March 2025 arXiv papers — page 36

Nedialko S. Nedialkov, John D. Pryce

A Taylor method for solving an ordinary differential equation initial-value problem $\dot x = f(t,x)$, $x(t_0) = x_0$, computes the Taylor series (TS) of the solution at the current point, truncated to some order, and then advances to the next point by summing the TS with a suitable step size. A standard ODE method (e.g. Runge-Kutta) treats function $f$ as a

Tsuyoshi Miezaki, Hiroshi Suzuki, Keisuke Uchida

In the present paper, we define the Terwilliger algebra of digraphs. Then, we determine the irreducible modules of the Terwilliger algebra of a Hamming digraph $H^*(d,3)$. As is well known, the representation of the Terwilliger algebra of a binary Hamming graph $H(d,2)$ is closely related to that of the Lie algebra $\mathit{sl}_2(\mathbb{C})$. We show that i

Yusong Hu, Zichen Liang, Fei Yang, Qibin Hou

Continual learning requires models to train continuously across consecutive tasks without forgetting. Most existing methods utilize linear classifiers, which struggle to maintain a stable classification space while learning new tasks. Inspired by the success of Kolmogorov-Arnold Networks (KAN) in preserving learning stability during simple continual regressi

Riju Basak, Daniel Spector, Dmitriy Stolyarov

We prove quantitative estimates for the decay of the Fourier transform of the Riesz potential of measures that are in homogeneous Besov spaces of negative exponent: \begin{align*} \|\widehat{I_{\alpha}\mu}\|_{L^{p, \infty}} \leq C \|\mu\|_{M_b}^{\frac{1}{2}}\left(\sup_{t>0} t^{\frac{d-\beta}{2}}\|p_{t}\ast \mu\|_{\infty}\right)^{\frac{1}{2}}, \end{align*} wh

Ooha Lakkadi Reddy

This thesis employs a hybrid CNN-Transformer architecture, alongside a detailed anthropological framework, to investigate potential historical connections between the visual morphology of the Indus Valley script and pictographic systems of the Tibetan-Yi Corridor. Through an ensemble methodology of three target scripts across 15 independently trained models,

Andrew Lee, Melanie Weber, Fernanda Viégas, Martin Wattenberg

Researchers have recently suggested that models share common representations. In our work, we find numerous geometric similarities across the token embeddings of large language models. First, we find ``global'' similarities: token embeddings often share similar relative orientations. Next, we characterize local geometry in two ways: (1) by using Locally Line

Judy X Yang, Jing Wang, Zhuanfeng, Li

The integration of hyperspectral imaging (HSI) and Light Detection and Ranging (LiDAR) data provides complementary spectral and spatial information for remote sensing applications. While previous studies have explored the role of band selection and grouping in HSI classification, little attention has been given to how the spectral sequence or band order affe

Yingyu Lin, Erchi Wang, Yi-An Ma, Yu-Xiang Wang

We propose a framework to convert $(\varepsilon, \delta)$-approximate Differential Privacy (DP) mechanisms into $(\varepsilon', 0)$-pure DP mechanisms under certain conditions, a process we call ``purification.'' This algorithmic technique leverages randomized post-processing with calibrated noise to eliminate the $\delta$ parameter while achieving near-opti

Tohid Kargar Tasooji, Sakineh Khodadadi

It is known that the conventional estimators such as extended Kalman filter (EKF) and unscented Kalman filter (UKF) may provide favorable performance; However, they may not guarantee the robustness against model uncertainty and cyber attacks. In this paper, we compare the performance of cubature Kalman filter (CKF) to the conventional nonlinear estimator, th

Fan Qi, Yu Duan, Changsheng Xu

Recent advances in text-guided diffusion models have revolutionized conditional image generation, yet they struggle to synthesize complex scenes with multiple objects due to imprecise spatial grounding and limited scalability. We address these challenges through two key modules: 1) Janus-Pro-driven Prompt Parsing, a prompt-layout parsing module that bridges

Manfred Einsiedler, Elon Lindenstrauss, Amir Mohammadi, Andreas Wieser

We prove an effective equidistribution theorem for semisimple closed orbits on compact adelic quotients. The obtained error depends polynomially on the minimal complexity of intermediate orbits and the complexity of the ambient space. The proof uses dynamical arguments, property $(\tau)$, Prasad's volume formula, an effective closing lemma, and a novel effec

Enzo B Onofre, Leonardo M P Moraes, Cristina D Aguiar

This paper introduces AskSport, a question-answering web application about sports. It allows users to ask questions using natural language and retrieve the three most relevant answers, including related information and documents. The paper describes the characteristics and functionalities of the application, including use cases demonstrating its ability to r

A. Zenteno, M. Kluge, R. Kharkrang, D. Hernandez-Lang

The Spectrum Roentgen Gamma/eROSITA first public release contains 12,247 clusters and groups. We use the offset between the Brightest Cluster Galaxy (BCG) and the X--ray peak (D$_{\rm BCG-X}$) to classify the cluster dynamical state of 3,946 galaxy clusters and groups. The X--ray peaks come from the eROSITA survey while the BCG positions come from the DECaLS

Filip Surma, Anahita Jamshidnejad

This paper introduces a novel concept, fuzzy-logic-based model predictive control (FLMPC), along with a multi-robot control approach for exploring unknown environments and locating targets. Traditional model predictive control (MPC) methods rely on Bayesian theory to represent environmental knowledge and optimize a stochastic cost function, often leading to

Elon Lindenstrauss, Amir Mohammadi, Zhiren Wang, Lei Yang

We establish effective equidistribution theorems, with a polynomial error rate, for orbits of unipotent subgroups in quotients of quasi-split, almost simple Linear algebraic groups of absolute rank 2. As an application, inspired by the results of Eskin, Margulis and Mozes, we establish quantitative results regarding the distribution of values of an indefinit

Ben Wilks, Michael H. Meylan, Fabien Montiel, Dasun Shalila Balasooriya

The design of optimal wave energy parks, namely, arrays of devices known as wave energy converters (WECs) that extract energy from water waves, is an important consideration for the renewable transition. In this paper, the problem of simultaneously optimising the layout and device parameters of a wave energy park is considered within the framework of linear

Santiago Palumbo, Pablo S. Cornaglia, Jorge I. Facio

A periodic lattice distortion that reduces the translational symmetry folds electron bands into a reduced Brillouin zone, leading to band mixing and a tendency to gap formation, as in the Peierls transition in one-dimensional systems. However, in higher dimensions, the resulting phase can present topological obstructions preventing a complete gap opening. We

Kangjian Chen, Chenhao Qi, Octavia A. Dobre

This paper proposes a dual-band reconfigurable antenna array (DBRAA), enabling wireless capabilities in both sub-6 GHz (sub-6G) and millimeter wave (mmWave) bands using a single array. For the sub-6G band, we propose a reconfigurable antenna selection structure, where each sub-6G antenna is formed by multiplexing several mmWave antennas, with its position dy

Mehraveh Javan Roshtkhari, Matthew Toews, Marco Pedersoli

Monte-Carlo Tree Search (MCTS) is a powerful tool for many non-differentiable search related problems such as adversarial games. However, the performance of such approach highly depends on the order of the nodes that are considered at each branching of the tree. If the first branches cannot distinguish between promising and deceiving configurations for the f

Ayumu Shoshi, Masayuki Yamaguchi, Takayuki Muto, Naomi Hirano

This study focuses on Class I, Flat Spectrum (FS), and Class II disks in the Ophiuchus molecular cloud, a nearby active star-forming region with numerous young stellar objects (YSOs), to unveil signs of substructure formation in these disks. We employ two-dimensional super-resolution imaging based on Sparse Modeling (SpM) for ALMA archival Band 6 continuum d

Jeremy Diamzon, Daniele Venturi

We develop new uncertainty propagation methods for feed-forward neural network architectures with leaky ReLU activation functions subject to random perturbations in the input vectors. In particular, we derive analytical expressions for the probability density function (PDF) of the neural network output and its statistical moments as a function of the input u

Vincent van Vliet, Menno van den Hout, Kadir Gümüş, Eduward Tangdiongga

We transmitted 5.7 Tb/s over a 4.6 km free-space optical link in an urban environment, spanning the city of Eindhoven, the Netherlands, using a 1.1 THz wide wavelength-division multiplexed signal.

Joy Carpio, Sulaiman Almatrudi, Nour Khoudari, Zhe Fu

Accurate estimation of vehicle fuel consumption typically requires detailed modeling of complex internal powertrain dynamics, often resulting in computationally intensive simulations. However, many transportation applications-such as traffic flow modeling, optimization, and control-require simplified models that are fast, interpretable, and easy to implement

Sebastian Barros

Latency remains a critical bottleneck for deploying foundational artificial intelligence (AI) models, such as large language models (LLMs), in customer-facing, real-time applications. While cloud-based inference offers scalability, it frequently introduces delays unacceptable for interactive experiences, such as semantic search, personalized recommendations,

Yiqing Shen, Bohan Liu, Chenjia Li, Lalithkumar Seenivasan

Reasoning segmentation (RS) aims to identify and segment objects of interest based on implicit text queries. As such, RS is a catalyst for embodied AI agents, enabling them to interpret high-level commands without requiring explicit step-by-step guidance. However, current RS approaches rely heavily on the visual perception capabilities of multimodal large la

Chi-Hsi Kung, Frangil Ramirez, Juhyung Ha, Yi-Ting Chen

Understanding a procedural activity requires modeling both how action steps transform the scene, and how evolving scene transformations can influence the sequence of action steps, even those that are accidental or erroneous. Existing work has studied procedure-aware video representations by modeling the temporal order of actions, but has not explicitly learn

Haomin Yu, Tianyi Li, Kristian Torp, Christian S. Jensen

Accurate vessel trajectory prediction facilitates improved navigational safety, routing, and environmental protection. However, existing prediction methods are challenged by the irregular sampling time intervals of the vessel tracking data from the global AIS system and the complexity of vessel movement. These aspects render model learning and generalization

Yuxiao Sun, Yao Zhao, Meiqin Liu, Chao Yao

Currently, video transmission serves not only the Human Visual System (HVS) for viewing but also machine perception for analysis. However, existing codecs are primarily optimized for pixel-domain and HVS-perception metrics rather than the needs of machine vision tasks. To address this issue, we propose a Compression Distortion Representation Embedding (CDRE)

Karol Kawka, Pawel Kempisty, Akira Kusaba, Krzysztof Golyga

Silicon (Si) is the primary donor dopant in gallium nitride (GaN), introduced through epitaxial growth or ion implantation. However, precise control over Si diffusion remains a critical challenge for high-performance device applications. This study investigates Si diffusion mechanisms in bulk GaN using first-principles density functional theory (DFT) calcula

Zengxing Lu, Jiatai Feng, Xuan Zheng, You-guo Shi

Ultrathin two-dimensional (2D) electronic systems at the interfaces of layered materials are highly desirable platforms for exploring of novel quantum phenomena and developing advanced device applications. Here, we investigate ultrathin heterostructures composed of SrIrO3 (SIO) and SrRuO3 (SRO) layers to uncover their emergent properties. Strikingly, despite

Jiang-Lin Zhou, Zou-Chen Fu, Choo Hiap Oh, Jing-Ling Chen

The Stern-Gerlach (SG) experiment is a fundamental experiment for revealing the existence of ``spin''. In such an experiment, beams of silver atoms were sent through inhomogeneous magnetic fields to observe their deflection. Thus, the conventional SG experiment can be actually viewed as a magnetic-type spin effect. In this work, we successfully gener

Yishen Liu

Large language models (LLMs) primarily trained on English texts, often face biases and inaccuracies in Chinese contexts. Their limitations are pronounced in fields like Traditional Chinese Medicine (TCM), where cultural and clinical subtleties are vital, further hindered by a lack of domain-specific data, such as rheumatoid arthritis (RA). To address these i

Xing Chen, Dongshu Liu, Jeremie Laydevant, Julie Grollier

Agents that operate autonomously benefit from lifelong learning capabilities. However, compatible training algorithms must comply with the decentralized nature of these systems, which imposes constraints on both the parameter counts and the computational resources. The Forward-Forward (FF) algorithm is one of these. FF relies only on feedforward operations,

Yiqing Shen, Chenjia Li, Bohan Liu, Cheng-Yi Li

Analyzing operating room (OR) workflows to derive quantitative insights into OR efficiency is important for hospitals to maximize patient care and financial sustainability. Prior work on OR-level workflow analysis has relied on end-to-end deep neural networks. While these approaches work well in constrained settings, they are limited to the conditions specif

Di Zhang, Suvrajeet Sen

Stochastic Optimization is a cornerstone of operations research, providing a framework to solve optimization problems under uncertainty. Despite the development of numerous algorithms to tackle these problems, several persistent challenges remain, including: (i) selecting an appropriate sample size, (ii) determining an effective search direction, and (iii) c

Lior Gishboliner, Ethan Honest

For $\ell \geq 3$, an $\ell$-uniform hypergraph is disperse if the number of edges induced by any set of $\ell+1$ vertices is 0, 1, $\ell$ or $\ell+1$. We show that every disperse $\ell$-uniform hypergraph on $n$ vertices contains a clique or independent set of size $n^{\Omega_{\ell}(1)}$, answering a question of the first author and Tomon. To this end, we p

Francisco F. Carrasco-Varela, Prasanta K. Nayak, Thomas H. Puzia

Context. The study of blue straggler stars (BSS) provides insight into the mechanisms of stellar mass exchange during binary stellar evolution and the complex gravitational interactions within dense stellar systems. In combination, they enhance our understanding of the possible life cycles of stars and the evolutionary pathways of star clusters. Aim. We stud

Pedro Duarte, Marcelo Durães, Tomé Graxinha, Silvius Klein

In this paper we establish a Bochi-Ma\~n\'e type dichotomy in the space of two dimensional, nonnegative determinant matrix valued, locally constant linear cocycles over a Bernoulli or Markov shift. Moreover, we prove that Lebesgue almost every such cocycle has finite first Lyapunov exponent, which then implies a break in the regularity of the Lyapunov expone

Dominik Kempa, Tomasz Kociumaka

In this work, we study the relative hardness of fundamental problems with state-of-the-art word RAM algorithms that take $O(n\sqrt{\log n})$ time for instances described in $\Theta(n)$ machine words ($\Theta(n\log n)$ bits). This complexity class, one of six hardness levels identified by Chan and P\u{a}tra\c{s}cu [SODA 2010], includes diverse problems from s

Ichiro Ohta, Shota Koyanagi, Kayo Kinjo, Jun Ohkubo

In recent years, there has been a growing interest in data-driven approaches in physics, such as extended dynamic mode decomposition (EDMD). The EDMD algorithm focuses on nonlinear time-evolution systems, and the constructed Koopman matrix yields the next-time prediction with only linear matrix-product operations. Note that data-driven approaches generally r

Alan Yang, Yulin Chen, Sean Lee, Venus Montes

Time series anomaly detection (TSAD) is of widespread interest across many industries, including finance, healthcare, and manufacturing. Despite the development of numerous automatic methods for detecting anomalies, human oversight remains necessary to review and act upon detected anomalies, as well as verify their accuracy. We study the use of multimodal la

Jeremias Ferrao, Rafael Cunha

Sparse reward environments pose a significant challenge for reinforcement learning due to the scarcity of feedback. Intrinsic motivation and transfer learning have emerged as promising strategies to address this issue. Change Based Exploration Transfer (CBET), a technique that combines these two approaches for model-free algorithms, has shown potential in ad

Kyung Ho Lim, Ujin Kang, Xiang Li, Jin Sung Kim

Large language models (LLMs) are increasingly used in healthcare, but their reliability is heavily influenced by user-driven factors such as question phrasing and the completeness of clinical information. In this study, we examined how misinformation framing, source authority, model persona, and omission of key clinical details affect the diagnostic accuracy

Fletcher Gates, Scott Rodney

In this paper we present a number of results concerning Alpert wavelet bases for $L^2(\mu)$, with $\mu$ a locally finite positive Borel measure on $\mathbb{R}^n$. We show that the properties of such a basis depend on linear dependences in $L^2(\mu)$ among the functions from which the wavelets are constructed; this result completes an investigation begun by R

Josep M. Trigo-Rodríguez, Damya Souami, Maria Gritsevich, Marcin Wesołowski

Comets and asteroids have long captured human curiosity, and until recently, all documented examples belonged to our Solar System. That changed with the discovery of the first known interstellar object, 1I/2017 U1 ('Oumuamua), in 2017. Two years later, Gennady Borisov discovered a second interstellar object: 2I/Borisov. From its initial images, the object's

Ching Hei Cheng, Jonathan Eden, Denny Oetomo, Ying Tan

Proprioception is essential for coordinating human movements and enhancing the performance of assistive robotic devices. Skin stretch feedback, which closely aligns with natural proprioception mechanisms, presents a promising method for conveying proprioceptive information. To better understand the impact of interference on skin stretch perception, we conduc

Keiji Hayashi, Alexander G. Kosovichev, Chunlei Liang

Fluid-dynamics simulations of global solar convection are a critically important tool for assessing the dynamics of the solar interior. However, simulation studies with a fully compressible hydrodynamics code are not yet common. The CHORUS++ code solves robustly and efficiently the fully compressible hydrodynamics equations using a compact local spectral met

Mohammad Javad Najafirad, Shirantha Welikala

This paper presents a novel dissipativity-based distributed droop-free control and communication topology co-design approach for voltage regulation and current sharing in DC microgrids (DC MGs) with generic ``ZIP'' (constant impedance (Z), current (I) and power (P)) loads. While ZIP loads accurately capture the varied nature of the consumer loads, its consta

Oskar Leimkuhler, K. Birgitta Whaley

We prove classical simulation hardness, under the generalized $\mathsf{P}\neq\mathsf{NP}$ conjecture, for quantum circuit families with applications in near-term chemical ground state estimation. The proof exploits a connection to particle number conserving matchgate circuits with fermionic magic state inputs, which are shown to be universal for quantum comp

Larry A. Dunning

In 1969 J. Verhoeff provided the first examples of a decimal error detecting code using a single check digit to provide protection against all single, transposition and adjacent twin errors. The three versions of such a code that he presented are length 3-digit codes with 2 information digits. Existence of a 4-digit code would imply the existence of 10 such

Amir Enayati Kafshgarkolaei, Maziar S. Hemati

Quadratic-bilinear (QB) systems arise in many areas of science and engineering. In this paper, we present a scalable approach for designing locally stabilizing state-feedback control laws and certifying the local stability of QB systems. Sufficient conditions are established for local stability and stabilization based on quadratic Lyapunov functions, which a

Hector Andres Chang-Lara, Sergio David Zapeta-Tzul

We revisit the classic problem of determining optimal routes in a graph for transporting two given distributions defined on its nodes, originally studied by Wardrop and Beckmann in the 1950s. The global congestion profile at any given time defines a dynamic metric on the graph, for which the routes must be geodesics. Our first contribution is the introductio

Chengyu Wang, A. Gupta, S. K. Singh, L. N. Pfeiffer

The fractional quantum Hall state (FQHS), an incompressible liquid state hosting anyonic excitations with fractional charge and statistics, represents a compelling many-body phase observed in clean two-dimensional (2D) carrier systems. The expected non-Abelian nature of the FQHSs at even-denominator Landau level (LL) fillings has particularly sparked conside

Gianmarco Lazzeri, Peter G. Bolhuis, Roberto Covino

We propose an efficient novel path sampling-based framework designed to accelerate the investigation of rare events in complex molecular systems. A key innovation is the shift from sampling restricted path ensemble distributions, as in transition path sampling, to directly sampling the distribution of shooting points. This allows for a rejection-free algorit

Yunnan Wu, Paul Chen, Deshank Baranwal, Jinlong Zhou

We present an agentic framework, Thinker, which achieves state of art performance in challenging reasoning tasks for realistic customer service scenarios that involve complex business logic and human interactions via long horizons. On the $\tau$-bench retail dataset, Thinker achieves 82.6\% success rate with GPT-4o (version 2024-06-01) (baseline: 68.3\%), an

Felipe Díaz-Jaramillo, Silvia Nagy, Giorgio Pizzolo

We present the first formulation of a homotopy algebra adapted to a $1/r$ expansion near future null infinity ($\mathcal{I^+}$). Focusing on self-dual Yang-Mills theory in Bondi coordinates, we demonstrate that imposing the homotopy algebra relations naturally yields the physically consistent fall-off behavior of the fields near $\mathcal{I^+}$. Furthermore,

Hidekazu Tanaka, Shuji Sasagawa

In this paper, we examine the properties of the solutions obtained by the Schwinger-Dyson equation (SDE). As a simple example, we consider a two-dimensional model including four-fermion interaction. It is shown that when this model is solved by an iterative method using the SDE at finite density, multiple solutions depending on the initial input values are o

Dimitar Mileski, Nikola Petrovski, Marjan Gusev

Training large language models requires extensive processing, made possible by many high-performance computing resources. This study compares multi-node and multi-GPU environments for training large language models of electrocardiograms. It provides a detailed mapping of current frameworks for distributed deep learning in multinode and multi-GPU settings, in

Jian-zhao Wang, Fran Bagenal, Stefan Eriksson, Robert E. Ergun

A key open question in astrophysics is how plasma is transported within strongly magnetized, rapidly rotating systems. Magnetic reconnection and flux tube interchange are possible mechanisms, with Jupiter serving as the best local analog for distant systems. However, magnetic reconnection at Jupiter remains poorly understood. A key indicator of active magnet

Gazi Nazia Nur, Mohammad Ahnaf Sadat, Basit Mahmud Shahriar

In this paper, we address the inherent limitations in traditional assembly line balancing, specifically the assumptions that task times are constant and no defective outputs occur. These assumptions often do not hold in practical scenarios, leading to inefficiencies. To address these challenges, we introduce a framework utilizing an "adjusted processing time

Malcolm Jones

We give a local treatment of finite alignment by identifying the finitely aligned part of any (not necessarily finitely aligned) higher-rank graph. We show the finitely aligned part is itself a constellation and forms a finitely aligned relative category of paths together with the original higher-rank graph. We show that the elements of the finitely aligned

Jungyeul Park, Yige Chen, Kyuwon Kim, KyungTae Lim

This paper introduces UniDive for Korean, an integrated framework that bridges Universal Dependencies (UD) and Universal Morphology (UniMorph) to enhance the representation and processing of Korean {morphosyntax}. Korean's rich inflectional morphology and flexible word order pose challenges for existing frameworks, which often treat morphology and syntax sep

M. J. Bertin, T. Zaïmi

Let K be a real algebraic number field and let P be the set of Pisot numbers generating K. We show that the elements of P-P are the algebraic integers of K whose images under the action of all embeddings of K into C, other than the identity of K, are of modulus less than 2. This completes certain previous results due to Dubickas and the second author. Also,

Tamal Kumar Dalui, Hari Paudyal, Durga Paudyal, Ramesh C Budhani

In Weyl semi-metals, the conduction and valence bands intersect at distinct points on the Brillouin zone (Weyl points), which act as monopoles of Berry curvature in momentum space. This nontrivial band topology, identified from electronic structure calculations, gives rise to various exotic magneto-transport properties. Hybrid functional calculations that in

Deng Wang

A key question in cosmology is whether massive neutrinos exist on cosmic scales. Current cosmological observations have severely compressed the viable range for neutrino masses and even prefer phenomenologically an effective negative mass. This poses a great challenge to the cosmological search for neutrinos. Based on current background and large scale struc

Aniket Abhishek Soni

Although speech recognition algorithms have developed quickly in recent years, achieving high transcription accuracy across diverse audio formats and acoustic environments remains a major challenge. This work explores how incorporating custom language models with the open-source Vosk Toolkit can improve speech-to-text accuracy in varied settings. Unlike many

Neal Dalal, Will J. Percival

Local-type primordial non-gaussianity generates a distinctive term in the clustering of tracers of large-scale structure, behaving as $k^{-2}$ at small wavenumbers $k$. In order to use this signal in a sample of galaxies to measure the amplitude of primordial non-gaussianity, $f_{NL}$, we need to independently determine the degenerate bias coefficient, $b_\P

Thomson Yen, Andrew Wei Tung Siah, Haozhe Chen, Tianyi Peng

Careful curation of data sources can significantly improve the performance of LLM pre-training, but predominant approaches rely heavily on intuition or costly trial-and-error, making them difficult to generalize across different data domains and downstream tasks. Although scaling laws can provide a principled and general approach for data curation, standard

Hyowon Kim, Navid~Amani, Musa Furkan Keskin, Zhongxia Simon He

In the upcoming vehicular networks, reconfigurable intelligent surfaces (RISs) are considered as a key enabler of user self-localization without the intervention of the access points (APs). In this paper, we investigate the feasibility of RIS-enabled self-localization with no APs. We first develop a digital signal processing (DSP) unit for estimating the geo

Sharat Paul, Md Nahid Hasan, Pai Wang

We propose complete tailoring procedures with analytical precision for band degeneracies in one-dimensional (1D) nonlocal phononic crystals, focusing on the role of beyond-nearest-neighbor (BNN) interactions. Unlike trivial Dirac cones at either the center or boundary of Brillouin zone (BZ), we demonstrate non-trivial Dirac-type and higher-order band crossin

V. V. Bobylev, N. R. Ikhsanov, A. T. Bajkova

Four samples of open star clusters (OSCs) with average ages of 5.2, 18.6, 40, and 61 Myr have been analyzed. The selection of these OSCs was carried out from a narrow region inclined to the galactic axis y at an angle of 25$^\circ$. The spectral analysis of the vertical positions and velocities of the selected clusters showed that the Radcliffe wave is assoc

Alexander Levine, Peter Stone, Amy Zhang

While sequential decision-making environments often involve high-dimensional observations, not all features of these observations are relevant for control. In particular, the observation space may capture factors of the environment which are not controllable by the agent, but which add complexity to the observation space. The need to ignore these "noise" fea

Joseph D. Clark, Tanner J. Dean, Diwakar Shukla

Deep learning models have become fundamental tools in drug design. In particular, large language models trained on biochemical sequences learn feature vectors that guide drug discovery through virtual screening. However, such models do not capture the molecular interactions important for binding affinity and specificity. Therefore, there is a need to 'compos

Hua-Chieh Shao, Xiaoxue Qian, Guoping Xu, Can Wu

Based on a 3D pre-treatment magnetic resonance (MR) scan, we developed DREME-MR to jointly reconstruct the reference patient anatomy and a data-driven, patient-specific cardiorespiratory motion model. Via a motion encoder simultaneously learned during the reconstruction, DREME-MR further enables real-time volumetric MR imaging and cardiorespiratory motion tr

Yufan Wei, Mickel Liu, Wenfei Wu

AllReduce is a technique in distributed computing which saw use in many critical applications of deep learning. Existing methods of AllReduce scheduling oftentimes lack flexibility due to being topology-specific or relying on extensive handcrafted designs that require domain-specific knowledge. In this work, we aim to alleviate this inflexibility by proposin

Jayita Dutta, Antonio Ferraro, Arnab Manna, Rui Chen

The development of compact, low-power, and high-performance integrated photonic phase shifters is critical for advancing emerging technologies such as light detection and ranging (LiDAR), optical information processing and quantum applications. Liquid crystal (LC)-based phase shifters offer a promising solution thanks to their large refractive index contrast

Ana Ma, Derek Powell

Prior work has shown that large language models (LLMs) can predict human attitudes based on other attitudes, but this work has largely focused on predictions from highly similar and interrelated attitudes. In contrast, human attitudes are often strongly associated even across disparate and dissimilar topics. Using a novel dataset of human responses toward di

Hilda Hadan, Derrick M. Wang, Lennart E. Nacke, Leah Zhang-Kennedy

Extended Reality (XR) technology is changing online interactions, but its granular data collection sensors may be more invasive to user privacy than web, mobile, and the Internet of Things technologies. Despite an increased interest in studying developers' concerns about XR device privacy, user perceptions have rarely been addressed. We surveyed 464 XR users

Tom Liu, Anna Wu, Chao Li

Self-training has become a popular semi-supervised learning technique for leveraging unlabeled data. However, the over-confidence of pseudo-labels remains a key challenge. In this paper, we propose a novel \emph{graph-based uncertainty-aware self-training} (GUST) framework to combat over-confidence in node classification. Drawing inspiration from the uncerta

Juan J. Lastra-Díaz, M. Teresa Ortuño

The irregular strip-packing problem consists of the computation of a non-overlapping placement of a set of polygons onto a rectangular strip of fixed width and the minimal length possible. Recent performance gains of the Mixed-Integer Linear Programming (MILP) solvers have encouraged the proposal of exact optimization models for nesting. The Dotted-Board (DB

Emily Wang, Michael Chen, Chao Li

In this paper, we propose a novel \emph{uncertainty-aware graph self-training} approach for semi-supervised node classification. Our method introduces an Expectation-Maximization (EM) regularization scheme to incorporate an uncertainty mechanism during pseudo-label generation and model retraining. Unlike conventional graph self-training pipelines that rely o

Nursel Erey, Takayuki Hibi

Given integers $2 \leq p \leq c \leq q$, we construct a finite simple graph $G$ with $\nu_1(G) = p$ and $\nu(G) = q$ for which the squarefree power $I(G)^{[k]}$ of the edge ideal $I(G)$ of $G$ has linear quotients for each $c \leq k \leq q$ and is not linearly related for each $1 \leq k < c$, where $\nu_1(G)$ is the induced matching number of $G$ and $\nu(G)

Daniel Adamiak, Nicholas Baldonado, Yuri V. Kovchegov, Ming Li

We perform a phenomenological study of helicity-dependent parton distribution functions (PDFs) using small-$x$ helicity evolution equations, incorporating for the first time single-inclusive jet production data in polarized proton-proton ($pp$) scattering at parton momentum fractions $x < 0.1$. We also simultaneously include double-longitudinal spin asymmetr

Gabriele Montefalcone, Dan Hooper, Katherine Freese, Chris Kelso

Semi-classical Hawking evaporation is expected to break down at some point in a black hole's evolution as the effects of quantum gravity become important. In particular, it has been argued that the so-called memory-burden effect could cause black holes to become stabilized by the information that they carry, thereby suppressing the rate at which they undergo

Mahmoud Alwakeel, Emory Buck, Jonathan G. Martin, Imran Aslam

Pulmonary embolism (PE) registries accelerate practice-improving research but depend on resource-intensive manual abstraction of radiology reports. We evaluated whether openly available large-language models (LLMs) can automate concept extraction from computed-tomography PE (CTPE) reports without sacrificing data quality. Four Llama-3 (L3) variants (3.0 8 B,

Alice Zhang, Chao Li

State-space modeling has emerged as a powerful paradigm for sequence analysis in various tasks such as natural language processing, time-series forecasting, and signal processing. In this work, we propose an \emph{Adaptive State-Space Mamba} (\textbf{ASSM}) framework for real-time sensor data anomaly detection. While state-space models have been previously e

Tai D. Nguyen, Aref Azizpour, Matthew C. Stamm

The emergence of advanced AI-based tools to generate realistic images poses significant challenges for forensic detection and source attribution, especially as new generative techniques appear rapidly. Traditional methods often fail to generalize to unseen generators due to reliance on features specific to known sources during training. To address this probl

Georgios Kalamakis, Anastasios C. Petkou

We present results for the numerical evaluation of scalar quasinormal modes in Taub-NUT-AdS$_4$ spacetimes. To achieve this we consider angular modes that correspond to non-unitary highest weight $SU(2)$ representations since global regularity is not consistent with the presence of complex quasinormal modes. We show that for any non-zero value of the NUT cha

Lynnette Hui Xian Ng, Kokil Jaidka, Kaiyuan Tay, Hansin Ahuja

Supervised machine-learning models often underperform in predicting user behaviors from conversational text, hindered by poor crowdsourced label quality and low NLP task accuracy. We introduce the Metadata-Sensitive Weighted-Encoding Ensemble Model (MSWEEM), which integrates annotator meta-features like fatigue and speeding. First, our results show MSWEEM ou

Wen Li, Sofia Martinez, Priyanka Shah

Text-driven voice conversion allows customization of speaker characteristics and prosodic elements using textual descriptions. However, most existing methods rely heavily on direct text-to-speech training, limiting their flexibility in controlling nuanced style elements or timbral features. In this paper, we propose a novel \textbf{Latent State-Space} approa

Lucas Myers, Carter Swift, Jonas Ronning, Luiza Angheluta

A singular potential method in the Q tensor order parameter representation of a nematic liquid crystal is used to study the equilibrium configuration of a disclination dipole. Unlike the well studied isotropic limit (the so called one constant approximation), we focus on the case of anisotropic Frank elasticity (bend/splay elastic constant contrast). Prior r

Ivo Schulthess, Federico Meloni

Beam-dump experiments offer an opportunity to search for new physics beyond the Standard Model of particle physics. In this work, we explore the use of a high-energy photon beam on a fixed target. Such photons can be produced via Compton backscattering when colliding high-energy electrons with a high-intensity laser pulse, such as in setups of strong-field q

Xiaomin Li, Xupeng Chen, Jingxuan Fan, Eric Hanchen Jiang

The safety alignment of large language models (LLMs) often relies on reinforcement learning from human feedback (RLHF), which requires human annotations to construct preference datasets. Given the challenge of assigning overall quality scores to data, recent works increasingly adopt fine-grained ratings based on multiple safety rules. In this paper, we disco

Cole Patten, Christopher Saunders, Michael Puthawala

We used contrastive neural networks to learn useful similarity scores between the 144 cartridge casings in the NBIDE dataset, under the common-but-unknown source paradigm. The common-but-unknown source problem is a problem archetype in forensics where the question is whether two objects share a common source (e.g. were two cartridge casings fired from the sa

Linda M. van Manen, M. Kemal Döner, André Großardt

We reconsider a thought experiment that employs the entanglement of the gravitational field with position space quantum states as a means for faster-than-light signaling. We present a protocol that includes the excitation to a higher internal energy level to increase sensitivity to gravitational phase shifts. We report that the explanations why previous vers

P. N. Bibikov

The free energy density of the XX chain in magnetic field is obtained in two alternative ways within the Quantum Transfer Matrix approach. In both the cases the proofs are complete and self-consistent. All the intermediate constructions are presented explicitly in detail.

Michael Brown, Sofia Martinez, Priya Singh

Text-driven speech style transfer aims to mold the intonation, pace, and timbre of a spoken utterance to match stylistic cues from text descriptions. While existing methods leverage large-scale neural architectures or pre-trained language models, the computational costs often remain high. In this paper, we present \emph{ReverBERT}, an efficient framework for

Tai D. Nguyen, Matthew C. Stamm

While videos can be falsified in many different ways, most existing forensic networks are specialized to detect only a single manipulation type (e.g. deepfake, inpainting). This poses a significant issue as the manipulation used to falsify a video is not known a priori. To address this problem, we propose MVFNet - a multipurpose video forensics network capab

Yupeng Cao, Haohang Li, Yangyang Yu, Shashidhar Reddy Javaji

Audio Large Language Models (AudioLLMs) have received widespread attention and have significantly improved performance on audio tasks such as conversation, audio understanding, and automatic speech recognition (ASR). Despite these advancements, there is an absence of a benchmark for assessing AudioLLMs in financial scenarios, where audio data, such as earnin

Gabriel Agostini, Rachel Young, Maria Fitzpatrick, Nikhil Garg

Fine-grained migration data illuminate demographic, environmental, and health phenomena. However, United States migration data have serious drawbacks: public data lack spatial granularity, and higher-resolution proprietary data suffer from multiple biases. To address this, we develop a method that fuses high-resolution proprietary data with coarse Census dat