affiliations_direct.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Affiliation extraction"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Adapted from code by Diego Charrez"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%load_ext autoreload\n",
    "%autoreload 2\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "from bs4 import BeautifulSoup\n",
    "import re\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import csv\n",
    "import json\n",
    "from collections import defaultdict\n",
    "from string_sim import cos_sim_pair"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Text scrape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_folder = \"data/neurips_2019/meta/\"\n",
    "out_folder = \"out/neurips_2019/\"\n",
    "data_fname = data_folder + \"raw_data_090819_edit.txt\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": "<p><b>Multimodal Model-Agnostic Meta-Learning via Task-Aware Modulation</b><br><i>Risto Vuorio (University of Michigan) &middot; Shao-Hua Sun (University of Southern California) &middot; Hexiang Hu (University of Southern California) &middot; Joseph Lim (University of Southern California)</i></p>\n      <p><b>ViLBERT: Pretraining Task-Agnostic Visiolinguistic Representations for Vision-and-Language Tasks</b><br><i>Jiasen Lu (Georgia Tech) &middot; Dhruv Batra (Georgia Tech / Facebook AI Research (FAIR)) &middot; Devi Parikh (Georgia Tech / Facebook AI Research (FAIR)) &middot; Stefan Lee (Georgia Institute of Technology)</i></p>\n      <p><b>Enabling hyperparameter optimization in sequential autoencoders for spiking neural data</b><br><i>Mohammad Reza Keshtkaran (Georgia Tech and Emory University) &middot; Chethan Pandarinath (Emory University and Georgia Tech)</i></p>\n      <p><b>A Meta-Analysis of Overfitting in Machine Learning</b><br><i>Rebecca Roelofs (University of California, Berkeley) &middot; Vaishaal Shankar (UC Berkeley) &middot; Benjamin Recht (UC Berkeley) &middot; Sara Fridovich-Keil (University of California, Berkeley) &middot; Moritz Hardt (University of California, Berkeley) &middot; John Miller (University of California, Berkeley) &middot; Ludwig Schmidt (UC Berkeley)</i></p>\n"
    }
   ],
   "source": [
    "!head -2 \"data/neurips_2019/meta/raw_data_090819_edit.txt\"\n",
    "!tail -2 \"data/neurips_2019/meta/raw_data_090819_edit.txt\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "tags": [
     "outputPrepend",
     "outputPrepend",
     "outputPrepend"
    ]
   },
   "outputs": [],
   "source": [
    "raw_affiliations = list()\n",
    "titles = list()\n",
    "raw_authors = list()\n",
    "pattern = '(\\([A-z\\.,/ ]+\\(.*?\\)[A-z\\.,/ ]+\\)|\\(.*?\\(?\\)?\\))' # '\\(.*?\\)'  # '(\\([A-z\\.,/ ]+\\(.*?\\)[A-z\\.,/ ]+\\)|\\(.*?\\(?\\)?\\))'\n",
    "with open(data_fname, 'r') as file:\n",
    "    for paper in file:\n",
    "        soup = BeautifulSoup(paper, features=\"html.parser\")\n",
    "        italic = soup.i.get_text()\n",
    "        raw_authors.append(italic)\n",
    "        raw_affiliations.append(''.join(re.findall(pattern, italic)))\n",
    "        titles.append(soup.b.get_text())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "(1430,\n 123896,\n ['(University of Michigan)(University of Southern California)(University of Southern California)(University of Southern California)',\n  '(Georgia Tech)(Georgia Tech / Facebook AI Research (FAIR))(Georgia Tech / Facebook AI Research (FAIR))(Georgia Institute of Technology)',\n  '(University of California, Davis)(University of California, Davis)(UCLA)(UC Davis)',\n  '(The University of Adelaide)(Tusimple)(Hong Kong University of Science and Technology)(The University of Adelaide)(University of Adelaide)(Nankai University)(University of Adelaide)',\n  '(Seoul National University)(Seoul National University)'])"
     },
     "metadata": {},
     "execution_count": 6
    }
   ],
   "source": [
    "len(raw_affiliations), len(''.join(raw_affiliations)), raw_affiliations[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "['Multimodal Model-Agnostic Meta-Learning via Task-Aware Modulation',\n 'ViLBERT: Pretraining Task-Agnostic Visiolinguistic Representations for Vision-and-Language Tasks',\n 'Stochastic Shared Embeddings: Data-driven Regularization of Embedding Layers',\n 'Unsupervised Scale-consistent Depth and Ego-motion Learning from Monocular Video',\n 'Zero-shot Learning via Simultaneous Generating and Learning']"
     },
     "metadata": {},
     "execution_count": 7
    }
   ],
   "source": [
    "titles[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "['Risto Vuorio (University of Michigan) · Shao-Hua Sun (University of Southern California) · Hexiang Hu (University of Southern California) · Joseph Lim (University of Southern California)',\n 'Jiasen Lu (Georgia Tech) · Dhruv Batra (Georgia Tech / Facebook AI Research (FAIR)) · Devi Parikh (Georgia Tech / Facebook AI Research (FAIR)) · Stefan Lee (Georgia Institute of Technology)',\n 'Liwei Wu (University of California, Davis) · Shuqing Li (University of California, Davis) · Cho-Jui Hsieh (UCLA) · James Sharpnack (UC Davis)',\n 'Jiawang Bian (The University of Adelaide) · Zhichao Li (Tusimple) · Naiyan Wang (Hong Kong University of Science and Technology) · Huangying Zhan (The University of Adelaide) · Chunhua Shen (University of Adelaide) · Ming-Ming Cheng (Nankai University) · Ian Reid (University of Adelaide)',\n 'Hyeonwoo Yu (Seoul National University) · Beomhee Lee (Seoul National University)']"
     },
     "metadata": {},
     "execution_count": 8
    }
   ],
   "source": [
    "raw_authors[:5]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## All affiliations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "def load_json(fname):\n",
    "    with open(fname, 'r') as f:\n",
    "        data = json.load(f)\n",
    "    return data\n",
    "\n",
    "def write_json(fname, output, indent=4):\n",
    "    with open(fname, 'w') as f:\n",
    "        json.dump(output, f, indent=indent)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "def title_nearest_neighbours(titles, names):\n",
    "    # name example: '8296-multimodal-model-agnostic-meta-learning-via-task-aware-modulation'\n",
    "    # matching title: 'Multimodal Model-Agnostic Meta-Learning via Task-Aware Modulation'\n",
    "    # non-matching title: 'ViLBERT: Pretraining Task-Agnostic Visiolinguistic Representations for Vision-and-Language Tasks'\n",
    "\n",
    "    nn = defaultdict(lambda: (-1, 0.0))\n",
    "    neighbours = defaultdict(list)\n",
    "    \n",
    "    for i, title in enumerate(titles):\n",
    "        for j, name in enumerate(names):\n",
    "            name_ = ' '.join(name.split('-')[1:])\n",
    "            title_ = title.lower().strip()\n",
    "            title_ = re.sub('[-]', ' ', title_)\n",
    "            title_ = re.sub('[\\(\\):,?]', '', title_)\n",
    "            \n",
    "            sim = cos_sim_pair(name_, title_)\n",
    "            \n",
    "            sim = round(sim, 4)\n",
    "            neighbours[j].append((i, sim))\n",
    "            if sim > nn[j][1]:\n",
    "                nn[j] = (i, sim)\n",
    "                \n",
    "    return nn, neighbours"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "def process_affiliations(name_idx, nn):\n",
    "    '''\n",
    "    Extract a clean list of affiliations from the raw affiliation data\n",
    "    that corresponds to `name_idx`, using nearest-neighbour table `nn`.\n",
    "    '''\n",
    "    # Get the entry index `i` in the raw data, such that\n",
    "    # the paper _title_ that corresponds to `i` is most similar \n",
    "    # to the paper _filename_ that corresponds to `name_idx`.\n",
    "    i = nn[name_idx]\n",
    "    affiliations = raw_affiliations[i]\n",
    "    # Split on the intervening brackets (should always work for well-formed data)\n",
    "    affiliations = affiliations.split(')(')\n",
    "    # Remove end brackets\n",
    "    affiliations[0] = affiliations[0][1:]\n",
    "    affiliations[-1] = affiliations[-1][:-1]\n",
    "    return affiliations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "metadata_fname = out_folder + 'papers_metadata.json'\n",
    "output_fname = out_folder + 'affiliations.json'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "metadatas = load_json(metadata_fname)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "names = list()\n",
    "for metadata in metadatas:\n",
    "    names.append(os.path.split(metadata['pdf'])[1].replace('.pdf', ''))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**WARNING**: below cell is expensive to compute. Run once and then load the result thereafter."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "# nn, neighbours = title_nearest_neighbours(titles, names)\n",
    "# write_json(out_folder + 'nn.json', nn, indent=4)\n",
    "# write_json(out_folder + 'title_similarity.json', neighbours, indent=None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "nn = load_json(out_folder + 'nn.json')\n",
    "nn = {int(k): v[0] for k, v in nn.items()}\n",
    "# Manually correct mismatches\n",
    "nn_correction = {1379: 63, 83: 1360, 451: 994, 92: 1347, 870: 569, 681: 763, 135: 1309, 1373: 65, 1060: 381, 1168: 273, 956: 488, 560: 884, 605: 832, 1117: 325, 64: 1384, 245: 1198, 1419: 9, 93: 1352, 1084: 356, 1343: 99, 100: 1341}\n",
    "for j, i in nn_correction.items():\n",
    "    nn[j] = i\n",
    "# Invert mapping\n",
    "inv_nn = {v: k for k, v in nn.items()}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "1428"
     },
     "metadata": {},
     "execution_count": 17
    }
   ],
   "source": [
    "len(nn)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": "8296-multimodal-model-agnostic-meta-learning-via-task-aware-modulation\nMultimodal Model-Agnostic Meta-Learning via Task-Aware Modulation\n8319-generalized-sliced-wasserstein-distances\nGeneralized Sliced Wasserstein Distances\n8318-asymptotic-guarantees-for-learning-generative-models-with-the-sliced-wasserstein-distance\nAsymptotic Guarantees for Learning Generative Models with the Sliced-Wasserstein Distance\n8321-blind-super-resolution-kernel-estimation-using-an-internal-gan\nBlind Super-Resolution Kernel Estimation using an Internal-GAN\n8325-provable-gradient-variance-guarantees-for-black-box-variational-inference\nProvable Gradient Variance Guarantees for Black-Box Variational Inference\n"
    }
   ],
   "source": [
    "for k, v in list(nn.items())[:5]:\n",
    "    print(names[k])\n",
    "    print(titles[v])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": "Multimodal Model-Agnostic Meta-Learning via Task-Aware Modulation\n8296-multimodal-model-agnostic-meta-learning-via-task-aware-modulation\nGeneralized Sliced Wasserstein Distances\n8319-generalized-sliced-wasserstein-distances\nAsymptotic Guarantees for Learning Generative Models with the Sliced-Wasserstein Distance\n8318-asymptotic-guarantees-for-learning-generative-models-with-the-sliced-wasserstein-distance\nBlind Super-Resolution Kernel Estimation using an Internal-GAN\n8321-blind-super-resolution-kernel-estimation-using-an-internal-gan\nProvable Gradient Variance Guarantees for Black-Box Variational Inference\n8325-provable-gradient-variance-guarantees-for-black-box-variational-inference\n"
    }
   ],
   "source": [
    "for k, v in list(inv_nn.items())[:5]:\n",
    "    print(titles[k])\n",
    "    print(names[v])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "output = list()\n",
    "for i, metadata in enumerate(metadatas):\n",
    "    pdf_fname = os.path.split(metadata['pdf'])[1]\n",
    "    paper_id = pdf_fname.split('-')[0]\n",
    "    \n",
    "    affiliations = process_affiliations(i, nn)\n",
    "\n",
    "    has_code = metadata['code']\n",
    "    data = {\n",
    "        'name': pdf_fname.replace('.pdf', ''),\n",
    "        'code': has_code,\n",
    "        'affiliations': affiliations\n",
    "    }\n",
    "    output.append(data)\n",
    "\n",
    "#     if i > 5: break\n",
    "\n",
    "write_json(output_fname, output)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Results on a selection of institutions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "1428"
     },
     "metadata": {},
     "execution_count": 21
    }
   ],
   "source": [
    "affiliation_data = load_json(output_fname)\n",
    "len(affiliation_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "institutions = {\n",
    "    'Amazon': [0, 0, 'amazon', 'industry'],\n",
    "    'Carnegie Mellon University': [0, 0, 'CMU, Carnegie Mellon University', 'academy'],\n",
    "    'Columbia University': [0, 0, 'columbia university, university of columbia', 'academy'],\n",
    "    'Cornell University': [0, 0, 'cornell', 'academy'],\n",
    "    # 'DeepMind': [0, 0, 'deepmind', 'industry'],\n",
    "    'Duke University': [0, 0, 'duke', 'academy'],\n",
    "    'EPFL': [0, 0, 'EPFL', 'academy'],\n",
    "    'ETH Zurich': [0, 0, 'ETH Zurich, ETHZ, ETH Zürich', 'academy'],\n",
    "    'Facebook AI Research': [0, 0, 'facebook', 'industry'],\n",
    "    'Georgia Institute of Technology': [0, 0, 'georgia tech, gatech, Georgia Institute of Technology', 'academy'],\n",
    "    # 'Google, Google Brain': [0, 0, 'google', 'industry'],\n",
    "    'Google, Google Brain, DeepMind': [0, 0, 'google, deepmind', 'industry'],\n",
    "    'Harvard University': [0, 0, 'harvard', 'academy'],\n",
    "    'IBM research': [0, 0, 'ibm', 'industry'],\n",
    "    'INRIA': [0, 0, 'inria', 'academy'],\n",
    "    'Imperial College London': [0, 0, 'imperial', 'academy'],\n",
    "    'MILA': [0, 0, 'mila', 'academy'],\n",
    "    'Massachusetts Institute of Technology': [0, 0, 'mit, massachusetts institute of technology', 'academy'],\n",
    "    'Microsoft Research': [0, 0, 'microsoft', 'industry'],\n",
    "    'New York University': [0, 0, 'nyu, New York University', 'academy'],\n",
    "    'Peking University': [0, 0, 'peking university', 'academy'],\n",
    "    'Princeton University': [0, 0, 'princeton', 'academy'],\n",
    "    'RIKEN': [0, 0, 'riken', 'industry'],\n",
    "    'Rutgers': [0, 0, 'Rutgers', 'academy'],\n",
    "    'Stanford University': [0, 0, 'stanford', 'academy'],\n",
    "    'Tencent': [0, 0, 'Tencent', 'industry'],\n",
    "    'Tsinghua University': [0, 0, 'tsinghua', 'academy'],\n",
    "    'University of California, Berkeley': [0, 0, 'berkeley', 'academy'],\n",
    "    'University of California, Los Angeles': [0, 0, 'ucla, University of California Los Angeles', 'academy'],\n",
    "    'University of Cambridge': [0, 0, 'university of cambridge', 'academy'],\n",
    "    'University of Illinois at Urbana-Champaign': [0, 0, 'uiuc, University of Illinois at Urbana', 'academy'],\n",
    "    'University of Massachusetts Amherst': [0, 0, 'Amherst', 'academy'],\n",
    "    'University of Oxford': [0, 0, 'oxford', 'academy'],\n",
    "    'University of Southern California': [0, 0, 'University of Southern California, USC', 'academy'],\n",
    "    'University of Texas at Austin': [0, 0, 'University of Texas at Austin, UAT, UT-Austin, UT Austin', 'academy'],\n",
    "    'University of Toronto': [0, 0, 'University of Toronto, uoft', 'academy'],\n",
    "    'University of Washington': [0, 0, 'University of Washington', 'academy'],\n",
    "    'covariant': [0, 0, 'covariant', 'industry'],\n",
    "    'nvidia': [0, 0, 'nvidia', 'industry'],\n",
    "    'uber': [0, 0, 'uber', 'industry']\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": "Missed at index 114: Gated CRF Loss for Weakly Supervised Semantic Image Segmentation | Anton Obukhov (ETH Zurich) · Stamatios Georgoulis (ETH Zurich) · Dengxin Dai (ETH Zurich) · Luc V Gool (Computer Vision Lab, ETH Zurich)\nMissed at index 1255: A First-Order Approach to Accelerated Value Iteration | Julien Grand Clement (IEOR Department, Columbia University) · Vineet Goyal (Columbia University)\n"
    }
   ],
   "source": [
    "missed = list()\n",
    "for i, _ in enumerate(raw_affiliations):\n",
    "    try:\n",
    "        j = inv_nn[i]\n",
    "    except KeyError as e:\n",
    "        missed.append(i)\n",
    "        print(f'Missed at index {i}: {titles[i]} | {raw_authors[i]}')\n",
    "        continue\n",
    "\n",
    "    entry = affiliation_data[j]\n",
    "\n",
    "    # All author affiliations\n",
    "    affiliations = entry['affiliations']\n",
    "\n",
    "    # Or select specific authors\n",
    "    # indices = [-1]\n",
    "    # affiliations = [entry['affiliations'][k] for k in indices]\n",
    "\n",
    "    affs = ' | '.join([a.lower() for a in affiliations])\n",
    "\n",
    "    for key in institutions:\n",
    "        flag = False\n",
    "        for keyword in institutions[key][2].split(\", \"):\n",
    "            if keyword.lower() in affs:\n",
    "                flag = True\n",
    "        if flag:\n",
    "            code = entry['code']  # whether code released\n",
    "            institutions[key][0] += int(code)  # no. papers with code\n",
    "            institutions[key][1] += 1  # no. papers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "{'Amazon': [19, 24, 'amazon', 'industry'],\n 'Carnegie Mellon University': [65,\n  83,\n  'CMU, Carnegie Mellon University',\n  'academy'],\n 'Columbia University': [33,\n  42,\n  'columbia university, university of columbia',\n  'academy'],\n 'Cornell University': [27, 32, 'cornell', 'academy'],\n 'Duke University': [12, 22, 'duke', 'academy'],\n 'EPFL': [23, 24, 'EPFL', 'academy'],\n 'ETH Zurich': [19, 25, 'ETH Zurich, ETHZ, ETH Zürich', 'academy'],\n 'Facebook AI Research': [33, 43, 'facebook', 'industry'],\n 'Georgia Institute of Technology': [21,\n  30,\n  'georgia tech, gatech, Georgia Institute of Technology',\n  'academy'],\n 'Google, Google Brain, DeepMind': [101, 170, 'google, deepmind', 'industry'],\n 'Harvard University': [20, 24, 'harvard', 'academy'],\n 'IBM research': [22, 36, 'ibm', 'industry'],\n 'INRIA': [31, 36, 'inria', 'academy'],\n 'Imperial College London': [16, 16, 'imperial', 'academy'],\n 'MILA': [27, 29, 'mila', 'academy'],\n 'Massachusetts Institute of Technology': [63,\n  86,\n  'mit, massachusetts institute of technology',\n  'academy'],\n 'Microsoft Research': [53, 76, 'microsoft', 'industry'],\n 'New York University': [22, 27, 'nyu, New York University', 'academy'],\n 'Peking University': [19, 25, 'peking university', 'academy'],\n 'Princeton University': [24, 54, 'princeton', 'academy'],\n 'RIKEN': [16, 21, 'riken', 'industry'],\n 'Rutgers': [12, 16, 'Rutgers', 'academy'],\n 'Stanford University': [66, 85, 'stanford', 'academy'],\n 'Tencent': [16, 18, 'Tencent', 'industry'],\n 'Tsinghua University': [26, 34, 'tsinghua', 'academy'],\n 'University of California, Berkeley': [43, 58, 'berkeley', 'academy'],\n 'University of California, Los Angeles': [19,\n  27,\n  'ucla, University of California Los Angeles',\n  'academy'],\n 'University of Cambridge': [19, 20, 'university of cambridge', 'academy'],\n 'University of Illinois at Urbana-Champaign': [19,\n  28,\n  'uiuc, University of Illinois at Urbana',\n  'academy'],\n 'University of Massachusetts Amherst': [8, 12, 'Amherst', 'academy'],\n 'University of Oxford': [36, 41, 'oxford', 'academy'],\n 'University of Southern California': [16,\n  22,\n  'University of Southern California, USC',\n  'academy'],\n 'University of Texas at Austin': [28,\n  34,\n  'University of Texas at Austin, UAT, UT-Austin, UT Austin',\n  'academy'],\n 'University of Toronto': [21, 28, 'University of Toronto, uoft', 'academy'],\n 'University of Washington': [21, 28, 'University of Washington', 'academy'],\n 'covariant': [8, 8, 'covariant', 'industry'],\n 'nvidia': [10, 10, 'nvidia', 'industry'],\n 'uber': [11, 11, 'uber', 'industry']}"
     },
     "metadata": {},
     "execution_count": 24
    }
   ],
   "source": [
    "institutions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "                                            code  papers  \\\nGoogle, Google Brain, DeepMind               101     170   \nMassachusetts Institute of Technology         63      86   \nStanford University                           66      85   \nCarnegie Mellon University                    65      83   \nMicrosoft Research                            53      76   \nUniversity of California, Berkeley            43      58   \nPrinceton University                          24      54   \nFacebook AI Research                          33      43   \nColumbia University                           33      42   \nUniversity of Oxford                          36      41   \nIBM research                                  22      36   \nINRIA                                         31      36   \nTsinghua University                           26      34   \nUniversity of Texas at Austin                 28      34   \nCornell University                            27      32   \nGeorgia Institute of Technology               21      30   \nMILA                                          27      29   \nUniversity of Washington                      21      28   \nUniversity of Illinois at Urbana-Champaign    19      28   \nUniversity of Toronto                         21      28   \nNew York University                           22      27   \nUniversity of California, Los Angeles         19      27   \nPeking University                             19      25   \nETH Zurich                                    19      25   \nAmazon                                        19      24   \nHarvard University                            20      24   \nEPFL                                          23      24   \nUniversity of Southern California             16      22   \nDuke University                               12      22   \nRIKEN                                         16      21   \nUniversity of Cambridge                       19      20   \nTencent                                       16      18   \nRutgers                                       12      16   \nImperial College London                       16      16   \nUniversity of Massachusetts Amherst            8      12   \nuber                                          11      11   \nnvidia                                        10      10   \ncovariant                                      8       8   \n\n                                                                                     keywords  \\\nGoogle, Google Brain, DeepMind                                               google, deepmind   \nMassachusetts Institute of Technology              mit, massachusetts institute of technology   \nStanford University                                                                  stanford   \nCarnegie Mellon University                                    CMU, Carnegie Mellon University   \nMicrosoft Research                                                                  microsoft   \nUniversity of California, Berkeley                                                   berkeley   \nPrinceton University                                                                princeton   \nFacebook AI Research                                                                 facebook   \nColumbia University                               columbia university, university of columbia   \nUniversity of Oxford                                                                   oxford   \nIBM research                                                                              ibm   \nINRIA                                                                                   inria   \nTsinghua University                                                                  tsinghua   \nUniversity of Texas at Austin               University of Texas at Austin, UAT, UT-Austin,...   \nCornell University                                                                    cornell   \nGeorgia Institute of Technology             georgia tech, gatech, Georgia Institute of Tec...   \nMILA                                                                                     mila   \nUniversity of Washington                                             University of Washington   \nUniversity of Illinois at Urbana-Champaign             uiuc, University of Illinois at Urbana   \nUniversity of Toronto                                             University of Toronto, uoft   \nNew York University                                                  nyu, New York University   \nUniversity of California, Los Angeles              ucla, University of California Los Angeles   \nPeking University                                                           peking university   \nETH Zurich                                                       ETH Zurich, ETHZ, ETH Zürich   \nAmazon                                                                                 amazon   \nHarvard University                                                                    harvard   \nEPFL                                                                                     EPFL   \nUniversity of Southern California                      University of Southern California, USC   \nDuke University                                                                          duke   \nRIKEN                                                                                   riken   \nUniversity of Cambridge                                               university of cambridge   \nTencent                                                                               Tencent   \nRutgers                                                                               Rutgers   \nImperial College London                                                              imperial   \nUniversity of Massachusetts Amherst                                                   Amherst   \nuber                                                                                     uber   \nnvidia                                                                                 nvidia   \ncovariant                                                                           covariant   \n\n                                            category  code_fraction  \nGoogle, Google Brain, DeepMind              industry       0.594118  \nMassachusetts Institute of Technology        academy       0.732558  \nStanford University                          academy       0.776471  \nCarnegie Mellon University                   academy       0.783133  \nMicrosoft Research                          industry       0.697368  \nUniversity of California, Berkeley           academy       0.741379  \nPrinceton University                         academy       0.444444  \nFacebook AI Research                        industry       0.767442  \nColumbia University                          academy       0.785714  \nUniversity of Oxford                         academy       0.878049  \nIBM research                                industry       0.611111  \nINRIA                                        academy       0.861111  \nTsinghua University                          academy       0.764706  \nUniversity of Texas at Austin                academy       0.823529  \nCornell University                           academy       0.843750  \nGeorgia Institute of Technology              academy       0.700000  \nMILA                                         academy       0.931034  \nUniversity of Washington                     academy       0.750000  \nUniversity of Illinois at Urbana-Champaign   academy       0.678571  \nUniversity of Toronto                        academy       0.750000  \nNew York University                          academy       0.814815  \nUniversity of California, Los Angeles        academy       0.703704  \nPeking University                            academy       0.760000  \nETH Zurich                                   academy       0.760000  \nAmazon                                      industry       0.791667  \nHarvard University                           academy       0.833333  \nEPFL                                         academy       0.958333  \nUniversity of Southern California            academy       0.727273  \nDuke University                              academy       0.545455  \nRIKEN                                       industry       0.761905  \nUniversity of Cambridge                      academy       0.950000  \nTencent                                     industry       0.888889  \nRutgers                                      academy       0.750000  \nImperial College London                      academy       1.000000  \nUniversity of Massachusetts Amherst          academy       0.666667  \nuber                                        industry       1.000000  \nnvidia                                      industry       1.000000  \ncovariant                                   industry       1.000000  ",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>code</th>\n      <th>papers</th>\n      <th>keywords</th>\n      <th>category</th>\n      <th>code_fraction</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>Google, Google Brain, DeepMind</th>\n      <td>101</td>\n      <td>170</td>\n      <td>google, deepmind</td>\n      <td>industry</td>\n      <td>0.594118</td>\n    </tr>\n    <tr>\n      <th>Massachusetts Institute of Technology</th>\n      <td>63</td>\n      <td>86</td>\n      <td>mit, massachusetts institute of technology</td>\n      <td>academy</td>\n      <td>0.732558</td>\n    </tr>\n    <tr>\n      <th>Stanford University</th>\n      <td>66</td>\n      <td>85</td>\n      <td>stanford</td>\n      <td>academy</td>\n      <td>0.776471</td>\n    </tr>\n    <tr>\n      <th>Carnegie Mellon University</th>\n      <td>65</td>\n      <td>83</td>\n      <td>CMU, Carnegie Mellon University</td>\n      <td>academy</td>\n      <td>0.783133</td>\n    </tr>\n    <tr>\n      <th>Microsoft Research</th>\n      <td>53</td>\n      <td>76</td>\n      <td>microsoft</td>\n      <td>industry</td>\n      <td>0.697368</td>\n    </tr>\n    <tr>\n      <th>University of California, Berkeley</th>\n      <td>43</td>\n      <td>58</td>\n      <td>berkeley</td>\n      <td>academy</td>\n      <td>0.741379</td>\n    </tr>\n    <tr>\n      <th>Princeton University</th>\n      <td>24</td>\n      <td>54</td>\n      <td>princeton</td>\n      <td>academy</td>\n      <td>0.444444</td>\n    </tr>\n    <tr>\n      <th>Facebook AI Research</th>\n      <td>33</td>\n      <td>43</td>\n      <td>facebook</td>\n      <td>industry</td>\n      <td>0.767442</td>\n    </tr>\n    <tr>\n      <th>Columbia University</th>\n      <td>33</td>\n      <td>42</td>\n      <td>columbia university, university of columbia</td>\n      <td>academy</td>\n      <td>0.785714</td>\n    </tr>\n    <tr>\n      <th>University of Oxford</th>\n      <td>36</td>\n      <td>41</td>\n      <td>oxford</td>\n      <td>academy</td>\n      <td>0.878049</td>\n    </tr>\n    <tr>\n      <th>IBM research</th>\n      <td>22</td>\n      <td>36</td>\n      <td>ibm</td>\n      <td>industry</td>\n      <td>0.611111</td>\n    </tr>\n    <tr>\n      <th>INRIA</th>\n      <td>31</td>\n      <td>36</td>\n      <td>inria</td>\n      <td>academy</td>\n      <td>0.861111</td>\n    </tr>\n    <tr>\n      <th>Tsinghua University</th>\n      <td>26</td>\n      <td>34</td>\n      <td>tsinghua</td>\n      <td>academy</td>\n      <td>0.764706</td>\n    </tr>\n    <tr>\n      <th>University of Texas at Austin</th>\n      <td>28</td>\n      <td>34</td>\n      <td>University of Texas at Austin, UAT, UT-Austin,...</td>\n      <td>academy</td>\n      <td>0.823529</td>\n    </tr>\n    <tr>\n      <th>Cornell University</th>\n      <td>27</td>\n      <td>32</td>\n      <td>cornell</td>\n      <td>academy</td>\n      <td>0.843750</td>\n    </tr>\n    <tr>\n      <th>Georgia Institute of Technology</th>\n      <td>21</td>\n      <td>30</td>\n      <td>georgia tech, gatech, Georgia Institute of Tec...</td>\n      <td>academy</td>\n      <td>0.700000</td>\n    </tr>\n    <tr>\n      <th>MILA</th>\n      <td>27</td>\n      <td>29</td>\n      <td>mila</td>\n      <td>academy</td>\n      <td>0.931034</td>\n    </tr>\n    <tr>\n      <th>University of Washington</th>\n      <td>21</td>\n      <td>28</td>\n      <td>University of Washington</td>\n      <td>academy</td>\n      <td>0.750000</td>\n    </tr>\n    <tr>\n      <th>University of Illinois at Urbana-Champaign</th>\n      <td>19</td>\n      <td>28</td>\n      <td>uiuc, University of Illinois at Urbana</td>\n      <td>academy</td>\n      <td>0.678571</td>\n    </tr>\n    <tr>\n      <th>University of Toronto</th>\n      <td>21</td>\n      <td>28</td>\n      <td>University of Toronto, uoft</td>\n      <td>academy</td>\n      <td>0.750000</td>\n    </tr>\n    <tr>\n      <th>New York University</th>\n      <td>22</td>\n      <td>27</td>\n      <td>nyu, New York University</td>\n      <td>academy</td>\n      <td>0.814815</td>\n    </tr>\n    <tr>\n      <th>University of California, Los Angeles</th>\n      <td>19</td>\n      <td>27</td>\n      <td>ucla, University of California Los Angeles</td>\n      <td>academy</td>\n      <td>0.703704</td>\n    </tr>\n    <tr>\n      <th>Peking University</th>\n      <td>19</td>\n      <td>25</td>\n      <td>peking university</td>\n      <td>academy</td>\n      <td>0.760000</td>\n    </tr>\n    <tr>\n      <th>ETH Zurich</th>\n      <td>19</td>\n      <td>25</td>\n      <td>ETH Zurich, ETHZ, ETH Zürich</td>\n      <td>academy</td>\n      <td>0.760000</td>\n    </tr>\n    <tr>\n      <th>Amazon</th>\n      <td>19</td>\n      <td>24</td>\n      <td>amazon</td>\n      <td>industry</td>\n      <td>0.791667</td>\n    </tr>\n    <tr>\n      <th>Harvard University</th>\n      <td>20</td>\n      <td>24</td>\n      <td>harvard</td>\n      <td>academy</td>\n      <td>0.833333</td>\n    </tr>\n    <tr>\n      <th>EPFL</th>\n      <td>23</td>\n      <td>24</td>\n      <td>EPFL</td>\n      <td>academy</td>\n      <td>0.958333</td>\n    </tr>\n    <tr>\n      <th>University of Southern California</th>\n      <td>16</td>\n      <td>22</td>\n      <td>University of Southern California, USC</td>\n      <td>academy</td>\n      <td>0.727273</td>\n    </tr>\n    <tr>\n      <th>Duke University</th>\n      <td>12</td>\n      <td>22</td>\n      <td>duke</td>\n      <td>academy</td>\n      <td>0.545455</td>\n    </tr>\n    <tr>\n      <th>RIKEN</th>\n      <td>16</td>\n      <td>21</td>\n      <td>riken</td>\n      <td>industry</td>\n      <td>0.761905</td>\n    </tr>\n    <tr>\n      <th>University of Cambridge</th>\n      <td>19</td>\n      <td>20</td>\n      <td>university of cambridge</td>\n      <td>academy</td>\n      <td>0.950000</td>\n    </tr>\n    <tr>\n      <th>Tencent</th>\n      <td>16</td>\n      <td>18</td>\n      <td>Tencent</td>\n      <td>industry</td>\n      <td>0.888889</td>\n    </tr>\n    <tr>\n      <th>Rutgers</th>\n      <td>12</td>\n      <td>16</td>\n      <td>Rutgers</td>\n      <td>academy</td>\n      <td>0.750000</td>\n    </tr>\n    <tr>\n      <th>Imperial College London</th>\n      <td>16</td>\n      <td>16</td>\n      <td>imperial</td>\n      <td>academy</td>\n      <td>1.000000</td>\n    </tr>\n    <tr>\n      <th>University of Massachusetts Amherst</th>\n      <td>8</td>\n      <td>12</td>\n      <td>Amherst</td>\n      <td>academy</td>\n      <td>0.666667</td>\n    </tr>\n    <tr>\n      <th>uber</th>\n      <td>11</td>\n      <td>11</td>\n      <td>uber</td>\n      <td>industry</td>\n      <td>1.000000</td>\n    </tr>\n    <tr>\n      <th>nvidia</th>\n      <td>10</td>\n      <td>10</td>\n      <td>nvidia</td>\n      <td>industry</td>\n      <td>1.000000</td>\n    </tr>\n    <tr>\n      <th>covariant</th>\n      <td>8</td>\n      <td>8</td>\n      <td>covariant</td>\n      <td>industry</td>\n      <td>1.000000</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 25
    }
   ],
   "source": [
    "df = pd.DataFrame.from_dict(institutions, orient='index', columns=['code', 'papers', 'keywords', 'category'])\n",
    "df['code_fraction'] = df.code / df.papers\n",
    "df = df.sort_values(by=['papers'], ascending=False)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.to_csv(out_folder + 'code_release_select.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "0.7437722419928826"
     },
     "metadata": {},
     "execution_count": 27
    }
   ],
   "source": [
    "df_tot = df.sum()\n",
    "df_tot.code / df_tot.papers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "code             27.500000\npapers           36.973684\ncode_fraction     0.784909\ndtype: float64"
     },
     "metadata": {},
     "execution_count": 28
    }
   ],
   "source": [
    "df.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "          code  papers  code_fraction\ncategory                             \nacademy    756     988       0.765182\nindustry   289     417       0.693046",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>code</th>\n      <th>papers</th>\n      <th>code_fraction</th>\n    </tr>\n    <tr>\n      <th>category</th>\n      <th></th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>academy</th>\n      <td>756</td>\n      <td>988</td>\n      <td>0.765182</td>\n    </tr>\n    <tr>\n      <th>industry</th>\n      <td>289</td>\n      <td>417</td>\n      <td>0.693046</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 29
    }
   ],
   "source": [
    "df_cat = df.groupby(['category']).sum()\n",
    "df_cat['code_fraction'] = df_cat.code / df_cat.papers\n",
    "df_cat"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "text/plain": "<Figure size 600x700 with 1 Axes>",
      "image/svg+xml": "<?xml version=\"1.0\" encoding=\"utf-8\" standalone=\"no\"?>\n<!DOCTYPE svg PUBLIC \"-//W3C//DTD SVG 1.1//EN\"\n  \"http://www.w3.org/Graphics/SVG/1.1/DTD/svg11.dtd\">\n<!-- Created with matplotlib (https://matplotlib.org/) -->\n<svg height=\"447.954375pt\" version=\"1.1\" viewBox=\"0 0 567.88125 447.954375\" width=\"567.88125pt\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\">\n <defs>\n  <style type=\"text/css\">\n*{stroke-linecap:butt;stroke-linejoin:round;}\n  </style>\n </defs>\n <g id=\"figure_1\">\n  <g id=\"patch_1\">\n   <path d=\"M 0 447.954375 \nL 567.88125 447.954375 \nL 567.88125 0 \nL 0 0 \nz\n\" style=\"fill:#ffffff;\"/>\n  </g>\n  <g id=\"axes_1\">\n   <g id=\"patch_2\">\n    <path d=\"M 225.88125 410.398125 \nL 560.68125 410.398125 \nL 560.68125 22.318125 \nL 225.88125 22.318125 \nz\n\" style=\"fill:#ffffff;\"/>\n   </g>\n   <g id=\"patch_3\">\n    <path clip-path=\"url(#pf30b7a255c)\" d=\"M 225.88125 39.958125 \nL 544.738393 39.958125 \nL 544.738393 47.424792 \nL 225.88125 47.424792 \nz\n\" style=\"fill:#d62728;\"/>\n   </g>\n   <g id=\"patch_4\">\n    <path clip-path=\"url(#pf30b7a255c)\" d=\"M 225.88125 49.291458 \nL 387.185452 49.291458 \nL 387.185452 56.758125 \nL 225.88125 56.758125 \nz\n\" style=\"fill:#d62728;\"/>\n   </g>\n   <g id=\"patch_5\">\n    <path clip-path=\"url(#pf30b7a255c)\" d=\"M 225.88125 58.624792 \nL 385.309821 58.624792 \nL 385.309821 66.091458 \nL 225.88125 66.091458 \nz\n\" style=\"fill:#d62728;\"/>\n   </g>\n   <g id=\"patch_6\">\n    <path clip-path=\"url(#pf30b7a255c)\" d=\"M 225.88125 67.958125 \nL 381.558561 67.958125 \nL 381.558561 75.424792 \nL 225.88125 75.424792 \nz\n\" style=\"fill:#d62728;\"/>\n   </g>\n   <g id=\"patch_7\">\n    <path clip-path=\"url(#pf30b7a255c)\" d=\"M 225.88125 77.291458 \nL 368.429149 77.291458 \nL 368.429149 84.758125 \nL 225.88125 84.758125 \nz\n\" style=\"fill:#d62728;\"/>\n   </g>\n   <g id=\"patch_8\">\n    <path clip-path=\"url(#pf30b7a255c)\" d=\"M 225.88125 86.624792 \nL 334.667805 86.624792 \nL 334.667805 94.091458 \nL 225.88125 94.091458 \nz\n\" style=\"fill:#d62728;\"/>\n   </g>\n   <g id=\"patch_9\">\n    <path clip-path=\"url(#pf30b7a255c)\" d=\"M 225.88125 95.958125 \nL 327.165284 95.958125 \nL 327.165284 103.424792 \nL 225.88125 103.424792 \nz\n\" style=\"fill:#d62728;\"/>\n   </g>\n   <g id=\"patch_10\">\n    <path clip-path=\"url(#pf30b7a255c)\" d=\"M 225.88125 105.291458 \nL 306.533351 105.291458 \nL 306.533351 112.758125 \nL 225.88125 112.758125 \nz\n\" style=\"fill:#d62728;\"/>\n   </g>\n   <g id=\"patch_11\">\n    <path clip-path=\"url(#pf30b7a255c)\" d=\"M 225.88125 114.624792 \nL 304.657721 114.624792 \nL 304.657721 122.091458 \nL 225.88125 122.091458 \nz\n\" style=\"fill:#d62728;\"/>\n   </g>\n   <g id=\"patch_12\">\n    <path clip-path=\"url(#pf30b7a255c)\" d=\"M 225.88125 123.958125 \nL 302.78209 123.958125 \nL 302.78209 131.424792 \nL 225.88125 131.424792 \nz\n\" style=\"fill:#d62728;\"/>\n   </g>\n   <g id=\"patch_13\">\n    <path clip-path=\"url(#pf30b7a255c)\" d=\"M 225.88125 133.291458 \nL 293.403939 133.291458 \nL 293.403939 140.758125 \nL 225.88125 140.758125 \nz\n\" style=\"fill:#d62728;\"/>\n   </g>\n   <g id=\"patch_14\">\n    <path clip-path=\"url(#pf30b7a255c)\" d=\"M 225.88125 142.624792 \nL 293.403939 142.624792 \nL 293.403939 150.091458 \nL 225.88125 150.091458 \nz\n\" style=\"fill:#d62728;\"/>\n   </g>\n   <g id=\"patch_15\">\n    <path clip-path=\"url(#pf30b7a255c)\" d=\"M 225.88125 151.958125 \nL 289.652679 151.958125 \nL 289.652679 159.424792 \nL 225.88125 159.424792 \nz\n\" style=\"fill:#d62728;\"/>\n   </g>\n   <g id=\"patch_16\">\n    <path clip-path=\"url(#pf30b7a255c)\" d=\"M 225.88125 161.291458 \nL 289.652679 161.291458 \nL 289.652679 168.758125 \nL 225.88125 168.758125 \nz\n\" style=\"fill:#d62728;\"/>\n   </g>\n   <g id=\"patch_17\">\n    <path clip-path=\"url(#pf30b7a255c)\" d=\"M 225.88125 170.624792 \nL 285.901418 170.624792 \nL 285.901418 178.091458 \nL 225.88125 178.091458 \nz\n\" style=\"fill:#d62728;\"/>\n   </g>\n   <g id=\"patch_18\">\n    <path clip-path=\"url(#pf30b7a255c)\" d=\"M 225.88125 179.958125 \nL 282.150158 179.958125 \nL 282.150158 187.424792 \nL 225.88125 187.424792 \nz\n\" style=\"fill:#d62728;\"/>\n   </g>\n   <g id=\"patch_19\">\n    <path clip-path=\"url(#pf30b7a255c)\" d=\"M 225.88125 189.291458 \nL 280.274527 189.291458 \nL 280.274527 196.758125 \nL 225.88125 196.758125 \nz\n\" style=\"fill:#d62728;\"/>\n   </g>\n   <g id=\"patch_20\">\n    <path clip-path=\"url(#pf30b7a255c)\" d=\"M 225.88125 198.624792 \nL 278.398897 198.624792 \nL 278.398897 206.091458 \nL 225.88125 206.091458 \nz\n\" style=\"fill:#d62728;\"/>\n   </g>\n   <g id=\"patch_21\">\n    <path clip-path=\"url(#pf30b7a255c)\" d=\"M 225.88125 207.958125 \nL 278.398897 207.958125 \nL 278.398897 215.424792 \nL 225.88125 215.424792 \nz\n\" style=\"fill:#d62728;\"/>\n   </g>\n   <g id=\"patch_22\">\n    <path clip-path=\"url(#pf30b7a255c)\" d=\"M 225.88125 217.291458 \nL 278.398897 217.291458 \nL 278.398897 224.758125 \nL 225.88125 224.758125 \nz\n\" style=\"fill:#d62728;\"/>\n   </g>\n   <g id=\"patch_23\">\n    <path clip-path=\"url(#pf30b7a255c)\" d=\"M 225.88125 226.624792 \nL 276.523267 226.624792 \nL 276.523267 234.091458 \nL 225.88125 234.091458 \nz\n\" style=\"fill:#d62728;\"/>\n   </g>\n   <g id=\"patch_24\">\n    <path clip-path=\"url(#pf30b7a255c)\" d=\"M 225.88125 235.958125 \nL 276.523267 235.958125 \nL 276.523267 243.424792 \nL 225.88125 243.424792 \nz\n\" style=\"fill:#d62728;\"/>\n   </g>\n   <g id=\"patch_25\">\n    <path clip-path=\"url(#pf30b7a255c)\" d=\"M 225.88125 245.291458 \nL 272.772006 245.291458 \nL 272.772006 252.758125 \nL 225.88125 252.758125 \nz\n\" style=\"fill:#d62728;\"/>\n   </g>\n   <g id=\"patch_26\">\n    <path clip-path=\"url(#pf30b7a255c)\" d=\"M 225.88125 254.624792 \nL 272.772006 254.624792 \nL 272.772006 262.091458 \nL 225.88125 262.091458 \nz\n\" style=\"fill:#d62728;\"/>\n   </g>\n   <g id=\"patch_27\">\n    <path clip-path=\"url(#pf30b7a255c)\" d=\"M 225.88125 263.958125 \nL 270.896376 263.958125 \nL 270.896376 271.424792 \nL 225.88125 271.424792 \nz\n\" style=\"fill:#d62728;\"/>\n   </g>\n   <g id=\"patch_28\">\n    <path clip-path=\"url(#pf30b7a255c)\" d=\"M 225.88125 273.291458 \nL 270.896376 273.291458 \nL 270.896376 280.758125 \nL 225.88125 280.758125 \nz\n\" style=\"fill:#d62728;\"/>\n   </g>\n   <g id=\"patch_29\">\n    <path clip-path=\"url(#pf30b7a255c)\" d=\"M 225.88125 282.624792 \nL 270.896376 282.624792 \nL 270.896376 290.091458 \nL 225.88125 290.091458 \nz\n\" style=\"fill:#d62728;\"/>\n   </g>\n   <g id=\"patch_30\">\n    <path clip-path=\"url(#pf30b7a255c)\" d=\"M 225.88125 291.958125 \nL 267.145116 291.958125 \nL 267.145116 299.424792 \nL 225.88125 299.424792 \nz\n\" style=\"fill:#d62728;\"/>\n   </g>\n   <g id=\"patch_31\">\n    <path clip-path=\"url(#pf30b7a255c)\" d=\"M 225.88125 301.291458 \nL 267.145116 301.291458 \nL 267.145116 308.758125 \nL 225.88125 308.758125 \nz\n\" style=\"fill:#d62728;\"/>\n   </g>\n   <g id=\"patch_32\">\n    <path clip-path=\"url(#pf30b7a255c)\" d=\"M 225.88125 310.624792 \nL 265.269485 310.624792 \nL 265.269485 318.091458 \nL 225.88125 318.091458 \nz\n\" style=\"fill:#d62728;\"/>\n   </g>\n   <g id=\"patch_33\">\n    <path clip-path=\"url(#pf30b7a255c)\" d=\"M 225.88125 319.958125 \nL 263.393855 319.958125 \nL 263.393855 327.424792 \nL 225.88125 327.424792 \nz\n\" style=\"fill:#d62728;\"/>\n   </g>\n   <g id=\"patch_34\">\n    <path clip-path=\"url(#pf30b7a255c)\" d=\"M 225.88125 329.291458 \nL 259.642595 329.291458 \nL 259.642595 336.758125 \nL 225.88125 336.758125 \nz\n\" style=\"fill:#d62728;\"/>\n   </g>\n   <g id=\"patch_35\">\n    <path clip-path=\"url(#pf30b7a255c)\" d=\"M 225.88125 338.624792 \nL 255.891334 338.624792 \nL 255.891334 346.091458 \nL 225.88125 346.091458 \nz\n\" style=\"fill:#d62728;\"/>\n   </g>\n   <g id=\"patch_36\">\n    <path clip-path=\"url(#pf30b7a255c)\" d=\"M 225.88125 347.958125 \nL 255.891334 347.958125 \nL 255.891334 355.424792 \nL 225.88125 355.424792 \nz\n\" style=\"fill:#d62728;\"/>\n   </g>\n   <g id=\"patch_37\">\n    <path clip-path=\"url(#pf30b7a255c)\" d=\"M 225.88125 357.291458 \nL 248.388813 357.291458 \nL 248.388813 364.758125 \nL 225.88125 364.758125 \nz\n\" style=\"fill:#d62728;\"/>\n   </g>\n   <g id=\"patch_38\">\n    <path clip-path=\"url(#pf30b7a255c)\" d=\"M 225.88125 366.624792 \nL 246.513183 366.624792 \nL 246.513183 374.091458 \nL 225.88125 374.091458 \nz\n\" style=\"fill:#d62728;\"/>\n   </g>\n   <g id=\"patch_39\">\n    <path clip-path=\"url(#pf30b7a255c)\" d=\"M 225.88125 375.958125 \nL 244.637553 375.958125 \nL 244.637553 383.424792 \nL 225.88125 383.424792 \nz\n\" style=\"fill:#d62728;\"/>\n   </g>\n   <g id=\"patch_40\">\n    <path clip-path=\"url(#pf30b7a255c)\" d=\"M 225.88125 385.291458 \nL 240.886292 385.291458 \nL 240.886292 392.758125 \nL 225.88125 392.758125 \nz\n\" style=\"fill:#d62728;\"/>\n   </g>\n   <g id=\"patch_41\">\n    <path clip-path=\"url(#pf30b7a255c)\" d=\"M 225.88125 39.958125 \nL 415.319905 39.958125 \nL 415.319905 47.424792 \nL 225.88125 47.424792 \nz\n\" style=\"fill:#1f77b4;\"/>\n   </g>\n   <g id=\"patch_42\">\n    <path clip-path=\"url(#pf30b7a255c)\" d=\"M 225.88125 49.291458 \nL 344.045956 49.291458 \nL 344.045956 56.758125 \nL 225.88125 56.758125 \nz\n\" style=\"fill:#1f77b4;\"/>\n   </g>\n   <g id=\"patch_43\">\n    <path clip-path=\"url(#pf30b7a255c)\" d=\"M 225.88125 58.624792 \nL 349.672847 58.624792 \nL 349.672847 66.091458 \nL 225.88125 66.091458 \nz\n\" style=\"fill:#1f77b4;\"/>\n   </g>\n   <g id=\"patch_44\">\n    <path clip-path=\"url(#pf30b7a255c)\" d=\"M 225.88125 67.958125 \nL 347.797216 67.958125 \nL 347.797216 75.424792 \nL 225.88125 75.424792 \nz\n\" style=\"fill:#1f77b4;\"/>\n   </g>\n   <g id=\"patch_45\">\n    <path clip-path=\"url(#pf30b7a255c)\" d=\"M 225.88125 77.291458 \nL 325.289653 77.291458 \nL 325.289653 84.758125 \nL 225.88125 84.758125 \nz\n\" style=\"fill:#1f77b4;\"/>\n   </g>\n   <g id=\"patch_46\">\n    <path clip-path=\"url(#pf30b7a255c)\" d=\"M 225.88125 86.624792 \nL 306.533351 86.624792 \nL 306.533351 94.091458 \nL 225.88125 94.091458 \nz\n\" style=\"fill:#1f77b4;\"/>\n   </g>\n   <g id=\"patch_47\">\n    <path clip-path=\"url(#pf30b7a255c)\" d=\"M 225.88125 95.958125 \nL 270.896376 95.958125 \nL 270.896376 103.424792 \nL 225.88125 103.424792 \nz\n\" style=\"fill:#1f77b4;\"/>\n   </g>\n   <g id=\"patch_48\">\n    <path clip-path=\"url(#pf30b7a255c)\" d=\"M 225.88125 105.291458 \nL 287.777048 105.291458 \nL 287.777048 112.758125 \nL 225.88125 112.758125 \nz\n\" style=\"fill:#1f77b4;\"/>\n   </g>\n   <g id=\"patch_49\">\n    <path clip-path=\"url(#pf30b7a255c)\" d=\"M 225.88125 114.624792 \nL 287.777048 114.624792 \nL 287.777048 122.091458 \nL 225.88125 122.091458 \nz\n\" style=\"fill:#1f77b4;\"/>\n   </g>\n   <g id=\"patch_50\">\n    <path clip-path=\"url(#pf30b7a255c)\" d=\"M 225.88125 123.958125 \nL 293.403939 123.958125 \nL 293.403939 131.424792 \nL 225.88125 131.424792 \nz\n\" style=\"fill:#1f77b4;\"/>\n   </g>\n   <g id=\"patch_51\">\n    <path clip-path=\"url(#pf30b7a255c)\" d=\"M 225.88125 133.291458 \nL 267.145116 133.291458 \nL 267.145116 140.758125 \nL 225.88125 140.758125 \nz\n\" style=\"fill:#1f77b4;\"/>\n   </g>\n   <g id=\"patch_52\">\n    <path clip-path=\"url(#pf30b7a255c)\" d=\"M 225.88125 142.624792 \nL 284.025788 142.624792 \nL 284.025788 150.091458 \nL 225.88125 150.091458 \nz\n\" style=\"fill:#1f77b4;\"/>\n   </g>\n   <g id=\"patch_53\">\n    <path clip-path=\"url(#pf30b7a255c)\" d=\"M 225.88125 151.958125 \nL 274.647637 151.958125 \nL 274.647637 159.424792 \nL 225.88125 159.424792 \nz\n\" style=\"fill:#1f77b4;\"/>\n   </g>\n   <g id=\"patch_54\">\n    <path clip-path=\"url(#pf30b7a255c)\" d=\"M 225.88125 161.291458 \nL 278.398897 161.291458 \nL 278.398897 168.758125 \nL 225.88125 168.758125 \nz\n\" style=\"fill:#1f77b4;\"/>\n   </g>\n   <g id=\"patch_55\">\n    <path clip-path=\"url(#pf30b7a255c)\" d=\"M 225.88125 170.624792 \nL 276.523267 170.624792 \nL 276.523267 178.091458 \nL 225.88125 178.091458 \nz\n\" style=\"fill:#1f77b4;\"/>\n   </g>\n   <g id=\"patch_56\">\n    <path clip-path=\"url(#pf30b7a255c)\" d=\"M 225.88125 179.958125 \nL 265.269485 179.958125 \nL 265.269485 187.424792 \nL 225.88125 187.424792 \nz\n\" style=\"fill:#1f77b4;\"/>\n   </g>\n   <g id=\"patch_57\">\n    <path clip-path=\"url(#pf30b7a255c)\" d=\"M 225.88125 189.291458 \nL 276.523267 189.291458 \nL 276.523267 196.758125 \nL 225.88125 196.758125 \nz\n\" style=\"fill:#1f77b4;\"/>\n   </g>\n   <g id=\"patch_58\">\n    <path clip-path=\"url(#pf30b7a255c)\" d=\"M 225.88125 198.624792 \nL 265.269485 198.624792 \nL 265.269485 206.091458 \nL 225.88125 206.091458 \nz\n\" style=\"fill:#1f77b4;\"/>\n   </g>\n   <g id=\"patch_59\">\n    <path clip-path=\"url(#pf30b7a255c)\" d=\"M 225.88125 207.958125 \nL 261.518225 207.958125 \nL 261.518225 215.424792 \nL 225.88125 215.424792 \nz\n\" style=\"fill:#1f77b4;\"/>\n   </g>\n   <g id=\"patch_60\">\n    <path clip-path=\"url(#pf30b7a255c)\" d=\"M 225.88125 217.291458 \nL 265.269485 217.291458 \nL 265.269485 224.758125 \nL 225.88125 224.758125 \nz\n\" style=\"fill:#1f77b4;\"/>\n   </g>\n   <g id=\"patch_61\">\n    <path clip-path=\"url(#pf30b7a255c)\" d=\"M 225.88125 226.624792 \nL 267.145116 226.624792 \nL 267.145116 234.091458 \nL 225.88125 234.091458 \nz\n\" style=\"fill:#1f77b4;\"/>\n   </g>\n   <g id=\"patch_62\">\n    <path clip-path=\"url(#pf30b7a255c)\" d=\"M 225.88125 235.958125 \nL 261.518225 235.958125 \nL 261.518225 243.424792 \nL 225.88125 243.424792 \nz\n\" style=\"fill:#1f77b4;\"/>\n   </g>\n   <g id=\"patch_63\">\n    <path clip-path=\"url(#pf30b7a255c)\" d=\"M 225.88125 245.291458 \nL 261.518225 245.291458 \nL 261.518225 252.758125 \nL 225.88125 252.758125 \nz\n\" style=\"fill:#1f77b4;\"/>\n   </g>\n   <g id=\"patch_64\">\n    <path clip-path=\"url(#pf30b7a255c)\" d=\"M 225.88125 254.624792 \nL 261.518225 254.624792 \nL 261.518225 262.091458 \nL 225.88125 262.091458 \nz\n\" style=\"fill:#1f77b4;\"/>\n   </g>\n   <g id=\"patch_65\">\n    <path clip-path=\"url(#pf30b7a255c)\" d=\"M 225.88125 263.958125 \nL 261.518225 263.958125 \nL 261.518225 271.424792 \nL 225.88125 271.424792 \nz\n\" style=\"fill:#1f77b4;\"/>\n   </g>\n   <g id=\"patch_66\">\n    <path clip-path=\"url(#pf30b7a255c)\" d=\"M 225.88125 273.291458 \nL 263.393855 273.291458 \nL 263.393855 280.758125 \nL 225.88125 280.758125 \nz\n\" style=\"fill:#1f77b4;\"/>\n   </g>\n   <g id=\"patch_67\">\n    <path clip-path=\"url(#pf30b7a255c)\" d=\"M 225.88125 282.624792 \nL 269.020746 282.624792 \nL 269.020746 290.091458 \nL 225.88125 290.091458 \nz\n\" style=\"fill:#1f77b4;\"/>\n   </g>\n   <g id=\"patch_68\">\n    <path clip-path=\"url(#pf30b7a255c)\" d=\"M 225.88125 291.958125 \nL 255.891334 291.958125 \nL 255.891334 299.424792 \nL 225.88125 299.424792 \nz\n\" style=\"fill:#1f77b4;\"/>\n   </g>\n   <g id=\"patch_69\">\n    <path clip-path=\"url(#pf30b7a255c)\" d=\"M 225.88125 301.291458 \nL 248.388813 301.291458 \nL 248.388813 308.758125 \nL 225.88125 308.758125 \nz\n\" style=\"fill:#1f77b4;\"/>\n   </g>\n   <g id=\"patch_70\">\n    <path clip-path=\"url(#pf30b7a255c)\" d=\"M 225.88125 310.624792 \nL 255.891334 310.624792 \nL 255.891334 318.091458 \nL 225.88125 318.091458 \nz\n\" style=\"fill:#1f77b4;\"/>\n   </g>\n   <g id=\"patch_71\">\n    <path clip-path=\"url(#pf30b7a255c)\" d=\"M 225.88125 319.958125 \nL 261.518225 319.958125 \nL 261.518225 327.424792 \nL 225.88125 327.424792 \nz\n\" style=\"fill:#1f77b4;\"/>\n   </g>\n   <g id=\"patch_72\">\n    <path clip-path=\"url(#pf30b7a255c)\" d=\"M 225.88125 329.291458 \nL 255.891334 329.291458 \nL 255.891334 336.758125 \nL 225.88125 336.758125 \nz\n\" style=\"fill:#1f77b4;\"/>\n   </g>\n   <g id=\"patch_73\">\n    <path clip-path=\"url(#pf30b7a255c)\" d=\"M 225.88125 338.624792 \nL 248.388813 338.624792 \nL 248.388813 346.091458 \nL 225.88125 346.091458 \nz\n\" style=\"fill:#1f77b4;\"/>\n   </g>\n   <g id=\"patch_74\">\n    <path clip-path=\"url(#pf30b7a255c)\" d=\"M 225.88125 347.958125 \nL 255.891334 347.958125 \nL 255.891334 355.424792 \nL 225.88125 355.424792 \nz\n\" style=\"fill:#1f77b4;\"/>\n   </g>\n   <g id=\"patch_75\">\n    <path clip-path=\"url(#pf30b7a255c)\" d=\"M 225.88125 357.291458 \nL 240.886292 357.291458 \nL 240.886292 364.758125 \nL 225.88125 364.758125 \nz\n\" style=\"fill:#1f77b4;\"/>\n   </g>\n   <g id=\"patch_76\">\n    <path clip-path=\"url(#pf30b7a255c)\" d=\"M 225.88125 366.624792 \nL 246.513183 366.624792 \nL 246.513183 374.091458 \nL 225.88125 374.091458 \nz\n\" style=\"fill:#1f77b4;\"/>\n   </g>\n   <g id=\"patch_77\">\n    <path clip-path=\"url(#pf30b7a255c)\" d=\"M 225.88125 375.958125 \nL 244.637553 375.958125 \nL 244.637553 383.424792 \nL 225.88125 383.424792 \nz\n\" style=\"fill:#1f77b4;\"/>\n   </g>\n   <g id=\"patch_78\">\n    <path clip-path=\"url(#pf30b7a255c)\" d=\"M 225.88125 385.291458 \nL 240.886292 385.291458 \nL 240.886292 392.758125 \nL 225.88125 392.758125 \nz\n\" style=\"fill:#1f77b4;\"/>\n   </g>\n   <g id=\"matplotlib.axis_1\">\n    <g id=\"xtick_1\">\n     <g id=\"line2d_1\">\n      <defs>\n       <path d=\"M 0 0 \nL 0 3.5 \n\" id=\"m7fc590a4b0\" style=\"stroke:#000000;stroke-width:0.8;\"/>\n      </defs>\n      <g>\n       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"225.88125\" xlink:href=\"#m7fc590a4b0\" y=\"410.398125\"/>\n      </g>\n     </g>\n     <g id=\"text_1\">\n      <!-- 0 -->\n      <defs>\n       <path d=\"M 31.78125 66.40625 \nQ 24.171875 66.40625 20.328125 58.90625 \nQ 16.5 51.421875 16.5 36.375 \nQ 16.5 21.390625 20.328125 13.890625 \nQ 24.171875 6.390625 31.78125 6.390625 \nQ 39.453125 6.390625 43.28125 13.890625 \nQ 47.125 21.390625 47.125 36.375 \nQ 47.125 51.421875 43.28125 58.90625 \nQ 39.453125 66.40625 31.78125 66.40625 \nz\nM 31.78125 74.21875 \nQ 44.046875 74.21875 50.515625 64.515625 \nQ 56.984375 54.828125 56.984375 36.375 \nQ 56.984375 17.96875 50.515625 8.265625 \nQ 44.046875 -1.421875 31.78125 -1.421875 \nQ 19.53125 -1.421875 13.0625 8.265625 \nQ 6.59375 17.96875 6.59375 36.375 \nQ 6.59375 54.828125 13.0625 64.515625 \nQ 19.53125 74.21875 31.78125 74.21875 \nz\n\" id=\"DejaVuSans-48\"/>\n      </defs>\n      <g transform=\"translate(222.7 424.996562)scale(0.1 -0.1)\">\n       <use xlink:href=\"#DejaVuSans-48\"/>\n      </g>\n     </g>\n    </g>\n    <g id=\"xtick_2\">\n     <g id=\"line2d_2\">\n      <g>\n       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"263.393855\" xlink:href=\"#m7fc590a4b0\" y=\"410.398125\"/>\n      </g>\n     </g>\n     <g id=\"text_2\">\n      <!-- 20 -->\n      <defs>\n       <path d=\"M 19.1875 8.296875 \nL 53.609375 8.296875 \nL 53.609375 0 \nL 7.328125 0 \nL 7.328125 8.296875 \nQ 12.9375 14.109375 22.625 23.890625 \nQ 32.328125 33.6875 34.8125 36.53125 \nQ 39.546875 41.84375 41.421875 45.53125 \nQ 43.3125 49.21875 43.3125 52.78125 \nQ 43.3125 58.59375 39.234375 62.25 \nQ 35.15625 65.921875 28.609375 65.921875 \nQ 23.96875 65.921875 18.8125 64.3125 \nQ 13.671875 62.703125 7.8125 59.421875 \nL 7.8125 69.390625 \nQ 13.765625 71.78125 18.9375 73 \nQ 24.125 74.21875 28.421875 74.21875 \nQ 39.75 74.21875 46.484375 68.546875 \nQ 53.21875 62.890625 53.21875 53.421875 \nQ 53.21875 48.921875 51.53125 44.890625 \nQ 49.859375 40.875 45.40625 35.40625 \nQ 44.1875 33.984375 37.640625 27.21875 \nQ 31.109375 20.453125 19.1875 8.296875 \nz\n\" id=\"DejaVuSans-50\"/>\n      </defs>\n      <g transform=\"translate(257.031355 424.996562)scale(0.1 -0.1)\">\n       <use xlink:href=\"#DejaVuSans-50\"/>\n       <use x=\"63.623047\" xlink:href=\"#DejaVuSans-48\"/>\n      </g>\n     </g>\n    </g>\n    <g id=\"xtick_3\">\n     <g id=\"line2d_3\">\n      <g>\n       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"300.90646\" xlink:href=\"#m7fc590a4b0\" y=\"410.398125\"/>\n      </g>\n     </g>\n     <g id=\"text_3\">\n      <!-- 40 -->\n      <defs>\n       <path d=\"M 37.796875 64.3125 \nL 12.890625 25.390625 \nL 37.796875 25.390625 \nz\nM 35.203125 72.90625 \nL 47.609375 72.90625 \nL 47.609375 25.390625 \nL 58.015625 25.390625 \nL 58.015625 17.1875 \nL 47.609375 17.1875 \nL 47.609375 0 \nL 37.796875 0 \nL 37.796875 17.1875 \nL 4.890625 17.1875 \nL 4.890625 26.703125 \nz\n\" id=\"DejaVuSans-52\"/>\n      </defs>\n      <g transform=\"translate(294.54396 424.996562)scale(0.1 -0.1)\">\n       <use xlink:href=\"#DejaVuSans-52\"/>\n       <use x=\"63.623047\" xlink:href=\"#DejaVuSans-48\"/>\n      </g>\n     </g>\n    </g>\n    <g id=\"xtick_4\">\n     <g id=\"line2d_4\">\n      <g>\n       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"338.419065\" xlink:href=\"#m7fc590a4b0\" y=\"410.398125\"/>\n      </g>\n     </g>\n     <g id=\"text_4\">\n      <!-- 60 -->\n      <defs>\n       <path d=\"M 33.015625 40.375 \nQ 26.375 40.375 22.484375 35.828125 \nQ 18.609375 31.296875 18.609375 23.390625 \nQ 18.609375 15.53125 22.484375 10.953125 \nQ 26.375 6.390625 33.015625 6.390625 \nQ 39.65625 6.390625 43.53125 10.953125 \nQ 47.40625 15.53125 47.40625 23.390625 \nQ 47.40625 31.296875 43.53125 35.828125 \nQ 39.65625 40.375 33.015625 40.375 \nz\nM 52.59375 71.296875 \nL 52.59375 62.3125 \nQ 48.875 64.0625 45.09375 64.984375 \nQ 41.3125 65.921875 37.59375 65.921875 \nQ 27.828125 65.921875 22.671875 59.328125 \nQ 17.53125 52.734375 16.796875 39.40625 \nQ 19.671875 43.65625 24.015625 45.921875 \nQ 28.375 48.1875 33.59375 48.1875 \nQ 44.578125 48.1875 50.953125 41.515625 \nQ 57.328125 34.859375 57.328125 23.390625 \nQ 57.328125 12.15625 50.6875 5.359375 \nQ 44.046875 -1.421875 33.015625 -1.421875 \nQ 20.359375 -1.421875 13.671875 8.265625 \nQ 6.984375 17.96875 6.984375 36.375 \nQ 6.984375 53.65625 15.1875 63.9375 \nQ 23.390625 74.21875 37.203125 74.21875 \nQ 40.921875 74.21875 44.703125 73.484375 \nQ 48.484375 72.75 52.59375 71.296875 \nz\n\" id=\"DejaVuSans-54\"/>\n      </defs>\n      <g transform=\"translate(332.056565 424.996562)scale(0.1 -0.1)\">\n       <use xlink:href=\"#DejaVuSans-54\"/>\n       <use x=\"63.623047\" xlink:href=\"#DejaVuSans-48\"/>\n      </g>\n     </g>\n    </g>\n    <g id=\"xtick_5\">\n     <g id=\"line2d_5\">\n      <g>\n       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"375.93167\" xlink:href=\"#m7fc590a4b0\" y=\"410.398125\"/>\n      </g>\n     </g>\n     <g id=\"text_5\">\n      <!-- 80 -->\n      <defs>\n       <path d=\"M 31.78125 34.625 \nQ 24.75 34.625 20.71875 30.859375 \nQ 16.703125 27.09375 16.703125 20.515625 \nQ 16.703125 13.921875 20.71875 10.15625 \nQ 24.75 6.390625 31.78125 6.390625 \nQ 38.8125 6.390625 42.859375 10.171875 \nQ 46.921875 13.96875 46.921875 20.515625 \nQ 46.921875 27.09375 42.890625 30.859375 \nQ 38.875 34.625 31.78125 34.625 \nz\nM 21.921875 38.8125 \nQ 15.578125 40.375 12.03125 44.71875 \nQ 8.5 49.078125 8.5 55.328125 \nQ 8.5 64.0625 14.71875 69.140625 \nQ 20.953125 74.21875 31.78125 74.21875 \nQ 42.671875 74.21875 48.875 69.140625 \nQ 55.078125 64.0625 55.078125 55.328125 \nQ 55.078125 49.078125 51.53125 44.71875 \nQ 48 40.375 41.703125 38.8125 \nQ 48.828125 37.15625 52.796875 32.3125 \nQ 56.78125 27.484375 56.78125 20.515625 \nQ 56.78125 9.90625 50.3125 4.234375 \nQ 43.84375 -1.421875 31.78125 -1.421875 \nQ 19.734375 -1.421875 13.25 4.234375 \nQ 6.78125 9.90625 6.78125 20.515625 \nQ 6.78125 27.484375 10.78125 32.3125 \nQ 14.796875 37.15625 21.921875 38.8125 \nz\nM 18.3125 54.390625 \nQ 18.3125 48.734375 21.84375 45.5625 \nQ 25.390625 42.390625 31.78125 42.390625 \nQ 38.140625 42.390625 41.71875 45.5625 \nQ 45.3125 48.734375 45.3125 54.390625 \nQ 45.3125 60.0625 41.71875 63.234375 \nQ 38.140625 66.40625 31.78125 66.40625 \nQ 25.390625 66.40625 21.84375 63.234375 \nQ 18.3125 60.0625 18.3125 54.390625 \nz\n\" id=\"DejaVuSans-56\"/>\n      </defs>\n      <g transform=\"translate(369.56917 424.996562)scale(0.1 -0.1)\">\n       <use xlink:href=\"#DejaVuSans-56\"/>\n       <use x=\"63.623047\" xlink:href=\"#DejaVuSans-48\"/>\n      </g>\n     </g>\n    </g>\n    <g id=\"xtick_6\">\n     <g id=\"line2d_6\">\n      <g>\n       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"413.444275\" xlink:href=\"#m7fc590a4b0\" y=\"410.398125\"/>\n      </g>\n     </g>\n     <g id=\"text_6\">\n      <!-- 100 -->\n      <defs>\n       <path d=\"M 12.40625 8.296875 \nL 28.515625 8.296875 \nL 28.515625 63.921875 \nL 10.984375 60.40625 \nL 10.984375 69.390625 \nL 28.421875 72.90625 \nL 38.28125 72.90625 \nL 38.28125 8.296875 \nL 54.390625 8.296875 \nL 54.390625 0 \nL 12.40625 0 \nz\n\" id=\"DejaVuSans-49\"/>\n      </defs>\n      <g transform=\"translate(403.900525 424.996562)scale(0.1 -0.1)\">\n       <use xlink:href=\"#DejaVuSans-49\"/>\n       <use x=\"63.623047\" xlink:href=\"#DejaVuSans-48\"/>\n       <use x=\"127.246094\" xlink:href=\"#DejaVuSans-48\"/>\n      </g>\n     </g>\n    </g>\n    <g id=\"xtick_7\">\n     <g id=\"line2d_7\">\n      <g>\n       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"450.95688\" xlink:href=\"#m7fc590a4b0\" y=\"410.398125\"/>\n      </g>\n     </g>\n     <g id=\"text_7\">\n      <!-- 120 -->\n      <g transform=\"translate(441.41313 424.996562)scale(0.1 -0.1)\">\n       <use xlink:href=\"#DejaVuSans-49\"/>\n       <use x=\"63.623047\" xlink:href=\"#DejaVuSans-50\"/>\n       <use x=\"127.246094\" xlink:href=\"#DejaVuSans-48\"/>\n      </g>\n     </g>\n    </g>\n    <g id=\"xtick_8\">\n     <g id=\"line2d_8\">\n      <g>\n       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"488.469485\" xlink:href=\"#m7fc590a4b0\" y=\"410.398125\"/>\n      </g>\n     </g>\n     <g id=\"text_8\">\n      <!-- 140 -->\n      <g transform=\"translate(478.925735 424.996562)scale(0.1 -0.1)\">\n       <use xlink:href=\"#DejaVuSans-49\"/>\n       <use x=\"63.623047\" xlink:href=\"#DejaVuSans-52\"/>\n       <use x=\"127.246094\" xlink:href=\"#DejaVuSans-48\"/>\n      </g>\n     </g>\n    </g>\n    <g id=\"xtick_9\">\n     <g id=\"line2d_9\">\n      <g>\n       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"525.98209\" xlink:href=\"#m7fc590a4b0\" y=\"410.398125\"/>\n      </g>\n     </g>\n     <g id=\"text_9\">\n      <!-- 160 -->\n      <g transform=\"translate(516.43834 424.996562)scale(0.1 -0.1)\">\n       <use xlink:href=\"#DejaVuSans-49\"/>\n       <use x=\"63.623047\" xlink:href=\"#DejaVuSans-54\"/>\n       <use x=\"127.246094\" xlink:href=\"#DejaVuSans-48\"/>\n      </g>\n     </g>\n    </g>\n    <g id=\"text_10\">\n     <!-- Papers -->\n     <defs>\n      <path d=\"M 19.671875 64.796875 \nL 19.671875 37.40625 \nL 32.078125 37.40625 \nQ 38.96875 37.40625 42.71875 40.96875 \nQ 46.484375 44.53125 46.484375 51.125 \nQ 46.484375 57.671875 42.71875 61.234375 \nQ 38.96875 64.796875 32.078125 64.796875 \nz\nM 9.8125 72.90625 \nL 32.078125 72.90625 \nQ 44.34375 72.90625 50.609375 67.359375 \nQ 56.890625 61.8125 56.890625 51.125 \nQ 56.890625 40.328125 50.609375 34.8125 \nQ 44.34375 29.296875 32.078125 29.296875 \nL 19.671875 29.296875 \nL 19.671875 0 \nL 9.8125 0 \nz\n\" id=\"DejaVuSans-80\"/>\n      <path d=\"M 34.28125 27.484375 \nQ 23.390625 27.484375 19.1875 25 \nQ 14.984375 22.515625 14.984375 16.5 \nQ 14.984375 11.71875 18.140625 8.90625 \nQ 21.296875 6.109375 26.703125 6.109375 \nQ 34.1875 6.109375 38.703125 11.40625 \nQ 43.21875 16.703125 43.21875 25.484375 \nL 43.21875 27.484375 \nz\nM 52.203125 31.203125 \nL 52.203125 0 \nL 43.21875 0 \nL 43.21875 8.296875 \nQ 40.140625 3.328125 35.546875 0.953125 \nQ 30.953125 -1.421875 24.3125 -1.421875 \nQ 15.921875 -1.421875 10.953125 3.296875 \nQ 6 8.015625 6 15.921875 \nQ 6 25.140625 12.171875 29.828125 \nQ 18.359375 34.515625 30.609375 34.515625 \nL 43.21875 34.515625 \nL 43.21875 35.40625 \nQ 43.21875 41.609375 39.140625 45 \nQ 35.0625 48.390625 27.6875 48.390625 \nQ 23 48.390625 18.546875 47.265625 \nQ 14.109375 46.140625 10.015625 43.890625 \nL 10.015625 52.203125 \nQ 14.9375 54.109375 19.578125 55.046875 \nQ 24.21875 56 28.609375 56 \nQ 40.484375 56 46.34375 49.84375 \nQ 52.203125 43.703125 52.203125 31.203125 \nz\n\" id=\"DejaVuSans-97\"/>\n      <path d=\"M 18.109375 8.203125 \nL 18.109375 -20.796875 \nL 9.078125 -20.796875 \nL 9.078125 54.6875 \nL 18.109375 54.6875 \nL 18.109375 46.390625 \nQ 20.953125 51.265625 25.265625 53.625 \nQ 29.59375 56 35.59375 56 \nQ 45.5625 56 51.78125 48.09375 \nQ 58.015625 40.1875 58.015625 27.296875 \nQ 58.015625 14.40625 51.78125 6.484375 \nQ 45.5625 -1.421875 35.59375 -1.421875 \nQ 29.59375 -1.421875 25.265625 0.953125 \nQ 20.953125 3.328125 18.109375 8.203125 \nz\nM 48.6875 27.296875 \nQ 48.6875 37.203125 44.609375 42.84375 \nQ 40.53125 48.484375 33.40625 48.484375 \nQ 26.265625 48.484375 22.1875 42.84375 \nQ 18.109375 37.203125 18.109375 27.296875 \nQ 18.109375 17.390625 22.1875 11.75 \nQ 26.265625 6.109375 33.40625 6.109375 \nQ 40.53125 6.109375 44.609375 11.75 \nQ 48.6875 17.390625 48.6875 27.296875 \nz\n\" id=\"DejaVuSans-112\"/>\n      <path d=\"M 56.203125 29.59375 \nL 56.203125 25.203125 \nL 14.890625 25.203125 \nQ 15.484375 15.921875 20.484375 11.0625 \nQ 25.484375 6.203125 34.421875 6.203125 \nQ 39.59375 6.203125 44.453125 7.46875 \nQ 49.3125 8.734375 54.109375 11.28125 \nL 54.109375 2.78125 \nQ 49.265625 0.734375 44.1875 -0.34375 \nQ 39.109375 -1.421875 33.890625 -1.421875 \nQ 20.796875 -1.421875 13.15625 6.1875 \nQ 5.515625 13.8125 5.515625 26.8125 \nQ 5.515625 40.234375 12.765625 48.109375 \nQ 20.015625 56 32.328125 56 \nQ 43.359375 56 49.78125 48.890625 \nQ 56.203125 41.796875 56.203125 29.59375 \nz\nM 47.21875 32.234375 \nQ 47.125 39.59375 43.09375 43.984375 \nQ 39.0625 48.390625 32.421875 48.390625 \nQ 24.90625 48.390625 20.390625 44.140625 \nQ 15.875 39.890625 15.1875 32.171875 \nz\n\" id=\"DejaVuSans-101\"/>\n      <path d=\"M 41.109375 46.296875 \nQ 39.59375 47.171875 37.8125 47.578125 \nQ 36.03125 48 33.890625 48 \nQ 26.265625 48 22.1875 43.046875 \nQ 18.109375 38.09375 18.109375 28.8125 \nL 18.109375 0 \nL 9.078125 0 \nL 9.078125 54.6875 \nL 18.109375 54.6875 \nL 18.109375 46.1875 \nQ 20.953125 51.171875 25.484375 53.578125 \nQ 30.03125 56 36.53125 56 \nQ 37.453125 56 38.578125 55.875 \nQ 39.703125 55.765625 41.0625 55.515625 \nz\n\" id=\"DejaVuSans-114\"/>\n      <path d=\"M 44.28125 53.078125 \nL 44.28125 44.578125 \nQ 40.484375 46.53125 36.375 47.5 \nQ 32.28125 48.484375 27.875 48.484375 \nQ 21.1875 48.484375 17.84375 46.4375 \nQ 14.5 44.390625 14.5 40.28125 \nQ 14.5 37.15625 16.890625 35.375 \nQ 19.28125 33.59375 26.515625 31.984375 \nL 29.59375 31.296875 \nQ 39.15625 29.25 43.1875 25.515625 \nQ 47.21875 21.78125 47.21875 15.09375 \nQ 47.21875 7.46875 41.1875 3.015625 \nQ 35.15625 -1.421875 24.609375 -1.421875 \nQ 20.21875 -1.421875 15.453125 -0.5625 \nQ 10.6875 0.296875 5.421875 2 \nL 5.421875 11.28125 \nQ 10.40625 8.6875 15.234375 7.390625 \nQ 20.0625 6.109375 24.8125 6.109375 \nQ 31.15625 6.109375 34.5625 8.28125 \nQ 37.984375 10.453125 37.984375 14.40625 \nQ 37.984375 18.0625 35.515625 20.015625 \nQ 33.0625 21.96875 24.703125 23.78125 \nL 21.578125 24.515625 \nQ 13.234375 26.265625 9.515625 29.90625 \nQ 5.8125 33.546875 5.8125 39.890625 \nQ 5.8125 47.609375 11.28125 51.796875 \nQ 16.75 56 26.8125 56 \nQ 31.78125 56 36.171875 55.265625 \nQ 40.578125 54.546875 44.28125 53.078125 \nz\n\" id=\"DejaVuSans-115\"/>\n     </defs>\n     <g transform=\"translate(376.516406 438.674687)scale(0.1 -0.1)\">\n      <use xlink:href=\"#DejaVuSans-80\"/>\n      <use x=\"55.802734\" xlink:href=\"#DejaVuSans-97\"/>\n      <use x=\"117.082031\" xlink:href=\"#DejaVuSans-112\"/>\n      <use x=\"180.558594\" xlink:href=\"#DejaVuSans-101\"/>\n      <use x=\"242.082031\" xlink:href=\"#DejaVuSans-114\"/>\n      <use x=\"283.195312\" xlink:href=\"#DejaVuSans-115\"/>\n     </g>\n    </g>\n   </g>\n   <g id=\"matplotlib.axis_2\">\n    <g id=\"ytick_1\">\n     <g id=\"line2d_10\">\n      <defs>\n       <path d=\"M 0 0 \nL -3.5 0 \n\" id=\"md95a43e5ce\" style=\"stroke:#000000;stroke-width:0.8;\"/>\n      </defs>\n      <g>\n       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"225.88125\" xlink:href=\"#md95a43e5ce\" y=\"43.691458\"/>\n      </g>\n     </g>\n     <g id=\"text_11\">\n      <!-- Google, Google Brain, DeepMind -->\n      <defs>\n       <path d=\"M 59.515625 10.40625 \nL 59.515625 29.984375 \nL 43.40625 29.984375 \nL 43.40625 38.09375 \nL 69.28125 38.09375 \nL 69.28125 6.78125 \nQ 63.578125 2.734375 56.6875 0.65625 \nQ 49.8125 -1.421875 42 -1.421875 \nQ 24.90625 -1.421875 15.25 8.5625 \nQ 5.609375 18.5625 5.609375 36.375 \nQ 5.609375 54.25 15.25 64.234375 \nQ 24.90625 74.21875 42 74.21875 \nQ 49.125 74.21875 55.546875 72.453125 \nQ 61.96875 70.703125 67.390625 67.28125 \nL 67.390625 56.78125 \nQ 61.921875 61.421875 55.765625 63.765625 \nQ 49.609375 66.109375 42.828125 66.109375 \nQ 29.4375 66.109375 22.71875 58.640625 \nQ 16.015625 51.171875 16.015625 36.375 \nQ 16.015625 21.625 22.71875 14.15625 \nQ 29.4375 6.6875 42.828125 6.6875 \nQ 48.046875 6.6875 52.140625 7.59375 \nQ 56.25 8.5 59.515625 10.40625 \nz\n\" id=\"DejaVuSans-71\"/>\n       <path d=\"M 30.609375 48.390625 \nQ 23.390625 48.390625 19.1875 42.75 \nQ 14.984375 37.109375 14.984375 27.296875 \nQ 14.984375 17.484375 19.15625 11.84375 \nQ 23.34375 6.203125 30.609375 6.203125 \nQ 37.796875 6.203125 41.984375 11.859375 \nQ 46.1875 17.53125 46.1875 27.296875 \nQ 46.1875 37.015625 41.984375 42.703125 \nQ 37.796875 48.390625 30.609375 48.390625 \nz\nM 30.609375 56 \nQ 42.328125 56 49.015625 48.375 \nQ 55.71875 40.765625 55.71875 27.296875 \nQ 55.71875 13.875 49.015625 6.21875 \nQ 42.328125 -1.421875 30.609375 -1.421875 \nQ 18.84375 -1.421875 12.171875 6.21875 \nQ 5.515625 13.875 5.515625 27.296875 \nQ 5.515625 40.765625 12.171875 48.375 \nQ 18.84375 56 30.609375 56 \nz\n\" id=\"DejaVuSans-111\"/>\n       <path d=\"M 45.40625 27.984375 \nQ 45.40625 37.75 41.375 43.109375 \nQ 37.359375 48.484375 30.078125 48.484375 \nQ 22.859375 48.484375 18.828125 43.109375 \nQ 14.796875 37.75 14.796875 27.984375 \nQ 14.796875 18.265625 18.828125 12.890625 \nQ 22.859375 7.515625 30.078125 7.515625 \nQ 37.359375 7.515625 41.375 12.890625 \nQ 45.40625 18.265625 45.40625 27.984375 \nz\nM 54.390625 6.78125 \nQ 54.390625 -7.171875 48.1875 -13.984375 \nQ 42 -20.796875 29.203125 -20.796875 \nQ 24.46875 -20.796875 20.265625 -20.09375 \nQ 16.0625 -19.390625 12.109375 -17.921875 \nL 12.109375 -9.1875 \nQ 16.0625 -11.328125 19.921875 -12.34375 \nQ 23.78125 -13.375 27.78125 -13.375 \nQ 36.625 -13.375 41.015625 -8.765625 \nQ 45.40625 -4.15625 45.40625 5.171875 \nL 45.40625 9.625 \nQ 42.625 4.78125 38.28125 2.390625 \nQ 33.9375 0 27.875 0 \nQ 17.828125 0 11.671875 7.65625 \nQ 5.515625 15.328125 5.515625 27.984375 \nQ 5.515625 40.671875 11.671875 48.328125 \nQ 17.828125 56 27.875 56 \nQ 33.9375 56 38.28125 53.609375 \nQ 42.625 51.21875 45.40625 46.390625 \nL 45.40625 54.6875 \nL 54.390625 54.6875 \nz\n\" id=\"DejaVuSans-103\"/>\n       <path d=\"M 9.421875 75.984375 \nL 18.40625 75.984375 \nL 18.40625 0 \nL 9.421875 0 \nz\n\" id=\"DejaVuSans-108\"/>\n       <path d=\"M 11.71875 12.40625 \nL 22.015625 12.40625 \nL 22.015625 4 \nL 14.015625 -11.625 \nL 7.71875 -11.625 \nL 11.71875 4 \nz\n\" id=\"DejaVuSans-44\"/>\n       <path id=\"DejaVuSans-32\"/>\n       <path d=\"M 19.671875 34.8125 \nL 19.671875 8.109375 \nL 35.5 8.109375 \nQ 43.453125 8.109375 47.28125 11.40625 \nQ 51.125 14.703125 51.125 21.484375 \nQ 51.125 28.328125 47.28125 31.5625 \nQ 43.453125 34.8125 35.5 34.8125 \nz\nM 19.671875 64.796875 \nL 19.671875 42.828125 \nL 34.28125 42.828125 \nQ 41.5 42.828125 45.03125 45.53125 \nQ 48.578125 48.25 48.578125 53.8125 \nQ 48.578125 59.328125 45.03125 62.0625 \nQ 41.5 64.796875 34.28125 64.796875 \nz\nM 9.8125 72.90625 \nL 35.015625 72.90625 \nQ 46.296875 72.90625 52.390625 68.21875 \nQ 58.5 63.53125 58.5 54.890625 \nQ 58.5 48.1875 55.375 44.234375 \nQ 52.25 40.28125 46.1875 39.3125 \nQ 53.46875 37.75 57.5 32.78125 \nQ 61.53125 27.828125 61.53125 20.40625 \nQ 61.53125 10.640625 54.890625 5.3125 \nQ 48.25 0 35.984375 0 \nL 9.8125 0 \nz\n\" id=\"DejaVuSans-66\"/>\n       <path d=\"M 9.421875 54.6875 \nL 18.40625 54.6875 \nL 18.40625 0 \nL 9.421875 0 \nz\nM 9.421875 75.984375 \nL 18.40625 75.984375 \nL 18.40625 64.59375 \nL 9.421875 64.59375 \nz\n\" id=\"DejaVuSans-105\"/>\n       <path d=\"M 54.890625 33.015625 \nL 54.890625 0 \nL 45.90625 0 \nL 45.90625 32.71875 \nQ 45.90625 40.484375 42.875 44.328125 \nQ 39.84375 48.1875 33.796875 48.1875 \nQ 26.515625 48.1875 22.3125 43.546875 \nQ 18.109375 38.921875 18.109375 30.90625 \nL 18.109375 0 \nL 9.078125 0 \nL 9.078125 54.6875 \nL 18.109375 54.6875 \nL 18.109375 46.1875 \nQ 21.34375 51.125 25.703125 53.5625 \nQ 30.078125 56 35.796875 56 \nQ 45.21875 56 50.046875 50.171875 \nQ 54.890625 44.34375 54.890625 33.015625 \nz\n\" id=\"DejaVuSans-110\"/>\n       <path d=\"M 19.671875 64.796875 \nL 19.671875 8.109375 \nL 31.59375 8.109375 \nQ 46.6875 8.109375 53.6875 14.9375 \nQ 60.6875 21.78125 60.6875 36.53125 \nQ 60.6875 51.171875 53.6875 57.984375 \nQ 46.6875 64.796875 31.59375 64.796875 \nz\nM 9.8125 72.90625 \nL 30.078125 72.90625 \nQ 51.265625 72.90625 61.171875 64.09375 \nQ 71.09375 55.28125 71.09375 36.53125 \nQ 71.09375 17.671875 61.125 8.828125 \nQ 51.171875 0 30.078125 0 \nL 9.8125 0 \nz\n\" id=\"DejaVuSans-68\"/>\n       <path d=\"M 9.8125 72.90625 \nL 24.515625 72.90625 \nL 43.109375 23.296875 \nL 61.8125 72.90625 \nL 76.515625 72.90625 \nL 76.515625 0 \nL 66.890625 0 \nL 66.890625 64.015625 \nL 48.09375 14.015625 \nL 38.1875 14.015625 \nL 19.390625 64.015625 \nL 19.390625 0 \nL 9.8125 0 \nz\n\" id=\"DejaVuSans-77\"/>\n       <path d=\"M 45.40625 46.390625 \nL 45.40625 75.984375 \nL 54.390625 75.984375 \nL 54.390625 0 \nL 45.40625 0 \nL 45.40625 8.203125 \nQ 42.578125 3.328125 38.25 0.953125 \nQ 33.9375 -1.421875 27.875 -1.421875 \nQ 17.96875 -1.421875 11.734375 6.484375 \nQ 5.515625 14.40625 5.515625 27.296875 \nQ 5.515625 40.1875 11.734375 48.09375 \nQ 17.96875 56 27.875 56 \nQ 33.9375 56 38.25 53.625 \nQ 42.578125 51.265625 45.40625 46.390625 \nz\nM 14.796875 27.296875 \nQ 14.796875 17.390625 18.875 11.75 \nQ 22.953125 6.109375 30.078125 6.109375 \nQ 37.203125 6.109375 41.296875 11.75 \nQ 45.40625 17.390625 45.40625 27.296875 \nQ 45.40625 37.203125 41.296875 42.84375 \nQ 37.203125 48.484375 30.078125 48.484375 \nQ 22.953125 48.484375 18.875 42.84375 \nQ 14.796875 37.203125 14.796875 27.296875 \nz\n\" id=\"DejaVuSans-100\"/>\n      </defs>\n      <g transform=\"translate(55.796875 47.490677)scale(0.1 -0.1)\">\n       <use xlink:href=\"#DejaVuSans-71\"/>\n       <use x=\"77.490234\" xlink:href=\"#DejaVuSans-111\"/>\n       <use x=\"138.671875\" xlink:href=\"#DejaVuSans-111\"/>\n       <use x=\"199.853516\" xlink:href=\"#DejaVuSans-103\"/>\n       <use x=\"263.330078\" xlink:href=\"#DejaVuSans-108\"/>\n       <use x=\"291.113281\" xlink:href=\"#DejaVuSans-101\"/>\n       <use x=\"352.636719\" xlink:href=\"#DejaVuSans-44\"/>\n       <use x=\"384.423828\" xlink:href=\"#DejaVuSans-32\"/>\n       <use x=\"416.210938\" xlink:href=\"#DejaVuSans-71\"/>\n       <use x=\"493.701172\" xlink:href=\"#DejaVuSans-111\"/>\n       <use x=\"554.882812\" xlink:href=\"#DejaVuSans-111\"/>\n       <use x=\"616.064453\" xlink:href=\"#DejaVuSans-103\"/>\n       <use x=\"679.541016\" xlink:href=\"#DejaVuSans-108\"/>\n       <use x=\"707.324219\" xlink:href=\"#DejaVuSans-101\"/>\n       <use x=\"768.847656\" xlink:href=\"#DejaVuSans-32\"/>\n       <use x=\"800.634766\" xlink:href=\"#DejaVuSans-66\"/>\n       <use x=\"869.238281\" xlink:href=\"#DejaVuSans-114\"/>\n       <use x=\"910.351562\" xlink:href=\"#DejaVuSans-97\"/>\n       <use x=\"971.630859\" xlink:href=\"#DejaVuSans-105\"/>\n       <use x=\"999.414062\" xlink:href=\"#DejaVuSans-110\"/>\n       <use x=\"1062.792969\" xlink:href=\"#DejaVuSans-44\"/>\n       <use x=\"1094.580078\" xlink:href=\"#DejaVuSans-32\"/>\n       <use x=\"1126.367188\" xlink:href=\"#DejaVuSans-68\"/>\n       <use x=\"1203.369141\" xlink:href=\"#DejaVuSans-101\"/>\n       <use x=\"1264.892578\" xlink:href=\"#DejaVuSans-101\"/>\n       <use x=\"1326.416016\" xlink:href=\"#DejaVuSans-112\"/>\n       <use x=\"1389.892578\" xlink:href=\"#DejaVuSans-77\"/>\n       <use x=\"1476.171875\" xlink:href=\"#DejaVuSans-105\"/>\n       <use x=\"1503.955078\" xlink:href=\"#DejaVuSans-110\"/>\n       <use x=\"1567.333984\" xlink:href=\"#DejaVuSans-100\"/>\n      </g>\n     </g>\n    </g>\n    <g id=\"ytick_2\">\n     <g id=\"line2d_11\">\n      <g>\n       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"225.88125\" xlink:href=\"#md95a43e5ce\" y=\"53.024792\"/>\n      </g>\n     </g>\n     <g id=\"text_12\">\n      <!-- Massachusetts Institute of Technology -->\n      <defs>\n       <path d=\"M 48.78125 52.59375 \nL 48.78125 44.1875 \nQ 44.96875 46.296875 41.140625 47.34375 \nQ 37.3125 48.390625 33.40625 48.390625 \nQ 24.65625 48.390625 19.8125 42.84375 \nQ 14.984375 37.3125 14.984375 27.296875 \nQ 14.984375 17.28125 19.8125 11.734375 \nQ 24.65625 6.203125 33.40625 6.203125 \nQ 37.3125 6.203125 41.140625 7.25 \nQ 44.96875 8.296875 48.78125 10.40625 \nL 48.78125 2.09375 \nQ 45.015625 0.34375 40.984375 -0.53125 \nQ 36.96875 -1.421875 32.421875 -1.421875 \nQ 20.0625 -1.421875 12.78125 6.34375 \nQ 5.515625 14.109375 5.515625 27.296875 \nQ 5.515625 40.671875 12.859375 48.328125 \nQ 20.21875 56 33.015625 56 \nQ 37.15625 56 41.109375 55.140625 \nQ 45.0625 54.296875 48.78125 52.59375 \nz\n\" id=\"DejaVuSans-99\"/>\n       <path d=\"M 54.890625 33.015625 \nL 54.890625 0 \nL 45.90625 0 \nL 45.90625 32.71875 \nQ 45.90625 40.484375 42.875 44.328125 \nQ 39.84375 48.1875 33.796875 48.1875 \nQ 26.515625 48.1875 22.3125 43.546875 \nQ 18.109375 38.921875 18.109375 30.90625 \nL 18.109375 0 \nL 9.078125 0 \nL 9.078125 75.984375 \nL 18.109375 75.984375 \nL 18.109375 46.1875 \nQ 21.34375 51.125 25.703125 53.5625 \nQ 30.078125 56 35.796875 56 \nQ 45.21875 56 50.046875 50.171875 \nQ 54.890625 44.34375 54.890625 33.015625 \nz\n\" id=\"DejaVuSans-104\"/>\n       <path d=\"M 8.5 21.578125 \nL 8.5 54.6875 \nL 17.484375 54.6875 \nL 17.484375 21.921875 \nQ 17.484375 14.15625 20.5 10.265625 \nQ 23.53125 6.390625 29.59375 6.390625 \nQ 36.859375 6.390625 41.078125 11.03125 \nQ 45.3125 15.671875 45.3125 23.6875 \nL 45.3125 54.6875 \nL 54.296875 54.6875 \nL 54.296875 0 \nL 45.3125 0 \nL 45.3125 8.40625 \nQ 42.046875 3.421875 37.71875 1 \nQ 33.40625 -1.421875 27.6875 -1.421875 \nQ 18.265625 -1.421875 13.375 4.4375 \nQ 8.5 10.296875 8.5 21.578125 \nz\nM 31.109375 56 \nz\n\" id=\"DejaVuSans-117\"/>\n       <path d=\"M 18.3125 70.21875 \nL 18.3125 54.6875 \nL 36.8125 54.6875 \nL 36.8125 47.703125 \nL 18.3125 47.703125 \nL 18.3125 18.015625 \nQ 18.3125 11.328125 20.140625 9.421875 \nQ 21.96875 7.515625 27.59375 7.515625 \nL 36.8125 7.515625 \nL 36.8125 0 \nL 27.59375 0 \nQ 17.1875 0 13.234375 3.875 \nQ 9.28125 7.765625 9.28125 18.015625 \nL 9.28125 47.703125 \nL 2.6875 47.703125 \nL 2.6875 54.6875 \nL 9.28125 54.6875 \nL 9.28125 70.21875 \nz\n\" id=\"DejaVuSans-116\"/>\n       <path d=\"M 9.8125 72.90625 \nL 19.671875 72.90625 \nL 19.671875 0 \nL 9.8125 0 \nz\n\" id=\"DejaVuSans-73\"/>\n       <path d=\"M 37.109375 75.984375 \nL 37.109375 68.5 \nL 28.515625 68.5 \nQ 23.6875 68.5 21.796875 66.546875 \nQ 19.921875 64.59375 19.921875 59.515625 \nL 19.921875 54.6875 \nL 34.71875 54.6875 \nL 34.71875 47.703125 \nL 19.921875 47.703125 \nL 19.921875 0 \nL 10.890625 0 \nL 10.890625 47.703125 \nL 2.296875 47.703125 \nL 2.296875 54.6875 \nL 10.890625 54.6875 \nL 10.890625 58.5 \nQ 10.890625 67.625 15.140625 71.796875 \nQ 19.390625 75.984375 28.609375 75.984375 \nz\n\" id=\"DejaVuSans-102\"/>\n       <path d=\"M -0.296875 72.90625 \nL 61.375 72.90625 \nL 61.375 64.59375 \nL 35.5 64.59375 \nL 35.5 0 \nL 25.59375 0 \nL 25.59375 64.59375 \nL -0.296875 64.59375 \nz\n\" id=\"DejaVuSans-84\"/>\n       <path d=\"M 32.171875 -5.078125 \nQ 28.375 -14.84375 24.75 -17.8125 \nQ 21.140625 -20.796875 15.09375 -20.796875 \nL 7.90625 -20.796875 \nL 7.90625 -13.28125 \nL 13.1875 -13.28125 \nQ 16.890625 -13.28125 18.9375 -11.515625 \nQ 21 -9.765625 23.484375 -3.21875 \nL 25.09375 0.875 \nL 2.984375 54.6875 \nL 12.5 54.6875 \nL 29.59375 11.921875 \nL 46.6875 54.6875 \nL 56.203125 54.6875 \nz\n\" id=\"DejaVuSans-121\"/>\n      </defs>\n      <g transform=\"translate(28.275 56.82401)scale(0.1 -0.1)\">\n       <use xlink:href=\"#DejaVuSans-77\"/>\n       <use x=\"86.279297\" xlink:href=\"#DejaVuSans-97\"/>\n       <use x=\"147.558594\" xlink:href=\"#DejaVuSans-115\"/>\n       <use x=\"199.658203\" xlink:href=\"#DejaVuSans-115\"/>\n       <use x=\"251.757812\" xlink:href=\"#DejaVuSans-97\"/>\n       <use x=\"313.037109\" xlink:href=\"#DejaVuSans-99\"/>\n       <use x=\"368.017578\" xlink:href=\"#DejaVuSans-104\"/>\n       <use x=\"431.396484\" xlink:href=\"#DejaVuSans-117\"/>\n       <use x=\"494.775391\" xlink:href=\"#DejaVuSans-115\"/>\n       <use x=\"546.875\" xlink:href=\"#DejaVuSans-101\"/>\n       <use x=\"608.398438\" xlink:href=\"#DejaVuSans-116\"/>\n       <use x=\"647.607422\" xlink:href=\"#DejaVuSans-116\"/>\n       <use x=\"686.816406\" xlink:href=\"#DejaVuSans-115\"/>\n       <use x=\"738.916016\" xlink:href=\"#DejaVuSans-32\"/>\n       <use x=\"770.703125\" xlink:href=\"#DejaVuSans-73\"/>\n       <use x=\"800.195312\" xlink:href=\"#DejaVuSans-110\"/>\n       <use x=\"863.574219\" xlink:href=\"#DejaVuSans-115\"/>\n       <use x=\"915.673828\" xlink:href=\"#DejaVuSans-116\"/>\n       <use x=\"954.882812\" xlink:href=\"#DejaVuSans-105\"/>\n       <use x=\"982.666016\" xlink:href=\"#DejaVuSans-116\"/>\n       <use x=\"1021.875\" xlink:href=\"#DejaVuSans-117\"/>\n       <use x=\"1085.253906\" xlink:href=\"#DejaVuSans-116\"/>\n       <use x=\"1124.462891\" xlink:href=\"#DejaVuSans-101\"/>\n       <use x=\"1185.986328\" xlink:href=\"#DejaVuSans-32\"/>\n       <use x=\"1217.773438\" xlink:href=\"#DejaVuSans-111\"/>\n       <use x=\"1278.955078\" xlink:href=\"#DejaVuSans-102\"/>\n       <use x=\"1314.160156\" xlink:href=\"#DejaVuSans-32\"/>\n       <use x=\"1345.947266\" xlink:href=\"#DejaVuSans-84\"/>\n       <use x=\"1390.03125\" xlink:href=\"#DejaVuSans-101\"/>\n       <use x=\"1451.554688\" xlink:href=\"#DejaVuSans-99\"/>\n       <use x=\"1506.535156\" xlink:href=\"#DejaVuSans-104\"/>\n       <use x=\"1569.914062\" xlink:href=\"#DejaVuSans-110\"/>\n       <use x=\"1633.292969\" xlink:href=\"#DejaVuSans-111\"/>\n       <use x=\"1694.474609\" xlink:href=\"#DejaVuSans-108\"/>\n       <use x=\"1722.257812\" xlink:href=\"#DejaVuSans-111\"/>\n       <use x=\"1783.439453\" xlink:href=\"#DejaVuSans-103\"/>\n       <use x=\"1846.916016\" xlink:href=\"#DejaVuSans-121\"/>\n      </g>\n     </g>\n    </g>\n    <g id=\"ytick_3\">\n     <g id=\"line2d_12\">\n      <g>\n       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"225.88125\" xlink:href=\"#md95a43e5ce\" y=\"62.358125\"/>\n      </g>\n     </g>\n     <g id=\"text_13\">\n      <!-- Stanford University -->\n      <defs>\n       <path d=\"M 53.515625 70.515625 \nL 53.515625 60.890625 \nQ 47.90625 63.578125 42.921875 64.890625 \nQ 37.9375 66.21875 33.296875 66.21875 \nQ 25.25 66.21875 20.875 63.09375 \nQ 16.5 59.96875 16.5 54.203125 \nQ 16.5 49.359375 19.40625 46.890625 \nQ 22.3125 44.4375 30.421875 42.921875 \nL 36.375 41.703125 \nQ 47.40625 39.59375 52.65625 34.296875 \nQ 57.90625 29 57.90625 20.125 \nQ 57.90625 9.515625 50.796875 4.046875 \nQ 43.703125 -1.421875 29.984375 -1.421875 \nQ 24.8125 -1.421875 18.96875 -0.25 \nQ 13.140625 0.921875 6.890625 3.21875 \nL 6.890625 13.375 \nQ 12.890625 10.015625 18.65625 8.296875 \nQ 24.421875 6.59375 29.984375 6.59375 \nQ 38.421875 6.59375 43.015625 9.90625 \nQ 47.609375 13.234375 47.609375 19.390625 \nQ 47.609375 24.75 44.3125 27.78125 \nQ 41.015625 30.8125 33.5 32.328125 \nL 27.484375 33.5 \nQ 16.453125 35.6875 11.515625 40.375 \nQ 6.59375 45.0625 6.59375 53.421875 \nQ 6.59375 63.09375 13.40625 68.65625 \nQ 20.21875 74.21875 32.171875 74.21875 \nQ 37.3125 74.21875 42.625 73.28125 \nQ 47.953125 72.359375 53.515625 70.515625 \nz\n\" id=\"DejaVuSans-83\"/>\n       <path d=\"M 8.6875 72.90625 \nL 18.609375 72.90625 \nL 18.609375 28.609375 \nQ 18.609375 16.890625 22.84375 11.734375 \nQ 27.09375 6.59375 36.625 6.59375 \nQ 46.09375 6.59375 50.34375 11.734375 \nQ 54.59375 16.890625 54.59375 28.609375 \nL 54.59375 72.90625 \nL 64.5 72.90625 \nL 64.5 27.390625 \nQ 64.5 13.140625 57.4375 5.859375 \nQ 50.390625 -1.421875 36.625 -1.421875 \nQ 22.796875 -1.421875 15.734375 5.859375 \nQ 8.6875 13.140625 8.6875 27.390625 \nz\n\" id=\"DejaVuSans-85\"/>\n       <path d=\"M 2.984375 54.6875 \nL 12.5 54.6875 \nL 29.59375 8.796875 \nL 46.6875 54.6875 \nL 56.203125 54.6875 \nL 35.6875 0 \nL 23.484375 0 \nz\n\" id=\"DejaVuSans-118\"/>\n      </defs>\n      <g transform=\"translate(122.601562 66.157344)scale(0.1 -0.1)\">\n       <use xlink:href=\"#DejaVuSans-83\"/>\n       <use x=\"63.476562\" xlink:href=\"#DejaVuSans-116\"/>\n       <use x=\"102.685547\" xlink:href=\"#DejaVuSans-97\"/>\n       <use x=\"163.964844\" xlink:href=\"#DejaVuSans-110\"/>\n       <use x=\"227.34375\" xlink:href=\"#DejaVuSans-102\"/>\n       <use x=\"262.548828\" xlink:href=\"#DejaVuSans-111\"/>\n       <use x=\"323.730469\" xlink:href=\"#DejaVuSans-114\"/>\n       <use x=\"363.09375\" xlink:href=\"#DejaVuSans-100\"/>\n       <use x=\"426.570312\" xlink:href=\"#DejaVuSans-32\"/>\n       <use x=\"458.357422\" xlink:href=\"#DejaVuSans-85\"/>\n       <use x=\"531.550781\" xlink:href=\"#DejaVuSans-110\"/>\n       <use x=\"594.929688\" xlink:href=\"#DejaVuSans-105\"/>\n       <use x=\"622.712891\" xlink:href=\"#DejaVuSans-118\"/>\n       <use x=\"681.892578\" xlink:href=\"#DejaVuSans-101\"/>\n       <use x=\"743.416016\" xlink:href=\"#DejaVuSans-114\"/>\n       <use x=\"784.529297\" xlink:href=\"#DejaVuSans-115\"/>\n       <use x=\"836.628906\" xlink:href=\"#DejaVuSans-105\"/>\n       <use x=\"864.412109\" xlink:href=\"#DejaVuSans-116\"/>\n       <use x=\"903.621094\" xlink:href=\"#DejaVuSans-121\"/>\n      </g>\n     </g>\n    </g>\n    <g id=\"ytick_4\">\n     <g id=\"line2d_13\">\n      <g>\n       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"225.88125\" xlink:href=\"#md95a43e5ce\" y=\"71.691458\"/>\n      </g>\n     </g>\n     <g id=\"text_14\">\n      <!-- Carnegie Mellon University -->\n      <defs>\n       <path d=\"M 64.40625 67.28125 \nL 64.40625 56.890625 \nQ 59.421875 61.53125 53.78125 63.8125 \nQ 48.140625 66.109375 41.796875 66.109375 \nQ 29.296875 66.109375 22.65625 58.46875 \nQ 16.015625 50.828125 16.015625 36.375 \nQ 16.015625 21.96875 22.65625 14.328125 \nQ 29.296875 6.6875 41.796875 6.6875 \nQ 48.140625 6.6875 53.78125 8.984375 \nQ 59.421875 11.28125 64.40625 15.921875 \nL 64.40625 5.609375 \nQ 59.234375 2.09375 53.4375 0.328125 \nQ 47.65625 -1.421875 41.21875 -1.421875 \nQ 24.65625 -1.421875 15.125 8.703125 \nQ 5.609375 18.84375 5.609375 36.375 \nQ 5.609375 53.953125 15.125 64.078125 \nQ 24.65625 74.21875 41.21875 74.21875 \nQ 47.75 74.21875 53.53125 72.484375 \nQ 59.328125 70.75 64.40625 67.28125 \nz\n\" id=\"DejaVuSans-67\"/>\n      </defs>\n      <g transform=\"translate(84.470312 75.490677)scale(0.1 -0.1)\">\n       <use xlink:href=\"#DejaVuSans-67\"/>\n       <use x=\"69.824219\" xlink:href=\"#DejaVuSans-97\"/>\n       <use x=\"131.103516\" xlink:href=\"#DejaVuSans-114\"/>\n       <use x=\"170.466797\" xlink:href=\"#DejaVuSans-110\"/>\n       <use x=\"233.845703\" xlink:href=\"#DejaVuSans-101\"/>\n       <use x=\"295.369141\" xlink:href=\"#DejaVuSans-103\"/>\n       <use x=\"358.845703\" xlink:href=\"#DejaVuSans-105\"/>\n       <use x=\"386.628906\" xlink:href=\"#DejaVuSans-101\"/>\n       <use x=\"448.152344\" xlink:href=\"#DejaVuSans-32\"/>\n       <use x=\"479.939453\" xlink:href=\"#DejaVuSans-77\"/>\n       <use x=\"566.21875\" xlink:href=\"#DejaVuSans-101\"/>\n       <use x=\"627.742188\" xlink:href=\"#DejaVuSans-108\"/>\n       <use x=\"655.525391\" xlink:href=\"#DejaVuSans-108\"/>\n       <use x=\"683.308594\" xlink:href=\"#DejaVuSans-111\"/>\n       <use x=\"744.490234\" xlink:href=\"#DejaVuSans-110\"/>\n       <use x=\"807.869141\" xlink:href=\"#DejaVuSans-32\"/>\n       <use x=\"839.65625\" xlink:href=\"#DejaVuSans-85\"/>\n       <use x=\"912.849609\" xlink:href=\"#DejaVuSans-110\"/>\n       <use x=\"976.228516\" xlink:href=\"#DejaVuSans-105\"/>\n       <use x=\"1004.011719\" xlink:href=\"#DejaVuSans-118\"/>\n       <use x=\"1063.191406\" xlink:href=\"#DejaVuSans-101\"/>\n       <use x=\"1124.714844\" xlink:href=\"#DejaVuSans-114\"/>\n       <use x=\"1165.828125\" xlink:href=\"#DejaVuSans-115\"/>\n       <use x=\"1217.927734\" xlink:href=\"#DejaVuSans-105\"/>\n       <use x=\"1245.710938\" xlink:href=\"#DejaVuSans-116\"/>\n       <use x=\"1284.919922\" xlink:href=\"#DejaVuSans-121\"/>\n      </g>\n     </g>\n    </g>\n    <g id=\"ytick_5\">\n     <g id=\"line2d_14\">\n      <g>\n       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"225.88125\" xlink:href=\"#md95a43e5ce\" y=\"81.024792\"/>\n      </g>\n     </g>\n     <g id=\"text_15\">\n      <!-- Microsoft Research -->\n      <defs>\n       <path d=\"M 44.390625 34.1875 \nQ 47.5625 33.109375 50.5625 29.59375 \nQ 53.5625 26.078125 56.59375 19.921875 \nL 66.609375 0 \nL 56 0 \nL 46.6875 18.703125 \nQ 43.0625 26.03125 39.671875 28.421875 \nQ 36.28125 30.8125 30.421875 30.8125 \nL 19.671875 30.8125 \nL 19.671875 0 \nL 9.8125 0 \nL 9.8125 72.90625 \nL 32.078125 72.90625 \nQ 44.578125 72.90625 50.734375 67.671875 \nQ 56.890625 62.453125 56.890625 51.90625 \nQ 56.890625 45.015625 53.6875 40.46875 \nQ 50.484375 35.9375 44.390625 34.1875 \nz\nM 19.671875 64.796875 \nL 19.671875 38.921875 \nL 32.078125 38.921875 \nQ 39.203125 38.921875 42.84375 42.21875 \nQ 46.484375 45.515625 46.484375 51.90625 \nQ 46.484375 58.296875 42.84375 61.546875 \nQ 39.203125 64.796875 32.078125 64.796875 \nz\n\" id=\"DejaVuSans-82\"/>\n      </defs>\n      <g transform=\"translate(124.335937 84.82401)scale(0.1 -0.1)\">\n       <use xlink:href=\"#DejaVuSans-77\"/>\n       <use x=\"86.279297\" xlink:href=\"#DejaVuSans-105\"/>\n       <use x=\"114.0625\" xlink:href=\"#DejaVuSans-99\"/>\n       <use x=\"169.042969\" xlink:href=\"#DejaVuSans-114\"/>\n       <use x=\"207.90625\" xlink:href=\"#DejaVuSans-111\"/>\n       <use x=\"269.087891\" xlink:href=\"#DejaVuSans-115\"/>\n       <use x=\"321.1875\" xlink:href=\"#DejaVuSans-111\"/>\n       <use x=\"382.369141\" xlink:href=\"#DejaVuSans-102\"/>\n       <use x=\"415.824219\" xlink:href=\"#DejaVuSans-116\"/>\n       <use x=\"455.033203\" xlink:href=\"#DejaVuSans-32\"/>\n       <use x=\"486.820312\" xlink:href=\"#DejaVuSans-82\"/>\n       <use x=\"551.802734\" xlink:href=\"#DejaVuSans-101\"/>\n       <use x=\"613.326172\" xlink:href=\"#DejaVuSans-115\"/>\n       <use x=\"665.425781\" xlink:href=\"#DejaVuSans-101\"/>\n       <use x=\"726.949219\" xlink:href=\"#DejaVuSans-97\"/>\n       <use x=\"788.228516\" xlink:href=\"#DejaVuSans-114\"/>\n       <use x=\"827.091797\" xlink:href=\"#DejaVuSans-99\"/>\n       <use x=\"882.072266\" xlink:href=\"#DejaVuSans-104\"/>\n      </g>\n     </g>\n    </g>\n    <g id=\"ytick_6\">\n     <g id=\"line2d_15\">\n      <g>\n       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"225.88125\" xlink:href=\"#md95a43e5ce\" y=\"90.358125\"/>\n      </g>\n     </g>\n     <g id=\"text_16\">\n      <!-- University of California, Berkeley -->\n      <defs>\n       <path d=\"M 9.078125 75.984375 \nL 18.109375 75.984375 \nL 18.109375 31.109375 \nL 44.921875 54.6875 \nL 56.390625 54.6875 \nL 27.390625 29.109375 \nL 57.625 0 \nL 45.90625 0 \nL 18.109375 26.703125 \nL 18.109375 0 \nL 9.078125 0 \nz\n\" id=\"DejaVuSans-107\"/>\n      </defs>\n      <g transform=\"translate(55.04375 94.157344)scale(0.1 -0.1)\">\n       <use xlink:href=\"#DejaVuSans-85\"/>\n       <use x=\"73.193359\" xlink:href=\"#DejaVuSans-110\"/>\n       <use x=\"136.572266\" xlink:href=\"#DejaVuSans-105\"/>\n       <use x=\"164.355469\" xlink:href=\"#DejaVuSans-118\"/>\n       <use x=\"223.535156\" xlink:href=\"#DejaVuSans-101\"/>\n       <use x=\"285.058594\" xlink:href=\"#DejaVuSans-114\"/>\n       <use x=\"326.171875\" xlink:href=\"#DejaVuSans-115\"/>\n       <use x=\"378.271484\" xlink:href=\"#DejaVuSans-105\"/>\n       <use x=\"406.054688\" xlink:href=\"#DejaVuSans-116\"/>\n       <use x=\"445.263672\" xlink:href=\"#DejaVuSans-121\"/>\n       <use x=\"504.443359\" xlink:href=\"#DejaVuSans-32\"/>\n       <use x=\"536.230469\" xlink:href=\"#DejaVuSans-111\"/>\n       <use x=\"597.412109\" xlink:href=\"#DejaVuSans-102\"/>\n       <use x=\"632.617188\" xlink:href=\"#DejaVuSans-32\"/>\n       <use x=\"664.404297\" xlink:href=\"#DejaVuSans-67\"/>\n       <use x=\"734.228516\" xlink:href=\"#DejaVuSans-97\"/>\n       <use x=\"795.507812\" xlink:href=\"#DejaVuSans-108\"/>\n       <use x=\"823.291016\" xlink:href=\"#DejaVuSans-105\"/>\n       <use x=\"851.074219\" xlink:href=\"#DejaVuSans-102\"/>\n       <use x=\"886.279297\" xlink:href=\"#DejaVuSans-111\"/>\n       <use x=\"947.460938\" xlink:href=\"#DejaVuSans-114\"/>\n       <use x=\"986.824219\" xlink:href=\"#DejaVuSans-110\"/>\n       <use x=\"1050.203125\" xlink:href=\"#DejaVuSans-105\"/>\n       <use x=\"1077.986328\" xlink:href=\"#DejaVuSans-97\"/>\n       <use x=\"1139.265625\" xlink:href=\"#DejaVuSans-44\"/>\n       <use x=\"1171.052734\" xlink:href=\"#DejaVuSans-32\"/>\n       <use x=\"1202.839844\" xlink:href=\"#DejaVuSans-66\"/>\n       <use x=\"1271.443359\" xlink:href=\"#DejaVuSans-101\"/>\n       <use x=\"1332.966797\" xlink:href=\"#DejaVuSans-114\"/>\n       <use x=\"1374.080078\" xlink:href=\"#DejaVuSans-107\"/>\n       <use x=\"1428.365234\" xlink:href=\"#DejaVuSans-101\"/>\n       <use x=\"1489.888672\" xlink:href=\"#DejaVuSans-108\"/>\n       <use x=\"1517.671875\" xlink:href=\"#DejaVuSans-101\"/>\n       <use x=\"1579.195312\" xlink:href=\"#DejaVuSans-121\"/>\n      </g>\n     </g>\n    </g>\n    <g id=\"ytick_7\">\n     <g id=\"line2d_16\">\n      <g>\n       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"225.88125\" xlink:href=\"#md95a43e5ce\" y=\"99.691458\"/>\n      </g>\n     </g>\n     <g id=\"text_17\">\n      <!-- Princeton University -->\n      <g transform=\"translate(118.15 103.490677)scale(0.1 -0.1)\">\n       <use xlink:href=\"#DejaVuSans-80\"/>\n       <use x=\"58.552734\" xlink:href=\"#DejaVuSans-114\"/>\n       <use x=\"99.666016\" xlink:href=\"#DejaVuSans-105\"/>\n       <use x=\"127.449219\" xlink:href=\"#DejaVuSans-110\"/>\n       <use x=\"190.828125\" xlink:href=\"#DejaVuSans-99\"/>\n       <use x=\"245.808594\" xlink:href=\"#DejaVuSans-101\"/>\n       <use x=\"307.332031\" xlink:href=\"#DejaVuSans-116\"/>\n       <use x=\"346.541016\" xlink:href=\"#DejaVuSans-111\"/>\n       <use x=\"407.722656\" xlink:href=\"#DejaVuSans-110\"/>\n       <use x=\"471.101562\" xlink:href=\"#DejaVuSans-32\"/>\n       <use x=\"502.888672\" xlink:href=\"#DejaVuSans-85\"/>\n       <use x=\"576.082031\" xlink:href=\"#DejaVuSans-110\"/>\n       <use x=\"639.460938\" xlink:href=\"#DejaVuSans-105\"/>\n       <use x=\"667.244141\" xlink:href=\"#DejaVuSans-118\"/>\n       <use x=\"726.423828\" xlink:href=\"#DejaVuSans-101\"/>\n       <use x=\"787.947266\" xlink:href=\"#DejaVuSans-114\"/>\n       <use x=\"829.060547\" xlink:href=\"#DejaVuSans-115\"/>\n       <use x=\"881.160156\" xlink:href=\"#DejaVuSans-105\"/>\n       <use x=\"908.943359\" xlink:href=\"#DejaVuSans-116\"/>\n       <use x=\"948.152344\" xlink:href=\"#DejaVuSans-121\"/>\n      </g>\n     </g>\n    </g>\n    <g id=\"ytick_8\">\n     <g id=\"line2d_17\">\n      <g>\n       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"225.88125\" xlink:href=\"#md95a43e5ce\" y=\"109.024792\"/>\n      </g>\n     </g>\n     <g id=\"text_18\">\n      <!-- Facebook AI Research -->\n      <defs>\n       <path d=\"M 9.8125 72.90625 \nL 51.703125 72.90625 \nL 51.703125 64.59375 \nL 19.671875 64.59375 \nL 19.671875 43.109375 \nL 48.578125 43.109375 \nL 48.578125 34.8125 \nL 19.671875 34.8125 \nL 19.671875 0 \nL 9.8125 0 \nz\n\" id=\"DejaVuSans-70\"/>\n       <path d=\"M 48.6875 27.296875 \nQ 48.6875 37.203125 44.609375 42.84375 \nQ 40.53125 48.484375 33.40625 48.484375 \nQ 26.265625 48.484375 22.1875 42.84375 \nQ 18.109375 37.203125 18.109375 27.296875 \nQ 18.109375 17.390625 22.1875 11.75 \nQ 26.265625 6.109375 33.40625 6.109375 \nQ 40.53125 6.109375 44.609375 11.75 \nQ 48.6875 17.390625 48.6875 27.296875 \nz\nM 18.109375 46.390625 \nQ 20.953125 51.265625 25.265625 53.625 \nQ 29.59375 56 35.59375 56 \nQ 45.5625 56 51.78125 48.09375 \nQ 58.015625 40.1875 58.015625 27.296875 \nQ 58.015625 14.40625 51.78125 6.484375 \nQ 45.5625 -1.421875 35.59375 -1.421875 \nQ 29.59375 -1.421875 25.265625 0.953125 \nQ 20.953125 3.328125 18.109375 8.203125 \nL 18.109375 0 \nL 9.078125 0 \nL 9.078125 75.984375 \nL 18.109375 75.984375 \nz\n\" id=\"DejaVuSans-98\"/>\n       <path d=\"M 34.1875 63.1875 \nL 20.796875 26.90625 \nL 47.609375 26.90625 \nz\nM 28.609375 72.90625 \nL 39.796875 72.90625 \nL 67.578125 0 \nL 57.328125 0 \nL 50.6875 18.703125 \nL 17.828125 18.703125 \nL 11.1875 0 \nL 0.78125 0 \nz\n\" id=\"DejaVuSans-65\"/>\n      </defs>\n      <g transform=\"translate(109.875 112.82401)scale(0.1 -0.1)\">\n       <use xlink:href=\"#DejaVuSans-70\"/>\n       <use x=\"48.394531\" xlink:href=\"#DejaVuSans-97\"/>\n       <use x=\"109.673828\" xlink:href=\"#DejaVuSans-99\"/>\n       <use x=\"164.654297\" xlink:href=\"#DejaVuSans-101\"/>\n       <use x=\"226.177734\" xlink:href=\"#DejaVuSans-98\"/>\n       <use x=\"289.654297\" xlink:href=\"#DejaVuSans-111\"/>\n       <use x=\"350.835938\" xlink:href=\"#DejaVuSans-111\"/>\n       <use x=\"412.017578\" xlink:href=\"#DejaVuSans-107\"/>\n       <use x=\"469.927734\" xlink:href=\"#DejaVuSans-32\"/>\n       <use x=\"501.714844\" xlink:href=\"#DejaVuSans-65\"/>\n       <use x=\"570.123047\" xlink:href=\"#DejaVuSans-73\"/>\n       <use x=\"599.615234\" xlink:href=\"#DejaVuSans-32\"/>\n       <use x=\"631.402344\" xlink:href=\"#DejaVuSans-82\"/>\n       <use x=\"696.384766\" xlink:href=\"#DejaVuSans-101\"/>\n       <use x=\"757.908203\" xlink:href=\"#DejaVuSans-115\"/>\n       <use x=\"810.007812\" xlink:href=\"#DejaVuSans-101\"/>\n       <use x=\"871.53125\" xlink:href=\"#DejaVuSans-97\"/>\n       <use x=\"932.810547\" xlink:href=\"#DejaVuSans-114\"/>\n       <use x=\"971.673828\" xlink:href=\"#DejaVuSans-99\"/>\n       <use x=\"1026.654297\" xlink:href=\"#DejaVuSans-104\"/>\n      </g>\n     </g>\n    </g>\n    <g id=\"ytick_9\">\n     <g id=\"line2d_18\">\n      <g>\n       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"225.88125\" xlink:href=\"#md95a43e5ce\" y=\"118.358125\"/>\n      </g>\n     </g>\n     <g id=\"text_19\">\n      <!-- Columbia University -->\n      <defs>\n       <path d=\"M 52 44.1875 \nQ 55.375 50.25 60.0625 53.125 \nQ 64.75 56 71.09375 56 \nQ 79.640625 56 84.28125 50.015625 \nQ 88.921875 44.046875 88.921875 33.015625 \nL 88.921875 0 \nL 79.890625 0 \nL 79.890625 32.71875 \nQ 79.890625 40.578125 77.09375 44.375 \nQ 74.3125 48.1875 68.609375 48.1875 \nQ 61.625 48.1875 57.5625 43.546875 \nQ 53.515625 38.921875 53.515625 30.90625 \nL 53.515625 0 \nL 44.484375 0 \nL 44.484375 32.71875 \nQ 44.484375 40.625 41.703125 44.40625 \nQ 38.921875 48.1875 33.109375 48.1875 \nQ 26.21875 48.1875 22.15625 43.53125 \nQ 18.109375 38.875 18.109375 30.90625 \nL 18.109375 0 \nL 9.078125 0 \nL 9.078125 54.6875 \nL 18.109375 54.6875 \nL 18.109375 46.1875 \nQ 21.1875 51.21875 25.484375 53.609375 \nQ 29.78125 56 35.6875 56 \nQ 41.65625 56 45.828125 52.96875 \nQ 50 49.953125 52 44.1875 \nz\n\" id=\"DejaVuSans-109\"/>\n      </defs>\n      <g transform=\"translate(118.046875 122.157344)scale(0.1 -0.1)\">\n       <use xlink:href=\"#DejaVuSans-67\"/>\n       <use x=\"69.824219\" xlink:href=\"#DejaVuSans-111\"/>\n       <use x=\"131.005859\" xlink:href=\"#DejaVuSans-108\"/>\n       <use x=\"158.789062\" xlink:href=\"#DejaVuSans-117\"/>\n       <use x=\"222.167969\" xlink:href=\"#DejaVuSans-109\"/>\n       <use x=\"319.580078\" xlink:href=\"#DejaVuSans-98\"/>\n       <use x=\"383.056641\" xlink:href=\"#DejaVuSans-105\"/>\n       <use x=\"410.839844\" xlink:href=\"#DejaVuSans-97\"/>\n       <use x=\"472.119141\" xlink:href=\"#DejaVuSans-32\"/>\n       <use x=\"503.90625\" xlink:href=\"#DejaVuSans-85\"/>\n       <use x=\"577.099609\" xlink:href=\"#DejaVuSans-110\"/>\n       <use x=\"640.478516\" xlink:href=\"#DejaVuSans-105\"/>\n       <use x=\"668.261719\" xlink:href=\"#DejaVuSans-118\"/>\n       <use x=\"727.441406\" xlink:href=\"#DejaVuSans-101\"/>\n       <use x=\"788.964844\" xlink:href=\"#DejaVuSans-114\"/>\n       <use x=\"830.078125\" xlink:href=\"#DejaVuSans-115\"/>\n       <use x=\"882.177734\" xlink:href=\"#DejaVuSans-105\"/>\n       <use x=\"909.960938\" xlink:href=\"#DejaVuSans-116\"/>\n       <use x=\"949.169922\" xlink:href=\"#DejaVuSans-121\"/>\n      </g>\n     </g>\n    </g>\n    <g id=\"ytick_10\">\n     <g id=\"line2d_19\">\n      <g>\n       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"225.88125\" xlink:href=\"#md95a43e5ce\" y=\"127.691458\"/>\n      </g>\n     </g>\n     <g id=\"text_20\">\n      <!-- University of Oxford -->\n      <defs>\n       <path d=\"M 39.40625 66.21875 \nQ 28.65625 66.21875 22.328125 58.203125 \nQ 16.015625 50.203125 16.015625 36.375 \nQ 16.015625 22.609375 22.328125 14.59375 \nQ 28.65625 6.59375 39.40625 6.59375 \nQ 50.140625 6.59375 56.421875 14.59375 \nQ 62.703125 22.609375 62.703125 36.375 \nQ 62.703125 50.203125 56.421875 58.203125 \nQ 50.140625 66.21875 39.40625 66.21875 \nz\nM 39.40625 74.21875 \nQ 54.734375 74.21875 63.90625 63.9375 \nQ 73.09375 53.65625 73.09375 36.375 \nQ 73.09375 19.140625 63.90625 8.859375 \nQ 54.734375 -1.421875 39.40625 -1.421875 \nQ 24.03125 -1.421875 14.8125 8.828125 \nQ 5.609375 19.09375 5.609375 36.375 \nQ 5.609375 53.65625 14.8125 63.9375 \nQ 24.03125 74.21875 39.40625 74.21875 \nz\n\" id=\"DejaVuSans-79\"/>\n       <path d=\"M 54.890625 54.6875 \nL 35.109375 28.078125 \nL 55.90625 0 \nL 45.3125 0 \nL 29.390625 21.484375 \nL 13.484375 0 \nL 2.875 0 \nL 24.125 28.609375 \nL 4.6875 54.6875 \nL 15.28125 54.6875 \nL 29.78125 35.203125 \nL 44.28125 54.6875 \nz\n\" id=\"DejaVuSans-120\"/>\n      </defs>\n      <g transform=\"translate(118.728125 131.490677)scale(0.1 -0.1)\">\n       <use xlink:href=\"#DejaVuSans-85\"/>\n       <use x=\"73.193359\" xlink:href=\"#DejaVuSans-110\"/>\n       <use x=\"136.572266\" xlink:href=\"#DejaVuSans-105\"/>\n       <use x=\"164.355469\" xlink:href=\"#DejaVuSans-118\"/>\n       <use x=\"223.535156\" xlink:href=\"#DejaVuSans-101\"/>\n       <use x=\"285.058594\" xlink:href=\"#DejaVuSans-114\"/>\n       <use x=\"326.171875\" xlink:href=\"#DejaVuSans-115\"/>\n       <use x=\"378.271484\" xlink:href=\"#DejaVuSans-105\"/>\n       <use x=\"406.054688\" xlink:href=\"#DejaVuSans-116\"/>\n       <use x=\"445.263672\" xlink:href=\"#DejaVuSans-121\"/>\n       <use x=\"504.443359\" xlink:href=\"#DejaVuSans-32\"/>\n       <use x=\"536.230469\" xlink:href=\"#DejaVuSans-111\"/>\n       <use x=\"597.412109\" xlink:href=\"#DejaVuSans-102\"/>\n       <use x=\"632.617188\" xlink:href=\"#DejaVuSans-32\"/>\n       <use x=\"664.404297\" xlink:href=\"#DejaVuSans-79\"/>\n       <use x=\"743.115234\" xlink:href=\"#DejaVuSans-120\"/>\n       <use x=\"802.294922\" xlink:href=\"#DejaVuSans-102\"/>\n       <use x=\"837.5\" xlink:href=\"#DejaVuSans-111\"/>\n       <use x=\"898.681641\" xlink:href=\"#DejaVuSans-114\"/>\n       <use x=\"938.044922\" xlink:href=\"#DejaVuSans-100\"/>\n      </g>\n     </g>\n    </g>\n    <g id=\"ytick_11\">\n     <g id=\"line2d_20\">\n      <g>\n       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"225.88125\" xlink:href=\"#md95a43e5ce\" y=\"137.024792\"/>\n      </g>\n     </g>\n     <g id=\"text_21\">\n      <!-- IBM research -->\n      <g transform=\"translate(154.0125 140.82401)scale(0.1 -0.1)\">\n       <use xlink:href=\"#DejaVuSans-73\"/>\n       <use x=\"29.492188\" xlink:href=\"#DejaVuSans-66\"/>\n       <use x=\"98.095703\" xlink:href=\"#DejaVuSans-77\"/>\n       <use x=\"184.375\" xlink:href=\"#DejaVuSans-32\"/>\n       <use x=\"216.162109\" xlink:href=\"#DejaVuSans-114\"/>\n       <use x=\"255.025391\" xlink:href=\"#DejaVuSans-101\"/>\n       <use x=\"316.548828\" xlink:href=\"#DejaVuSans-115\"/>\n       <use x=\"368.648438\" xlink:href=\"#DejaVuSans-101\"/>\n       <use x=\"430.171875\" xlink:href=\"#DejaVuSans-97\"/>\n       <use x=\"491.451172\" xlink:href=\"#DejaVuSans-114\"/>\n       <use x=\"530.314453\" xlink:href=\"#DejaVuSans-99\"/>\n       <use x=\"585.294922\" xlink:href=\"#DejaVuSans-104\"/>\n      </g>\n     </g>\n    </g>\n    <g id=\"ytick_12\">\n     <g id=\"line2d_21\">\n      <g>\n       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"225.88125\" xlink:href=\"#md95a43e5ce\" y=\"146.358125\"/>\n      </g>\n     </g>\n     <g id=\"text_22\">\n      <!-- INRIA -->\n      <defs>\n       <path d=\"M 9.8125 72.90625 \nL 23.09375 72.90625 \nL 55.421875 11.921875 \nL 55.421875 72.90625 \nL 64.984375 72.90625 \nL 64.984375 0 \nL 51.703125 0 \nL 19.390625 60.984375 \nL 19.390625 0 \nL 9.8125 0 \nz\n\" id=\"DejaVuSans-78\"/>\n      </defs>\n      <g transform=\"translate(191.710938 150.157344)scale(0.1 -0.1)\">\n       <use xlink:href=\"#DejaVuSans-73\"/>\n       <use x=\"29.492188\" xlink:href=\"#DejaVuSans-78\"/>\n       <use x=\"104.296875\" xlink:href=\"#DejaVuSans-82\"/>\n       <use x=\"173.779297\" xlink:href=\"#DejaVuSans-73\"/>\n       <use x=\"203.271484\" xlink:href=\"#DejaVuSans-65\"/>\n      </g>\n     </g>\n    </g>\n    <g id=\"ytick_13\">\n     <g id=\"line2d_22\">\n      <g>\n       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"225.88125\" xlink:href=\"#md95a43e5ce\" y=\"155.691458\"/>\n      </g>\n     </g>\n     <g id=\"text_23\">\n      <!-- Tsinghua University -->\n      <g transform=\"translate(121.325 159.490677)scale(0.1 -0.1)\">\n       <use xlink:href=\"#DejaVuSans-84\"/>\n       <use x=\"44.583984\" xlink:href=\"#DejaVuSans-115\"/>\n       <use x=\"96.683594\" xlink:href=\"#DejaVuSans-105\"/>\n       <use x=\"124.466797\" xlink:href=\"#DejaVuSans-110\"/>\n       <use x=\"187.845703\" xlink:href=\"#DejaVuSans-103\"/>\n       <use x=\"251.322266\" xlink:href=\"#DejaVuSans-104\"/>\n       <use x=\"314.701172\" xlink:href=\"#DejaVuSans-117\"/>\n       <use x=\"378.080078\" xlink:href=\"#DejaVuSans-97\"/>\n       <use x=\"439.359375\" xlink:href=\"#DejaVuSans-32\"/>\n       <use x=\"471.146484\" xlink:href=\"#DejaVuSans-85\"/>\n       <use x=\"544.339844\" xlink:href=\"#DejaVuSans-110\"/>\n       <use x=\"607.71875\" xlink:href=\"#DejaVuSans-105\"/>\n       <use x=\"635.501953\" xlink:href=\"#DejaVuSans-118\"/>\n       <use x=\"694.681641\" xlink:href=\"#DejaVuSans-101\"/>\n       <use x=\"756.205078\" xlink:href=\"#DejaVuSans-114\"/>\n       <use x=\"797.318359\" xlink:href=\"#DejaVuSans-115\"/>\n       <use x=\"849.417969\" xlink:href=\"#DejaVuSans-105\"/>\n       <use x=\"877.201172\" xlink:href=\"#DejaVuSans-116\"/>\n       <use x=\"916.410156\" xlink:href=\"#DejaVuSans-121\"/>\n      </g>\n     </g>\n    </g>\n    <g id=\"ytick_14\">\n     <g id=\"line2d_23\">\n      <g>\n       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"225.88125\" xlink:href=\"#md95a43e5ce\" y=\"165.024792\"/>\n      </g>\n     </g>\n     <g id=\"text_24\">\n      <!-- University of Texas at Austin -->\n      <g transform=\"translate(76.971875 168.82401)scale(0.1 -0.1)\">\n       <use xlink:href=\"#DejaVuSans-85\"/>\n       <use x=\"73.193359\" xlink:href=\"#DejaVuSans-110\"/>\n       <use x=\"136.572266\" xlink:href=\"#DejaVuSans-105\"/>\n       <use x=\"164.355469\" xlink:href=\"#DejaVuSans-118\"/>\n       <use x=\"223.535156\" xlink:href=\"#DejaVuSans-101\"/>\n       <use x=\"285.058594\" xlink:href=\"#DejaVuSans-114\"/>\n       <use x=\"326.171875\" xlink:href=\"#DejaVuSans-115\"/>\n       <use x=\"378.271484\" xlink:href=\"#DejaVuSans-105\"/>\n       <use x=\"406.054688\" xlink:href=\"#DejaVuSans-116\"/>\n       <use x=\"445.263672\" xlink:href=\"#DejaVuSans-121\"/>\n       <use x=\"504.443359\" xlink:href=\"#DejaVuSans-32\"/>\n       <use x=\"536.230469\" xlink:href=\"#DejaVuSans-111\"/>\n       <use x=\"597.412109\" xlink:href=\"#DejaVuSans-102\"/>\n       <use x=\"632.617188\" xlink:href=\"#DejaVuSans-32\"/>\n       <use x=\"664.404297\" xlink:href=\"#DejaVuSans-84\"/>\n       <use x=\"708.488281\" xlink:href=\"#DejaVuSans-101\"/>\n       <use x=\"768.261719\" xlink:href=\"#DejaVuSans-120\"/>\n       <use x=\"827.441406\" xlink:href=\"#DejaVuSans-97\"/>\n       <use x=\"888.720703\" xlink:href=\"#DejaVuSans-115\"/>\n       <use x=\"940.820312\" xlink:href=\"#DejaVuSans-32\"/>\n       <use x=\"972.607422\" xlink:href=\"#DejaVuSans-97\"/>\n       <use x=\"1033.886719\" xlink:href=\"#DejaVuSans-116\"/>\n       <use x=\"1073.095703\" xlink:href=\"#DejaVuSans-32\"/>\n       <use x=\"1104.882812\" xlink:href=\"#DejaVuSans-65\"/>\n       <use x=\"1173.291016\" xlink:href=\"#DejaVuSans-117\"/>\n       <use x=\"1236.669922\" xlink:href=\"#DejaVuSans-115\"/>\n       <use x=\"1288.769531\" xlink:href=\"#DejaVuSans-116\"/>\n       <use x=\"1327.978516\" xlink:href=\"#DejaVuSans-105\"/>\n       <use x=\"1355.761719\" xlink:href=\"#DejaVuSans-110\"/>\n      </g>\n     </g>\n    </g>\n    <g id=\"ytick_15\">\n     <g id=\"line2d_24\">\n      <g>\n       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"225.88125\" xlink:href=\"#md95a43e5ce\" y=\"174.358125\"/>\n      </g>\n     </g>\n     <g id=\"text_25\">\n      <!-- Cornell University -->\n      <g transform=\"translate(130.175 178.157344)scale(0.1 -0.1)\">\n       <use xlink:href=\"#DejaVuSans-67\"/>\n       <use x=\"69.824219\" xlink:href=\"#DejaVuSans-111\"/>\n       <use x=\"131.005859\" xlink:href=\"#DejaVuSans-114\"/>\n       <use x=\"170.369141\" xlink:href=\"#DejaVuSans-110\"/>\n       <use x=\"233.748047\" xlink:href=\"#DejaVuSans-101\"/>\n       <use x=\"295.271484\" xlink:href=\"#DejaVuSans-108\"/>\n       <use x=\"323.054688\" xlink:href=\"#DejaVuSans-108\"/>\n       <use x=\"350.837891\" xlink:href=\"#DejaVuSans-32\"/>\n       <use x=\"382.625\" xlink:href=\"#DejaVuSans-85\"/>\n       <use x=\"455.818359\" xlink:href=\"#DejaVuSans-110\"/>\n       <use x=\"519.197266\" xlink:href=\"#DejaVuSans-105\"/>\n       <use x=\"546.980469\" xlink:href=\"#DejaVuSans-118\"/>\n       <use x=\"606.160156\" xlink:href=\"#DejaVuSans-101\"/>\n       <use x=\"667.683594\" xlink:href=\"#DejaVuSans-114\"/>\n       <use x=\"708.796875\" xlink:href=\"#DejaVuSans-115\"/>\n       <use x=\"760.896484\" xlink:href=\"#DejaVuSans-105\"/>\n       <use x=\"788.679688\" xlink:href=\"#DejaVuSans-116\"/>\n       <use x=\"827.888672\" xlink:href=\"#DejaVuSans-121\"/>\n      </g>\n     </g>\n    </g>\n    <g id=\"ytick_16\">\n     <g id=\"line2d_25\">\n      <g>\n       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"225.88125\" xlink:href=\"#md95a43e5ce\" y=\"183.691458\"/>\n      </g>\n     </g>\n     <g id=\"text_26\">\n      <!-- Georgia Institute of Technology -->\n      <g transform=\"translate(62.953125 187.490677)scale(0.1 -0.1)\">\n       <use xlink:href=\"#DejaVuSans-71\"/>\n       <use x=\"77.490234\" xlink:href=\"#DejaVuSans-101\"/>\n       <use x=\"139.013672\" xlink:href=\"#DejaVuSans-111\"/>\n       <use x=\"200.195312\" xlink:href=\"#DejaVuSans-114\"/>\n       <use x=\"239.558594\" xlink:href=\"#DejaVuSans-103\"/>\n       <use x=\"303.035156\" xlink:href=\"#DejaVuSans-105\"/>\n       <use x=\"330.818359\" xlink:href=\"#DejaVuSans-97\"/>\n       <use x=\"392.097656\" xlink:href=\"#DejaVuSans-32\"/>\n       <use x=\"423.884766\" xlink:href=\"#DejaVuSans-73\"/>\n       <use x=\"453.376953\" xlink:href=\"#DejaVuSans-110\"/>\n       <use x=\"516.755859\" xlink:href=\"#DejaVuSans-115\"/>\n       <use x=\"568.855469\" xlink:href=\"#DejaVuSans-116\"/>\n       <use x=\"608.064453\" xlink:href=\"#DejaVuSans-105\"/>\n       <use x=\"635.847656\" xlink:href=\"#DejaVuSans-116\"/>\n       <use x=\"675.056641\" xlink:href=\"#DejaVuSans-117\"/>\n       <use x=\"738.435547\" xlink:href=\"#DejaVuSans-116\"/>\n       <use x=\"777.644531\" xlink:href=\"#DejaVuSans-101\"/>\n       <use x=\"839.167969\" xlink:href=\"#DejaVuSans-32\"/>\n       <use x=\"870.955078\" xlink:href=\"#DejaVuSans-111\"/>\n       <use x=\"932.136719\" xlink:href=\"#DejaVuSans-102\"/>\n       <use x=\"967.341797\" xlink:href=\"#DejaVuSans-32\"/>\n       <use x=\"999.128906\" xlink:href=\"#DejaVuSans-84\"/>\n       <use x=\"1043.212891\" xlink:href=\"#DejaVuSans-101\"/>\n       <use x=\"1104.736328\" xlink:href=\"#DejaVuSans-99\"/>\n       <use x=\"1159.716797\" xlink:href=\"#DejaVuSans-104\"/>\n       <use x=\"1223.095703\" xlink:href=\"#DejaVuSans-110\"/>\n       <use x=\"1286.474609\" xlink:href=\"#DejaVuSans-111\"/>\n       <use x=\"1347.65625\" xlink:href=\"#DejaVuSans-108\"/>\n       <use x=\"1375.439453\" xlink:href=\"#DejaVuSans-111\"/>\n       <use x=\"1436.621094\" xlink:href=\"#DejaVuSans-103\"/>\n       <use x=\"1500.097656\" xlink:href=\"#DejaVuSans-121\"/>\n      </g>\n     </g>\n    </g>\n    <g id=\"ytick_17\">\n     <g id=\"line2d_26\">\n      <g>\n       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"225.88125\" xlink:href=\"#md95a43e5ce\" y=\"193.024792\"/>\n      </g>\n     </g>\n     <g id=\"text_27\">\n      <!-- MILA -->\n      <defs>\n       <path d=\"M 9.8125 72.90625 \nL 19.671875 72.90625 \nL 19.671875 8.296875 \nL 55.171875 8.296875 \nL 55.171875 0 \nL 9.8125 0 \nz\n\" id=\"DejaVuSans-76\"/>\n      </defs>\n      <g transform=\"translate(194.665625 196.82401)scale(0.1 -0.1)\">\n       <use xlink:href=\"#DejaVuSans-77\"/>\n       <use x=\"86.279297\" xlink:href=\"#DejaVuSans-73\"/>\n       <use x=\"115.771484\" xlink:href=\"#DejaVuSans-76\"/>\n       <use x=\"173.734375\" xlink:href=\"#DejaVuSans-65\"/>\n      </g>\n     </g>\n    </g>\n    <g id=\"ytick_18\">\n     <g id=\"line2d_27\">\n      <g>\n       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"225.88125\" xlink:href=\"#md95a43e5ce\" y=\"202.358125\"/>\n      </g>\n     </g>\n     <g id=\"text_28\">\n      <!-- University of Washington -->\n      <defs>\n       <path d=\"M 3.328125 72.90625 \nL 13.28125 72.90625 \nL 28.609375 11.28125 \nL 43.890625 72.90625 \nL 54.984375 72.90625 \nL 70.3125 11.28125 \nL 85.59375 72.90625 \nL 95.609375 72.90625 \nL 77.296875 0 \nL 64.890625 0 \nL 49.515625 63.28125 \nL 33.984375 0 \nL 21.578125 0 \nz\n\" id=\"DejaVuSans-87\"/>\n      </defs>\n      <g transform=\"translate(93.676562 206.157344)scale(0.1 -0.1)\">\n       <use xlink:href=\"#DejaVuSans-85\"/>\n       <use x=\"73.193359\" xlink:href=\"#DejaVuSans-110\"/>\n       <use x=\"136.572266\" xlink:href=\"#DejaVuSans-105\"/>\n       <use x=\"164.355469\" xlink:href=\"#DejaVuSans-118\"/>\n       <use x=\"223.535156\" xlink:href=\"#DejaVuSans-101\"/>\n       <use x=\"285.058594\" xlink:href=\"#DejaVuSans-114\"/>\n       <use x=\"326.171875\" xlink:href=\"#DejaVuSans-115\"/>\n       <use x=\"378.271484\" xlink:href=\"#DejaVuSans-105\"/>\n       <use x=\"406.054688\" xlink:href=\"#DejaVuSans-116\"/>\n       <use x=\"445.263672\" xlink:href=\"#DejaVuSans-121\"/>\n       <use x=\"504.443359\" xlink:href=\"#DejaVuSans-32\"/>\n       <use x=\"536.230469\" xlink:href=\"#DejaVuSans-111\"/>\n       <use x=\"597.412109\" xlink:href=\"#DejaVuSans-102\"/>\n       <use x=\"632.617188\" xlink:href=\"#DejaVuSans-32\"/>\n       <use x=\"664.404297\" xlink:href=\"#DejaVuSans-87\"/>\n       <use x=\"756.90625\" xlink:href=\"#DejaVuSans-97\"/>\n       <use x=\"818.185547\" xlink:href=\"#DejaVuSans-115\"/>\n       <use x=\"870.285156\" xlink:href=\"#DejaVuSans-104\"/>\n       <use x=\"933.664062\" xlink:href=\"#DejaVuSans-105\"/>\n       <use x=\"961.447266\" xlink:href=\"#DejaVuSans-110\"/>\n       <use x=\"1024.826172\" xlink:href=\"#DejaVuSans-103\"/>\n       <use x=\"1088.302734\" xlink:href=\"#DejaVuSans-116\"/>\n       <use x=\"1127.511719\" xlink:href=\"#DejaVuSans-111\"/>\n       <use x=\"1188.693359\" xlink:href=\"#DejaVuSans-110\"/>\n      </g>\n     </g>\n    </g>\n    <g id=\"ytick_19\">\n     <g id=\"line2d_28\">\n      <g>\n       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"225.88125\" xlink:href=\"#md95a43e5ce\" y=\"211.691458\"/>\n      </g>\n     </g>\n     <g id=\"text_29\">\n      <!-- University of Illinois at Urbana-Champaign -->\n      <defs>\n       <path d=\"M 4.890625 31.390625 \nL 31.203125 31.390625 \nL 31.203125 23.390625 \nL 4.890625 23.390625 \nz\n\" id=\"DejaVuSans-45\"/>\n      </defs>\n      <g transform=\"translate(7.2 215.490677)scale(0.1 -0.1)\">\n       <use xlink:href=\"#DejaVuSans-85\"/>\n       <use x=\"73.193359\" xlink:href=\"#DejaVuSans-110\"/>\n       <use x=\"136.572266\" xlink:href=\"#DejaVuSans-105\"/>\n       <use x=\"164.355469\" xlink:href=\"#DejaVuSans-118\"/>\n       <use x=\"223.535156\" xlink:href=\"#DejaVuSans-101\"/>\n       <use x=\"285.058594\" xlink:href=\"#DejaVuSans-114\"/>\n       <use x=\"326.171875\" xlink:href=\"#DejaVuSans-115\"/>\n       <use x=\"378.271484\" xlink:href=\"#DejaVuSans-105\"/>\n       <use x=\"406.054688\" xlink:href=\"#DejaVuSans-116\"/>\n       <use x=\"445.263672\" xlink:href=\"#DejaVuSans-121\"/>\n       <use x=\"504.443359\" xlink:href=\"#DejaVuSans-32\"/>\n       <use x=\"536.230469\" xlink:href=\"#DejaVuSans-111\"/>\n       <use x=\"597.412109\" xlink:href=\"#DejaVuSans-102\"/>\n       <use x=\"632.617188\" xlink:href=\"#DejaVuSans-32\"/>\n       <use x=\"664.404297\" xlink:href=\"#DejaVuSans-73\"/>\n       <use x=\"693.896484\" xlink:href=\"#DejaVuSans-108\"/>\n       <use x=\"721.679688\" xlink:href=\"#DejaVuSans-108\"/>\n       <use x=\"749.462891\" xlink:href=\"#DejaVuSans-105\"/>\n       <use x=\"777.246094\" xlink:href=\"#DejaVuSans-110\"/>\n       <use x=\"840.625\" xlink:href=\"#DejaVuSans-111\"/>\n       <use x=\"901.806641\" xlink:href=\"#DejaVuSans-105\"/>\n       <use x=\"929.589844\" xlink:href=\"#DejaVuSans-115\"/>\n       <use x=\"981.689453\" xlink:href=\"#DejaVuSans-32\"/>\n       <use x=\"1013.476562\" xlink:href=\"#DejaVuSans-97\"/>\n       <use x=\"1074.755859\" xlink:href=\"#DejaVuSans-116\"/>\n       <use x=\"1113.964844\" xlink:href=\"#DejaVuSans-32\"/>\n       <use x=\"1145.751953\" xlink:href=\"#DejaVuSans-85\"/>\n       <use x=\"1218.945312\" xlink:href=\"#DejaVuSans-114\"/>\n       <use x=\"1260.058594\" xlink:href=\"#DejaVuSans-98\"/>\n       <use x=\"1323.535156\" xlink:href=\"#DejaVuSans-97\"/>\n       <use x=\"1384.814453\" xlink:href=\"#DejaVuSans-110\"/>\n       <use x=\"1448.193359\" xlink:href=\"#DejaVuSans-97\"/>\n       <use x=\"1509.472656\" xlink:href=\"#DejaVuSans-45\"/>\n       <use x=\"1545.556641\" xlink:href=\"#DejaVuSans-67\"/>\n       <use x=\"1615.380859\" xlink:href=\"#DejaVuSans-104\"/>\n       <use x=\"1678.759766\" xlink:href=\"#DejaVuSans-97\"/>\n       <use x=\"1740.039062\" xlink:href=\"#DejaVuSans-109\"/>\n       <use x=\"1837.451172\" xlink:href=\"#DejaVuSans-112\"/>\n       <use x=\"1900.927734\" xlink:href=\"#DejaVuSans-97\"/>\n       <use x=\"1962.207031\" xlink:href=\"#DejaVuSans-105\"/>\n       <use x=\"1989.990234\" xlink:href=\"#DejaVuSans-103\"/>\n       <use x=\"2053.466797\" xlink:href=\"#DejaVuSans-110\"/>\n      </g>\n     </g>\n    </g>\n    <g id=\"ytick_20\">\n     <g id=\"line2d_29\">\n      <g>\n       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"225.88125\" xlink:href=\"#md95a43e5ce\" y=\"221.024792\"/>\n      </g>\n     </g>\n     <g id=\"text_30\">\n      <!-- University of Toronto -->\n      <g transform=\"translate(115.534375 224.82401)scale(0.1 -0.1)\">\n       <use xlink:href=\"#DejaVuSans-85\"/>\n       <use x=\"73.193359\" xlink:href=\"#DejaVuSans-110\"/>\n       <use x=\"136.572266\" xlink:href=\"#DejaVuSans-105\"/>\n       <use x=\"164.355469\" xlink:href=\"#DejaVuSans-118\"/>\n       <use x=\"223.535156\" xlink:href=\"#DejaVuSans-101\"/>\n       <use x=\"285.058594\" xlink:href=\"#DejaVuSans-114\"/>\n       <use x=\"326.171875\" xlink:href=\"#DejaVuSans-115\"/>\n       <use x=\"378.271484\" xlink:href=\"#DejaVuSans-105\"/>\n       <use x=\"406.054688\" xlink:href=\"#DejaVuSans-116\"/>\n       <use x=\"445.263672\" xlink:href=\"#DejaVuSans-121\"/>\n       <use x=\"504.443359\" xlink:href=\"#DejaVuSans-32\"/>\n       <use x=\"536.230469\" xlink:href=\"#DejaVuSans-111\"/>\n       <use x=\"597.412109\" xlink:href=\"#DejaVuSans-102\"/>\n       <use x=\"632.617188\" xlink:href=\"#DejaVuSans-32\"/>\n       <use x=\"664.404297\" xlink:href=\"#DejaVuSans-84\"/>\n       <use x=\"708.488281\" xlink:href=\"#DejaVuSans-111\"/>\n       <use x=\"769.669922\" xlink:href=\"#DejaVuSans-114\"/>\n       <use x=\"808.533203\" xlink:href=\"#DejaVuSans-111\"/>\n       <use x=\"869.714844\" xlink:href=\"#DejaVuSans-110\"/>\n       <use x=\"933.09375\" xlink:href=\"#DejaVuSans-116\"/>\n       <use x=\"972.302734\" xlink:href=\"#DejaVuSans-111\"/>\n      </g>\n     </g>\n    </g>\n    <g id=\"ytick_21\">\n     <g id=\"line2d_30\">\n      <g>\n       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"225.88125\" xlink:href=\"#md95a43e5ce\" y=\"230.358125\"/>\n      </g>\n     </g>\n     <g id=\"text_31\">\n      <!-- New York University -->\n      <defs>\n       <path d=\"M 4.203125 54.6875 \nL 13.1875 54.6875 \nL 24.421875 12.015625 \nL 35.59375 54.6875 \nL 46.1875 54.6875 \nL 57.421875 12.015625 \nL 68.609375 54.6875 \nL 77.59375 54.6875 \nL 63.28125 0 \nL 52.6875 0 \nL 40.921875 44.828125 \nL 29.109375 0 \nL 18.5 0 \nz\n\" id=\"DejaVuSans-119\"/>\n       <path d=\"M -0.203125 72.90625 \nL 10.40625 72.90625 \nL 30.609375 42.921875 \nL 50.6875 72.90625 \nL 61.28125 72.90625 \nL 35.5 34.71875 \nL 35.5 0 \nL 25.59375 0 \nL 25.59375 34.71875 \nz\n\" id=\"DejaVuSans-89\"/>\n      </defs>\n      <g transform=\"translate(119.465625 234.157344)scale(0.1 -0.1)\">\n       <use xlink:href=\"#DejaVuSans-78\"/>\n       <use x=\"74.804688\" xlink:href=\"#DejaVuSans-101\"/>\n       <use x=\"136.328125\" xlink:href=\"#DejaVuSans-119\"/>\n       <use x=\"218.115234\" xlink:href=\"#DejaVuSans-32\"/>\n       <use x=\"249.902344\" xlink:href=\"#DejaVuSans-89\"/>\n       <use x=\"297.736328\" xlink:href=\"#DejaVuSans-111\"/>\n       <use x=\"358.917969\" xlink:href=\"#DejaVuSans-114\"/>\n       <use x=\"400.03125\" xlink:href=\"#DejaVuSans-107\"/>\n       <use x=\"457.941406\" xlink:href=\"#DejaVuSans-32\"/>\n       <use x=\"489.728516\" xlink:href=\"#DejaVuSans-85\"/>\n       <use x=\"562.921875\" xlink:href=\"#DejaVuSans-110\"/>\n       <use x=\"626.300781\" xlink:href=\"#DejaVuSans-105\"/>\n       <use x=\"654.083984\" xlink:href=\"#DejaVuSans-118\"/>\n       <use x=\"713.263672\" xlink:href=\"#DejaVuSans-101\"/>\n       <use x=\"774.787109\" xlink:href=\"#DejaVuSans-114\"/>\n       <use x=\"815.900391\" xlink:href=\"#DejaVuSans-115\"/>\n       <use x=\"868\" xlink:href=\"#DejaVuSans-105\"/>\n       <use x=\"895.783203\" xlink:href=\"#DejaVuSans-116\"/>\n       <use x=\"934.992188\" xlink:href=\"#DejaVuSans-121\"/>\n      </g>\n     </g>\n    </g>\n    <g id=\"ytick_22\">\n     <g id=\"line2d_31\">\n      <g>\n       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"225.88125\" xlink:href=\"#md95a43e5ce\" y=\"239.691458\"/>\n      </g>\n     </g>\n     <g id=\"text_32\">\n      <!-- University of California, Los Angeles -->\n      <g transform=\"translate(38.876562 243.490677)scale(0.1 -0.1)\">\n       <use xlink:href=\"#DejaVuSans-85\"/>\n       <use x=\"73.193359\" xlink:href=\"#DejaVuSans-110\"/>\n       <use x=\"136.572266\" xlink:href=\"#DejaVuSans-105\"/>\n       <use x=\"164.355469\" xlink:href=\"#DejaVuSans-118\"/>\n       <use x=\"223.535156\" xlink:href=\"#DejaVuSans-101\"/>\n       <use x=\"285.058594\" xlink:href=\"#DejaVuSans-114\"/>\n       <use x=\"326.171875\" xlink:href=\"#DejaVuSans-115\"/>\n       <use x=\"378.271484\" xlink:href=\"#DejaVuSans-105\"/>\n       <use x=\"406.054688\" xlink:href=\"#DejaVuSans-116\"/>\n       <use x=\"445.263672\" xlink:href=\"#DejaVuSans-121\"/>\n       <use x=\"504.443359\" xlink:href=\"#DejaVuSans-32\"/>\n       <use x=\"536.230469\" xlink:href=\"#DejaVuSans-111\"/>\n       <use x=\"597.412109\" xlink:href=\"#DejaVuSans-102\"/>\n       <use x=\"632.617188\" xlink:href=\"#DejaVuSans-32\"/>\n       <use x=\"664.404297\" xlink:href=\"#DejaVuSans-67\"/>\n       <use x=\"734.228516\" xlink:href=\"#DejaVuSans-97\"/>\n       <use x=\"795.507812\" xlink:href=\"#DejaVuSans-108\"/>\n       <use x=\"823.291016\" xlink:href=\"#DejaVuSans-105\"/>\n       <use x=\"851.074219\" xlink:href=\"#DejaVuSans-102\"/>\n       <use x=\"886.279297\" xlink:href=\"#DejaVuSans-111\"/>\n       <use x=\"947.460938\" xlink:href=\"#DejaVuSans-114\"/>\n       <use x=\"986.824219\" xlink:href=\"#DejaVuSans-110\"/>\n       <use x=\"1050.203125\" xlink:href=\"#DejaVuSans-105\"/>\n       <use x=\"1077.986328\" xlink:href=\"#DejaVuSans-97\"/>\n       <use x=\"1139.265625\" xlink:href=\"#DejaVuSans-44\"/>\n       <use x=\"1171.052734\" xlink:href=\"#DejaVuSans-32\"/>\n       <use x=\"1202.839844\" xlink:href=\"#DejaVuSans-76\"/>\n       <use x=\"1256.802734\" xlink:href=\"#DejaVuSans-111\"/>\n       <use x=\"1317.984375\" xlink:href=\"#DejaVuSans-115\"/>\n       <use x=\"1370.083984\" xlink:href=\"#DejaVuSans-32\"/>\n       <use x=\"1401.871094\" xlink:href=\"#DejaVuSans-65\"/>\n       <use x=\"1470.279297\" xlink:href=\"#DejaVuSans-110\"/>\n       <use x=\"1533.658203\" xlink:href=\"#DejaVuSans-103\"/>\n       <use x=\"1597.134766\" xlink:href=\"#DejaVuSans-101\"/>\n       <use x=\"1658.658203\" xlink:href=\"#DejaVuSans-108\"/>\n       <use x=\"1686.441406\" xlink:href=\"#DejaVuSans-101\"/>\n       <use x=\"1747.964844\" xlink:href=\"#DejaVuSans-115\"/>\n      </g>\n     </g>\n    </g>\n    <g id=\"ytick_23\">\n     <g id=\"line2d_32\">\n      <g>\n       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"225.88125\" xlink:href=\"#md95a43e5ce\" y=\"249.024792\"/>\n      </g>\n     </g>\n     <g id=\"text_33\">\n      <!-- Peking University -->\n      <g transform=\"translate(132.184375 252.82401)scale(0.1 -0.1)\">\n       <use xlink:href=\"#DejaVuSans-80\"/>\n       <use x=\"56.677734\" xlink:href=\"#DejaVuSans-101\"/>\n       <use x=\"118.201172\" xlink:href=\"#DejaVuSans-107\"/>\n       <use x=\"176.111328\" xlink:href=\"#DejaVuSans-105\"/>\n       <use x=\"203.894531\" xlink:href=\"#DejaVuSans-110\"/>\n       <use x=\"267.273438\" xlink:href=\"#DejaVuSans-103\"/>\n       <use x=\"330.75\" xlink:href=\"#DejaVuSans-32\"/>\n       <use x=\"362.537109\" xlink:href=\"#DejaVuSans-85\"/>\n       <use x=\"435.730469\" xlink:href=\"#DejaVuSans-110\"/>\n       <use x=\"499.109375\" xlink:href=\"#DejaVuSans-105\"/>\n       <use x=\"526.892578\" xlink:href=\"#DejaVuSans-118\"/>\n       <use x=\"586.072266\" xlink:href=\"#DejaVuSans-101\"/>\n       <use x=\"647.595703\" xlink:href=\"#DejaVuSans-114\"/>\n       <use x=\"688.708984\" xlink:href=\"#DejaVuSans-115\"/>\n       <use x=\"740.808594\" xlink:href=\"#DejaVuSans-105\"/>\n       <use x=\"768.591797\" xlink:href=\"#DejaVuSans-116\"/>\n       <use x=\"807.800781\" xlink:href=\"#DejaVuSans-121\"/>\n      </g>\n     </g>\n    </g>\n    <g id=\"ytick_24\">\n     <g id=\"line2d_33\">\n      <g>\n       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"225.88125\" xlink:href=\"#md95a43e5ce\" y=\"258.358125\"/>\n      </g>\n     </g>\n     <g id=\"text_34\">\n      <!-- ETH Zurich -->\n      <defs>\n       <path d=\"M 9.8125 72.90625 \nL 55.90625 72.90625 \nL 55.90625 64.59375 \nL 19.671875 64.59375 \nL 19.671875 43.015625 \nL 54.390625 43.015625 \nL 54.390625 34.71875 \nL 19.671875 34.71875 \nL 19.671875 8.296875 \nL 56.78125 8.296875 \nL 56.78125 0 \nL 9.8125 0 \nz\n\" id=\"DejaVuSans-69\"/>\n       <path d=\"M 9.8125 72.90625 \nL 19.671875 72.90625 \nL 19.671875 43.015625 \nL 55.515625 43.015625 \nL 55.515625 72.90625 \nL 65.375 72.90625 \nL 65.375 0 \nL 55.515625 0 \nL 55.515625 34.71875 \nL 19.671875 34.71875 \nL 19.671875 0 \nL 9.8125 0 \nz\n\" id=\"DejaVuSans-72\"/>\n       <path d=\"M 5.609375 72.90625 \nL 62.890625 72.90625 \nL 62.890625 65.375 \nL 16.796875 8.296875 \nL 64.015625 8.296875 \nL 64.015625 0 \nL 4.5 0 \nL 4.5 7.515625 \nL 50.59375 64.59375 \nL 5.609375 64.59375 \nz\n\" id=\"DejaVuSans-90\"/>\n      </defs>\n      <g transform=\"translate(163.84375 262.157344)scale(0.1 -0.1)\">\n       <use xlink:href=\"#DejaVuSans-69\"/>\n       <use x=\"63.183594\" xlink:href=\"#DejaVuSans-84\"/>\n       <use x=\"124.267578\" xlink:href=\"#DejaVuSans-72\"/>\n       <use x=\"199.462891\" xlink:href=\"#DejaVuSans-32\"/>\n       <use x=\"231.25\" xlink:href=\"#DejaVuSans-90\"/>\n       <use x=\"299.755859\" xlink:href=\"#DejaVuSans-117\"/>\n       <use x=\"363.134766\" xlink:href=\"#DejaVuSans-114\"/>\n       <use x=\"404.248047\" xlink:href=\"#DejaVuSans-105\"/>\n       <use x=\"432.03125\" xlink:href=\"#DejaVuSans-99\"/>\n       <use x=\"487.011719\" xlink:href=\"#DejaVuSans-104\"/>\n      </g>\n     </g>\n    </g>\n    <g id=\"ytick_25\">\n     <g id=\"line2d_34\">\n      <g>\n       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"225.88125\" xlink:href=\"#md95a43e5ce\" y=\"267.691458\"/>\n      </g>\n     </g>\n     <g id=\"text_35\">\n      <!-- Amazon -->\n      <defs>\n       <path d=\"M 5.515625 54.6875 \nL 48.1875 54.6875 \nL 48.1875 46.484375 \nL 14.40625 7.171875 \nL 48.1875 7.171875 \nL 48.1875 0 \nL 4.296875 0 \nL 4.296875 8.203125 \nL 38.09375 47.515625 \nL 5.515625 47.515625 \nz\n\" id=\"DejaVuSans-122\"/>\n      </defs>\n      <g transform=\"translate(178.467187 271.490677)scale(0.1 -0.1)\">\n       <use xlink:href=\"#DejaVuSans-65\"/>\n       <use x=\"68.408203\" xlink:href=\"#DejaVuSans-109\"/>\n       <use x=\"165.820312\" xlink:href=\"#DejaVuSans-97\"/>\n       <use x=\"227.099609\" xlink:href=\"#DejaVuSans-122\"/>\n       <use x=\"279.589844\" xlink:href=\"#DejaVuSans-111\"/>\n       <use x=\"340.771484\" xlink:href=\"#DejaVuSans-110\"/>\n      </g>\n     </g>\n    </g>\n    <g id=\"ytick_26\">\n     <g id=\"line2d_35\">\n      <g>\n       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"225.88125\" xlink:href=\"#md95a43e5ce\" y=\"277.024792\"/>\n      </g>\n     </g>\n     <g id=\"text_36\">\n      <!-- Harvard University -->\n      <g transform=\"translate(125.16875 280.82401)scale(0.1 -0.1)\">\n       <use xlink:href=\"#DejaVuSans-72\"/>\n       <use x=\"75.195312\" xlink:href=\"#DejaVuSans-97\"/>\n       <use x=\"136.474609\" xlink:href=\"#DejaVuSans-114\"/>\n       <use x=\"177.587891\" xlink:href=\"#DejaVuSans-118\"/>\n       <use x=\"236.767578\" xlink:href=\"#DejaVuSans-97\"/>\n       <use x=\"298.046875\" xlink:href=\"#DejaVuSans-114\"/>\n       <use x=\"337.410156\" xlink:href=\"#DejaVuSans-100\"/>\n       <use x=\"400.886719\" xlink:href=\"#DejaVuSans-32\"/>\n       <use x=\"432.673828\" xlink:href=\"#DejaVuSans-85\"/>\n       <use x=\"505.867188\" xlink:href=\"#DejaVuSans-110\"/>\n       <use x=\"569.246094\" xlink:href=\"#DejaVuSans-105\"/>\n       <use x=\"597.029297\" xlink:href=\"#DejaVuSans-118\"/>\n       <use x=\"656.208984\" xlink:href=\"#DejaVuSans-101\"/>\n       <use x=\"717.732422\" xlink:href=\"#DejaVuSans-114\"/>\n       <use x=\"758.845703\" xlink:href=\"#DejaVuSans-115\"/>\n       <use x=\"810.945312\" xlink:href=\"#DejaVuSans-105\"/>\n       <use x=\"838.728516\" xlink:href=\"#DejaVuSans-116\"/>\n       <use x=\"877.9375\" xlink:href=\"#DejaVuSans-121\"/>\n      </g>\n     </g>\n    </g>\n    <g id=\"ytick_27\">\n     <g id=\"line2d_36\">\n      <g>\n       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"225.88125\" xlink:href=\"#md95a43e5ce\" y=\"286.358125\"/>\n      </g>\n     </g>\n     <g id=\"text_37\">\n      <!-- EPFL -->\n      <g transform=\"translate(195.209375 290.157344)scale(0.1 -0.1)\">\n       <use xlink:href=\"#DejaVuSans-69\"/>\n       <use x=\"63.183594\" xlink:href=\"#DejaVuSans-80\"/>\n       <use x=\"123.486328\" xlink:href=\"#DejaVuSans-70\"/>\n       <use x=\"181.005859\" xlink:href=\"#DejaVuSans-76\"/>\n      </g>\n     </g>\n    </g>\n    <g id=\"ytick_28\">\n     <g id=\"line2d_37\">\n      <g>\n       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"225.88125\" xlink:href=\"#md95a43e5ce\" y=\"295.691458\"/>\n      </g>\n     </g>\n     <g id=\"text_38\">\n      <!-- University of Southern California -->\n      <g transform=\"translate(56.289062 299.490677)scale(0.1 -0.1)\">\n       <use xlink:href=\"#DejaVuSans-85\"/>\n       <use x=\"73.193359\" xlink:href=\"#DejaVuSans-110\"/>\n       <use x=\"136.572266\" xlink:href=\"#DejaVuSans-105\"/>\n       <use x=\"164.355469\" xlink:href=\"#DejaVuSans-118\"/>\n       <use x=\"223.535156\" xlink:href=\"#DejaVuSans-101\"/>\n       <use x=\"285.058594\" xlink:href=\"#DejaVuSans-114\"/>\n       <use x=\"326.171875\" xlink:href=\"#DejaVuSans-115\"/>\n       <use x=\"378.271484\" xlink:href=\"#DejaVuSans-105\"/>\n       <use x=\"406.054688\" xlink:href=\"#DejaVuSans-116\"/>\n       <use x=\"445.263672\" xlink:href=\"#DejaVuSans-121\"/>\n       <use x=\"504.443359\" xlink:href=\"#DejaVuSans-32\"/>\n       <use x=\"536.230469\" xlink:href=\"#DejaVuSans-111\"/>\n       <use x=\"597.412109\" xlink:href=\"#DejaVuSans-102\"/>\n       <use x=\"632.617188\" xlink:href=\"#DejaVuSans-32\"/>\n       <use x=\"664.404297\" xlink:href=\"#DejaVuSans-83\"/>\n       <use x=\"727.880859\" xlink:href=\"#DejaVuSans-111\"/>\n       <use x=\"789.0625\" xlink:href=\"#DejaVuSans-117\"/>\n       <use x=\"852.441406\" xlink:href=\"#DejaVuSans-116\"/>\n       <use x=\"891.650391\" xlink:href=\"#DejaVuSans-104\"/>\n       <use x=\"955.029297\" xlink:href=\"#DejaVuSans-101\"/>\n       <use x=\"1016.552734\" xlink:href=\"#DejaVuSans-114\"/>\n       <use x=\"1055.916016\" xlink:href=\"#DejaVuSans-110\"/>\n       <use x=\"1119.294922\" xlink:href=\"#DejaVuSans-32\"/>\n       <use x=\"1151.082031\" xlink:href=\"#DejaVuSans-67\"/>\n       <use x=\"1220.90625\" xlink:href=\"#DejaVuSans-97\"/>\n       <use x=\"1282.185547\" xlink:href=\"#DejaVuSans-108\"/>\n       <use x=\"1309.96875\" xlink:href=\"#DejaVuSans-105\"/>\n       <use x=\"1337.751953\" xlink:href=\"#DejaVuSans-102\"/>\n       <use x=\"1372.957031\" xlink:href=\"#DejaVuSans-111\"/>\n       <use x=\"1434.138672\" xlink:href=\"#DejaVuSans-114\"/>\n       <use x=\"1473.501953\" xlink:href=\"#DejaVuSans-110\"/>\n       <use x=\"1536.880859\" xlink:href=\"#DejaVuSans-105\"/>\n       <use x=\"1564.664062\" xlink:href=\"#DejaVuSans-97\"/>\n      </g>\n     </g>\n    </g>\n    <g id=\"ytick_29\">\n     <g id=\"line2d_38\">\n      <g>\n       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"225.88125\" xlink:href=\"#md95a43e5ce\" y=\"305.024792\"/>\n      </g>\n     </g>\n     <g id=\"text_39\">\n      <!-- Duke University -->\n      <g transform=\"translate(139.640625 308.82401)scale(0.1 -0.1)\">\n       <use xlink:href=\"#DejaVuSans-68\"/>\n       <use x=\"77.001953\" xlink:href=\"#DejaVuSans-117\"/>\n       <use x=\"140.380859\" xlink:href=\"#DejaVuSans-107\"/>\n       <use x=\"194.666016\" xlink:href=\"#DejaVuSans-101\"/>\n       <use x=\"256.189453\" xlink:href=\"#DejaVuSans-32\"/>\n       <use x=\"287.976562\" xlink:href=\"#DejaVuSans-85\"/>\n       <use x=\"361.169922\" xlink:href=\"#DejaVuSans-110\"/>\n       <use x=\"424.548828\" xlink:href=\"#DejaVuSans-105\"/>\n       <use x=\"452.332031\" xlink:href=\"#DejaVuSans-118\"/>\n       <use x=\"511.511719\" xlink:href=\"#DejaVuSans-101\"/>\n       <use x=\"573.035156\" xlink:href=\"#DejaVuSans-114\"/>\n       <use x=\"614.148438\" xlink:href=\"#DejaVuSans-115\"/>\n       <use x=\"666.248047\" xlink:href=\"#DejaVuSans-105\"/>\n       <use x=\"694.03125\" xlink:href=\"#DejaVuSans-116\"/>\n       <use x=\"733.240234\" xlink:href=\"#DejaVuSans-121\"/>\n      </g>\n     </g>\n    </g>\n    <g id=\"ytick_30\">\n     <g id=\"line2d_39\">\n      <g>\n       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"225.88125\" xlink:href=\"#md95a43e5ce\" y=\"314.358125\"/>\n      </g>\n     </g>\n     <g id=\"text_40\">\n      <!-- RIKEN -->\n      <defs>\n       <path d=\"M 9.8125 72.90625 \nL 19.671875 72.90625 \nL 19.671875 42.09375 \nL 52.390625 72.90625 \nL 65.09375 72.90625 \nL 28.90625 38.921875 \nL 67.671875 0 \nL 54.6875 0 \nL 19.671875 35.109375 \nL 19.671875 0 \nL 9.8125 0 \nz\n\" id=\"DejaVuSans-75\"/>\n      </defs>\n      <g transform=\"translate(188.625 318.157344)scale(0.1 -0.1)\">\n       <use xlink:href=\"#DejaVuSans-82\"/>\n       <use x=\"69.482422\" xlink:href=\"#DejaVuSans-73\"/>\n       <use x=\"98.974609\" xlink:href=\"#DejaVuSans-75\"/>\n       <use x=\"164.550781\" xlink:href=\"#DejaVuSans-69\"/>\n       <use x=\"227.734375\" xlink:href=\"#DejaVuSans-78\"/>\n      </g>\n     </g>\n    </g>\n    <g id=\"ytick_31\">\n     <g id=\"line2d_40\">\n      <g>\n       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"225.88125\" xlink:href=\"#md95a43e5ce\" y=\"323.691458\"/>\n      </g>\n     </g>\n     <g id=\"text_41\">\n      <!-- University of Cambridge -->\n      <g transform=\"translate(97.503125 327.490677)scale(0.1 -0.1)\">\n       <use xlink:href=\"#DejaVuSans-85\"/>\n       <use x=\"73.193359\" xlink:href=\"#DejaVuSans-110\"/>\n       <use x=\"136.572266\" xlink:href=\"#DejaVuSans-105\"/>\n       <use x=\"164.355469\" xlink:href=\"#DejaVuSans-118\"/>\n       <use x=\"223.535156\" xlink:href=\"#DejaVuSans-101\"/>\n       <use x=\"285.058594\" xlink:href=\"#DejaVuSans-114\"/>\n       <use x=\"326.171875\" xlink:href=\"#DejaVuSans-115\"/>\n       <use x=\"378.271484\" xlink:href=\"#DejaVuSans-105\"/>\n       <use x=\"406.054688\" xlink:href=\"#DejaVuSans-116\"/>\n       <use x=\"445.263672\" xlink:href=\"#DejaVuSans-121\"/>\n       <use x=\"504.443359\" xlink:href=\"#DejaVuSans-32\"/>\n       <use x=\"536.230469\" xlink:href=\"#DejaVuSans-111\"/>\n       <use x=\"597.412109\" xlink:href=\"#DejaVuSans-102\"/>\n       <use x=\"632.617188\" xlink:href=\"#DejaVuSans-32\"/>\n       <use x=\"664.404297\" xlink:href=\"#DejaVuSans-67\"/>\n       <use x=\"734.228516\" xlink:href=\"#DejaVuSans-97\"/>\n       <use x=\"795.507812\" xlink:href=\"#DejaVuSans-109\"/>\n       <use x=\"892.919922\" xlink:href=\"#DejaVuSans-98\"/>\n       <use x=\"956.396484\" xlink:href=\"#DejaVuSans-114\"/>\n       <use x=\"997.509766\" xlink:href=\"#DejaVuSans-105\"/>\n       <use x=\"1025.292969\" xlink:href=\"#DejaVuSans-100\"/>\n       <use x=\"1088.769531\" xlink:href=\"#DejaVuSans-103\"/>\n       <use x=\"1152.246094\" xlink:href=\"#DejaVuSans-101\"/>\n      </g>\n     </g>\n    </g>\n    <g id=\"ytick_32\">\n     <g id=\"line2d_41\">\n      <g>\n       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"225.88125\" xlink:href=\"#md95a43e5ce\" y=\"333.024792\"/>\n      </g>\n     </g>\n     <g id=\"text_42\">\n      <!-- Tencent -->\n      <g transform=\"translate(180.073437 336.82401)scale(0.1 -0.1)\">\n       <use xlink:href=\"#DejaVuSans-84\"/>\n       <use x=\"44.083984\" xlink:href=\"#DejaVuSans-101\"/>\n       <use x=\"105.607422\" xlink:href=\"#DejaVuSans-110\"/>\n       <use x=\"168.986328\" xlink:href=\"#DejaVuSans-99\"/>\n       <use x=\"223.966797\" xlink:href=\"#DejaVuSans-101\"/>\n       <use x=\"285.490234\" xlink:href=\"#DejaVuSans-110\"/>\n       <use x=\"348.869141\" xlink:href=\"#DejaVuSans-116\"/>\n      </g>\n     </g>\n    </g>\n    <g id=\"ytick_33\">\n     <g id=\"line2d_42\">\n      <g>\n       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"225.88125\" xlink:href=\"#md95a43e5ce\" y=\"342.358125\"/>\n      </g>\n     </g>\n     <g id=\"text_43\">\n      <!-- Rutgers -->\n      <g transform=\"translate(180.303125 346.157344)scale(0.1 -0.1)\">\n       <use xlink:href=\"#DejaVuSans-82\"/>\n       <use x=\"64.982422\" xlink:href=\"#DejaVuSans-117\"/>\n       <use x=\"128.361328\" xlink:href=\"#DejaVuSans-116\"/>\n       <use x=\"167.570312\" xlink:href=\"#DejaVuSans-103\"/>\n       <use x=\"231.046875\" xlink:href=\"#DejaVuSans-101\"/>\n       <use x=\"292.570312\" xlink:href=\"#DejaVuSans-114\"/>\n       <use x=\"333.683594\" xlink:href=\"#DejaVuSans-115\"/>\n      </g>\n     </g>\n    </g>\n    <g id=\"ytick_34\">\n     <g id=\"line2d_43\">\n      <g>\n       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"225.88125\" xlink:href=\"#md95a43e5ce\" y=\"351.691458\"/>\n      </g>\n     </g>\n     <g id=\"text_44\">\n      <!-- Imperial College London -->\n      <g transform=\"translate(97.567187 355.490677)scale(0.1 -0.1)\">\n       <use xlink:href=\"#DejaVuSans-73\"/>\n       <use x=\"29.492188\" xlink:href=\"#DejaVuSans-109\"/>\n       <use x=\"126.904297\" xlink:href=\"#DejaVuSans-112\"/>\n       <use x=\"190.380859\" xlink:href=\"#DejaVuSans-101\"/>\n       <use x=\"251.904297\" xlink:href=\"#DejaVuSans-114\"/>\n       <use x=\"293.017578\" xlink:href=\"#DejaVuSans-105\"/>\n       <use x=\"320.800781\" xlink:href=\"#DejaVuSans-97\"/>\n       <use x=\"382.080078\" xlink:href=\"#DejaVuSans-108\"/>\n       <use x=\"409.863281\" xlink:href=\"#DejaVuSans-32\"/>\n       <use x=\"441.650391\" xlink:href=\"#DejaVuSans-67\"/>\n       <use x=\"511.474609\" xlink:href=\"#DejaVuSans-111\"/>\n       <use x=\"572.65625\" xlink:href=\"#DejaVuSans-108\"/>\n       <use x=\"600.439453\" xlink:href=\"#DejaVuSans-108\"/>\n       <use x=\"628.222656\" xlink:href=\"#DejaVuSans-101\"/>\n       <use x=\"689.746094\" xlink:href=\"#DejaVuSans-103\"/>\n       <use x=\"753.222656\" xlink:href=\"#DejaVuSans-101\"/>\n       <use x=\"814.746094\" xlink:href=\"#DejaVuSans-32\"/>\n       <use x=\"846.533203\" xlink:href=\"#DejaVuSans-76\"/>\n       <use x=\"900.496094\" xlink:href=\"#DejaVuSans-111\"/>\n       <use x=\"961.677734\" xlink:href=\"#DejaVuSans-110\"/>\n       <use x=\"1025.056641\" xlink:href=\"#DejaVuSans-100\"/>\n       <use x=\"1088.533203\" xlink:href=\"#DejaVuSans-111\"/>\n       <use x=\"1149.714844\" xlink:href=\"#DejaVuSans-110\"/>\n      </g>\n     </g>\n    </g>\n    <g id=\"ytick_35\">\n     <g id=\"line2d_44\">\n      <g>\n       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"225.88125\" xlink:href=\"#md95a43e5ce\" y=\"361.024792\"/>\n      </g>\n     </g>\n     <g id=\"text_45\">\n      <!-- University of Massachusetts Amherst -->\n      <g transform=\"translate(33.0625 364.82401)scale(0.1 -0.1)\">\n       <use xlink:href=\"#DejaVuSans-85\"/>\n       <use x=\"73.193359\" xlink:href=\"#DejaVuSans-110\"/>\n       <use x=\"136.572266\" xlink:href=\"#DejaVuSans-105\"/>\n       <use x=\"164.355469\" xlink:href=\"#DejaVuSans-118\"/>\n       <use x=\"223.535156\" xlink:href=\"#DejaVuSans-101\"/>\n       <use x=\"285.058594\" xlink:href=\"#DejaVuSans-114\"/>\n       <use x=\"326.171875\" xlink:href=\"#DejaVuSans-115\"/>\n       <use x=\"378.271484\" xlink:href=\"#DejaVuSans-105\"/>\n       <use x=\"406.054688\" xlink:href=\"#DejaVuSans-116\"/>\n       <use x=\"445.263672\" xlink:href=\"#DejaVuSans-121\"/>\n       <use x=\"504.443359\" xlink:href=\"#DejaVuSans-32\"/>\n       <use x=\"536.230469\" xlink:href=\"#DejaVuSans-111\"/>\n       <use x=\"597.412109\" xlink:href=\"#DejaVuSans-102\"/>\n       <use x=\"632.617188\" xlink:href=\"#DejaVuSans-32\"/>\n       <use x=\"664.404297\" xlink:href=\"#DejaVuSans-77\"/>\n       <use x=\"750.683594\" xlink:href=\"#DejaVuSans-97\"/>\n       <use x=\"811.962891\" xlink:href=\"#DejaVuSans-115\"/>\n       <use x=\"864.0625\" xlink:href=\"#DejaVuSans-115\"/>\n       <use x=\"916.162109\" xlink:href=\"#DejaVuSans-97\"/>\n       <use x=\"977.441406\" xlink:href=\"#DejaVuSans-99\"/>\n       <use x=\"1032.421875\" xlink:href=\"#DejaVuSans-104\"/>\n       <use x=\"1095.800781\" xlink:href=\"#DejaVuSans-117\"/>\n       <use x=\"1159.179688\" xlink:href=\"#DejaVuSans-115\"/>\n       <use x=\"1211.279297\" xlink:href=\"#DejaVuSans-101\"/>\n       <use x=\"1272.802734\" xlink:href=\"#DejaVuSans-116\"/>\n       <use x=\"1312.011719\" xlink:href=\"#DejaVuSans-116\"/>\n       <use x=\"1351.220703\" xlink:href=\"#DejaVuSans-115\"/>\n       <use x=\"1403.320312\" xlink:href=\"#DejaVuSans-32\"/>\n       <use x=\"1435.107422\" xlink:href=\"#DejaVuSans-65\"/>\n       <use x=\"1503.515625\" xlink:href=\"#DejaVuSans-109\"/>\n       <use x=\"1600.927734\" xlink:href=\"#DejaVuSans-104\"/>\n       <use x=\"1664.306641\" xlink:href=\"#DejaVuSans-101\"/>\n       <use x=\"1725.830078\" xlink:href=\"#DejaVuSans-114\"/>\n       <use x=\"1766.943359\" xlink:href=\"#DejaVuSans-115\"/>\n       <use x=\"1819.042969\" xlink:href=\"#DejaVuSans-116\"/>\n      </g>\n     </g>\n    </g>\n    <g id=\"ytick_36\">\n     <g id=\"line2d_45\">\n      <g>\n       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"225.88125\" xlink:href=\"#md95a43e5ce\" y=\"370.358125\"/>\n      </g>\n     </g>\n     <g id=\"text_46\">\n      <!-- uber -->\n      <g transform=\"translate(195.93125 374.157344)scale(0.1 -0.1)\">\n       <use xlink:href=\"#DejaVuSans-117\"/>\n       <use x=\"63.378906\" xlink:href=\"#DejaVuSans-98\"/>\n       <use x=\"126.855469\" xlink:href=\"#DejaVuSans-101\"/>\n       <use x=\"188.378906\" xlink:href=\"#DejaVuSans-114\"/>\n      </g>\n     </g>\n    </g>\n    <g id=\"ytick_37\">\n     <g id=\"line2d_46\">\n      <g>\n       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"225.88125\" xlink:href=\"#md95a43e5ce\" y=\"379.691458\"/>\n      </g>\n     </g>\n     <g id=\"text_47\">\n      <!-- nvidia -->\n      <g transform=\"translate(188.592187 383.490677)scale(0.1 -0.1)\">\n       <use xlink:href=\"#DejaVuSans-110\"/>\n       <use x=\"63.378906\" xlink:href=\"#DejaVuSans-118\"/>\n       <use x=\"122.558594\" xlink:href=\"#DejaVuSans-105\"/>\n       <use x=\"150.341797\" xlink:href=\"#DejaVuSans-100\"/>\n       <use x=\"213.818359\" xlink:href=\"#DejaVuSans-105\"/>\n       <use x=\"241.601562\" xlink:href=\"#DejaVuSans-97\"/>\n      </g>\n     </g>\n    </g>\n    <g id=\"ytick_38\">\n     <g id=\"line2d_47\">\n      <g>\n       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"225.88125\" xlink:href=\"#md95a43e5ce\" y=\"389.024792\"/>\n      </g>\n     </g>\n     <g id=\"text_48\">\n      <!-- covariant -->\n      <g transform=\"translate(171.942187 392.82401)scale(0.1 -0.1)\">\n       <use xlink:href=\"#DejaVuSans-99\"/>\n       <use x=\"54.980469\" xlink:href=\"#DejaVuSans-111\"/>\n       <use x=\"116.162109\" xlink:href=\"#DejaVuSans-118\"/>\n       <use x=\"175.341797\" xlink:href=\"#DejaVuSans-97\"/>\n       <use x=\"236.621094\" xlink:href=\"#DejaVuSans-114\"/>\n       <use x=\"277.734375\" xlink:href=\"#DejaVuSans-105\"/>\n       <use x=\"305.517578\" xlink:href=\"#DejaVuSans-97\"/>\n       <use x=\"366.796875\" xlink:href=\"#DejaVuSans-110\"/>\n       <use x=\"430.175781\" xlink:href=\"#DejaVuSans-116\"/>\n      </g>\n     </g>\n    </g>\n   </g>\n   <g id=\"patch_79\">\n    <path d=\"M 225.88125 410.398125 \nL 225.88125 22.318125 \n\" style=\"fill:none;stroke:#000000;stroke-linecap:square;stroke-linejoin:miter;stroke-width:0.8;\"/>\n   </g>\n   <g id=\"patch_80\">\n    <path d=\"M 560.68125 410.398125 \nL 560.68125 22.318125 \n\" style=\"fill:none;stroke:#000000;stroke-linecap:square;stroke-linejoin:miter;stroke-width:0.8;\"/>\n   </g>\n   <g id=\"patch_81\">\n    <path d=\"M 225.88125 410.398125 \nL 560.68125 410.398125 \n\" style=\"fill:none;stroke:#000000;stroke-linecap:square;stroke-linejoin:miter;stroke-width:0.8;\"/>\n   </g>\n   <g id=\"patch_82\">\n    <path d=\"M 225.88125 22.318125 \nL 560.68125 22.318125 \n\" style=\"fill:none;stroke:#000000;stroke-linecap:square;stroke-linejoin:miter;stroke-width:0.8;\"/>\n   </g>\n   <g id=\"text_49\">\n    <!-- Papers with code - author affiliations -->\n    <g transform=\"translate(283.295625 16.318125)scale(0.12 -0.12)\">\n     <use xlink:href=\"#DejaVuSans-80\"/>\n     <use x=\"55.802734\" xlink:href=\"#DejaVuSans-97\"/>\n     <use x=\"117.082031\" xlink:href=\"#DejaVuSans-112\"/>\n     <use x=\"180.558594\" xlink:href=\"#DejaVuSans-101\"/>\n     <use x=\"242.082031\" xlink:href=\"#DejaVuSans-114\"/>\n     <use x=\"283.195312\" xlink:href=\"#DejaVuSans-115\"/>\n     <use x=\"335.294922\" xlink:href=\"#DejaVuSans-32\"/>\n     <use x=\"367.082031\" xlink:href=\"#DejaVuSans-119\"/>\n     <use x=\"448.869141\" xlink:href=\"#DejaVuSans-105\"/>\n     <use x=\"476.652344\" xlink:href=\"#DejaVuSans-116\"/>\n     <use x=\"515.861328\" xlink:href=\"#DejaVuSans-104\"/>\n     <use x=\"579.240234\" xlink:href=\"#DejaVuSans-32\"/>\n     <use x=\"611.027344\" xlink:href=\"#DejaVuSans-99\"/>\n     <use x=\"666.007812\" xlink:href=\"#DejaVuSans-111\"/>\n     <use x=\"727.189453\" xlink:href=\"#DejaVuSans-100\"/>\n     <use x=\"790.666016\" xlink:href=\"#DejaVuSans-101\"/>\n     <use x=\"852.189453\" xlink:href=\"#DejaVuSans-32\"/>\n     <use x=\"883.976562\" xlink:href=\"#DejaVuSans-45\"/>\n     <use x=\"920.060547\" xlink:href=\"#DejaVuSans-32\"/>\n     <use x=\"951.847656\" xlink:href=\"#DejaVuSans-97\"/>\n     <use x=\"1013.126953\" xlink:href=\"#DejaVuSans-117\"/>\n     <use x=\"1076.505859\" xlink:href=\"#DejaVuSans-116\"/>\n     <use x=\"1115.714844\" xlink:href=\"#DejaVuSans-104\"/>\n     <use x=\"1179.09375\" xlink:href=\"#DejaVuSans-111\"/>\n     <use x=\"1240.275391\" xlink:href=\"#DejaVuSans-114\"/>\n     <use x=\"1281.388672\" xlink:href=\"#DejaVuSans-32\"/>\n     <use x=\"1313.175781\" xlink:href=\"#DejaVuSans-97\"/>\n     <use x=\"1374.455078\" xlink:href=\"#DejaVuSans-102\"/>\n     <use x=\"1409.660156\" xlink:href=\"#DejaVuSans-102\"/>\n     <use x=\"1444.865234\" xlink:href=\"#DejaVuSans-105\"/>\n     <use x=\"1472.648438\" xlink:href=\"#DejaVuSans-108\"/>\n     <use x=\"1500.431641\" xlink:href=\"#DejaVuSans-105\"/>\n     <use x=\"1528.214844\" xlink:href=\"#DejaVuSans-97\"/>\n     <use x=\"1589.494141\" xlink:href=\"#DejaVuSans-116\"/>\n     <use x=\"1628.703125\" xlink:href=\"#DejaVuSans-105\"/>\n     <use x=\"1656.486328\" xlink:href=\"#DejaVuSans-111\"/>\n     <use x=\"1717.667969\" xlink:href=\"#DejaVuSans-110\"/>\n     <use x=\"1781.046875\" xlink:href=\"#DejaVuSans-115\"/>\n    </g>\n   </g>\n   <g id=\"legend_1\">\n    <g id=\"patch_83\">\n     <path d=\"M 481.928125 405.398125 \nL 553.68125 405.398125 \nQ 555.68125 405.398125 555.68125 403.398125 \nL 555.68125 375.041875 \nQ 555.68125 373.041875 553.68125 373.041875 \nL 481.928125 373.041875 \nQ 479.928125 373.041875 479.928125 375.041875 \nL 479.928125 403.398125 \nQ 479.928125 405.398125 481.928125 405.398125 \nz\n\" style=\"fill:#ffffff;opacity:0.8;stroke:#cccccc;stroke-linejoin:miter;\"/>\n    </g>\n    <g id=\"patch_84\">\n     <path d=\"M 483.928125 384.640312 \nL 503.928125 384.640312 \nL 503.928125 377.640312 \nL 483.928125 377.640312 \nz\n\" style=\"fill:#d62728;\"/>\n    </g>\n    <g id=\"text_50\">\n     <!-- no code -->\n     <g transform=\"translate(511.928125 384.640312)scale(0.1 -0.1)\">\n      <use xlink:href=\"#DejaVuSans-110\"/>\n      <use x=\"63.378906\" xlink:href=\"#DejaVuSans-111\"/>\n      <use x=\"124.560547\" xlink:href=\"#DejaVuSans-32\"/>\n      <use x=\"156.347656\" xlink:href=\"#DejaVuSans-99\"/>\n      <use x=\"211.328125\" xlink:href=\"#DejaVuSans-111\"/>\n      <use x=\"272.509766\" xlink:href=\"#DejaVuSans-100\"/>\n      <use x=\"335.986328\" xlink:href=\"#DejaVuSans-101\"/>\n     </g>\n    </g>\n    <g id=\"patch_85\">\n     <path d=\"M 483.928125 399.318437 \nL 503.928125 399.318437 \nL 503.928125 392.318437 \nL 483.928125 392.318437 \nz\n\" style=\"fill:#1f77b4;\"/>\n    </g>\n    <g id=\"text_51\">\n     <!-- code -->\n     <g transform=\"translate(511.928125 399.318437)scale(0.1 -0.1)\">\n      <use xlink:href=\"#DejaVuSans-99\"/>\n      <use x=\"54.980469\" xlink:href=\"#DejaVuSans-111\"/>\n      <use x=\"116.162109\" xlink:href=\"#DejaVuSans-100\"/>\n      <use x=\"179.638672\" xlink:href=\"#DejaVuSans-101\"/>\n     </g>\n    </g>\n   </g>\n  </g>\n </g>\n <defs>\n  <clipPath id=\"pf30b7a255c\">\n   <rect height=\"388.08\" width=\"334.8\" x=\"225.88125\" y=\"22.318125\"/>\n  </clipPath>\n </defs>\n</svg>\n",
      "image/png": "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\n"
     },
     "metadata": {}
    }
   ],
   "source": [
    "plt.rcdefaults()\n",
    "fig, ax1 = plt.subplots(figsize=(6, 7))\n",
    "\n",
    "institution_names = list(df.index.values)\n",
    "paper_counts = df['papers'].tolist()\n",
    "code_counts = df['code'].tolist()\n",
    "\n",
    "ax1.barh(institution_names, paper_counts, color='tab:red')\n",
    "ax1.barh(institution_names, code_counts, color='tab:blue')\n",
    "ax1.invert_yaxis()  # labels read top-to-bottom\n",
    "ax1.set_xlabel('Papers')\n",
    "ax1.set_title('Papers with code - author affiliations')\n",
    "ax1.legend(['no code', 'code'])\n",
    "\n",
    "plt.savefig(out_folder + 'papers_with_code.png', bbox_inches='tight')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.1-final"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}