October 2024 arXiv papers — page 30

Yamin Arefeen, Brett Levac, Jonathan I. Tamir

We present the first acquisition-agnostic diffusion generative model for Magnetic Resonance Imaging (MRI) in the neonatal intensive care unit (NICU) to solve a range of inverse problems for shortening scan time and improving motion robustness. In-NICU MRI scanners leverage permanent magnets at lower field-strengths (i.e., below 1.5 Tesla) for non-invasive as

Tyler Sam, Yudong Chen, Christina Lee Yu

Many reinforcement learning (RL) algorithms are too costly to use in practice due to the large sizes $S, A$ of the problem's state and action space. To resolve this issue, we study transfer RL with latent low rank structure. We consider the problem of transferring a latent low rank representation when the source and target MDPs have transition kernels with T

Zhenxian Chen, Changchang Xi

We show that the Terwilliger algebra of a quasi-thin association scheme over a field is always a quasi-hereditary cellular algebra in the sense of Cline-Parshall-Scott and of Graham-Lehrer, repsectively, and that the basic algebra of the Terwilliger algebra is the dual extension of a star with all arrows pointing to its center if the field has characteristic

Reuben Luera, Ryan A. Rossi, Alexa Siu, Franck Dernoncourt

The applications of generative AI have become extremely impressive, and the interplay between users and AI is even more so. Current human-AI interaction literature has taken a broad look at how humans interact with generative AI, but it lacks specificity regarding the user interface designs and patterns used to create these applications. Therefore, we presen

Damiano Anselmi

We investigate the relationship between nonlocal and local quantum field theories, and search for a viable notion of "local limit" to relate the unitary models. In Euclidean space it is relatively easy to have nonlocal theories with well-behaved local limits. In Minkowski spacetime, instead, singular behaviors are generically expected. Relaxing some assumpti

John M. Carpenter, Taran L. Esplin, Kevin L. Luhman, Eric E. Mamajek

We present ALMA Band 7 continuum (340 GHz) and CO J=3-2 observations for an extended sample of disks in the Upper Scorpius OB Association (Upper Sco, age ~ 10 Myr). The targets were selected from previous studies that identified new members of Upper Sco using photometry and astrometry from the Gaia mission, and the presence of a disk has been inferred from m

David Yunis, Siyu Huo, Chulaka Gunasekara, Danish Contractor

Large language models (LLMs) are deployed in a wide variety of user-facing applications. Typically, these deployments have some specific purpose, like answering questions grounded on documentation or acting as coding assistants, but they require general language understanding. In such deployments, LLMs should respond only to queries that align with the inten

Auren R. Liu, Pat Pataranutaporn, Pattie Maes

Companion chatbots offer a potential solution to the growing epidemic of loneliness, but their impact on users' psychosocial well-being remains poorly understood, raising critical ethical questions about their deployment and design. This study presents a large-scale survey (n = 404) of regular users of companion chatbots, investigating the relationship betwe

Dimitris Bertsimas, Lisa Everest, Jiayi Gu, Matthew Peroni

Decision Trees have remained a popular machine learning method for tabular datasets, mainly due to their interpretability. However, they lack the expressiveness needed to handle highly nonlinear or unstructured datasets. Motivated by recent advances in tree-based machine learning (ML) techniques and first-order optimization methods, we introduce Generalized

Yifei Tang, Yoshihiko Umemoto, Yo Kawamoto, Masahiro Kawamata

Spin dynamics in the van der Waals antiferromagnet FeGa$_2$S$_4$ with triangular lattices are investigated using magnetometry, neutron scattering, and muon spin relaxation measurements. The characteristic spin relaxation time is thoroughly clarified over thirteen orders of magnitude. Although the temperature dependence of DC and AC susceptibilities recalls a

Neil Shah

Decentralized Autonomous Organizations (DAOs), based on block-chain systems such as Ethereum, are emerging governance protocols that enable decentralized community management without a central authority. For instance, UniswapDAO allows members to vote on policy changes for the Uniswap exchange. However, DAOs face challenges regarding scalability, governance,

Charles Paquette, Deepanshu Prasad, David Wehlau

For an algebraically closed field $\mathbb{K}$, we consider a Galois $G$-covering $\mathcal{B} \to \mathcal{A}$ between locally bounded $\mathbb{K}$-categories given by bound quivers, where $G$ is torsion-free and acts freely on the objects of $\mathcal{B}$. We define the notion of $(G,\tau_{\mathcal{B}})$-rigid subcategory and of support $(G,\tau_{\mathcal{

Zifeng Wang, Benjamin Danek, Ziwei Yang, Zheng Chen

Data science plays a critical role in biomedical research, but it requires professionals with expertise in coding and medical data analysis. Large language models (LLMs) have shown great potential in supporting medical tasks and performing well in general coding tests. However, existing evaluations fail to assess their capability in biomedical data science,

Mia Kumamoto, Junwu Huang, Christian Drischler, Masha Baryakhtar

We present a comprehensive study of axion condensed neutron stars that arise in models of an exceptionally light axion that couples to quantum chromodynamics (QCD). These axions solve the strong-charge-parity (CP) problem, but have a mass-squared lighter than that due to QCD by a factor of $\varepsilon<1$. Inside dense matter, the axion potential is altered,

Rhett Hanscom, Tamara Silbergleit Lehman, Qin Lv, Shivakant Mishra

Social media platforms have evolved rapidly in modernity without strong regulation. One clear obstacle faced by current users is that of toxicity. Toxicity on social media manifests through a number of forms, including harassment, negativity, misinformation or other means of divisiveness. In this paper, we characterize literature surrounding toxicity, formal

Christophe Lohou

In this paper, we adapt the two topological numbers, which have been proposed to efficiently characterize simple points in specific neighborhoods for 3D binary images, to the case of 2D binary images. Unlike the 3D case, we only use a single neighborhood to define these two topological numbers for the 2D case. Then, we characterize simple points either by us

Chirag Modi

Hamiltonian Monte-Carlo (HMC) and its auto-tuned variant, the No U-Turn Sampler (NUTS) can struggle to accurately sample distributions with complex geometries, e.g., varying curvature, due to their constant step size for leapfrog integration and fixed mass matrix. In this work, we develop a strategy to locally adapt the step size parameter of HMC at every it

Mohamad Jouni, Daniele Picone, Mauro Dalla Mura

Hyperspectral imaging systems based on multiple-beam interference (MBI), such as Fabry-Perot interferometry, are attracting interest due to their compact design, high throughput, and fine resolution. Unlike dispersive devices, which measure spectra directly, the desired spectra in interferometric systems are reconstructed from measured interferograms. Althou

Jean Leneutre, Vadim Malvone, James Ortiz

In this paper, we propose a novel formalism called Probabilistic Obstruction Temporal Logic (POTL), which extends Obstruction Logic (OL) by incorporating probabilistic elements. POTL provides a robust framework for reasoning about the probabilistic behaviors and strategic interactions between attackers and defenders in environments where probabilistic events

Patrick M. Shober, Ariane Courtot, Jeremie Vaubaillon

Context. Orbital similarity measures, such as the D-values, have been extensively used in meteor science to identify meteoroid streams and associate meteorite falls with near-Earth objects (NEOs). However, the chaotic nature of near-Earth space challenges the long-term reliability of these measures for stream identification, and the increasing size of our fi

Yao Tang, Jonathan J. Fortney, Francis Nimmo, Daniel Thorngren

We present a novel python-based 1D sub-Neptune evolution model that emphasizes the thermal evolution and potential solidification of the rock/iron core and the structure of the radiative atmosphere. This model explores planetary structure from the molten center to nbar pressure levels. Treating the radiative atmosphere is crucial for sub-Neptunes, due to the

Seung Whan Chung, Youngsoo Choi, Pratanu Roy, Thomas Roy

Computational physics simulation can be a powerful tool to accelerate industry deployment of new scientific technologies. However, it must address the challenge of computationally tractable, moderately accurate prediction at large industry scales, and training a model without data at such large scales. A recently proposed component reduced order modeling (CR

Jaedong Hwang, Brian Cheung, Zhang-Wei Hong, Akhilan Boopathy

Large-scale pretrained models are widely leveraged as foundations for learning new specialized tasks via fine-tuning, with the goal of maintaining the general performance of the model while allowing it to gain new skills. A valuable goal for all such models is robustness: the ability to perform well on out-of-distribution (OOD) tasks. We assess whether fine-

Alvin Y. Sukmadji, Frank R. Kschischang

Concatenated bit-interleaved and multilevel coded modulation with outer Reed--Solomon codes, inner Chase-algorithm-based soft-decision-decoded Bose--Ray-Chaudhuri--Hocquenghem codes, and four-level pulse amplitude modulation is considered. A semi-analytical formula is derived for estimating the decoded frame error rate (FER) at the output of the additive whi

Zifeng Wang, Hanyin Wang, Benjamin Danek, Ying Li

The integration of Large Language Models (LLMs) into medical applications has sparked widespread interest across the healthcare industry, from drug discovery and development to clinical decision support, assisting telemedicine, medical devices, and healthcare insurance applications. This perspective paper aims to discuss the inner workings of building LLM-po

Elliot Blackstone, Peter D. Miller, Matthew D. Mitchell

We examine the solution of the Benjamin-Ono Cauchy problem for rational initial data in three types of double-scaling limits in which the dispersion tends to zero while simultaneously the independent variables either approach a point on one of the two branches of the caustic curve of the inviscid Burgers equation, or approach the critical point where the bra

Angus Beane, James Johnson, Vadim Semenov, Lars Hernquist

The Milky Way hosts at least two modes in its present day distribution of Fe and alpha-elements. The exact cause of this bimodality is disputed, but one class of explanations involves the merger between the Milky Way and a relatively massive satellite (Gaia-Sausage-Enceladus) at z~2. However, reproducing this bimodality in simulations is not straightforward,

Seungsoo Hong, Dongwook Lim, Young-Wook Lee

We examined the double red clump (RC) observed in the Galactic bulge, interpreted as a difference in distance ("X-shaped bulge scenario") or in chemical composition ("multiple population scenario"). To verify chemical differences between the RC groups, we performed low-resolution spectroscopy for RC and red giant branch (RGB) stars using Gemini-South/GMOS in

Dongwook Lim, Sang-Hyun Chun, Young-Wook Lee, Chul Chung

Context. With the increasing number of discoveries of globular clusters in the inner Milky Way, the need for spectroscopic confirmation and further investigation of their stellar populations and chemodynamical properties has become crucial. Aims. Gran 5 is a newly reported low-mass globular cluster located close to the Galactic center, and it is thought to b

Paul C. W. Davies, Damien A. Easson, Phillip B. Levin

It is commonly assumed that low-mass primordial black holes cannot constitute a significant fraction of the dark matter in our universe due to their predicted short lifetimes from the conventional Hawking radiation and evaporation process. Assuming physical black holes are nonsingular--likely due to quantum gravity or other high-energy physics--we demonstrat

A. Plati, R. Maire, F. Boulogne, F. Restagno

We report the experimental observation of a square crystalline phase in a vibrated binary mixture of spherical grains. This structure spontaneously forms from a disordered state, consistently with predictions obtained in an equilibrium system with similar geometrical properties under conservative dynamics. By varying the area fraction, we also observe stable

Eugene M. Chudnovsky, Dmitry A. Garanin

Static and dynamic properties of magnetically soft amorphous ferromagnets have been studied analytically and numerically within random-field and random-anisotropy models. External field and coherent anisotropy that are weak compared to their random counterparts are sufficient to bring the magnet close to saturation. The scaling of spin-spin correlations in t

Olaf Sassnick, Georg Schäfer, Thomas Rosenstatter, Stefan Huber

Industrial Operational Technology (OT) systems are increasingly targeted by cyber-attacks due to their integration with Information Technology (IT) systems in the Industry 4.0 era. Besides intrusion detection systems, honeypots can effectively detect these attacks. However, creating realistic honeypots for brownfield systems is particularly challenging. This

Samuel Cahyawijaya, Ruochen Zhang, Holy Lovenia, Jan Christian Blaise Cruz

Multilingual large language models (LLMs) have gained prominence, but concerns arise regarding their reliability beyond English. This study addresses the gap in cross-lingual semantic evaluation by introducing a novel benchmark for cross-lingual sense disambiguation, StingrayBench. In this paper, we demonstrate using false friends -- words that are orthograp

Marie Davidsen Buhl, Gaurav Sett, Leonie Koessler, Jonas Schuett

As frontier artificial intelligence (AI) systems become more capable, it becomes more important that developers can explain why their systems are sufficiently safe. One way to do so is via safety cases: reports that make a structured argument, supported by evidence, that a system is safe enough in a given operational context. Safety cases are already common

Adán Flores-Ramírez, Ángel Mario Alarcón-López, Sofía Vaca-Narvaja, Daniela Leo-Orozco

Beyond conventional productivity metrics, human interaction and collaboration dynamics merit careful consideration in our increasingly digital workspace. This research proposes a conjectural neuro-adaptive room that enhances group interactions by adjusting the physical environment to desired internal states. Drawing inspiration from previous work on collecti

Joel Ferguson, Saeed Ahmed, Juan E. Machado, Michele Cucuzzella

We develop a novel switching dynamics that converges to the Karush-Kuhn-Tucker (KKT) point of a nonlinear optimisation problem. This new approach is particularly notable for its lower dimensionality compared to conventional primal-dual dynamics, as it focuses exclusively on estimating the primal variable. Our method is successfully illustrated on general qua

Daniel Lokshtanov, Paweł Rzążewski, Saket Saurabh, Roohani Sharma

In this article we show that Maximum Partial List H-Coloring is polynomial-time solvable on P_5-free graphs for every fixed graph H. In particular, this implies that Maximum k-Colorable Subgraph is polynomial-time solvable on P_5-free graphs. This answers an open question from Agrawal, Lima, Lokshtanov, Saurabh & Sharma [SODA 2024]. This also improves the $n

Thomas A. Schad, Gordon J. D. Petrie, Jeffrey R. Kuhn, Andre Fehlmann

Regular remote sensing of the magnetic field embedded within the million-degree solar corona is severely lacking. This reality impedes fundamental investigations of the nature of coronal heating, the generation of solar and stellar winds, and the impulsive release of energy into the solar system via flares and other eruptive phenomena. Resulting from advance

Rômulo Damasclin Chaves dos Santos, Jorge Henrique de Oliveira Sales

This article proposes a hybrid adaptive numerical method based on the Dual Reciprocity Method (DRM) to solve problems with non-linear boundary conditions and large-scale problems, named Hybrid Adaptive Dual Reciprocity Method (H-DRM). The method uses a combination of DRM to handle non-homogeneous terms, iterative techniques to deal with non-linear boundary c

Yating Xu, Chen Li, Gim Hee Lee

The key challenge of multi-view indoor 3D object detection is to infer accurate geometry information from images for precise 3D detection. Previous method relies on NeRF for geometry reasoning. However, the geometry extracted from NeRF is generally inaccurate, which leads to sub-optimal detection performance. In this paper, we propose MVSDet which utilizes p

Callum S. Sambridge, Kirk McKenzie

This letter presents a dual quadrature phasemeter, an implementation of a phase-locked loop designed to track the phase of homodyne and heterodyne inter-satellite laser links. The dual quadrature phasemeters use dual quadrature optical detection to enable an alternate phase readout scheme that operates with wider bandwidths and on signals with carrier freque

Juyoung Yun

In deep learning, Residual Networks (ResNets) have proven effective in addressing the vanishing gradient problem, allowing for the successful training of very deep networks. However, skip connections in ResNets can lead to gradient overlap, where gradients from both the learned transformation and the skip connection combine, potentially resulting in overesti

Benoît Assi, Adam Martin

We study Higgs boson production via vector boson fusion at the LHC, focusing on the process $pp \to H + jj$ and capturing the leading energy-enhanced contributions within the Standard Model Effective Field Theory (SMEFT) up to order $1/\Lambda^4$. Employing energy-scaling arguments, we predict the magnitude of each higher-dimensional operator's contribution.

Yuan Huang, Fugen Zhou, Jerome Gilles

In this paper, we propose a new approach to perform supervised texture classification/segmentation. The proposed idea is to feed a Fully Convolutional Network with specific texture descriptors. These texture features are extracted from images by using an empirical curvelet transform. We propose a method to build a unique empirical curvelet filter bank adapte

Noel Elias

Modern day audio signal classification techniques lack the ability to classify low feature audio signals in the form of spectrographic temporal frequency data representations. Additionally, currently utilized techniques rely on full diverse data sets that are often not representative of real-world distributions. This paper derives several first-of-its-kind m

Amaya Gallagher-Syed, Elena Pontarini, Myles J. Lewis, Michael R. Barnes

This study evaluates the generalisation capabilities of state-of-the-art histopathology foundation models on out-of-distribution multi-stain autoimmune Immunohistochemistry datasets. We compare 13 feature extractor models, including ImageNet-pretrained networks, and histopathology foundation models trained on both public and proprietary data, on Rheumatoid A

Pierdomenico Duttilo, Stefano Antonio Gattone

Mixtures of generalized normal distributions (MGND) have gained popularity for modelling datasets with complex statistical behaviours. However, the estimation of the shape parameter within the maximum likelihood framework is quite complex, presenting the risk of numerical and degeneracy issues. This study introduced an expectation conditional maximization al

Joachim Andreassen, Donn Morrison

We study the problem of streamlining reverse engineering (RE) of binary programs from unknown instruction set architectures (ISA). We focus on two fundamental ISA characteristics to beginning the RE process: identification of endianness and whether the instruction width is a fixed or variable. For ISAs with a fixed instruction width, we also present methods

Noel Elias

Sonar based audio classification techniques are a growing area of research in the field of underwater acoustics. Usually, underwater noise picked up by passive sonar transducers contains all types of signals that travel through the ocean and is transformed into spectrographic images. As a result, the corresponding spectrograms intended to display the tempora

Alexander Christie, Matan Leibovich, Miguel Moscoso, Alexei Novikov

We propose a methodology that exploits large and diverse data sets to accurately estimate the ambient medium's Green's functions in strongly scattering media. Given these estimates, obtained with and without the use of neural networks, excellent imaging results are achieved, with a resolution that is better than that of a homogeneous medium. This phenomenon,

Ferdinand Omlor, Benedikt Tissot, Guido Burkard

A general entanglement generation protocol between remote stationary qubits using single-photon reflection in a photonic network is explored theoretically. The nodes of the network consist of single qubits that are typically represented by the spin of a color center, each localized in a separate optical cavity and linked to other nodes via photonic links suc

Shaorong Zhang, Yuanbin Cheng, Greg Ver Steeg

Diffusion bridge models and stochastic interpolants enable high-quality image-to-image (I2I) translation by creating paths between distributions in pixel space. However, the proliferation of techniques based on incompatible mathematical assumptions have impeded progress. In this work, we unify and expand the space of bridge models by extending Stochastic Int

Adam S. Sikora

We propose conjectural generalizations of the Fermat-Catalan conjecture, the Tijdeman-Zagier conjecture, and of the Fermat Last Theorem, in which powers are replaced by products of integers. We also formulate a new explicit version of the abc conjecture.

Jerome Gilles, Francis Alvarez, Nicholas B. Ferrante, Margaret Fortman

In this paper, we investigate how moving objects can be detected when images are impacted by atmospheric turbulence. We present a geometric spatio-temporal point of view to the problem and show that it is possible to distinguish movement due to the turbulence vs. moving objects. To perform this task, we propose an extension of 2D cartoon+texture decompositio

Aleksandros Sobczyk

In this work we revisit the arithmetic and bit complexity of Hermitian eigenproblems. Recently, [BGVKS, FOCS 2020] proved that a (non-Hermitian) matrix can be diagonalized with a randomized algorithm in $O(n^{\omega}\log^2(n/\epsilon))$ arithmetic operations, where $\omega\lesssim 2.371$ is the square matrix multiplication exponent, and [Shah, SODA 2025] sig

Jhordan Silveira Borba, Sebastian Gonçalves, Celia Anteneodo

We analyze inequality aspects of the agent-based model of capitalist economy named it Social Architecture of Capitalism that has been introduced by Ian Wright. The model contemplates two main types of agents, workers and capitalists, which can also be unemployed. Starting from a state where all agents are unemployed and possess the same initial wealth, the s

Chujie Zheng, Jeffrey Wang, Shuqian Albee Zhang, Anand Kishore

We propose a novel method for evaluating the performance of a content search system that measures the semantic match between a query and the results returned by the search system. We introduce a metric called "on-topic rate" to measure the percentage of results that are relevant to the query. To achieve this, we design a pipeline that defines a golden query

Lucas Spangher, Tianle Li, William F. Arnold, Nick Masiewicki

There exists an extremely wide array of LLM benchmarking tasks, whereas oftentimes a single number is the most actionable for decision-making, especially by non-experts. No such aggregation schema exists that is not Elo-based, which could be costly or time-consuming. Here we propose a method to aggregate performance across a general space of benchmarks, nick

Noel Elias, Homa Esfahanizadeh, Kaan Kale, Sriram Vishwanath

Large language models have drastically changed the prospects of AI by introducing technologies for more complex natural language processing. However, current methodologies to train such LLMs require extensive resources including but not limited to large amounts of data, expensive machinery, and lengthy training. To solve this problem, this paper proposes a n

Pouya M. Ghari, Yanning Shen

Federated learning is renowned for its efficacy in distributed model training, ensuring that users, called clients, retain data privacy by not disclosing their data to the central server that orchestrates collaborations. Most previous work on federated learning assumes that clients possess static batches of training data. However, clients may also need to ma

Khai Yi Chin, Carlo Pinciroli

Collective perception is a fundamental problem in swarm robotics, often cast as best-of-$n$ decision-making. Past studies involve robots with perfect sensing or with small numbers of faulty robots. We previously addressed these limitations by proposing an algorithm, here referred to as Minimalistic Collective Perception (MCP) [arxiv:2209.12858], to reach cor

Taneesh Gupta, Shivam Shandilya, Xuchao Zhang, Rahul Madhavan

Reward modeling in large language models is susceptible to reward hacking, causing models to latch onto superficial features such as the tendency to generate lists or unnecessarily long responses. In reinforcement learning from human feedback (RLHF) and more generally during post-training flawed reward signals often lead to outputs that optimize for these sp

Jiaxin Zhang

We develop a theory of multiple radial SLE(0) -- a smooth system of curves in a simply connected domain $\Omega$ with marked boundary points $z_1, \ldots, z_n \in \partial \Omega$ and a marked interior point $q$ -- arising as the deterministic limit of random multiple radial SLE($\kappa$) systems. We construct multiple radial SLE(0) systems by starting from

Yoshihisa Asada, Guillaume Desprez, Chris J. Willott, Marcin Sawicki

We present a new analytical model for the attenuation to Epoch of Reionization (EoR) galaxies by proximate neutral hydrogen gas. Many galaxy spectra in the EoR taken by JWST have shown a flux deficit at wavelengths just redward of the Lyman break, and this has been regarded as resulting from Ly$\alpha$ damping wing absorption by the increasing amount of neut

Rania Kousovista, Georgina Cosma, Emeka Abakasanga, Ashley Akbari

Identifying and understanding the co-occurrence of multiple long-term conditions (MLTC) in individuals with intellectual disabilities (ID) is vital for effective healthcare management. These individuals often face earlier onset and higher prevalence of MLTCs, yet specific co-occurrence patterns remain unexplored. This study applies an unsupervised approach t

Marcelo Ferreira, Rodrigo F. L. Holanda, Javier E. Gonzalez, L. R. Colaço

Testing possible variations in fundamental constants of nature is a crucial endeavor in observational cosmology. This paper investigates potential cosmological variations in the fine structure constant ($\alpha$) through a non-parametric approach, using galaxy cluster observations as the primary cosmological probe. We employ two methodologies based on galaxy

S. E. Chorfi, A. Habbal, M. Jahid, L. Maniar

We investigate inverse backward-in-time problems for a class of second-order degenerate Mean-Field Game (MFG) systems. More precisely, given the final datum $(u(\cdot, T),m(\cdot, T))$ of a solution to the one-dimensional mean-field game system with a degenerate diffusion coefficient, we aim to determine the intermediate states $(u(\cdot,t_{0}),m(\cdot,t_{0}

Patrick Clearwater, Andrew Melatos, Surya Nepal, Matthew Bailes

Searches for continuous-wave gravitational radiation in data collected by modern long-baseline interferometers, such as the Laser Interferometer Gravitational-wave Observatory (LIGO), the Virgo interferometer and the Kamioka Gravitational Wave Detector (KAGRA), can be memory intensive. A digitisation scheme is described that reduces the 64-bit interferometer

Muhammad Farhan Tanvir, Md Maruf Hossain, Md Asifuzzaman Jishan

In the highly competitive environment of the banking industry, it is essential to precisely forecast the behavior of customers in order to maximize the effectiveness of marketing initiatives and improve financial consequences. The purpose of this research is to examine the efficacy of logit and probit models in predicting term deposit subscriptions using a P

Pierre Fraigniaud, Minh Hang Nguyen, Ami Paz

Consensus is arguably the most studied problem in distributed computing as a whole, and particularly in the distributed message-passing setting. In this latter framework, research on consensus has considered various hypotheses regarding the failure types, the memory constraints, the algorithmic performances (e.g., early stopping and obliviousness), etc. Surp

Maxime Ramzi

We prove that for any presentably symmetric monoidal $\infty$-category $\mathcal{V}$, the $\infty$-category $\mathbf{Mod}_\mathcal{V}(\mathbf{Pr}^{\mathrm{L}})^{\mathrm{dbl}}$ of dualizable presentable $\mathcal{V}$-modules and internal left adjoints between them is itself presentable. Along the way, we survey formal properties of these dualizable $\mathcal

Wei Dong, Han Zhou, Yulun Zhang, Xiaohong Liu

Exposure Correction (EC) aims to recover proper exposure conditions for images captured under over-exposure or under-exposure scenarios. While existing deep learning models have shown promising results, few have fully embedded Retinex theory into their architecture, highlighting a gap in current methodologies. Additionally, the balance between high performan

Seung Whan Chung, Youngsoo Choi, Pratanu Roy, Thomas Roy

Scaling up new scientific technologies from laboratory to industry often involves demonstrating performance on a larger scale. Computer simulations can accelerate design and predictions in the deployment process, though traditional numerical methods are computationally intractable even for intermediate pilot plant scales. Recently, component reduced order mo

Guneet S. Dhillon, Xingjian Shi, Yee Whye Teh, Alex Smola

Supervised fine-tuning (SFT) and alignment of large language models (LLMs) are key steps in providing a good user experience. However, the concept of an appropriate alignment is inherently application-dependent, and current methods often rely on heuristic choices to drive optimization. In this work, we formulate SFT and alignment as a constrained optimizatio

Majd Kosta, Oded Amir

In this study, we investigate and compare formulations for computing shape derivatives in bi-material level-set optimization with precise modeling of the interface. The level-set function is parameterized using B-splines, whose coordinates serve as design variables. A precise mechanical model is obtained every design cycle, replicating the exact geometry of

Sophia M Rein, Jing Li, Miguel Hernan, Andrew Beam

The g-formula can be used to estimate causal effects of sustained treatment strategies using observational data under the identifying assumptions of consistency, positivity, and exchangeability. The non-iterative conditional expectation (NICE) estimator of the g-formula also requires correct estimation of the conditional distribution of the time-varying trea

Hussein Talib, Phillip D. Sewell, Ana Vukovic, Sendy Phang

A design framework to implement non-unitary input-output operations to a practical unitary photonic integrated circuit is described. This is achieved by utilising the cosine-sine decomposition to recover the unitarity of the original operation. The recovered unitary operation is decomposed into fundamental unitary building blocks, forming a photonic integrat

Weronika Łajewska, Krisztian Balog, Damiano Spina, Johanne Trippas

Information-seeking dialogues span a wide range of questions, from simple factoid to complex queries that require exploring multiple facets and viewpoints. When performing exploratory searches in unfamiliar domains, users may lack background knowledge and struggle to verify the system-provided information, making them vulnerable to misinformation. We investi

Christian Ennis, Barbara Rüdiger, Padmanabhan Sundar

The time evolution of moderately dense gas evolving in vacuum described by the Boltzmann-Enskog equation is studied. The associated stochastic process, the Boltzmann-Enskog process, was constructed by Albeverio, R\"udiger and Sundar (2017) and further studied by Friesen, R\"udiger and Sundar (2019, 2022). The process is given by the solution of a McKean-Vlas

Benjamin Brindle, Thomas Derrick Hull, Matteo Malgaroli, Nicolas Charon

We introduce VISTA, a clustering approach for multivariate and irregularly sampled time series based on a parametric state space mixture model. VISTA is specifically designed for the unsupervised identification of groups in datasets originating from healthcare and psychology where such sampling issues are commonplace. Our approach adapts linear Gaussian stat

Hsun-Yu Kuo, Yin-Hsiang Liao, Yu-Chieh Chao, Wei-Yun Ma

Synthetic data augmentation via large language models (LLMs) allows researchers to leverage additional training data, thus enhancing the performance of downstream tasks, especially when real-world data is scarce. However, the generated data can deviate from the real-world data, and this misalignment can bring deficient outcomes while applying the trained mod

Talia Shlomovich

We present a quantitative version of Guessing Geodesics, which is a well-known theorem that provides a set of conditions to prove hyperbolicity of a given metric space. This version adds to the existing result by determining an explicit estimate of the hyperbolicity constant. As a sample application of this result, we estimate the hyperbolicity constant for

Maxime Ramzi

We develop the theory of locally rigid and rigid symmetric monoidal $\infty$-categories over an arbitrary base $\mathcal{V}\in\mathrm{CAlg}(\mathbf{Pr}^\mathrm{L})$. Among other things, we prove that every locally rigid commutative $\mathcal{V}$-algebra arises as a ``completion'' of a rigid commutative $\mathcal{V}$-algebra. Along the way, we introduce and s

Hengrui Zhang, Liancheng Fang, Qitian Wu, Philip S. Yu

Autoregressive models are predominant in natural language generation, while their application in tabular data remains underexplored. We posit that this can be attributed to two factors: 1) tabular data contains heterogeneous data type, while the autoregressive model is primarily designed to model discrete-valued data; 2) tabular data is column permutation-in

Morgan S. Porter

This study investigates the potential of deterministic systems, specifically large language models (LLMs), to exhibit the functional capacities of moral agency and compatibilist free will. We develop a functional definition of free will grounded in Dennett's compatibilist framework, building on an interdisciplinary theoretical foundation that integrates Shan

Yijian Wu, Thomas Philippe

The classical nucleation theory (CNT) and its modified versions provide a convenient framework for describing the nucleation process under the capillary approximation. However, these models often predict nucleation rates that depart significantly from simulation results, even for a simple Lennard-Jones fluid. This large discrepancy is likely due to the inacc

Yoel Shoshan, Moshiko Raboh, Michal Ozery-Flato, Vadim Ratner

Large language models applied to vast biological datasets have the potential to transform biology by uncovering disease mechanisms and accelerating drug development. However, current models are often siloed, trained separately on small-molecules, proteins, or transcriptomic data, limiting their ability to capture complex, multi-modal interactions. Effective

Sriniketh Vangaru, Daniel Rosen, Dylan Green, Raphael Rodriguez

Technological trends show that Radio Frequency Reinforcement Learning (RFRL) will play a prominent role in the wireless communication systems of the future. Applications of RFRL range from military communications jamming to enhancing WiFi networks. Before deploying algorithms for these purposes, they must be trained in a simulation environment to ensure adeq

Xinrui He, Yikun Ban, Jiaru Zou, Tianxin Wei

Missing data imputation is a critical challenge in various domains, such as healthcare and finance, where data completeness is vital for accurate analysis. Large language models (LLMs), trained on vast corpora, have shown strong potential in data generation, making them a promising tool for data imputation. However, challenges persist in designing effective

Ygor de Jesus, Luis Pedro Piñeyrúa, Sergio Romaña

This paper presents a new construction of non-Anosov Partially Hyperbolic Geodesic flows. Our construction is closely related to the construction made by Carneiro and Pujals, the novelty is the use of conformal deformations to produce the examples. Some of the necessary conditions appear more naturally and are easier to check. Besides that, we could enumerat

Wenxian Shi, Menghua Wu, Regina Barzilay

Forecasting the change in the distribution of viral variants is crucial for therapeutic design and disease surveillance. This task poses significant modeling challenges due to the sharp differences in virus distributions across sub-populations (e.g., countries) and their dynamic interactions. Existing machine learning approaches that model the variant distri

Laura Clinton, Toby S. Cubitt, Raul Garcia-Patron, Ashley Montanaro

In this work we demonstrate the use of adapted classical phase retrieval algorithms to perform control-free quantum phase estimation. We eliminate the costly controlled time evolution and Hadamard test commonly required to access the complex time-series needed to reconstruct the spectrum. This significant reduction of the number of coherent controlled-operat

Mahdi Goldani

Political stability is crucial for the socioeconomic development of nations, particularly in geopolitically sensitive regions such as the Gulf Cooperation Council Countries, Saudi Arabia, UAE, Kuwait, Qatar, Oman, and Bahrain. This study focuses on predicting the political stability index for these six countries using machine learning techniques. The study u

Lukas Grunwald, Emil Viñas Boström, Mark Kamper Svendsen, Dante M. Kennes

Embedding materials in optical cavities has emerged as an intriguing perspective for controlling quantum materials, but a key challenge lies in measuring properties of the embedded matter. Here, we propose a framework for probing strongly correlated cavity-embedded materials through direct measurements of cavity photons. We derive general relations between p

Joe Benton, Misha Wagner, Eric Christiansen, Cem Anil

Sufficiently capable models could subvert human oversight and decision-making in important contexts. For example, in the context of AI development, models could covertly sabotage efforts to evaluate their own dangerous capabilities, to monitor their behavior, or to make decisions about their deployment. We refer to this family of abilities as sabotage capabi

Sourav Chatterjee, Souvik Ray

Consider the Euclidean traveling salesman problem with $n$ random points on the plane. Suppose that one of the points is shifted to a new random location. This gives us a new optimal path. Consider such shifts for each of the $n$ points. Do we get $n$ very different optimal paths? In this article, we show that this is not the case - in fact, the number of tr

Jamal Al-Nabulsi, Mohammad Al-Sayed Ahmad, Baraa Hasaneiah, Fayhaa AlZoubi

Diagnosing knee osteoarthritis (OA) early is crucial for managing symptoms and preventing further joint damage, ultimately improving patient outcomes and quality of life. In this paper, a bioimpedance-based diagnostic tool that combines precise hardware and deep learning for effective non-invasive diagnosis is proposed. system features a relay-based circuit

Shriyan Reyya, Yao Cheng

Many agencies have adopted the FHWA-recommended systemic approach to traffic safety, an essential supplement to the traditional hotspot crash analysis which develops region-wide safety projects based on identified risk factors. However, this approach narrows analysis to specific crash and facility types. This specification causes inefficient use of crash and

Mahdi Goldani

The GCC region includes Saudi Arabia, UAE, Bahrain, Kuwait, Qatar, and Oman, which are of critical geopolitical and economic importance, being rich in oil and positioned along vital maritime routes. However, the region faces complex security challenges, ranging from traditional threats like interstate conflicts to nontraditional risks such as cyber-attacks,