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class="help"><a href="https://info.arxiv.org/help">Help</a> | <a href="https://arxiv.org/search/advanced">Advanced Search</a></p> </div> <div class="control"> <div class="select is-small"> <select name="searchtype" aria-label="Field to search"> <option value="all" selected="selected">All fields</option> <option value="title">Title</option> <option value="author">Author</option> <option value="abstract">Abstract</option> <option value="comments">Comments</option> <option value="journal_ref">Journal reference</option> <option value="acm_class">ACM classification</option> <option value="msc_class">MSC classification</option> <option value="report_num">Report number</option> <option value="paper_id">arXiv identifier</option> <option value="doi">DOI</option> <option value="orcid">ORCID</option> <option value="author_id">arXiv author ID</option> <option value="help">Help pages</option> <option value="full_text">Full text</option> </select> </div> </div> <input type="hidden" name="source" 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name="searchtype"><option value="all">All fields</option><option value="title">Title</option><option selected value="author">Author(s)</option><option value="abstract">Abstract</option><option value="comments">Comments</option><option value="journal_ref">Journal reference</option><option value="acm_class">ACM classification</option><option value="msc_class">MSC classification</option><option value="report_num">Report number</option><option value="paper_id">arXiv identifier</option><option value="doi">DOI</option><option value="orcid">ORCID</option><option value="license">License (URI)</option><option value="author_id">arXiv author ID</option><option value="help">Help pages</option><option value="full_text">Full text</option></select> <input id="query" name="query" type="text" value="Kramer, A"> <ul id="abstracts"><li><input checked id="abstracts-0" name="abstracts" type="radio" value="show"> <label for="abstracts-0">Show abstracts</label></li><li><input id="abstracts-1" 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is-small is-link">Go</button> </div> </div> </form> </div> </div> <ol class="breathe-horizontal" start="1"> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2406.06657">arXiv:2406.06657</a> <span> [<a href="https://arxiv.org/pdf/2406.06657">pdf</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Computation and Language">cs.CL</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Artificial Intelligence">cs.AI</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Information Retrieval">cs.IR</span> </div> </div> <p class="title is-5 mathjax"> Harnessing AI for efficient analysis of complex policy documents: a case study of Executive Order 14110 </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Kramer%2C+M+A">Mark A. Kramer</a>, <a href="/search/cs?searchtype=author&query=Leavens%2C+A">Allen Leavens</a>, <a href="/search/cs?searchtype=author&query=Scarlat%2C+A">Alexander Scarlat</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2406.06657v1-abstract-short" style="display: inline;"> Policy documents, such as legislation, regulations, and executive orders, are crucial in shaping society. However, their length and complexity make interpretation and application challenging and time-consuming. Artificial intelligence (AI), particularly large language models (LLMs), has the potential to automate the process of analyzing these documents, improving accuracy and efficiency. This stud… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2406.06657v1-abstract-full').style.display = 'inline'; document.getElementById('2406.06657v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2406.06657v1-abstract-full" style="display: none;"> Policy documents, such as legislation, regulations, and executive orders, are crucial in shaping society. However, their length and complexity make interpretation and application challenging and time-consuming. Artificial intelligence (AI), particularly large language models (LLMs), has the potential to automate the process of analyzing these documents, improving accuracy and efficiency. This study aims to evaluate the potential of AI in streamlining policy analysis and to identify the strengths and limitations of current AI approaches. The research focuses on question answering and tasks involving content extraction from policy documents. A case study was conducted using Executive Order 14110 on "Safe, Secure, and Trustworthy Development and Use of Artificial Intelligence" as a test case. Four commercial AI systems were used to analyze the document and answer a set of representative policy questions. The performance of the AI systems was compared to manual analysis conducted by human experts. The study found that two AI systems, Gemini 1.5 Pro and Claude 3 Opus, demonstrated significant potential for supporting policy analysis, providing accurate and reliable information extraction from complex documents. They performed comparably to human analysts but with significantly higher efficiency. However, achieving reproducibility remains a challenge, necessitating further research and development. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2406.06657v1-abstract-full').style.display = 'none'; document.getElementById('2406.06657v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 10 June, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> June 2024. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">28 pages, 1 figure</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2405.00600">arXiv:2405.00600</a> <span> [<a href="https://arxiv.org/pdf/2405.00600">pdf</a>, <a href="https://arxiv.org/format/2405.00600">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Robotics">cs.RO</span> </div> </div> <p class="title is-5 mathjax"> Radar-Based Localization For Autonomous Ground Vehicles In Suburban Neighborhoods </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Kramer%2C+A+J">Andrew J. Kramer</a>, <a href="/search/cs?searchtype=author&query=Heckman%2C+C">Christoffer Heckman</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2405.00600v1-abstract-short" style="display: inline;"> For autonomous ground vehicles (AGVs) deployed in suburban neighborhoods and other human-centric environments the problem of localization remains a fundamental challenge. There are well established methods for localization with GPS, lidar, and cameras. But even in ideal conditions these have limitations. GPS is not always available and is often not accurate enough on its own, visual methods have d… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2405.00600v1-abstract-full').style.display = 'inline'; document.getElementById('2405.00600v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2405.00600v1-abstract-full" style="display: none;"> For autonomous ground vehicles (AGVs) deployed in suburban neighborhoods and other human-centric environments the problem of localization remains a fundamental challenge. There are well established methods for localization with GPS, lidar, and cameras. But even in ideal conditions these have limitations. GPS is not always available and is often not accurate enough on its own, visual methods have difficulty coping with appearance changes due to weather and other factors, and lidar methods are prone to defective solutions due to ambiguous scene geometry. Radar on the other hand is not highly susceptible to these problems, owing in part to its longer range. Further, radar is also robust to challenging conditions that interfere with vision and lidar including fog, smoke, rain, and darkness. We present a radar-based localization system that includes a novel method for highly-accurate radar odometry for smooth, high-frequency relative pose estimation and a novel method for radar-based place recognition and relocalization. We present experiments demonstrating our methods' accuracy and reliability, which are comparable with \new{other methods' published results for radar localization and we find outperform a similar method as ours applied to lidar measurements}. Further, we show our methods are lightweight enough to run on common low-power embedded hardware with ample headroom for other autonomy functions. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2405.00600v1-abstract-full').style.display = 'none'; document.getElementById('2405.00600v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 1 May, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> May 2024. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">Accepted to Field Robotics, 1. May 2024</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2310.03121">arXiv:2310.03121</a> <span> [<a href="https://arxiv.org/pdf/2310.03121">pdf</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Chemical Physics">physics.chem-ph</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> </div> </div> <p class="title is-5 mathjax"> OpenMM 8: Molecular Dynamics Simulation with Machine Learning Potentials </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Eastman%2C+P">Peter Eastman</a>, <a href="/search/cs?searchtype=author&query=Galvelis%2C+R">Raimondas Galvelis</a>, <a href="/search/cs?searchtype=author&query=Pel%C3%A1ez%2C+R+P">Ra煤l P. Pel谩ez</a>, <a href="/search/cs?searchtype=author&query=Abreu%2C+C+R+A">Charlles R. A. Abreu</a>, <a href="/search/cs?searchtype=author&query=Farr%2C+S+E">Stephen E. Farr</a>, <a href="/search/cs?searchtype=author&query=Gallicchio%2C+E">Emilio Gallicchio</a>, <a href="/search/cs?searchtype=author&query=Gorenko%2C+A">Anton Gorenko</a>, <a href="/search/cs?searchtype=author&query=Henry%2C+M+M">Michael M. Henry</a>, <a href="/search/cs?searchtype=author&query=Hu%2C+F">Frank Hu</a>, <a href="/search/cs?searchtype=author&query=Huang%2C+J">Jing Huang</a>, <a href="/search/cs?searchtype=author&query=Kr%C3%A4mer%2C+A">Andreas Kr盲mer</a>, <a href="/search/cs?searchtype=author&query=Michel%2C+J">Julien Michel</a>, <a href="/search/cs?searchtype=author&query=Mitchell%2C+J+A">Joshua A. Mitchell</a>, <a href="/search/cs?searchtype=author&query=Pande%2C+V+S">Vijay S. Pande</a>, <a href="/search/cs?searchtype=author&query=Rodrigues%2C+J+P">Jo茫o PGLM Rodrigues</a>, <a href="/search/cs?searchtype=author&query=Rodriguez-Guerra%2C+J">Jaime Rodriguez-Guerra</a>, <a href="/search/cs?searchtype=author&query=Simmonett%2C+A+C">Andrew C. Simmonett</a>, <a href="/search/cs?searchtype=author&query=Singh%2C+S">Sukrit Singh</a>, <a href="/search/cs?searchtype=author&query=Swails%2C+J">Jason Swails</a>, <a href="/search/cs?searchtype=author&query=Turner%2C+P">Philip Turner</a>, <a href="/search/cs?searchtype=author&query=Wang%2C+Y">Yuanqing Wang</a>, <a href="/search/cs?searchtype=author&query=Zhang%2C+I">Ivy Zhang</a>, <a href="/search/cs?searchtype=author&query=Chodera%2C+J+D">John D. Chodera</a>, <a href="/search/cs?searchtype=author&query=De+Fabritiis%2C+G">Gianni De Fabritiis</a>, <a href="/search/cs?searchtype=author&query=Markland%2C+T+E">Thomas E. Markland</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2310.03121v2-abstract-short" style="display: inline;"> Machine learning plays an important and growing role in molecular simulation. The newest version of the OpenMM molecular dynamics toolkit introduces new features to support the use of machine learning potentials. Arbitrary PyTorch models can be added to a simulation and used to compute forces and energy. A higher-level interface allows users to easily model their molecules of interest with general… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2310.03121v2-abstract-full').style.display = 'inline'; document.getElementById('2310.03121v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2310.03121v2-abstract-full" style="display: none;"> Machine learning plays an important and growing role in molecular simulation. The newest version of the OpenMM molecular dynamics toolkit introduces new features to support the use of machine learning potentials. Arbitrary PyTorch models can be added to a simulation and used to compute forces and energy. A higher-level interface allows users to easily model their molecules of interest with general purpose, pretrained potential functions. A collection of optimized CUDA kernels and custom PyTorch operations greatly improves the speed of simulations. We demonstrate these features on simulations of cyclin-dependent kinase 8 (CDK8) and the green fluorescent protein (GFP) chromophore in water. Taken together, these features make it practical to use machine learning to improve the accuracy of simulations at only a modest increase in cost. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2310.03121v2-abstract-full').style.display = 'none'; document.getElementById('2310.03121v2-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 29 November, 2023; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 4 October, 2023; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> October 2023. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">16 pages, 5 figures</span> </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">ACM Class:</span> J.2; J.3 </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2306.15030">arXiv:2306.15030</a> <span> [<a href="https://arxiv.org/pdf/2306.15030">pdf</a>, <a href="https://arxiv.org/format/2306.15030">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Machine Learning">stat.ML</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Chemical Physics">physics.chem-ph</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Computational Physics">physics.comp-ph</span> </div> </div> <p class="title is-5 mathjax"> Equivariant flow matching </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Klein%2C+L">Leon Klein</a>, <a href="/search/cs?searchtype=author&query=Kr%C3%A4mer%2C+A">Andreas Kr盲mer</a>, <a href="/search/cs?searchtype=author&query=No%C3%A9%2C+F">Frank No茅</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2306.15030v2-abstract-short" style="display: inline;"> Normalizing flows are a class of deep generative models that are especially interesting for modeling probability distributions in physics, where the exact likelihood of flows allows reweighting to known target energy functions and computing unbiased observables. For instance, Boltzmann generators tackle the long-standing sampling problem in statistical physics by training flows to produce equilibr… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2306.15030v2-abstract-full').style.display = 'inline'; document.getElementById('2306.15030v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2306.15030v2-abstract-full" style="display: none;"> Normalizing flows are a class of deep generative models that are especially interesting for modeling probability distributions in physics, where the exact likelihood of flows allows reweighting to known target energy functions and computing unbiased observables. For instance, Boltzmann generators tackle the long-standing sampling problem in statistical physics by training flows to produce equilibrium samples of many-body systems such as small molecules and proteins. To build effective models for such systems, it is crucial to incorporate the symmetries of the target energy into the model, which can be achieved by equivariant continuous normalizing flows (CNFs). However, CNFs can be computationally expensive to train and generate samples from, which has hampered their scalability and practical application. In this paper, we introduce equivariant flow matching, a new training objective for equivariant CNFs that is based on the recently proposed optimal transport flow matching. Equivariant flow matching exploits the physical symmetries of the target energy for efficient, simulation-free training of equivariant CNFs. We demonstrate the effectiveness of flow matching on rotation and permutation invariant many-particle systems and a small molecule, alanine dipeptide, where for the first time we obtain a Boltzmann generator with significant sampling efficiency without relying on tailored internal coordinate featurization. Our results show that the equivariant flow matching objective yields flows with shorter integration paths, improved sampling efficiency, and higher scalability compared to existing methods. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2306.15030v2-abstract-full').style.display = 'none'; document.getElementById('2306.15030v2-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 23 November, 2023; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 26 June, 2023; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> June 2023. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2203.11167">arXiv:2203.11167</a> <span> [<a href="https://arxiv.org/pdf/2203.11167">pdf</a>, <a href="https://arxiv.org/format/2203.11167">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Computational Physics">physics.comp-ph</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Biological Physics">physics.bio-ph</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Chemical Physics">physics.chem-ph</span> </div> <div class="is-inline-block" style="margin-left: 0.5rem"> <div class="tags has-addons"> <span class="tag is-dark is-size-7">doi</span> <span class="tag is-light is-size-7"><a class="" href="https://doi.org/10.1021/acs.jctc.3c00016">10.1021/acs.jctc.3c00016 <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </div> </div> <p class="title is-5 mathjax"> Flow-matching -- efficient coarse-graining of molecular dynamics without forces </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=K%C3%B6hler%2C+J">Jonas K枚hler</a>, <a href="/search/cs?searchtype=author&query=Chen%2C+Y">Yaoyi Chen</a>, <a href="/search/cs?searchtype=author&query=Kr%C3%A4mer%2C+A">Andreas Kr盲mer</a>, <a href="/search/cs?searchtype=author&query=Clementi%2C+C">Cecilia Clementi</a>, <a href="/search/cs?searchtype=author&query=No%C3%A9%2C+F">Frank No茅</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2203.11167v4-abstract-short" style="display: inline;"> Coarse-grained (CG) molecular simulations have become a standard tool to study molecular processes on time- and length-scales inaccessible to all-atom simulations. Parameterizing CG force fields to match all-atom simulations has mainly relied on force-matching or relative entropy minimization, which require many samples from costly simulations with all-atom or CG resolutions, respectively. Here we… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2203.11167v4-abstract-full').style.display = 'inline'; document.getElementById('2203.11167v4-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2203.11167v4-abstract-full" style="display: none;"> Coarse-grained (CG) molecular simulations have become a standard tool to study molecular processes on time- and length-scales inaccessible to all-atom simulations. Parameterizing CG force fields to match all-atom simulations has mainly relied on force-matching or relative entropy minimization, which require many samples from costly simulations with all-atom or CG resolutions, respectively. Here we present flow-matching, a new training method for CG force fields that combines the advantages of both methods by leveraging normalizing flows, a generative deep learning method. Flow-matching first trains a normalizing flow to represent the CG probability density, which is equivalent to minimizing the relative entropy without requiring iterative CG simulations. Subsequently, the flow generates samples and forces according to the learned distribution in order to train the desired CG free energy model via force matching. Even without requiring forces from the all-atom simulations, flow-matching outperforms classical force-matching by an order of magnitude in terms of data efficiency, and produces CG models that can capture the folding and unfolding transitions of small proteins. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2203.11167v4-abstract-full').style.display = 'none'; document.getElementById('2203.11167v4-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 5 February, 2023; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 21 March, 2022; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> March 2022. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Journal ref:</span> J. Chem. Theory Comput. 2023, XXXX, XXX, XXX-XXX </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2112.02622">arXiv:2112.02622</a> <span> [<a href="https://arxiv.org/pdf/2112.02622">pdf</a>, <a href="https://arxiv.org/format/2112.02622">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Artificial Intelligence">cs.AI</span> </div> <div class="is-inline-block" style="margin-left: 0.5rem"> <div class="tags has-addons"> <span class="tag is-dark is-size-7">doi</span> <span class="tag is-light is-size-7"><a class="" href="https://doi.org/10.3390/s21238009">10.3390/s21238009 <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </div> </div> <p class="title is-5 mathjax"> Probabilistic Deep Learning to Quantify Uncertainty in Air Quality Forecasting </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Murad%2C+A">Abdulmajid Murad</a>, <a href="/search/cs?searchtype=author&query=Kraemer%2C+F+A">Frank Alexander Kraemer</a>, <a href="/search/cs?searchtype=author&query=Bach%2C+K">Kerstin Bach</a>, <a href="/search/cs?searchtype=author&query=Taylor%2C+G">Gavin Taylor</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2112.02622v1-abstract-short" style="display: inline;"> Data-driven forecasts of air quality have recently achieved more accurate short-term predictions. Despite their success, most of the current data-driven solutions lack proper quantifications of model uncertainty that communicate how much to trust the forecasts. Recently, several practical tools to estimate uncertainty have been developed in probabilistic deep learning. However, there have not been… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2112.02622v1-abstract-full').style.display = 'inline'; document.getElementById('2112.02622v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2112.02622v1-abstract-full" style="display: none;"> Data-driven forecasts of air quality have recently achieved more accurate short-term predictions. Despite their success, most of the current data-driven solutions lack proper quantifications of model uncertainty that communicate how much to trust the forecasts. Recently, several practical tools to estimate uncertainty have been developed in probabilistic deep learning. However, there have not been empirical applications and extensive comparisons of these tools in the domain of air quality forecasts. Therefore, this work applies state-of-the-art techniques of uncertainty quantification in a real-world setting of air quality forecasts. Through extensive experiments, we describe training probabilistic models and evaluate their predictive uncertainties based on empirical performance, reliability of confidence estimate, and practical applicability. We also propose improving these models using "free" adversarial training and exploiting temporal and spatial correlation inherent in air quality data. Our experiments demonstrate that the proposed models perform better than previous works in quantifying uncertainty in data-driven air quality forecasts. Overall, Bayesian neural networks provide a more reliable uncertainty estimate but can be challenging to implement and scale. Other scalable methods, such as deep ensemble, Monte Carlo (MC) dropout, and stochastic weight averaging-Gaussian (SWAG), can perform well if applied correctly but with different tradeoffs and slight variations in performance metrics. Finally, our results show the practical impact of uncertainty estimation and demonstrate that, indeed, probabilistic models are more suitable for making informed decisions. Code and dataset are available at \url{https://github.com/Abdulmajid-Murad/deep_probabilistic_forecast} <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2112.02622v1-abstract-full').style.display = 'none'; document.getElementById('2112.02622v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 5 December, 2021; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> December 2021. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Journal ref:</span> Sensors, 21(23) 2021) </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2110.00351">arXiv:2110.00351</a> <span> [<a href="https://arxiv.org/pdf/2110.00351">pdf</a>, <a href="https://arxiv.org/format/2110.00351">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Machine Learning">stat.ML</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Chemical Physics">physics.chem-ph</span> </div> </div> <p class="title is-5 mathjax"> Smooth Normalizing Flows </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=K%C3%B6hler%2C+J">Jonas K枚hler</a>, <a href="/search/cs?searchtype=author&query=Kr%C3%A4mer%2C+A">Andreas Kr盲mer</a>, <a href="/search/cs?searchtype=author&query=No%C3%A9%2C+F">Frank No茅</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2110.00351v2-abstract-short" style="display: inline;"> Normalizing flows are a promising tool for modeling probability distributions in physical systems. While state-of-the-art flows accurately approximate distributions and energies, applications in physics additionally require smooth energies to compute forces and higher-order derivatives. Furthermore, such densities are often defined on non-trivial topologies. A recent example are Boltzmann Generato… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2110.00351v2-abstract-full').style.display = 'inline'; document.getElementById('2110.00351v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2110.00351v2-abstract-full" style="display: none;"> Normalizing flows are a promising tool for modeling probability distributions in physical systems. While state-of-the-art flows accurately approximate distributions and energies, applications in physics additionally require smooth energies to compute forces and higher-order derivatives. Furthermore, such densities are often defined on non-trivial topologies. A recent example are Boltzmann Generators for generating 3D-structures of peptides and small proteins. These generative models leverage the space of internal coordinates (dihedrals, angles, and bonds), which is a product of hypertori and compact intervals. In this work, we introduce a class of smooth mixture transformations working on both compact intervals and hypertori. Mixture transformations employ root-finding methods to invert them in practice, which has so far prevented bi-directional flow training. To this end, we show that parameter gradients and forces of such inverses can be computed from forward evaluations via the inverse function theorem. We demonstrate two advantages of such smooth flows: they allow training by force matching to simulation data and can be used as potentials in molecular dynamics simulations. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2110.00351v2-abstract-full').style.display = 'none'; document.getElementById('2110.00351v2-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 30 November, 2021; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 1 October, 2021; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> October 2021. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">Neural Information Proceessing Systems (NeurIPS) 2021</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2106.12929">arXiv:2106.12929</a> <span> [<a href="https://arxiv.org/pdf/2106.12929">pdf</a>, <a href="https://arxiv.org/format/2106.12929">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Computational Physics">physics.comp-ph</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Fluid Dynamics">physics.flu-dyn</span> </div> <div class="is-inline-block" style="margin-left: 0.5rem"> <div class="tags has-addons"> <span class="tag is-dark is-size-7">doi</span> <span class="tag is-light is-size-7"><a class="" href="https://doi.org/10.1007/978-3-030-90539-2_3">10.1007/978-3-030-90539-2_3 <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </div> </div> <p class="title is-5 mathjax"> Lettuce: PyTorch-based Lattice Boltzmann Framework </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Bedrunka%2C+M+C">Mario Christopher Bedrunka</a>, <a href="/search/cs?searchtype=author&query=Wilde%2C+D">Dominik Wilde</a>, <a href="/search/cs?searchtype=author&query=Kliemank%2C+M">Martin Kliemank</a>, <a href="/search/cs?searchtype=author&query=Reith%2C+D">Dirk Reith</a>, <a href="/search/cs?searchtype=author&query=Foysi%2C+H">Holger Foysi</a>, <a href="/search/cs?searchtype=author&query=Kr%C3%A4mer%2C+A">Andreas Kr盲mer</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2106.12929v2-abstract-short" style="display: inline;"> The lattice Boltzmann method (LBM) is an efficient simulation technique for computational fluid mechanics and beyond. It is based on a simple stream-and-collide algorithm on Cartesian grids, which is easily compatible with modern machine learning architectures. While it is becoming increasingly clear that deep learning can provide a decisive stimulus for classical simulation techniques, recent stu… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2106.12929v2-abstract-full').style.display = 'inline'; document.getElementById('2106.12929v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2106.12929v2-abstract-full" style="display: none;"> The lattice Boltzmann method (LBM) is an efficient simulation technique for computational fluid mechanics and beyond. It is based on a simple stream-and-collide algorithm on Cartesian grids, which is easily compatible with modern machine learning architectures. While it is becoming increasingly clear that deep learning can provide a decisive stimulus for classical simulation techniques, recent studies have not addressed possible connections between machine learning and LBM. Here, we introduce Lettuce, a PyTorch-based LBM code with a threefold aim. Lettuce enables GPU accelerated calculations with minimal source code, facilitates rapid prototyping of LBM models, and enables integrating LBM simulations with PyTorch's deep learning and automatic differentiation facility. As a proof of concept for combining machine learning with the LBM, a neural collision model is developed, trained on a doubly periodic shear layer and then transferred to a different flow, a decaying turbulence. We also exemplify the added benefit of PyTorch's automatic differentiation framework in flow control and optimization. To this end, the spectrum of a forced isotropic turbulence is maintained without further constraining the velocity field. The source code is freely available from https://github.com/lettucecfd/lettuce. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2106.12929v2-abstract-full').style.display = 'none'; document.getElementById('2106.12929v2-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 17 November, 2021; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 24 June, 2021; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> June 2021. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Journal ref:</span> In International Conference on High Performance Computing (pp. 40-55). Springer, Cham (2021) </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2103.11470">arXiv:2103.11470</a> <span> [<a href="https://arxiv.org/pdf/2103.11470">pdf</a>, <a href="https://arxiv.org/format/2103.11470">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Robotics">cs.RO</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Artificial Intelligence">cs.AI</span> </div> </div> <p class="title is-5 mathjax"> NeBula: Quest for Robotic Autonomy in Challenging Environments; TEAM CoSTAR at the DARPA Subterranean Challenge </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Agha%2C+A">Ali Agha</a>, <a href="/search/cs?searchtype=author&query=Otsu%2C+K">Kyohei Otsu</a>, <a href="/search/cs?searchtype=author&query=Morrell%2C+B">Benjamin Morrell</a>, <a href="/search/cs?searchtype=author&query=Fan%2C+D+D">David D. Fan</a>, <a href="/search/cs?searchtype=author&query=Thakker%2C+R">Rohan Thakker</a>, <a href="/search/cs?searchtype=author&query=Santamaria-Navarro%2C+A">Angel Santamaria-Navarro</a>, <a href="/search/cs?searchtype=author&query=Kim%2C+S">Sung-Kyun Kim</a>, <a href="/search/cs?searchtype=author&query=Bouman%2C+A">Amanda Bouman</a>, <a href="/search/cs?searchtype=author&query=Lei%2C+X">Xianmei Lei</a>, <a href="/search/cs?searchtype=author&query=Edlund%2C+J">Jeffrey Edlund</a>, <a href="/search/cs?searchtype=author&query=Ginting%2C+M+F">Muhammad Fadhil Ginting</a>, <a href="/search/cs?searchtype=author&query=Ebadi%2C+K">Kamak Ebadi</a>, <a href="/search/cs?searchtype=author&query=Anderson%2C+M">Matthew Anderson</a>, <a href="/search/cs?searchtype=author&query=Pailevanian%2C+T">Torkom Pailevanian</a>, <a href="/search/cs?searchtype=author&query=Terry%2C+E">Edward Terry</a>, <a href="/search/cs?searchtype=author&query=Wolf%2C+M">Michael Wolf</a>, <a href="/search/cs?searchtype=author&query=Tagliabue%2C+A">Andrea Tagliabue</a>, <a href="/search/cs?searchtype=author&query=Vaquero%2C+T+S">Tiago Stegun Vaquero</a>, <a href="/search/cs?searchtype=author&query=Palieri%2C+M">Matteo Palieri</a>, <a href="/search/cs?searchtype=author&query=Tepsuporn%2C+S">Scott Tepsuporn</a>, <a href="/search/cs?searchtype=author&query=Chang%2C+Y">Yun Chang</a>, <a href="/search/cs?searchtype=author&query=Kalantari%2C+A">Arash Kalantari</a>, <a href="/search/cs?searchtype=author&query=Chavez%2C+F">Fernando Chavez</a>, <a href="/search/cs?searchtype=author&query=Lopez%2C+B">Brett Lopez</a>, <a href="/search/cs?searchtype=author&query=Funabiki%2C+N">Nobuhiro Funabiki</a> , et al. (47 additional authors not shown) </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2103.11470v4-abstract-short" style="display: inline;"> This paper presents and discusses algorithms, hardware, and software architecture developed by the TEAM CoSTAR (Collaborative SubTerranean Autonomous Robots), competing in the DARPA Subterranean Challenge. Specifically, it presents the techniques utilized within the Tunnel (2019) and Urban (2020) competitions, where CoSTAR achieved 2nd and 1st place, respectively. We also discuss CoSTAR's demonstr… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2103.11470v4-abstract-full').style.display = 'inline'; document.getElementById('2103.11470v4-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2103.11470v4-abstract-full" style="display: none;"> This paper presents and discusses algorithms, hardware, and software architecture developed by the TEAM CoSTAR (Collaborative SubTerranean Autonomous Robots), competing in the DARPA Subterranean Challenge. Specifically, it presents the techniques utilized within the Tunnel (2019) and Urban (2020) competitions, where CoSTAR achieved 2nd and 1st place, respectively. We also discuss CoSTAR's demonstrations in Martian-analog surface and subsurface (lava tubes) exploration. The paper introduces our autonomy solution, referred to as NeBula (Networked Belief-aware Perceptual Autonomy). NeBula is an uncertainty-aware framework that aims at enabling resilient and modular autonomy solutions by performing reasoning and decision making in the belief space (space of probability distributions over the robot and world states). We discuss various components of the NeBula framework, including: (i) geometric and semantic environment mapping; (ii) a multi-modal positioning system; (iii) traversability analysis and local planning; (iv) global motion planning and exploration behavior; (i) risk-aware mission planning; (vi) networking and decentralized reasoning; and (vii) learning-enabled adaptation. We discuss the performance of NeBula on several robot types (e.g. wheeled, legged, flying), in various environments. We discuss the specific results and lessons learned from fielding this solution in the challenging courses of the DARPA Subterranean Challenge competition. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2103.11470v4-abstract-full').style.display = 'none'; document.getElementById('2103.11470v4-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 18 October, 2021; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 21 March, 2021; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> March 2021. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">For team website, see https://costar.jpl.nasa.gov/. Accepted for publication in the Journal of Field Robotics, 2021</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2103.04510">arXiv:2103.04510</a> <span> [<a href="https://arxiv.org/pdf/2103.04510">pdf</a>, <a href="https://arxiv.org/format/2103.04510">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Robotics">cs.RO</span> </div> </div> <p class="title is-5 mathjax"> ColoRadar: The Direct 3D Millimeter Wave Radar Dataset </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Kramer%2C+A">Andrew Kramer</a>, <a href="/search/cs?searchtype=author&query=Harlow%2C+K">Kyle Harlow</a>, <a href="/search/cs?searchtype=author&query=Williams%2C+C">Christopher Williams</a>, <a href="/search/cs?searchtype=author&query=Heckman%2C+C">Christoffer Heckman</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2103.04510v1-abstract-short" style="display: inline;"> Millimeter wave radar is becoming increasingly popular as a sensing modality for robotic mapping and state estimation. However, there are very few publicly available datasets that include dense, high-resolution millimeter wave radar scans and there are none focused on 3D odometry and mapping. In this paper we present a solution to that problem. The ColoRadar dataset includes 3 different forms of d… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2103.04510v1-abstract-full').style.display = 'inline'; document.getElementById('2103.04510v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2103.04510v1-abstract-full" style="display: none;"> Millimeter wave radar is becoming increasingly popular as a sensing modality for robotic mapping and state estimation. However, there are very few publicly available datasets that include dense, high-resolution millimeter wave radar scans and there are none focused on 3D odometry and mapping. In this paper we present a solution to that problem. The ColoRadar dataset includes 3 different forms of dense, high-resolution radar data from 2 FMCW radar sensors as well as 3D lidar, IMU, and highly accurate groundtruth for the sensor rig's pose over approximately 2 hours of data collection in highly diverse 3D environments. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2103.04510v1-abstract-full').style.display = 'none'; document.getElementById('2103.04510v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 7 March, 2021; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> March 2021. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">8 pages, 7 figures, 3863 words, also submitted to IJRR</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2012.12106">arXiv:2012.12106</a> <span> [<a href="https://arxiv.org/pdf/2012.12106">pdf</a>, <a href="https://arxiv.org/format/2012.12106">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Chemical Physics">physics.chem-ph</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Artificial Intelligence">cs.AI</span> </div> <div class="is-inline-block" style="margin-left: 0.5rem"> <div class="tags has-addons"> <span class="tag is-dark is-size-7">doi</span> <span class="tag is-light is-size-7"><a class="" href="https://doi.org/10.1021/acs.jctc.0c01343">10.1021/acs.jctc.0c01343 <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </div> </div> <p class="title is-5 mathjax"> TorchMD: A deep learning framework for molecular simulations </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Doerr%2C+S">Stefan Doerr</a>, <a href="/search/cs?searchtype=author&query=Majewsk%2C+M">Maciej Majewsk</a>, <a href="/search/cs?searchtype=author&query=P%C3%A9rez%2C+A">Adri脿 P茅rez</a>, <a href="/search/cs?searchtype=author&query=Kr%C3%A4mer%2C+A">Andreas Kr盲mer</a>, <a href="/search/cs?searchtype=author&query=Clementi%2C+C">Cecilia Clementi</a>, <a href="/search/cs?searchtype=author&query=Noe%2C+F">Frank Noe</a>, <a href="/search/cs?searchtype=author&query=Giorgino%2C+T">Toni Giorgino</a>, <a href="/search/cs?searchtype=author&query=De+Fabritiis%2C+G">Gianni De Fabritiis</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2012.12106v1-abstract-short" style="display: inline;"> Molecular dynamics simulations provide a mechanistic description of molecules by relying on empirical potentials. The quality and transferability of such potentials can be improved leveraging data-driven models derived with machine learning approaches. Here, we present TorchMD, a framework for molecular simulations with mixed classical and machine learning potentials. All of force computations inc… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2012.12106v1-abstract-full').style.display = 'inline'; document.getElementById('2012.12106v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2012.12106v1-abstract-full" style="display: none;"> Molecular dynamics simulations provide a mechanistic description of molecules by relying on empirical potentials. The quality and transferability of such potentials can be improved leveraging data-driven models derived with machine learning approaches. Here, we present TorchMD, a framework for molecular simulations with mixed classical and machine learning potentials. All of force computations including bond, angle, dihedral, Lennard-Jones and Coulomb interactions are expressed as PyTorch arrays and operations. Moreover, TorchMD enables learning and simulating neural network potentials. We validate it using standard Amber all-atom simulations, learning an ab-initio potential, performing an end-to-end training and finally learning and simulating a coarse-grained model for protein folding. We believe that TorchMD provides a useful tool-set to support molecular simulations of machine learning potentials. Code and data are freely available at \url{github.com/torchmd}. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2012.12106v1-abstract-full').style.display = 'none'; document.getElementById('2012.12106v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 22 December, 2020; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> December 2020. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2010.07033">arXiv:2010.07033</a> <span> [<a href="https://arxiv.org/pdf/2010.07033">pdf</a>, <a href="https://arxiv.org/format/2010.07033">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Machine Learning">stat.ML</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Optimization and Control">math.OC</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Chemical Physics">physics.chem-ph</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Data Analysis, Statistics and Probability">physics.data-an</span> </div> </div> <p class="title is-5 mathjax"> Training Invertible Linear Layers through Rank-One Perturbations </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Kr%C3%A4mer%2C+A">Andreas Kr盲mer</a>, <a href="/search/cs?searchtype=author&query=K%C3%B6hler%2C+J">Jonas K枚hler</a>, <a href="/search/cs?searchtype=author&query=No%C3%A9%2C+F">Frank No茅</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2010.07033v2-abstract-short" style="display: inline;"> Many types of neural network layers rely on matrix properties such as invertibility or orthogonality. Retaining such properties during optimization with gradient-based stochastic optimizers is a challenging task, which is usually addressed by either reparameterization of the affected parameters or by directly optimizing on the manifold. This work presents a novel approach for training invertible l… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2010.07033v2-abstract-full').style.display = 'inline'; document.getElementById('2010.07033v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2010.07033v2-abstract-full" style="display: none;"> Many types of neural network layers rely on matrix properties such as invertibility or orthogonality. Retaining such properties during optimization with gradient-based stochastic optimizers is a challenging task, which is usually addressed by either reparameterization of the affected parameters or by directly optimizing on the manifold. This work presents a novel approach for training invertible linear layers. In lieu of directly optimizing the network parameters, we train rank-one perturbations and add them to the actual weight matrices infrequently. This P$^{4}$Inv update allows keeping track of inverses and determinants without ever explicitly computing them. We show how such invertible blocks improve the mixing and thus the mode separation of the resulting normalizing flows. Furthermore, we outline how the P$^4$ concept can be utilized to retain properties other than invertibility. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2010.07033v2-abstract-full').style.display = 'none'; document.getElementById('2010.07033v2-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 30 November, 2020; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 14 October, 2020; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> October 2020. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">17 pages, 10 figures</span> </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">MSC Class:</span> 68T07; 82-10 </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2010.04112">arXiv:2010.04112</a> <span> [<a href="https://arxiv.org/pdf/2010.04112">pdf</a>, <a href="https://arxiv.org/format/2010.04112">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Artificial Intelligence">cs.AI</span> </div> <div class="is-inline-block" style="margin-left: 0.5rem"> <div class="tags has-addons"> <span class="tag is-dark is-size-7">doi</span> <span class="tag is-light is-size-7"><a class="" href="https://doi.org/10.1145/3410992.3411001">10.1145/3410992.3411001 <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </div> </div> <p class="title is-5 mathjax"> Information-Driven Adaptive Sensing Based on Deep Reinforcement Learning </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Murad%2C+A">Abdulmajid Murad</a>, <a href="/search/cs?searchtype=author&query=Kraemer%2C+F+A">Frank Alexander Kraemer</a>, <a href="/search/cs?searchtype=author&query=Bach%2C+K">Kerstin Bach</a>, <a href="/search/cs?searchtype=author&query=Taylor%2C+G">Gavin Taylor</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2010.04112v1-abstract-short" style="display: inline;"> In order to make better use of deep reinforcement learning in the creation of sensing policies for resource-constrained IoT devices, we present and study a novel reward function based on the Fisher information value. This reward function enables IoT sensor devices to learn to spend available energy on measurements at otherwise unpredictable moments, while conserving energy at times when measuremen… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2010.04112v1-abstract-full').style.display = 'inline'; document.getElementById('2010.04112v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2010.04112v1-abstract-full" style="display: none;"> In order to make better use of deep reinforcement learning in the creation of sensing policies for resource-constrained IoT devices, we present and study a novel reward function based on the Fisher information value. This reward function enables IoT sensor devices to learn to spend available energy on measurements at otherwise unpredictable moments, while conserving energy at times when measurements would provide little new information. This is a highly general approach, which allows for a wide range of use cases without significant human design effort or hyper-parameter tuning. We illustrate the approach in a scenario of workplace noise monitoring, where results show that the learned behavior outperforms a uniform sampling strategy and comes close to a near-optimal oracle solution. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2010.04112v1-abstract-full').style.display = 'none'; document.getElementById('2010.04112v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 8 October, 2020; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> October 2020. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">8 pages, 8 figures</span> </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Journal ref:</span> 10th International Conference on the Internet of Things (IoT20), October 6-9, 2020, Malmo, Sweden </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2006.13062">arXiv:2006.13062</a> <span> [<a href="https://arxiv.org/pdf/2006.13062">pdf</a>, <a href="https://arxiv.org/format/2006.13062">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Computers and Society">cs.CY</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Optimization and Control">math.OC</span> </div> <div class="is-inline-block" style="margin-left: 0.5rem"> <div class="tags has-addons"> <span class="tag is-dark is-size-7">doi</span> <span class="tag is-light is-size-7"><a class="" href="https://doi.org/10.1080/09537287.2021.1996651">10.1080/09537287.2021.1996651 <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </div> </div> <p class="title is-5 mathjax"> Successful implementation of discrete event simulation: the case of an Italian emergency department </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Kramer%2C+A">Arthur Kramer</a>, <a href="/search/cs?searchtype=author&query=Dosi%2C+C">Clio Dosi</a>, <a href="/search/cs?searchtype=author&query=Iori%2C+M">Manuel Iori</a>, <a href="/search/cs?searchtype=author&query=Vignoli%2C+M">Matteo Vignoli</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2006.13062v1-abstract-short" style="display: inline;"> This paper focuses on the study of a practical management problem faced by a healthcare {\it emergency department} (ED) located in the north of Italy. The objective of our study was to propose organisational changes in the selected ED, which admits approximately 7000 patients per month, aiming at improving key performance indicators related to patient satisfaction, such as the waiting time. Our st… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2006.13062v1-abstract-full').style.display = 'inline'; document.getElementById('2006.13062v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2006.13062v1-abstract-full" style="display: none;"> This paper focuses on the study of a practical management problem faced by a healthcare {\it emergency department} (ED) located in the north of Italy. The objective of our study was to propose organisational changes in the selected ED, which admits approximately 7000 patients per month, aiming at improving key performance indicators related to patient satisfaction, such as the waiting time. Our study is based on a design thinking process that adopts a {\it discrete event simulation} (DES) model as the main tool for proposing changes. We used the DES model to propose and evaluate the impact of different improving scenarios. The model is based on historical data, on the observation of the current ED situation, and information obtained from the ED staff. The results obtained by the DES model have been compared with those related to the existing ED setting, and then validated by the ED managers. Based on the results we obtained, one of the tested scenarios was selected by the ED for implementation. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2006.13062v1-abstract-full').style.display = 'none'; document.getElementById('2006.13062v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 23 June, 2020; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> June 2020. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">24 pages, 8 figures and 4 tables</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2006.08327">arXiv:2006.08327</a> <span> [<a href="https://arxiv.org/pdf/2006.08327">pdf</a>, <a href="https://arxiv.org/format/2006.08327">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Artificial Intelligence">cs.AI</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Optimization and Control">math.OC</span> </div> </div> <p class="title is-5 mathjax"> Exact and heuristic methods for the discrete parallel machine scheduling location problem </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Kramer%2C+R">Raphael Kramer</a>, <a href="/search/cs?searchtype=author&query=Kramer%2C+A">Arthur Kramer</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2006.08327v1-abstract-short" style="display: inline;"> The discrete parallel machine makespan scheduling location (ScheLoc) problem is an integrated combinatorial optimization problem that combines facility location and job scheduling. The problem consists in choosing the locations of $p$ machines among a finite set of candidates and scheduling a set of jobs on these machines, aiming to minimize the makespan. Depending on the machine location, the job… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2006.08327v1-abstract-full').style.display = 'inline'; document.getElementById('2006.08327v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2006.08327v1-abstract-full" style="display: none;"> The discrete parallel machine makespan scheduling location (ScheLoc) problem is an integrated combinatorial optimization problem that combines facility location and job scheduling. The problem consists in choosing the locations of $p$ machines among a finite set of candidates and scheduling a set of jobs on these machines, aiming to minimize the makespan. Depending on the machine location, the jobs may have different release dates, and thus the location decisions have a direct impact on the scheduling decisions. To solve the problem, it is proposed a new arc-flow formulation, a column generation and three heuristic procedures that are evaluated through extensive computational experiments. By embedding the proposed procedures into a framework algorithm, we are able to find proven optimal solutions for all benchmark instances from the related literature and to obtain small percentage gaps for a new set of challenging instances. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2006.08327v1-abstract-full').style.display = 'none'; document.getElementById('2006.08327v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 8 June, 2020; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> June 2020. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">25 pages, 5 figures, 7 tables</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/1908.10109">arXiv:1908.10109</a> <span> [<a href="https://arxiv.org/pdf/1908.10109">pdf</a>, <a href="https://arxiv.org/format/1908.10109">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Computer Vision and Pattern Recognition">cs.CV</span> </div> </div> <p class="title is-5 mathjax"> Synthetic patches, real images: screening for centrosome aberrations in EM images of human cancer cells </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Lukoyanov%2C+A">Artem Lukoyanov</a>, <a href="/search/cs?searchtype=author&query=Haberbosch%2C+I">Isabella Haberbosch</a>, <a href="/search/cs?searchtype=author&query=Pape%2C+C">Constantin Pape</a>, <a href="/search/cs?searchtype=author&query=Kraemer%2C+A">Alwin Kraemer</a>, <a href="/search/cs?searchtype=author&query=Schwab%2C+Y">Yannick Schwab</a>, <a href="/search/cs?searchtype=author&query=Kreshuk%2C+A">Anna Kreshuk</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="1908.10109v1-abstract-short" style="display: inline;"> Recent advances in high-throughput electron microscopy imaging enable detailed study of centrosome aberrations in cancer cells. While the image acquisition in such pipelines is automated, manual detection of centrioles is still necessary to select cells for re-imaging at higher magnification. In this contribution we propose an algorithm which performs this step automatically and with high accuracy… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1908.10109v1-abstract-full').style.display = 'inline'; document.getElementById('1908.10109v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="1908.10109v1-abstract-full" style="display: none;"> Recent advances in high-throughput electron microscopy imaging enable detailed study of centrosome aberrations in cancer cells. While the image acquisition in such pipelines is automated, manual detection of centrioles is still necessary to select cells for re-imaging at higher magnification. In this contribution we propose an algorithm which performs this step automatically and with high accuracy. From the image labels produced by human experts and a 3D model of a centriole we construct an additional training set with patch-level labels. A two-level DenseNet is trained on the hybrid training data with synthetic patches and real images, achieving much better results on real patient data than training only at the image-level. The code can be found at https://github.com/kreshuklab/centriole_detection. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1908.10109v1-abstract-full').style.display = 'none'; document.getElementById('1908.10109v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 27 August, 2019; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> August 2019. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">Accepted at MICCAI 2019</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/1905.04181">arXiv:1905.04181</a> <span> [<a href="https://arxiv.org/pdf/1905.04181">pdf</a>, <a href="https://arxiv.org/format/1905.04181">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Artificial Intelligence">cs.AI</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Signal Processing">eess.SP</span> </div> <div class="is-inline-block" style="margin-left: 0.5rem"> <div class="tags has-addons"> <span class="tag is-dark is-size-7">doi</span> <span class="tag is-light is-size-7"><a class="" href="https://doi.org/10.1109/SASO.2019.00015">10.1109/SASO.2019.00015 <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </div> </div> <p class="title is-5 mathjax"> Autonomous Management of Energy-Harvesting IoT Nodes Using Deep Reinforcement Learning </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Murad%2C+A">Abdulmajid Murad</a>, <a href="/search/cs?searchtype=author&query=Kraemer%2C+F+A">Frank Alexander Kraemer</a>, <a href="/search/cs?searchtype=author&query=Bach%2C+K">Kerstin Bach</a>, <a href="/search/cs?searchtype=author&query=Taylor%2C+G">Gavin Taylor</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="1905.04181v1-abstract-short" style="display: inline;"> Reinforcement learning (RL) is capable of managing wireless, energy-harvesting IoT nodes by solving the problem of autonomous management in non-stationary, resource-constrained settings. We show that the state-of-the-art policy-gradient approaches to RL are appropriate for the IoT domain and that they outperform previous approaches. Due to the ability to model continuous observation and action spa… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1905.04181v1-abstract-full').style.display = 'inline'; document.getElementById('1905.04181v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="1905.04181v1-abstract-full" style="display: none;"> Reinforcement learning (RL) is capable of managing wireless, energy-harvesting IoT nodes by solving the problem of autonomous management in non-stationary, resource-constrained settings. We show that the state-of-the-art policy-gradient approaches to RL are appropriate for the IoT domain and that they outperform previous approaches. Due to the ability to model continuous observation and action spaces, as well as improved function approximation capability, the new approaches are able to solve harder problems, permitting reward functions that are better aligned with the actual application goals. We show such a reward function and use policy-gradient approaches to learn capable policies, leading to behavior more appropriate for IoT nodes with less manual design effort, increasing the level of autonomy in IoT. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1905.04181v1-abstract-full').style.display = 'none'; document.getElementById('1905.04181v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 10 May, 2019; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> May 2019. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Journal ref:</span> IEEE 13th International Conference on Self-Adaptive and Self-Organizing Systems (SASO) 2019 Jun 16 (pp. 43-51) </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/1901.08129">arXiv:1901.08129</a> <span> [<a href="https://arxiv.org/pdf/1901.08129">pdf</a>, <a href="https://arxiv.org/ps/1901.08129">ps</a>, <a href="https://arxiv.org/format/1901.08129">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Artificial Intelligence">cs.AI</span> </div> </div> <p class="title is-5 mathjax"> The Multi-Agent Reinforcement Learning in Malm脰 (MARL脰) Competition </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Perez-Liebana%2C+D">Diego Perez-Liebana</a>, <a href="/search/cs?searchtype=author&query=Hofmann%2C+K">Katja Hofmann</a>, <a href="/search/cs?searchtype=author&query=Mohanty%2C+S+P">Sharada Prasanna Mohanty</a>, <a href="/search/cs?searchtype=author&query=Kuno%2C+N">Noburu Kuno</a>, <a href="/search/cs?searchtype=author&query=Kramer%2C+A">Andre Kramer</a>, <a href="/search/cs?searchtype=author&query=Devlin%2C+S">Sam Devlin</a>, <a href="/search/cs?searchtype=author&query=Gaina%2C+R+D">Raluca D. Gaina</a>, <a href="/search/cs?searchtype=author&query=Ionita%2C+D">Daniel Ionita</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="1901.08129v1-abstract-short" style="display: inline;"> Learning in multi-agent scenarios is a fruitful research direction, but current approaches still show scalability problems in multiple games with general reward settings and different opponent types. The Multi-Agent Reinforcement Learning in Malm脰 (MARL脰) competition is a new challenge that proposes research in this domain using multiple 3D games. The goal of this contest is to foster research in… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1901.08129v1-abstract-full').style.display = 'inline'; document.getElementById('1901.08129v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="1901.08129v1-abstract-full" style="display: none;"> Learning in multi-agent scenarios is a fruitful research direction, but current approaches still show scalability problems in multiple games with general reward settings and different opponent types. The Multi-Agent Reinforcement Learning in Malm脰 (MARL脰) competition is a new challenge that proposes research in this domain using multiple 3D games. The goal of this contest is to foster research in general agents that can learn across different games and opponent types, proposing a challenge as a milestone in the direction of Artificial General Intelligence. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1901.08129v1-abstract-full').style.display = 'none'; document.getElementById('1901.08129v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 23 January, 2019; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> January 2019. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">2 pages plus references</span> </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Journal ref:</span> Challenges in Machine Learning (NIPS Workshop), 2018 </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/1808.10661">arXiv:1808.10661</a> <span> [<a href="https://arxiv.org/pdf/1808.10661">pdf</a>, <a href="https://arxiv.org/format/1808.10661">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Data Structures and Algorithms">cs.DS</span> </div> <div class="is-inline-block" style="margin-left: 0.5rem"> <div class="tags has-addons"> <span class="tag is-dark is-size-7">doi</span> <span class="tag is-light is-size-7"><a class="" href="https://doi.org/10.1016/j.ejor.2018.11.039">10.1016/j.ejor.2018.11.039 <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </div> </div> <p class="title is-5 mathjax"> Enhanced arc-flow formulations to minimize weighted completion time on identical parallel machines </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Kramer%2C+A">Arthur Kramer</a>, <a href="/search/cs?searchtype=author&query=Dell%27Amico%2C+M">Mauro Dell'Amico</a>, <a href="/search/cs?searchtype=author&query=Iori%2C+M">Manuel Iori</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="1808.10661v1-abstract-short" style="display: inline;"> We consider the problem of scheduling a set of jobs on a set of identical parallel machines, with the aim of minimizing the total weighted completion time. The problem has been solved in the literature with a number of mathematical formulations, some of which require the implementation of tailored branch-and-price methods. In our work, we solve the problem instead by means of new arc-flow formulat… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1808.10661v1-abstract-full').style.display = 'inline'; document.getElementById('1808.10661v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="1808.10661v1-abstract-full" style="display: none;"> We consider the problem of scheduling a set of jobs on a set of identical parallel machines, with the aim of minimizing the total weighted completion time. The problem has been solved in the literature with a number of mathematical formulations, some of which require the implementation of tailored branch-and-price methods. In our work, we solve the problem instead by means of new arc-flow formulations, by first representing it on a capacitated network and then invoking a mixed integer linear model with a pseudo-polynomial number of variables and constraints. According to our computational tests, existing formulations from the literature can solve to proven optimality benchmark instances with up to 100 jobs, whereas our most performing arc-flow formulation solves all instances with up to 400 jobs and provides very low gap for larger instances with up to 1000 jobs. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1808.10661v1-abstract-full').style.display = 'none'; document.getElementById('1808.10661v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 31 August, 2018; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> August 2018. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">25 pages</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/1802.03211">arXiv:1802.03211</a> <span> [<a href="https://arxiv.org/pdf/1802.03211">pdf</a>, <a href="https://arxiv.org/format/1802.03211">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Computational Engineering, Finance, and Science">cs.CE</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Computational Physics">physics.comp-ph</span> </div> </div> <p class="title is-5 mathjax"> Towards realistic HPC models of the neuromuscular system </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Bradley%2C+C">Chris Bradley</a>, <a href="/search/cs?searchtype=author&query=Emamy%2C+N">Nehzat Emamy</a>, <a href="/search/cs?searchtype=author&query=Ertl%2C+T">Thomas Ertl</a>, <a href="/search/cs?searchtype=author&query=G%C3%B6ddeke%2C+D">Dominik G枚ddeke</a>, <a href="/search/cs?searchtype=author&query=Hessenthaler%2C+A">Andreas Hessenthaler</a>, <a href="/search/cs?searchtype=author&query=Klotz%2C+T">Thomas Klotz</a>, <a href="/search/cs?searchtype=author&query=Kr%C3%A4mer%2C+A">Aaron Kr盲mer</a>, <a href="/search/cs?searchtype=author&query=Krone%2C+M">Michael Krone</a>, <a href="/search/cs?searchtype=author&query=Maier%2C+B">Benjamin Maier</a>, <a href="/search/cs?searchtype=author&query=Mehl%2C+M">Miriam Mehl</a>, <a href="/search/cs?searchtype=author&query=Rau%2C+T">Tobias Rau</a>, <a href="/search/cs?searchtype=author&query=R%C3%B6hrle%2C+O">Oliver R枚hrle</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="1802.03211v1-abstract-short" style="display: inline;"> Realistic simulations of detailed, biophysics-based, multi-scale models require very high resolution and, thus, large-scale compute facilities. Existing simulation environments, especially for biomedical applications, are designed to allow for a high flexibility and generality in model development. Flexibility and model development, however, are often a limiting factor for large-scale simulations.… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1802.03211v1-abstract-full').style.display = 'inline'; document.getElementById('1802.03211v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="1802.03211v1-abstract-full" style="display: none;"> Realistic simulations of detailed, biophysics-based, multi-scale models require very high resolution and, thus, large-scale compute facilities. Existing simulation environments, especially for biomedical applications, are designed to allow for a high flexibility and generality in model development. Flexibility and model development, however, are often a limiting factor for large-scale simulations. Therefore, new models are typically tested and run on small-scale compute facilities. By using a detailed biophysics-based, chemo-electromechanical skeletal muscle model and the international open-source software library OpenCMISS as an example, we present an approach to upgrade an existing muscle simulation framework from a moderately parallel version towards a massively parallel one that scales both in terms of problem size and in terms of the number of parallel processes. For this purpose, we investigate different modeling, algorithmic and implementational aspects. We present improvements addressing both numerical and parallel scalability. In addition, our approach includes a novel visualization environment, which is based on the MegaMol environment capable of handling large amounts of simulated data. It offers a platform for fast visualization prototyping, distributed rendering, and advanced visualization techniques. We present results of a variety of scaling studies at the Tier-1 supercomputer HazelHen at the High Performance Computing Center Stuttgart (HLRS). We improve the overall runtime by a factor of up to 2.6 and achieved good scalability on up to 768 cores, where the previous implementation used only 4 cores. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1802.03211v1-abstract-full').style.display = 'none'; document.getElementById('1802.03211v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 9 February, 2018; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> February 2018. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">MSC Class:</span> 65L99; 65M99 </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/1802.01482">arXiv:1802.01482</a> <span> [<a href="https://arxiv.org/pdf/1802.01482">pdf</a>, <a href="https://arxiv.org/format/1802.01482">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Artificial Intelligence">cs.AI</span> </div> </div> <p class="title is-5 mathjax"> The Sea Exploration Problem: Data-driven Orienteering on a Continuous Surface </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Pedroso%2C+J+P">Jo茫o Pedro Pedroso</a>, <a href="/search/cs?searchtype=author&query=Kramer%2C+A+V">Alpar Vajk Kramer</a>, <a href="/search/cs?searchtype=author&query=Zhang%2C+K">Ke Zhang</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="1802.01482v2-abstract-short" style="display: inline;"> This paper describes a problem arising in sea exploration, where the aim is to schedule the expedition of a ship for collecting information about the resources on the seafloor. The aim is to collect data by probing on a set of carefully chosen locations, so that the information available is optimally enriched. This problem has similarities with the orienteering problem, where the aim is to plan a… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1802.01482v2-abstract-full').style.display = 'inline'; document.getElementById('1802.01482v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="1802.01482v2-abstract-full" style="display: none;"> This paper describes a problem arising in sea exploration, where the aim is to schedule the expedition of a ship for collecting information about the resources on the seafloor. The aim is to collect data by probing on a set of carefully chosen locations, so that the information available is optimally enriched. This problem has similarities with the orienteering problem, where the aim is to plan a time-limited trip for visiting a set of vertices, collecting a prize at each of them, in such a way that the total value collected is maximum. In our problem, the score at each vertex is associated with an estimation of the level of the resource on the given surface, which is done by regression using Gaussian processes. Hence, there is a correlation among scores on the selected vertices; this is a first difference with respect to the standard orienteering problem. The second difference is the location of each vertex, which in our problem is a freely chosen point on a given surface. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1802.01482v2-abstract-full').style.display = 'none'; document.getElementById('1802.01482v2-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 6 April, 2019; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 5 February, 2018; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> February 2018. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/1705.04919">arXiv:1705.04919</a> <span> [<a href="https://arxiv.org/pdf/1705.04919">pdf</a>, <a href="https://arxiv.org/format/1705.04919">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Computer Vision and Pattern Recognition">cs.CV</span> </div> </div> <p class="title is-5 mathjax"> Discovery and visualization of structural biomarkers from MRI using transport-based morphometry </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Kundu%2C+S">Shinjini Kundu</a>, <a href="/search/cs?searchtype=author&query=Kolouri%2C+S">Soheil Kolouri</a>, <a href="/search/cs?searchtype=author&query=Erickson%2C+K+I">Kirk I Erickson</a>, <a href="/search/cs?searchtype=author&query=Kramer%2C+A+F">Arthur F Kramer</a>, <a href="/search/cs?searchtype=author&query=McAuley%2C+E">Edward McAuley</a>, <a href="/search/cs?searchtype=author&query=Rohde%2C+G+K">Gustavo K Rohde</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="1705.04919v1-abstract-short" style="display: inline;"> Disease in the brain is often associated with subtle, spatially diffuse, or complex tissue changes that may lie beneath the level of gross visual inspection, even on magnetic resonance imaging (MRI). Unfortunately, current computer-assisted approaches that examine pre-specified features, whether anatomically-defined (i.e. thalamic volume, cortical thickness) or based on pixelwise comparison (i.e.… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1705.04919v1-abstract-full').style.display = 'inline'; document.getElementById('1705.04919v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="1705.04919v1-abstract-full" style="display: none;"> Disease in the brain is often associated with subtle, spatially diffuse, or complex tissue changes that may lie beneath the level of gross visual inspection, even on magnetic resonance imaging (MRI). Unfortunately, current computer-assisted approaches that examine pre-specified features, whether anatomically-defined (i.e. thalamic volume, cortical thickness) or based on pixelwise comparison (i.e. deformation-based methods), are prone to missing a vast array of physical changes that are not well-encapsulated by these metrics. In this paper, we have developed a technique for automated pattern analysis that can fully determine the relationship between brain structure and observable phenotype without requiring any a priori features. Our technique, called transport-based morphometry (TBM), is an image transformation that maps brain images losslessly to a domain where they become much more separable. The new approach is validated on structural brain images of healthy older adult subjects where even linear models for discrimination, regression, and blind source separation enable TBM to independently discover the characteristic changes of aging and highlight potential mechanisms by which aerobic fitness may mediate brain health later in life. TBM is a generative approach that can provide visualization of physically meaningful shifts in tissue distribution through inverse transformation. The proposed framework is a powerful technique that can potentially elucidate genotype-structural-behavioral associations in myriad diseases. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1705.04919v1-abstract-full').style.display = 'none'; document.getElementById('1705.04919v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 14 May, 2017; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> May 2017. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/1509.02384">arXiv:1509.02384</a> <span> [<a href="https://arxiv.org/pdf/1509.02384">pdf</a>, <a href="https://arxiv.org/ps/1509.02384">ps</a>, <a href="https://arxiv.org/format/1509.02384">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Artificial Intelligence">cs.AI</span> </div> </div> <p class="title is-5 mathjax"> A unified heuristic and an annotated bibliography for a large class of earliness-tardiness scheduling problems </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Kramer%2C+A">Arthur Kramer</a>, <a href="/search/cs?searchtype=author&query=Subramanian%2C+A">Anand Subramanian</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="1509.02384v3-abstract-short" style="display: inline;"> This work proposes a unified heuristic algorithm for a large class of earliness-tardiness (E-T) scheduling problems. We consider single/parallel machine E-T problems that may or may not consider some additional features such as idle time, setup times and release dates. In addition, we also consider those problems whose objective is to minimize either the total (average) weighted completion time or… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1509.02384v3-abstract-full').style.display = 'inline'; document.getElementById('1509.02384v3-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="1509.02384v3-abstract-full" style="display: none;"> This work proposes a unified heuristic algorithm for a large class of earliness-tardiness (E-T) scheduling problems. We consider single/parallel machine E-T problems that may or may not consider some additional features such as idle time, setup times and release dates. In addition, we also consider those problems whose objective is to minimize either the total (average) weighted completion time or the total (average) weighted flow time, which arise as particular cases when the due dates of all jobs are either set to zero or to their associated release dates, respectively. The developed local search based metaheuristic framework is quite simple, but at the same time relies on sophisticated procedures for efficiently performing local search according to the characteristics of the problem. We present efficient move evaluation approaches for some parallel machine problems that generalize the existing ones for single machine problems. The algorithm was tested in hundreds of instances of several E-T problems and particular cases. The results obtained show that our unified heuristic is capable of producing high quality solutions when compared to the best ones available in the literature that were obtained by specific methods. Moreover, we provide an extensive annotated bibliography on the problems related to those considered in this work, where we not only indicate the approach(es) used in each publication, but we also point out the characteristics of the problem(s) considered. Beyond that, we classify the existing methods in different categories so as to have a better idea of the popularity of each type of solution procedure. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1509.02384v3-abstract-full').style.display = 'none'; document.getElementById('1509.02384v3-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 10 January, 2017; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 8 September, 2015; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> September 2015. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/1303.1498">arXiv:1303.1498</a> <span> [<a href="https://arxiv.org/pdf/1303.1498">pdf</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Artificial Intelligence">cs.AI</span> </div> </div> <p class="title is-5 mathjax"> GALGO: A Genetic ALGOrithm Decision Support Tool for Complex Uncertain Systems Modeled with Bayesian Belief Networks </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Rojas-Guzman%2C+C">Carlos Rojas-Guzman</a>, <a href="/search/cs?searchtype=author&query=Kramer%2C+M+A">Mark A. Kramer</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="1303.1498v1-abstract-short" style="display: inline;"> Bayesian belief networks can be used to represent and to reason about complex systems with uncertain, incomplete and conflicting information. Belief networks are graphs encoding and quantifying probabilistic dependence and conditional independence among variables. One type of reasoning of interest in diagnosis is called abductive inference (determination of the global most probable system descri… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1303.1498v1-abstract-full').style.display = 'inline'; document.getElementById('1303.1498v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="1303.1498v1-abstract-full" style="display: none;"> Bayesian belief networks can be used to represent and to reason about complex systems with uncertain, incomplete and conflicting information. Belief networks are graphs encoding and quantifying probabilistic dependence and conditional independence among variables. One type of reasoning of interest in diagnosis is called abductive inference (determination of the global most probable system description given the values of any partial subset of variables). In some cases, abductive inference can be performed with exact algorithms using distributed network computations but it is an NP-hard problem and complexity increases drastically with the presence of undirected cycles, number of discrete states per variable, and number of variables in the network. This paper describes an approximate method based on genetic algorithms to perform abductive inference in large, multiply connected networks for which complexity is a concern when using most exact methods and for which systematic search methods are not feasible. The theoretical adequacy of the method is discussed and preliminary experimental results are presented. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1303.1498v1-abstract-full').style.display = 'none'; document.getElementById('1303.1498v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 6 March, 2013; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> March 2013. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">Appears in Proceedings of the Ninth Conference on Uncertainty in Artificial Intelligence (UAI1993)</span> </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Report number:</span> UAI-P-1993-PG-368-375 </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/1112.1038">arXiv:1112.1038</a> <span> [<a href="https://arxiv.org/pdf/1112.1038">pdf</a>, <a href="https://arxiv.org/format/1112.1038">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Computers and Society">cs.CY</span> </div> </div> <p class="title is-5 mathjax"> Yahtzee: An Anonymized Group Level Matching Procedure </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Jones%2C+J+J">Jason J. Jones</a>, <a href="/search/cs?searchtype=author&query=Bond%2C+R+M">Robert M. Bond</a>, <a href="/search/cs?searchtype=author&query=Fariss%2C+C+J">Christopher J. Fariss</a>, <a href="/search/cs?searchtype=author&query=Settle%2C+J+E">Jaime E. Settle</a>, <a href="/search/cs?searchtype=author&query=Kramer%2C+A">Adam Kramer</a>, <a href="/search/cs?searchtype=author&query=Marlow%2C+C">Cameron Marlow</a>, <a href="/search/cs?searchtype=author&query=Fowler%2C+J+H">James H. Fowler</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="1112.1038v1-abstract-short" style="display: inline;"> Researchers often face the problem of needing to protect the privacy of subjects while also needing to integrate data that contains personal information from diverse data sources in order to conduct their research. The advent of computational social science and the enormous amount of data about people that is being collected makes protecting the privacy of research subjects evermore important. How… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1112.1038v1-abstract-full').style.display = 'inline'; document.getElementById('1112.1038v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="1112.1038v1-abstract-full" style="display: none;"> Researchers often face the problem of needing to protect the privacy of subjects while also needing to integrate data that contains personal information from diverse data sources in order to conduct their research. The advent of computational social science and the enormous amount of data about people that is being collected makes protecting the privacy of research subjects evermore important. However, strict privacy procedures can make joining diverse sources of data that contain information about specific individual behaviors difficult. In this paper we present a procedure to keep information about specific individuals from being "leaked" or shared in either direction between two sources of data. To achieve this goal, we randomly assign individuals to anonymous groups before combining the anonymized information between the two sources of data. We refer to this method as the Yahtzee procedure, and show that it performs as expected theoretically when we apply it to data from Facebook and public voter records. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1112.1038v1-abstract-full').style.display = 'none'; document.getElementById('1112.1038v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 5 December, 2011; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> December 2011. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/cs/9606101">arXiv:cs/9606101</a> <span> [<a href="https://arxiv.org/pdf/cs/9606101">pdf</a>, <a href="https://arxiv.org/ps/cs/9606101">ps</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Artificial Intelligence">cs.AI</span> </div> </div> <p class="title is-5 mathjax"> A Principled Approach Towards Symbolic Geometric Constraint Satisfaction </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Bhansali%2C+S">S. Bhansali</a>, <a href="/search/cs?searchtype=author&query=Kramer%2C+G+A">G. A. Kramer</a>, <a href="/search/cs?searchtype=author&query=Hoar%2C+T+J">T. J. Hoar</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="cs/9606101v1-abstract-short" style="display: inline;"> An important problem in geometric reasoning is to find the configuration of a collection of geometric bodies so as to satisfy a set of given constraints. Recently, it has been suggested that this problem can be solved efficiently by symbolically reasoning about geometry. This approach, called degrees of freedom analysis, employs a set of specialized routines called plan fragments that specify ho… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('cs/9606101v1-abstract-full').style.display = 'inline'; document.getElementById('cs/9606101v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="cs/9606101v1-abstract-full" style="display: none;"> An important problem in geometric reasoning is to find the configuration of a collection of geometric bodies so as to satisfy a set of given constraints. Recently, it has been suggested that this problem can be solved efficiently by symbolically reasoning about geometry. This approach, called degrees of freedom analysis, employs a set of specialized routines called plan fragments that specify how to change the configuration of a set of bodies to satisfy a new constraint while preserving existing constraints. A potential drawback, which limits the scalability of this approach, is concerned with the difficulty of writing plan fragments. In this paper we address this limitation by showing how these plan fragments can be automatically synthesized using first principles about geometric bodies, actions, and topology. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('cs/9606101v1-abstract-full').style.display = 'none'; document.getElementById('cs/9606101v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 31 May, 1996; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> June 1996. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">See http://www.jair.org/ for an online appendix and other files accompanying this article</span> </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Journal ref:</span> Journal of Artificial Intelligence Research, Vol 4, (1996), 419-443 </p> </li> </ol> <div class="is-hidden-tablet">  <span class="help" style="display: inline-block;"><a href="https://github.com/arXiv/arxiv-search/releases">Search v0.5.6 released 2020-02-24</a>  </span> </div> </div> </main> <footer> <div class="columns is-desktop" role="navigation" aria-label="Secondary">  <div class="column"> <div class="columns"> <div class="column"> <ul class="nav-spaced"> <li><a href="https://info.arxiv.org/about">About</a></li> <li><a href="https://info.arxiv.org/help">Help</a></li> </ul> </div> <div class="column"> <ul class="nav-spaced"> <li> <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 512 512" class="icon filter-black" role="presentation"><title>contact arXiv</title><desc>Click here to contact arXiv</desc><path d="M502.3 190.8c3.9-3.1 9.7-.2 9.7 4.7V400c0 26.5-21.5 48-48 48H48c-26.5 0-48-21.5-48-48V195.6c0-5 5.7-7.8 9.7-4.7 22.4 17.4 52.1 39.5 154.1 113.6 21.1 15.4 56.7 47.8 92.2 47.6 35.7.3 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