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breathe-horizontal" role="navigation" aria-label="pagination"> <a href="" class="pagination-previous is-invisible">Previous </a> <a href="/search/?searchtype=author&query=Bhatt%2C+S&start=50" class="pagination-next" >Next </a> <ul class="pagination-list"> <li> <a href="/search/?searchtype=author&query=Bhatt%2C+S&start=0" class="pagination-link is-current" aria-label="Goto page 1">1 </a> </li> <li> <a href="/search/?searchtype=author&query=Bhatt%2C+S&start=50" class="pagination-link " aria-label="Page 2" aria-current="page">2 </a> </li> <li> <a href="/search/?searchtype=author&query=Bhatt%2C+S&start=100" class="pagination-link " aria-label="Page 3" aria-current="page">3 </a> </li> </ul> </nav> <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/2502.05994">arXiv:2502.05994</a> <span> [<a href="https://arxiv.org/pdf/2502.05994">pdf</a>, <a href="https://arxiv.org/format/2502.05994">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> </div> </div> <p class="title is-5 mathjax"> Diffusion Models for Inverse Problems in the Exponential Family </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Micheli%2C+A">Alessandro Micheli</a>, <a href="/search/?searchtype=author&query=Monod%2C+M">M茅lodie Monod</a>, <a href="/search/?searchtype=author&query=Bhatt%2C+S">Samir Bhatt</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="2502.05994v1-abstract-short" style="display: inline;"> Diffusion models have emerged as powerful tools for solving inverse problems, yet prior work has primarily focused on observations with Gaussian measurement noise, restricting their use in real-world scenarios. This limitation persists due to the intractability of the likelihood score, which until now has only been approximated in the simpler case of Gaussian likelihoods. In this work, we extend d… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2502.05994v1-abstract-full').style.display = 'inline'; document.getElementById('2502.05994v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2502.05994v1-abstract-full" style="display: none;"> Diffusion models have emerged as powerful tools for solving inverse problems, yet prior work has primarily focused on observations with Gaussian measurement noise, restricting their use in real-world scenarios. This limitation persists due to the intractability of the likelihood score, which until now has only been approximated in the simpler case of Gaussian likelihoods. In this work, we extend diffusion models to handle inverse problems where the observations follow a distribution from the exponential family, such as a Poisson or a Binomial distribution. By leveraging the conjugacy properties of exponential family distributions, we introduce the evidence trick, a method that provides a tractable approximation to the likelihood score. In our experiments, we demonstrate that our methodology effectively performs Bayesian inference on spatially inhomogeneous Poisson processes with intensities as intricate as ImageNet images. Furthermore, we demonstrate the real-world impact of our methodology by showing that it performs competitively with the current state-of-the-art in predicting malaria prevalence estimates in Sub-Saharan Africa. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2502.05994v1-abstract-full').style.display = 'none'; document.getElementById('2502.05994v1-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, 2025; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> February 2025. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2502.04067">arXiv:2502.04067</a> <span> [<a href="https://arxiv.org/pdf/2502.04067">pdf</a>, <a href="https://arxiv.org/format/2502.04067">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Populations and Evolution">q-bio.PE</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Statistics Theory">math.ST</span> </div> </div> <p class="title is-5 mathjax"> Generalised Bayesian distance-based phylogenetics for the genomics era </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Penn%2C+M+J">Matthew J. Penn</a>, <a href="/search/?searchtype=author&query=Scheidwasser%2C+N">Neil Scheidwasser</a>, <a href="/search/?searchtype=author&query=Khurana%2C+M+P">Mark P. Khurana</a>, <a href="/search/?searchtype=author&query=Donnelly%2C+C+A">Christl A. Donnelly</a>, <a href="/search/?searchtype=author&query=Duch%C3%AAne%2C+D+A">David A. Duch锚ne</a>, <a href="/search/?searchtype=author&query=Bhatt%2C+S">Samir Bhatt</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="2502.04067v1-abstract-short" style="display: inline;"> As whole genomes become widely available, maximum likelihood and Bayesian phylogenetic methods are demonstrating their limits in meeting the escalating computational demands. Conversely, distance-based phylogenetic methods are efficient, but are rarely favoured due to their inferior performance. Here, we extend distance-based phylogenetics using an entropy-based likelihood of the evolution among p… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2502.04067v1-abstract-full').style.display = 'inline'; document.getElementById('2502.04067v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2502.04067v1-abstract-full" style="display: none;"> As whole genomes become widely available, maximum likelihood and Bayesian phylogenetic methods are demonstrating their limits in meeting the escalating computational demands. Conversely, distance-based phylogenetic methods are efficient, but are rarely favoured due to their inferior performance. Here, we extend distance-based phylogenetics using an entropy-based likelihood of the evolution among pairs of taxa, allowing for fast Bayesian inference in genome-scale datasets. We provide evidence of a close link between the inference criteria used in distance methods and Felsenstein's likelihood, such that the methods are expected to have comparable performance in practice. Using the entropic likelihood, we perform Bayesian inference on three phylogenetic benchmark datasets and find that estimates closely correspond with previous inferences. We also apply this rapid inference approach to a 60-million-site alignment from 363 avian taxa, covering most avian families. The method has outstanding performance and reveals substantial uncertainty in the avian diversification events immediately after the K-Pg transition event. The entropic likelihood allows for efficient Bayesian phylogenetic inference, accommodating the analysis demands of the genomic era. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2502.04067v1-abstract-full').style.display = 'none'; document.getElementById('2502.04067v1-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 February, 2025; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> February 2025. </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">53 pages, 6 figures</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2501.11796">arXiv:2501.11796</a> <span> [<a href="https://arxiv.org/pdf/2501.11796">pdf</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Mesoscale and Nanoscale Physics">cond-mat.mes-hall</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Instrumentation and Detectors">physics.ins-det</span> </div> </div> <p class="title is-5 mathjax"> Self-aligned multilayered nitrogen vacancy diamond nanoparticles for high spatial resolution magnetometry of microelectronic currents </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Gokhale%2C+Y">Yash Gokhale</a>, <a href="/search/?searchtype=author&query=Coventry%2C+B+S">Brandon S Coventry</a>, <a href="/search/?searchtype=author&query=Rogers%2C+T">Tsani Rogers</a>, <a href="/search/?searchtype=author&query=Lines%2C+M">Maya Lines</a>, <a href="/search/?searchtype=author&query=Vena%2C+A">Anna Vena</a>, <a href="/search/?searchtype=author&query=Phillips%2C+J">Jack Phillips</a>, <a href="/search/?searchtype=author&query=Zhu%2C+T">Tianxiang Zhu</a>, <a href="/search/?searchtype=author&query=Bok%2C+I">Ilhan Bok</a>, <a href="/search/?searchtype=author&query=Troche%2C+D+J">Dariana J. Troche</a>, <a href="/search/?searchtype=author&query=Glodowski%2C+M">Mitchell Glodowski</a>, <a href="/search/?searchtype=author&query=Vareberg%2C+A">Adam Vareberg</a>, <a href="/search/?searchtype=author&query=Bhatt%2C+S">Suyash Bhatt</a>, <a href="/search/?searchtype=author&query=Ashtiani%2C+A">Alireza Ashtiani</a>, <a href="/search/?searchtype=author&query=Eliceiri%2C+K+W">Kevin W. Eliceiri</a>, <a href="/search/?searchtype=author&query=Hai%2C+A">Aviad Hai</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="2501.11796v2-abstract-short" style="display: inline;"> Nitrogen Vacancy diamond nanoparticles (NVNPs) are increasingly integrated with methods for optical detection of magnetic resonance (ODMR), providing new opportunities in magnetic characterization that span the visualization of magnetic fields in microelectronic circuits, environmental sensing and biology. However, only a small number of studies utilize aggregates of NVNPs for surface-wide magneto… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2501.11796v2-abstract-full').style.display = 'inline'; document.getElementById('2501.11796v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2501.11796v2-abstract-full" style="display: none;"> Nitrogen Vacancy diamond nanoparticles (NVNPs) are increasingly integrated with methods for optical detection of magnetic resonance (ODMR), providing new opportunities in magnetic characterization that span the visualization of magnetic fields in microelectronic circuits, environmental sensing and biology. However, only a small number of studies utilize aggregates of NVNPs for surface-wide magnetometry being that spin orientations in aggregate NVNPs are inherently misaligned, precluding their use for proper magnetic field detection compared with expensive monocrystalline diamonds. A postprocessing method for layering NVNPs with aligned NV center orientations can potentially facilitate superior NV magnetometry by allowing sensitive detection combined with simplified probe preparation. We present a novel technology for creating densely stacked monolayers of NVNP with inherent interlayer alignment for sensitive measurement of local magnetic field perturbations in microelectronic traces. We establish spatial characteristics of deposited aggregates and demonstrate their ability to capture magnetic dipoles from conducting microwires via ODMR. Our approach forms a novel accessible protocol that can be used for broad applications in micromagnetometry. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2501.11796v2-abstract-full').style.display = 'none'; document.getElementById('2501.11796v2-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 February, 2025; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 20 January, 2025; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> January 2025. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2501.01111">arXiv:2501.01111</a> <span> [<a href="https://arxiv.org/pdf/2501.01111">pdf</a>, <a href="https://arxiv.org/format/2501.01111">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Computer Science and Game Theory">cs.GT</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"> Regularized Proportional Fairness Mechanism for Resource Allocation Without Money </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Zeng%2C+S">Sihan Zeng</a>, <a href="/search/?searchtype=author&query=Bhatt%2C+S">Sujay Bhatt</a>, <a href="/search/?searchtype=author&query=Koppel%2C+A">Alec Koppel</a>, <a href="/search/?searchtype=author&query=Ganesh%2C+S">Sumitra Ganesh</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="2501.01111v1-abstract-short" style="display: inline;"> Mechanism design in resource allocation studies dividing limited resources among self-interested agents whose satisfaction with the allocation depends on privately held utilities. We consider the problem in a payment-free setting, with the aim of maximizing social welfare while enforcing incentive compatibility (IC), i.e., agents cannot inflate allocations by misreporting their utilities. The well… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2501.01111v1-abstract-full').style.display = 'inline'; document.getElementById('2501.01111v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2501.01111v1-abstract-full" style="display: none;"> Mechanism design in resource allocation studies dividing limited resources among self-interested agents whose satisfaction with the allocation depends on privately held utilities. We consider the problem in a payment-free setting, with the aim of maximizing social welfare while enforcing incentive compatibility (IC), i.e., agents cannot inflate allocations by misreporting their utilities. The well-known proportional fairness (PF) mechanism achieves the maximum possible social welfare but incurs an undesirably high exploitability (the maximum unilateral inflation in utility from misreport and a measure of deviation from IC). In fact, it is known that no mechanism can achieve the maximum social welfare and exact incentive compatibility (IC) simultaneously without the use of monetary incentives (Cole et al., 2013). Motivated by this fact, we propose learning an approximate mechanism that desirably trades off the competing objectives. Our main contribution is to design an innovative neural network architecture tailored to the resource allocation problem, which we name Regularized Proportional Fairness Network (RPF-Net). RPF-Net regularizes the output of the PF mechanism by a learned function approximator of the most exploitable allocation, with the aim of reducing the incentive for any agent to misreport. We derive generalization bounds that guarantee the mechanism performance when trained under finite and out-of-distribution samples and experimentally demonstrate the merits of the proposed mechanism compared to the state-of-the-art. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2501.01111v1-abstract-full').style.display = 'none'; document.getElementById('2501.01111v1-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> 2 January, 2025; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> January 2025. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2412.13972">arXiv:2412.13972</a> <span> [<a href="https://arxiv.org/pdf/2412.13972">pdf</a>, <a href="https://arxiv.org/format/2412.13972">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Computer Science and Game Theory">cs.GT</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Multiagent Systems">cs.MA</span> </div> </div> <p class="title is-5 mathjax"> Decentralized Convergence to Equilibrium Prices in Trading Networks </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Lock%2C+E">Edwin Lock</a>, <a href="/search/?searchtype=author&query=Evans%2C+B+P">Benjamin Patrick Evans</a>, <a href="/search/?searchtype=author&query=Kreacic%2C+E">Eleonora Kreacic</a>, <a href="/search/?searchtype=author&query=Bhatt%2C+S">Sujay Bhatt</a>, <a href="/search/?searchtype=author&query=Koppel%2C+A">Alec Koppel</a>, <a href="/search/?searchtype=author&query=Ganesh%2C+S">Sumitra Ganesh</a>, <a href="/search/?searchtype=author&query=Goldberg%2C+P+W">Paul W. Goldberg</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="2412.13972v2-abstract-short" style="display: inline;"> We propose a decentralized market model in which agents can negotiate bilateral contracts. This builds on a similar, but centralized, model of trading networks introduced by Hatfield et al. in 2013. Prior work has established that fully-substitutable preferences guarantee the existence of competitive equilibria which can be centrally computed. Our motivation comes from the fact that prices in mark… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2412.13972v2-abstract-full').style.display = 'inline'; document.getElementById('2412.13972v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2412.13972v2-abstract-full" style="display: none;"> We propose a decentralized market model in which agents can negotiate bilateral contracts. This builds on a similar, but centralized, model of trading networks introduced by Hatfield et al. in 2013. Prior work has established that fully-substitutable preferences guarantee the existence of competitive equilibria which can be centrally computed. Our motivation comes from the fact that prices in markets such as over-the-counter markets and used car markets arise from decentralized negotiation among agents, which has left open an important question as to whether equilibrium prices can emerge from agent-to-agent bilateral negotiations. We design a best response dynamic intended to capture such negotiations between market participants. We assume fully substitutable preferences for market participants. In this setting, we provide proofs of convergence for sparse markets (covering many real world markets of interest), and experimental results for more general cases, demonstrating that prices indeed reach equilibrium, quickly, via bilateral negotiations. Our best response dynamic, and its convergence behavior, forms an important first step in understanding how decentralized markets reach, and retain, equilibrium. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2412.13972v2-abstract-full').style.display = 'none'; document.getElementById('2412.13972v2-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> 28 January, 2025; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 18 December, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> December 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">Extended version of paper accepted at AAAI'25</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2412.12827">arXiv:2412.12827</a> <span> [<a href="https://arxiv.org/pdf/2412.12827">pdf</a>, <a href="https://arxiv.org/format/2412.12827">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"> TabSniper: Towards Accurate Table Detection & Structure Recognition for Bank Statements </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Trivedi%2C+A">Abhishek Trivedi</a>, <a href="/search/?searchtype=author&query=Mukherjee%2C+S">Sourajit Mukherjee</a>, <a href="/search/?searchtype=author&query=Singh%2C+R+K">Rajat Kumar Singh</a>, <a href="/search/?searchtype=author&query=Agarwal%2C+V">Vani Agarwal</a>, <a href="/search/?searchtype=author&query=Ramakrishnan%2C+S">Sriranjani Ramakrishnan</a>, <a href="/search/?searchtype=author&query=Bhatt%2C+H+S">Himanshu S. Bhatt</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="2412.12827v1-abstract-short" style="display: inline;"> Extraction of transaction information from bank statements is required to assess one's financial well-being for credit rating and underwriting decisions. Unlike other financial documents such as tax forms or financial statements, extracting the transaction descriptions from bank statements can provide a comprehensive and recent view into the cash flows and spending patterns. With multiple variatio… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2412.12827v1-abstract-full').style.display = 'inline'; document.getElementById('2412.12827v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2412.12827v1-abstract-full" style="display: none;"> Extraction of transaction information from bank statements is required to assess one's financial well-being for credit rating and underwriting decisions. Unlike other financial documents such as tax forms or financial statements, extracting the transaction descriptions from bank statements can provide a comprehensive and recent view into the cash flows and spending patterns. With multiple variations in layout and templates across several banks, extracting transactional level information from different table categories is an arduous task. Existing table structure recognition approaches produce sub optimal results for long, complex tables and are unable to capture all transactions accurately. This paper proposes TabSniper, a novel approach for efficient table detection, categorization and structure recognition from bank statements. The pipeline starts with detecting and categorizing tables of interest from the bank statements. The extracted table regions are then processed by the table structure recognition model followed by a post-processing module to transform the transactional data into a structured and standardised format. The detection and structure recognition architectures are based on DETR, fine-tuned with diverse bank statements along with additional feature enhancements. Results on challenging datasets demonstrate that TabSniper outperforms strong baselines and produces high-quality extraction of transaction information from bank and other financial documents across multiple layouts and templates. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2412.12827v1-abstract-full').style.display = 'none'; document.getElementById('2412.12827v1-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 December, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> December 2024. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2412.11240">arXiv:2412.11240</a> <span> [<a href="https://arxiv.org/pdf/2412.11240">pdf</a>, <a href="https://arxiv.org/format/2412.11240">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Materials Science">cond-mat.mtrl-sci</span> </div> </div> <p class="title is-5 mathjax"> Revisiting altermagnetism in RuO2: a study of laser-pulse induced charge dynamics by time-domain terahertz spectroscopy </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Plouff%2C+D+T">David T. Plouff</a>, <a href="/search/?searchtype=author&query=Scheuer%2C+L">Laura Scheuer</a>, <a href="/search/?searchtype=author&query=Shrestha%2C+S">Shreya Shrestha</a>, <a href="/search/?searchtype=author&query=Wu%2C+W">Weipeng Wu</a>, <a href="/search/?searchtype=author&query=Parvez%2C+N+J">Nawsher J. Parvez</a>, <a href="/search/?searchtype=author&query=Bhatt%2C+S">Subhash Bhatt</a>, <a href="/search/?searchtype=author&query=Wang%2C+X">Xinhao Wang</a>, <a href="/search/?searchtype=author&query=Gundlach%2C+L">Lars Gundlach</a>, <a href="/search/?searchtype=author&query=Jungfleisch%2C+M+B">M. Benjamin Jungfleisch</a>, <a href="/search/?searchtype=author&query=Xiao%2C+J+Q">John Q. Xiao</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="2412.11240v1-abstract-short" style="display: inline;"> Altermagnets are a recently discovered class of magnetic material with great potential for applications in the field of spintronics, owing to their non-relativistic spin-splitting and simultaneous antiferromagnetic order. One of the most studied candidates for altermagnetic materials is rutile structured RuO2. However, it has recently come under significant scrutiny as evidence emerged for its lac… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2412.11240v1-abstract-full').style.display = 'inline'; document.getElementById('2412.11240v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2412.11240v1-abstract-full" style="display: none;"> Altermagnets are a recently discovered class of magnetic material with great potential for applications in the field of spintronics, owing to their non-relativistic spin-splitting and simultaneous antiferromagnetic order. One of the most studied candidates for altermagnetic materials is rutile structured RuO2. However, it has recently come under significant scrutiny as evidence emerged for its lack of any magnetic order. In this work, we study bilayers of epitaxial RuO2 and ferromagnetic permalloy (Fe19Ni81) by time-domain terahertz spectroscopy, probing for three possible mechanisms of laser-induced charge dynamics: the inverse spin Hall effect (ISHE), electrical anisotropic conductivity (EAC), and inverse altermagnetic spin-splitting effect (IASSE). We examine films of four common RuO2 layer orientations: (001), (100), (110), and (101). If RuO2 is altermagnetic, then the (100) and (101) oriented samples are expected to produce anisotropic emission from the IASSE, however, our results do not indicate the presence of IASSE for either as-deposited or field annealed samples. The THz emission from all samples is instead consistent with charge dynamics induced by only the relativistic ISHE and the non-relativistic and non-magnetic EAC, casting further doubt on the existence of altermagnetism in RuO2. In addition, we find that in the (101) oriented RuO2 sample, the combination of ISHE and EAC emission mechanisms produces THz emission which is tunable between linear and elliptical polarization by modulation of the external magnetic field. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2412.11240v1-abstract-full').style.display = 'none'; document.getElementById('2412.11240v1-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> 15 December, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> December 2024. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2411.17535">arXiv:2411.17535</a> <span> [<a href="https://arxiv.org/pdf/2411.17535">pdf</a>, <a href="https://arxiv.org/format/2411.17535">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"> IMPROVE: Improving Medical Plausibility without Reliance on HumanValidation -- An Enhanced Prototype-Guided Diffusion Framework </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Shandilya%2C+A">Anurag Shandilya</a>, <a href="/search/?searchtype=author&query=Bhat%2C+S">Swapnil Bhat</a>, <a href="/search/?searchtype=author&query=Gautam%2C+A">Akshat Gautam</a>, <a href="/search/?searchtype=author&query=Yadav%2C+S">Subhash Yadav</a>, <a href="/search/?searchtype=author&query=Bhatt%2C+S">Siddharth Bhatt</a>, <a href="/search/?searchtype=author&query=Mehta%2C+D">Deval Mehta</a>, <a href="/search/?searchtype=author&query=Jadhav%2C+K">Kshitij Jadhav</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="2411.17535v1-abstract-short" style="display: inline;"> Generative models have proven to be very effective in generating synthetic medical images and find applications in downstream tasks such as enhancing rare disease datasets, long-tailed dataset augmentation, and scaling machine learning algorithms. For medical applications, the synthetically generated medical images by such models are still reasonable in quality when evaluated based on traditional… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2411.17535v1-abstract-full').style.display = 'inline'; document.getElementById('2411.17535v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2411.17535v1-abstract-full" style="display: none;"> Generative models have proven to be very effective in generating synthetic medical images and find applications in downstream tasks such as enhancing rare disease datasets, long-tailed dataset augmentation, and scaling machine learning algorithms. For medical applications, the synthetically generated medical images by such models are still reasonable in quality when evaluated based on traditional metrics such as FID score, precision, and recall. However, these metrics fail to capture the medical/biological plausibility of the generated images. Human expert feedback has been used to get biological plausibility which demonstrates that these generated images have very low plausibility. Recently, the research community has further integrated this human feedback through Reinforcement Learning from Human Feedback(RLHF), which generates more medically plausible images. However, incorporating human feedback is a costly and slow process. In this work, we propose a novel approach to improve the medical plausibility of generated images without the need for human feedback. We introduce IMPROVE:Improving Medical Plausibility without Reliance on Human Validation - An Enhanced Prototype-Guided Diffusion Framework, a prototype-guided diffusion process for medical image generation and show that it substantially enhances the biological plausibility of the generated medical images without the need for any human feedback. We perform experiments on Bone Marrow and HAM10000 datasets and show that medical accuracy can be substantially increased without human feedback. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2411.17535v1-abstract-full').style.display = 'none'; document.getElementById('2411.17535v1-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> 26 November, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> November 2024. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2411.16956">arXiv:2411.16956</a> <span> [<a href="https://arxiv.org/pdf/2411.16956">pdf</a>, <a href="https://arxiv.org/format/2411.16956">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Image and Video Processing">eess.IV</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="Computer Vision and Pattern Recognition">cs.CV</span> </div> </div> <p class="title is-5 mathjax"> Contrastive Deep Learning Reveals Age Biomarkers in Histopathological Skin Biopsies </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Chakradeo%2C+K">Kaustubh Chakradeo</a>, <a href="/search/?searchtype=author&query=Nielsen%2C+P">Pernille Nielsen</a>, <a href="/search/?searchtype=author&query=Gjerdrum%2C+L+M+R">Lise Mette Rahbek Gjerdrum</a>, <a href="/search/?searchtype=author&query=Hansen%2C+G+S">Gry Sahl Hansen</a>, <a href="/search/?searchtype=author&query=Duch%C3%AAne%2C+D+A">David A Duch锚ne</a>, <a href="/search/?searchtype=author&query=Mortensen%2C+L+H">Laust H Mortensen</a>, <a href="/search/?searchtype=author&query=Jensen%2C+M+K">Majken K Jensen</a>, <a href="/search/?searchtype=author&query=Bhatt%2C+S">Samir Bhatt</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="2411.16956v1-abstract-short" style="display: inline;"> As global life expectancy increases, so does the burden of chronic diseases, yet individuals exhibit considerable variability in the rate at which they age. Identifying biomarkers that distinguish fast from slow ageing is crucial for understanding the biology of ageing, enabling early disease detection, and improving prevention strategies. Using contrastive deep learning, we show that skin biopsy… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2411.16956v1-abstract-full').style.display = 'inline'; document.getElementById('2411.16956v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2411.16956v1-abstract-full" style="display: none;"> As global life expectancy increases, so does the burden of chronic diseases, yet individuals exhibit considerable variability in the rate at which they age. Identifying biomarkers that distinguish fast from slow ageing is crucial for understanding the biology of ageing, enabling early disease detection, and improving prevention strategies. Using contrastive deep learning, we show that skin biopsy images alone are sufficient to determine an individual's age. We then use visual features in histopathology slides of the skin biopsies to construct a novel biomarker of ageing. By linking with comprehensive health registers in Denmark, we demonstrate that visual features in histopathology slides of skin biopsies predict mortality and the prevalence of chronic age-related diseases. Our work highlights how routinely collected health data can provide additional value when used together with deep learning, by creating a new biomarker for ageing which can be actively used to determine mortality over time. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2411.16956v1-abstract-full').style.display = 'none'; document.getElementById('2411.16956v1-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> 25 November, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> November 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">20 pages, 5 tables, 5 figures Under review: npj Digital Medicine</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2411.07567">arXiv:2411.07567</a> <span> [<a href="https://arxiv.org/pdf/2411.07567">pdf</a>, <a href="https://arxiv.org/format/2411.07567">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Image and Video Processing">eess.IV</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Computer Vision and Pattern Recognition">cs.CV</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"> Uncertainty-Aware Test-Time Adaptation for Inverse Consistent Diffeomorphic Lung Image Registration </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Chaudhary%2C+M+F+A">Muhammad F. A. Chaudhary</a>, <a href="/search/?searchtype=author&query=Aguilera%2C+S+M">Stephanie M. Aguilera</a>, <a href="/search/?searchtype=author&query=Nakhmani%2C+A">Arie Nakhmani</a>, <a href="/search/?searchtype=author&query=Reinhardt%2C+J+M">Joseph M. Reinhardt</a>, <a href="/search/?searchtype=author&query=Bhatt%2C+S+P">Surya P. Bhatt</a>, <a href="/search/?searchtype=author&query=Bodduluri%2C+S">Sandeep Bodduluri</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="2411.07567v1-abstract-short" style="display: inline;"> Diffeomorphic deformable image registration ensures smooth invertible transformations across inspiratory and expiratory chest CT scans. Yet, in practice, deep learning-based diffeomorphic methods struggle to capture large deformations between inspiratory and expiratory volumes, and therefore lack inverse consistency. Existing methods also fail to account for model uncertainty, which can be useful… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2411.07567v1-abstract-full').style.display = 'inline'; document.getElementById('2411.07567v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2411.07567v1-abstract-full" style="display: none;"> Diffeomorphic deformable image registration ensures smooth invertible transformations across inspiratory and expiratory chest CT scans. Yet, in practice, deep learning-based diffeomorphic methods struggle to capture large deformations between inspiratory and expiratory volumes, and therefore lack inverse consistency. Existing methods also fail to account for model uncertainty, which can be useful for improving performance. We propose an uncertainty-aware test-time adaptation framework for inverse consistent diffeomorphic lung registration. Our method uses Monte Carlo (MC) dropout to estimate spatial uncertainty that is used to improve model performance. We train and evaluate our method for inspiratory-to-expiratory CT registration on a large cohort of 675 subjects from the COPDGene study, achieving a higher Dice similarity coefficient (DSC) between the lung boundaries (0.966) compared to both VoxelMorph (0.953) and TransMorph (0.953). Our method demonstrates consistent improvements in the inverse registration direction as well with an overall DSC of 0.966, higher than VoxelMorph (0.958) and TransMorph (0.956). Paired t-tests indicate statistically significant improvements. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2411.07567v1-abstract-full').style.display = 'none'; document.getElementById('2411.07567v1-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> 12 November, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> November 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">5 pages, 4 figures</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2411.04225">arXiv:2411.04225</a> <span> [<a href="https://arxiv.org/pdf/2411.04225">pdf</a>, <a href="https://arxiv.org/format/2411.04225">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> </div> </div> <p class="title is-5 mathjax"> Approximate Equivariance in Reinforcement Learning </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Park%2C+J+Y">Jung Yeon Park</a>, <a href="/search/?searchtype=author&query=Bhatt%2C+S">Sujay Bhatt</a>, <a href="/search/?searchtype=author&query=Zeng%2C+S">Sihan Zeng</a>, <a href="/search/?searchtype=author&query=Wong%2C+L+L+S">Lawson L. S. Wong</a>, <a href="/search/?searchtype=author&query=Koppel%2C+A">Alec Koppel</a>, <a href="/search/?searchtype=author&query=Ganesh%2C+S">Sumitra Ganesh</a>, <a href="/search/?searchtype=author&query=Walters%2C+R">Robin Walters</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="2411.04225v1-abstract-short" style="display: inline;"> Equivariant neural networks have shown great success in reinforcement learning, improving sample efficiency and generalization when there is symmetry in the task. However, in many problems, only approximate symmetry is present, which makes imposing exact symmetry inappropriate. Recently, approximately equivariant networks have been proposed for supervised classification and modeling physical syste… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2411.04225v1-abstract-full').style.display = 'inline'; document.getElementById('2411.04225v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2411.04225v1-abstract-full" style="display: none;"> Equivariant neural networks have shown great success in reinforcement learning, improving sample efficiency and generalization when there is symmetry in the task. However, in many problems, only approximate symmetry is present, which makes imposing exact symmetry inappropriate. Recently, approximately equivariant networks have been proposed for supervised classification and modeling physical systems. In this work, we develop approximately equivariant algorithms in reinforcement learning (RL). We define approximately equivariant MDPs and theoretically characterize the effect of approximate equivariance on the optimal Q function. We propose novel RL architectures using relaxed group convolutions and experiment on several continuous control domains and stock trading with real financial data. Our results demonstrate that approximate equivariance matches prior work when exact symmetries are present, and outperforms them when domains exhibit approximate symmetry. As an added byproduct of these techniques, we observe increased robustness to noise at test time. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2411.04225v1-abstract-full').style.display = 'none'; document.getElementById('2411.04225v1-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 November, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> November 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">Preprint</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2410.20490">arXiv:2410.20490</a> <span> [<a href="https://arxiv.org/pdf/2410.20490">pdf</a>, <a href="https://arxiv.org/format/2410.20490">other</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> </div> </div> <p class="title is-5 mathjax"> $\textit{Who Speaks Matters}$: Analysing the Influence of the Speaker's Ethnicity on Hate Classification </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Malik%2C+A">Ananya Malik</a>, <a href="/search/?searchtype=author&query=Sharma%2C+K">Kartik Sharma</a>, <a href="/search/?searchtype=author&query=Ng%2C+L+H+X">Lynnette Hui Xian Ng</a>, <a href="/search/?searchtype=author&query=Bhatt%2C+S">Shaily Bhatt</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="2410.20490v1-abstract-short" style="display: inline;"> Large Language Models (LLMs) offer a lucrative promise for scalable content moderation, including hate speech detection. However, they are also known to be brittle and biased against marginalised communities and dialects. This requires their applications to high-stakes tasks like hate speech detection to be critically scrutinized. In this work, we investigate the robustness of hate speech classifi… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2410.20490v1-abstract-full').style.display = 'inline'; document.getElementById('2410.20490v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2410.20490v1-abstract-full" style="display: none;"> Large Language Models (LLMs) offer a lucrative promise for scalable content moderation, including hate speech detection. However, they are also known to be brittle and biased against marginalised communities and dialects. This requires their applications to high-stakes tasks like hate speech detection to be critically scrutinized. In this work, we investigate the robustness of hate speech classification using LLMs, particularly when explicit and implicit markers of the speaker's ethnicity are injected into the input. For the explicit markers, we inject a phrase that mentions the speaker's identity. For the implicit markers, we inject dialectal features. By analysing how frequently model outputs flip in the presence of these markers, we reveal varying degrees of brittleness across 4 popular LLMs and 5 ethnicities. We find that the presence of implicit dialect markers in inputs causes model outputs to flip more than the presence of explicit markers. Further, the percentage of flips varies across ethnicities. Finally, we find that larger models are more robust. Our findings indicate the need for exercising caution in deploying LLMs for high-stakes tasks like hate speech detection. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2410.20490v1-abstract-full').style.display = 'none'; document.getElementById('2410.20490v1-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 October, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> October 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">9 pages, 3 figures, 3 tables. To appear in NeurIPS SafeGenAI 2024 Workshop</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2410.06167">arXiv:2410.06167</a> <span> [<a href="https://arxiv.org/pdf/2410.06167">pdf</a>, <a href="https://arxiv.org/format/2410.06167">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Materials Science">cond-mat.mtrl-sci</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Mesoscale and Nanoscale Physics">cond-mat.mes-hall</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Other Condensed Matter">cond-mat.other</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Applied Physics">physics.app-ph</span> </div> </div> <p class="title is-5 mathjax"> Broken intrinsic symmetry induced magnon-magnon coupling in synthetic ferrimagnets </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Hossain%2C+M+T">Mohammad Tomal Hossain</a>, <a href="/search/?searchtype=author&query=Chen%2C+H">Hang Chen</a>, <a href="/search/?searchtype=author&query=Bhatt%2C+S">Subhash Bhatt</a>, <a href="/search/?searchtype=author&query=Kaffash%2C+M+T">Mojtaba Taghipour Kaffash</a>, <a href="/search/?searchtype=author&query=Xiao%2C+J+Q">John Q. Xiao</a>, <a href="/search/?searchtype=author&query=Sklenar%2C+J">Joseph Sklenar</a>, <a href="/search/?searchtype=author&query=Jungfleisch%2C+M+B">M. Benjamin Jungfleisch</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="2410.06167v1-abstract-short" style="display: inline;"> Synthetic antiferromagnets offer rich magnon energy spectra in which optical and acoustic magnon branches can hybridize. Here, we demonstrate a broken intrinsic symmetry induced coupling of acoustic and optical magnons in a synthetic ferrimagnet consisting of two dissimilar antiferromagnetically interacting ferromagnetic metals. Two distinct magnon modes hybridize at degeneracy points, as indicate… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2410.06167v1-abstract-full').style.display = 'inline'; document.getElementById('2410.06167v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2410.06167v1-abstract-full" style="display: none;"> Synthetic antiferromagnets offer rich magnon energy spectra in which optical and acoustic magnon branches can hybridize. Here, we demonstrate a broken intrinsic symmetry induced coupling of acoustic and optical magnons in a synthetic ferrimagnet consisting of two dissimilar antiferromagnetically interacting ferromagnetic metals. Two distinct magnon modes hybridize at degeneracy points, as indicated by an avoided level-crossing. The avoided level-crossing gap depends on the interlayer exchange interaction between the magnetic layers, which can be controlled by adjusting the non-magnetic interlayer thickness. An exceptionally large avoided level crossing gap of 6 GHz is revealed, exceeding the coupling strength that is typically found in other magnonic hybrid systems based on a coupling of magnons with photons or magnons and phonons. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2410.06167v1-abstract-full').style.display = 'none'; document.getElementById('2410.06167v1-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, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> October 2024. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2409.11521">arXiv:2409.11521</a> <span> [<a href="https://arxiv.org/pdf/2409.11521">pdf</a>, <a href="https://arxiv.org/format/2409.11521">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="Machine Learning">stat.ML</span> </div> </div> <p class="title is-5 mathjax"> Partially Observable Contextual Bandits with Linear Payoffs </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Zeng%2C+S">Sihan Zeng</a>, <a href="/search/?searchtype=author&query=Bhatt%2C+S">Sujay Bhatt</a>, <a href="/search/?searchtype=author&query=Koppel%2C+A">Alec Koppel</a>, <a href="/search/?searchtype=author&query=Ganesh%2C+S">Sumitra Ganesh</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="2409.11521v1-abstract-short" style="display: inline;"> The standard contextual bandit framework assumes fully observable and actionable contexts. In this work, we consider a new bandit setting with partially observable, correlated contexts and linear payoffs, motivated by the applications in finance where decision making is based on market information that typically displays temporal correlation and is not fully observed. We make the following contrib… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2409.11521v1-abstract-full').style.display = 'inline'; document.getElementById('2409.11521v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2409.11521v1-abstract-full" style="display: none;"> The standard contextual bandit framework assumes fully observable and actionable contexts. In this work, we consider a new bandit setting with partially observable, correlated contexts and linear payoffs, motivated by the applications in finance where decision making is based on market information that typically displays temporal correlation and is not fully observed. We make the following contributions marrying ideas from statistical signal processing with bandits: (i) We propose an algorithmic pipeline named EMKF-Bandit, which integrates system identification, filtering, and classic contextual bandit algorithms into an iterative method alternating between latent parameter estimation and decision making. (ii) We analyze EMKF-Bandit when we select Thompson sampling as the bandit algorithm and show that it incurs a sub-linear regret under conditions on filtering. (iii) We conduct numerical simulations that demonstrate the benefits and practical applicability of the proposed pipeline. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2409.11521v1-abstract-full').style.display = 'none'; document.getElementById('2409.11521v1-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 September, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> September 2024. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2408.04780">arXiv:2408.04780</a> <span> [<a href="https://arxiv.org/pdf/2408.04780">pdf</a>, <a href="https://arxiv.org/format/2408.04780">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Optimization and Control">math.OC</span> </div> </div> <p class="title is-5 mathjax"> Learning in Herding Mean Field Games: Single-Loop Algorithm with Finite-Time Convergence Analysis </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Zeng%2C+S">Sihan Zeng</a>, <a href="/search/?searchtype=author&query=Bhatt%2C+S">Sujay Bhatt</a>, <a href="/search/?searchtype=author&query=Koppel%2C+A">Alec Koppel</a>, <a href="/search/?searchtype=author&query=Ganesh%2C+S">Sumitra Ganesh</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="2408.04780v5-abstract-short" style="display: inline;"> We consider discrete-time stationary mean field games (MFG) with unknown dynamics and design algorithms for finding the equilibrium with finite-time complexity guarantees. Prior solutions to the problem assume either the contraction of a mean field optimality-consistency operator or strict weak monotonicity, which may be overly restrictive. In this work, we introduce a new class of solvable MFGs,… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2408.04780v5-abstract-full').style.display = 'inline'; document.getElementById('2408.04780v5-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2408.04780v5-abstract-full" style="display: none;"> We consider discrete-time stationary mean field games (MFG) with unknown dynamics and design algorithms for finding the equilibrium with finite-time complexity guarantees. Prior solutions to the problem assume either the contraction of a mean field optimality-consistency operator or strict weak monotonicity, which may be overly restrictive. In this work, we introduce a new class of solvable MFGs, named the "fully herding class", which expands the known solvable class of MFGs and for the first time includes problems with multiple equilibria. We propose a direct policy optimization method, Accelerated Single-loop Actor Critic Algorithm for Mean Field Games (ASAC-MFG), that provably finds a global equilibrium for MFGs within this class, under suitable access to a single trajectory of Markovian samples. Different from the prior methods, ASAC-MFG is single-loop and single-sample-path. We establish the finite-time and finite-sample convergence of ASAC-MFG to a mean field equilibrium via new techniques that we develop for multi-time-scale stochastic approximation. We support the theoretical results with illustrative numerical simulations. When the mean field does not affect the transition and reward, a MFG reduces to a Markov decision process (MDP) and ASAC-MFG becomes an actor-critic algorithm for finding the optimal policy in average-reward MDPs, with a sample complexity matching the state-of-the-art. Previous works derive the complexity assuming a contraction on the Bellman operator, which is invalid for average-reward MDPs. We match the rate while removing the untenable assumption through an improved Lyapunov function. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2408.04780v5-abstract-full').style.display = 'none'; document.getElementById('2408.04780v5-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> 11 February, 2025; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 8 August, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> August 2024. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2408.03167">arXiv:2408.03167</a> <span> [<a href="https://arxiv.org/pdf/2408.03167">pdf</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Materials Science">cond-mat.mtrl-sci</span> </div> </div> <p class="title is-5 mathjax"> Stacking fault segregation imaging with analytical field ion microscopy </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Morgado%2C+F+F">F. F. Morgado</a>, <a href="/search/?searchtype=author&query=Stephenson%2C+L+T">L. T. Stephenson</a>, <a href="/search/?searchtype=author&query=Bhatt%2C+S">S. Bhatt</a>, <a href="/search/?searchtype=author&query=Freysoldt%2C+C">C. Freysoldt</a>, <a href="/search/?searchtype=author&query=Neumeier%2C+S">S. Neumeier</a>, <a href="/search/?searchtype=author&query=Katnagallu%2C+S">S. Katnagallu</a>, <a href="/search/?searchtype=author&query=Subramanyam%2C+A+P+A">A. P. A. Subramanyam</a>, <a href="/search/?searchtype=author&query=Pietka%2C+I">I. Pietka</a>, <a href="/search/?searchtype=author&query=Hammerschmidt%2C+T">T. Hammerschmidt</a>, <a href="/search/?searchtype=author&query=Vurpillot%2C+F">F. Vurpillot</a>, <a href="/search/?searchtype=author&query=Gault%2C+B">B. Gault</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="2408.03167v1-abstract-short" style="display: inline;"> Stacking faults (SF) are important structural defects that play an essential role in the deformation of engineering alloys. However, direct observation of stacking faults at the atomic scale can be challenging. Here, we use the analytical field ion microscopy (aFIM), including density-functional theory informed contrast estimation, to image local elemental segregation at SFs in a creep-deformed so… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2408.03167v1-abstract-full').style.display = 'inline'; document.getElementById('2408.03167v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2408.03167v1-abstract-full" style="display: none;"> Stacking faults (SF) are important structural defects that play an essential role in the deformation of engineering alloys. However, direct observation of stacking faults at the atomic scale can be challenging. Here, we use the analytical field ion microscopy (aFIM), including density-functional theory informed contrast estimation, to image local elemental segregation at SFs in a creep-deformed solid solution single crystal alloy of Ni-2 at.% W. The segregated atoms are imaged brightly, and time-of-flight spectrometry allows for their identification as W. We also provide the first quantitative analysis of trajectory aberration, with a deviation of approximately 0.4 nm, explaining why atom probe tomography could not resolve these segregations. Atomistic simulations of substitutional W atoms at an edge dislocation in fcc Ni using an analytic bond-order potential indicate that the experimentally observed segregation is due to the energetic preference of W for the center of the stacking fault, contrasting with e.g., Re segregating to partial dislocations. Solute segregation to SF can hinder dislocation motion, increasing the strength of Ni-based superalloys. Yet direct substitution of Re by W envisaged to lower superalloys' costs, requires extra consideration in alloy design since these two solutes do not have comparable interactions with structural defects during deformation. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2408.03167v1-abstract-full').style.display = 'none'; document.getElementById('2408.03167v1-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 August, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> August 2024. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2407.05986">arXiv:2407.05986</a> <span> [<a href="https://arxiv.org/pdf/2407.05986">pdf</a>, <a href="https://arxiv.org/format/2407.05986">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> <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"> KidSat: satellite imagery to map childhood poverty dataset and benchmark </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Sharma%2C+M">Makkunda Sharma</a>, <a href="/search/?searchtype=author&query=Yang%2C+F">Fan Yang</a>, <a href="/search/?searchtype=author&query=Vo%2C+D">Duy-Nhat Vo</a>, <a href="/search/?searchtype=author&query=Suel%2C+E">Esra Suel</a>, <a href="/search/?searchtype=author&query=Mishra%2C+S">Swapnil Mishra</a>, <a href="/search/?searchtype=author&query=Bhatt%2C+S">Samir Bhatt</a>, <a href="/search/?searchtype=author&query=Fiala%2C+O">Oliver Fiala</a>, <a href="/search/?searchtype=author&query=Rudgard%2C+W">William Rudgard</a>, <a href="/search/?searchtype=author&query=Flaxman%2C+S">Seth Flaxman</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="2407.05986v1-abstract-short" style="display: inline;"> Satellite imagery has emerged as an important tool to analyse demographic, health, and development indicators. While various deep learning models have been built for these tasks, each is specific to a particular problem, with few standard benchmarks available. We propose a new dataset pairing satellite imagery and high-quality survey data on child poverty to benchmark satellite feature representat… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2407.05986v1-abstract-full').style.display = 'inline'; document.getElementById('2407.05986v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2407.05986v1-abstract-full" style="display: none;"> Satellite imagery has emerged as an important tool to analyse demographic, health, and development indicators. While various deep learning models have been built for these tasks, each is specific to a particular problem, with few standard benchmarks available. We propose a new dataset pairing satellite imagery and high-quality survey data on child poverty to benchmark satellite feature representations. Our dataset consists of 33,608 images, each 10 km $\times$ 10 km, from 19 countries in Eastern and Southern Africa in the time period 1997-2022. As defined by UNICEF, multidimensional child poverty covers six dimensions and it can be calculated from the face-to-face Demographic and Health Surveys (DHS) Program . As part of the benchmark, we test spatial as well as temporal generalization, by testing on unseen locations, and on data after the training years. Using our dataset we benchmark multiple models, from low-level satellite imagery models such as MOSAIKS , to deep learning foundation models, which include both generic vision models such as Self-Distillation with no Labels (DINOv2) models and specific satellite imagery models such as SatMAE. We provide open source code for building the satellite dataset, obtaining ground truth data from DHS and running various models assessed in our work. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2407.05986v1-abstract-full').style.display = 'none'; document.getElementById('2407.05986v1-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 July, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> July 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">15 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/2406.11565">arXiv:2406.11565</a> <span> [<a href="https://arxiv.org/pdf/2406.11565">pdf</a>, <a href="https://arxiv.org/format/2406.11565">other</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="Computers and Society">cs.CY</span> </div> </div> <p class="title is-5 mathjax"> Extrinsic Evaluation of Cultural Competence in Large Language Models </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Bhatt%2C+S">Shaily Bhatt</a>, <a href="/search/?searchtype=author&query=Diaz%2C+F">Fernando Diaz</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="2406.11565v3-abstract-short" style="display: inline;"> Productive interactions between diverse users and language technologies require outputs from the latter to be culturally relevant and sensitive. Prior works have evaluated models' knowledge of cultural norms, values, and artifacts, without considering how this knowledge manifests in downstream applications. In this work, we focus on extrinsic evaluation of cultural competence in two text generatio… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2406.11565v3-abstract-full').style.display = 'inline'; document.getElementById('2406.11565v3-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2406.11565v3-abstract-full" style="display: none;"> Productive interactions between diverse users and language technologies require outputs from the latter to be culturally relevant and sensitive. Prior works have evaluated models' knowledge of cultural norms, values, and artifacts, without considering how this knowledge manifests in downstream applications. In this work, we focus on extrinsic evaluation of cultural competence in two text generation tasks, open-ended question answering and story generation. We quantitatively and qualitatively evaluate model outputs when an explicit cue of culture, specifically nationality, is perturbed in the prompts. Although we find that model outputs do vary when varying nationalities and feature culturally relevant words, we also find weak correlations between text similarity of outputs for different countries and the cultural values of these countries. Finally, we discuss important considerations in designing comprehensive evaluation of cultural competence in user-facing tasks. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2406.11565v3-abstract-full').style.display = 'none'; document.getElementById('2406.11565v3-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> 3 October, 2024; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 17 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">Accepted to EMNLP Findings 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/2405.02379">arXiv:2405.02379</a> <span> [<a href="https://arxiv.org/pdf/2405.02379">pdf</a>, <a href="https://arxiv.org/ps/2405.02379">ps</a>, <a href="https://arxiv.org/format/2405.02379">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Populations and Evolution">q-bio.PE</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Probability">math.PR</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Quantitative Methods">q-bio.QM</span> </div> </div> <p class="title is-5 mathjax"> Modelling the Stochastic Importation Dynamics and Establishment of Novel Pathogenic Strains using a General Branching Processes Framework </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Curran-Sebastian%2C+J">Jacob Curran-Sebastian</a>, <a href="/search/?searchtype=author&query=Andersen%2C+F+M">Frederik M酶lkj忙r Andersen</a>, <a href="/search/?searchtype=author&query=Bhatt%2C+S">Samir Bhatt</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="2405.02379v2-abstract-short" style="display: inline;"> The importation and subsequent establishment of novel pathogenic strains in a population is subject to a large degree of uncertainty due to the stochastic nature of the disease dynamics. Mathematical models need to take this stochasticity in the early phase of an outbreak in order to adequately capture the uncertainty in disease forecasts. We propose a general branching process model of disease sp… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2405.02379v2-abstract-full').style.display = 'inline'; document.getElementById('2405.02379v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2405.02379v2-abstract-full" style="display: none;"> The importation and subsequent establishment of novel pathogenic strains in a population is subject to a large degree of uncertainty due to the stochastic nature of the disease dynamics. Mathematical models need to take this stochasticity in the early phase of an outbreak in order to adequately capture the uncertainty in disease forecasts. We propose a general branching process model of disease spread that includes host-level heterogeneity, and that can be straightforwardly tailored to capture the salient aspects of a particular disease outbreak. We combine this with a model of case importation that occurs via an independent marked Poisson process. We use this framework to investigate the impact of different control strategies, particularly on the time to establishment of an invading, exogenous strain, using parameters taken from the literature for COVID-19 as an example. We also demonstrate how to combine our model with a deterministic approximation, such that longer term projections can be generated that still incorporate the uncertainty from the early growth phase of the epidemic. Our approach produces meaningful short- and medium-term projections of the course of a disease outbreak when model parameters are still uncertain and when stochasticity still has a large effect on the population dynamics. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2405.02379v2-abstract-full').style.display = 'none'; document.getElementById('2405.02379v2-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> 28 November, 2024; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 3 May, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> May 2024. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2401.12784">arXiv:2401.12784</a> <span> [<a href="https://arxiv.org/pdf/2401.12784">pdf</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Materials Science">cond-mat.mtrl-sci</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"> Understanding atom probe's analytical performance for iron oxides using correlation histograms and ab initio calculations </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Kim%2C+S">Se-Ho Kim</a>, <a href="/search/?searchtype=author&query=Bhatt%2C+S">Shalini Bhatt</a>, <a href="/search/?searchtype=author&query=Schreiber%2C+D+K">Daniel K. Schreiber</a>, <a href="/search/?searchtype=author&query=Neugebauer%2C+J">J枚rg Neugebauer</a>, <a href="/search/?searchtype=author&query=Freysoldt%2C+C">Christoph Freysoldt</a>, <a href="/search/?searchtype=author&query=Gault%2C+B">Baptiste Gault</a>, <a href="/search/?searchtype=author&query=Katnagallu%2C+S">Shyam Katnagallu</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="2401.12784v1-abstract-short" style="display: inline;"> Field evaporation from ionic or covalently bonded materials often leads to the emission of molecular ions. The metastability of these molecular ions, particularly under the influence of the intense electrostatic field (1010 Vm-1), makes them prone to dissociation with or without an exchange of energy amongst them. These processes can affect the analytical performance of atom probe tomography (APT)… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2401.12784v1-abstract-full').style.display = 'inline'; document.getElementById('2401.12784v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2401.12784v1-abstract-full" style="display: none;"> Field evaporation from ionic or covalently bonded materials often leads to the emission of molecular ions. The metastability of these molecular ions, particularly under the influence of the intense electrostatic field (1010 Vm-1), makes them prone to dissociation with or without an exchange of energy amongst them. These processes can affect the analytical performance of atom probe tomography (APT). For instance, neutral species formed through dissociation may not be detected at all or with a time of flight no longer related to their mass, causing their loss from the analysis. Here, we evaluated the changes in the measured composition of FeO, Fe2O3 and Fe3O4 across a wide range of analysis conditions. Possible dissociation reactions are predicted by density-functional theory (DFT) calculations considering the spin states of the molecules. The energetically favoured reactions are traced on to the multi-hit ion correlation histograms, to confirm their existence within experiments, using an automated Python-based routine. The detected reactions are carefully analysed to reflect upon the influence of these neutrals from dissociation reactions on the performance of APT for analysing iron oxides. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2401.12784v1-abstract-full').style.display = 'none'; document.getElementById('2401.12784v1-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, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> January 2024. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2312.06189">arXiv:2312.06189</a> <span> [<a href="https://arxiv.org/pdf/2312.06189">pdf</a>, <a href="https://arxiv.org/ps/2312.06189">ps</a>, <a href="https://arxiv.org/format/2312.06189">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Operator Algebras">math.OA</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="K-Theory and Homology">math.KT</span> </div> </div> <p class="title is-5 mathjax"> $K$-stability of $C^*$-algebras generated by isometries and unitaries with twisted commutation relations </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Bhatt%2C+S+S">Shreema Subhash Bhatt</a>, <a href="/search/?searchtype=author&query=Saurabh%2C+B">Bipul Saurabh</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="2312.06189v1-abstract-short" style="display: inline;"> In this article, we prove $K$-stability for a family of $C^*$-algebras, which are generated by a finite set of unitaries and isometries satisfying twisted commutation relations. This family includes the $C^*$-algebra of doubly non-commuting isometries and free twist of isometries. Next, we consider the $C^*$-algebra $A_{\mathcal{V}}$ generated by an $n$-tuple of $\mathcal{U}$-twisted isometries… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2312.06189v1-abstract-full').style.display = 'inline'; document.getElementById('2312.06189v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2312.06189v1-abstract-full" style="display: none;"> In this article, we prove $K$-stability for a family of $C^*$-algebras, which are generated by a finite set of unitaries and isometries satisfying twisted commutation relations. This family includes the $C^*$-algebra of doubly non-commuting isometries and free twist of isometries. Next, we consider the $C^*$-algebra $A_{\mathcal{V}}$ generated by an $n$-tuple of $\mathcal{U}$-twisted isometries $\mathcal{V}$ with respect to a fixed $n\choose 2$-tuple $\mathcal{U}=\{U_{ij}:1\leq i<j \leq n\}$ of commuting unitaries (see \cite{NarJaySur-2022aa}). Under the assumption that the spectrum of the commutative $C^*$-algebra generated by $(\{U_{ij}:1\leq i<j \leq n\})$ does not contain any element of finite order in the torus group $\bbbt^{n\choose 2}$, we show that $A_{\mathcal{V}}$ is $K$-stable. Finally, we prove the same result for the $C^*$-algebra generated by a tuple of free $\mathcal{U}$-twisted isometries. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2312.06189v1-abstract-full').style.display = 'none'; document.getElementById('2312.06189v1-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> 11 December, 2023; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> December 2023. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">MSC Class:</span> 58B32; 58B34; 46L89; 47L55 </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2311.14642">arXiv:2311.14642</a> <span> [<a href="https://arxiv.org/pdf/2311.14642">pdf</a>, <a href="https://arxiv.org/format/2311.14642">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> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Multiagent Systems">cs.MA</span> </div> </div> <p class="title is-5 mathjax"> Continuous football player tracking from discrete broadcast data </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Penn%2C+M+J">Matthew J. Penn</a>, <a href="/search/?searchtype=author&query=Donnelly%2C+C+A">Christl A. Donnelly</a>, <a href="/search/?searchtype=author&query=Bhatt%2C+S">Samir Bhatt</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="2311.14642v1-abstract-short" style="display: inline;"> Player tracking data remains out of reach for many professional football teams as their video feeds are not sufficiently high quality for computer vision technologies to be used. To help bridge this gap, we present a method that can estimate continuous full-pitch tracking data from discrete data made from broadcast footage. Such data could be collected by clubs or players at a similar cost to even… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2311.14642v1-abstract-full').style.display = 'inline'; document.getElementById('2311.14642v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2311.14642v1-abstract-full" style="display: none;"> Player tracking data remains out of reach for many professional football teams as their video feeds are not sufficiently high quality for computer vision technologies to be used. To help bridge this gap, we present a method that can estimate continuous full-pitch tracking data from discrete data made from broadcast footage. Such data could be collected by clubs or players at a similar cost to event data, which is widely available down to semi-professional level. We test our method using open-source tracking data, and include a version that can be applied to a large set of over 200 games with such discrete data. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2311.14642v1-abstract-full').style.display = 'none'; document.getElementById('2311.14642v1-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> 24 November, 2023; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> November 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">12 pages, 3 figures</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2311.10927">arXiv:2311.10927</a> <span> [<a href="https://arxiv.org/pdf/2311.10927">pdf</a>, <a href="https://arxiv.org/format/2311.10927">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Computer Science and Game Theory">cs.GT</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"> Learning Payment-Free Resource Allocation Mechanisms </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Zeng%2C+S">Sihan Zeng</a>, <a href="/search/?searchtype=author&query=Bhatt%2C+S">Sujay Bhatt</a>, <a href="/search/?searchtype=author&query=Kreacic%2C+E">Eleonora Kreacic</a>, <a href="/search/?searchtype=author&query=Hassanzadeh%2C+P">Parisa Hassanzadeh</a>, <a href="/search/?searchtype=author&query=Koppel%2C+A">Alec Koppel</a>, <a href="/search/?searchtype=author&query=Ganesh%2C+S">Sumitra Ganesh</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="2311.10927v3-abstract-short" style="display: inline;"> We consider the design of mechanisms that allocate limited resources among self-interested agents using neural networks. Unlike the recent works that leverage machine learning for revenue maximization in auctions, we consider welfare maximization as the key objective in the payment-free setting. Without payment exchange, it is unclear how we can align agents' incentives to achieve the desired obje… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2311.10927v3-abstract-full').style.display = 'inline'; document.getElementById('2311.10927v3-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2311.10927v3-abstract-full" style="display: none;"> We consider the design of mechanisms that allocate limited resources among self-interested agents using neural networks. Unlike the recent works that leverage machine learning for revenue maximization in auctions, we consider welfare maximization as the key objective in the payment-free setting. Without payment exchange, it is unclear how we can align agents' incentives to achieve the desired objectives of truthfulness and social welfare simultaneously, without resorting to approximations. Our work makes novel contributions by designing an approximate mechanism that desirably trade-off social welfare with truthfulness. Specifically, (i) we contribute a new end-to-end neural network architecture, ExS-Net, that accommodates the idea of "money-burning" for mechanism design without payments; (ii)~we provide a generalization bound that guarantees the mechanism performance when trained under finite samples; and (iii) we provide an experimental demonstration of the merits of the proposed mechanism. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2311.10927v3-abstract-full').style.display = 'none'; document.getElementById('2311.10927v3-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 August, 2024; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 17 November, 2023; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> November 2023. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2311.02480">arXiv:2311.02480</a> <span> [<a href="https://arxiv.org/pdf/2311.02480">pdf</a>, <a href="https://arxiv.org/format/2311.02480">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Image and Video Processing">eess.IV</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Computer Vision and Pattern Recognition">cs.CV</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</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/SSP53291.2023.10207960">10.1109/SSP53291.2023.10207960 <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </div> </div> <p class="title is-5 mathjax"> A Strictly Bounded Deep Network for Unpaired Cyclic Translation of Medical Images </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Rai%2C+S">Swati Rai</a>, <a href="/search/?searchtype=author&query=Bhatt%2C+J+S">Jignesh S. Bhatt</a>, <a href="/search/?searchtype=author&query=Patra%2C+S+K">Sarat Kumar Patra</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="2311.02480v1-abstract-short" style="display: inline;"> Medical image translation is an ill-posed problem. Unlike existing paired unbounded unidirectional translation networks, in this paper, we consider unpaired medical images and provide a strictly bounded network that yields a stable bidirectional translation. We propose a patch-level concatenated cyclic conditional generative adversarial network (pCCGAN) embedded with adaptive dictionary learning.… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2311.02480v1-abstract-full').style.display = 'inline'; document.getElementById('2311.02480v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2311.02480v1-abstract-full" style="display: none;"> Medical image translation is an ill-posed problem. Unlike existing paired unbounded unidirectional translation networks, in this paper, we consider unpaired medical images and provide a strictly bounded network that yields a stable bidirectional translation. We propose a patch-level concatenated cyclic conditional generative adversarial network (pCCGAN) embedded with adaptive dictionary learning. It consists of two cyclically connected CGANs of 47 layers each; where both generators (each of 32 layers) are conditioned with concatenation of alternate unpaired patches from input and target modality images (not ground truth) of the same organ. The key idea is to exploit cross-neighborhood contextual feature information that bounds the translation space and boosts generalization. The generators are further equipped with adaptive dictionaries learned from the contextual patches to reduce possible degradation. Discriminators are 15-layer deep networks that employ minimax function to validate the translated imagery. A combined loss function is formulated with adversarial, non-adversarial, forward-backward cyclic, and identity losses that further minimize the variance of the proposed learning machine. Qualitative, quantitative, and ablation analysis show superior results on real CT and MRI. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2311.02480v1-abstract-full').style.display = 'none'; document.getElementById('2311.02480v1-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> 4 November, 2023; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> November 2023. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Journal ref:</span> 2023 IEEE Statistical Signal Processing Workshop (SSP), Hanoi, Vietnam, 2023, pp. 61-65 </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2310.19898">arXiv:2310.19898</a> <span> [<a href="https://arxiv.org/pdf/2310.19898">pdf</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> <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"> MIST: Medical Image Segmentation Transformer with Convolutional Attention Mixing (CAM) Decoder </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Rahman%2C+M+M">Md Motiur Rahman</a>, <a href="/search/?searchtype=author&query=Shokouhmand%2C+S">Shiva Shokouhmand</a>, <a href="/search/?searchtype=author&query=Bhatt%2C+S">Smriti Bhatt</a>, <a href="/search/?searchtype=author&query=Faezipour%2C+M">Miad Faezipour</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="2310.19898v1-abstract-short" style="display: inline;"> One of the common and promising deep learning approaches used for medical image segmentation is transformers, as they can capture long-range dependencies among the pixels by utilizing self-attention. Despite being successful in medical image segmentation, transformers face limitations in capturing local contexts of pixels in multimodal dimensions. We propose a Medical Image Segmentation Transforme… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2310.19898v1-abstract-full').style.display = 'inline'; document.getElementById('2310.19898v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2310.19898v1-abstract-full" style="display: none;"> One of the common and promising deep learning approaches used for medical image segmentation is transformers, as they can capture long-range dependencies among the pixels by utilizing self-attention. Despite being successful in medical image segmentation, transformers face limitations in capturing local contexts of pixels in multimodal dimensions. We propose a Medical Image Segmentation Transformer (MIST) incorporating a novel Convolutional Attention Mixing (CAM) decoder to address this issue. MIST has two parts: a pre-trained multi-axis vision transformer (MaxViT) is used as an encoder, and the encoded feature representation is passed through the CAM decoder for segmenting the images. In the CAM decoder, an attention-mixer combining multi-head self-attention, spatial attention, and squeeze and excitation attention modules is introduced to capture long-range dependencies in all spatial dimensions. Moreover, to enhance spatial information gain, deep and shallow convolutions are used for feature extraction and receptive field expansion, respectively. The integration of low-level and high-level features from different network stages is enabled by skip connections, allowing MIST to suppress unnecessary information. The experiments show that our MIST transformer with CAM decoder outperforms the state-of-the-art models specifically designed for medical image segmentation on the ACDC and Synapse datasets. Our results also demonstrate that adding the CAM decoder with a hierarchical transformer improves segmentation performance significantly. Our model with data and code is publicly available on GitHub. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2310.19898v1-abstract-full').style.display = 'none'; document.getElementById('2310.19898v1-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 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">10 pages, 2 figures, 3 tables, accepted for publication in WACV 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/2306.05739">arXiv:2306.05739</a> <span> [<a href="https://arxiv.org/pdf/2306.05739">pdf</a>, <a href="https://arxiv.org/format/2306.05739">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Populations and Evolution">q-bio.PE</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</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/10.1093/gbe/evad213">10.1093/gbe/evad213 <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </div> </div> <p class="title is-5 mathjax"> Leaping through tree space: continuous phylogenetic inference for rooted and unrooted trees </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Penn%2C+M+J">Matthew J Penn</a>, <a href="/search/?searchtype=author&query=Scheidwasser%2C+N">Neil Scheidwasser</a>, <a href="/search/?searchtype=author&query=Penn%2C+J">Joseph Penn</a>, <a href="/search/?searchtype=author&query=Donnelly%2C+C+A">Christl A Donnelly</a>, <a href="/search/?searchtype=author&query=Duch%C3%AAne%2C+D+A">David A Duch锚ne</a>, <a href="/search/?searchtype=author&query=Bhatt%2C+S">Samir Bhatt</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="2306.05739v4-abstract-short" style="display: inline;"> Phylogenetics is now fundamental in life sciences, providing insights into the earliest branches of life and the origins and spread of epidemics. However, finding suitable phylogenies from the vast space of possible trees remains challenging. To address this problem, for the first time, we perform both tree exploration and inference in a continuous space where the computation of gradients is possi… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2306.05739v4-abstract-full').style.display = 'inline'; document.getElementById('2306.05739v4-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2306.05739v4-abstract-full" style="display: none;"> Phylogenetics is now fundamental in life sciences, providing insights into the earliest branches of life and the origins and spread of epidemics. However, finding suitable phylogenies from the vast space of possible trees remains challenging. To address this problem, for the first time, we perform both tree exploration and inference in a continuous space where the computation of gradients is possible. This continuous relaxation allows for major leaps across tree space in both rooted and unrooted trees, and is less susceptible to convergence to local minima. Our approach outperforms the current best methods for inference on unrooted trees and, in simulation, accurately infers the tree and root in ultrametric cases. The approach is effective in cases of empirical data with negligible amounts of data, which we demonstrate on the phylogeny of jawed vertebrates. Indeed, only a few genes with an ultrametric signal were generally sufficient for resolving the major lineages of vertebrates. Optimisation is possible via automatic differentiation and our method presents an effective way forwards for exploring the most difficult, data-deficient phylogenetic questions. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2306.05739v4-abstract-full').style.display = 'none'; document.getElementById('2306.05739v4-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, 2024; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 9 June, 2023; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> June 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">26 pages, 3 figures, 2 tables, 20 supplementary pages, 3 supplementary figures</span> </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Journal ref:</span> Genome Biol. Evol. 15 (2023) evad213 </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2305.19779">arXiv:2305.19779</a> <span> [<a href="https://arxiv.org/pdf/2305.19779">pdf</a>, <a href="https://arxiv.org/format/2305.19779">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="Machine Learning">stat.ML</span> </div> </div> <p class="title is-5 mathjax"> Deep learning and MCMC with aggVAE for shifting administrative boundaries: mapping malaria prevalence in Kenya </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Semenova%2C+E">Elizaveta Semenova</a>, <a href="/search/?searchtype=author&query=Mishra%2C+S">Swapnil Mishra</a>, <a href="/search/?searchtype=author&query=Bhatt%2C+S">Samir Bhatt</a>, <a href="/search/?searchtype=author&query=Flaxman%2C+S">Seth Flaxman</a>, <a href="/search/?searchtype=author&query=Unwin%2C+H+J+T">H Juliette T Unwin</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="2305.19779v3-abstract-short" style="display: inline;"> Model-based disease mapping remains a fundamental policy-informing tool in the fields of public health and disease surveillance. Hierarchical Bayesian models have emerged as the state-of-the-art approach for disease mapping since they are able to both capture structure in the data and robustly characterise uncertainty. When working with areal data, e.g.~aggregates at the administrative unit level… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2305.19779v3-abstract-full').style.display = 'inline'; document.getElementById('2305.19779v3-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2305.19779v3-abstract-full" style="display: none;"> Model-based disease mapping remains a fundamental policy-informing tool in the fields of public health and disease surveillance. Hierarchical Bayesian models have emerged as the state-of-the-art approach for disease mapping since they are able to both capture structure in the data and robustly characterise uncertainty. When working with areal data, e.g.~aggregates at the administrative unit level such as district or province, current models rely on the adjacency structure of areal units to account for spatial correlations and perform shrinkage. The goal of disease surveillance systems is to track disease outcomes over time. This task is especially challenging in crisis situations which often lead to redrawn administrative boundaries, meaning that data collected before and after the crisis are no longer directly comparable. Moreover, the adjacency-based approach ignores the continuous nature of spatial processes and cannot solve the change-of-support problem, i.e.~when estimates are required to be produced at different administrative levels or levels of aggregation. We present a novel, practical, and easy to implement solution to solve these problems relying on a methodology combining deep generative modelling and fully Bayesian inference: we build on the recently proposed PriorVAE method able to encode spatial priors over small areas with variational autoencoders by encoding aggregates over administrative units. We map malaria prevalence in Kenya, a country in which administrative boundaries changed in 2010. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2305.19779v3-abstract-full').style.display = 'none'; document.getElementById('2305.19779v3-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> 15 July, 2023; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 31 May, 2023; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> May 2023. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2305.06020">arXiv:2305.06020</a> <span> [<a href="https://arxiv.org/pdf/2305.06020">pdf</a>, <a href="https://arxiv.org/format/2305.06020">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Mesoscale and Nanoscale Physics">cond-mat.mes-hall</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Materials Science">cond-mat.mtrl-sci</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/10.1103/PhysRevB.108.085435">10.1103/PhysRevB.108.085435 <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </div> </div> <p class="title is-5 mathjax"> Giant spin Nernst effect in a two-dimensional antiferromagnet due to magnetoelastic coupling-induced gaps and interband transitions between magnon-like bands </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=To%2C+D+-">D. -Q. To</a>, <a href="/search/?searchtype=author&query=Ameyaw%2C+C+Y">C. Y. Ameyaw</a>, <a href="/search/?searchtype=author&query=Suresh%2C+A">A. Suresh</a>, <a href="/search/?searchtype=author&query=Bhatt%2C+S">S. Bhatt</a>, <a href="/search/?searchtype=author&query=Ku%2C+M+J+H">M. J. H. Ku</a>, <a href="/search/?searchtype=author&query=Jungfleisch%2C+M+B">M. B. Jungfleisch</a>, <a href="/search/?searchtype=author&query=Xiao%2C+J+Q">J. Q. Xiao</a>, <a href="/search/?searchtype=author&query=Zide%2C+J+M+O">J. M. O. Zide</a>, <a href="/search/?searchtype=author&query=Nikolic%2C+B+K">B. K. Nikolic</a>, <a href="/search/?searchtype=author&query=Doty%2C+M+F">M. F. Doty</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="2305.06020v2-abstract-short" style="display: inline;"> We analyze theoretically the origin of the spin Nernst and thermal Hall effects in FePS3 as a realization of two-dimensional antiferromagnet (2D AFM). We find that a strong magnetoelastic coupling, hybridizing magnetic excitation (magnon) and elastic excitation (phonon), combined with time-reversal-symmetry-breaking, results in a Berry curvature hotspots in the region of anticrossing between the t… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2305.06020v2-abstract-full').style.display = 'inline'; document.getElementById('2305.06020v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2305.06020v2-abstract-full" style="display: none;"> We analyze theoretically the origin of the spin Nernst and thermal Hall effects in FePS3 as a realization of two-dimensional antiferromagnet (2D AFM). We find that a strong magnetoelastic coupling, hybridizing magnetic excitation (magnon) and elastic excitation (phonon), combined with time-reversal-symmetry-breaking, results in a Berry curvature hotspots in the region of anticrossing between the two distinct hybridized bands. Furthermore, large spin Berry curvature emerges due to interband transitions between two magnon-like bands, where a small energy gap is induced by magnetoelastic coupling between such bands that are energetically distant from anticrossing of hybridized bands. These nonzero Berry curvatures generate topological transverse transport (i.e., the thermal Hall effect) of hybrid excitations, dubbed magnon-polaron, as well as of spin (i.e., the spin Nernst effect) carried by them, in response to applied longitudinal temperature gradient. We investigate the dependence of the spin Nernst and thermal Hall conductivities on the applied magnetic field and temperature, unveiling very large spin Nernst conductivity even at zero magnetic field. Our results suggest FePS3 AFM, which is already available in 2D form experimentally, as a promising platform to explore the topological transport of the magnon-polaron quasiparticles at THz frequencies. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2305.06020v2-abstract-full').style.display = 'none'; document.getElementById('2305.06020v2-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> 16 May, 2023; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 10 May, 2023; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> May 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">11 pages, 7 figures; Supplemental Materials is available from https://mrsec.udel.edu/publications/</span> </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Journal ref:</span> Physical Review B, 2023 </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2305.00933">arXiv:2305.00933</a> <span> [<a href="https://arxiv.org/pdf/2305.00933">pdf</a>, <a href="https://arxiv.org/format/2305.00933">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Applications">stat.AP</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="Populations and Evolution">q-bio.PE</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Machine Learning">stat.ML</span> </div> </div> <p class="title is-5 mathjax"> A comparison of short-term probabilistic forecasts for the incidence of COVID-19 using mechanistic and statistical time series models </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Banholzer%2C+N">Nicolas Banholzer</a>, <a href="/search/?searchtype=author&query=Mellan%2C+T">Thomas Mellan</a>, <a href="/search/?searchtype=author&query=Unwin%2C+H+J+T">H Juliette T Unwin</a>, <a href="/search/?searchtype=author&query=Feuerriegel%2C+S">Stefan Feuerriegel</a>, <a href="/search/?searchtype=author&query=Mishra%2C+S">Swapnil Mishra</a>, <a href="/search/?searchtype=author&query=Bhatt%2C+S">Samir Bhatt</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="2305.00933v1-abstract-short" style="display: inline;"> Short-term forecasts of infectious disease spread are a critical component in risk evaluation and public health decision making. While different models for short-term forecasting have been developed, open questions about their relative performance remain. Here, we compare short-term probabilistic forecasts of popular mechanistic models based on the renewal equation with forecasts of statistical ti… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2305.00933v1-abstract-full').style.display = 'inline'; document.getElementById('2305.00933v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2305.00933v1-abstract-full" style="display: none;"> Short-term forecasts of infectious disease spread are a critical component in risk evaluation and public health decision making. While different models for short-term forecasting have been developed, open questions about their relative performance remain. Here, we compare short-term probabilistic forecasts of popular mechanistic models based on the renewal equation with forecasts of statistical time series models. Our empirical comparison is based on data of the daily incidence of COVID-19 across six large US states over the first pandemic year. We find that, on average, probabilistic forecasts from statistical time series models are overall at least as accurate as forecasts from mechanistic models. Moreover, statistical time series models better capture volatility. Our findings suggest that domain knowledge, which is integrated into mechanistic models by making assumptions about disease dynamics, does not improve short-term forecasts of disease incidence. We note, however, that forecasting is often only one of many objectives and thus mechanistic models remain important, for example, to model the impact of vaccines or the emergence of new variants. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2305.00933v1-abstract-full').style.display = 'none'; document.getElementById('2305.00933v1-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, 2023; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> May 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">37 pages, 4 Figures, 9 Appendix figures</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2304.12693">arXiv:2304.12693</a> <span> [<a href="https://arxiv.org/pdf/2304.12693">pdf</a>, <a href="https://arxiv.org/format/2304.12693">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Populations and Evolution">q-bio.PE</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="Quantitative Methods">q-bio.QM</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/10.1093/sysbio/syae030">10.1093/sysbio/syae030 <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </div> </div> <p class="title is-5 mathjax"> Phylo2Vec: a vector representation for binary trees </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Penn%2C+M+J">Matthew J Penn</a>, <a href="/search/?searchtype=author&query=Scheidwasser%2C+N">Neil Scheidwasser</a>, <a href="/search/?searchtype=author&query=Khurana%2C+M+P">Mark P Khurana</a>, <a href="/search/?searchtype=author&query=Duch%C3%AAne%2C+D+A">David A Duch锚ne</a>, <a href="/search/?searchtype=author&query=Donnelly%2C+C+A">Christl A Donnelly</a>, <a href="/search/?searchtype=author&query=Bhatt%2C+S">Samir Bhatt</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="2304.12693v4-abstract-short" style="display: inline;"> Binary phylogenetic trees inferred from biological data are central to understanding the shared history among evolutionary units. However, inferring the placement of latent nodes in a tree is computationally expensive. State-of-the-art methods rely on carefully designed heuristics for tree search, using different data structures for easy manipulation (e.g., classes in object-oriented programming l… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2304.12693v4-abstract-full').style.display = 'inline'; document.getElementById('2304.12693v4-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2304.12693v4-abstract-full" style="display: none;"> Binary phylogenetic trees inferred from biological data are central to understanding the shared history among evolutionary units. However, inferring the placement of latent nodes in a tree is computationally expensive. State-of-the-art methods rely on carefully designed heuristics for tree search, using different data structures for easy manipulation (e.g., classes in object-oriented programming languages) and readable representation of trees (e.g., Newick-format strings). Here, we present Phylo2Vec, a parsimonious encoding for phylogenetic trees that serves as a unified approach for both manipulating and representing phylogenetic trees. Phylo2Vec maps any binary tree with $n$ leaves to a unique integer vector of length $n-1$. The advantages of Phylo2Vec are fourfold: i) fast tree sampling, (ii) compressed tree representation compared to a Newick string, iii) quick and unambiguous verification if two binary trees are identical topologically, and iv) systematic ability to traverse tree space in very large or small jumps. As a proof of concept, we use Phylo2Vec for maximum likelihood inference on five real-world datasets and show that a simple hill-climbing-based optimisation scheme can efficiently traverse the vastness of tree space from a random to an optimal tree. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2304.12693v4-abstract-full').style.display = 'none'; document.getElementById('2304.12693v4-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> 4 November, 2024; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 25 April, 2023; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> April 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">38 pages, 9 figures, 1 table, 2 supplementary figures</span> </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Journal ref:</span> Systematic Biology, 2024, syae030 </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2304.04307">arXiv:2304.04307</a> <span> [<a href="https://arxiv.org/pdf/2304.04307">pdf</a>, <a href="https://arxiv.org/format/2304.04307">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> </div> </div> <p class="title is-5 mathjax"> PriorCVAE: scalable MCMC parameter inference with Bayesian deep generative modelling </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Semenova%2C+E">Elizaveta Semenova</a>, <a href="/search/?searchtype=author&query=Verma%2C+P">Prakhar Verma</a>, <a href="/search/?searchtype=author&query=Cairney-Leeming%2C+M">Max Cairney-Leeming</a>, <a href="/search/?searchtype=author&query=Solin%2C+A">Arno Solin</a>, <a href="/search/?searchtype=author&query=Bhatt%2C+S">Samir Bhatt</a>, <a href="/search/?searchtype=author&query=Flaxman%2C+S">Seth Flaxman</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="2304.04307v3-abstract-short" style="display: inline;"> Recent advances have shown that GP priors, or their finite realisations, can be encoded using deep generative models such as variational autoencoders (VAEs). These learned generators can serve as drop-in replacements for the original priors during MCMC inference. While this approach enables efficient inference, it loses information about the hyperparameters of the original models, and consequently… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2304.04307v3-abstract-full').style.display = 'inline'; document.getElementById('2304.04307v3-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2304.04307v3-abstract-full" style="display: none;"> Recent advances have shown that GP priors, or their finite realisations, can be encoded using deep generative models such as variational autoencoders (VAEs). These learned generators can serve as drop-in replacements for the original priors during MCMC inference. While this approach enables efficient inference, it loses information about the hyperparameters of the original models, and consequently makes inference over hyperparameters impossible and the learned priors indistinct. To overcome this limitation, we condition the VAE on stochastic process hyperparameters. This allows the joint encoding of hyperparameters with GP realizations and their subsequent estimation during inference. Further, we demonstrate that our proposed method, PriorCVAE, is agnostic to the nature of the models which it approximates, and can be used, for instance, to encode solutions of ODEs. It provides a practical tool for approximate inference and shows potential in real-life spatial and spatiotemporal applications. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2304.04307v3-abstract-full').style.display = 'none'; document.getElementById('2304.04307v3-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 November, 2023; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 9 April, 2023; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> April 2023. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2303.14461">arXiv:2303.14461</a> <span> [<a href="https://arxiv.org/pdf/2303.14461">pdf</a>, <a href="https://arxiv.org/format/2303.14461">other</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> </div> </div> <p class="title is-5 mathjax"> Indian Language Summarization using Pretrained Sequence-to-Sequence Models </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Urlana%2C+A">Ashok Urlana</a>, <a href="/search/?searchtype=author&query=Bhatt%2C+S+M">Sahil Manoj Bhatt</a>, <a href="/search/?searchtype=author&query=Surange%2C+N">Nirmal Surange</a>, <a href="/search/?searchtype=author&query=Shrivastava%2C+M">Manish Shrivastava</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="2303.14461v1-abstract-short" style="display: inline;"> The ILSUM shared task focuses on text summarization for two major Indian languages- Hindi and Gujarati, along with English. In this task, we experiment with various pretrained sequence-to-sequence models to find out the best model for each of the languages. We present a detailed overview of the models and our approaches in this paper. We secure the first rank across all three sub-tasks (English, H… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2303.14461v1-abstract-full').style.display = 'inline'; document.getElementById('2303.14461v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2303.14461v1-abstract-full" style="display: none;"> The ILSUM shared task focuses on text summarization for two major Indian languages- Hindi and Gujarati, along with English. In this task, we experiment with various pretrained sequence-to-sequence models to find out the best model for each of the languages. We present a detailed overview of the models and our approaches in this paper. We secure the first rank across all three sub-tasks (English, Hindi and Gujarati). This paper also extensively analyzes the impact of k-fold cross-validation while experimenting with limited data size, and we also perform various experiments with a combination of the original and a filtered version of the data to determine the efficacy of the pretrained models. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2303.14461v1-abstract-full').style.display = 'none'; document.getElementById('2303.14461v1-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> 25 March, 2023; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> March 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">Accepted at FIRE-2022, Indian Language Summarization (ILSUM) track</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2302.01960">arXiv:2302.01960</a> <span> [<a href="https://arxiv.org/pdf/2302.01960">pdf</a>, <a href="https://arxiv.org/format/2302.01960">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Atmospheric and Oceanic Physics">physics.ao-ph</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Chaotic Dynamics">nlin.CD</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Geophysics">physics.geo-ph</span> </div> </div> <p class="title is-5 mathjax"> Dynamic Arctic weather variability and connectivity </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Meng%2C+J">Jun Meng</a>, <a href="/search/?searchtype=author&query=Fan%2C+J">Jingfang Fan</a>, <a href="/search/?searchtype=author&query=Bhatt%2C+U+S">Uma S Bhatt</a>, <a href="/search/?searchtype=author&query=Kurths%2C+J">J眉rgen Kurths</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="2302.01960v1-abstract-short" style="display: inline;"> The rapidly shrinking Arctic sea ice is changing weather patterns and disrupting the balance of nature. Dynamics of Arctic weather variability (WV) plays a crucial role in weather forecasting and is closely related to extreme weather events. Yet, assessing and quantifying the WV for both local Arctic regions and its planetary impacts under anthropogenic climate change is still unknown. Here, we de… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2302.01960v1-abstract-full').style.display = 'inline'; document.getElementById('2302.01960v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2302.01960v1-abstract-full" style="display: none;"> The rapidly shrinking Arctic sea ice is changing weather patterns and disrupting the balance of nature. Dynamics of Arctic weather variability (WV) plays a crucial role in weather forecasting and is closely related to extreme weather events. Yet, assessing and quantifying the WV for both local Arctic regions and its planetary impacts under anthropogenic climate change is still unknown. Here, we develop a complexity-based approach to systematically evaluate and analyze the dynamic behaviour of WV. We reveal that the WV within and around the Arctic is statistically correlated to the Arctic Oscillation at the intraseasonal time scale. We further find that the variability of the daily Arctic sea ice is increasing due to its dramatic decline under a warming climate. Unstable Arctic weather conditions can disturb regional weather patterns through atmospheric teleconnection pathways, resulting in higher risk to human activities and greater weather forecast uncertainty. A multivariate climate network analysis reveals the existence of such teleconnections and implies a positive feedback loop between the Arctic and global weather instabilities. This enhances the mechanistic understanding of the influence of Arctic amplification on mid-latitude severe weather. Our framework provides a fresh perspective on the linkage of complexity science, WV and the Arctic. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2302.01960v1-abstract-full').style.display = 'none'; document.getElementById('2302.01960v1-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> 3 February, 2023; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> February 2023. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2212.10910">arXiv:2212.10910</a> <span> [<a href="https://arxiv.org/pdf/2212.10910">pdf</a>, <a href="https://arxiv.org/format/2212.10910">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Materials Science">cond-mat.mtrl-sci</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/10.1103/PhysRevB.107.235413">10.1103/PhysRevB.107.235413 <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </div> </div> <p class="title is-5 mathjax"> Accurate computation of chemical contrast in field ion microscopy </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Bhatt%2C+S">Shalini Bhatt</a>, <a href="/search/?searchtype=author&query=Katnagallu%2C+S">Shyam Katnagallu</a>, <a href="/search/?searchtype=author&query=Neugebauer%2C+J">J枚rg Neugebauer</a>, <a href="/search/?searchtype=author&query=Freysoldt%2C+C">Christoph Freysoldt</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="2212.10910v2-abstract-short" style="display: inline;"> We present a computational approach to simulate local contrast observed in Field Ion Microscopy (FIM). It is based on density-functional theory utilizing the Tersoff-Hamann approach as done in Scanning Tunneling Microscopy (STM). A key requirement is the highly accurate computation of the surface states' wave-function tails. To refine the Kohn-Sham states from standard iterative global solvers we… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2212.10910v2-abstract-full').style.display = 'inline'; document.getElementById('2212.10910v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2212.10910v2-abstract-full" style="display: none;"> We present a computational approach to simulate local contrast observed in Field Ion Microscopy (FIM). It is based on density-functional theory utilizing the Tersoff-Hamann approach as done in Scanning Tunneling Microscopy (STM). A key requirement is the highly accurate computation of the surface states' wave-function tails. To refine the Kohn-Sham states from standard iterative global solvers we introduce and discuss the EXtrapolation of Tails via Reverse integration Algorithm (EXTRA). The decaying tails are obtained by reverse integration (from outside in) using a Numerov-like algorithm. The starting conditions are then iteratively adapted to match the values of plane-wave Kohn-Sham wave functions close to the surface. We demonstrate the performance of the proposed algorithm by analysing and showing the chemical contrast for Ta at Ni surface. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2212.10910v2-abstract-full').style.display = 'none'; document.getElementById('2212.10910v2-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, 2023; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 21 December, 2022; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> December 2022. </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">submitted to Phys. Rev. B</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2212.02656">arXiv:2212.02656</a> <span> [<a href="https://arxiv.org/pdf/2212.02656">pdf</a>, <a href="https://arxiv.org/format/2212.02656">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Mesoscale and Nanoscale Physics">cond-mat.mes-hall</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Other Condensed Matter">cond-mat.other</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/10.1103/PhysRevMaterials.7.045201">10.1103/PhysRevMaterials.7.045201 <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </div> </div> <p class="title is-5 mathjax"> Phonon-mediated strong coupling between a three-dimensional topological insulator and a two-dimensional antiferromagnetic material </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=To%2C+D+Q">D. Quang To</a>, <a href="/search/?searchtype=author&query=Wu%2C+W">Weipeng Wu</a>, <a href="/search/?searchtype=author&query=Bhatt%2C+S">Subhash Bhatt</a>, <a href="/search/?searchtype=author&query=Liu%2C+Y">Yongchen Liu</a>, <a href="/search/?searchtype=author&query=Janotti%2C+A">Anderson Janotti</a>, <a href="/search/?searchtype=author&query=Zide%2C+J+M+O">Joshua M. O. Zide</a>, <a href="/search/?searchtype=author&query=Ku%2C+M+J+H">Mark J. H. Ku</a>, <a href="/search/?searchtype=author&query=Xiao%2C+J+Q">John Q. Xiao</a>, <a href="/search/?searchtype=author&query=Jungfleisch%2C+M+B">M. Benjamin Jungfleisch</a>, <a href="/search/?searchtype=author&query=Law%2C+S">Stephanie Law</a>, <a href="/search/?searchtype=author&query=Doty%2C+M+F">Matthew F. Doty</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="2212.02656v1-abstract-short" style="display: inline;"> Van der Waals antiferromagnetic and topological insulator materials provide powerful platforms for modern optical, electronic, and spintronic devices applications. The interaction between an antiferromagnet (AFM) and a topological insulator (TI), if sufficiently strong, could offer emergent hybrid material properties that enable new functionality exceeding what is possible in any individual materi… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2212.02656v1-abstract-full').style.display = 'inline'; document.getElementById('2212.02656v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2212.02656v1-abstract-full" style="display: none;"> Van der Waals antiferromagnetic and topological insulator materials provide powerful platforms for modern optical, electronic, and spintronic devices applications. The interaction between an antiferromagnet (AFM) and a topological insulator (TI), if sufficiently strong, could offer emergent hybrid material properties that enable new functionality exceeding what is possible in any individual material constituent. In this work, we study strong coupling between THz excitations in a three dimensional (3D) topological insulator and a quasi-two dimensional (2D) antiferromagnetic material resulting in a new hybridized mode, namely a surface Dirac plasmon-phonon-magnon polariton. We find that the interaction between a surface Dirac plasmon polariton in the 3D TI and a magnon polariton in the 2D AFM is mediated by the phonon coupling in the 3D TI material. The coupling of phonons with an electromagnetic wave propagating in the 3D TI enhances the permittivity of the TI thin film in a way that results in a strong correlation between the dispersion of Dirac plasmon polaritons on the surfaces of the TI with the thickness of the TI. As a result, the dispersion of surface Dirac plasmon polaritons in the TI can be tuned toward resonance with the magnon polariton in the AFM material by varying the TI's thickness, thereby enhancing the strength of the coupling between the excitations in the two materials. The strength of this coupling, which results in the surface Dirac plasmon-phonon-magnon polariton, can be parameterized by the amplitude of the avoided-crossing splitting between the two polariton branches at the magnon resonance frequency... <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2212.02656v1-abstract-full').style.display = 'none'; document.getElementById('2212.02656v1-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, 2022; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> December 2022. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Report number:</span> 7, 045201 </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Journal ref:</span> Phys. Rev. Materials 2023 </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2212.00529">arXiv:2212.00529</a> <span> [<a href="https://arxiv.org/pdf/2212.00529">pdf</a>, <a href="https://arxiv.org/format/2212.00529">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="General Relativity and Quantum Cosmology">gr-qc</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Cosmology and Nongalactic Astrophysics">astro-ph.CO</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="High Energy Physics - Phenomenology">hep-ph</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="High Energy Physics - Theory">hep-th</span> </div> </div> <p class="title is-5 mathjax"> Numerical simulations of inflationary dynamics: slow roll and beyond </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Bhatt%2C+S+S">Siddharth S. Bhatt</a>, <a href="/search/?searchtype=author&query=Mishra%2C+S+S">Swagat S. Mishra</a>, <a href="/search/?searchtype=author&query=Basak%2C+S">Soumen Basak</a>, <a href="/search/?searchtype=author&query=Sahoo%2C+S+N">Surya N. Sahoo</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="2212.00529v3-abstract-short" style="display: inline;"> Numerical simulations of the inflationary dynamics are presented here for a single canonical scalar field minimally coupled to gravity. We spell out the basic equations governing the inflationary dynamics in terms of cosmic time $t$ and define a set of dimensionless variables convenient for numerical analysis. We then provide a link to our simple numerical Python code on GitHub that can be used to… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2212.00529v3-abstract-full').style.display = 'inline'; document.getElementById('2212.00529v3-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2212.00529v3-abstract-full" style="display: none;"> Numerical simulations of the inflationary dynamics are presented here for a single canonical scalar field minimally coupled to gravity. We spell out the basic equations governing the inflationary dynamics in terms of cosmic time $t$ and define a set of dimensionless variables convenient for numerical analysis. We then provide a link to our simple numerical Python code on GitHub that can be used to simulate the background dynamics as well as the evolution of linear perturbations during inflation. The code computes both scalar and tensor power spectra for a given inflaton potential $V(蠁)$. We discuss a concrete algorithm to use the code for various purposes, especially for computing the enhanced scalar power spectrum in the context of Primordial Black Holes and scalar-induced Gravitational Waves. We also compare the efficiency of different variables used in the literature to compute the scalar fluctuations. We intend to extend the framework to simulate the dynamics of a number of different quantities, including the computation of scalar-induced second-order tensor power spectrum in the near future. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2212.00529v3-abstract-full').style.display = 'none'; document.getElementById('2212.00529v3-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, 2024; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 1 December, 2022; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> December 2022. </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">40 pages, 19 figures, GitHub link to codes provided in the paper, Revised version: Typos corrected, Additional text and references</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2211.11206">arXiv:2211.11206</a> <span> [<a href="https://arxiv.org/pdf/2211.11206">pdf</a>, <a href="https://arxiv.org/format/2211.11206">other</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="Computers and Society">cs.CY</span> </div> </div> <p class="title is-5 mathjax"> Cultural Re-contextualization of Fairness Research in Language Technologies in India </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Bhatt%2C+S">Shaily Bhatt</a>, <a href="/search/?searchtype=author&query=Dev%2C+S">Sunipa Dev</a>, <a href="/search/?searchtype=author&query=Talukdar%2C+P">Partha Talukdar</a>, <a href="/search/?searchtype=author&query=Dave%2C+S">Shachi Dave</a>, <a href="/search/?searchtype=author&query=Prabhakaran%2C+V">Vinodkumar Prabhakaran</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="2211.11206v1-abstract-short" style="display: inline;"> Recent research has revealed undesirable biases in NLP data and models. However, these efforts largely focus on social disparities in the West, and are not directly portable to other geo-cultural contexts. In this position paper, we outline a holistic research agenda to re-contextualize NLP fairness research for the Indian context, accounting for Indian societal context, bridging technological gap… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2211.11206v1-abstract-full').style.display = 'inline'; document.getElementById('2211.11206v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2211.11206v1-abstract-full" style="display: none;"> Recent research has revealed undesirable biases in NLP data and models. However, these efforts largely focus on social disparities in the West, and are not directly portable to other geo-cultural contexts. In this position paper, we outline a holistic research agenda to re-contextualize NLP fairness research for the Indian context, accounting for Indian societal context, bridging technological gaps in capability and resources, and adapting to Indian cultural values. We also summarize findings from an empirical study on various social biases along different axes of disparities relevant to India, demonstrating their prevalence in corpora and models. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2211.11206v1-abstract-full').style.display = 'none'; document.getElementById('2211.11206v1-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> 21 November, 2022; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> November 2022. </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 NeurIPS Workshop on "Cultures in AI/AI in Culture". This is a non-archival short version, to cite please refer to our complete paper: arXiv:2209.12226</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2211.05230">arXiv:2211.05230</a> <span> [<a href="https://arxiv.org/pdf/2211.05230">pdf</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Medical Physics">physics.med-ph</span> </div> </div> <p class="title is-5 mathjax"> Multimodal Optical Techniques in Pre-Clinical Evaluation of Oral Cancer: Fluorescence Imaging and Spectroscopic Devices </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Thapa%2C+P">Pramila Thapa</a>, <a href="/search/?searchtype=author&query=Singh%2C+V">Veena Singh</a>, <a href="/search/?searchtype=author&query=Kumar%2C+V">Virendra Kumar</a>, <a href="/search/?searchtype=author&query=Bhatt%2C+S">Sunil Bhatt</a>, <a href="/search/?searchtype=author&query=Maurya%2C+K">Kiran Maurya</a>, <a href="/search/?searchtype=author&query=Nayyar%2C+V">Vivek Nayyar</a>, <a href="/search/?searchtype=author&query=Jot%2C+K">Kiran Jot</a>, <a href="/search/?searchtype=author&query=Mishra%2C+D">Deepika Mishra</a>, <a href="/search/?searchtype=author&query=Shrivastava%2C+A">Anurag Shrivastava</a>, <a href="/search/?searchtype=author&query=Mehta%2C+D+S">Dalip Singh Mehta</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="2211.05230v1-abstract-short" style="display: inline;"> Objective: Survival rate of oral squamous cell carcinoma (OSCC) patients is very poor and can be improved using highly sensitive, specific and accurate techniques. Autofluorescence and fluorescence techniques are very sensitive and useful in cancer screening. Furthermore, fluorescence spectroscopy is directly linked with molecular levels of human tissue and can be used as quantitative tool for can… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2211.05230v1-abstract-full').style.display = 'inline'; document.getElementById('2211.05230v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2211.05230v1-abstract-full" style="display: none;"> Objective: Survival rate of oral squamous cell carcinoma (OSCC) patients is very poor and can be improved using highly sensitive, specific and accurate techniques. Autofluorescence and fluorescence techniques are very sensitive and useful in cancer screening. Furthermore, fluorescence spectroscopy is directly linked with molecular levels of human tissue and can be used as quantitative tool for cancer detection. Materials and Methods: Here, we report development of multi-modal autofluorescence and fluorescence imaging and spectroscopic (MAF-IS) smartphone-based systems for fast and real time oral cancer screening. Fluorescence-autofluorescence images and spectroscopic datasets shows significant change in oral cancer and normal tissue in terms of fluorescence-intensity, spectral-shape, and red-shift respectively. Results: In this study, total 68 samples (33 cancerous and 35 normal) of 18 OSCC patients and 13 patients of precancerous tissues (dysplasia and fibrosis) are screened. Main remarkable finding of the study is presence of three peaks viz ~636 nm, ~680 nm and ~705 nm with decrease in intensity around 450 nm ~ 520 nm in OSCC in case of autofluorescence. Another finding is red shift in fluorescence spectroscopy of OSCC, dysplasia and fibrosis from normal which is 6.59+-4.54 nm, 3+-4.78 nm and 1.5+-0.5 nm respectively and can be used as cancer marker in real-time screening. Finally, support vector machine (SVM) based classifier is applied for classification of OSCC tissue from normal tissue. The average sensitivity, specificity and accuracy are found as 88.89% ,100 %, and 95%, respectively. Conclusion: Autofluorescence and fluorescence-based imaging and spectroscopy is used for pre-clinical screening of different oral lesions. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2211.05230v1-abstract-full').style.display = 'none'; document.getElementById('2211.05230v1-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> 3 November, 2022; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> November 2022. </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">12 pages, 7 figures</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2211.00054">arXiv:2211.00054</a> <span> [<a href="https://arxiv.org/pdf/2211.00054">pdf</a>, <a href="https://arxiv.org/format/2211.00054">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Applications">stat.AP</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="Populations and Evolution">q-bio.PE</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/10.48550/arXiv.2209.01487">10.48550/arXiv.2209.01487 <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </div> </div> <p class="title is-5 mathjax"> The interaction of transmission intensity, mortality, and the economy: a retrospective analysis of the COVID-19 pandemic </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Morgenstern%2C+C">Christian Morgenstern</a>, <a href="/search/?searchtype=author&query=Laydon%2C+D+J">Daniel J. Laydon</a>, <a href="/search/?searchtype=author&query=Whittaker%2C+C">Charles Whittaker</a>, <a href="/search/?searchtype=author&query=Mishra%2C+S">Swapnil Mishra</a>, <a href="/search/?searchtype=author&query=Haw%2C+D">David Haw</a>, <a href="/search/?searchtype=author&query=Bhatt%2C+S">Samir Bhatt</a>, <a href="/search/?searchtype=author&query=Ferguson%2C+N+M">Neil M. Ferguson</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="2211.00054v2-abstract-short" style="display: inline;"> The COVID-19 pandemic has caused over 6.4 million registered deaths to date and has had a profound impact on economic activity. Here, we study the interaction of transmission, mortality, and the economy during the SARS-CoV-2 pandemic from January 2020 to December 2022 across 25 European countries. We adopt a Bayesian Mixed Effects model with auto-regressive terms. We find that increases in disease… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2211.00054v2-abstract-full').style.display = 'inline'; document.getElementById('2211.00054v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2211.00054v2-abstract-full" style="display: none;"> The COVID-19 pandemic has caused over 6.4 million registered deaths to date and has had a profound impact on economic activity. Here, we study the interaction of transmission, mortality, and the economy during the SARS-CoV-2 pandemic from January 2020 to December 2022 across 25 European countries. We adopt a Bayesian Mixed Effects model with auto-regressive terms. We find that increases in disease transmission intensity decreases Gross domestic product (GDP) and increases daily excess deaths, with a longer lasting impact on excess deaths in comparison to GDP, which recovers more rapidly. Broadly, our results reinforce the intuitive phenomenon that significant economic activity arises from diverse person-to-person interactions. We report on the effectiveness of non-pharmaceutical interventions (NPIs) on transmission intensity, excess deaths, and changes in GDP, and resulting implications for policy makers. Our results highlight a complex cost-benefit trade off from individual NPIs. For example, banning international travel increases GDP and reduces excess deaths. We consider country random effects and their associations with excess changes in GDP and excess deaths. For example, more developed countries in Europe typically had more cautious approaches to the COVID-19 pandemic, prioritising healthcare, and excess deaths over economic performance. Long term economic impairments are not fully captured by our model, as well as long term disease effects (Long Covid). Our results highlight that the impact of disease on a country is complex and multifaceted, and simple heuristic conclusions to extract the best outcome from the economy and disease burden are challenging. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2211.00054v2-abstract-full').style.display = 'none'; document.getElementById('2211.00054v2-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> 15 February, 2023; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 31 October, 2022; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> November 2022. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2210.14221">arXiv:2210.14221</a> <span> [<a href="https://arxiv.org/pdf/2210.14221">pdf</a>, <a href="https://arxiv.org/format/2210.14221">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Populations and Evolution">q-bio.PE</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Probability">math.PR</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Statistics Theory">math.ST</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Applications">stat.AP</span> </div> </div> <p class="title is-5 mathjax"> Intrinsic Randomness in Epidemic Modelling Beyond Statistical Uncertainty </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Penn%2C+M+J">Matthew J. Penn</a>, <a href="/search/?searchtype=author&query=Laydon%2C+D+J">Daniel J. Laydon</a>, <a href="/search/?searchtype=author&query=Penn%2C+J">Joseph Penn</a>, <a href="/search/?searchtype=author&query=Whittaker%2C+C">Charles Whittaker</a>, <a href="/search/?searchtype=author&query=Morgenstern%2C+C">Christian Morgenstern</a>, <a href="/search/?searchtype=author&query=Ratmann%2C+O">Oliver Ratmann</a>, <a href="/search/?searchtype=author&query=Mishra%2C+S">Swapnil Mishra</a>, <a href="/search/?searchtype=author&query=Pakkanen%2C+M+S">Mikko S. Pakkanen</a>, <a href="/search/?searchtype=author&query=Donnelly%2C+C+A">Christl A. Donnelly</a>, <a href="/search/?searchtype=author&query=Bhatt%2C+S">Samir Bhatt</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="2210.14221v2-abstract-short" style="display: inline;"> Uncertainty can be classified as either aleatoric (intrinsic randomness) or epistemic (imperfect knowledge of parameters). The majority of frameworks assessing infectious disease risk consider only epistemic uncertainty. We only ever observe a single epidemic, and therefore cannot empirically determine aleatoric uncertainty. Here, we characterise both epistemic and aleatoric uncertainty using a ti… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2210.14221v2-abstract-full').style.display = 'inline'; document.getElementById('2210.14221v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2210.14221v2-abstract-full" style="display: none;"> Uncertainty can be classified as either aleatoric (intrinsic randomness) or epistemic (imperfect knowledge of parameters). The majority of frameworks assessing infectious disease risk consider only epistemic uncertainty. We only ever observe a single epidemic, and therefore cannot empirically determine aleatoric uncertainty. Here, we characterise both epistemic and aleatoric uncertainty using a time-varying general branching process. Our framework explicitly decomposes aleatoric variance into mechanistic components, quantifying the contribution to uncertainty produced by each factor in the epidemic process, and how these contributions vary over time. The aleatoric variance of an outbreak is itself a renewal equation where past variance affects future variance. We find that, superspreading is not necessary for substantial uncertainty, and profound variation in outbreak size can occur even without overdispersion in the offspring distribution (i.e. the distribution of the number of secondary infections an infected person produces). Aleatoric forecasting uncertainty grows dynamically and rapidly, and so forecasting using only epistemic uncertainty is a significant underestimate. Therefore, failure to account for aleatoric uncertainty will ensure that policymakers are misled about the substantially higher true extent of potential risk. We demonstrate our method, and the extent to which potential risk is underestimated, using two historical examples. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2210.14221v2-abstract-full').style.display = 'none'; document.getElementById('2210.14221v2-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, 2023; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 25 October, 2022; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> October 2022. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2210.11844">arXiv:2210.11844</a> <span> [<a href="https://arxiv.org/pdf/2210.11844">pdf</a>, <a href="https://arxiv.org/format/2210.11844">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> </div> </div> <p class="title is-5 mathjax"> Cox-Hawkes: doubly stochastic spatiotemporal Poisson processes </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Miscouridou%2C+X">Xenia Miscouridou</a>, <a href="/search/?searchtype=author&query=Bhatt%2C+S">Samir Bhatt</a>, <a href="/search/?searchtype=author&query=Mohler%2C+G">George Mohler</a>, <a href="/search/?searchtype=author&query=Flaxman%2C+S">Seth Flaxman</a>, <a href="/search/?searchtype=author&query=Mishra%2C+S">Swapnil Mishra</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="2210.11844v1-abstract-short" style="display: inline;"> Hawkes processes are point process models that have been used to capture self-excitatory behavior in social interactions, neural activity, earthquakes and viral epidemics. They can model the occurrence of the times and locations of events. Here we develop a new class of spatiotemporal Hawkes processes that can capture both triggering and clustering behavior and we provide an efficient method for p… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2210.11844v1-abstract-full').style.display = 'inline'; document.getElementById('2210.11844v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2210.11844v1-abstract-full" style="display: none;"> Hawkes processes are point process models that have been used to capture self-excitatory behavior in social interactions, neural activity, earthquakes and viral epidemics. They can model the occurrence of the times and locations of events. Here we develop a new class of spatiotemporal Hawkes processes that can capture both triggering and clustering behavior and we provide an efficient method for performing inference. We use a log-Gaussian Cox process (LGCP) as prior for the background rate of the Hawkes process which gives arbitrary flexibility to capture a wide range of underlying background effects (for infectious diseases these are called endemic effects). The Hawkes process and LGCP are computationally expensive due to the former having a likelihood with quadratic complexity in the number of observations and the latter involving inversion of the precision matrix which is cubic in observations. Here we propose a novel approach to perform MCMC sampling for our Hawkes process with LGCP background, using pre-trained Gaussian Process generators which provide direct and cheap access to samples during inference. We show the efficacy and flexibility of our approach in experiments on simulated data and use our methods to uncover the trends in a dataset of reported crimes in the US. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2210.11844v1-abstract-full').style.display = 'none'; document.getElementById('2210.11844v1-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> 21 October, 2022; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> October 2022. </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 Figures, 27 pages without references, 3 pages of references</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2210.11358">arXiv:2210.11358</a> <span> [<a href="https://arxiv.org/pdf/2210.11358">pdf</a>, <a href="https://arxiv.org/format/2210.11358">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Applications">stat.AP</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/10.1371/journal.pcbi.1011191">10.1371/journal.pcbi.1011191 <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </div> </div> <p class="title is-5 mathjax"> Estimating fine age structure and time trends in human contact patterns from coarse contact data: the Bayesian rate consistency model </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Dan%2C+S">Shozen Dan</a>, <a href="/search/?searchtype=author&query=Chen%2C+Y">Yu Chen</a>, <a href="/search/?searchtype=author&query=Chen%2C+Y">Yining Chen</a>, <a href="/search/?searchtype=author&query=Monod%2C+M">Melodie Monod</a>, <a href="/search/?searchtype=author&query=Jaeger%2C+V+K">Veronika K. Jaeger</a>, <a href="/search/?searchtype=author&query=Bhatt%2C+S">Samir Bhatt</a>, <a href="/search/?searchtype=author&query=Karch%2C+A">Andre Karch</a>, <a href="/search/?searchtype=author&query=Ratmann%2C+O">Oliver Ratmann</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="2210.11358v1-abstract-short" style="display: inline;"> Since the emergence of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), many contact surveys have been conducted to measure changes in human interactions in the face of the pandemic and non-pharmaceutical interventions. These surveys were typically conducted longitudinally, using protocols that differ from those used in the pre-pandemic era. We present a model-based statistical approa… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2210.11358v1-abstract-full').style.display = 'inline'; document.getElementById('2210.11358v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2210.11358v1-abstract-full" style="display: none;"> Since the emergence of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), many contact surveys have been conducted to measure changes in human interactions in the face of the pandemic and non-pharmaceutical interventions. These surveys were typically conducted longitudinally, using protocols that differ from those used in the pre-pandemic era. We present a model-based statistical approach that can reconstruct contact patterns at 1-year resolution even when the age of the contacts is reported coarsely by 5 or 10-year age bands. This innovation is rooted in population-level consistency constraints in how contacts between groups must add up, which prompts us to call the approach presented here the Bayesian rate consistency model. The model incorporates computationally efficient Hilbert Space Gaussian process priors to infer the dynamics in age- and gender-structured social contacts and is designed to adjust for reporting fatigue in longitudinal surveys. We demonstrate on simulations the ability to reconstruct contact patterns by gender and 1-year age interval from coarse data with adequate accuracy and within a fully Bayesian framework to quantify uncertainty. We investigate the patterns of social contact data collected in Germany from April to June 2020 across five longitudinal survey waves. We reconstruct the fine age structure in social contacts during the early stages of the pandemic and demonstrate that social contacts rebounded in a structured, non-homogeneous manner. We also show that by July 2020, social contact intensities remained well below pre-pandemic values despite a considerable easing of non-pharmaceutical interventions. This model-based inference approach is open access, computationally tractable enabling full Bayesian uncertainty quantification, and readily applicable to contemporary survey data as long as the exact age of survey participants is reported. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2210.11358v1-abstract-full').style.display = 'none'; document.getElementById('2210.11358v1-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> 20 October, 2022; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> October 2022. </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">39 pages, 16 figures</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2209.12226">arXiv:2209.12226</a> <span> [<a href="https://arxiv.org/pdf/2209.12226">pdf</a>, <a href="https://arxiv.org/format/2209.12226">other</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="Computers and Society">cs.CY</span> </div> </div> <p class="title is-5 mathjax"> Re-contextualizing Fairness in NLP: The Case of India </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Bhatt%2C+S">Shaily Bhatt</a>, <a href="/search/?searchtype=author&query=Dev%2C+S">Sunipa Dev</a>, <a href="/search/?searchtype=author&query=Talukdar%2C+P">Partha Talukdar</a>, <a href="/search/?searchtype=author&query=Dave%2C+S">Shachi Dave</a>, <a href="/search/?searchtype=author&query=Prabhakaran%2C+V">Vinodkumar Prabhakaran</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="2209.12226v5-abstract-short" style="display: inline;"> Recent research has revealed undesirable biases in NLP data and models. However, these efforts focus on social disparities in West, and are not directly portable to other geo-cultural contexts. In this paper, we focus on NLP fair-ness in the context of India. We start with a brief account of the prominent axes of social disparities in India. We build resources for fairness evaluation in the Indian… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2209.12226v5-abstract-full').style.display = 'inline'; document.getElementById('2209.12226v5-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2209.12226v5-abstract-full" style="display: none;"> Recent research has revealed undesirable biases in NLP data and models. However, these efforts focus on social disparities in West, and are not directly portable to other geo-cultural contexts. In this paper, we focus on NLP fair-ness in the context of India. We start with a brief account of the prominent axes of social disparities in India. We build resources for fairness evaluation in the Indian context and use them to demonstrate prediction biases along some of the axes. We then delve deeper into social stereotypes for Region andReligion, demonstrating its prevalence in corpora and models. Finally, we outline a holistic research agenda to re-contextualize NLP fairness research for the Indian context, ac-counting for Indian societal context, bridging technological gaps in NLP capabilities and re-sources, and adapting to Indian cultural values. While we focus on India, this framework can be generalized to other geo-cultural contexts. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2209.12226v5-abstract-full').style.display = 'none'; document.getElementById('2209.12226v5-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> 21 November, 2022; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 25 September, 2022; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> September 2022. </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 AACL-IJCNLP 2022</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2209.09617">arXiv:2209.09617</a> <span> [<a href="https://arxiv.org/pdf/2209.09617">pdf</a>, <a href="https://arxiv.org/format/2209.09617">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="Probability">math.PR</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Populations and Evolution">q-bio.PE</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Machine Learning">stat.ML</span> </div> </div> <p class="title is-5 mathjax"> Seq2Seq Surrogates of Epidemic Models to Facilitate Bayesian Inference </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Charles%2C+G">Giovanni Charles</a>, <a href="/search/?searchtype=author&query=Wolock%2C+T+M">Timothy M. Wolock</a>, <a href="/search/?searchtype=author&query=Winskill%2C+P">Peter Winskill</a>, <a href="/search/?searchtype=author&query=Ghani%2C+A">Azra Ghani</a>, <a href="/search/?searchtype=author&query=Bhatt%2C+S">Samir Bhatt</a>, <a href="/search/?searchtype=author&query=Flaxman%2C+S">Seth Flaxman</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="2209.09617v2-abstract-short" style="display: inline;"> Epidemic models are powerful tools in understanding infectious disease. However, as they increase in size and complexity, they can quickly become computationally intractable. Recent progress in modelling methodology has shown that surrogate models can be used to emulate complex epidemic models with a high-dimensional parameter space. We show that deep sequence-to-sequence (seq2seq) models can serv… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2209.09617v2-abstract-full').style.display = 'inline'; document.getElementById('2209.09617v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2209.09617v2-abstract-full" style="display: none;"> Epidemic models are powerful tools in understanding infectious disease. However, as they increase in size and complexity, they can quickly become computationally intractable. Recent progress in modelling methodology has shown that surrogate models can be used to emulate complex epidemic models with a high-dimensional parameter space. We show that deep sequence-to-sequence (seq2seq) models can serve as accurate surrogates for complex epidemic models with sequence based model parameters, effectively replicating seasonal and long-term transmission dynamics. Once trained, our surrogate can predict scenarios a several thousand times faster than the original model, making them ideal for policy exploration. We demonstrate that replacing a traditional epidemic model with a learned simulator facilitates robust Bayesian inference. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2209.09617v2-abstract-full').style.display = 'none'; document.getElementById('2209.09617v2-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 March, 2023; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 20 September, 2022; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> September 2022. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2208.11639">arXiv:2208.11639</a> <span> [<a href="https://arxiv.org/pdf/2208.11639">pdf</a>, <a href="https://arxiv.org/ps/2208.11639">ps</a>, <a href="https://arxiv.org/format/2208.11639">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="Computer Science and Game Theory">cs.GT</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Multiagent Systems">cs.MA</span> </div> </div> <p class="title is-5 mathjax"> Oracle-free Reinforcement Learning in Mean-Field Games along a Single Sample Path </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Zaman%2C+M+A+u">Muhammad Aneeq uz Zaman</a>, <a href="/search/?searchtype=author&query=Koppel%2C+A">Alec Koppel</a>, <a href="/search/?searchtype=author&query=Bhatt%2C+S">Sujay Bhatt</a>, <a href="/search/?searchtype=author&query=Ba%C5%9Far%2C+T">Tamer Ba艧ar</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="2208.11639v3-abstract-short" style="display: inline;"> We consider online reinforcement learning in Mean-Field Games (MFGs). Unlike traditional approaches, we alleviate the need for a mean-field oracle by developing an algorithm that approximates the Mean-Field Equilibrium (MFE) using the single sample path of the generic agent. We call this {\it Sandbox Learning}, as it can be used as a warm-start for any agent learning in a multi-agent non-cooperati… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2208.11639v3-abstract-full').style.display = 'inline'; document.getElementById('2208.11639v3-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2208.11639v3-abstract-full" style="display: none;"> We consider online reinforcement learning in Mean-Field Games (MFGs). Unlike traditional approaches, we alleviate the need for a mean-field oracle by developing an algorithm that approximates the Mean-Field Equilibrium (MFE) using the single sample path of the generic agent. We call this {\it Sandbox Learning}, as it can be used as a warm-start for any agent learning in a multi-agent non-cooperative setting. We adopt a two time-scale approach in which an online fixed-point recursion for the mean-field operates on a slower time-scale, in tandem with a control policy update on a faster time-scale for the generic agent. Given that the underlying Markov Decision Process (MDP) of the agent is communicating, we provide finite sample convergence guarantees in terms of convergence of the mean-field and control policy to the mean-field equilibrium. The sample complexity of the Sandbox learning algorithm is $\tilde{\mathcal{O}}(蔚^{-4})$ where $蔚$ is the MFE approximation error. This is similar to works which assume access to oracle. Finally, we empirically demonstrate the effectiveness of the sandbox learning algorithm in diverse scenarios, including those where the MDP does not necessarily have a single communicating class. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2208.11639v3-abstract-full').style.display = 'none'; document.getElementById('2208.11639v3-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> 11 April, 2023; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 24 August, 2022; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> August 2022. </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 for publication in AISTATS 2023</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2208.04153">arXiv:2208.04153</a> <span> [<a href="https://arxiv.org/pdf/2208.04153">pdf</a>, <a href="https://arxiv.org/format/2208.04153">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"> [Reproducibility Report] Path Planning using Neural A* Search </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Bhatt%2C+S">Shreya Bhatt</a>, <a href="/search/?searchtype=author&query=Jain%2C+A">Aayush Jain</a>, <a href="/search/?searchtype=author&query=Maheshwari%2C+P">Parv Maheshwari</a>, <a href="/search/?searchtype=author&query=Jha%2C+A">Animesh Jha</a>, <a href="/search/?searchtype=author&query=Chakravarty%2C+D">Debashish Chakravarty</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="2208.04153v1-abstract-short" style="display: inline;"> The following paper is a reproducibility report for "Path Planning using Neural A* Search" published in ICML2 2021 as part of the ML Reproducibility Challenge 2021. The original paper proposes the Neural A* planner, and claims it achieves an optimal balance between the reduction of node expansions and path accuracy. We verify this claim by reimplementing the model in a different framework and repr… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2208.04153v1-abstract-full').style.display = 'inline'; document.getElementById('2208.04153v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2208.04153v1-abstract-full" style="display: none;"> The following paper is a reproducibility report for "Path Planning using Neural A* Search" published in ICML2 2021 as part of the ML Reproducibility Challenge 2021. The original paper proposes the Neural A* planner, and claims it achieves an optimal balance between the reduction of node expansions and path accuracy. We verify this claim by reimplementing the model in a different framework and reproduce the data published in the original paper. We have also provided a code-flow diagram to aid comprehension of the code structure. As extensions to the original paper, we explore the effects of (1) generalizing the model by training it on a shuffled dataset, (2) introducing dropout, (3) implementing empirically chosen hyperparameters as trainable parameters in the model, (4) altering the network model to Generative Adversarial Networks (GANs) to introduce stochasticity, (5) modifying the encoder from Unet to Unet++, (6) incorporating cost maps obtained from the Neural A* module in other variations of A* search. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2208.04153v1-abstract-full').style.display = 'none'; document.getElementById('2208.04153v1-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> 16 July, 2022; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> August 2022. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2208.03185">arXiv:2208.03185</a> <span> [<a href="https://arxiv.org/pdf/2208.03185">pdf</a>, <a href="https://arxiv.org/ps/2208.03185">ps</a>, <a href="https://arxiv.org/format/2208.03185">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Statistics Theory">math.ST</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="Machine Learning">stat.ML</span> </div> </div> <p class="title is-5 mathjax"> Catoni-style Confidence Sequences under Infinite Variance </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Bhatt%2C+S">Sujay Bhatt</a>, <a href="/search/?searchtype=author&query=Fang%2C+G">Guanhua Fang</a>, <a href="/search/?searchtype=author&query=Li%2C+P">Ping Li</a>, <a href="/search/?searchtype=author&query=Samorodnitsky%2C+G">Gennady Samorodnitsky</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="2208.03185v1-abstract-short" style="display: inline;"> In this paper, we provide an extension of confidence sequences for settings where the variance of the data-generating distribution does not exist or is infinite. Confidence sequences furnish confidence intervals that are valid at arbitrary data-dependent stopping times, naturally having a wide range of applications. We first establish a lower bound for the width of the Catoni-style confidence sequ… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2208.03185v1-abstract-full').style.display = 'inline'; document.getElementById('2208.03185v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2208.03185v1-abstract-full" style="display: none;"> In this paper, we provide an extension of confidence sequences for settings where the variance of the data-generating distribution does not exist or is infinite. Confidence sequences furnish confidence intervals that are valid at arbitrary data-dependent stopping times, naturally having a wide range of applications. We first establish a lower bound for the width of the Catoni-style confidence sequences for the finite variance case to highlight the looseness of the existing results. Next, we derive tight Catoni-style confidence sequences for data distributions having a relaxed bounded~$p^{th}-$moment, where~$p \in (1,2]$, and strengthen the results for the finite variance case of~$p =2$. The derived results are shown to better than confidence sequences obtained using Dubins-Savage inequality. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2208.03185v1-abstract-full').style.display = 'none'; document.getElementById('2208.03185v1-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 August, 2022; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> August 2022. </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">10 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/2208.00385">arXiv:2208.00385</a> <span> [<a href="https://arxiv.org/pdf/2208.00385">pdf</a>, <a href="https://arxiv.org/format/2208.00385">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> <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"> Evaluating Table Structure Recognition: A New Perspective </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Kumar%2C+T">Tarun Kumar</a>, <a href="/search/?searchtype=author&query=Bhatt%2C+H+S">Himanshu Sharad Bhatt</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="2208.00385v1-abstract-short" style="display: inline;"> Existing metrics used to evaluate table structure recognition algorithms have shortcomings with regard to capturing text and empty cells alignment. In this paper, we build on prior work and propose a new metric - TEDS based IOU similarity (TEDS (IOU)) for table structure recognition which uses bounding boxes instead of text while simultaneously being robust against the above disadvantages. We demo… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2208.00385v1-abstract-full').style.display = 'inline'; document.getElementById('2208.00385v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2208.00385v1-abstract-full" style="display: none;"> Existing metrics used to evaluate table structure recognition algorithms have shortcomings with regard to capturing text and empty cells alignment. In this paper, we build on prior work and propose a new metric - TEDS based IOU similarity (TEDS (IOU)) for table structure recognition which uses bounding boxes instead of text while simultaneously being robust against the above disadvantages. We demonstrate the effectiveness of our metric against previous metrics through various examples. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2208.00385v1-abstract-full').style.display = 'none'; document.getElementById('2208.00385v1-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 July, 2022; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> August 2022. </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">4 pages, 2 figures, 1 table, 15th IAPR International Workshop on Document Analysis System (DAS 2022)</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2206.11214">arXiv:2206.11214</a> <span> [<a href="https://arxiv.org/pdf/2206.11214">pdf</a>, <a href="https://arxiv.org/format/2206.11214">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Methodology">stat.ME</span> </div> </div> <p class="title is-5 mathjax"> Offline Change Detection under Contamination </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Bhatt%2C+S">Sujay Bhatt</a>, <a href="/search/?searchtype=author&query=Fang%2C+G">Guanhua Fang</a>, <a href="/search/?searchtype=author&query=Li%2C+P">Ping Li</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="2206.11214v2-abstract-short" style="display: inline;"> In this work, we propose a non-parametric and robust change detection algorithm to detect multiple change points in time series data under contamination. The contamination model is sufficiently general, in that, the most common model used in the context of change detection -- Huber contamination model -- is a special case. Also, the contamination model is oblivious and arbitrary. The change detect… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2206.11214v2-abstract-full').style.display = 'inline'; document.getElementById('2206.11214v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2206.11214v2-abstract-full" style="display: none;"> In this work, we propose a non-parametric and robust change detection algorithm to detect multiple change points in time series data under contamination. The contamination model is sufficiently general, in that, the most common model used in the context of change detection -- Huber contamination model -- is a special case. Also, the contamination model is oblivious and arbitrary. The change detection algorithm is designed for the offline setting, where the objective is to detect changes when all data are received. We only make weak moment assumptions on the inliers (uncorrupted data) to handle a large class of distributions. The robust scan statistic in the algorithm is fashioned using mean estimators based on influence functions. We establish the consistency of the estimated change point indexes as the number of samples increases, and provide empirical evidence to support the consistency results. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2206.11214v2-abstract-full').style.display = 'none'; document.getElementById('2206.11214v2-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, 2022; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 22 June, 2022; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> June 2022. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2206.07562">arXiv:2206.07562</a> <span> [<a href="https://arxiv.org/pdf/2206.07562">pdf</a>, <a href="https://arxiv.org/format/2206.07562">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="Machine Learning">stat.ML</span> </div> </div> <p class="title is-5 mathjax"> Federated Learning with Uncertainty via Distilled Predictive Distributions </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Bhatt%2C+S">Shrey Bhatt</a>, <a href="/search/?searchtype=author&query=Gupta%2C+A">Aishwarya Gupta</a>, <a href="/search/?searchtype=author&query=Rai%2C+P">Piyush Rai</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="2206.07562v2-abstract-short" style="display: inline;"> Most existing federated learning methods are unable to estimate model/predictive uncertainty since the client models are trained using the standard loss function minimization approach which ignores such uncertainties. In many situations, however, especially in limited data settings, it is beneficial to take into account the uncertainty in the model parameters at each client as it leads to more acc… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2206.07562v2-abstract-full').style.display = 'inline'; document.getElementById('2206.07562v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2206.07562v2-abstract-full" style="display: none;"> Most existing federated learning methods are unable to estimate model/predictive uncertainty since the client models are trained using the standard loss function minimization approach which ignores such uncertainties. In many situations, however, especially in limited data settings, it is beneficial to take into account the uncertainty in the model parameters at each client as it leads to more accurate predictions and also because reliable estimates of uncertainty can be used for tasks, such as out-of-distribution (OOD) detection, and sequential decision-making tasks, such as active learning. We present a framework for federated learning with uncertainty where, in each round, each client infers the posterior distribution over its parameters as well as the posterior predictive distribution (PPD), distills the PPD into a single deep neural network, and sends this network to the server. Unlike some of the recent Bayesian approaches to federated learning, our approach does not require sending the whole posterior distribution of the parameters from each client to the server but only the PPD in the distilled form as a deep neural network. In addition, when making predictions at test time, it does not require computationally expensive Monte-Carlo averaging over the posterior distribution because our approach always maintains the PPD in the form of a single deep neural network. Moreover, our approach does not make any restrictive assumptions, such as the form of the clients' posterior distributions, or of their PPDs. We evaluate our approach on classification in federated setting, as well as active learning and OOD detection in federated settings, on which our approach outperforms various existing federated learning baselines. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2206.07562v2-abstract-full').style.display = 'none'; document.getElementById('2206.07562v2-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 October, 2023; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 15 June, 2022; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> June 2022. </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 ACML 2023; 21 pages(14 pages of main content, 2 pages of references, and 5 pages of supplementary content)</span> </p> </li> </ol> <nav class="pagination is-small is-centered breathe-horizontal" role="navigation" aria-label="pagination"> <a href="" class="pagination-previous is-invisible">Previous </a> <a href="/search/?searchtype=author&query=Bhatt%2C+S&start=50" class="pagination-next" >Next </a> <ul class="pagination-list"> <li> <a href="/search/?searchtype=author&query=Bhatt%2C+S&start=0" class="pagination-link is-current" aria-label="Goto page 1">1 </a> </li> <li> <a href="/search/?searchtype=author&query=Bhatt%2C+S&start=50" class="pagination-link " aria-label="Page 2" aria-current="page">2 </a> </li> <li> <a href="/search/?searchtype=author&query=Bhatt%2C+S&start=100" class="pagination-link " aria-label="Page 3" aria-current="page">3 </a> </li> </ul> </nav> <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> 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