December 2024 arXiv papers — page 73

Huihui Zhu, Bing Dong

Let $S$ be a $*$-monoid and let $a,b,c$ be elements of $S$. We say that $a$ is $(b,c)$-core-EP invertible if there exist some $x$ in $S$ and some nonnegative integer $k$ such that $cax(ca)^{k}c=(ca)^{k}c$, $x{\mathcal R}(ca)^{k}b$ and $x{\mathcal L}((ca)^{k}c)^{*}$. This terminology can be seen as an extension of the $w$-core-EP inverse and the $(b,c)$-core

Koshiro Saito, Sakae Mizuki, Masanari Ohi, Taishi Nakamura

Why do we build local large language models (LLMs)? What should a local LLM learn from the target language? Which abilities can be transferred from other languages? Do language-specific scaling laws exist? To explore these research questions, we evaluated 35 Japanese, English, and multilingual LLMs on 19 evaluation benchmarks for Japanese and English, taking

Yue Zhang, Liqiang Jing, Vibhav Gogate

We introduce a new task called Defeasible Visual Entailment (DVE), where the goal is to allow the modification of the entailment relationship between an image premise and a text hypothesis based on an additional update. While this concept is well-established in Natural Language Inference, it remains unexplored in visual entailment. At a high level, DVE enabl

Zhexin Zhang, Shiyao Cui, Yida Lu, Jingzhuo Zhou

As large language models (LLMs) are increasingly deployed as agents, their integration into interactive environments and tool use introduce new safety challenges beyond those associated with the models themselves. However, the absence of comprehensive benchmarks for evaluating agent safety presents a significant barrier to effective assessment and further im

Bingyi Han, Simon Coghlan, George Buchanan, Dana McKay

AI's integration into education promises to equip teachers with data-driven insights and intervene in student learning. Despite the intended advancements, there is a lack of understanding of interactions and emerging dynamics in classrooms where various stakeholders including teachers, students, and AI, collaborate. This paper aims to understand how students

Aditya Desai, Shuo Yang, Alejandro Cuadron, Matei Zaharia

Leveraging long contexts is crucial for advanced AI systems, but attention computation poses a scalability challenge. While scaled dot-product attention (SDPA) exhibits token sparsity, i.e. only a few pivotal tokens significantly contribute to output, exploiting this sparsity remains challenging. Existing methods either suffer from quality degradation or req

Arthur Amorim, Trevor Kann, Max Taylor, Lance Joneckis

Industrial control systems (ICSs) increasingly rely on digital technologies vulnerable to cyber attacks. Cyber attackers can infiltrate ICSs and execute malicious actions. Individually, each action seems innocuous. But taken together, they cause the system to enter an unsafe state. These attacks have resulted in dramatic consequences such as physical damage,

Hiroshi Yano, Masaya Kohda, Shoichiro Tsutsui, Ryosuke Imai

Expectation value estimation is ubiquitous in quantum algorithms. The expectation value of a Hamiltonian, which is essential in various practical applications, is often estimated by measuring a large number of Pauli strings on quantum computers and performing classical post-processing. In the case of $n$-qubit molecular Hamiltonians in quantum chemistry calc

Wengyi Zhan, Mingbao Lin, Shuicheng Yan, Rongrong Ji

We introduce DiffusionTrend for virtual fashion try-on, which forgoes the need for retraining diffusion models. Using advanced diffusion models, DiffusionTrend harnesses latent information rich in prior information to capture the nuances of garment details. Throughout the diffusion denoising process, these details are seamlessly integrated into the model ima

Tung Do, Thuan Hoang Nguyen, Anh Tuan Tran, Rang Nguyen

We propose a new view synthesis method via synthesizing a 3D neural field from both single or few-view input images. To address the ill-posed nature of the image-to-3D generation problem, we devise a two-stage method that involves a reconstruction model and a diffusion model for view synthesis. Our reconstruction model first lifts one or more input images to

Shuo Zhang, Shinichi Kotani, Jiahao Xu

A Toda flow is constructed on a space of bounded initial data through Sato-Segal-Wilson theory. The flow is described by the Weyl functions of the underlying Jacobi operators. This is a continuation of the previous work on the KdV flow.

Jixuan He, Wanhua Li, Ye Liu, Junsik Kim

As a common image editing operation, image composition involves integrating foreground objects into background scenes. In this paper, we expand the application of the concept of Affordance from human-centered image composition tasks to a more general object-scene composition framework, addressing the complex interplay between foreground objects and backgroun

Xiang Cheng, Raveesh Mayya, João Sedoc

Unstructured text data annotation is foundational to management research. LLMs offer a cost-effective and scalable alternative to human annotation, but they introduce a novel challenge: the annotator itself can be retired. Proprietary models undergo regular deprecation cycles, threatening long-term reproducibility. Hence, the ability to reproduce annotation

Nicholas Mueller, Yiran Zhao, Santiago Badia, Tiangang Cui

In this manuscript, we introduce the tensor-train reduced basis method, a novel projection-based reduced-order model designed for the efficient solution of parameterized partial differential equations. While reduced-order models are widely used for their computational efficiency compared to full-order models, they often involve significant offline computatio

Wei Xu

This paper is devoted to establishing the full scaling limit theorems for multivariate Hawkes processes. Under some mild conditions on the exciting kernels, we develop a new way to prove that after a suitable time-spatial scaling, the asymptotically critical multivariate Hawkes processes converge weakly to the unique solution of a multidimensional stochastic

Daniel R. Fuhrmann, Muhammad Fahad

An abstraction for multisensor communication termed the Gaussian Multiplex Channel is presented and analyzed. In this model, the sensor outputs can be added together in any combination through a network of switches, and the combinations can be changed arbitrarily during the observation interval. The sensor output sums are observed in additive Gaussian noise.

Xueguang Ma, Shengyao Zhuang, Bevan Koopman, Guido Zuccon

Generation with source attribution is important for enhancing the verifiability of retrieval-augmented generation (RAG) systems. However, existing approaches in RAG primarily link generated content to document-level references, making it challenging for users to locate evidence among multiple content-rich retrieved documents. To address this challenge, we pr

Chao Wang, Zhihao Xia, Thomas Leimkuehler, Karol Myszkowski

While consumer displays increasingly support more than 10 stops of dynamic range, most image assets such as internet photographs and generative AI content remain limited to 8-bit low dynamic range (LDR), constraining their utility across high dynamic range (HDR) applications. Currently, no generative model can produce high-bit, high-dynamic range content in

Andrei Mikhailov

We give examples of cohomologies of the superconformal algebra, relevant to computations in the AdS supergravity. Our main examples are deformations of $AdS_5\times S^5$ transforming in finite-dimensional representations of the superconformal algebra at the linearized level. In the study of correlation functions, it is important to compute the resolution of

Genki Kusano, Kosuke Akimoto, Kunihiro Takeoka

In large language models (LLM)-based recommendation systems (LLM-RSs), accurately predicting user preferences by leveraging the general knowledge of LLMs is possible without requiring extensive training data. By converting recommendation tasks into natural language inputs called prompts, LLM-RSs can efficiently solve issues that have been difficult to addres

Shengqi Liu, Yuhao Cheng, Zhuo Chen, Xingyu Ren

Generating sewing patterns in garment design is receiving increasing attention due to its CG-friendly and flexible-editing nature. Previous sewing pattern generation methods have been able to produce exquisite clothing, but struggle to design complex garments with detailed control. To address these issues, we propose SewingLDM, a multi-modal generative model

Junyi Cheng, Rabia Husain, Lingfeng Li, Matthew J. Strassler

The ATLAS collaboration, using 139 fb$^{-1}$ of 13 TeV collisions from the Large Hadron Collider, has placed limits on the decay of a $Z$ boson to three dark photons. We reproduce the results of the ATLAS analysis, and then recast it as a limit on a exotic Higgs decay mode, in which the Higgs boson decays via a pair of intermediate (pseudo)scalars $a$ to fou

Yiming Xu, Bin Shi, Teng Ma, Bo Dong

The graph with complex annotations is the most potent data type, whose constantly evolving motivates further exploration of the unsupervised dynamic graph representation. One of the representative paradigms is graph contrastive learning. It constructs self-supervised signals by maximizing the mutual information between the statistic graph's augmentation view

Rodolfo Capdevilla, Graciela B. Gelmini, Jonah Hyman, Alexander J. Millar

Searches for high frequency gravitational waves using cavities based on the Gertsenshtein effect were recently proposed, building off existing axion dark matter experiments. In particular, the sensitivity of axion dark matter experiments using metamaterial plasmas (tunable plasma haloscopes) to gravitational waves has not been explored in detail. Here we per

Jingwei Bao, Yu Liu, Zeliang Li, Shuyuan Zhu

Video-based point cloud compression (V-PCC) converts the dynamic point cloud data into video sequences using traditional video codecs for efficient encoding. However, this lossy compression scheme introduces artifacts that degrade the color attributes of the data. This paper introduces a framework designed to enhance the color quality in the V-PCC compressed

Sergey Masaev

A review of economic approaches showed the lack of a universal method for assessing management decisions in the face of an increasing volume of analyzed data and changing parameters of the external environment. The method of integral indicators is proposed. Integral indicators are one of the modern methods for researching the behavior of an enterprise. It pr

Sunny Karim, Matthew D. Webb

This paper introduces the two-way common causal covariates (CCC) assumption, which is necessary to get an unbiased estimate of the ATT when using time-varying covariates in existing Difference-in-Differences methods. The two-way CCC assumption implies that the effect of the covariates remain the same between groups and across time periods. This assumption ha

Yi Xu, Yuxin Hu, Zaiwei Zhang, Gregory P. Meyer

Human drivers rely on commonsense reasoning to navigate diverse and dynamic real-world scenarios. Existing end-to-end (E2E) autonomous driving (AD) models are typically optimized to mimic driving patterns observed in data, without capturing the underlying reasoning processes. This limitation constrains their ability to handle challenging driving scenarios. T

Rodrigo Perusquía Cortés, Pablo Padilla Longoria

The survival of the domestic cat (Felis catus) in various ecosystems has become increasingly relevant due to its impact on wildlife, public health, and society. In countries like Mexico, social factors such as abandonment have led to the feralization of the species and an unexpected increase in its population in urban areas. To design and implement effective

Muhammad Usama Saleem, Ekkasit Pinyoanuntapong, Pu Wang, Hongfei Xue

Human mesh recovery (HMR) is crucial in many computer vision applications; from health to arts and entertainment. HMR from monocular images has predominantly been addressed by deterministic methods that output a single prediction for a given 2D image. However, HMR from a single image is an ill-posed problem due to depth ambiguity and occlusions. Probabilisti

Simão M. João, Ottavio Bassano, Johannes Lischner

Energetic or "hot" electrons and holes generated from the decay of localized surface plasmons in metallic nanoparticles have great potential for applications in photocatalysis, photovoltaics, and sensing. Here, we study the generation of hot carriers in brick-shaped gold nanoparticles using a recently developed modelling approach that combines a solution to

Saiqian Peng, Duanfeng Chu, Guanjie Li, Liping Lu

Trajectory prediction plays a crucial role in improving the safety of autonomous vehicles. However, due to the highly dynamic and multimodal nature of the task, accurately predicting the future trajectory of a target vehicle remains a significant challenge. To address this challenge, we propose an Ego vehicle Planning-informed Network (EPN) for multimodal tr

Quentin F. Stout

Obeying constraints imposed by classical physics, we give optimal fine-grained algorithms for matrix multiplication and problems involving graphs and mazes, where all calculations are done in 3-dimensional space. We assume that whatever the technology is, a bit requires a minimum volume and communication travels at a bounded speed. These imply that multiplyi

Lei Xu, Muming Li, Zhilong Yu, Zheyu Liu

This paper proposes a dual-color grating chip design method for simultaneously capturing dual atomic clouds (87Rb and 133Cs). By simulating key parameters such as the grating period, etching depth, duty cycle, coating material, and thickness, the optimal design parameters were determined to ensure efficient dual-wavelength diffraction and maximize the number

Eslam Ahmed, Shun Tamura, Yukio Tanaka, Jorge Cayao

Majorana and trivial Andreev bound states are predicted to appear in superconductor-semiconductor hybrid systems, but their identification is still a challenging task. Here we consider superconducting junctions with Rashba spin-orbit coupling and explore the signatures of Majorana and trivial Andreev bound states in the emergent superconducting correlations

Xiaochun Duan, Wenxin Geng, Huaqing Luo, Yaoyao Xu

The sensitive axes of atom gravimeters are defined by the directions of the respective Raman lasers. Any tilt of the Raman lasers with respect to the vertical direction introduces errors in gravity measurements. In this work, we report a fast determination of the tilt of Raman lasers, where the fringe of the atom interferometer is scanned by varying the tilt

W. H. Dai, J. K. Chen, H. Ma, Z. Zeng

Ge-68 is a cosmogenic isotope in germanium with a half-life of 270.9 days.Ge-68 and its decay daughter Ga-68 contribute considerable background with energy up to 3 MeV to low background $\gamma$ spectrometers using high purity germanium (HPGe) detectors. In this paper, we evaluated the background of Ge-68 and Ga-68 in a $p$-type coaxial HPGe detector operate

Eric Modesitt, Ke Yang, Spencer Hulsey, Chengxiang Zhai

Recent advances in language modeling demonstrate the need for high-quality domain-specific training data, especially for tasks that require specialized knowledge. General-purpose models, while versatile, often lack the depth needed for expert-level tasks because of limited domain-specific information. Domain adaptation training can enhance these models, but

Luis Roque, Carlos Soares, Vitor Cerqueira, Luis Torgo

The importance of time series forecasting drives continuous research and the development of new approaches to tackle this problem. Typically, these methods are introduced through empirical studies that frequently claim superior accuracy for the proposed approaches. Nevertheless, concerns are rising about the reliability and generalizability of these results

Nathan Seiberg

We discuss a large class of non-relativistic continuum field theories where the Euclidean spatial symmetry of the classical theory is violated in the quantum theory by an Adler-Bell-Jackiw-like anomaly. In particular, the continuous translation symmetry of the classical theory is broken in the quantum theory to a discrete symmetry. Furthermore, that discrete

Sophie A. Young, Ross J. Turner, Stanislav S. Shabala, Georgia S. C. Stewart

We investigate the evolution of active galactic nucleus jets on kiloparsec-scales due to their interaction with the clumpy interstellar medium (ISM) of the host galaxy and, subsequently, the surrounding circumgalactic environment. Hydrodynamic simulations of this jet-environment interaction are presented for a range of jet kinetic powers, peak densities of t

Shayne Longpre, Nikhil Singh, Manuel Cherep, Kushagra Tiwary

Progress in AI is driven largely by the scale and quality of training data. Despite this, there is a deficit of empirical analysis examining the attributes of well-established datasets beyond text. In this work we conduct the largest and first-of-its-kind longitudinal audit across modalities--popular text, speech, and video datasets--from their detailed sour

Anand Kumar, Jiteng Mu, Nuno Vasconcelos

Text-to-image (T2I) models have recently gained widespread adoption. This has spurred concerns about safeguarding intellectual property rights and an increasing demand for mechanisms that prevent the generation of specific artistic styles. Existing methods for style extraction typically necessitate the collection of custom datasets and the training of specia

Coralia Cartis, Lindon Roberts

We consider model-based derivative-free optimization (DFO) for large-scale problems, based on iterative minimization in random subspaces. We provide the first worst-case complexity bound for such methods for convergence to approximate second-order critical points, and show that these bounds have significantly improved dimension dependence compared to standar

Hongye Xu, Jan Wasilewski, Bartosz Krawczyk

Continual learning in deep neural networks often suffers from catastrophic forgetting, where representations for previous tasks are overwritten during subsequent training. We propose a novel sample retrieval strategy from the memory buffer that leverages both gradient-conflicting and gradient-aligned samples to effectively retain knowledge about past tasks w

Paul S. Rosenbloom, John E. Laird, Christian Lebiere, Andrea Stocco

Cognition and emotion must be partnered in any complete model of a humanlike mind. This article proposes an extension to the Common Model of Cognition -- a developing consensus concerning what is required in such a mind -- for emotion that includes a linked pair of modules for emotion and metacognitive assessment, plus pervasive connections between these two

Song Dai, Qiongling Li

A rank $n$ Higgs bundle $(E,\theta)$ is called generically regular nilpotent if $\theta^n=0$ but $\theta^{n-1}\neq 0$. We show that for a generically regular nilpotent Higgs bundle, if it admits a harmonic metric, then its graded Higgs bundle admits a unique maximal harmonic metric. The proof relies on a generalization of Kalka-Yang's theorem for prescribed

Rangel Daroya, Elijah Cole, Oisin Mac Aodha, Grant Van Horn

Species distributions encode valuable ecological and environmental information, yet their potential for guiding representation learning in remote sensing remains underexplored. We introduce WildSAT, which pairs satellite images with millions of geo-tagged wildlife observations readily-available on citizen science platforms. WildSAT employs a contrastive lear

Adam H. Hamilton, Matthew Roughan, Anna Kalenkova

This paper studies how the Elo rating system behaves when the underlying modelling assumptions are not met.

Lei Lu, Zhepeng Wang, Runxue Bao, Mengbing Wang

Existing pruning techniques for large language models (LLMs) targeting domain-specific applications typically follow a two-stage process: pruning the pretrained general-purpose LLMs and then fine-tuning the pruned LLMs on specific domains. However, the pruning decisions, derived from the pretrained weights, remain unchanged during fine-tuning, even if the we

Adrien Devolder, Timur V. Tscherbul, Paul Brumer

Coherent control of atomic and molecular scattering relies on the preparation of colliding particles in superpositions of internal states, establishing interfering pathways that can be used to tune the outcome of a scattering process. However, incoherent addition of different partial wave contributions to the integral cross sections (partial wave scrambling)

Pramit Saha, Divyanshu Mishra, Felix Wagner, Konstantinos Kamnitsas

Large Vision-Language Models typically require large text and image datasets for effective fine-tuning. However, collecting data from various sites, especially in healthcare, is challenging due to strict privacy regulations. An alternative is to fine-tune these models on end-user devices, such as in medical clinics, without sending data to a server. These lo

Sifan Liu, Snigdha Panigrahi, Jake A. Soloff

We introduce a new cross-validation method based on an equicorrelated Gaussian randomization scheme. Our method is well-suited for problems where sample splitting is infeasible, either because the data violate the assumption of independent and identically distributed samples, or because there are insufficient samples to form representative train-test data pa

Jaineet Shah, Michael Gromis, Rickston Pinto

This report presents the comprehensive implementation, evaluation, and optimization of Denoising Diffusion Probabilistic Models (DDPMs) and Denoising Diffusion Implicit Models (DDIMs), which are state-of-the-art generative models. During inference, these models take random noise as input and iteratively generate high-quality images as output. The study focus

Amin Nejatbakhsh, Victor Geadah, Alex H. Williams, David Lipshutz

Biological and artificial neural systems form high-dimensional neural representations that underpin their computational capabilities. Methods for quantifying geometric similarity in neural representations have become a popular tool for identifying computational principles that are potentially shared across neural systems. These methods generally assume that

Ernie Croot, Junzhe Mao

We prove a structural theorem for generalized arithmetic progressions in $\F_p$ which contain a large product set of two other progressions.

Sára Lévay, Axel Katona, Hartmut Löwen, Raúl Cruz Hidalgo

We present an experimental study on the collective behavior of macroscopic self-propelled particles that are externally excited by light. This property allows testing the system response to the excitation intensity in a very versatile manner. We discover that for low excitation intensities, clustering at the boundaries is always present, even when this is pr

Xijun Liu, Yifan Zhou, Yuxiang Guo, Rama Chellappa

Significant progress has been made in photo-realistic scene reconstruction over recent years. Various disparate efforts have enabled capabilities such as multi-appearance or large-scale modeling; however, there lacks a welldesigned dataset that can evaluate the holistic progress of scene reconstruction. We introduce a collection of imagery of the Johns Hopki

Takumi Hachimine, Jun Morimoto, Takamitsu Matsubara

Automating object shaping by grinding with a robot is a crucial industrial process that involves removing material with a rotating grinding belt. This process generates removal resistance depending on such process conditions as material type, removal volume, and robot grinding posture, all of which complicate the analytical modeling of shape transitions. Add

Takashi Uneyama

We analyze the stress tensor and the gyration tensor of an unentangled polymer melt under flow by using a Rouse-type single chain model. We employ the bead-spring type single chain model, in which beads interact each other via nonlinear potentials such as the finite-extensible nonlinear elasticity (FENE) potential. Beads are assumed to obey the Langevin equa

Xin Huang, Eric M. Wolff, Paul Vernaza, Tung Phan-Minh

We present DriveGPT, a scalable behavior model for autonomous driving. We model driving as a sequential decision-making task, and learn a transformer model to predict future agent states as tokens in an autoregressive fashion. We scale up our model parameters and training data by multiple orders of magnitude, enabling us to explore the scaling properties in

Pham Vu Tuan Dat, Long Doan, Huynh Thi Thanh Binh

Automatic Heuristic Design (AHD) is an active research area due to its utility in solving complex search and NP-hard combinatorial optimization problems in the real world. The recent advancements in Large Language Models (LLMs) introduce new possibilities by coupling LLMs with evolutionary computation to automatically generate heuristics, known as LLM-based

Niccolò Sellati, Lara Benfatto

The experimental measurement of collective charge fluctuations in metals and superconductors is a preferential tool to benchmark fundamental interactions in solids. Recent experiments in multicomponent systems, from superconducting layered cuprates to multiband metals, highlighted striking effects due to the interplay between different degrees of freedom. In

D. Martínez, L. Pereira, K. Sawada, P. González

Point tomography is a new approach to the problem of state estimation, which is arguably the most efficient and simple method for modern high-precision quantum information experiments. In this scenario, the experimenter knows the target state that their device should prepare, except that intrinsic systematic errors will create small discrepancies in the stat

Oron Binyamin, Guy Shapira, Noam Nahum, Avishai Sintov

Robotic hands offer advanced manipulation capabilities, while their complexity and cost often limit their real-world applications. In contrast, simple parallel grippers, though affordable, are restricted to basic tasks like pick-and-place. Recently, a vibration-based mechanism was proposed to augment parallel grippers and enable in-hand manipulation capabili

Il-Seok Oh

Fruit tree image segmentation is an essential problem in automating a variety of agricultural tasks such as phenotyping, harvesting, spraying, and pruning. Many research papers have proposed a diverse spectrum of solutions suitable to specific tasks and environments. The review scope of this paper is confined to the front views of fruit trees and based on 15

Nina Mohebbi, Joonha Hwang, Matthew K. Fu, John O. Dabiri

Hydrodynamic interactions between swimming or flying organisms can lead to complex flows on the scale of the group. These emergent fluid dynamics are often more complex than a linear superposition of individual organism flows, especially at intermediate Reynolds numbers. This paper presents an approach to estimate the flow induced by multiple swimmer wakes i

Zhongxiao Jia, Qingqing Zheng

We establish a general convergence theory of the Rayleigh--Ritz method and the refined Rayleigh--Ritz method for computing some simple eigenpair $(λ_{*},x_{*})$ of a given analytic regular nonlinear eigenvalue problem (NEP). In terms of the deviation $\varepsilon$ of $x_{*}$ from a given subspace $\mathcal{W}$, we establish a priori convergence results on th

Yu-Jui Huang, Zhenhua Wang, Zhou Zhou

For a general entropy-regularized stochastic control problem on an infinite horizon, we prove that a policy iteration algorithm (PIA) converges to an optimal relaxed control. Contrary to the standard stochastic control literature, classical Hölder estimates of value functions do not ensure the convergence of the PIA, due to the added entropy-regularizing ter

Rômulo Damasclin Chaves dos Santos

In this work, we present three important theorems related to the corrected Smagorinsky model for turbulence in time-dependent domains. The first theorem establishes an improved regularity criterion for the solution of the corrected Smagorinsky model in Sobolev spaces $H^s(\Omega(t))$ with smooth and evolving boundaries. The result provides a bound on the Sob

Buddhika Nettasinghe, Ashwin Rao, Bohan Jiang, Allon Percus

Affective polarization, the emotional divide between ideological groups marked by in-group love and out-group hate, has intensified in the United States, driving contentious issues like masking and lockdowns during the COVID-19 pandemic. Despite its societal impact, existing models of opinion change fail to account for emotional dynamics nor offer methods to

David Bałut, Xuefei Guo, Niels de Vries, Dipanjan Chaudhuri

The density-density response in optimally doped Bi$_2$Sr$_2$CaCu$_2$O$_{8+x}$ has recently been shown to exhibit conformal symmetry. Using, the experimentally inferred conformal dynamic susceptibility, we compute the resultant quantum Fisher information (QFI), a witness to multi-partite entanglement. For a Fermi liquid, we find that the QFI grows quadratical

Juliusz Banecki

We prove that a $k$-regulous function defined on a non-singular affine variety can always be extended to the entire affine space.

Yuan Gao, Artur Stephan

Microscopic behaviors of chemical reactions can be described by a random time-changed Poisson process, whose large-volume limit determines the macroscopic behaviors of species concentrations, including both typical and non-typical trajectories. When the reaction intensities (or fluxes) exhibit a separation of fast-slow scales, the macroscopic typical traject

Nestor Colin, Rita Jiménez Rolland, Porfirio L. León Álvarez, Luis Jorge Sánchez Saldaña

We show that the {\it full} mapping class group of any orientable closed surface with punctures admits a cocompact classifying space for proper actions of dimension equal to its virtual cohomological dimension. This was proved for closed orientable surfaces and for {\it pure} mapping class groups by Aramayona and Mart\'inez P\'erez. As a consequence of our r

Junyang Cai, Taoan Huang, Bistra Dilkina

Mixed Integer Linear Programs (MILPs) are highly flexible and powerful tools for modeling and solving complex real-world combinatorial optimization problems. Recently, machine learning (ML)-guided approaches have demonstrated significant potential in improving MILP-solving efficiency. However, these methods typically rely on separate offline data collection

Eduardo H. da Silva Neto, Alex Frano, Fabio Boschini

Charge order has been a central focus in the study of cuprate high-temperature superconductors due to its intriguing yet not fully understood connection to superconductivity. Recent advances in resonant inelastic x-ray scattering (RIXS) in the soft x-ray regime have enabled the first momentum-resolved studies of dynamic charge order correlations in the cupra

MicroBooNE collaboration, P. Abratenko, D. Andrade Aldana, L. Arellano

This Letter presents an investigation of low-energy electron-neutrino interactions in the Fermilab Booster Neutrino Beam by the MicroBooNE experiment, motivated by the excess of electron-neutrino-like events observed by the MiniBooNE experiment. This is the first measurement to use data from all five years of operation of the MicroBooNE experiment, correspon

Haowei Liu, Xuyang Wu, Guohao Sun, Zhiqiang Tao

In information retrieval, large language models (LLMs) have demonstrated remarkable potential in text reranking tasks by leveraging their sophisticated natural language understanding and advanced reasoning capabilities. However, conventional supervised fine-tuning approaches for specializing LLMs in ranking tasks often lead to significant degradation of the

Temirlan Meiramkhanov, Arailym Tleubayeva

Fingerprint recognition systems stand as pillars in the realm of biometric authentication, providing indispensable security measures across various domains. This study investigates integrating Convolutional Neural Networks (CNNs) with Gabor filters to improve fingerprint recognition accuracy and robustness. Leveraging a diverse dataset sourced from the Sokot

Seyedalireza Abootorabi, Stefano Leonardi, Mario Rotea, Armin Zare

We propose a short-term wind forecasting framework for predicting real-time variations in atmospheric turbulence based on nacelle-mounted anemometer and ground-level air-pressure measurements. Our approach combines linear stochastic estimation and Kalman filtering algorithms to assimilate and process real-time field measurements with the predictions of a sto

Chen Xie, Chengyan Xie, Bin B. Ren, Myriam Benisty

In the early stages of planetary system formation, young exoplanets gravitationally interact with their surrounding environments and leave observable signatures on protoplanetary disks. Among these structures, a pair of nearly symmetric spiral arms can be driven by a giant protoplanet. For the double-spiraled SAO 206462 protoplanetary disk, we obtained three

Ainaz Eftekhar, Rose Hendrix, Luca Weihs, Jiafei Duan

Modern robots vary significantly in shape, size, and sensor configurations used to perceive and interact with their environments. However, most navigation policies are embodiment-specific--a policy trained on one robot typically fails to generalize to another, even with minor changes in body size or camera viewpoint. As custom hardware becomes increasingly c

Anton Kolotilin, Hongyi Li, Andriy Zapechelnyuk

We study monotone persuasion in the linear case, where posterior distributions over states are summarized by their mean. We solve the two leading cases where optimal unrestricted signals can be nonmonotone. First, if the objective is s-shaped and the state is discrete, then optimal monotone signals are upper censorship, whereas optimal unrestricted signals m

Chengpeng Wang, Yifei Gao, Wuqi Zhang, Xuwei Liu

Static program analysis plays an essential role in program optimization, bug detection, and debugging. However, reliance on compilation and limited customization hinder its adoption in the real world. This paper presents a compositional neuro-symbolic approach named NESA that facilitates compilation-free and customizable static program analysis using large l

David Baraglia, Hokuto Konno

We prove that a variety of examples of minimal complex surfaces admit exotic diffeomorphisms, providing the first known instances of exotic diffeomorphisms of irreducible 4-manifolds. We also give sufficient conditions for the boundary Dehn twist on a spin 4-manifold with $S^3$ boundary to be non-trivial in the relative mapping class group. This gives many n

Yana A. Butko, Merten Mlinarzik

We define a fractional Ito stochastic integral with respect to a randomly scaled fractional Brownian motion via an $S$-transform approach. We investigate the properties of this stochastic integral, prove the Ito formula for functions of such stochastic integrals and apply this Ito formula for investigation of related generalized time-fractional evolution equ

Xin Lyu, Kunal Talwar

Fingerprinting codes are a crucial tool for proving lower bounds in differential privacy. They have been used to prove tight lower bounds for several fundamental questions, especially in the ``low accuracy'' regime. Unlike reconstruction/discrepancy approaches however, they are more suited for query sets that arise naturally from the fingerprinting codes con

Gabriela Jaramillo, Cristian Meraz

We establish the existence of weak solutions for a nonlocal Klausmeier model within a small time interval $[0, T)$. The Klausmeier model is a coupled, nonlinear system of partial differential equations governing plant biomass and water dynamics in semiarid regions. The original model posits that plants disperse their seed according to classical diffusion. In

Lei Li, Siyu Liu, Antonio M. Peralta

The contributions in this note begin with a new characterization of (positive) scalar multiples of minimal tripotents in a general JB$^*$-triple $E$, proving that a non-zero element $a\in E$ is a positive scalar multiple of a minimal tripotent in $E$ if, and only if, its inner quadratic annihilator (that is, the set $^{\perp_{q}}\!\{a\} = \{ b\in E: \{a,b,a\

Daniele Fargion

UHECR are evaluated in the frame role of different nuclei composition. Most of the past and present models are considering proton or iron as their main courier. Some attention has been paid to the role of the UHECR light nuclei in recent years. We update here the lightest nuclei UHECR model, able to explain the nearest AGN or Star Burst sources with the few

Dongfang Zhao

Machine learning (ML) systems that guarantee security and privacy often rely on Fully Homomorphic Encryption (FHE) as a cornerstone technique, enabling computations on encrypted data without exposing sensitive information. However, a critical limitation of FHE is its computational inefficiency, making it impractical for large-scale applications. In this work

Kenny Chiu, Alex Sharp, Benjamin Bloem-Reddy

Symmetry plays a central role in the sciences, machine learning, and statistics. While statistical tests for the presence of distributional invariance with respect to groups have a long history, tests for conditional symmetry in the form of equivariance or conditional invariance are absent from the literature. This work initiates the study of nonparametric r

Renato M. Fonseca

Computations with tensors are ubiquitous in fundamental physics, and so is the usage of Einstein's dummy index convention for the contraction of indices. For instance, $T_{ia}U_{aj}$ is readily recognized as the same as $T_{ib}U_{bj}$, but a computer does not know that T[i,a]U[a,j] is equal to T[i,b]U[b,j]. Furthermore, tensors may have symmetries which can

Sébastien Alvarez, Ben Lowe, Graham Smith

Labourie raised the question of determining the possible asymptotics for the growth rate of compact $k$-surfaces, counted according to energy, in negatively curved $3$-manifolds, indicating the possibility of a theory of thermodynamical formalism for this class of surfaces. Motivated by this question and by analogous results for the geodesic flow, we prove a

Annette Lopez, Cody A. Melton, Jeonghwan Ahn, Brenda M. Rubenstein

Topological insulators are characterized by insulating bulk states and robust metallic surface states. Band inversion is a hallmark of topological insulators: at time-reversal invariant points in the Brillouin zone, spin-orbit coupling (SOC) induces a swapping of orbital character at the bulk band edges. In this work, we develop a novel method to detect band

Berkan Çakır

Managing clinical trial information is currently a significant challenge for the medical industry, as traditional methods are both time-consuming and costly. This paper proposes a simple yet effective methodology to extract and integrate clinical trial data in a cost-effective and time-efficient manner. Allowing the medical industry to stay up-to-date with m

Abderrahman Rachidi, Tarek El Bardouni, Otman El Hajjaji

$^{7}Be$ an isotope emanating from cosmogenic origins due to high energy cosmic rays, is studied from its production to its detection in the surface, in order to elucidate atmospheric circulation phenomena and analyze the vertical transport of air masses. This can be illustrated briefly by the monsoon model in Kerala in India, where the application of the $^

Jiayin Gu

Tensor networks are employed to characterize the current fluctuations in one-dimensional diffusion-reaction systems. The representative system under study is a semiconducting material where holes and electrons constitute two types of charge carriers. These holes and electrons diffuse in the system with the reactions of pair-generation and -recombination occu

Na Min An, Eunki Kim, James Thorne, Hyunjung Shim

Contrastive Language-Image Pretraining (CLIP) enables zero-shot inference in downstream tasks such as image-text retrieval and classification. However, recent works extending CLIP suffer from the issue of modality gap, which arises when the image and text embeddings are projected to disparate manifolds, deviating from the intended objective of image-text con