December 2024 arXiv papers — page 170

Deepank Singh, Vedhus Hoskere, Pietro Milillo

Timely and accurate assessments of building damage are crucial for effective response and recovery in the aftermath of earthquakes. Conventional preliminary damage assessments (PDA) often rely on manual door-to-door inspections, which are not only time-consuming but also pose significant safety risks. To safely expedite the PDA process, researchers have stud

Fei Huang, Junhao Shen, Yanrong Yang, Ran Zhao

Fairness-aware statistical learning is essential for mitigating discrimination against protected attributes such as gender, race, and ethnicity in data-driven decision-making. This is particularly critical in high-stakes applications like insurance underwriting and annuity pricing, where biased business decisions can have significant financial and social con

Oleg Karpenkov, Anna Pratoussevitch, Rebecca Sheppard

Integer geometry on a plane deals with objects whose vertices are points in $\mathbb Z^2$. The congruence relation is provided by all affine transformations preserving the lattice $\mathbb Z^2$. In this paper we study circumscribed circles in integer geometry. We introduce the notions of integer and rational circumscribed circles of integer sets. We determin

Manish Bhattarai, Ryan Barron, Maksim Eren, Minh Vu

Retrieval-Augmented Generation (RAG) enhances Large Language Models (LLMs) by integrating external document retrieval to provide domain-specific or up-to-date knowledge. The effectiveness of RAG depends on the relevance of retrieved documents, which is influenced by the semantic alignment of embeddings with the domain's specialized content. Although full fin

Wangzheng Zhang, Ming-chung Chu, Shihong Liao, Shek Yeung

The Hubble constant $H_0$, the current expansion rate of the universe, is one of the most important parameters in cosmology. The cosmic expansion regulates the mutually approaching motion of a pair of celestial objects due to their gravity. Therefore, the mean pairwise peculiar velocity of celestial objects, which quantifies their relative motion, is sensiti

Srikar Gouru, Siddharth Lakkoju, Rohan Chandra

Robots in densely populated real-world environments frequently encounter constrained and cluttered situations such as passing through narrow doorways, hallways, and corridor intersections, where conflicts over limited space result in collisions or deadlocks among the robots. Current decentralized state-of-the-art optimization- and neural network-based approa

William D. Compton, Max H. Cohen, Aaron D. Ames

Safety filters leveraging control barrier functions (CBFs) are highly effective for enforcing safe behavior on complex systems. It is often easier to synthesize CBFs for a Reduced order Model (RoM), and track the resulting safe behavior on the Full order Model (FoM) -- yet gaps between the RoM and FoM can result in safety violations. This paper introduces \e

Florian Beguet, Sandrine Lanquetin, Romain Raffin

This paper presents a new mesh segmentation method that integrates geometrical and topological features through a flexible Reeb graph representation. The algorithm consists of three phases: construction of the Reeb graph using the improved topological skeleton approach, topological simplification of the graph by cancelling critical points while preserving es

Forough Majidi, Foutse Khomh, Heng Li, Amin Nikanjam

In recent years, many industries have utilized machine learning (ML) models in their systems. Ideally, ML models should be trained on and applied to data from the same distributions. However, the data evolves over time in many application areas, leading to concept drift, which in turn causes the performance of the ML models to degrade over time. Therefore, m

Aaron Tran, Samuel J. Frank, Ari Y. Le, Adam J. Stanier

The kinetic stability of collisionless, sloshing beam-ion (45{\deg} pitch angle) plasma is studied in a 3D simple magnetic mirror, mimicking the Wisconsin High-temperature superconductor Axisymmetric Mirror (WHAM) experiment. The collisional Fokker-Planck code CQL3D-m provides a slowing-down beam-ion distribution to initialize the kinetic-ion/fluid-electron

Brian Hsu, Cyrus DiCiccio, Natesh Sivasubramoniapillai, Hongseok Namkoong

Fairness research in machine learning often centers on ensuring equitable performance of individual models. However, real-world recommendation systems are built on multiple models and even multiple stages, from candidate retrieval to scoring and serving, which raises challenges for responsible development and deployment. This system-level view, as highlighte

Chen Shen, Zhixue He, Lei Shi, Jun Tanimoto

Agent-based simulations are essential for studying cooperation on spatial networks. However, finite-size effects -- random fluctuations due to limited network sizes -- can cause certain strategies to unexpectedly dominate or disappear, leading to unreliable outcomes. While enlarging network sizes or carefully preparing initial states can reduce these effects

Kasra Arabi, Benjamin Feuer, R. Teal Witter, Chinmay Hegde

As the quality of image generators continues to improve, deepfakes become a topic of considerable societal debate. Image watermarking allows responsible model owners to detect and label their AI-generated content, which can mitigate the harm. Yet, current state-of-the-art methods in image watermarking remain vulnerable to forgery and removal attacks. This vu

Xiaohuan Pei, Tao Huang, Chang Xu

KV cache pruning has emerged as a promising technique for reducing memory and computation costs in long-context auto-regressive generation. Existing methods for vision-language models (VLMs) typically rely on self-attention scores from large language models (LLMs) to identify and prune irrelevant tokens. However, these approaches overlook the inherent distri

Thomas Führer, Gregor Gantner

We develop Aubin--Nitsche-type estimates for recently proposed first-order system least-squares finite element methods (FOSLS) for the heat equation. Under certain assumptions, which are satisfied if the spatial domain is convex and the heat source and initial datum are sufficiently smooth, we prove that the $L^2$ error of approximations of the scalar field

Ziyao Wang, Bowei Tian, Yexiao He, Zheyu Shen

The recent advancement of foundation models (FMs) has increased the demand for fine-tuning these models on large-scale cross-domain datasets. To address this, federated fine-tuning has emerged, allowing FMs to be fine-tuned on distributed datasets across multiple devices while ensuring data privacy. However, the substantial parameter size and the multi-round

Simone Borelli, Francesco Giovinazzo, Francesco Grella, Giorgio Cannata

This paper presents a novel control algorithm for robotic manipulators in unstructured environments using proximity sensors partially distributed on the platform. The proposed approach exploits arrays of multi zone Time-of-Flight (ToF) sensors to generate a sparse point cloud representation of the robot surroundings. By employing computational geometry techn

Brayan Monroy, Jorge Bacca, Julián Tachella

Recorrupted-to-Recorrupted (R2R) has emerged as a methodology for training deep networks for image restoration in a self-supervised manner from noisy measurement data alone, demonstrating equivalence in expectation to the supervised squared loss in the case of Gaussian noise. However, its effectiveness with non-Gaussian noise remains unexplored. In this pape

Minati De, Satyam Singh, Csaba D. Tóth

We present algorithms for the online minimum hitting set problem in geometric range spaces: given a set $P$ of $n$ points in the plane and a sequence of geometric objects that arrive one-by-one, we need to maintain a hitting set at all times by making irrevocable decisions. For disks of radii in the interval $[1,M]$, we present an $O(\log M \log n)$-competit

Toby Simonds, Jey Han Lau, Chaithanya Bandi

Recent developments, particularly OpenAI's O1 model, have demonstrated the remarkable potential of Large Language Models (LLMs) for complex reasoning tasks. Through analysis of O1's outputs and provided sample Chain-of-Thought (CoT) demonstrations, we observe that it approaches problem-solving in a distinctly human-like manner, systematically brainstorming i

Petit Jean-Pierre, Margnat Florent, Zejli Hicham

The standard cosmological model, based on Cold Dark Matter and Dark Energy ({\Lambda}CDM), faces several challenges. Among these is the need to adjust the scenario to account for he presence of vast voids in the large-scale structure of the universe, as well as the early formation of the first stars and galaxies. Additionally, the observed matter-antimatter

Ohad Lib, Shuheng Liu, Ronen Shekel, Qiongyi He

High-dimensional entangled states offer higher information capacity and stronger resilience to noise compared with two-dimensional systems. However, the large number of modes and sensitivity to random rotations complicate experimental entanglement certification. Here, we experimentally certify three-dimensional entanglement in a five-dimensional two-photon s

Valeriia Cherepanova, Chia-Jung Lee, Nil-Jana Akpinar, Riccardo Fogliato

Large language models (LLMs) have been shown to be effective on tabular prediction tasks in the low-data regime, leveraging their internal knowledge and ability to learn from instructions and examples. However, LLMs can fail to generate predictions that satisfy group fairness, that is, produce equitable outcomes across groups. Critically, conventional debias

Debo Cheng, Jiuyong Li, Lin Liu, Ziqi Xu

Latent confounders are a fundamental challenge for inferring causal effects from observational data. The instrumental variable (IV) approach is a practical way to address this challenge. Existing IV based estimators need a known IV or other strong assumptions, such as the existence of two or more IVs in the system, which limits the application of the IV appr

Sen Lin, Ao Kong, Robert Azencott

We introduce and study Multi-Quantile estimators for the parameters $( \xi, \sigma, \mu)$ of Generalized Extreme Value (GEV) distributions to provide a robust approach to extreme value modeling. Unlike classical estimators, such as the Maximum Likelihood Estimation (MLE) estimator and the Probability Weighted Moments (PWM) estimator, which impose strict cons

Marc Vornehm, Chong Chen, Muhammad Ahmad Sultan, Syed Murtaza Arshad

Cardiovascular magnetic resonance imaging is a powerful diagnostic tool for assessing cardiac structure and function. However, traditional breath-held imaging protocols pose challenges for patients with arrhythmias or limited breath-holding capacity. This work aims to overcome these limitations by developing a reconstruction framework that enables high-quali

Rachael Huxford, Richard George, Max Trevor, Zach Yarbrough

The gravitational wave detectors used by the LIGO Scientific Collaboration, and the Virgo Collaboration are incredibly sensitive instruments which frequently detect non-stationary, non-Gaussian noise transients. iDQ is a statistical inference framework which leverages the use of auxiliary degrees of freedom monitored in the detectors to identify such transie

Alessandro Magnani, Feng Liu, Suthee Chaidaroon, Sachin Yadav

In product search, the retrieval of candidate products before re-ranking is more critical and challenging than other search like web search, especially for tail queries, which have a complex and specific search intent. In this paper, we present a hybrid system for e-commerce search deployed at Walmart that combines traditional inverted index and embedding-ba

Gregory R. Chambers, Jared Marx-Kuo

In this note, we extend diameter bounds of Simon, Topping, and Wu--Zheng to submanifolds with boundary and (potentially non-compact) ambient manifolds with minor curvature restrictions. The bound is dependent on both an integral of mean curvature and the area of the manifold. We apply our diameter bounds to minimal, constant mean curvature, and prescribed me

Wance Wang, Sarthak Subhankar, Joseph W. Britton

The Pound-Drever-Hall (PDH) technique for laser linewidth narrowing is widely used by AMO experimentalists. However, achieving a high-performance PDH locking requires substantial engineering experience, which is scattered across literature and often lacks a cohesive control-theory perspective. Excellent pedagogical papers exist on the theory of the PDH error

Mikhail Dereviannykh, Dmitrii Klepikov, Johannes Hanika, Carsten Dachsbacher

We introduce an efficient Two-Level Monte Carlo (subset of Multi-Level Monte Carlo, MLMC) estimator for real-time rendering of scenes with global illumination. Using MLMC we split the shading integral into two parts: the radiance cache integral and the residual error integral that compensates for the bias of the first one. For the first part, we developed th

Mengchen Liu, Jessica J. Hong, Elias Sebti, Ke Zhou

Sulfide solid state electrolytes are promising candidates to realize all solid state batteries due to their superior ionic conductivity and excellent ductility. However, their hypersensitivity to moisture requires processing environments that are not compatible with todays lithium ion battery manufacturing infrastructure. Herein, we present a reversible surf

Abhishek Jha

We obtain a totient analogue for Linnik's theorem in arithmetic progressions. Specifically, for any coprime pair of positive integers $(m,a)$ such that $m$ is odd, there exists $n\le m^{2+o(1)}$ such that $\varphi(n)\equiv a\,\mathrm{mod}\,{m}$.

Marwan Gebran, Ian Bentley, Rose Brienza, Frédéric Paletou

In this third paper in a series, we investigate the need of spectra denoising for the derivation of stellar parameters. We have used two distinct datasets for this work. The first one contains spectra in the range of 4450-5400 {\AA} at a resolution of 42000 and the second in the range of 8400-8800 {\AA} at a resolution of 11500. We constructed two denoising

Tadele Mengesha, Abner J. Salgado, Joshua M. Siktar

We study optimal design problems where the design corresponds to a coefficient in the principal part of the state equation. The state equation, in addition, is parameter dependent, and we allow it to change type in the limit of this (modeling) parameter. We develop a framework that guarantees asymptotic compatibility, that is unconditional convergence with r

Filippo Ziliotto, Tommaso Campari, Luciano Serafini, Lamberto Ballan

Large Language Models (LLMs) have demonstrated excellent capabilities in composing various modules together to create programs that can perform complex reasoning tasks on images. In this paper, we propose TANGO, an approach that extends the program composition via LLMs already observed for images, aiming to integrate those capabilities into embodied agents c

Li Shi, Houjiang Liu, Yian Wong, Utkarsh Mujumdar

Multi-persona debate systems powered by large language models (LLMs) show promise in reducing confirmation bias, which can fuel echo chambers and social polarization. However, empirical evidence remains limited on whether they meaningfully shift user attention toward belief-challenging content, promote belief change, or outperform traditional debiasing strat

Taneesh Gupta, Rahul Madhavan, Xuchao Zhang, Nagarajan Natarajan

Direct Preference Optimization (DPO) has become a popular approach for aligning language models using pairwise preferences. However, in practical post-training pipelines, on-policy generation typically yields multiple candidate responses per prompt, which are scored by a reward model to guide learning. In this setting, we propose $\textbf{Multi-Preference Op

Ya-Ting Yang, Quanyan Zhu

The growing complexity and interconnectivity of Intelligent Transportation Systems (ITS) make them increasingly vulnerable to advanced cyber threats, particularly deceptive information attacks. These sophisticated threats exploit vulnerabilities to manipulate data integrity and decision-making processes through techniques such as data poisoning, spoofing, an

Juan Rodriguez, Xiangru Jian, Siba Smarak Panigrahi, Tianyu Zhang

Multimodal AI has the potential to significantly enhance document-understanding tasks, such as processing receipts, understanding workflows, extracting data from documents, and summarizing reports. Code generation tasks that require long-structured outputs can also be enhanced by multimodality. Despite this, their use in commercial applications is often limi

Guillaume Van Dessel, François Glineur

We consider the global minimization of a particular type of minimum structured optimization problems wherein the variables must belong to some basic set, the feasible domain is described by the intersection of a large number of functional constraints and the objective stems as the pointwise minimum of a collection of functional pieces. Among others, this set

R. de la Reza

The existence of one percent of lithium-rich giant stars among normal, lithium-poor giant stars continues to be poorly explained. By merging two catalogues, one containing 10,535 lithium-rich giant stars with lithium abundances ranging from 1.5 to 4.9 dex, and the other detecting infrared sources, we have found 421 clump giant stars and 196 first-ascending g

Herbert Batte, Florian Luca

Let $(L_n^{(k)})_{n\geq 2-k}$ be the sequence of $k$--generalized Lucas numbers for some fixed integer $k\ge 2$ whose first $k$ terms are $0,\ldots,0,2,1$ and each term afterwards is the sum of the preceding $k$ terms. In this paper, we completely solve the nonlinear Diophantine equation $\left(L_{n+1}^{(k)}\right)^x+\left(L_{n}^{(k)}\right)^x-\left(L_{n-1}^

Kaihua Chen, Deva Ramanan, Tarasha Khurana

Object permanence in humans is a fundamental cue that helps in understanding persistence of objects, even when they are fully occluded in the scene. Present day methods in object segmentation do not account for this amodal nature of the world, and only work for segmentation of visible or modal objects. Few amodal methods exist; single-image segmentation meth

Changpeng Ti, Usman Hassan, Sairam Sri Vatsavai, Margaret McCarter

Patterned nanomagnet arrays (PNAs) have been shown to exhibit a strong geometrically frustrated dipole interaction. Some PNAs have also shown emergent domain wall dynamics. Previous works have demonstrated methods to physically probe these magnetization dynamics of PNAs to realize neuromorphic reservoir systems that exhibit chaotic dynamical behavior and hig

Puttipong Pongtanapaisan, Daniel Rodman

For any pair of integers $m$ and $n$ such that $3<m<n$, we provide an infinite family of links, where each link in the family has a locally minimal $n$-bridge position and a globally minimal $m$-bridge position. We accomplish this by applying the criterion of Takao et al. The $n$-bridge position is interesting because the corresponding bridge sphere is unper

Tian Qin, Naomi Saphra, David Alvarez-Melis

Early in training, LMs can behave like n-gram models, but eventually they often learn tree-based syntactic rules and generalize hierarchically out of distribution (OOD). We study this shift using controlled grammar-learning tasks: question formation and tense inflection. We find that a model learns to generalize hierarchically if its training data is _comple

Matthew Hase-Liu, Amal Mattoo

We show that the moduli space of degree $e$ maps from smooth genus $g \ge 1$ curves to an arbitrary low degree smooth hypersurface is singular when $e$ is large compared to $g$. We also give a lower bound for the dimension of the singular locus.

Carlos Arauz-Moreno, Keyvan Piroird, Elise Lorenceau

Laminated safety glass (LSG) is a composite assembly of glass and polyvinyl butyral (PVB), a viscoelastic polymer. LSG can be found in building facades, important landmarks around the world, and every major form of transportation. Yet, the assembly suffers from unwanted bubbles which are anathema to one of the most important features of glass: optical transp

Le Zhang, Qian Yang, Aishwarya Agrawal

How well are unimodal vision and language models aligned? Although prior work have approached answering this question, their assessment methods do not directly translate to how these models are used in practical vision-language tasks. In this paper, we propose a direct assessment method, inspired by linear probing, to assess vision-language alignment. We ide

Akanksha Devkar

Superposition or Neuron Polysemanticity are important concepts in the field of interpretability and one might say they are these most intricately beautiful blockers in our path of decoding the Machine Learning black-box. The idea behind this paper is to examine whether it is possible to decode Superposition using Active Learning methods. While it seems that

Leonid A. Bunimovich, Yaofeng Su

The paper addresses for the first time some basic questions in the theory of finite time dynamics and finite time predictions for slowly mixing non-uniformly hyperbolic dynamical systems. It is concerned with transport in phase spaces of such systems, and analyzes which subsets and when the orbits prefer to visit. An asymptotic expansion of the decay of poly

Jiahai Feng, Stuart Russell, Jacob Steinhardt

Pretrained language models (LMs) can generalize to implications of facts that they are finetuned on. For example, if finetuned on ``John Doe lives in Tokyo," LMs can correctly answer ``What language do the people in John Doe's city speak?'' with ``Japanese''. However, little is known about the mechanisms that enable this generalization or how they are learne

Zhejun Zhang, Peter Karkus, Maximilian Igl, Wenhao Ding

Traffic simulation aims to learn a policy for traffic agents that, when unrolled in closed-loop, faithfully recovers the joint distribution of trajectories observed in the real world. Inspired by large language models, tokenized multi-agent policies have recently become the state-of-the-art in traffic simulation. However, they are typically trained through o

Gabriela S. Militani, Artur Matoso, Denise F. Ávila, Raul Corrêa

In this work we experimentally demonstrate the quantum interference of force effect using pairs of entangled photons. Although photons are massless particles, they have linear momentum, and our experiments show that the quantum superposition of a positive momentum transfer with a null momentum transfer may result in a negative momentum transfer to an ensembl

Dali Zangurashvili

The criteria for a baric algebra $A$ (over a field $K$) to have a unique weight homomorphism are found. One of them requires a certain system of equations to have a unique non-trivial solution in the field $K$. Applying this criterion, we provide an example showing that Holgate's well-known sufficient condition for the uniqueness of a weight homomorphism is

Mukremin Kilic, Pierre Bergeron, Simon Blouin, Adam Moss

We increase the spectroscopic completeness of the 100 pc white dwarf sample in the SDSS footprint with 840 additional spectra. Our spectroscopy is 86% complete for white dwarfs hotter than $T_{\rm eff}= 5000$ K, where H$\alpha$ remains visible and provides reliable constraints on the atmospheric composition. We identify 2108 DA white dwarfs with pure hydroge

Eleanor Row, Ivan Shanin, György Fazekas

This paper explores transformer-based models for music overpainting, focusing on jazz piano variations. Music overpainting generates new variations while preserving the melodic and harmonic structure of the input. Existing approaches are limited by small datasets, restricting scalability and diversity. We introduce VAR4000, a subset of a larger dataset for j

Christopher D. Carone, Noah L. Donald

It has recently been suggested that tuning towards the boundary of the positivity domain of the scalar potential may explain the separation between the electroweak scale and the unification scale in a grand unified theory. Here we explore the possibility that the same type of tuning might account for the generation of the electroweak scale from a much lighte

Christina Meng

We study a model of colored multiwebs, which generalizes the dimer model to allow each vertex to be adjacent to \(n_v\) edges. These objects can be formulated as a random tiling of a graph with partial dimer covers. We examine the case of a cycle graph, and in particular we describe the local correlations of tiles in this setting.

Chenyu Wang, Weichao Zhou, Shantanu Ghosh, Kayhan Batmanghelich

Radiology report generation (RRG) has shown great potential in assisting radiologists by automating the labor-intensive task of report writing. While recent advancements have improved the quality and coherence of generated reports, ensuring their factual correctness remains a critical challenge. Although generative medical Vision Large Language Models (VLLMs

Francois Chollet, Mike Knoop, Gregory Kamradt, Bryan Landers

As of December 2024, the ARC-AGI benchmark is five years old and remains unbeaten. We believe it is currently the most important unsolved AI benchmark in the world because it seeks to measure generalization on novel tasks -- the essence of intelligence -- as opposed to skill at tasks that can be prepared for in advance. This year, we launched ARC Prize, a gl

Higinio Serrano, Bernardo Uribe, Miguel A. Xicoténcatl

We introduce the magnetic equivariant K-theory groups as the K-theory groups associated to magnetic groups and their respective magnetic equivariant complex bundles. We restrict the magnetic group to its subgroup of elements that act complex linearly, and we show that this restriction induces a rational isomorphism with the conjugation invariant part of the

Rafayel Petrosyan

Problems in probability theory prove to be one of the most challenging for students. Here, we formulate and discuss four related problems in probability theory that proved difficult for first to fourth-year undergraduate students whose first language was not English. These examples emphasize how crucial it is to understand the conditions and requirements of

Marin Donchev, Dylan Smyth

Non-Terrestrial Networks (NTNs) and satellite systems have become an important component of modern data communication systems in recent years. Despite their importance, the security of these systems is often limited, leaving them vulnerable to determined attackers. In this paper, we outline a scenario in which an attacker can infect an in-orbit NASA Core Fli

Nicholas Fisher, Gregory Fasshauer, Wenwu Gao

The paper aims at proposing an efficient and stable quasi-interpolation based method for numerically computing the Helmholtz-Hodge decomposition of a vector field. To this end, we first explicitly construct a matrix kernel in a general form from polyharmonic splines such that it includes divergence-free/curl-free/harmonic matrix kernels as special cases. The

Eric E. Mamajek, Adam J. Burgasser

The L9 dwarf SDSS J100711.74+193056.2 is situated 7$^{\circ}$.5 north of the nearest B-type star Regulus (d = 24.3+-0.2 pc), part of a stellar quadruplet. The object is at similar distance (d = 21.9+-1.0 pc) as Regulus, with a 3D separation of 3.9+0.6-0.5 pc ($\sim$1.6 tidal radii from Regulus), and shares tangential motion within 2 km/s, hinting at a physic

Anthony Seret, Guy Libourel

As some of the most ancient materials in our Solar System, chondritic meteorites offer a valuable window into the early stages of planetary formation, particularly the accretion processes that built the most primitive asteroids. Until now, high energy shocks and collisions have been invoked to explain the deformation and fragmentation of chondrules, the main

Alexia Bravo, Adam C. Schneider, Sarah Casewell, Austin Rothermich

We conducted a search for new ultracool companions to nearby white dwarfs using multiple methods, including the analysis of colors and examination of images in both the optical and the infrared. Through this process, we identified fifty-one previously unrecognized systems with candidate ultracool companions. Thirty-one of these systems are resolved in at lea

Mats G. Larson, Carl Lundholm, Anna Persson

We develop and evaluate a method for learning solution operators to nonlinear problems governed by partial differential equations (PDEs). The approach is based on a finite element discretization and aims at representing the solution operator by a multilayer perceptron (MLP) that takes problem data variables as input and gives a prediction of the finite eleme

Xuanzhao Gao, Shidong Jiang, Jiuyang Liang, Zhenli Xu

The quasi-2D electrostatic systems, characterized by periodicity in two dimensions with a free third dimension, have garnered significant interest in many fields. We apply the sum-of-Gaussians (SOG) approximation to the Laplace kernel, dividing the interactions into near-field, mid-range, and long-range components. The near-field component, singular but comp

Putri A. van der Linden, Alejandro García-Castellanos, Sharvaree Vadgama, Thijs P. Kuipers

Group equivariance has emerged as a valuable inductive bias in deep learning, enhancing generalization, data efficiency, and robustness. Classically, group equivariant methods require the groups of interest to be known beforehand, which may not be realistic for real-world data. Additionally, baking in fixed group equivariance may impose overly restrictive co

Rafael Gonzalez-Hernandez, Higinio Serrano, Bernardo Uribe

This work explores the topological properties of altermagnets, a novel class of collinear magnetic materials. We employ equivariant K-theory of magnetic groups and Hamiltonian models to formulate a robust $C^z_4 \mathbb{T}$ topological invariant to classify 2D and 3D altermagnetic systems. Our findings demonstrate that the spin Chern number serves as a robus

Ahmad Darkhalil, Rhodri Guerrier, Adam W. Harley, Dima Damen

We introduce EgoPoints, a benchmark for point tracking in egocentric videos. We annotate 4.7K challenging tracks in egocentric sequences. Compared to the popular TAP-Vid-DAVIS evaluation benchmark, we include 9x more points that go out-of-view and 59x more points that require re-identification (ReID) after returning to view. To measure the performance of mod

Byeonghyeon Lee, Youbin Kim, Yongjae Jo, Hyunsu Kim

Metalens is an emerging optical system with an irreplaceable merit in that it can be manufactured in ultra-thin and compact sizes, which shows great promise in various applications. Despite its advantage in miniaturization, its practicality is constrained by spatially varying aberrations and distortions, which significantly degrade the image quality. Several

Soumit Kanti Saha, Fazle Rabbi, Song Wang, Jinqiu Yang

Large Language Models (LLMs) are increasingly being applied across various domains, including code-related tasks such as code translation. Previous studies have explored using LLMs for translating code between different programming languages. Since LLMs are more effective with natural language, using natural language as an intermediate representation in code

Martin Larsson, Shukun Long

Markovian projections arise in problems where we aim to mimic the one-dimensional marginal laws of an It\^o semimartingale by using another It\^o process with Markovian dynamics. In applications, Markovian projections are useful in calibrating jump-diffusion models with both local and stochastic features, leading to the study of the inversion problems. In th

Jennifer E. Doyle, Maya M. Martirossyan, Julia Dshemuchadse, Erin G. Teich

The mechanical, optical, and chemical properties of a wide variety of soft materials are enabled and constrained by their bulk structure. How this structure emerges at small system sizes during self-assembly has been the subject of decades of research, with the aim of designing and controlling material functionality. Despite these efforts, it is still not fu

Matthias C. Löbl, Love A. Pettersson, Andrew Jena, Luca Dellantonio

Graph states are the key resources for measurement- and fusion-based quantum computing with photons, yet their creation is experimentally challenging. We optimize a hybrid graph-state generation scheme using a single quantum emitter and linear optics Bell-state measurements called fusions. We first generate a restricted class of states from a single quantum

Ivan Gadjev, Parvan Parvanov, Rumen Uluchev

A new Goodman-Sharma modification of the Baskakov operator is presented for approximation of bounded and continuous on $[0,\,\infty)$ functions. In our study on the approximation error of the proposed operator we prove direct and strong converse theorems with respect to a related K-functional. This operator is linear but not positive. However it has the adva

Fazlollah Hajkarim, Jürgen Schaffner-Bielich, Laura Tolos

We consider a thermodynamically consistent approach for the computation of the masses, radii, and tidal deformabilities of compact stars consisting of two interacting fluids with separately conserved quantum numbers. We apply this interacting fluid approach to the case of compact stars of neutron star matter with the Higgs portal fermionic dark matter model

Alberto Ceria, Frank W. Takes

Higher-order networks effectively represent complex systems with group interactions. Existing methods usually overlook the relative contribution of group interactions (hyperlinks) of different sizes to the overall network structure. Yet, this has many important applications, especially when the network has meaningful node labels. In this work, we propose a c

Garett Brown, Renu Malhotra, Hanno Rein

The modestly eccentric and non-coplanar orbits of the giant planets pose a challenge to solar system formation theories which generally indicate that the giant planets emerged from the protoplanetary disk in nearly perfectly circular and coplanar orbits. We demonstrate that a single encounter with a 2-50 Jupiter-mass object, passing through the solar system

Matt I. B. Oddo, Stephen Kobourov, Tamara Munzner

An invariant descriptor captures meaningful structural features of networks, useful where traditional visualizations, like node-link views, face challenges like the hairball phenomenon (inscrutable overlap of points and lines). Designing invariant descriptors involves balancing abstraction and information retention, as richer data summaries demand more stora

Nuno J. Alves, Peter Markowich, Athanasios E. Tzavaras

We study a one-dimensional nonlinear Vlasov equation with a local self-consistent force field generated by the density, where the force is given by the spatial derivative of a real-analytic nonlinearity. For small analytic initial data, we prove local-in-time existence and uniqueness of analytic solutions. In particular, this yields a perturbative well-posed

Xuenan Cao

This paper argues that we should perceive LLMs as machines of extrapolation. Extrapolation is a statistical function for predicting the next value in a series. Extrapolation contributes to both GPT successes and controversies surrounding its hallucination. The term hallucination implies a malfunction, yet this paper contends that it in fact indicates the cha

Daniela Ivanova, Marco Aversa, Paul Henderson, John Williamson

Accurately detecting and classifying damage in analogue media such as paintings, photographs, textiles, mosaics, and frescoes is essential for cultural heritage preservation. While machine learning models excel in correcting degradation if the damage operator is known a priori, we show that they fail to robustly predict where the damage is even after supervi

Dustin Enyeart, Guang Lin

Koopman autoencoders are a prevalent architecture in operator learning. But, the loss functions and the form of the operator vary significantly in the literature. This paper presents a fair and systemic study of these options. Furthermore, it introduces novel loss terms.

Indu Kant Deo, Youngsoo Choi, Saad A. Khairallah, Alexandre Reikher

In Laser Powder Bed Fusion (LPBF), the applied laser energy produces high thermal gradients that lead to unacceptable final part distortion. Accurate distortion prediction is essential for optimizing the 3D printing process and manufacturing a part that meets geometric accuracy requirements. This study introduces data-driven parameterized reduced-order model

Brandon Jaipersaud, Zining Zhu, Frank Rudzicz, Elliot Creager

Tools for analyzing character portrayal in fiction are valuable for writers and literary scholars in developing and interpreting compelling stories. Existing tools, such as visualization tools for analyzing fictional characters, primarily rely on explicit textual indicators of character attributes. However, portrayal is often implicit, revealed through actio

C. E. Lamb, D. G. Brown, M. R. Grossman

We have undertaken a mixed-methods study of fiction writers' experiences and attitudes with generative AI, primarily focused on the experiences of queer and disabled writers. We find that queer and disabled writers are markedly more pessimistic than non-queer and non-disabled writers about the impact of AI on their industry, although pessimism is the majorit

Fan Bai, Keith Harrigian, Joel Stremmel, Hamid Hassanzadeh

Clinical Question Answering (QA) systems enable doctors to quickly access patient information from electronic health records (EHRs). However, training these systems requires significant annotated data, which is limited due to the expertise needed and the privacy concerns associated with clinical data. This paper explores generating Clinical QA data using lar

Juxiang Li, Huayang Song, Shufang Su, Wei Su

In this paper, we present a comprehensive study of the collider search limits on the charged Higgses in the four types of Two Higgs Double Models (2HDM). In addition to constraints from flavor physics measurements, we include both the LEP charged Higgs search channels, as well as the LHC search results on the light and heavy charged Higgses. We consider both

Graham Findlay, William Marshall, Larissa Albantakis, Isaac David

Developments in machine learning and computing power suggest that artificial general intelligence is within reach. This raises the question of artificial consciousness: if a computer were to be functionally equivalent to a human, being able to do all we do, would it experience sights, sounds, and thoughts, as we do when we are conscious? Answering this quest

Leonardo Chiesa, Maitraya Bhattacharyya, Filippo Mazzini, Federico Maria Guercilena

A realistic and detailed description of neutrinos in binary neutron star (BNS) mergers is essential to build reliable models of such systems. To this end, we present BNS_NURATES, a novel open-source numerical library designed for the efficient on-the-fly computation of neutrino interactions, with particular focus on regimes relevant to BNS mergers. BNS_NURAT

Ayush Gundawar, Euijun Chung, Hyesoon Kim

The exponential growth of data-intensive machine learning workloads has exposed significant limitations in conventional GPU-accelerated systems, especially when processing datasets exceeding GPU DRAM capacity. We propose MQMS, an augmented in-storage GPU architecture and simulator that is aware of internal SSD states and operations, enabling intelligent sche

Tiago F. L. L. Pinheiro, Rafael Sfair, Giovana Ramon

Numerical N-body simulations are commonly used to explore stability regions around exoplanets, offering insights into the possible existence of satellites and ring systems. This study aims to utilize Machine Learning (ML) techniques to generate predictive maps of stable regions surrounding a hypothetical planet. The approach can also be extended to planet-sa

F. Urcelay, E. Jullo, L. F. Barrientos, X. Huang

In the era of large-scale astronomical surveys, fast modeling of strong lens systems has become increasingly vital. While significant progress has been made for galaxy-scale lenses, the development of automated methods for modeling larger systems, such as groups and clusters, is not as extensive. Our study aims to extend the capabilities of the GIGA-Lens cod

Irshad A. Meer, Karl-Ludwig Besser, Mustafa Ozger, Dominic Schupke

Multi-connectivity involves dynamic cluster formation among distributed access points (APs) and coordinated resource allocation from these APs, highlighting the need for efficient mobility management strategies for users with multi-connectivity. In this paper, we propose a novel mobility management scheme for unmanned aerial vehicles (UAVs) that uses dynamic

Xuan Liu, Changxing Miao, Jiqiang Zheng

We prove global well-posedness and scattering for solutions to the mass-critical inhomogeneous nonlinear Schr\"odinger equation $i\partial_{t}u+\Delta u=\pm |x|^{-b}|u|^{\frac{4-2b}{d}}u$ for large $L^2(\mathbb{R} ^d)$ initial data with $d\ge3,0<b<\min \left\{ 2,\frac{d}{2} \right\}$; in the focusing case, we require that the mass is strictly less than that