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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=Acharya%2C+A&start=50" class="pagination-next" >Next </a> <ul class="pagination-list"> <li> <a href="/search/?searchtype=author&query=Acharya%2C+A&start=0" class="pagination-link is-current" aria-label="Goto page 1">1 </a> </li> <li> <a href="/search/?searchtype=author&query=Acharya%2C+A&start=50" class="pagination-link " aria-label="Page 2" aria-current="page">2 </a> </li> <li> <a href="/search/?searchtype=author&query=Acharya%2C+A&start=100" class="pagination-link " aria-label="Page 3" aria-current="page">3 </a> </li> <li> <a href="/search/?searchtype=author&query=Acharya%2C+A&start=150" class="pagination-link " aria-label="Page 4" aria-current="page">4 </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/2411.17635">arXiv:2411.17635</a> <span> [<a href="https://arxiv.org/pdf/2411.17635">pdf</a>, <a href="https://arxiv.org/format/2411.17635">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Mathematical Physics">math-ph</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Analysis of PDEs">math.AP</span> </div> </div> <p class="title is-5 mathjax"> Variational Dual Solutions of Chern-Simons Theory </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Acharya%2C+A">Amit Acharya</a>, <a href="/search/?searchtype=author&query=Ginster%2C+J">Janusz Ginster</a>, <a href="/search/?searchtype=author&query=Sengupta%2C+A+N">Ambar N. Sengupta</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="2411.17635v1-abstract-short" style="display: inline;"> A scheme for generating weakly lower semi-continuous action functionals corresponding to the Euler-Lagrange equations of Chern-Simons theory is described. Coercivity is deduced for such a functional in appropriate function spaces to prove the existence of a minimizer, which constitutes a solution to the Euler-Lagrange equations of Chern-Simons theory in a relaxed sense. A geometric analysis is als… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2411.17635v1-abstract-full').style.display = 'inline'; document.getElementById('2411.17635v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2411.17635v1-abstract-full" style="display: none;"> A scheme for generating weakly lower semi-continuous action functionals corresponding to the Euler-Lagrange equations of Chern-Simons theory is described. Coercivity is deduced for such a functional in appropriate function spaces to prove the existence of a minimizer, which constitutes a solution to the Euler-Lagrange equations of Chern-Simons theory in a relaxed sense. A geometric analysis is also made, especially for the gauge group SU(2), relating connection forms on the bundle to corresponding forms in the dual scheme. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2411.17635v1-abstract-full').style.display = 'none'; document.getElementById('2411.17635v1-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/2411.03542">arXiv:2411.03542</a> <span> [<a href="https://arxiv.org/pdf/2411.03542">pdf</a>, <a href="https://arxiv.org/format/2411.03542">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"> Exploring the Benefits of Domain-Pretraining of Generative Large Language Models for Chemistry </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Acharya%2C+A">Anurag Acharya</a>, <a href="/search/?searchtype=author&query=Sharma%2C+S">Shivam Sharma</a>, <a href="/search/?searchtype=author&query=Cosbey%2C+R">Robin Cosbey</a>, <a href="/search/?searchtype=author&query=Subramanian%2C+M">Megha Subramanian</a>, <a href="/search/?searchtype=author&query=Howland%2C+S">Scott Howland</a>, <a href="/search/?searchtype=author&query=Glenski%2C+M">Maria Glenski</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="2411.03542v1-abstract-short" style="display: inline;"> A proliferation of Large Language Models (the GPT series, BLOOM, LLaMA, and more) are driving forward novel development of multipurpose AI for a variety of tasks, particularly natural language processing (NLP) tasks. These models demonstrate strong performance on a range of tasks; however, there has been evidence of brittleness when applied to more niche or narrow domains where hallucinations or f… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2411.03542v1-abstract-full').style.display = 'inline'; document.getElementById('2411.03542v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2411.03542v1-abstract-full" style="display: none;"> A proliferation of Large Language Models (the GPT series, BLOOM, LLaMA, and more) are driving forward novel development of multipurpose AI for a variety of tasks, particularly natural language processing (NLP) tasks. These models demonstrate strong performance on a range of tasks; however, there has been evidence of brittleness when applied to more niche or narrow domains where hallucinations or fluent but incorrect responses reduce performance. Given the complex nature of scientific domains, it is prudent to investigate the trade-offs of leveraging off-the-shelf versus more targeted foundation models for scientific domains. In this work, we examine the benefits of in-domain pre-training for a given scientific domain, chemistry, and compare these to open-source, off-the-shelf models with zero-shot and few-shot prompting. Our results show that not only do in-domain base models perform reasonably well on in-domain tasks in a zero-shot setting but that further adaptation using instruction fine-tuning yields impressive performance on chemistry-specific tasks such as named entity recognition and molecular formula generation. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2411.03542v1-abstract-full').style.display = 'none'; document.getElementById('2411.03542v1-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 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/2410.11620">arXiv:2410.11620</a> <span> [<a href="https://arxiv.org/pdf/2410.11620">pdf</a>, <a href="https://arxiv.org/format/2410.11620">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link 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="Astrophysics of Galaxies">astro-ph.GA</span> </div> </div> <p class="title is-5 mathjax"> Exploring the effect of different cosmologies on the Epoch of Reionization 21-cm signal with POLAR </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Acharya%2C+A">Anshuman Acharya</a>, <a href="/search/?searchtype=author&query=Ma%2C+Q">Qing-bo Ma</a>, <a href="/search/?searchtype=author&query=Giri%2C+S+K">Sambit K. Giri</a>, <a href="/search/?searchtype=author&query=Ciardi%2C+B">Benedetta Ciardi</a>, <a href="/search/?searchtype=author&query=Ghara%2C+R">Raghunath Ghara</a>, <a href="/search/?searchtype=author&query=Mellema%2C+G">Garrelt Mellema</a>, <a href="/search/?searchtype=author&query=Zaroubi%2C+S">Saleem Zaroubi</a>, <a href="/search/?searchtype=author&query=Hothi%2C+I">Ian Hothi</a>, <a href="/search/?searchtype=author&query=Iliev%2C+I+T">Ilian T. Iliev</a>, <a href="/search/?searchtype=author&query=Koopmans%2C+L+V+E">L茅on V. E. Koopmans</a>, <a href="/search/?searchtype=author&query=Bianco%2C+M">Michele Bianco</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="2410.11620v1-abstract-short" style="display: inline;"> A detection of the 21-cm signal power spectrum from the Epoch of Reionization is imminent, thanks to consistent advancements from telescopes such as LOFAR, MWA, and HERA, along with the development of SKA. In light of this progress, it is crucial to expand the parameter space of simulations used to infer astrophysical properties from this signal. In this work, we explore the role of cosmological p… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2410.11620v1-abstract-full').style.display = 'inline'; document.getElementById('2410.11620v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2410.11620v1-abstract-full" style="display: none;"> A detection of the 21-cm signal power spectrum from the Epoch of Reionization is imminent, thanks to consistent advancements from telescopes such as LOFAR, MWA, and HERA, along with the development of SKA. In light of this progress, it is crucial to expand the parameter space of simulations used to infer astrophysical properties from this signal. In this work, we explore the role of cosmological parameters such as the Hubble constant $H_0$ and the matter clustering amplitude $蟽_8$, whose values as provided by measurements at different redshifts are in tension. We run $N$-body simulations using GADGET-4, and post-process them with the reionization simulation code POLAR, that uses L-GALAXIES to include galaxy formation and evolution properties and GRIZZLY to execute 1-D radiative transfer of ionizing photons in the intergalactic medium (IGM). We compare our results with the latest JWST observations and explore which astrophysical properties for different cosmologies are necessary to match the observed UV luminosity functions at redshifts $z = 10$ and 9. Additionally, we explore the impact of these parameters on the observed 21-cm signal power spectrum, focusing on the redshifts within the range of LOFAR 21-cm signal observations ($z \approx 8.5-10$). Despite differences in cosmological and astrophysical parameters, the 21-cm power spectrum at these redshifts agrees with presently observed upper limits. This suggests the need for broader physical parameter spaces for inference modeling to account for all models that agree with observations. However, we also propose stronger constraining power by using a combination of galactic and IGM observables. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2410.11620v1-abstract-full').style.display = 'none'; document.getElementById('2410.11620v1-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 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">16 pages, 8 figures, 2 tables. Submitted to the Monthly Notices of the Royal Astronomical Society (MNRAS)</span> </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Report number:</span> NORDITA-2024-035 </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2409.17194">arXiv:2409.17194</a> <span> [<a href="https://arxiv.org/pdf/2409.17194">pdf</a>, <a href="https://arxiv.org/format/2409.17194">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"> A finite deformation theory of dislocation thermomechanics </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Lima-Chaves%2C+G+D">Gabriel Dante Lima-Chaves</a>, <a href="/search/?searchtype=author&query=Acharya%2C+A">Amit Acharya</a>, <a href="/search/?searchtype=author&query=Upadhyay%2C+M+V">Manas Vijay Upadhyay</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="2409.17194v1-abstract-short" style="display: inline;"> A geometrically nonlinear theory for field dislocation thermomechanics based entirely on measurable state variables is proposed. Instead of starting from an ordering-dependent multiplicative decomposition of the total deformation gradient tensor, the additive decomposition of the velocity gradient into elastic, plastic and thermal distortion rates is obtained as a natural consequence of the conser… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2409.17194v1-abstract-full').style.display = 'inline'; document.getElementById('2409.17194v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2409.17194v1-abstract-full" style="display: none;"> A geometrically nonlinear theory for field dislocation thermomechanics based entirely on measurable state variables is proposed. Instead of starting from an ordering-dependent multiplicative decomposition of the total deformation gradient tensor, the additive decomposition of the velocity gradient into elastic, plastic and thermal distortion rates is obtained as a natural consequence of the conservation of the Burgers vector. Based on this equation, the theory consistently captures the contribution of transient heterogeneous temperature fields on the evolution of the (polar) dislocation density. The governing equations of the model are obtained from the conservation of Burgers vector, mass, linear and angular momenta, and the First Law. The Second Law is used to deduce the thermodynamical driving forces for dislocation velocity. An evolution equation for temperature is obtained from the First Law and the Helmholtz free energy density, which is taken as a function of the following measurable quantities: elastic distortion, temperature and the dislocation density (the theory allows prescribing additional measurable quantities as internal state variables if needed). Furthermore, the theory allows one to compute the Taylor-Quinney factor, which is material and strain rate dependent. Accounting for the polar dislocation density as a state variable in the Helmholtz free energy of the system allows for temperature solutions in the form of dispersive waves with finite propagation speed, despite using Fourier's law of heat conduction as the constitutive assumption for the heat flux vector. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2409.17194v1-abstract-full').style.display = 'none'; document.getElementById('2409.17194v1-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 September, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> September 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">34 pages, 3 figures, preprint submitted to journal</span> </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">MSC Class:</span> 74C20; 74F05 <span class="has-text-black-bis has-text-weight-semibold">ACM Class:</span> J.2 </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2409.11779">arXiv:2409.11779</a> <span> [<a href="https://arxiv.org/pdf/2409.11779">pdf</a>, <a href="https://arxiv.org/format/2409.11779">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Computational Geometry">cs.CG</span> </div> </div> <p class="title is-5 mathjax"> Evolving Distributions Under Local Motion </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Acharya%2C+A">Aditya Acharya</a>, <a href="/search/?searchtype=author&query=Mount%2C+D+M">David M. Mount</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="2409.11779v1-abstract-short" style="display: inline;"> Geometric data sets arising in modern applications are often very large and change dynamically over time. A popular framework for dealing with such data sets is the evolving data framework, where a discrete structure continuously varies over time due to the unseen actions of an evolver, which makes small changes to the data. An algorithm probes the current state through an oracle, and the objectiv… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2409.11779v1-abstract-full').style.display = 'inline'; document.getElementById('2409.11779v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2409.11779v1-abstract-full" style="display: none;"> Geometric data sets arising in modern applications are often very large and change dynamically over time. A popular framework for dealing with such data sets is the evolving data framework, where a discrete structure continuously varies over time due to the unseen actions of an evolver, which makes small changes to the data. An algorithm probes the current state through an oracle, and the objective is to maintain a hypothesis of the data set's current state that is close to its actual state at all times. In this paper, we apply this framework to maintaining a set of $n$ point objects in motion in $d$-dimensional Euclidean space. To model the uncertainty in the object locations, both the ground truth and hypothesis are based on spatial probability distributions, and the distance between them is measured by the Kullback-Leibler divergence (relative entropy). We introduce a simple and intuitive motion model where with each time step, the distance that any object can move is a fraction of the distance to its nearest neighbor. We present an algorithm that, in steady state, guarantees a distance of $O(n)$ between the true and hypothesized placements. We also show that for any algorithm in this model, there is an evolver that can generate a distance of $惟(n)$, implying that our algorithm is asymptotically optimal. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2409.11779v1-abstract-full').style.display = 'none'; document.getElementById('2409.11779v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 18 September, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> September 2024. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">ACM Class:</span> F.2.2 </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2409.10301">arXiv:2409.10301</a> <span> [<a href="https://arxiv.org/pdf/2409.10301">pdf</a>, <a href="https://arxiv.org/format/2409.10301">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> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Data Analysis, Statistics and Probability">physics.data-an</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Portfolio Management">q-fin.PM</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Risk Management">q-fin.RM</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Quantum Physics">quant-ph</span> </div> </div> <p class="title is-5 mathjax"> Decomposition Pipeline for Large-Scale Portfolio Optimization with Applications to Near-Term Quantum Computing </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Acharya%2C+A">Atithi Acharya</a>, <a href="/search/?searchtype=author&query=Yalovetzky%2C+R">Romina Yalovetzky</a>, <a href="/search/?searchtype=author&query=Minssen%2C+P">Pierre Minssen</a>, <a href="/search/?searchtype=author&query=Chakrabarti%2C+S">Shouvanik Chakrabarti</a>, <a href="/search/?searchtype=author&query=Shaydulin%2C+R">Ruslan Shaydulin</a>, <a href="/search/?searchtype=author&query=Raymond%2C+R">Rudy Raymond</a>, <a href="/search/?searchtype=author&query=Sun%2C+Y">Yue Sun</a>, <a href="/search/?searchtype=author&query=Herman%2C+D">Dylan Herman</a>, <a href="/search/?searchtype=author&query=Andrist%2C+R+S">Ruben S. Andrist</a>, <a href="/search/?searchtype=author&query=Salton%2C+G">Grant Salton</a>, <a href="/search/?searchtype=author&query=Schuetz%2C+M+J+A">Martin J. A. Schuetz</a>, <a href="/search/?searchtype=author&query=Katzgraber%2C+H+G">Helmut G. Katzgraber</a>, <a href="/search/?searchtype=author&query=Pistoia%2C+M">Marco Pistoia</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="2409.10301v2-abstract-short" style="display: inline;"> Industrially relevant constrained optimization problems, such as portfolio optimization and portfolio rebalancing, are often intractable or difficult to solve exactly. In this work, we propose and benchmark a decomposition pipeline targeting portfolio optimization and rebalancing problems with constraints. The pipeline decomposes the optimization problem into constrained subproblems, which are the… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2409.10301v2-abstract-full').style.display = 'inline'; document.getElementById('2409.10301v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2409.10301v2-abstract-full" style="display: none;"> Industrially relevant constrained optimization problems, such as portfolio optimization and portfolio rebalancing, are often intractable or difficult to solve exactly. In this work, we propose and benchmark a decomposition pipeline targeting portfolio optimization and rebalancing problems with constraints. The pipeline decomposes the optimization problem into constrained subproblems, which are then solved separately and aggregated to give a final result. Our pipeline includes three main components: preprocessing of correlation matrices based on random matrix theory, modified spectral clustering based on Newman's algorithm, and risk rebalancing. Our empirical results show that our pipeline consistently decomposes real-world portfolio optimization problems into subproblems with a size reduction of approximately 80%. Since subproblems are then solved independently, our pipeline drastically reduces the total computation time for state-of-the-art solvers. Moreover, by decomposing large problems into several smaller subproblems, the pipeline enables the use of near-term quantum devices as solvers, providing a path toward practical utility of quantum computers in portfolio optimization. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2409.10301v2-abstract-full').style.display = 'none'; document.getElementById('2409.10301v2-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 November, 2024; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 16 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/2409.05401">arXiv:2409.05401</a> <span> [<a href="https://arxiv.org/pdf/2409.05401">pdf</a>, <a href="https://arxiv.org/format/2409.05401">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Information Retrieval">cs.IR</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Computation and Language">cs.CL</span> </div> </div> <p class="title is-5 mathjax"> Benchmarking and Building Zero-Shot Hindi Retrieval Model with Hindi-BEIR and NLLB-E5 </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Acharya%2C+A">Arkadeep Acharya</a>, <a href="/search/?searchtype=author&query=Murthy%2C+R">Rudra Murthy</a>, <a href="/search/?searchtype=author&query=Kumar%2C+V">Vishwajeet Kumar</a>, <a href="/search/?searchtype=author&query=Sen%2C+J">Jaydeep Sen</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="2409.05401v2-abstract-short" style="display: inline;"> Given the large number of Hindi speakers worldwide, there is a pressing need for robust and efficient information retrieval systems for Hindi. Despite ongoing research, comprehensive benchmarks for evaluating retrieval models in Hindi are lacking. To address this gap, we introduce the Hindi-BEIR benchmark, comprising 15 datasets across seven distinct tasks. We evaluate state-of-the-art multilingua… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2409.05401v2-abstract-full').style.display = 'inline'; document.getElementById('2409.05401v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2409.05401v2-abstract-full" style="display: none;"> Given the large number of Hindi speakers worldwide, there is a pressing need for robust and efficient information retrieval systems for Hindi. Despite ongoing research, comprehensive benchmarks for evaluating retrieval models in Hindi are lacking. To address this gap, we introduce the Hindi-BEIR benchmark, comprising 15 datasets across seven distinct tasks. We evaluate state-of-the-art multilingual retrieval models on the Hindi-BEIR benchmark, identifying task and domain-specific challenges that impact Hindi retrieval performance. Building on the insights from these results, we introduce NLLB-E5, a multilingual retrieval model that leverages a zero-shot approach to support Hindi without the need for Hindi training data. We believe our contributions, which include the release of the Hindi-BEIR benchmark and the NLLB-E5 model, will prove to be a valuable resource for researchers and promote advancements in multilingual retrieval models. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2409.05401v2-abstract-full').style.display = 'none'; document.getElementById('2409.05401v2-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 October, 2024; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 9 September, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> September 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">arXiv admin note: substantial text overlap with arXiv:2408.09437</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2409.04911">arXiv:2409.04911</a> <span> [<a href="https://arxiv.org/pdf/2409.04911">pdf</a>, <a href="https://arxiv.org/ps/2409.04911">ps</a>, <a href="https://arxiv.org/format/2409.04911">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Analysis of PDEs">math.AP</span> </div> </div> <p class="title is-5 mathjax"> Variational Dual Solutions for Incompressible Fluids </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Acharya%2C+A">Amit Acharya</a>, <a href="/search/?searchtype=author&query=Stroffolini%2C+B">Bianca Stroffolini</a>, <a href="/search/?searchtype=author&query=Zarnescu%2C+A">Arghir Zarnescu</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="2409.04911v1-abstract-short" style="display: inline;"> We consider a construction proposed in \cite{acharyaQAM} that builds on the notion of weak solutions for incompressible fluids to provide a scheme that generates variationally a certain type of dual solutions. If these dual solutions are regular enough one can use them to recover standard solutions. The scheme provides a generalisation of a construction of Y. Brenier for the Euler equations. We ri… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2409.04911v1-abstract-full').style.display = 'inline'; document.getElementById('2409.04911v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2409.04911v1-abstract-full" style="display: none;"> We consider a construction proposed in \cite{acharyaQAM} that builds on the notion of weak solutions for incompressible fluids to provide a scheme that generates variationally a certain type of dual solutions. If these dual solutions are regular enough one can use them to recover standard solutions. The scheme provides a generalisation of a construction of Y. Brenier for the Euler equations. We rigorously analyze the scheme, extending the work of Y.Brenier for Euler, and also provide an extension of it to the case of the Navier-Stokes equations. Furthermore we obtain the inviscid limit of Navier-Stokes to Euler as a $螕$-limit. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2409.04911v1-abstract-full').style.display = 'none'; document.getElementById('2409.04911v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 7 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/2408.11800">arXiv:2408.11800</a> <span> [<a href="https://arxiv.org/pdf/2408.11800">pdf</a>, <a href="https://arxiv.org/format/2408.11800">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"> WeQA: A Benchmark for Retrieval Augmented Generation in Wind Energy Domain </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Meyur%2C+R">Rounak Meyur</a>, <a href="/search/?searchtype=author&query=Phan%2C+H">Hung Phan</a>, <a href="/search/?searchtype=author&query=Wagle%2C+S">Sridevi Wagle</a>, <a href="/search/?searchtype=author&query=Strube%2C+J">Jan Strube</a>, <a href="/search/?searchtype=author&query=Halappanavar%2C+M">Mahantesh Halappanavar</a>, <a href="/search/?searchtype=author&query=Horawalavithana%2C+S">Sameera Horawalavithana</a>, <a href="/search/?searchtype=author&query=Acharya%2C+A">Anurag Acharya</a>, <a href="/search/?searchtype=author&query=Munikoti%2C+S">Sai Munikoti</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="2408.11800v2-abstract-short" style="display: inline;"> In the rapidly evolving landscape of Natural Language Processing (NLP) and text generation, the emergence of Retrieval Augmented Generation (RAG) presents a promising avenue for improving the quality and reliability of generated text by leveraging information retrieved from user specified database. Benchmarking is essential to evaluate and compare the performance of the different RAG configuration… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2408.11800v2-abstract-full').style.display = 'inline'; document.getElementById('2408.11800v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2408.11800v2-abstract-full" style="display: none;"> In the rapidly evolving landscape of Natural Language Processing (NLP) and text generation, the emergence of Retrieval Augmented Generation (RAG) presents a promising avenue for improving the quality and reliability of generated text by leveraging information retrieved from user specified database. Benchmarking is essential to evaluate and compare the performance of the different RAG configurations in terms of retriever and generator, providing insights into their effectiveness, scalability, and suitability for the specific domain and applications. In this paper, we present a comprehensive framework to generate a domain relevant RAG benchmark. Our framework is based on automatic question-answer generation with Human (domain experts)-AI Large Language Model (LLM) teaming. As a case study, we demonstrate the framework by introducing WeQA, a first-of-its-kind benchmark on the wind energy domain which comprises of multiple scientific documents/reports related to environmental impact of wind energy projects. Our framework systematically evaluates RAG performance using diverse metrics and multiple question types with varying complexity level. We also demonstrate the performance of different models on our benchmark. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2408.11800v2-abstract-full').style.display = 'none'; document.getElementById('2408.11800v2-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 September, 2024; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 21 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/2408.10051">arXiv:2408.10051</a> <span> [<a href="https://arxiv.org/pdf/2408.10051">pdf</a>, <a href="https://arxiv.org/format/2408.10051">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link 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="Astrophysics of Galaxies">astro-ph.GA</span> </div> </div> <p class="title is-5 mathjax"> Revised LOFAR upper limits on the 21-cm signal power spectrum at $\mathbf{z\approx9.1}$ using Machine Learning and Gaussian Process Regression </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Acharya%2C+A">Anshuman Acharya</a>, <a href="/search/?searchtype=author&query=Mertens%2C+F">Florent Mertens</a>, <a href="/search/?searchtype=author&query=Ciardi%2C+B">Benedetta Ciardi</a>, <a href="/search/?searchtype=author&query=Ghara%2C+R">Raghunath Ghara</a>, <a href="/search/?searchtype=author&query=Koopmans%2C+L+V+E">L茅on V. E. Koopmans</a>, <a href="/search/?searchtype=author&query=Zaroubi%2C+S">Saleem Zaroubi</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="2408.10051v2-abstract-short" style="display: inline;"> The use of Gaussian Process Regression (GPR) for foregrounds mitigation in data collected by the LOw-Frequency ARray (LOFAR) to measure the high-redshift 21-cm signal power spectrum has been shown to have issues of signal loss when the 21-cm signal covariance is misestimated. To address this problem, we have recently introduced covariance kernels obtained by using a Machine Learning based Variatio… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2408.10051v2-abstract-full').style.display = 'inline'; document.getElementById('2408.10051v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2408.10051v2-abstract-full" style="display: none;"> The use of Gaussian Process Regression (GPR) for foregrounds mitigation in data collected by the LOw-Frequency ARray (LOFAR) to measure the high-redshift 21-cm signal power spectrum has been shown to have issues of signal loss when the 21-cm signal covariance is misestimated. To address this problem, we have recently introduced covariance kernels obtained by using a Machine Learning based Variational Auto-Encoder (VAE) algorithm in combination with simulations of the 21-cm signal. In this work, we apply this framework to 141 hours ($\approx 10$ nights) of LOFAR data at $z \approx 9.1$, and report revised upper limits of the 21-cm signal power spectrum. Overall, we agree with past results reporting a 2-$蟽$ upper limit of $螖^2_{21} < (80)^2~\rm mK^2$ at $k = 0.075~h~\rm Mpc^{-1}$. Further, the VAE-based kernel has a smaller correlation with the systematic excess noise, and the overall GPR-based approach is shown to be a good model for the data. Assuming an accurate bias correction for the excess noise, we report a 2-$蟽$ upper limit of $螖^2_{21} < (25)^2~\rm mK^2$ at $k = 0.075~h~\rm Mpc^{-1}$. However, we still caution to take the more conservative approach to jointly report the upper limits of the excess noise and the 21-cm signal components. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2408.10051v2-abstract-full').style.display = 'none'; document.getElementById('2408.10051v2-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 August, 2024; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 19 August, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> August 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, 3 figures, 2 tables. Accepted for publication in the Monthly Notices of the Royal Astronomical Society (MNRAS) Letters</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2408.09437">arXiv:2408.09437</a> <span> [<a href="https://arxiv.org/pdf/2408.09437">pdf</a>, <a href="https://arxiv.org/format/2408.09437">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Information Retrieval">cs.IR</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Computation and Language">cs.CL</span> </div> </div> <p class="title is-5 mathjax"> Hindi-BEIR : A Large Scale Retrieval Benchmark in Hindi </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Acharya%2C+A">Arkadeep Acharya</a>, <a href="/search/?searchtype=author&query=Murthy%2C+R">Rudra Murthy</a>, <a href="/search/?searchtype=author&query=Kumar%2C+V">Vishwajeet Kumar</a>, <a href="/search/?searchtype=author&query=Sen%2C+J">Jaydeep Sen</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="2408.09437v1-abstract-short" style="display: inline;"> Given the large number of Hindi speakers worldwide, there is a pressing need for robust and efficient information retrieval systems for Hindi. Despite ongoing research, there is a lack of comprehensive benchmark for evaluating retrieval models in Hindi. To address this gap, we introduce the Hindi version of the BEIR benchmark, which includes a subset of English BEIR datasets translated to Hindi, e… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2408.09437v1-abstract-full').style.display = 'inline'; document.getElementById('2408.09437v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2408.09437v1-abstract-full" style="display: none;"> Given the large number of Hindi speakers worldwide, there is a pressing need for robust and efficient information retrieval systems for Hindi. Despite ongoing research, there is a lack of comprehensive benchmark for evaluating retrieval models in Hindi. To address this gap, we introduce the Hindi version of the BEIR benchmark, which includes a subset of English BEIR datasets translated to Hindi, existing Hindi retrieval datasets, and synthetically created datasets for retrieval. The benchmark is comprised of $15$ datasets spanning across $8$ distinct tasks. We evaluate state-of-the-art multilingual retrieval models on this benchmark to identify task and domain-specific challenges and their impact on retrieval performance. By releasing this benchmark and a set of relevant baselines, we enable researchers to understand the limitations and capabilities of current Hindi retrieval models, promoting advancements in this critical area. The datasets from Hindi-BEIR are publicly available. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2408.09437v1-abstract-full').style.display = 'none'; document.getElementById('2408.09437v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 18 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/2407.07321">arXiv:2407.07321</a> <span> [<a href="https://arxiv.org/pdf/2407.07321">pdf</a>, <a href="https://arxiv.org/format/2407.07321">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"> Examining Long-Context Large Language Models for Environmental Review Document Comprehension </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Phan%2C+H">Hung Phan</a>, <a href="/search/?searchtype=author&query=Acharya%2C+A">Anurag Acharya</a>, <a href="/search/?searchtype=author&query=Meyur%2C+R">Rounak Meyur</a>, <a href="/search/?searchtype=author&query=Chaturvedi%2C+S">Sarthak Chaturvedi</a>, <a href="/search/?searchtype=author&query=Sharma%2C+S">Shivam Sharma</a>, <a href="/search/?searchtype=author&query=Parker%2C+M">Mike Parker</a>, <a href="/search/?searchtype=author&query=Nally%2C+D">Dan Nally</a>, <a href="/search/?searchtype=author&query=Jannesari%2C+A">Ali Jannesari</a>, <a href="/search/?searchtype=author&query=Pazdernik%2C+K">Karl Pazdernik</a>, <a href="/search/?searchtype=author&query=Halappanavar%2C+M">Mahantesh Halappanavar</a>, <a href="/search/?searchtype=author&query=Munikoti%2C+S">Sai Munikoti</a>, <a href="/search/?searchtype=author&query=Horawalavithana%2C+S">Sameera Horawalavithana</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="2407.07321v2-abstract-short" style="display: inline;"> As LLMs become increasingly ubiquitous, researchers have tried various techniques to augment the knowledge provided to these models. Long context and retrieval-augmented generation (RAG) are two such methods that have recently gained popularity. In this work, we examine the benefits of both of these techniques by utilizing question answering (QA) task in a niche domain. While the effectiveness of… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2407.07321v2-abstract-full').style.display = 'inline'; document.getElementById('2407.07321v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2407.07321v2-abstract-full" style="display: none;"> As LLMs become increasingly ubiquitous, researchers have tried various techniques to augment the knowledge provided to these models. Long context and retrieval-augmented generation (RAG) are two such methods that have recently gained popularity. In this work, we examine the benefits of both of these techniques by utilizing question answering (QA) task in a niche domain. While the effectiveness of LLM-based QA systems has already been established at an acceptable level in popular domains such as trivia and literature, it has not often been established in niche domains that traditionally require specialized expertise. We construct the NEPAQuAD1.0 benchmark to evaluate the performance of five long-context LLMs -- Claude Sonnet, Gemini, GPT-4, Llama 3.1, and Mistral -- when answering questions originating from Environmental Impact Statements prepared by U.S. federal government agencies in accordance with the National Environmental Environmental Act (NEPA). We specifically measure the ability of LLMs to understand the nuances of legal, technical, and compliance-related information present in NEPA documents in different contextual scenarios. We test the LLMs' internal prior NEPA knowledge by providing questions without any context, as well as assess how LLMs synthesize the contextual information present in long NEPA documents to facilitate the question/answering task. We compare the performance of the models in handling different types of questions (e.g., problem-solving, divergent, etc.). Our results suggest that RAG powered models significantly outperform those provided with only the PDF context in terms of answer accuracy, regardless of the choice of the LLM. Our further analysis reveals that many models perform better answering closed type questions (Yes/No) than divergent and problem-solving questions. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2407.07321v2-abstract-full').style.display = 'none'; document.getElementById('2407.07321v2-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 October, 2024; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 9 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">14 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/2407.03523">arXiv:2407.03523</a> <span> [<a href="https://arxiv.org/pdf/2407.03523">pdf</a>, <a href="https://arxiv.org/format/2407.03523">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Cosmology and Nongalactic Astrophysics">astro-ph.CO</span> </div> </div> <p class="title is-5 mathjax"> Inferring IGM parameters from the redshifted 21-cm Power Spectrum using Artificial Neural Networks </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Choudhury%2C+M">Madhurima Choudhury</a>, <a href="/search/?searchtype=author&query=Ghara%2C+R">Raghunath Ghara</a>, <a href="/search/?searchtype=author&query=Zaroubi%2C+S">Saleem Zaroubi</a>, <a href="/search/?searchtype=author&query=Ciardi%2C+B">Benedetta Ciardi</a>, <a href="/search/?searchtype=author&query=Koopmans%2C+L+V+E">Leon V. E. Koopmans</a>, <a href="/search/?searchtype=author&query=Mellema%2C+G">Garrelt Mellema</a>, <a href="/search/?searchtype=author&query=Shaw%2C+A+K">Abinash Kumar Shaw</a>, <a href="/search/?searchtype=author&query=Acharya%2C+A">Anshuman Acharya</a>, <a href="/search/?searchtype=author&query=Iliev%2C+I+T">I. T. Iliev</a>, <a href="/search/?searchtype=author&query=Ma%2C+Q">Qing-Bo Ma</a>, <a href="/search/?searchtype=author&query=Giri%2C+S+K">Sambit K. Giri</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="2407.03523v1-abstract-short" style="display: inline;"> The high redshift 21-cm signal promises to be a crucial probe of the state of the intergalactic medium (IGM). Understanding the connection between the observed 21-cm power spectrum and the physical quantities intricately associated with the IGM is crucial to fully understand the evolution of our Universe. In this study, we develop an emulator using artificial neural network (ANN) to predict the 21… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2407.03523v1-abstract-full').style.display = 'inline'; document.getElementById('2407.03523v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2407.03523v1-abstract-full" style="display: none;"> The high redshift 21-cm signal promises to be a crucial probe of the state of the intergalactic medium (IGM). Understanding the connection between the observed 21-cm power spectrum and the physical quantities intricately associated with the IGM is crucial to fully understand the evolution of our Universe. In this study, we develop an emulator using artificial neural network (ANN) to predict the 21-cm power spectrum from a given set of IGM properties, namely, the bubble size distribution and the volume averaged ionization fraction. This emulator is implemented within a standard Bayesian framework to constrain the IGM parameters from a given 21-cm power spectrum. We compare the performance of the Bayesian method to an alternate method using ANN to predict the IGM parameters from a given input power spectrum, and find that both methods yield similar levels of accuracy, while the ANN is significantly faster. We also use this ANN method of parameter estimation to predict the IGM parameters from a test set contaminated with noise levels expected from the SKA-LOW instrument after 1000 hours of observation. Finally, we train a separate ANN to predict the source parameters from the IGM parameters directly, at a redshift of $z=9.1$, demonstrating the possibility of a non-analytic inference of the source parameters from the IGM parameters for the first time. We achieve high accuracies, with R2-scores ranging between $0.898-0.978$ for the ANN emulator and between $0.966-0.986$ and $0.817-0.981$ for the predictions of IGM parameters from 21-cm power spectrum and source parameters from IGM parameters, respectively. The predictions of the IGM parameters from the Bayesian method incorporating the ANN emulator leads to tight constraints with error bars around $\pm{0.14}$ on the IGM parameters. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2407.03523v1-abstract-full').style.display = 'none'; document.getElementById('2407.03523v1-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 July, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> July 2024. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2406.18545">arXiv:2406.18545</a> <span> [<a href="https://arxiv.org/pdf/2406.18545">pdf</a>, <a href="https://arxiv.org/format/2406.18545">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"> Visual Analysis of Prediction Uncertainty in Neural Networks for Deep Image Synthesis </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Dutta%2C+S">Soumya Dutta</a>, <a href="/search/?searchtype=author&query=Nizar%2C+F">Faheem Nizar</a>, <a href="/search/?searchtype=author&query=Amaan%2C+A">Ahmad Amaan</a>, <a href="/search/?searchtype=author&query=Acharya%2C+A">Ayan Acharya</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="2406.18545v1-abstract-short" style="display: inline;"> Ubiquitous applications of Deep neural networks (DNNs) in different artificial intelligence systems have led to their adoption in solving challenging visualization problems in recent years. While sophisticated DNNs offer an impressive generalization, it is imperative to comprehend the quality, confidence, robustness, and uncertainty associated with their prediction. A thorough understanding of the… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2406.18545v1-abstract-full').style.display = 'inline'; document.getElementById('2406.18545v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2406.18545v1-abstract-full" style="display: none;"> Ubiquitous applications of Deep neural networks (DNNs) in different artificial intelligence systems have led to their adoption in solving challenging visualization problems in recent years. While sophisticated DNNs offer an impressive generalization, it is imperative to comprehend the quality, confidence, robustness, and uncertainty associated with their prediction. A thorough understanding of these quantities produces actionable insights that help application scientists make informed decisions. Unfortunately, the intrinsic design principles of the DNNs cannot beget prediction uncertainty, necessitating separate formulations for robust uncertainty-aware models for diverse visualization applications. To that end, this contribution demonstrates how the prediction uncertainty and sensitivity of DNNs can be estimated efficiently using various methods and then interactively compared and contrasted for deep image synthesis tasks. Our inspection suggests that uncertainty-aware deep visualization models generate illustrations of informative and superior quality and diversity. Furthermore, prediction uncertainty improves the robustness and interpretability of deep visualization models, making them practical and convenient for various scientific domains that thrive on visual analyses. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2406.18545v1-abstract-full').style.display = 'none'; document.getElementById('2406.18545v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 22 May, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> June 2024. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2406.17188">arXiv:2406.17188</a> <span> [<a href="https://arxiv.org/pdf/2406.17188">pdf</a>, <a href="https://arxiv.org/format/2406.17188">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Artificial Intelligence">cs.AI</span> </div> </div> <p class="title is-5 mathjax"> Geometric Median (GM) Matching for Robust Data Pruning </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Acharya%2C+A">Anish Acharya</a>, <a href="/search/?searchtype=author&query=Dhillon%2C+I+S">Inderjit S Dhillon</a>, <a href="/search/?searchtype=author&query=Sanghavi%2C+S">Sujay Sanghavi</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="2406.17188v1-abstract-short" style="display: inline;"> Data pruning, the combinatorial task of selecting a small and informative subset from a large dataset, is crucial for mitigating the enormous computational costs associated with training data-hungry modern deep learning models at scale. Since large-scale data collections are invariably noisy, developing data pruning strategies that remain robust even in the presence of corruption is critical in pr… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2406.17188v1-abstract-full').style.display = 'inline'; document.getElementById('2406.17188v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2406.17188v1-abstract-full" style="display: none;"> Data pruning, the combinatorial task of selecting a small and informative subset from a large dataset, is crucial for mitigating the enormous computational costs associated with training data-hungry modern deep learning models at scale. Since large-scale data collections are invariably noisy, developing data pruning strategies that remain robust even in the presence of corruption is critical in practice. Unfortunately, the existing heuristics for (robust) data pruning lack theoretical coherence and rely on heroic assumptions, that are, often unattainable, by the very nature of the problem setting. Moreover, these strategies often yield sub-optimal neural scaling laws even compared to random sampling, especially in scenarios involving strong corruption and aggressive pruning rates -- making provably robust data pruning an open challenge. In response, in this work, we propose Geometric Median ($\gm$) Matching -- a herding~\citep{welling2009herding} style greedy algorithm -- that yields a $k$-subset such that the mean of the subset approximates the geometric median of the (potentially) noisy dataset. Theoretically, we show that $\gm$ Matching enjoys an improved $\gO(1/k)$ scaling over $\gO(1/\sqrt{k})$ scaling of uniform sampling; while achieving the optimal breakdown point of 1/2 even under arbitrary corruption. Extensive experiments across popular deep learning benchmarks indicate that $\gm$ Matching consistently outperforms prior state-of-the-art; the gains become more profound at high rates of corruption and aggressive pruning rates; making $\gm$ Matching a strong baseline for future research in robust data pruning. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2406.17188v1-abstract-full').style.display = 'none'; document.getElementById('2406.17188v1-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 June, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> June 2024. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2405.20513">arXiv:2405.20513</a> <span> [<a href="https://arxiv.org/pdf/2405.20513">pdf</a>, <a href="https://arxiv.org/format/2405.20513">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="Computer Vision and Pattern Recognition">cs.CV</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Robotics">cs.RO</span> </div> </div> <p class="title is-5 mathjax"> Deep Modeling of Non-Gaussian Aleatoric Uncertainty </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Acharya%2C+A">Aastha Acharya</a>, <a href="/search/?searchtype=author&query=Lee%2C+C">Caleb Lee</a>, <a href="/search/?searchtype=author&query=D%27Alonzo%2C+M">Marissa D'Alonzo</a>, <a href="/search/?searchtype=author&query=Shamwell%2C+J">Jared Shamwell</a>, <a href="/search/?searchtype=author&query=Ahmed%2C+N+R">Nisar R. Ahmed</a>, <a href="/search/?searchtype=author&query=Russell%2C+R">Rebecca Russell</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="2405.20513v1-abstract-short" style="display: inline;"> Deep learning offers promising new ways to accurately model aleatoric uncertainty in robotic estimation systems, particularly when the uncertainty distributions do not conform to traditional assumptions of being fixed and Gaussian. In this study, we formulate and evaluate three fundamental deep learning approaches for conditional probability density modeling to quantify non-Gaussian aleatoric unce… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2405.20513v1-abstract-full').style.display = 'inline'; document.getElementById('2405.20513v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2405.20513v1-abstract-full" style="display: none;"> Deep learning offers promising new ways to accurately model aleatoric uncertainty in robotic estimation systems, particularly when the uncertainty distributions do not conform to traditional assumptions of being fixed and Gaussian. In this study, we formulate and evaluate three fundamental deep learning approaches for conditional probability density modeling to quantify non-Gaussian aleatoric uncertainty: parametric, discretized, and generative modeling. We systematically compare the respective strengths and weaknesses of these three methods on simulated non-Gaussian densities as well as on real-world terrain-relative navigation data. Our results show that these deep learning methods can accurately capture complex uncertainty patterns, highlighting their potential for improving the reliability and robustness of estimation systems. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2405.20513v1-abstract-full').style.display = 'none'; document.getElementById('2405.20513v1-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 May, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> May 2024. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">8 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/2404.11686">arXiv:2404.11686</a> <span> [<a href="https://arxiv.org/pdf/2404.11686">pdf</a>, <a href="https://arxiv.org/format/2404.11686">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Cosmology and Nongalactic Astrophysics">astro-ph.CO</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/10.1051/0004-6361/202449444">10.1051/0004-6361/202449444 <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </div> </div> <p class="title is-5 mathjax"> Probing the intergalactic medium during the Epoch of Reionization using 21-cm signal power spectra </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Ghara%2C+R">Raghunath Ghara</a>, <a href="/search/?searchtype=author&query=Shaw%2C+A+K">Abinash Kumar Shaw</a>, <a href="/search/?searchtype=author&query=Zaroubi%2C+S">Saleem Zaroubi</a>, <a href="/search/?searchtype=author&query=Ciardi%2C+B">Benedetta Ciardi</a>, <a href="/search/?searchtype=author&query=Mellema%2C+G">Garrelt Mellema</a>, <a href="/search/?searchtype=author&query=Koopmans%2C+L+V+E">L茅on V. E. Koopmans</a>, <a href="/search/?searchtype=author&query=Acharya%2C+A">Anshuman Acharya</a>, <a href="/search/?searchtype=author&query=Choudhury%2C+M">Madhurima Choudhury</a>, <a href="/search/?searchtype=author&query=Giri%2C+S+K">Sambit K. Giri</a>, <a href="/search/?searchtype=author&query=Iliev%2C+I+T">Ilian T. Iliev</a>, <a href="/search/?searchtype=author&query=Ma%2C+Q">Qing-Bo Ma</a>, <a href="/search/?searchtype=author&query=Mertens%2C+F">Florent Mertens</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="2404.11686v1-abstract-short" style="display: inline;"> The redshifted 21-cm signal from the epoch of reionization (EoR) directly probes the ionization and thermal states of the intergalactic medium during that period. In particular, the distribution of the ionized regions around the radiating sources during EoR introduces scale-dependent features in the spherically-averaged EoR 21-cm signal power spectrum. The goal is to study these scale-dependent fe… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2404.11686v1-abstract-full').style.display = 'inline'; document.getElementById('2404.11686v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2404.11686v1-abstract-full" style="display: none;"> The redshifted 21-cm signal from the epoch of reionization (EoR) directly probes the ionization and thermal states of the intergalactic medium during that period. In particular, the distribution of the ionized regions around the radiating sources during EoR introduces scale-dependent features in the spherically-averaged EoR 21-cm signal power spectrum. The goal is to study these scale-dependent features at different stages of reionization using numerical simulations and build a source model-independent framework to probe the properties of the intergalactic medium using EoR 21-cm signal power spectrum measurements. Under the assumption of high spin temperature, we modelled the redshift evolution of the ratio of EoR 21-cm brightness temperature power spectrum and the corresponding density power spectrum using an ansatz consisting of a set of redshift and scale-independent parameters. This set of eight parameters probes the redshift evolution of the average ionization fraction and the quantities related to the morphology of the ionized regions. We have tested this ansatz on different reionization scenarios generated using different simulation algorithms and found that it is able to recover the redshift evolution of the average neutral fraction within an absolute deviation $\lesssim 0.1$. Our framework allows us to interpret 21-cm signal power spectra in terms of parameters related to the state of the IGM. This source model-independent framework can efficiently constrain reionization scenarios using multi-redshift power spectrum measurements with ongoing and future radio telescopes such as LOFAR, MWA, HERA, and SKA. This will add independent information regarding the EoR IGM properties. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2404.11686v1-abstract-full').style.display = 'none'; document.getElementById('2404.11686v1-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 April, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> April 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">16 pages, 13 figures, 2 tables, Accepted for publication in Astronomy and Astrophysics</span> </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Report number:</span> NORDITA 2024-009 </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Journal ref:</span> A&A 687, A252 (2024) </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2404.07232">arXiv:2404.07232</a> <span> [<a href="https://arxiv.org/pdf/2404.07232">pdf</a>, <a href="https://arxiv.org/ps/2404.07232">ps</a>, <a href="https://arxiv.org/format/2404.07232">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Analysis of PDEs">math.AP</span> <span class="tag is-small is-grey 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="Mathematical Physics">math-ph</span> </div> </div> <p class="title is-5 mathjax"> Ideal Magnetohydrodynamics and Field Dislocation Mechanics </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Acharya%2C+A">Amit Acharya</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="2404.07232v2-abstract-short" style="display: inline;"> The fully nonlinear (geometric and material) system of Field Dislocation Mechanics is reviewed to establish an exact analogy with the equations of ideal magnetohydrodynamics (ideal MHD) under suitable physically simplifying circumstances. Weak solutions with various conservation properties have been established for ideal MHD recently by Faraco, Lindberg, and Szekelyhidi using the techniques of com… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2404.07232v2-abstract-full').style.display = 'inline'; document.getElementById('2404.07232v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2404.07232v2-abstract-full" style="display: none;"> The fully nonlinear (geometric and material) system of Field Dislocation Mechanics is reviewed to establish an exact analogy with the equations of ideal magnetohydrodynamics (ideal MHD) under suitable physically simplifying circumstances. Weak solutions with various conservation properties have been established for ideal MHD recently by Faraco, Lindberg, and Szekelyhidi using the techniques of compensated compactness of Tartar and Murat and convex integration; by the established analogy, these results would seem to be transferable to the idealization of Field Dislocation Mechanics considered. A dual variational principle is designed and discussed for this system of PDE, with the technique transferable to the study of MHD as well. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2404.07232v2-abstract-full').style.display = 'none'; document.getElementById('2404.07232v2-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 18 April, 2024; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 3 April, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> April 2024. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2404.07214">arXiv:2404.07214</a> <span> [<a href="https://arxiv.org/pdf/2404.07214">pdf</a>, <a href="https://arxiv.org/format/2404.07214">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="Artificial Intelligence">cs.AI</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Computation and Language">cs.CL</span> </div> </div> <p class="title is-5 mathjax"> Exploring the Frontier of Vision-Language Models: A Survey of Current Methodologies and Future Directions </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Ghosh%2C+A">Akash Ghosh</a>, <a href="/search/?searchtype=author&query=Acharya%2C+A">Arkadeep Acharya</a>, <a href="/search/?searchtype=author&query=Saha%2C+S">Sriparna Saha</a>, <a href="/search/?searchtype=author&query=Jain%2C+V">Vinija Jain</a>, <a href="/search/?searchtype=author&query=Chadha%2C+A">Aman Chadha</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="2404.07214v2-abstract-short" style="display: inline;"> The advent of Large Language Models (LLMs) has significantly reshaped the trajectory of the AI revolution. Nevertheless, these LLMs exhibit a notable limitation, as they are primarily adept at processing textual information. To address this constraint, researchers have endeavored to integrate visual capabilities with LLMs, resulting in the emergence of Vision-Language Models (VLMs). These advanced… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2404.07214v2-abstract-full').style.display = 'inline'; document.getElementById('2404.07214v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2404.07214v2-abstract-full" style="display: none;"> The advent of Large Language Models (LLMs) has significantly reshaped the trajectory of the AI revolution. Nevertheless, these LLMs exhibit a notable limitation, as they are primarily adept at processing textual information. To address this constraint, researchers have endeavored to integrate visual capabilities with LLMs, resulting in the emergence of Vision-Language Models (VLMs). These advanced models are instrumental in tackling more intricate tasks such as image captioning and visual question answering. In our comprehensive survey paper, we delve into the key advancements within the realm of VLMs. Our classification organizes VLMs into three distinct categories: models dedicated to vision-language understanding, models that process multimodal inputs to generate unimodal (textual) outputs and models that both accept and produce multimodal inputs and outputs.This classification is based on their respective capabilities and functionalities in processing and generating various modalities of data.We meticulously dissect each model, offering an extensive analysis of its foundational architecture, training data sources, as well as its strengths and limitations wherever possible, providing readers with a comprehensive understanding of its essential components. We also analyzed the performance of VLMs in various benchmark datasets. By doing so, we aim to offer a nuanced understanding of the diverse landscape of VLMs. Additionally, we underscore potential avenues for future research in this dynamic domain, anticipating further breakthroughs and advancements. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2404.07214v2-abstract-full').style.display = 'none'; document.getElementById('2404.07214v2-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 April, 2024; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 20 February, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> April 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">The most extensive and up to date Survey on Visual Language Models covering 76 Visual Language Models</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2403.06061">arXiv:2403.06061</a> <span> [<a href="https://arxiv.org/pdf/2403.06061">pdf</a>, <a href="https://arxiv.org/format/2403.06061">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="Mathematical Physics">math-ph</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Classical Physics">physics.class-ph</span> </div> </div> <p class="title is-5 mathjax"> Coupled Dislocations and Fracture dynamics at finite deformation: model derivation, and physical questions </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Acharya%2C+A">Amit Acharya</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="2403.06061v2-abstract-short" style="display: inline;"> A continuum mechanical model of coupled dislocation based plasticity and fracture at finite deformation is proposed. Motivating questions and target applications of the model are sketched. </span> <span class="abstract-full has-text-grey-dark mathjax" id="2403.06061v2-abstract-full" style="display: none;"> A continuum mechanical model of coupled dislocation based plasticity and fracture at finite deformation is proposed. Motivating questions and target applications of the model are sketched. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2403.06061v2-abstract-full').style.display = 'none'; document.getElementById('2403.06061v2-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 April, 2024; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 9 March, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> March 2024. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2403.00779">arXiv:2403.00779</a> <span> [<a href="https://arxiv.org/pdf/2403.00779">pdf</a>, <a href="https://arxiv.org/format/2403.00779">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Analysis of PDEs">math.AP</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Soft Condensed Matter">cond-mat.soft</span> </div> </div> <p class="title is-5 mathjax"> Mid-surface scaling invariance of some bending strain measures </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Acharya%2C+A">Amit Acharya</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="2403.00779v3-abstract-short" style="display: inline;"> The mid-surface scaling invariance of bending strain measures proposed in [Acharya (2000)] is discussed in light of the work of [arXiv:2010.14308]. </span> <span class="abstract-full has-text-grey-dark mathjax" id="2403.00779v3-abstract-full" style="display: none;"> The mid-surface scaling invariance of bending strain measures proposed in [Acharya (2000)] is discussed in light of the work of [arXiv:2010.14308]. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2403.00779v3-abstract-full').style.display = 'none'; document.getElementById('2403.00779v3-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 April, 2024; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 15 February, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> March 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">Reply to arXiv:2010.14308. This preprint has been classified as math.AP by arXiv moderators</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2402.06038">arXiv:2402.06038</a> <span> [<a href="https://arxiv.org/pdf/2402.06038">pdf</a>, <a href="https://arxiv.org/format/2402.06038">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="Computer Vision and Pattern Recognition">cs.CV</span> </div> </div> <p class="title is-5 mathjax"> Contrastive Approach to Prior Free Positive Unlabeled Learning </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Acharya%2C+A">Anish Acharya</a>, <a href="/search/?searchtype=author&query=Sanghavi%2C+S">Sujay Sanghavi</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="2402.06038v1-abstract-short" style="display: inline;"> Positive Unlabeled (PU) learning refers to the task of learning a binary classifier given a few labeled positive samples, and a set of unlabeled samples (which could be positive or negative). In this paper, we propose a novel PU learning framework, that starts by learning a feature space through pretext-invariant representation learning and then applies pseudo-labeling to the unlabeled examples, l… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2402.06038v1-abstract-full').style.display = 'inline'; document.getElementById('2402.06038v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2402.06038v1-abstract-full" style="display: none;"> Positive Unlabeled (PU) learning refers to the task of learning a binary classifier given a few labeled positive samples, and a set of unlabeled samples (which could be positive or negative). In this paper, we propose a novel PU learning framework, that starts by learning a feature space through pretext-invariant representation learning and then applies pseudo-labeling to the unlabeled examples, leveraging the concentration property of the embeddings. Overall, our proposed approach handily outperforms state-of-the-art PU learning methods across several standard PU benchmark datasets, while not requiring a-priori knowledge or estimate of class prior. Remarkably, our method remains effective even when labeled data is scant, where most PU learning algorithms falter. We also provide simple theoretical analysis motivating our proposed algorithms and establish generalization guarantee for our approach. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2402.06038v1-abstract-full').style.display = 'none'; document.getElementById('2402.06038v1-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, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> February 2024. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2402.02811">arXiv:2402.02811</a> <span> [<a href="https://arxiv.org/pdf/2402.02811">pdf</a>, <a href="https://arxiv.org/format/2402.02811">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="Quantitative Methods">q-bio.QM</span> </div> </div> <p class="title is-5 mathjax"> Multi-scale fMRI time series analysis for understanding neurodegeneration in MCI </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=R.%2C+A">Ammu R.</a>, <a href="/search/?searchtype=author&query=Bhattacharya%2C+D">Debanjali Bhattacharya</a>, <a href="/search/?searchtype=author&query=Acharya%2C+A">Ameiy Acharya</a>, <a href="/search/?searchtype=author&query=Aithal%2C+N">Ninad Aithal</a>, <a href="/search/?searchtype=author&query=Sinha%2C+N">Neelam Sinha</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="2402.02811v1-abstract-short" style="display: inline;"> In this study, we present a technique that spans multi-scale views (global scale -- meaning brain network-level and local scale -- examining each individual ROI that constitutes the network) applied to resting-state fMRI volumes. Deep learning based classification is utilized in understanding neurodegeneration. The novelty of the proposed approach lies in utilizing two extreme scales of analysis.… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2402.02811v1-abstract-full').style.display = 'inline'; document.getElementById('2402.02811v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2402.02811v1-abstract-full" style="display: none;"> In this study, we present a technique that spans multi-scale views (global scale -- meaning brain network-level and local scale -- examining each individual ROI that constitutes the network) applied to resting-state fMRI volumes. Deep learning based classification is utilized in understanding neurodegeneration. The novelty of the proposed approach lies in utilizing two extreme scales of analysis. One branch considers the entire network within graph-analysis framework. Concurrently, the second branch scrutinizes each ROI within a network independently, focusing on evolution of dynamics. For each subject, graph-based approach employs partial correlation to profile the subject in a single graph where each ROI is a node, providing insights into differences in levels of participation. In contrast, non-linear analysis employs recurrence plots to profile a subject as a multichannel 2D image, revealing distinctions in underlying dynamics. The proposed approach is employed for classification of a cohort of 50 healthy control (HC) and 50 Mild Cognitive Impairment (MCI), sourced from ADNI dataset. Results point to: (1) reduced activity in ROIs such as PCC in MCI (2) greater activity in occipital in MCI, which is not seen in HC (3) when analysed for dynamics, all ROIs in MCI show greater predictability in time-series. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2402.02811v1-abstract-full').style.display = 'none'; document.getElementById('2402.02811v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 5 February, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> February 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">12 pages, 3 figures and 4 tables</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2401.12332">arXiv:2401.12332</a> <span> [<a href="https://arxiv.org/pdf/2401.12332">pdf</a>, <a href="https://arxiv.org/format/2401.12332">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="Optimization and Control">math.OC</span> </div> </div> <p class="title is-5 mathjax"> A Precise Characterization of SGD Stability Using Loss Surface Geometry </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Dexter%2C+G">Gregory Dexter</a>, <a href="/search/?searchtype=author&query=Ocejo%2C+B">Borja Ocejo</a>, <a href="/search/?searchtype=author&query=Keerthi%2C+S">Sathiya Keerthi</a>, <a href="/search/?searchtype=author&query=Gupta%2C+A">Aman Gupta</a>, <a href="/search/?searchtype=author&query=Acharya%2C+A">Ayan Acharya</a>, <a href="/search/?searchtype=author&query=Khanna%2C+R">Rajiv Khanna</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="2401.12332v1-abstract-short" style="display: inline;"> Stochastic Gradient Descent (SGD) stands as a cornerstone optimization algorithm with proven real-world empirical successes but relatively limited theoretical understanding. Recent research has illuminated a key factor contributing to its practical efficacy: the implicit regularization it instigates. Several studies have investigated the linear stability property of SGD in the vicinity of a statio… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2401.12332v1-abstract-full').style.display = 'inline'; document.getElementById('2401.12332v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2401.12332v1-abstract-full" style="display: none;"> Stochastic Gradient Descent (SGD) stands as a cornerstone optimization algorithm with proven real-world empirical successes but relatively limited theoretical understanding. Recent research has illuminated a key factor contributing to its practical efficacy: the implicit regularization it instigates. Several studies have investigated the linear stability property of SGD in the vicinity of a stationary point as a predictive proxy for sharpness and generalization error in overparameterized neural networks (Wu et al., 2022; Jastrzebski et al., 2019; Cohen et al., 2021). In this paper, we delve deeper into the relationship between linear stability and sharpness. More specifically, we meticulously delineate the necessary and sufficient conditions for linear stability, contingent on hyperparameters of SGD and the sharpness at the optimum. Towards this end, we introduce a novel coherence measure of the loss Hessian that encapsulates pertinent geometric properties of the loss function that are relevant to the linear stability of SGD. It enables us to provide a simplified sufficient condition for identifying linear instability at an optimum. Notably, compared to previous works, our analysis relies on significantly milder assumptions and is applicable for a broader class of loss functions than known before, encompassing not only mean-squared error but also cross-entropy loss. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2401.12332v1-abstract-full').style.display = 'none'; document.getElementById('2401.12332v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 22 January, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> January 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">To appear at ICLR 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/2401.08814">arXiv:2401.08814</a> <span> [<a href="https://arxiv.org/pdf/2401.08814">pdf</a>, <a href="https://arxiv.org/format/2401.08814">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Numerical Analysis">math.NA</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Mathematical Physics">math-ph</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Analysis of PDEs">math.AP</span> </div> </div> <p class="title is-5 mathjax"> Inviscid Burgers as a degenerate elliptic problem </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Kouskiya%2C+U">Uditnarayan Kouskiya</a>, <a href="/search/?searchtype=author&query=Acharya%2C+A">Amit Acharya</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="2401.08814v6-abstract-short" style="display: inline;"> We demonstrate the feasibility of a scheme to obtain approximate weak solutions to the (inviscid) Burgers equation in conservation and Hamilton-Jacobi form, treated as degenerate elliptic problems. We show different variants recover non-unique weak solutions as appropriate, and also specific constructive approaches to recover the corresponding entropy solutions. </span> <span class="abstract-full has-text-grey-dark mathjax" id="2401.08814v6-abstract-full" style="display: none;"> We demonstrate the feasibility of a scheme to obtain approximate weak solutions to the (inviscid) Burgers equation in conservation and Hamilton-Jacobi form, treated as degenerate elliptic problems. We show different variants recover non-unique weak solutions as appropriate, and also specific constructive approaches to recover the corresponding entropy solutions. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2401.08814v6-abstract-full').style.display = 'none'; document.getElementById('2401.08814v6-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 June, 2024; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 16 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/2401.08538">arXiv:2401.08538</a> <span> [<a href="https://arxiv.org/pdf/2401.08538">pdf</a>, <a href="https://arxiv.org/format/2401.08538">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Analysis of PDEs">math.AP</span> <span class="tag is-small is-grey 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="Mathematical Physics">math-ph</span> </div> </div> <p class="title is-5 mathjax"> A Hidden Convexity of Nonlinear Elasticity </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Singh%2C+S">Siddharth Singh</a>, <a href="/search/?searchtype=author&query=Ginster%2C+J">Janusz Ginster</a>, <a href="/search/?searchtype=author&query=Acharya%2C+A">Amit Acharya</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="2401.08538v3-abstract-short" style="display: inline;"> A technique for developing convex dual variational principles for the governing PDE of nonlinear elastostatics and elastodynamics is presented. This allows the definition of notions of a variational dual solution and a dual solution corresponding to the PDEs of nonlinear elasticity, even when the latter arise as formal Euler-Lagrange equations corresponding to non-quasiconvex elastic energy functi… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2401.08538v3-abstract-full').style.display = 'inline'; document.getElementById('2401.08538v3-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2401.08538v3-abstract-full" style="display: none;"> A technique for developing convex dual variational principles for the governing PDE of nonlinear elastostatics and elastodynamics is presented. This allows the definition of notions of a variational dual solution and a dual solution corresponding to the PDEs of nonlinear elasticity, even when the latter arise as formal Euler-Lagrange equations corresponding to non-quasiconvex elastic energy functionals whose energy minimizers do not exist. This is demonstrated rigorously in the case of elastostatics for the Saint-Venant Kirchhoff material (in all dimensions), where the existence of variational dual solutions is also proven. The existence of a variational dual solution for the incompressible neo-Hookean material in 2-d is also shown. Stressed and unstressed elastostatic and elastodynamic solutions in 1 space dimension corresponding to a non-convex, double-well energy are computed using the dual methodology. In particular, we show the stability of a dual elastodynamic equilibrium solution for which there are regions of non-vanishing length with negative elastic stiffness, i.e.~non-hyperbolic regions, for which the corresponding primal problem is ill-posed and demonstrates an explosive `Hadamard instability;' this appears to have implications for the modeling of physically observed softening behavior in macroscopic mechanical response. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2401.08538v3-abstract-full').style.display = 'none'; document.getElementById('2401.08538v3-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 July, 2024; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 16 January, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> January 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 in Journal of Elasticity</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2401.06372">arXiv:2401.06372</a> <span> [<a href="https://arxiv.org/pdf/2401.06372">pdf</a>, <a href="https://arxiv.org/format/2401.06372">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Instrumentation and Methods for Astrophysics">astro-ph.IM</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="High Energy Astrophysical Phenomena">astro-ph.HE</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/10.3847/2515-5172/ad18b5">10.3847/2515-5172/ad18b5 <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </div> </div> <p class="title is-5 mathjax"> Spectral fit residuals as an indicator to increase model complexity </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Acharya%2C+A">Anshuman Acharya</a>, <a href="/search/?searchtype=author&query=Kashyap%2C+V+L">Vinay L. Kashyap</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="2401.06372v1-abstract-short" style="display: inline;"> Spectral fitting of X-ray data usually involves minimizing statistics like the chi-square and the Cash statistic. Here we discuss their limitations and introduce two measures based on the cumulative sum (CuSum) of model residuals to evaluate whether model complexity could be increased: the percentage of bins exceeding a nominal threshold in a CuSum array (pct$_{CuSum}$), and the excess area under… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2401.06372v1-abstract-full').style.display = 'inline'; document.getElementById('2401.06372v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2401.06372v1-abstract-full" style="display: none;"> Spectral fitting of X-ray data usually involves minimizing statistics like the chi-square and the Cash statistic. Here we discuss their limitations and introduce two measures based on the cumulative sum (CuSum) of model residuals to evaluate whether model complexity could be increased: the percentage of bins exceeding a nominal threshold in a CuSum array (pct$_{CuSum}$), and the excess area under the CuSum compared to the nominal (p$_\textit{area}$). We demonstrate their use with an application to a $\textit{Chandra}$ ACIS spectral fit. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2401.06372v1-abstract-full').style.display = 'none'; document.getElementById('2401.06372v1-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 January, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> January 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">3 pages, 1 figure, published in the Research Notes of the American Astronomical Society (RNAAS)</span> </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Journal ref:</span> Res. Notes AAS 8 1 (2024) </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2401.01596">arXiv:2401.01596</a> <span> [<a href="https://arxiv.org/pdf/2401.01596">pdf</a>, <a href="https://arxiv.org/format/2401.01596">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Artificial Intelligence">cs.AI</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Computation and Language">cs.CL</span> </div> </div> <p class="title is-5 mathjax"> MedSumm: A Multimodal Approach to Summarizing Code-Mixed Hindi-English Clinical Queries </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Ghosh%2C+A">Akash Ghosh</a>, <a href="/search/?searchtype=author&query=Acharya%2C+A">Arkadeep Acharya</a>, <a href="/search/?searchtype=author&query=Jha%2C+P">Prince Jha</a>, <a href="/search/?searchtype=author&query=Gaudgaul%2C+A">Aniket Gaudgaul</a>, <a href="/search/?searchtype=author&query=Majumdar%2C+R">Rajdeep Majumdar</a>, <a href="/search/?searchtype=author&query=Saha%2C+S">Sriparna Saha</a>, <a href="/search/?searchtype=author&query=Chadha%2C+A">Aman Chadha</a>, <a href="/search/?searchtype=author&query=Jain%2C+R">Raghav Jain</a>, <a href="/search/?searchtype=author&query=Sinha%2C+S">Setu Sinha</a>, <a href="/search/?searchtype=author&query=Agarwal%2C+S">Shivani Agarwal</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="2401.01596v1-abstract-short" style="display: inline;"> In the healthcare domain, summarizing medical questions posed by patients is critical for improving doctor-patient interactions and medical decision-making. Although medical data has grown in complexity and quantity, the current body of research in this domain has primarily concentrated on text-based methods, overlooking the integration of visual cues. Also prior works in the area of medical quest… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2401.01596v1-abstract-full').style.display = 'inline'; document.getElementById('2401.01596v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2401.01596v1-abstract-full" style="display: none;"> In the healthcare domain, summarizing medical questions posed by patients is critical for improving doctor-patient interactions and medical decision-making. Although medical data has grown in complexity and quantity, the current body of research in this domain has primarily concentrated on text-based methods, overlooking the integration of visual cues. Also prior works in the area of medical question summarisation have been limited to the English language. This work introduces the task of multimodal medical question summarization for codemixed input in a low-resource setting. To address this gap, we introduce the Multimodal Medical Codemixed Question Summarization MMCQS dataset, which combines Hindi-English codemixed medical queries with visual aids. This integration enriches the representation of a patient's medical condition, providing a more comprehensive perspective. We also propose a framework named MedSumm that leverages the power of LLMs and VLMs for this task. By utilizing our MMCQS dataset, we demonstrate the value of integrating visual information from images to improve the creation of medically detailed summaries. This multimodal strategy not only improves healthcare decision-making but also promotes a deeper comprehension of patient queries, paving the way for future exploration in personalized and responsive medical care. Our dataset, code, and pre-trained models will be made publicly available. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2401.01596v1-abstract-full').style.display = 'none'; document.getElementById('2401.01596v1-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 January, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> January 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">ECIR 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/2312.14288">arXiv:2312.14288</a> <span> [<a href="https://arxiv.org/pdf/2312.14288">pdf</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Tissues and Organs">q-bio.TO</span> </div> </div> <p class="title is-5 mathjax"> The Status and Prospects of Phytoremediation of Heavy Metals </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Acharya%2C+A">Aniruddha Acharya</a>, <a href="/search/?searchtype=author&query=Perez%2C+E">Enrique Perez</a>, <a href="/search/?searchtype=author&query=Maddox-Mandolini%2C+M">Miller Maddox-Mandolini</a>, <a href="/search/?searchtype=author&query=De+La+Fuente%2C+H">Hania De La Fuente</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="2312.14288v1-abstract-short" style="display: inline;"> The release of heavy metals into the agricultural soil and waterbodies has been accelerated due to anthropogenic activities. They are not usually required for biological functions thus, their accumulation in biological system poses serious threat to health and environment globally. Phytoremediation offers a safe, inexpensive, and ecologically sustainable technique to clean habitats contaminated wi… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2312.14288v1-abstract-full').style.display = 'inline'; document.getElementById('2312.14288v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2312.14288v1-abstract-full" style="display: none;"> The release of heavy metals into the agricultural soil and waterbodies has been accelerated due to anthropogenic activities. They are not usually required for biological functions thus, their accumulation in biological system poses serious threat to health and environment globally. Phytoremediation offers a safe, inexpensive, and ecologically sustainable technique to clean habitats contaminated with heavy metals. Though several plants have been identified and used as a potential candidate for such phytoremediation, the technique is still at its formative stage and has been mostly confined to laboratory and greenhouses. However, recently several field studies have shown promising results that can propel large-scale implementation of this technology in industrial sites and urban agriculture. Realistically, the commercialization of this technique is possible if interdisciplinary approach is employed to increase its efficiency. This review presents a comprehensive narration of the status and future of the technique. It illustrates the concept of phytoremediation, the ecological and commercial benefits, and the types of phytoremediation. The candidate plants and factors that influences phytoremediation has been discussed. Finally, the physiological and molecular mechanism along with the future of the technique has been described. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2312.14288v1-abstract-full').style.display = 'none'; document.getElementById('2312.14288v1-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 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">Comments:</span> <span class="has-text-grey-dark mathjax">34 pages, 3 figures, 2 tables, review paper</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2312.11541">arXiv:2312.11541</a> <span> [<a href="https://arxiv.org/pdf/2312.11541">pdf</a>, <a href="https://arxiv.org/format/2312.11541">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Artificial Intelligence">cs.AI</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Computation and Language">cs.CL</span> </div> </div> <p class="title is-5 mathjax"> CLIPSyntel: CLIP and LLM Synergy for Multimodal Question Summarization in Healthcare </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Ghosh%2C+A">Akash Ghosh</a>, <a href="/search/?searchtype=author&query=Acharya%2C+A">Arkadeep Acharya</a>, <a href="/search/?searchtype=author&query=Jain%2C+R">Raghav Jain</a>, <a href="/search/?searchtype=author&query=Saha%2C+S">Sriparna Saha</a>, <a href="/search/?searchtype=author&query=Chadha%2C+A">Aman Chadha</a>, <a href="/search/?searchtype=author&query=Sinha%2C+S">Setu Sinha</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="2312.11541v1-abstract-short" style="display: inline;"> In the era of modern healthcare, swiftly generating medical question summaries is crucial for informed and timely patient care. Despite the increasing complexity and volume of medical data, existing studies have focused solely on text-based summarization, neglecting the integration of visual information. Recognizing the untapped potential of combining textual queries with visual representations of… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2312.11541v1-abstract-full').style.display = 'inline'; document.getElementById('2312.11541v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2312.11541v1-abstract-full" style="display: none;"> In the era of modern healthcare, swiftly generating medical question summaries is crucial for informed and timely patient care. Despite the increasing complexity and volume of medical data, existing studies have focused solely on text-based summarization, neglecting the integration of visual information. Recognizing the untapped potential of combining textual queries with visual representations of medical conditions, we introduce the Multimodal Medical Question Summarization (MMQS) Dataset. This dataset, a major contribution to our work, pairs medical queries with visual aids, facilitating a richer and more nuanced understanding of patient needs. We also propose a framework, utilizing the power of Contrastive Language Image Pretraining(CLIP) and Large Language Models(LLMs), consisting of four modules that identify medical disorders, generate relevant context, filter medical concepts, and craft visually aware summaries. Our comprehensive framework harnesses the power of CLIP, a multimodal foundation model, and various general-purpose LLMs, comprising four main modules: the medical disorder identification module, the relevant context generation module, the context filtration module for distilling relevant medical concepts and knowledge, and finally, a general-purpose LLM to generate visually aware medical question summaries. Leveraging our MMQS dataset, we showcase how visual cues from images enhance the generation of medically nuanced summaries. This multimodal approach not only enhances the decision-making process in healthcare but also fosters a more nuanced understanding of patient queries, laying the groundwork for future research in personalized and responsive medical care <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2312.11541v1-abstract-full').style.display = 'none'; document.getElementById('2312.11541v1-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, 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">Comments:</span> <span class="has-text-grey-dark mathjax">AAAI 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/2312.09378">arXiv:2312.09378</a> <span> [<a href="https://arxiv.org/pdf/2312.09378">pdf</a>, <a href="https://arxiv.org/format/2312.09378">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> </div> </div> <p class="title is-5 mathjax"> Emergent fault friction and supershear in a continuum model of geophysical rupture </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Arora%2C+A">Abhishek Arora</a>, <a href="/search/?searchtype=author&query=Acharya%2C+A">Amit Acharya</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="2312.09378v1-abstract-short" style="display: inline;"> Important physical observations in rupture dynamics such as static fault friction, short-slip, self-healing, and supershear phenomenon in cracks are studied. A continuum model of rupture dynamics is developed using the field dislocation mechanics (FDM) theory. The energy density function in our model encodes accepted and simple physical facts related to rocks and granular materials under compressi… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2312.09378v1-abstract-full').style.display = 'inline'; document.getElementById('2312.09378v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2312.09378v1-abstract-full" style="display: none;"> Important physical observations in rupture dynamics such as static fault friction, short-slip, self-healing, and supershear phenomenon in cracks are studied. A continuum model of rupture dynamics is developed using the field dislocation mechanics (FDM) theory. The energy density function in our model encodes accepted and simple physical facts related to rocks and granular materials under compression. We work within a 2-dimensional ansatz of FDM where the rupture front is allowed to move only in a horizontal fault layer sandwiched between elastic blocks. Damage via the degradation of elastic modulus is allowed to occur only in the fault layer, characterized by the amount of plastic slip. The theory dictates the evolution equation of the plastic shear strain to be a Hamilton-Jacobi (H-J) equation, resulting in the representation of a propagating rupture front, which is fully coupled to elastodynamics in the whole domain. Our simulations recover static friction laws as emergent features of our model, without putting in by hand any such discontinuous criteria. Estimates of material parameters of cohesion and friction angle are deduced. Short-slip and slip-weakening (crack-like) behaviors are also reproduced as a function of the degree of damage behind the rupture front. The long-time behavior of a moving rupture front is probed, and it is deduced that the equilibrium profiles under no shear stress are not traveling wave profiles under non-zero shear load. However, it is shown that a traveling wave structure is likely attained in the limit of long times. Finally, a crack-like damage front is driven by an initial impact loading, and it is observed in our simulations that an upper bound to the crack speed is the dilatational wave speed of the material unless the material is put under pre-stressed conditions, in which case supersonic motion can be obtained. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2312.09378v1-abstract-full').style.display = 'none'; document.getElementById('2312.09378v1-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 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">Comments:</span> <span class="has-text-grey-dark mathjax">keywords: Dynamic Rupture; fault friction; supershear; yield criterion</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2312.03742">arXiv:2312.03742</a> <span> [<a href="https://arxiv.org/pdf/2312.03742">pdf</a>, <a href="https://arxiv.org/format/2312.03742">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="Machine Learning">cs.LG</span> </div> </div> <p class="title is-5 mathjax"> Clinical Risk Prediction Using Language Models: Benefits And Considerations </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Acharya%2C+A">Angeela Acharya</a>, <a href="/search/?searchtype=author&query=Shrestha%2C+S">Sulabh Shrestha</a>, <a href="/search/?searchtype=author&query=Chen%2C+A">Anyi Chen</a>, <a href="/search/?searchtype=author&query=Conte%2C+J">Joseph Conte</a>, <a href="/search/?searchtype=author&query=Avramovic%2C+S">Sanja Avramovic</a>, <a href="/search/?searchtype=author&query=Sikdar%2C+S">Siddhartha Sikdar</a>, <a href="/search/?searchtype=author&query=Anastasopoulos%2C+A">Antonios Anastasopoulos</a>, <a href="/search/?searchtype=author&query=Das%2C+S">Sanmay Das</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="2312.03742v1-abstract-short" style="display: inline;"> The utilization of Electronic Health Records (EHRs) for clinical risk prediction is on the rise. However, strict privacy regulations limit access to comprehensive health records, making it challenging to apply standard machine learning algorithms in practical real-world scenarios. Previous research has addressed this data limitation by incorporating medical ontologies and employing transfer learni… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2312.03742v1-abstract-full').style.display = 'inline'; document.getElementById('2312.03742v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2312.03742v1-abstract-full" style="display: none;"> The utilization of Electronic Health Records (EHRs) for clinical risk prediction is on the rise. However, strict privacy regulations limit access to comprehensive health records, making it challenging to apply standard machine learning algorithms in practical real-world scenarios. Previous research has addressed this data limitation by incorporating medical ontologies and employing transfer learning methods. In this study, we investigate the potential of leveraging language models (LMs) as a means to incorporate supplementary domain knowledge for improving the performance of various EHR-based risk prediction tasks. Unlike applying LMs to unstructured EHR data such as clinical notes, this study focuses on using textual descriptions within structured EHR to make predictions exclusively based on that information. We extensively compare against previous approaches across various data types and sizes. We find that employing LMs to represent structured EHRs, such as diagnostic histories, leads to improved or at least comparable performance in diverse risk prediction tasks. Furthermore, LM-based approaches offer numerous advantages, including few-shot learning, the capability to handle previously unseen medical concepts, and adaptability to various medical vocabularies. Nevertheless, we underscore, through various experiments, the importance of being cautious when employing such models, as concerns regarding the reliability of LMs persist. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2312.03742v1-abstract-full').style.display = 'none'; document.getElementById('2312.03742v1-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, 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">Comments:</span> <span class="has-text-grey-dark mathjax">12 pages, 6 figures, 4 tables</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2312.01768">arXiv:2312.01768</a> <span> [<a href="https://arxiv.org/pdf/2312.01768">pdf</a>, <a href="https://arxiv.org/format/2312.01768">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"> Localizing and Assessing Node Significance in Default Mode Network using Sub-Community Detection in Mild Cognitive Impairment </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Acharya%2C+A">Ameiy Acharya</a>, <a href="/search/?searchtype=author&query=Pradeep%2C+C+S">Chakka Sai Pradeep</a>, <a href="/search/?searchtype=author&query=Sinha%2C+N">Neelam Sinha</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="2312.01768v1-abstract-short" style="display: inline;"> Our study aims to utilize fMRI to identify the affected brain regions within the Default Mode Network (DMN) in subjects with Mild Cognitive Impairment (MCI), using a novel Node Significance Score (NSS). We construct subject-specific DMN graphs by employing partial correlation of Regions of Interest (ROIs) that make-up the DMN. For the DMN graph, ROIs are the nodes and edges are determined based on… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2312.01768v1-abstract-full').style.display = 'inline'; document.getElementById('2312.01768v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2312.01768v1-abstract-full" style="display: none;"> Our study aims to utilize fMRI to identify the affected brain regions within the Default Mode Network (DMN) in subjects with Mild Cognitive Impairment (MCI), using a novel Node Significance Score (NSS). We construct subject-specific DMN graphs by employing partial correlation of Regions of Interest (ROIs) that make-up the DMN. For the DMN graph, ROIs are the nodes and edges are determined based on partial correlation. Four popular community detection algorithms (Clique Percolation Method (CPM), Louvain algorithm, Greedy Modularity and Leading Eigenvectors) are applied to determine the largest sub-community. NSS ratings are derived for each node, considering (I) frequency in the largest sub-community within a class across all subjects and (II) occurrence in the largest sub-community according to all four methods. After computing the NSS of each ROI in both healthy and MCI subjects, we quantify the score disparity to identify nodes most impacted by MCI. The results reveal a disparity exceeding 20% for 10 DMN nodes, maximally for PCC and Fusiform, showing 45.69% and 43.08% disparity. This aligns with existing medical literature, additionally providing a quantitative measure that enables the ordering of the affected ROIs. These findings offer valuable insights and could lead to treatment strategies aggressively targeting the affected nodes. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2312.01768v1-abstract-full').style.display = 'none'; document.getElementById('2312.01768v1-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 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">Comments:</span> <span class="has-text-grey-dark mathjax">4 pages, 2 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/2311.16633">arXiv:2311.16633</a> <span> [<a href="https://arxiv.org/pdf/2311.16633">pdf</a>, <a href="https://arxiv.org/format/2311.16633">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link 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="Instrumentation and Methods for Astrophysics">astro-ph.IM</span> </div> </div> <p class="title is-5 mathjax"> 21-cm Signal from the Epoch of Reionization: A Machine Learning upgrade to Foreground Removal with Gaussian Process Regression </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Acharya%2C+A">Anshuman Acharya</a>, <a href="/search/?searchtype=author&query=Mertens%2C+F">Florent Mertens</a>, <a href="/search/?searchtype=author&query=Ciardi%2C+B">Benedetta Ciardi</a>, <a href="/search/?searchtype=author&query=Ghara%2C+R">Raghunath Ghara</a>, <a href="/search/?searchtype=author&query=Koopmans%2C+L+V+E">L茅on V. E. Koopmans</a>, <a href="/search/?searchtype=author&query=Giri%2C+S+K">Sambit K. Giri</a>, <a href="/search/?searchtype=author&query=Hothi%2C+I">Ian Hothi</a>, <a href="/search/?searchtype=author&query=Ma%2C+Q">Qing-Bo Ma</a>, <a href="/search/?searchtype=author&query=Mellema%2C+G">Garrelt Mellema</a>, <a href="/search/?searchtype=author&query=Munshi%2C+S">Satyapan Munshi</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="2311.16633v2-abstract-short" style="display: inline;"> In recent years, a Gaussian Process Regression (GPR) based framework has been developed for foreground mitigation from data collected by the LOw-Frequency ARray (LOFAR), to measure the 21-cm signal power spectrum from the Epoch of Reionization (EoR) and Cosmic Dawn. However, it has been noted that through this method there can be a significant amount of signal loss if the EoR signal covariance is… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2311.16633v2-abstract-full').style.display = 'inline'; document.getElementById('2311.16633v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2311.16633v2-abstract-full" style="display: none;"> In recent years, a Gaussian Process Regression (GPR) based framework has been developed for foreground mitigation from data collected by the LOw-Frequency ARray (LOFAR), to measure the 21-cm signal power spectrum from the Epoch of Reionization (EoR) and Cosmic Dawn. However, it has been noted that through this method there can be a significant amount of signal loss if the EoR signal covariance is misestimated. To obtain better covariance models, we propose to use a kernel trained on the {\tt GRIZZLY} simulations using a Variational Auto-Encoder (VAE) based algorithm. In this work, we explore the abilities of this Machine Learning based kernel (VAE kernel) used with GPR, by testing it on mock signals from a variety of simulations, exploring noise levels corresponding to $\approx$10 nights ($\approx$141 hours) and $\approx$100 nights ($\approx$1410 hours) of observations with LOFAR. Our work suggests the possibility of successful extraction of the 21-cm signal within 2$蟽$ uncertainty in most cases using the VAE kernel, with better recovery of both shape and power than with previously used covariance models. We also explore the role of the excess noise component identified in past applications of GPR and additionally analyse the possibility of redshift dependence on the performance of the VAE kernel. The latter allows us to prepare for future LOFAR observations at a range of redshifts, as well as compare with results from other telescopes. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2311.16633v2-abstract-full').style.display = 'none'; document.getElementById('2311.16633v2-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 29 November, 2023; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 28 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">13 pages, 7 figures, 3 tables. Accepted for publication in the Monthly Notices of the Royal Astronomical Society</span> </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Report number:</span> NORDITA 2023-074 </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2311.12289">arXiv:2311.12289</a> <span> [<a href="https://arxiv.org/pdf/2311.12289">pdf</a>, <a href="https://arxiv.org/format/2311.12289">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"> ATLANTIC: Structure-Aware Retrieval-Augmented Language Model for Interdisciplinary Science </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Munikoti%2C+S">Sai Munikoti</a>, <a href="/search/?searchtype=author&query=Acharya%2C+A">Anurag Acharya</a>, <a href="/search/?searchtype=author&query=Wagle%2C+S">Sridevi Wagle</a>, <a href="/search/?searchtype=author&query=Horawalavithana%2C+S">Sameera Horawalavithana</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="2311.12289v1-abstract-short" style="display: inline;"> Large language models record impressive performance on many natural language processing tasks. However, their knowledge capacity is limited to the pretraining corpus. Retrieval augmentation offers an effective solution by retrieving context from external knowledge sources to complement the language model. However, existing retrieval augmentation techniques ignore the structural relationships betwe… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2311.12289v1-abstract-full').style.display = 'inline'; document.getElementById('2311.12289v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2311.12289v1-abstract-full" style="display: none;"> Large language models record impressive performance on many natural language processing tasks. However, their knowledge capacity is limited to the pretraining corpus. Retrieval augmentation offers an effective solution by retrieving context from external knowledge sources to complement the language model. However, existing retrieval augmentation techniques ignore the structural relationships between these documents. Furthermore, retrieval models are not explored much in scientific tasks, especially in regard to the faithfulness of retrieved documents. In this paper, we propose a novel structure-aware retrieval augmented language model that accommodates document structure during retrieval augmentation. We create a heterogeneous document graph capturing multiple types of relationships (e.g., citation, co-authorship, etc.) that connect documents from more than 15 scientific disciplines (e.g., Physics, Medicine, Chemistry, etc.). We train a graph neural network on the curated document graph to act as a structural encoder for the corresponding passages retrieved during the model pretraining. Particularly, along with text embeddings of the retrieved passages, we obtain structural embeddings of the documents (passages) and fuse them together before feeding them to the language model. We evaluate our model extensively on various scientific benchmarks that include science question-answering and scientific document classification tasks. Experimental results demonstrate that structure-aware retrieval improves retrieving more coherent, faithful and contextually relevant passages, while showing a comparable performance in the overall accuracy. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2311.12289v1-abstract-full').style.display = 'none'; document.getElementById('2311.12289v1-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 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">ACM Class:</span> I.2.7 </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2311.09358">arXiv:2311.09358</a> <span> [<a href="https://arxiv.org/pdf/2311.09358">pdf</a>, <a href="https://arxiv.org/format/2311.09358">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"> Empirical evaluation of Uncertainty Quantification in Retrieval-Augmented Language Models for Science </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Wagle%2C+S">Sridevi Wagle</a>, <a href="/search/?searchtype=author&query=Munikoti%2C+S">Sai Munikoti</a>, <a href="/search/?searchtype=author&query=Acharya%2C+A">Anurag Acharya</a>, <a href="/search/?searchtype=author&query=Smith%2C+S">Sara Smith</a>, <a href="/search/?searchtype=author&query=Horawalavithana%2C+S">Sameera Horawalavithana</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="2311.09358v1-abstract-short" style="display: inline;"> Large language models (LLMs) have shown remarkable achievements in natural language processing tasks, producing high-quality outputs. However, LLMs still exhibit limitations, including the generation of factually incorrect information. In safety-critical applications, it is important to assess the confidence of LLM-generated content to make informed decisions. Retrieval Augmented Language Models (… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2311.09358v1-abstract-full').style.display = 'inline'; document.getElementById('2311.09358v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2311.09358v1-abstract-full" style="display: none;"> Large language models (LLMs) have shown remarkable achievements in natural language processing tasks, producing high-quality outputs. However, LLMs still exhibit limitations, including the generation of factually incorrect information. In safety-critical applications, it is important to assess the confidence of LLM-generated content to make informed decisions. Retrieval Augmented Language Models (RALMs) is relatively a new area of research in NLP. RALMs offer potential benefits for scientific NLP tasks, as retrieved documents, can serve as evidence to support model-generated content. This inclusion of evidence enhances trustworthiness, as users can verify and explore the retrieved documents to validate model outputs. Quantifying uncertainty in RALM generations further improves trustworthiness, with retrieved text and confidence scores contributing to a comprehensive and reliable model for scientific applications. However, there is limited to no research on UQ for RALMs, particularly in scientific contexts. This study aims to address this gap by conducting a comprehensive evaluation of UQ in RALMs, focusing on scientific tasks. This research investigates how uncertainty scores vary when scientific knowledge is incorporated as pretraining and retrieval data and explores the relationship between uncertainty scores and the accuracy of model-generated outputs. We observe that an existing RALM finetuned with scientific knowledge as the retrieval data tends to be more confident in generating predictions compared to the model pretrained only with scientific knowledge. We also found that RALMs are overconfident in their predictions, making inaccurate predictions more confidently than accurate ones. Scientific knowledge provided either as pretraining or retrieval corpus does not help alleviate this issue. We released our code, data and dashboards at https://github.com/pnnl/EXPERT2. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2311.09358v1-abstract-full').style.display = 'none'; document.getElementById('2311.09358v1-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 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">ACM Class:</span> I.2.7 </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2311.05592">arXiv:2311.05592</a> <span> [<a href="https://arxiv.org/pdf/2311.05592">pdf</a>, <a href="https://arxiv.org/format/2311.05592">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Quantum Physics">quant-ph</span> </div> </div> <p class="title is-5 mathjax"> Fixed-point Grover Adaptive Search for Quadratic Binary Optimization Problems </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Nagy%2C+%C3%81">脕kos Nagy</a>, <a href="/search/?searchtype=author&query=Park%2C+J">Jaime Park</a>, <a href="/search/?searchtype=author&query=Zhang%2C+C">Cindy Zhang</a>, <a href="/search/?searchtype=author&query=Acharya%2C+A">Atithi Acharya</a>, <a href="/search/?searchtype=author&query=Khan%2C+A">Alex Khan</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="2311.05592v5-abstract-short" style="display: inline;"> We study a Grover-type method for Quadratic Unconstrained Binary Optimization (QUBO) problems. For an $n$-dimensional QUBO problem with $m$ nonzero terms, we construct a marker oracle for such problems with a tuneable parameter, $螞\in \left[ 1, m \right] \cap \mathbb{Z}$. At $d \in \mathbb{Z}_+$ precision, the oracle uses $O (n + 螞d)$ qubits, has total depth of… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2311.05592v5-abstract-full').style.display = 'inline'; document.getElementById('2311.05592v5-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2311.05592v5-abstract-full" style="display: none;"> We study a Grover-type method for Quadratic Unconstrained Binary Optimization (QUBO) problems. For an $n$-dimensional QUBO problem with $m$ nonzero terms, we construct a marker oracle for such problems with a tuneable parameter, $螞\in \left[ 1, m \right] \cap \mathbb{Z}$. At $d \in \mathbb{Z}_+$ precision, the oracle uses $O (n + 螞d)$ qubits, has total depth of $O \left( \tfrac{m}螞 \log_2 (n) + \log_2 (d) \right)$, and non-Clifford depth of $O \left( \tfrac{m}螞 \right)$. Moreover, each qubit required to be connected to at most $O \left( \log_2 (螞+ d) \right)$ other qubits. In the case of a maximum graph cuts, as $d = 2 \left\lceil \log_2 (n) \right\rceil$ always suffices, the depth of the marker oracle can be made as shallow as $O (\log_2 (n))$. For all values of $螞$, the non-Clifford gate count of these oracles is strictly lower (at least by a factor of $\sim 2$) than previous constructions. Furthermore, we introduce a novel \textit{Fixed-point Grover Adaptive Search for QUBO Problems}, using our oracle design and a hybrid Fixed-point Grover Search, motivated by the works of Boyer et al. and Li et al. This method has better performance guarantees than previous Grover Adaptive Search methods. Some of our results are novel and useful for any method based on Fixed-point Grover Search. Finally, we give a heuristic argument that, with high probability and in $O \left( \tfrac{\log_2 (n)}{\sqrt蔚} \right)$ time, this adaptive method finds a configuration that is among the best $蔚2^n$ ones. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2311.05592v5-abstract-full').style.display = 'none'; document.getElementById('2311.05592v5-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> 19 October, 2024; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 9 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">25 pages, 5 figures, 1 table. Accepted by IEEE Transactions on Quantum Engineering</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2311.04348">arXiv:2311.04348</a> <span> [<a href="https://arxiv.org/pdf/2311.04348">pdf</a>, <a href="https://arxiv.org/format/2311.04348">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"> Evaluating the Effectiveness of Retrieval-Augmented Large Language Models in Scientific Document Reasoning </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Munikoti%2C+S">Sai Munikoti</a>, <a href="/search/?searchtype=author&query=Acharya%2C+A">Anurag Acharya</a>, <a href="/search/?searchtype=author&query=Wagle%2C+S">Sridevi Wagle</a>, <a href="/search/?searchtype=author&query=Horawalavithana%2C+S">Sameera Horawalavithana</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="2311.04348v1-abstract-short" style="display: inline;"> Despite the dramatic progress in Large Language Model (LLM) development, LLMs often provide seemingly plausible but not factual information, often referred to as hallucinations. Retrieval-augmented LLMs provide a non-parametric approach to solve these issues by retrieving relevant information from external data sources and augment the training process. These models help to trace evidence from an e… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2311.04348v1-abstract-full').style.display = 'inline'; document.getElementById('2311.04348v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2311.04348v1-abstract-full" style="display: none;"> Despite the dramatic progress in Large Language Model (LLM) development, LLMs often provide seemingly plausible but not factual information, often referred to as hallucinations. Retrieval-augmented LLMs provide a non-parametric approach to solve these issues by retrieving relevant information from external data sources and augment the training process. These models help to trace evidence from an externally provided knowledge base allowing the model predictions to be better interpreted and verified. In this work, we critically evaluate these models in their ability to perform in scientific document reasoning tasks. To this end, we tuned multiple such model variants with science-focused instructions and evaluated them on a scientific document reasoning benchmark for the usefulness of the retrieved document passages. Our findings suggest that models justify predictions in science tasks with fabricated evidence and leveraging scientific corpus as pretraining data does not alleviate the risk of evidence fabrication. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2311.04348v1-abstract-full').style.display = 'none'; document.getElementById('2311.04348v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 7 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">5 pages</span> </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">ACM Class:</span> I.2.7 </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2311.00667">arXiv:2311.00667</a> <span> [<a href="https://arxiv.org/pdf/2311.00667">pdf</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Quantitative Methods">q-bio.QM</span> </div> </div> <p class="title is-5 mathjax"> Development and application of SEM/EDS in biological, biomedical & nanotechnological research </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Acharya%2C+A">Aniruddha Acharya</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="2311.00667v1-abstract-short" style="display: inline;"> This comprehensive review discusses the development of scanning electron microscopy and the application of this technology in different fields such as biology, nanobiotechnology and biomedical science. Besides being a tool for high resolution imaging of surface or topography, the technology is coupled with analytical techniques such as energy dispersive spectroscopy for elemental mapping. Since th… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2311.00667v1-abstract-full').style.display = 'inline'; document.getElementById('2311.00667v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2311.00667v1-abstract-full" style="display: none;"> This comprehensive review discusses the development of scanning electron microscopy and the application of this technology in different fields such as biology, nanobiotechnology and biomedical science. Besides being a tool for high resolution imaging of surface or topography, the technology is coupled with analytical techniques such as energy dispersive spectroscopy for elemental mapping. Since the commercialization of the technology, it has developed manifold and currently very high-resolution nano scale imaging is possible by this technology. The development of FIB-SEM has allowed three-dimensional imaging of materials while the development of cryostage allows imaging of hydrated biological samples. Though variable pressure or environmental SEM can be used for imaging hydrated samples, they cannot capture a high-resolution image. SBEM and ATUM-SEM has automated the sampling process while improved and more powerful software along with user-friendly computer interface has made image analysis faster and more reliable. This review presents one of the most widely used analytical techniques used across the globe for scientific investigation. The power and potential of SEM is expanding with the development of accessory technology. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2311.00667v1-abstract-full').style.display = 'none'; document.getElementById('2311.00667v1-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 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">32 pages, 5 figures, 1 table, unpublished work</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2311.00106">arXiv:2311.00106</a> <span> [<a href="https://arxiv.org/pdf/2311.00106">pdf</a>, <a href="https://arxiv.org/format/2311.00106">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Mathematical Physics">math-ph</span> </div> </div> <p class="title is-5 mathjax"> Variational principle for a damped, quadratically interacting particle chain with nonconservative forcing </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Acharya%2C+A">Amit Acharya</a>, <a href="/search/?searchtype=author&query=Sengupta%2C+A+N">Ambar N. Sengupta</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="2311.00106v2-abstract-short" style="display: inline;"> A method for designing variational principles for the dynamics of a possibly dissipative and non-conservatively forced chain of particles is demonstrated. Some qualitative features of the formulation are discussed. </span> <span class="abstract-full has-text-grey-dark mathjax" id="2311.00106v2-abstract-full" style="display: none;"> A method for designing variational principles for the dynamics of a possibly dissipative and non-conservatively forced chain of particles is demonstrated. Some qualitative features of the formulation are discussed. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2311.00106v2-abstract-full').style.display = 'none'; document.getElementById('2311.00106v2-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 April, 2024; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 31 October, 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/2310.17688">arXiv:2310.17688</a> <span> [<a href="https://arxiv.org/pdf/2310.17688">pdf</a>, <a href="https://arxiv.org/format/2310.17688">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Computers and Society">cs.CY</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Artificial Intelligence">cs.AI</span> <span class="tag is-small is-grey 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="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/10.1126/science.adn0117">10.1126/science.adn0117 <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </div> </div> <p class="title is-5 mathjax"> Managing extreme AI risks amid rapid progress </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Bengio%2C+Y">Yoshua Bengio</a>, <a href="/search/?searchtype=author&query=Hinton%2C+G">Geoffrey Hinton</a>, <a href="/search/?searchtype=author&query=Yao%2C+A">Andrew Yao</a>, <a href="/search/?searchtype=author&query=Song%2C+D">Dawn Song</a>, <a href="/search/?searchtype=author&query=Abbeel%2C+P">Pieter Abbeel</a>, <a href="/search/?searchtype=author&query=Darrell%2C+T">Trevor Darrell</a>, <a href="/search/?searchtype=author&query=Harari%2C+Y+N">Yuval Noah Harari</a>, <a href="/search/?searchtype=author&query=Zhang%2C+Y">Ya-Qin Zhang</a>, <a href="/search/?searchtype=author&query=Xue%2C+L">Lan Xue</a>, <a href="/search/?searchtype=author&query=Shalev-Shwartz%2C+S">Shai Shalev-Shwartz</a>, <a href="/search/?searchtype=author&query=Hadfield%2C+G">Gillian Hadfield</a>, <a href="/search/?searchtype=author&query=Clune%2C+J">Jeff Clune</a>, <a href="/search/?searchtype=author&query=Maharaj%2C+T">Tegan Maharaj</a>, <a href="/search/?searchtype=author&query=Hutter%2C+F">Frank Hutter</a>, <a href="/search/?searchtype=author&query=Baydin%2C+A+G">At谋l谋m G眉ne艧 Baydin</a>, <a href="/search/?searchtype=author&query=McIlraith%2C+S">Sheila McIlraith</a>, <a href="/search/?searchtype=author&query=Gao%2C+Q">Qiqi Gao</a>, <a href="/search/?searchtype=author&query=Acharya%2C+A">Ashwin Acharya</a>, <a href="/search/?searchtype=author&query=Krueger%2C+D">David Krueger</a>, <a href="/search/?searchtype=author&query=Dragan%2C+A">Anca Dragan</a>, <a href="/search/?searchtype=author&query=Torr%2C+P">Philip Torr</a>, <a href="/search/?searchtype=author&query=Russell%2C+S">Stuart Russell</a>, <a href="/search/?searchtype=author&query=Kahneman%2C+D">Daniel Kahneman</a>, <a href="/search/?searchtype=author&query=Brauner%2C+J">Jan Brauner</a>, <a href="/search/?searchtype=author&query=Mindermann%2C+S">S枚ren Mindermann</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="2310.17688v3-abstract-short" style="display: inline;"> Artificial Intelligence (AI) is progressing rapidly, and companies are shifting their focus to developing generalist AI systems that can autonomously act and pursue goals. Increases in capabilities and autonomy may soon massively amplify AI's impact, with risks that include large-scale social harms, malicious uses, and an irreversible loss of human control over autonomous AI systems. Although rese… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2310.17688v3-abstract-full').style.display = 'inline'; document.getElementById('2310.17688v3-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2310.17688v3-abstract-full" style="display: none;"> Artificial Intelligence (AI) is progressing rapidly, and companies are shifting their focus to developing generalist AI systems that can autonomously act and pursue goals. Increases in capabilities and autonomy may soon massively amplify AI's impact, with risks that include large-scale social harms, malicious uses, and an irreversible loss of human control over autonomous AI systems. Although researchers have warned of extreme risks from AI, there is a lack of consensus about how exactly such risks arise, and how to manage them. Society's response, despite promising first steps, is incommensurate with the possibility of rapid, transformative progress that is expected by many experts. AI safety research is lagging. Present governance initiatives lack the mechanisms and institutions to prevent misuse and recklessness, and barely address autonomous systems. In this short consensus paper, we describe extreme risks from upcoming, advanced AI systems. Drawing on lessons learned from other safety-critical technologies, we then outline a comprehensive plan combining technical research and development with proactive, adaptive governance mechanisms for a more commensurate preparation. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2310.17688v3-abstract-full').style.display = 'none'; document.getElementById('2310.17688v3-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 22 May, 2024; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 26 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">Published in Science: https://www.science.org/doi/10.1126/science.adn0117</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2310.15180">arXiv:2310.15180</a> <span> [<a href="https://arxiv.org/pdf/2310.15180">pdf</a>, <a href="https://arxiv.org/format/2310.15180">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> </div> </div> <p class="title is-5 mathjax"> Lorentzian path integral in Kantowski-Sachs anisotropic cosmology </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Ghosh%2C+S">Saumya Ghosh</a>, <a href="/search/?searchtype=author&query=Acharya%2C+A">Arnab Acharya</a>, <a href="/search/?searchtype=author&query=Gangopadhyay%2C+S">Sunandan Gangopadhyay</a>, <a href="/search/?searchtype=author&query=Panigrahi%2C+P+K">Prasanta K. Panigrahi</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="2310.15180v1-abstract-short" style="display: inline;"> Motivated by the recent development in quantum cosmology, we revisit the anisotropic Kantowski-Sachs model in the light of a Lorentzian path integral formalism. Studies so far have considered the Euclidean method where the choice of the lapse integration contour is constrained by certain physical considerations rather than mathematical justification. In this paper, we have studied the Hartle-Hawki… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2310.15180v1-abstract-full').style.display = 'inline'; document.getElementById('2310.15180v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2310.15180v1-abstract-full" style="display: none;"> Motivated by the recent development in quantum cosmology, we revisit the anisotropic Kantowski-Sachs model in the light of a Lorentzian path integral formalism. Studies so far have considered the Euclidean method where the choice of the lapse integration contour is constrained by certain physical considerations rather than mathematical justification. In this paper, we have studied the Hartle-Hawking no-boundary proposal along with the use of Picard-Lefschetz theory in performing the lapse integration. In an isotropic limit, we show our results agree with the studies made in FLRW cosmology. We also observe that in the large scale structure the no-boundary proposal tends towards a conical singularity at the beginning of time. We have also performed a massless scalar perturbation analysis with no back reaction. This reveals that if there were any perturbation present at the beginning of the universe then that would flare up at the final boundary. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2310.15180v1-abstract-full').style.display = 'none'; document.getElementById('2310.15180v1-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> 19 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">9 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/2310.13401">arXiv:2310.13401</a> <span> [<a href="https://arxiv.org/pdf/2310.13401">pdf</a>, <a href="https://arxiv.org/format/2310.13401">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link 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="Astrophysics of Galaxies">astro-ph.GA</span> </div> </div> <p class="title is-5 mathjax"> Cosmic variance suppression in radiation-hydrodynamic modeling of the reionization-era 21-cm signal </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Acharya%2C+A">Anshuman Acharya</a>, <a href="/search/?searchtype=author&query=Garaldi%2C+E">Enrico Garaldi</a>, <a href="/search/?searchtype=author&query=Ciardi%2C+B">Benedetta Ciardi</a>, <a href="/search/?searchtype=author&query=Ma%2C+Q">Qing-bo Ma</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="2310.13401v2-abstract-short" style="display: inline;"> The 21-cm line emitted by neutral hydrogen is the most promising probe of the Epoch of Reionization (EoR). Multiple radio interferometric instruments are on the cusp of detecting its power spectrum. It is therefore essential to deliver robust theoretical predictions, enabling sound inference of the coeval Universe properties. The nature of this signal traditionally required the modelling of… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2310.13401v2-abstract-full').style.display = 'inline'; document.getElementById('2310.13401v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2310.13401v2-abstract-full" style="display: none;"> The 21-cm line emitted by neutral hydrogen is the most promising probe of the Epoch of Reionization (EoR). Multiple radio interferometric instruments are on the cusp of detecting its power spectrum. It is therefore essential to deliver robust theoretical predictions, enabling sound inference of the coeval Universe properties. The nature of this signal traditionally required the modelling of $\mathcal{O}(10^{7-8} \, {\rm Mpc}^3)$ volumes to suppress the impact of cosmic variance. However, the recently-proposed Fixed & Paired (F&P) approach uses carefully-crafted simulation pairs to achieve equal results in smaller volumes. In this work, we thoroughly test the applicability of and improvement granted by this technique to different observables of the 21-cm signal from the EoR. We employ radiation-magneto-hydrodynamics simulations to ensure the most realistic physical description of this epoch, greatly improving over previous studies using a semi-numerical approach without accurate galaxy formation physics and radiative transfer. We estimate the statistical improvement granted by the F&P technique on predictions of the skewness, power spectrum, bispectrum and ionized regions size distribution of the 21-cm signal at redshift $7 \leq z \leq 10$ (corresponding to $\geq80\%$ of the gas being neutral). We find that the effective volume of F&P simulations is at least 3.5 times larger than traditional simulations. This directly translates into an equal improvement in the computational cost (in terms of time and memory). Finally, we confirm that a combination of different observables like skewness, power spectrum and bispectrum across different redshifts can be utilised to maximise the improvement. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2310.13401v2-abstract-full').style.display = 'none'; document.getElementById('2310.13401v2-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 March, 2024; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 20 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">13 pages, 11 figures, 2 tables. Accepted for publication in the Monthly Notices of the Royal Astronomical Society (MNRAS)</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2310.10920">arXiv:2310.10920</a> <span> [<a href="https://arxiv.org/pdf/2310.10920">pdf</a>, <a href="https://arxiv.org/format/2310.10920">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"> NuclearQA: A Human-Made Benchmark for Language Models for the Nuclear Domain </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Acharya%2C+A">Anurag Acharya</a>, <a href="/search/?searchtype=author&query=Munikoti%2C+S">Sai Munikoti</a>, <a href="/search/?searchtype=author&query=Hellinger%2C+A">Aaron Hellinger</a>, <a href="/search/?searchtype=author&query=Smith%2C+S">Sara Smith</a>, <a href="/search/?searchtype=author&query=Wagle%2C+S">Sridevi Wagle</a>, <a href="/search/?searchtype=author&query=Horawalavithana%2C+S">Sameera Horawalavithana</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="2310.10920v1-abstract-short" style="display: inline;"> As LLMs have become increasingly popular, they have been used in almost every field. But as the application for LLMs expands from generic fields to narrow, focused science domains, there exists an ever-increasing gap in ways to evaluate their efficacy in those fields. For the benchmarks that do exist, a lot of them focus on questions that don't require proper understanding of the subject in questi… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2310.10920v1-abstract-full').style.display = 'inline'; document.getElementById('2310.10920v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2310.10920v1-abstract-full" style="display: none;"> As LLMs have become increasingly popular, they have been used in almost every field. But as the application for LLMs expands from generic fields to narrow, focused science domains, there exists an ever-increasing gap in ways to evaluate their efficacy in those fields. For the benchmarks that do exist, a lot of them focus on questions that don't require proper understanding of the subject in question. In this paper, we present NuclearQA, a human-made benchmark of 100 questions to evaluate language models in the nuclear domain, consisting of a varying collection of questions that have been specifically designed by experts to test the abilities of language models. We detail our approach and show how the mix of several types of questions makes our benchmark uniquely capable of evaluating models in the nuclear domain. We also present our own evaluation metric for assessing LLM's performances due to the limitations of existing ones. Our experiments on state-of-the-art models suggest that even the best LLMs perform less than satisfactorily on our benchmark, demonstrating the scientific knowledge gap of existing LLMs. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2310.10920v1-abstract-full').style.display = 'none'; document.getElementById('2310.10920v1-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 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">9 pages</span> </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">ACM Class:</span> I.2.7 </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2310.03201">arXiv:2310.03201</a> <span> [<a href="https://arxiv.org/pdf/2310.03201">pdf</a>, <a href="https://arxiv.org/format/2310.03201">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Analysis of PDEs">math.AP</span> </div> </div> <p class="title is-5 mathjax"> A Hidden Convexity in Continuum Mechanics, with application to classical, continuous-time, rate-(in)dependent plasticity </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Acharya%2C+A">Amit Acharya</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="2310.03201v3-abstract-short" style="display: inline;"> A methodology for defining variational principles for a class of PDE models from continuum mechanics is demonstrated, and some of its features explored. The scheme is applied to quasi-static and dynamic models of rate-independent and rate-dependent, single crystal plasticity at finite deformation. </span> <span class="abstract-full has-text-grey-dark mathjax" id="2310.03201v3-abstract-full" style="display: none;"> A methodology for defining variational principles for a class of PDE models from continuum mechanics is demonstrated, and some of its features explored. The scheme is applied to quasi-static and dynamic models of rate-independent and rate-dependent, single crystal plasticity at finite deformation. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2310.03201v3-abstract-full').style.display = 'none'; document.getElementById('2310.03201v3-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 June, 2024; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 4 October, 2023; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> October 2023. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">This preprint has been classified as math.AP by arXiv moderators. This paper is to appear in Mathematics and Mechanics of Solids</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2309.12631">arXiv:2309.12631</a> <span> [<a href="https://arxiv.org/pdf/2309.12631">pdf</a>, <a href="https://arxiv.org/format/2309.12631">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Quantum Physics">quant-ph</span> </div> </div> <p class="title is-5 mathjax"> Learning the eigenstructure of quantum dynamics using classical shadows </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Acharya%2C+A">Atithi Acharya</a>, <a href="/search/?searchtype=author&query=Saha%2C+S">Siddhartha Saha</a>, <a href="/search/?searchtype=author&query=Sridharan%2C+S">Shagesh Sridharan</a>, <a href="/search/?searchtype=author&query=Bahroun%2C+Y">Yanis Bahroun</a>, <a href="/search/?searchtype=author&query=Sengupta%2C+A+M">Anirvan M. Sengupta</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="2309.12631v1-abstract-short" style="display: inline;"> Learning dynamics from repeated observation of the time evolution of an open quantum system, namely, the problem of quantum process tomography is an important task. This task is difficult in general, but, with some additional constraints could be tractable. This motivates us to look at the problem of Lindblad operator discovery from observations. We point out that for moderate size Hilbert spaces,… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2309.12631v1-abstract-full').style.display = 'inline'; document.getElementById('2309.12631v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2309.12631v1-abstract-full" style="display: none;"> Learning dynamics from repeated observation of the time evolution of an open quantum system, namely, the problem of quantum process tomography is an important task. This task is difficult in general, but, with some additional constraints could be tractable. This motivates us to look at the problem of Lindblad operator discovery from observations. We point out that for moderate size Hilbert spaces, low Kraus rank of the channel, and short time steps, the eigenvalues of the Choi matrix corresponding to the channel have a special structure. We use the least-square method for the estimation of a channel where, for fixed inputs, we estimate the outputs by classical shadows. The resultant noisy estimate of the channel can then be denoised by diagonalizing the nominal Choi matrix, truncating some eigenvalues, and altering it to a genuine Choi matrix. This processed Choi matrix is then compared to the original one. We see that as the number of samples increases, our reconstruction becomes more accurate. We also use tools from random matrix theory to understand the effect of estimation noise in the eigenspectrum of the estimated Choi matrix. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2309.12631v1-abstract-full').style.display = 'none'; document.getElementById('2309.12631v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 22 September, 2023; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> September 2023. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2309.01885">arXiv:2309.01885</a> <span> [<a href="https://arxiv.org/pdf/2309.01885">pdf</a>, <a href="https://arxiv.org/format/2309.01885">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="Computation and Language">cs.CL</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"> QuantEase: Optimization-based Quantization for Language Models </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Behdin%2C+K">Kayhan Behdin</a>, <a href="/search/?searchtype=author&query=Acharya%2C+A">Ayan Acharya</a>, <a href="/search/?searchtype=author&query=Gupta%2C+A">Aman Gupta</a>, <a href="/search/?searchtype=author&query=Song%2C+Q">Qingquan Song</a>, <a href="/search/?searchtype=author&query=Zhu%2C+S">Siyu Zhu</a>, <a href="/search/?searchtype=author&query=Keerthi%2C+S">Sathiya Keerthi</a>, <a href="/search/?searchtype=author&query=Mazumder%2C+R">Rahul Mazumder</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="2309.01885v2-abstract-short" style="display: inline;"> With the rising popularity of Large Language Models (LLMs), there has been an increasing interest in compression techniques that enable their efficient deployment. This study focuses on the Post-Training Quantization (PTQ) of LLMs. Drawing from recent advances, our work introduces QuantEase, a layer-wise quantization framework where individual layers undergo separate quantization. The problem is f… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2309.01885v2-abstract-full').style.display = 'inline'; document.getElementById('2309.01885v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2309.01885v2-abstract-full" style="display: none;"> With the rising popularity of Large Language Models (LLMs), there has been an increasing interest in compression techniques that enable their efficient deployment. This study focuses on the Post-Training Quantization (PTQ) of LLMs. Drawing from recent advances, our work introduces QuantEase, a layer-wise quantization framework where individual layers undergo separate quantization. The problem is framed as a discrete-structured non-convex optimization, prompting the development of algorithms rooted in Coordinate Descent (CD) techniques. These CD-based methods provide high-quality solutions to the complex non-convex layer-wise quantization problems. Notably, our CD-based approach features straightforward updates, relying solely on matrix and vector operations, circumventing the need for matrix inversion or decomposition. We also explore an outlier-aware variant of our approach, allowing for retaining significant weights (outliers) with complete precision. Our proposal attains state-of-the-art performance in terms of perplexity and zero-shot accuracy in empirical evaluations across various LLMs and datasets, with relative improvements up to 15% over methods such as GPTQ. Leveraging careful linear algebra optimizations, QuantEase can quantize models like Falcon-180B on a single NVIDIA A100 GPU in $\sim$3 hours. Particularly noteworthy is our outlier-aware algorithm's capability to achieve near or sub-3-bit quantization of LLMs with an acceptable drop in accuracy, obviating the need for non-uniform quantization or grouping techniques, improving upon methods such as SpQR by up to two times in terms of perplexity. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2309.01885v2-abstract-full').style.display = 'none'; document.getElementById('2309.01885v2-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 December, 2023; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 4 September, 2023; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> September 2023. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2308.14040">arXiv:2308.14040</a> <span> [<a href="https://arxiv.org/pdf/2308.14040">pdf</a>, <a href="https://arxiv.org/format/2308.14040">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Statistical Mechanics">cond-mat.stat-mech</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Quantum Physics">quant-ph</span> </div> <div class="is-inline-block" style="margin-left: 0.5rem"> <div class="tags has-addons"> <span class="tag is-dark is-size-7">doi</span> <span class="tag is-light is-size-7"><a class="" href="https://doi.org/10.1103/PhysRevE.108.064125">10.1103/PhysRevE.108.064125 <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </div> </div> <p class="title is-5 mathjax"> Tight-binding model subject to conditional resets at random times </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Acharya%2C+A">Anish Acharya</a>, <a href="/search/?searchtype=author&query=Gupta%2C+S">Shamik Gupta</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="2308.14040v2-abstract-short" style="display: inline;"> We investigate the dynamics of a quantum system subjected to a time-dependent and conditional resetting protocol. Namely, we ask: what happens when the unitary evolution of the system is repeatedly interrupted at random time instants with an instantaneous reset to a specified set of reset configurations taking place with a probability that depends on the current configuration of the system at the… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2308.14040v2-abstract-full').style.display = 'inline'; document.getElementById('2308.14040v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2308.14040v2-abstract-full" style="display: none;"> We investigate the dynamics of a quantum system subjected to a time-dependent and conditional resetting protocol. Namely, we ask: what happens when the unitary evolution of the system is repeatedly interrupted at random time instants with an instantaneous reset to a specified set of reset configurations taking place with a probability that depends on the current configuration of the system at the instant of reset? Analyzing the protocol in the framework of the so-called tight-binding model describing the hopping of a quantum particle to nearest-neighbour sites in a one-dimensional open lattice, we obtain analytical results for the probability of finding the particle on the different sites of the lattice. We explore a variety of dynamical scenarios, including the one in which the resetting time intervals are sampled from an exponential as well as from a power-law distribution, and a set-up that includes a Floquet-type Hamiltonian involving an external periodic forcing. Under exponential resetting, and in both presence and absence of the external forcing, the system relaxes to a stationary state characterized by localization of the particle around the reset sites. The choice of the reset sites plays a defining role in dictating the relative probability of finding the particle at the reset sites as well as in determining the overall spatial profile of the site-occupation probability. Indeed, a simple choice can be engineered that makes the spatial profile highly asymmetric even when the bare dynamics does not involve the effect of any bias. Furthermore, analyzing the case of power-law resetting serves to demonstrate that the attainment of the stationary state in this quantum problem is not always evident and depends crucially on whether the distribution of reset time intervals has a finite or an infinite mean. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2308.14040v2-abstract-full').style.display = 'none'; document.getElementById('2308.14040v2-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> 19 December, 2023; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 27 August, 2023; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> August 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">23 pages, 8 figures; v2: close to published version, 25 pages, 8 figures</span> </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Journal ref:</span> Phys. Rev. E 108, 064125 (2023) </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2308.02427">arXiv:2308.02427</a> <span> [<a href="https://arxiv.org/pdf/2308.02427">pdf</a>, <a href="https://arxiv.org/format/2308.02427">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Neural and Evolutionary Computing">cs.NE</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="Machine Learning">cs.LG</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Neurons and Cognition">q-bio.NC</span> </div> </div> <p class="title is-5 mathjax"> Unlocking the Potential of Similarity Matching: Scalability, Supervision and Pre-training </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Bahroun%2C+Y">Yanis Bahroun</a>, <a href="/search/?searchtype=author&query=Sridharan%2C+S">Shagesh Sridharan</a>, <a href="/search/?searchtype=author&query=Acharya%2C+A">Atithi Acharya</a>, <a href="/search/?searchtype=author&query=Chklovskii%2C+D+B">Dmitri B. Chklovskii</a>, <a href="/search/?searchtype=author&query=Sengupta%2C+A+M">Anirvan M. Sengupta</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="2308.02427v1-abstract-short" style="display: inline;"> While effective, the backpropagation (BP) algorithm exhibits limitations in terms of biological plausibility, computational cost, and suitability for online learning. As a result, there has been a growing interest in developing alternative biologically plausible learning approaches that rely on local learning rules. This study focuses on the primarily unsupervised similarity matching (SM) framewor… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2308.02427v1-abstract-full').style.display = 'inline'; document.getElementById('2308.02427v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2308.02427v1-abstract-full" style="display: none;"> While effective, the backpropagation (BP) algorithm exhibits limitations in terms of biological plausibility, computational cost, and suitability for online learning. As a result, there has been a growing interest in developing alternative biologically plausible learning approaches that rely on local learning rules. This study focuses on the primarily unsupervised similarity matching (SM) framework, which aligns with observed mechanisms in biological systems and offers online, localized, and biologically plausible algorithms. i) To scale SM to large datasets, we propose an implementation of Convolutional Nonnegative SM using PyTorch. ii) We introduce a localized supervised SM objective reminiscent of canonical correlation analysis, facilitating stacking SM layers. iii) We leverage the PyTorch implementation for pre-training architectures such as LeNet and compare the evaluation of features against BP-trained models. This work combines biologically plausible algorithms with computational efficiency opening multiple avenues for further explorations. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2308.02427v1-abstract-full').style.display = 'none'; document.getElementById('2308.02427v1-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 August, 2023; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> August 2023. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2306.10616">arXiv:2306.10616</a> <span> [<a href="https://arxiv.org/pdf/2306.10616">pdf</a>, <a href="https://arxiv.org/ps/2306.10616">ps</a>, <a href="https://arxiv.org/format/2306.10616">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Mathematical Physics">math-ph</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Dynamical Systems">math.DS</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Classical Physics">physics.class-ph</span> </div> </div> <p class="title is-5 mathjax"> Action principles for dissipative, non-holonomic Newtonian mechanics </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Acharya%2C+A">Amit Acharya</a>, <a href="/search/?searchtype=author&query=Sengupta%2C+A+N">Ambar N. Sengupta</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="2306.10616v4-abstract-short" style="display: inline;"> A methodology for deriving dual variational principles for the classical Newtonian mechanics of mass points in the presence of applied forces, interaction forces, and constraints, all with a general dependence on particle velocities and positions, is presented. Methods for incorporating constraints are critically assessed. General theory, as well as explicitly worked out variational principles for… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2306.10616v4-abstract-full').style.display = 'inline'; document.getElementById('2306.10616v4-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2306.10616v4-abstract-full" style="display: none;"> A methodology for deriving dual variational principles for the classical Newtonian mechanics of mass points in the presence of applied forces, interaction forces, and constraints, all with a general dependence on particle velocities and positions, is presented. Methods for incorporating constraints are critically assessed. General theory, as well as explicitly worked out variational principles for a dissipative system (due to Lorenz) and a system with anholonomic constraints (due to Pars) are demonstrated. Conditions under which a (family of) dual Hamiltonian flow(s), as well as a constant(s) of motion, may be associated with a conservative or dissipative, and possibly constrained, primal system naturally emerge in this work. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2306.10616v4-abstract-full').style.display = 'none'; document.getElementById('2306.10616v4-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 June, 2024; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 18 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">to be published in Proceedings of the Royal Society A</span> </p> </li> </ol> 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