November 2024 arXiv papers — page 167

Chihaya Matsuhira, Marc A. Kastner, Takahiro Komamizu, Takatsugu Hirayama

Text-to-image diffusion models sometimes depict blended concepts in the generated images. One promising use case of this effect would be the nonword-to-image generation task which attempts to generate images intuitively imaginable from a non-existing word (nonword). To realize nonword-to-image generation, an existing study focused on associating nonwords wit

Yingzhi Du, Hairong Liu

This paper concerns an initial boundary value problem of compressible Navier-Stokes-Poisson equations with the non-flat doping profile in a 3-D exterior domain.The global existence of strong solutions near a steady state for compressible Navier-Stokes-Poisson equations with the general Navier-slip boundary conditions is established. For our setting, not only

Jorge A. Zavala, Tom Bakx, Ikki Mitsuhashi, Marco Castellano

We present ALMA observations on the high-redshift galaxy GHZ2 and report a successful detection of the rest-frame 88um atomic transition from doubly-ionized Oxygen at z=12.3327+/-0.0005. Based on these observations, combined with additional constraints on the [OIII] 52um line luminosity and previous JWST data, we argue that GHZ2 is likely powered by compact

Fu-Xing Li, Sheng-Bang Qian, Li-ying Zhu, Wen-Ping Liao

The massive semi-detached binary with a long-term decreasing orbital period may involve a rapid mass-transfer phase in Case A, and thus they are good astrophysical laboratories for investigating the evolution of massive binary stars. In this work, by using the long-term observational light curves from the OGLE project and other data in the low-metallicity LM

Yitian Shi, Edgar Welte, Maximilian Gilles, Rania Rayyes

Grasp learning in noisy environments, such as occlusions, sensor noise, and out-of-distribution (OOD) objects, poses significant challenges. Recent learning-based approaches focus primarily on capturing aleatoric uncertainty from inherent data noise. The epistemic uncertainty, which represents the OOD recognition, is often addressed by ensembles with multipl

Harsha Nori, Naoto Usuyama, Nicholas King, Scott Mayer McKinney

Run-time steering strategies like Medprompt are valuable for guiding large language models (LLMs) to top performance on challenging tasks. Medprompt demonstrates that a general LLM can be focused to deliver state-of-the-art performance on specialized domains like medicine by using a prompt to elicit a run-time strategy involving chain of thought reasoning an

Cheng-Rong Deng, Chun-Sheng An

We systematically explore the trimeson states $\bar{B}\bar{B}^*\bar{B}^*$ with various isospin-spin configurations in the quark model by solving exactly the six-body Schr\"{o}dinger equations with the Gaussian expansion method. The configuration $\left[[\bar{B} \bar{B}^*]^1_0\bar{B}^*\right]^0_{\frac{1}{2}}$ is not only approximately 10.2 MeV lower than the

Qiyuan Zhu, A. K. Qin, Hussein Dia, Adriana-Simona Mihaita

Traffic flow forecasting is a crucial task in intelligent transport systems. Deep learning offers an effective solution, capturing complex patterns in time-series traffic flow data to enable the accurate prediction. However, deep learning models are prone to overfitting the intricate details of flow data, leading to poor generalisation. Recent studies sugges

Bingzhi Zhang, Peng Xu, Xiaohui Chen, Quntao Zhuang

Randomness is a cornerstone of science, underpinning fields such as statistics, information theory, dynamical systems, and thermodynamics. In quantum science, quantum randomness, especially random pure states, plays a pivotal role in fundamental questions like black hole physics and quantum complexity, as well as in practical applications such as quantum dev

Thomas Barthelmé, Christian Bonatti, Kathryn Mann

We study (topological) pseudo-Anosov flows from the perspective of the associated group actions on their orbit spaces and boundary at infinity. We extend the definition of Anosov-like action from [BFM22] from the transitive to the general non-transitive context and show that one can recover the basic sets of a flow, the Smale order on basic sets, and their e

Wahidah Md Shah, M Azim. Adnan, Aslinda Hassan, Norharyati Harum

In Petanque, each player aims to throw the boule closer to the jack. The closest boule to the jack among players will score the point. Currently, the distance of the boule to the jack is still measured using manual measurement tools such as measuring tape, string, and calipers. The manual measurement method is considered time-consuming and prone to inconsist

Hongji Yu, Dmitry Green, Claudio Chamon

Building on the principle of combinatorial gauge symmetry, lattice gauge theories can be formulated with only one- and two-body interactions that ensure the exact realization of the symmetry rather than its approximate emergence in a perturbative regime. This paper extends the framework to encompass generic non-Abelian finite gauge groups by expanding on pre

Yiding Feng, Yaonan Jin

A large proportion of the Bayesian mechanism design literature is restricted to the family of regular distributions $\mathbb{F}_{\tt reg}$ [Mye81] or the family of monotone hazard rate (MHR) distributions $\mathbb{F}_{\tt MHR}$ [BMP63], which overshadows this beautiful and well-developed theory. We (re-)introduce two generalizations, the family of quasi-regu

Chinmay Maheshwari, Maria G. Mendoza, Victoria Marie Tuck, Pan-Yang Su

Advanced Air Mobility (AAM) operations are expected to transform air transportation while challenging current air traffic management practices. By introducing a novel market-based mechanism, we address the problem of on-demand allocation of capacity-constrained airspace to AAM vehicles with heterogeneous and private valuations. We model airspace and air infr

Rajul Kumar, Adam Bhatti, Ningshi Yao

Consensus between humans and robots is crucial as robotic agents become more prevalent and deeply integrated into our daily lives. This integration presents both unprecedented opportunities and notable challenges for effective collaboration. However, the active guidance of human actions and their integration in co-learning processes, where humans and robots

Anteneh Z. Deriba, David Y. Yang

Accurately assessing failure risk due to asset deterioration and/or extreme events is essential for efficient transportation asset management. Traditional risk assessment is conducted for individual assets by either focusing on the economic risk to asset owners or relying on empirical proxies of systemwide consequences. Risk assessment directly based on syst

Sam Cuthbertson, Glen Wheeler, Valentina-Mira Wheeler

In this paper we consider the anisotropic curve shortening flow in the plane in the presence of an ambient force. We consider force fields in which all their derivatives are bounded in the $L^{\infty}$ sense. We prove that closed embedded curves that have a minimum of curvature sufficiently large shrink to round points. The method of proof follows along the

Jeffrey Cheng

In this paper, we study stability properties of solutions to scalar conservation laws with a class of non-convex fluxes. Using the theory of $a$-contraction with shifts, we show $L^2$-stability for shocks among a class of large perturbations, and give estimates on the weight coefficient $a$ in regimes where the shock amplitude is both large and small. Then,

Kazunori Ando, Hiroshi Isozaki, Hisashi Morioka

We prove a Rellich-Vekua type theorem for Schr\"{o}dinger operators with exponentially decreasing potentials on a class of lattices including square, triangular, hexagonal lattices and their ladders. We also discuss the unique continuation theorem and the non-existence of eigenvalues embedded in the continuous spectrum.

Arunkumar Rathinam, Leo Pauly, Abd El Rahman Shabayek, Wassim Rharbaoui

Multispectral pedestrian detection has gained significant attention in recent years, particularly in autonomous driving applications. To address the challenges posed by adversarial illumination conditions, the combination of thermal and visible images has demonstrated its advantages. However, existing fusion methods rely on the critical assumption that the R

Thomas Sandholm, Sarah Dong, Sayandev Mukherjee, John Feland

We present a novel AI-based ideation assistant and evaluate it in a user study with a group of innovators. The key contribution of our work is twofold: we propose a method of idea exploration in a constrained domain by means of LLM-supported semantic navigation of problem and solution spaces, and employ novel automated data input filtering to improve generat

Howard Cohl, Hans Volkmer

In this paper we evaluate integrals of products of gamma functions of Ramanujan type in terms of bilateral hypergeometric series. In cases where the bilateral hypergeometric series are summable, then we evaluate these integral as beta integrals. In addition, we obtain integral representations for bilateral hypergeometric series.

Zongze Liu

For $(R, R^{+})$ an analytic perfectoid ring in char $p$, let $A_{\text{inf}}(R^{+})$ be the ring of Witt vectors with the induced topology from $(R, R^{+})$. We prove that $\text{Spa}(A_{\text{inf}}(R^{+}),A_{\text{inf}}(R^{+}))$ is sheafy and its structure sheaf is acyclic. We first show $A_{\text{inf}}(R^{+})$ is a stably uniform Banach ring using element

Yuxin Dong, Shuo Wang, Hongye Zheng, Jiajing Chen

This study aims to optimize the existing retrieval-augmented generation model (RAG) by introducing a graph structure to improve the performance of the model in dealing with complex knowledge reasoning tasks. The traditional RAG model has the problem of insufficient processing efficiency when facing complex graph structure information (such as knowledge graph

Howard Cohl, Michael Schlosser

Ismail and Wilson derived a generating function for Askey--Wilson polynomials which is given by a product of $q$-Gauss (Heine) nonterminating basic hypergeometric functions. We provide a generalization of that generating function which contains an extra parameter. A special case gives a closed form summation formula for a quadruple basic hypergeometric sum.

Adam R. Klivans, Konstantinos Stavropoulos, Arsen Vasilyan

The seminal work of Linial, Mansour, and Nisan gave a quasipolynomial-time algorithm for learning constant-depth circuits ($\mathsf{AC}^0$) with respect to the uniform distribution on the hypercube. Extending their algorithm to the setting of malicious noise, where both covariates and labels can be adversarially corrupted, has remained open. Here we achieve

Pengju Wang, Bochao Liu, Weijia Guo, Yong Li

Federated learning is a distributed machine learning paradigm designed to protect data privacy. However, data heterogeneity across various clients results in catastrophic forgetting, where the model rapidly forgets previous knowledge while acquiring new knowledge. To address this challenge, personalized federated learning has emerged to customize a personali

Lee Kezar, Nidhi Munikote, Zian Zeng, Zed Sehyr

Language models for American Sign Language (ASL) could make language technologies substantially more accessible to those who sign. To train models on tasks such as isolated sign recognition (ISR) and ASL-to-English translation, datasets provide annotated video examples of ASL signs. To facilitate the generalizability and explainability of these models, we in

Joshua Cooper, Krystal Guo, Utku Okur

It is a classical result due to Jacobi in algebraic combinatorics that the generating function of closed walks at a vertex $u$ in a graph $G$ is determined by the rational function \[ \frac{\phi_{G-u}(t)}{\phi_G(t)} \] where $\phi_G(t)$ is the characteristic polynomial of $G$. In this paper, we show that the corresponding rational function for a hypergraph i

Lennart Maximilian Seifert, Victor E. Colussi, Michael A. Perlin, Pranav Gokhale

Programming a quantum device describes the usage of quantum logic gates, agnostic of hardware specifics, to perform a sequence of operations with (typically) a computing or sensing task in mind. Such programs have been executed on digital quantum computers, which despite their noisy character, have shown the ability to optimize metrological functions, for ex

Mingjun Ying, Dipankar Shakya, Theodore S. Rappaport, Peijie Ma

This paper presents detailed radio propagation measurements for an indoor factory (InF) environment at 6.75 GHz and 16.95 GHz using a 1 GHz bandwidth channel sounder. Conducted at the NYU MakerSpace in the NYU Tandon School of Engineering campus in Brooklyn, NY, USA, our measurement campaign characterizes the radio propagation in a representative small facto

Keisuke Nishimura, Hiroto Sakai, Takafumi Tomita, Sylvain de Léséleuc

Aligning light spots into arbitrary shapes is a fundamental challenge in holography, leading to various applications across diverse fields in science and engineering. However, as the spot interval approaches the wavelength of light, interference effects among the spots become prominent, which complicates the generation of a distortion-free alignment. Herein,

Anja Wenger, Armando Consiglio, Hendrik Hohmann, Matteo Dürrnagel

Kagome metals have established a new arena for correlated electron physics. To date, the predominant experimental evidence centers around unconventional charge order, nematicity, and superconductivity, while magnetic fluctuations due to electronic interactions, i.e., beyond local atomic magnetism, have largely been elusive. We find the challenge of locating

Milena Veneva

This paper presents a generalised symbolic algorithm for solving systems of linear algebraic equations with multi-diagonal coefficient matrices. The algorithm is given in a pseudocode. A theorem which gives the condition for correctness of the algorithm is formulated and proven. Formula for the complexity of the multi-diagonal numerical algorithm is obtained

Braulio Gutierrez Pimenta, Luís Paulo de Queiroz Moreira, Adriano Possebon Rosa, Roberto Francisco Bobenrieth Miserda

The aerodynamic influence of a wing-equipped motorcycle on a pursuing motorcycle presents critical implications for stability and performance. This study investigates the induced flow dynamics, characterized by a turbulent and complex wake that significantly affects the aerodynamic forces and moments experienced by the following motorcycle. The presence of a

Yu-Han Ma, Xiu-Hua Zhao

In this paper, we summarize the historical development of finite-time thermodynamics and review the current state of research over the past two decades in this field, focusing on fundamental constraints of finite-time thermodynamic cycles, optimal control and optimization of thermodynamic processes, the operation of unconventional heat engines, and experimen

F. Arpanaei, C. Natalino, M. Ranjbar Zefreh, S. Yan

In the ultra-low inter-core crosstalk working zone of terrestrial multi-band and multi-core fiber (MCF) elastic optical networks (EONs), the ICXT in all channels of all cores remains below the ICXT threshold of the highest modulation format level (64QAM) for long-haul distances (10000 km). This paper analyzes the performance of this type of MCF in multi-band

Pengjie Tian, Yanqiu Wang

The gradient bounds of generalized barycentric coordinates play an essential role in the $H^1$ norm approximation error estimate of generalized barycentric interpolations. Similarly, the $H^k$ norm, $k>1$, estimate needs upper bounds of high-order derivatives, which are not available in the literature. In this paper, we derive such upper bounds for the Wachs

Adrian Höhl, Ivica Obadic, Miguel Ángel Fernández Torres, Hiba Najjar

In recent years, black-box machine learning approaches have become a dominant modeling paradigm for knowledge extraction in remote sensing. Despite the potential benefits of uncovering the inner workings of these models with explainable AI, a comprehensive overview summarizing the explainable AI methods used and their objectives, findings, and challenges in

Abdulhafeez A. Abdulsalam

In this study, we present a new closed form for the generalized integral $$\int_0^1 \frac{\mathrm{Li}_2(z) \ln(1+az)}{z}\, \mathrm{d}z,$$ where $a \in \mathbb{C} \setminus(-\infty, -1)$ and $\mathrm{Li}_2(z)$ is the dilogarithm function. This generalization is achieved by leveraging our established findings in conjunction with Vălean's results. Furthermo

Abdulhafeez A. Abdulsalam

This paper discusses generalizations of logarithmic and hyperbolic integrals. A new proof for an integral presented by Vardi and several other integrals in relation to known mathematical constants are discovered. We introduce the signed generalized Stirling polynomials of the first kind from the generalized Stirling polynomials of the first kind, and we give

Antoine Grosnit, Alexandre Maraval, Refinath S N, Zichao Zhao

Human expertise emerges through iterative cycles of interaction, reflection, and internal model updating, which are central to cognitive theories such as Kolb's experiential learning and Vygotsky's zone of proximal development. In contrast, current AI systems, particularly LLM agents, rely on static pre-training or rigid workflows, lacking mechanisms for con

Seunggeun Chi, Pin-Hao Huang, Enna Sachdeva, Hengbo Ma

We study the problem of estimating the body movements of a camera wearer from egocentric videos. Current methods for ego-body pose estimation rely on temporally dense sensor data, such as IMU measurements from spatially sparse body parts like the head and hands. However, we propose that even temporally sparse observations, such as hand poses captured intermi

Siyuan Tang

We study the dynamics of $SL_{2}(\mathbb{R})$ on the stratum of translation surfaces $\mathcal{H}(2)$. Especially, we obtain effective density theorems on $\mathcal{H}(2)$ for orbits of the upper triangular subgroup $P$ of $SL_{2}(\mathbb{R})$ with the based surfaces near a small Teichm\"{u}ller curve. The proof is based on the use of McMullen's classificati

Srikumar Balasubramanian, Andres Goza

Passive flow control via fluid-structure interaction (FSI) is a promising paradigm for unmanned aerial vehicles operating in vortex-dominated low Reynolds number regimes. A flexible structure has the potential to passively alter key unsteady vortex structures through its vibrations if its intrinsic modal dynamics are carefully aligned with the driving flow p

Gabriel Goldberg, Jonathan Osinski, Alejandro Poveda

In the first part of the manuscript, we establish several consistency results concerning Woodin's $\HOD$ hypothesis and large cardinals around the level of extendibility. First, we prove that the first extendible cardinal can be the first strongly compact in HOD. We extend a former result of Woodin by showing that under the HOD hypothesis the first extendibl

Yu-Neng Wang, Sara Achour

As the demand for efficient data processing escalates, reconfigurable analog hardware which implements novel analog compute paradigms, is promising for energy-efficient computing at the sensing and actuation boundaries. These analog computing platforms embed information in physical properties and then use the physics of materials, devices, and circuits to pe

Chenyu Yang, Davide Liconti, Robert K. Katzschmann

Dexterous robotic manipulation remains a significant challenge due to the high dimensionality and complexity of hand movements required for tasks like in-hand manipulation and object grasping. This paper addresses this issue by introducing Vector Quantized Action Chunking Embedding (VQ-ACE), a novel framework that compresses human hand motion into a quantize

Nizar Islah, Justine Gehring, Diganta Misra, Eilif Muller

The rapid evolution of software libraries presents a significant challenge for code generation models, which must adapt to frequent version updates while maintaining compatibility with previous versions. Existing code completion benchmarks often overlook this dynamic aspect, and the one that does consider it relies on static code prediction tasks without exe

Michael Büttner, Jonathan Francis, Helge Rhodin, Andrew Melnik

This paper introduces a method to enhance Interactive Imitation Learning (IIL) by extracting touch interaction points and tracking object movement from video demonstrations. The approach extends current IIL systems by providing robots with detailed knowledge of both where and how to interact with objects, particularly complex articulated ones like doors and

Yingzi Ma, Jiongxiao Wang, Fei Wang, Siyuan Ma

Machine unlearning has emerged as an effective strategy for forgetting specific information in the training data. However, with the increasing integration of visual data, privacy concerns in Vision Language Models (VLMs) remain underexplored. To address this, we introduce Facial Identity Unlearning Benchmark (FIUBench), a novel VLM unlearning benchmark desig

P. D. Marinos, T. A. Porter, G. P. Rowell, G. Jóhannesson

We use the GALPROP cosmic ray (CR) framework to model the Galactic CR distributions and associated non-thermal diffuse emissions up to PeV energies. We consider ensembles of discrete, finite lifetime CR sources, e.g.\ supernova remnants (SNRs), for a range of creation rates and lifetimes. We find that global properties of the CR sources are likely not direct

Holger Bech Nielsen

We discuss a series of 8 energy scales, some of which just speculated by ourselves, and fit the logarithms of these energies as a straight line versus a quantity related to the dimensionalities of action terms in a way to be defined in the article. These terms in the action are related to the energy scales in question. So e.g. the dimensionality of Einstein

Zhiling Yue, Yingying Fang, Liutao Yang, Nikhil Baid

Fibrotic Lung Disease (FLD) is a severe condition marked by lung stiffening and scarring, leading to respiratory decline. High-resolution computed tomography (HRCT) is critical for diagnosing and monitoring FLD; however, fibrosis appears as irregular, diffuse patterns with unclear boundaries, leading to high inter-observer variability and time-intensive manu

Zixin Tang, Janet G. van Hell

People tend to distribute information evenly in language production for better and clearer communication. In this study, we compared essays written by second language learners with various native language (L1) backgrounds to investigate how they distribute information in their non-native language (L2) production. Analyses of surprisal and constancy of entrop

S. K. Leggett, Pascal Tremblin

JWST is opening many avenues for exploration. For cold brown dwarfs and exoplanets, JWST has opened the door to the mid-infrared wavelength region, where such objects emit significant energy. For the first time, astronomers have access to mid-infrared spectroscopy for objects colder than 600 K. The first spectra appear to validate the model suite known as AT

Amit Jamadagni, Eugene Dumitrescu

We provide theory, algorithms, and simulations of non-equilibrium quantum systems using a one-dimensional (1D) completely-positive (CP), matrix-product (MP) density-operator ($\rho$) representation. By generalizing the matrix product state's orthogonality center, to additionally store positive classical mixture correlations, the MP$\rho$ factorization natura

Daniel Lozano-Gómez, Owen Benton, Michel J. P. Gingras, Han Yan

Frustrated magnetism in the pyrochlore lattice magnet has proven to be a most fruitful setting for the experimental and theoretical search for spin liquids. Besides the canonical case of spin ice, recent works have identified a variety of new classical and quantum spin liquids engendered by the generic nearest-neighbor anisotropic spin Hamiltonian for that l

A. Bulgac, M. Kafker, I. Abdurrahman, I. Stetcu

We will cover briefly the time-dependent density functional theory approach, extended to include pairing correlations, to induced fission of $^{238}$U(n,f), $^{241,243}$Pu, and $^{238}$Np, and also results on scission neutrons and a number of very nontrivial aspects of induced fission.

Adrià Garcés, Demian Levis

We present a general framework for incorporating non-reciprocal interactions into the Ising model with Glauber dynamics, without requiring multiple species. We then focus on a model with vision-cone type interactions. We solve it in a fully connected network (mean-field) and perform extensive numerical simulations of the model in the square lattice. We find

J. T. De Chant, K. Nakamura, Q. Ji, L. Obst-Huebl

Laser-driven (LD) ion acceleration has been explored in a newly constructed short focal length beamline at the BELLA petawatt facility (interaction point 2, iP2). For applications utilizing such LD ion beams, a beam transport system is required, which for reasons of compactness be ideally contained within 3 m. While they are generated from a micron-scale sou

Anurag Acharya, Shivam Sharma, Robin Cosbey, Megha Subramanian

A proliferation of Large Language Models (the GPT series, BLOOM, LLaMA, and more) are driving forward novel development of multipurpose AI for a variety of tasks, particularly natural language processing (NLP) tasks. These models demonstrate strong performance on a range of tasks; however, there has been evidence of brittleness when applied to more niche or

Shamima Nasrin Tumpa, Kehelwala Dewage Gayan Maduranga

This study explores the use of Recurrent Neural Networks (RNN) for real-time cryptocurrency price prediction and optimized trading strategies. Given the high volatility of the cryptocurrency market, traditional forecasting models often fall short. By leveraging RNNs' capability to capture long-term patterns in time-series data, this research aims to improve

Tanishq Kumar, Blake Bordelon, Cengiz Pehlevan, Venkatesh N. Murthy

Does learning of task-relevant representations stop when behavior stops changing? Motivated by recent theoretical advances in machine learning and the intuitive observation that human experts continue to learn from practice even after mastery, we hypothesize that task-specific representation learning can continue, even when behavior plateaus. In a novel rean

Haochen Zhang, Nader Zantout, Pujith Kachana, Zongyuan Wu

With the recent rise of Large Language Models (LLMs), Vision-Language Models (VLMs), and other general foundation models, there is growing potential for multimodal, multi-task embodied agents that can operate in diverse environments given only natural language as input. One such application area is indoor navigation using natural language instructions. Howev

Beatriz Romeo, Damiano Stramaccioni, Javier Menéndez, Jose Javier Valiente-Dobón

We investigate the experimental feasibility of detecting second-order double-magnetic dipole ($\gamma\gamma$-$M1M1$) decays from double isobaric analog states (DIAS), which have recently been found to be strongly correlated with the nuclear matrix elements of neutrinoless $\beta\beta$ decay. Using the nuclear shell model, we compute theoretical branching rat

Quinn Leng, Jacob Portes, Sam Havens, Matei Zaharia

Retrieval Augmented Generation (RAG) has emerged as a crucial technique for enhancing the accuracy of Large Language Models (LLMs) by incorporating external information. With the advent of LLMs that support increasingly longer context lengths, there is a growing interest in understanding how these models perform in RAG scenarios. Can these new long context m

Kevin Tirta Wijaya, Minghao Guo, Michael Sun, Hans-Peter Seidel

Molecular deep learning models have achieved remarkable success in property prediction, but they often require large amounts of labeled data. The challenge is that, in real-world applications, labels are extremely scarce, as obtaining them through laboratory experimentation is both expensive and time-consuming. In this work, we introduce MoleVers, a versatil

Luiz Fernando Ziebell, Rudi Gaelzer

We study the effect of the velocity distributions of the plasma particles on the equilibrium charge of dust particles which suffer collisional charging, considering different forms of both isotropic and anisotropic Kappa distributions for ions and electrons. This paper is the first of a series of two papers on this subject. Here, we consider two different fo

Matteo Cacciola, Alexandre Forel, Antonio Frangioni, Andrea Lodi

Although nearly 20 years have passed since its conception, the feasibility pump algorithm remains a widely used heuristic to find feasible primal solutions to mixed-integer linear problems. Many extensions of the initial algorithm have been proposed. Yet, its core algorithm remains centered around two key steps: solving the linear relaxation of the original

Michael Stewart

The spectral transformation Lanczos method for the sparse symmetric definite generalized eigenvalue problem for matrices $A$ and $B$ is an iterative method that addresses the case of semidefinite or ill conditioned $B$ using a shifted and inverted formulation of the problem. This paper proposes the same approach for dense problems and shows that with a shift

Kavitha Chandrasekar, Laxmikant Kale

Message aggregation is often used with a goal to reduce communication cost in HPC applications. The difference in the order of overhead of sending a message and cost of per byte transferred motivates the need for message aggregation, for several irregular fine-grained messaging applications like graph algorithms and parallel discrete event simulation (PDES).

Duncan Calvert, Luigi Penco, Dexton Anderson, Tomasz Bialek

Towards the role of humanoid robots as squad mates in urban operations and other domains, we identified doors as a major area lacking capability development. In this paper, we focus on the ability of humanoid robots to navigate and deal with doors. Human-sized doors are ubiquitous in many environment domains and the humanoid form factor is uniquely suited to

Brian Chen, Xiangyuan Zhao, Yingnan Zhu

Video summarization techniques have been proven to improve the overall user experience when it comes to accessing and comprehending video content. If the user's preference is known, video summarization can identify significant information or relevant content from an input video, aiding them in obtaining the necessary information or determining their interest

Tanmoy Das, Dohyeon Lee, Arnab Sinha

In industry, online randomized controlled experiment (a.k.a. A/B experiment) is a standard approach to measure the impact of a causal change. These experiments have small treatment effect to reduce the potential blast radius. As a result, these experiments often lack statistical significance due to low signal-to-noise ratio. A standard approach for improving

Felipe García-Ramos, Irma León-Torres

The coincidence rank, introduced by Barge and Kwapisz, measures the regularity of the maximal equicontinuous factor of minimal dynamical systems. We provide a characterization of the finiteness of coincidence rank using a multivariate notion of equicontinuity.

Richard C. Bradley

Consider the class of (functions of) strictly stationary Markov chains in which (i) the second moments are finite and (ii) absolute regularity (beta-mixing) is satisfied with exponential mixing rate. For (functions of) Markov chains in that class that are also reversible, the central limit theorem holds, as a well known byproduct of results of Roberts, Rosen

Hanqing Zhu, Wenyan Cong, Guojin Chen, Shupeng Ning

Electromagnetic field simulation is central to designing, optimizing, and validating photonic devices and circuits. However, costly computation associated with numerical simulation poses a significant bottleneck, hindering scalability and turnaround time in the photonic circuit design process. Neural operators offer a promising alternative, but existing SOTA

Emiliano Manuel Fortes Soplanes, Eduardo Javier Pérez Sánchez, Ambrus Both, Temistocle Grenga

Preferential diffusion plays a critical role in the evolution of lean premixed hydrogen flames, influencing flame surface corrugation and overall flame behavior. Simulating such flames with tabulated chemistry (TC) methods remains challenging due to the complexity of flame dynamics. A detailed assessment of flamelet-based manifolds for capturing these dynami

Gedeana Pantoja da Silva, Alexandre Casassola Gonçalves, Ferreira da Silva Rafael

We improve a specific method to obtain the dimension of the eigenspaces of the Laplace-Beltrami operator on lens spaces and establish some applications related to the explicit description of the dimension of the smallest positive eigenvalue and the parity of the dimensions of the eigenspaces.

Geza Kovacs, Daniel Deutsch, Markus Freitag

While Minimum Bayes Risk (MBR) decoding using metrics such as COMET or MetricX has outperformed traditional decoding methods such as greedy or beam search, it introduces a challenge we refer to as metric bias. As MBR decoding aims to produce translations that score highly according to a specific utility metric, this very process makes it impossible to use th

Daniel McBride, Ioannis Sgouralis

We present a scalable Bayesian framework for the analysis of confocal fluorescence spectroscopy data, addressing key limitations in traditional fluorescence correlation spectroscopy methods. Our framework captures molecular motion, microscope optics, and photon detection with high fidelity, enabling statistical inference of molecule trajectories from raw pho

Wei Wang, Zhichao Hou, Xiaorui Liu, Xinxia Peng

Research on long non-coding RNAs (lncRNAs) has garnered significant attention due to their critical roles in gene regulation and disease mechanisms. However, the complexity and diversity of lncRNA sequences, along with the limited knowledge of their functional mechanisms and the regulation of their expressions, pose significant challenges to lncRNA studies.

Adrián Morales-Pastor, Raquel Vázquez-Reza, Miłosz Wieczór, Clàudia Valverde

RNA is a vital biomolecule with numerous roles and functions within cells, and interest in targeting it for therapeutic purposes has grown significantly in recent years. However, fully understanding and predicting RNA behavior, particularly for applications in drug discovery, remains a challenge due to the complexity of RNA structures and interactions. While

Ahsan Mujtaba, Evgenii Ievlev, Matthew J. Gorban, Michael R. R. Good

Erasing a black hole leaves spacetime flat, so light passing through the region before any star forms and after black hole's evaporation shows no time delay, just like a flying mirror that returns to its initial starting point. Quantum radiation from a round-trip flying mirror has not been solved despite the model's mathematical simplicity and physical clari

Tito Homem-de-Mello, Juan Valencia, Felipe Lagos, Guido Lagos

We study a class of two-stage stochastic programs, namely, those with fixed recourse matrix and fixed costs, and linear second stage. We show that, under mild assumptions, the problem can be solved with just one scenario, which we call an ``optimal scenario.'' Such a scenario does not have to be unique and may fall outside the support of the underlying distr

Zhiqiang Xie, Hao Kang, Ying Sheng, Tushar Krishna

With more advanced natural language understanding and reasoning capabilities, large language model (LLM)-powered agents are increasingly developed in simulated environments to perform complex tasks, interact with other agents, and exhibit emergent behaviors relevant to social science and gaming. However, current multi-agent simulations frequently suffer from

Parikshit Bansal, Ali Kavis, Sujay Sanghavi

Self-supervised learning attempts to learn representations from un-labeled data; it does so via a loss function that encourages the embedding of a point to be close to that of its augmentations. This simple idea performs remarkably well, yet it is not precisely theoretically understood why this is the case. In this paper we analyze self-supervised learning i

Alexander Cerjan, Terry A. Loring

Recently, the spectral localizer framework has emerged as an efficient approach to classifying topology in photonic systems featuring local nonlinearities and radiative environments. In nonlinear systems, this framework provides rigorous definitions for concepts like topological solitons and topological dynamics, where a system's occupation induces a local c

Vagelis Plevris, Afaq Ahmad

This study investigates the use of symbolic computation in Matrix Structural Analysis (MSA) for continuous beams, leveraging the MATLAB Symbolic Math Toolbox. By employing symbolic MSA, analytical expressions for displacements, support reactions, and internal forces are derived, offering deeper insights into structural behavior. This approach facilitates eff

Razvan-Gabriel Dumitru, Paul-Ioan Clotan, Vikas Yadav, Darius Peteleaza

This paper introduces a novel model compression approach through dynamic layer-specific pruning in Large Language Models (LLMs), enhancing the traditional methodology established by SliceGPT. By transitioning from constant to dynamic slicing, our method leverages the newly proposed Layer Redundancy (LR) score, which assesses how much change each layer change

Wen Huang, Chunlin Liu, Shige Peng, Baoyou Qu

We utilize an ergodic theory framework to explore sublinear expectation theory. Specifically, we investigate the pointwise Birkhoff's ergodic theorem for invariant sublinear expectation systems. By further assuming that these sublinear expectation systems are ergodic, we derive stronger results. Furthermore, we relax the conditions for the law of large numbe

Viktoria Ehm, Nafie El Amrani, Yizheng Xie, Lennart Bastian

Finding correspondences between 3D deformable shapes is an important and long-standing problem in geometry processing, computer vision, graphics, and beyond. While various shape matching datasets exist, they are mostly static or limited in size, restricting their adaptation to different problem settings, including both full and partial shape matching. In par

Abdessatar Souissi, Amenallah Andolsi

In this work, we present a novel representation of matrix product states (MPS) within the framework of quasi-local algebras. By introducing an enhanced compatibility condition, we enable the extension of finite MPS to an infinite-volume state, providing new insights into complex, high-dimensional quantum systems. As an illustrative example, we apply this met

Cristian Martinez

We give a proof of the Kodaira vanishing theorem on smooth complex surfaces using geometric stability conditions. Likewise, we give a new proof of a result of Xie characterizing the counterexamples of the Kodaira vanishing theorem in positive characteristic.

León Carvajales, Pablo Lessa, Rafael Potrie

We study quasi-isometric representations of finitely generated non-abelian free groups into some higher rank semi-simple Lie groups which are not Anosov, nor approximated by Anosov. We show in some cases that these can be perturbed to be non-quasi-isometric, or to have some instability properties with respect to their action on the flag space.

Boris M. Bekker, Yuri G. Zarhin

Let $d\geq 2$ be a positive integer, $K$ an algebraically closed field of characteristic not dividing $d$, $n\geq d+1$ a positive integer that is prime to $d$, $f(x)\in K[x]$ a degree $n$ monic polynomial without multiple roots, $C_{f,d}: y^d=f(x)$ the corresponding smooth plane affine curve over $K$, $\mathcal{C}_{f,d}$ a smooth projective model of $C_{f,d}

Shridhar Mehendale, Ankit Walishetti

Individuals with fine motor impairments, such as those caused by conditions like Parkinson's disease, cerebral palsy, or dyspraxia, face significant challenges in interacting with traditional computer interfaces. Historically, scripted automation has offered some assistance, but these solutions are often too rigid and task-specific, failing to adapt to the d

Hanwen Zhang, Mingzhe Chen, Alireza Vahid, Feng Ye

Next generation communications demand for better spectrum management, lower latency, and guaranteed quality-of-service (QoS). Recently, Artificial intelligence (AI) has been widely introduced to advance these aspects in next generation wireless systems. However, such AI applications suffer from limited training data, low robustness, and poor generalization c

Jeff Meilander, Chloe Herman, Andrew Manley, Georgia Augustine

Human excrement composting (HEC) is a sustainable strategy for human excrement (HE) management that recycles nutrients and mitigates health risks while reducing reliance on freshwater, fossil fuels, and fertilizers. We present a comprehensive microbial time series analysis of HEC and show that the initial gut-like microbiome of HEC systems transitions to a m