May 2025 arXiv papers — page 26

Bhanuka Gamage, Leona Holloway, Nicola McDowell, Thanh-Toan Do

Over the past decade, considerable research has investigated Vision-Based Assistive Technologies (VBAT) to support people with vision impairments to understand and interact with their immediate environment using machine learning, computer vision, image enhancement, and/or augmented/virtual reality. However, this has almost totally overlooked a growing demogr

Christoph Luckeneder, Ralph Hoch, Hermann Kaindl

Safety verification of robot applications is extremely challenging due to the complexity of the environment that a robot typically operates in. Formal verification with model-checking provides guarantees but it may often take too long or even fail for complex models of the environment. A usual solution approach is abstraction, more precisely behavioral abstr

Siyang Liu, Sahand Sabour, Xiaoyang Wang, Rada Mihalcea

User studies are central to user experience research, yet recruiting participant is expensive, slow, and limited in diversity. Recent work has explored using Large Language Models as simulated users, but doubts about fidelity have hindered practical adoption. We deepen this line of research by asking whether scale itself can enable useful simulation, even if

Aimon Rahman, Jiang Liu, Ze Wang, Ximeng Sun

Recent advances in diffusion-based text-to-video (T2V) models have demonstrated remarkable progress, but these models still face challenges in generating videos with multiple objects. Most models struggle with accurately capturing complex object interactions, often treating some objects as static background elements and limiting their movement. In addition,

Bengisu Guresti, Chongjie Zhang, Yevgeniy Vorobeychik

An important challenge in non-cooperative game theory is coordinating on a single (approximate) equilibrium from many possibilities - a challenge that becomes even more complex when players hold private information. Recommender mechanisms tackle this problem by recommending strategies to players based on their reported type profiles. A key consideration in s

Youngju Na, Taeyeon Kim, Jumin Lee, Kyu Beom Han

While generalizable 3D Gaussian splatting enables efficient, high-quality rendering of unseen scenes, it heavily depends on precise camera poses for accurate geometry. In real-world scenarios, obtaining accurate poses is challenging, leading to noisy pose estimates and geometric misalignments. To address this, we introduce SHARE, a pose-free, feed-forward Ga

Nic Dobbins, Christelle Xiong, Kristine Lan, Meliha Yetisgen

Objective: To demonstrate the capabilities of Large Language Models (LLMs) as autonomous agents to reproduce findings of published research studies using the same or similar dataset. Materials and Methods: We used the "Quick Access" dataset of the National Alzheimer's Coordinating Center (NACC). We identified highly cited published research manuscripts using

Shuolin Xu, Siming Zheng, Ziyi Wang, HC Yu

Recent advances in diffusion models have significantly improved conditional video generation, particularly in the pose-guided human image animation task. Although existing methods are capable of generating high-fidelity and time-consistent animation sequences in regular motions and static scenes. However there are still obvious limitations when facing comple

Kewei Lian, Shaofei Cai, Yitao Liang, Anji Liu

The ability to simulate the world in a spatially consistent manner is a crucial requirement for effective world models. Such a model enables high-quality visual generation, and also ensures the reliability of world models for downstream tasks such as simulation and planning. It must not only retain long-horizon observational information, but also enables the

Paweł Goldstein, Piotr Hajłasz

We prove that if $f:(a,b)\to\mathbb{R}$ is convex, then for any $\varepsilon>0$ there is a convex function $g\in C^2(a,b)$ such that $|\{f\neq g\}|<\varepsilon$ and $\Vert f-g\Vert_\infty<\varepsilon$.

Dimitrios Kollias, Damith C. Senadeera, Jianian Zheng, Kaushal K. K. Yadav

Violence Detection (VD) has become an increasingly vital area of research. Existing automated VD efforts are hindered by the limited availability of diverse, well-annotated databases. Existing databases suffer from coarse video-level annotations, limited scale and diversity, and lack of metadata, restricting the generalization of models. To address these cha

Yuntao Ma, Andrei Cramariuc, Farbod Farshidian, Marco Hutter

Coordinating the motion between lower and upper limbs and aligning limb control with perception are substantial challenges in robotics, particularly in dynamic environments. To this end, we introduce an approach for enabling legged mobile manipulators to play badminton, a task that requires precise coordination of perception, locomotion, and arm swinging. We

Bahareh Tolooshams, Aditi Chandrashekar, Rayhan Zirvi, Abbas Mammadov

Diffusion models represent the state-of-the-art for solving inverse problems such as image restoration tasks. Diffusion-based inverse solvers incorporate a likelihood term to guide prior sampling, generating data consistent with the posterior distribution. However, due to the intractability of the likelihood, most methods rely on isotropic Gaussian approxima

Jianjun Jin

Recently, Bansah and Sehba studied in [3] the boundedness of a family of Hilbert-type integral operators, where they characterized the $L^{p}-L^{q}$ boundedness of the operators for $1\leq p\leq q\leq \infty$. In this paper, we deal with the corresponding discrete Hilbert-type operators acting on the weighted sequence spaces. We establish some sufficient and

Yu Yuan, Yiheng Chi, Xingguang Zhang, Stanley Chan

High dynamic range (HDR) imaging aims to obtain a high-quality HDR image by fusing information from multiple low dynamic range (LDR) images. Numerous learning-based HDR imaging methods have been proposed to achieve this for static and dynamic scenes. However, their architectures are mostly tailored for a fixed number (e.g., three) of inputs and, therefore, c

Xiaoyi Liu, Hao Tang

Physical intelligence -- anticipating and shaping the world from partial, multisensory observations -- is critical for next-generation world models. We propose FOLIAGE, a physics-informed multimodal world model for unbounded accretive surface growth. In its Action-Perception loop, a unified context encoder maps images, mesh connectivity, and point clouds to

Jiuda Wu, Behrooz Yousefzadeh

We investigate the phenomenon of parametric instability in discrete models of spatiotemporally modulated materials. These materials are celebrated in part because they exhibit nonreciprocal transmission characteristics. However, parametric instability may occur for strong modulations, or occasionally even at very small modulation amplitudes, and prevent the

RoshikNagaSai Patibandla, Ross Greer

The increasing number of accidents caused by alcohol-impaired driving has prompted the development of integrated safety systems in vehicles to monitor driver behavior and prevent crashes. This paper explores how drivers perceive these systems, focusing on their comfort, trust, privacy concerns, and willingness to adopt the technology. Through a survey of 115

Aaron D. Ames, Sébastien Mattenet, Joe Moeller

Lyapunov's theorem provides a foundational characterization of stable equilibrium points in dynamical systems. In this paper, we develop a framework for stability for F-coalgebras. We give two definitions for a categorical setting in which we can study the stability of a coalgebra for an endofunctor F. One is minimal and better suited for concrete settings,

Chengqi Zheng, Jianda Chen, Yueming Lyu, Wen Zheng Terence Ng

Despite the promise of autonomous agentic reasoning, existing workflow generation methods frequently produce fragile, unexecutable plans due to unconstrained LLM-driven construction. We introduce MermaidFlow, a framework that redefines the agentic search space through safety-constrained graph evolution. At its core, MermaidFlow represent workflows as a verif

Jia Zhou

~Let $(g,~[-,-],~\omega)$ be a finite-dimensional complex $\omega$-Lie superalgebra. This paper explores the algbaraic structures of generalized derivation superalgebra ${\rm GDer}(g)$, compatatible generalized derivations algebra ${\rm GDer}^{\omega}(g)$, and their subvarieties such as quasiderivation superalgebra ${\rm QDer}(g)$(${\rm QDer}^{\omega}(g)$),

Lucas A. Oliveira, Wei Chen

The electronic local density of states of solids, if normalized correctly, represents the probability density that the electron at a specific position has a particular energy. Because this probability density can vary in space in disordered systems, we propose that one can either treat the energy as a random variable and position as an external parameter to

Sheng Zhang, Qin Liu, Naoto Usuyama, Cliff Wong

The emergence of scaling laws has profoundly shaped the development of large language models (LLMs), enabling predictable performance gains through systematic increases in model size, dataset volume, and compute. Yet, these principles remain largely unexplored in the context of electronic health records (EHRs) -- a rich, sequential, and globally abundant dat

Zhuoran Duan, Guoshun Nan, Rushan Li, Zijun Wang

The upcoming 6G will fundamentally reshape mobile networks beyond communications, unlocking a multitude of applications that were once considered unimaginable. Meanwhile, security and resilience are especially highlighted in the 6G design principles. However, safeguarding 6G networks will be quite challenging due to various known and unknown threats from hig

Tolulope Oshinowo, Sohyeon Hwang, Amy X. Zhang, Andrés Monroy-Hernández

Calls to decentralize feed-based social media have been driven by concerns about the concentrated power of centralized platforms and their societal impact. In response, numerous decentralized social media protocols have emerged, each interpreting "decentralization" in different ways. We analyze four such protocols -- ActivityPub, AT Protocol, Nostr, and Farc

Yongjin Yang, Euiin Yi, Jongwoo Ko, Kimin Lee

The remarkable growth in large language model (LLM) capabilities has spurred exploration into multi-agent systems, with debate frameworks emerging as a promising avenue for enhanced problem-solving. These multi-agent debate (MAD) approaches, where agents collaboratively present, critique, and refine arguments, potentially offer improved reasoning, robustness

Jianwei Wang, Mengqi Wang, Yinsi Zhou, Zhenchang Xing

Health, Safety, and Environment (HSE) compliance assessment demands dynamic real-time decision-making under complicated regulations and complex human-machine-environment interactions. While large language models (LLMs) hold significant potential for decision intelligence and contextual dialogue, their capacity for domain-specific knowledge in HSE and structu

Andrea Marino

Recently, Sinha defined a spectral sequence approximating the (co)homology of the space of long knots in R^m modulo immersions, stemming from a cosimplicial structure on the compactified configuration spaces \`a la Kontsevich. We provide an equivalent cosimplicial structure on (the barycentric subdivision of) a regular CW complex with cells indexed by Fox-Ne

Lijie Ding, Egang Lu, Kin Cheung

We introduce a fast and flexible Machine Learning (ML) framework for pricing derivative products whose valuation depends on volatility surfaces. By parameterizing volatility surfaces with the 5-parameter stochastic volatility inspired (SVI) model augmented by a one-factor term structure adjustment, we first generate numerous volatility surfaces over realisti

Matteo Guida, Yulia Otmakhova, Eduard Hovy, Lea Frermann

Automated large-scale analysis of public discussions around contested issues like abortion requires detecting and understanding the use of arguments. While Large Language Models (LLMs) have shown promise in language processing tasks, their performance in mining topic-specific, pre-defined arguments in online comments remains underexplored. We evaluate four s

Hong Tang, Jiang Wei, Gabor I. Csonka, Adrienn Ruzsinszky

Ta2Ni3Te5, a layered transition metal chalcogenide with quasi-one-dimensional electronic states, exhibits rich topological and correlated phenomena. Using first-principles calculations, we explore Ta2Ni3Te5 nanoribbons, demonstrating tunable electronic and magnetic properties-ranging from metallic to semimetallic and semiconducting (band gaps of 29.7-60.8 me

Jenny Zhang, Shengran Hu, Cong Lu, Robert Lange

Today's AI systems have human-designed, fixed architectures and cannot autonomously and continuously improve themselves. The advance of AI could itself be automated. If done safely, that would accelerate AI development and allow us to reap its benefits much sooner. Meta-learning can automate the discovery of novel algorithms, but is limited by first-order im

Rasim Volga Ovali, Mehmet Emre Tasgin

The propagation distance of surface plasmon polaritons (SPPs) on metal nanowires is severely limited by their short lifetime, primarily due to strong metallic losses. In this work, we show that the lifetime-and thus the propagation distance-of SPPs can be significantly extended through the use of Fano resonances. Our FDTD simulations demonstrate that the SPP

Juan Francisco García-Camacho, Oliver Contreras-Vergara, Norma Sánchez-Salas, Gonzalo Ares de Parga

In this work, we study non-Markovian electronic plasma diffusion from a classical point of view, taking into account the effects of the radiation reaction force. The electron Brownian motion is described by a Generalized Langevin Equation (GLE) characterized by an Ornstein-Uhlenbeck-type friction memory kernel. To take into account the effects of the radiati

Cheongho Han, Chung-Uk Lee, Andrzej Udalski, Ian A. Bond

The light curves of the microlensing events MOA-2022-BLG-091 and KMT-2024-BLG-1209 exhibit anomalies with very similar features. These anomalies appear near the peaks of the light curves, where the magnifications are moderately high, and are distinguished by weak caustic-crossing features with minimal distortion while the source remains inside the caustic. T

Haohan Yuan, Sukhwa Hong, Haopeng Zhang

Large language models (LLMs) have shown strong performance in zero-shot summarization, but often struggle to model document structure and identify salient information in long texts. In this work, we introduce StrucSum, a training-free prompting framework that enhances LLM reasoning through sentence-level graph structures. StrucSum injects structural signals

Michael Sun, Orion Foo, Gang Liu, Wojciech Matusik

Directed acyclic graphs (DAGs) are a class of graphs commonly used in practice, with examples that include electronic circuits, Bayesian networks, and neural architectures. While many effective encoders exist for DAGs, it remains challenging to decode them in a principled manner, because the nodes of a DAG can have many different topological orders. In this

Michael Sun, Weize Yuan, Gang Liu, Wojciech Matusik

Recent data-efficient molecular generation approaches exploit graph grammars to introduce interpretability into the generative models. However, grammar learning therein relies on expert annotation or unreliable heuristics for algorithmic inference. We propose Foundation Molecular Grammar (FMG), which leverages multi-modal foundation models (MMFMs) to induce

Ayla Gafni, Terence Tao

For a fixed exponent $0 < θ\leq 1$, it is expected that we have the prime number theorem in short intervals $\sum_{x \leq n < x+x^θ} Λ(n) \sim x^θ$ as $x \to \infty$. From the recent zero density estimates of Guth and Maynard, this result is known for all $x$ for $θ> \frac{17}{30}$ and for almost all $x$ for $θ> \frac{2}{15}$. Prior to this work, Bazzanella

Siddharth Iyer

Let $\triangle$ denote the integers represented by the quadratic form $x^2+xy+y^2$ and $\square_{2}$ denote the numbers represented as a sum of two squares. For a non-zero integer $a$, let $S(\triangle,\square_{2},a)$ be the set of integers $n$ such that $n \in \triangle$, and $n + a \in \square_{2}$. We conduct a census of $S(\triangle,\square_{2},a)$ in sh

Goro Miki, Yasuhiro Tokura

With the rapid development of quantum computers in recent years, the importance of performance evaluation in quantum algorithms has been increasing. One method that has gained attention for performing this evaluation on classical computers is tensor networks. Tensor networks not only reduce the computational cost required for simulations by using approximati

Premchand V. Chandra, Pradip Dutta

Jet impingement enhances heat transfer and is characterised by the complex flow patterns formed when a jet impacts a plate aligned normal to it. While traditional round jet impingement has been extensively studied to understand flow and associated heat transfer, there is still room for research in investigating flow structures in swirl jet impingement. This

Ruitong Sun, Guanqi Ye, Fusheng Ma

Optical chaotic comb, possessing the key metrics of intrinsic random amplitude, phase, and frequency modulation of comb lines, emerges as a novel chaotic source in information systems for coherence tomography, parallel ranging, and secure communications. Considering the analogies between magnons and photons, the magnonic analog of optical chaotic combs is ex

Guanqi Ye, Ruitong Sun, Junning Zhao, Fusheng Ma

Magnetostriction, mechanical-to-magnetic or magnetic-to-mechanical response, plays a pivotal role in magneto-mechanical systems. Here, we propose and experimentally demonstrate a magneto-mechanical frequency comb via the three-wave mixing mechanism, which solely requires the involvement of the fundamental mode f0 of a magnetostrictive macroresonator. Two typ

Osman Mert Yilmaz, Tayfun Yilmaz, Ali Gorcin, Ibrahim Hokelek

By intelligently reconfiguring wireless propagation environment, reconfigurable intelligent surfaces (RISs) can enhance signal quality, suppress interference, and improve channel conditions, thereby serving as a powerful complement to multiple-input multiple-output (MIMO) architectures. However, jointly optimizing the RIS phase shifts and the MIMO transmit p

Jorge Fariña-Asategui

Abért and Virág proved in 2005 that the Hausdorff dimension of a non-trivial normal subgroup of a level-transitive 1-dimensional subgroup of the group of $p$-adic automorphisms $W_p$ is always 1. They further asked whether the same holds replacing 1-dimensional with positive dimensional. On the one hand, we provide a negative answer in general by giving coun

Kasturi Barkataki, Eleni Panagiotou

Knots, links and entangled filaments appear in many physical systems of interest in biology and engineering. Classifying knots and measuring entanglement is of interest both for advancing knot theory, as well as for analyzing large data that become available through experiments or Artificial Intelligence. In this context, the efficient computation of topolog

Hongrong Yin, Jinhong Huang, Yao Li, Yunwei Dong

Mobile application development is a fast-paced process where maintaining high-quality user experiences is crucial. Bug reproduction, a key aspect of maintaining app quality, often faces significant challenges. Specifically, when descriptions in bug reports are ambiguous or difficult to comprehend, current approaches fail to extract accurate information. More

Yucheng Liu, Gordon Slade

We analyse generating functions for trees and for connected subgraphs on the complete graph, and identify a single scaling profile which applies for both generating functions in a critical window. Our motivation comes from the analysis of the finite-size scaling of lattice trees and lattice animals on a high-dimensional discrete torus, for which we conjectur

Christopher Caruvana

In this note, we compare and contrast various selective divergence properties such as the properties of being discretely selective and selectively highly divergent. We identify and incorporate a class of subsemigroups of the semigroup of strictly increasing maps from the naturals to themselves. We investigate certain implications for hyperspaces of finite su

Yuhui Zhang, Yuchang Su, Yiming Liu, Serena Yeung-Levy

Negation is a fundamental linguistic phenomenon that can entirely reverse the meaning of a sentence. As vision language models (VLMs) continue to advance and are deployed in high-stakes applications, assessing their ability to comprehend negation becomes essential. To address this, we introduce NegVQA, a visual question answering (VQA) benchmark consisting o

Alisha Srivastava, Emir Korukluoglu, Minh Nhat Le, Duyen Tran

Large language models (LLMs) are known to memorize and recall English text from their pretraining data. However, the extent to which this ability generalizes to non-English languages or transfers across languages remains unclear. This paper investigates multilingual and cross-lingual memorization in LLMs, probing if memorized content in one language (e.g., E

Angtian Wang, Haibin Huang, Jacob Zhiyuan Fang, Yiding Yang

We propose a unified framework for motion control in video generation that seamlessly integrates camera movement, object-level translation, and fine-grained local motion using trajectory-based inputs. In contrast to prior methods that address these motion types through separate modules or task-specific designs, our approach offers a cohesive solution by proj

Jaewoo Ahn, Heeseung Yun, Dayoon Ko, Gunhee Kim

While pre-trained multimodal representations (e.g., CLIP) have shown impressive capabilities, they exhibit significant compositional vulnerabilities leading to counterintuitive judgments. We introduce Multimodal Adversarial Compositionality (MAC), a benchmark that leverages large language models (LLMs) to generate deceptive text samples to exploit these vuln

Yuchen Zhuang, Di Jin, Jiaao Chen, Wenqi Shi

Large language models (LLMs)-empowered web agents enables automating complex, real-time web navigation tasks in enterprise environments. However, existing web agents relying on supervised fine-tuning (SFT) often struggle with generalization and robustness due to insufficient reasoning capabilities when handling the inherently dynamic nature of web interactio

Guilherme Adamatti Bridi, André Luis Alves Martins, Franklin de Lima Marquezino, Celina Miraglia Herrera de Figueiredo

Graph pebbling is a game played on graphs with pebbles on their vertices. A pebbling move removes two pebbles from one vertex and places one pebble on an adjacent vertex. The pebbling number is the smallest $t$ so that from any initial configuration of $t$ pebbles it is possible, after a sequence of pebbling moves, to place a pebble on any given target verte

Daniel Aronoff, Robert M. Townsend

We construct an empirically founded model of a repo trade intermediated by two broker-dealers and prove multiple equilibrium and the existence of equilibrium at the joint profit maximizing volume of trade. We then present a smart contract that resolves multiple equilibrium by requiring each broker-dealer to report its client schedule and its minimum hurdle s

Niclas Boehmer, Sara Fish, Ariel D. Procaccia

A key task in certain democratic processes is to produce a concise slate of statements that proportionally represents the full spectrum of user opinions. This task is similar to committee elections, but unlike traditional settings, the candidate set comprises all possible statements of varying lengths, and so it can only be accessed through specific queries.

Ben Weiss

Median filtering is a cornerstone of computational image processing. It provides an effective means of image smoothing, with minimal blurring or softening of edges, invariance to monotonic transformations such as gamma adjustment, and robustness to noise and outliers. However, known algorithms have all suffered from practical limitations: the bit depth of th

Ngeyen Yinkfu

This study presents an efficient transformer-based question-answering (QA) model optimized for deployment on a 13th Gen Intel i7-1355U CPU, using the Stanford Question Answering Dataset (SQuAD) v1.1. Leveraging exploratory data analysis, data augmentation, and fine-tuning of a DistilBERT architecture, the model achieves a validation F1 score of 0.6536 with a

Facundo Oliú

In a probabilistic mean field game driven by a L\'evy process an individual player aims to minimize a long run discounted/ergodic cost by controlling the process through a pair of increasing and decreasing c\`adl\`ag processes, while he is interacting with an aggregate of players through the expectation of a controlled process by another pair of c\`adl\`ag p

Jipeng Li, Yanning Shen

Explicit noise-level conditioning is widely regarded as essential for the effective operation of Graph Diffusion Models (GDMs). In this work, we challenge this assumption by investigating whether denoisers can implicitly infer noise levels directly from corrupted graph structures, potentially eliminating the need for explicit noise conditioning. To this end,

Satyam Anand, Guanming Zhang, Stefano Martiniani

Self-organization through noisy interactions is ubiquitous across physics, mathematics, and machine learning, yet how long-range structure emerges from local noisy dynamics remains poorly understood. Here, we investigate three paradigmatic random-organizing particle systems drawn from distinct domains: models from soft matter physics (random organization, bi

Mohammad Shojaeifard, Mattia Bacca

Hyperelastic material characterization is crucial for understanding the behavior of soft materials -- such as tissues, rubbers, hydrogels, and polymers -- under quasi-static loading before failure. Traditional methods typically rely on uniaxial tensile tests, which require the cumbersome preparation of dumbbell-shaped samples for clamping in a uniaxial testi

Kevin Han, Bowen Deng, Amir Barati Farimani, Gerbrand Ceder

Large-scale atomistic simulations are essential to bridge computational materials and chemistry to realistic materials and drug discovery applications. In the past few years, rapid developments of machine learning interatomic potentials (MLIPs) have offered a solution to scale up quantum mechanical calculations. Parallelizing these interatomic potentials acr

Ammar Kirmani, Elijah Pelofske, Andreas Bärtschi, Stephan Eidenbenz

Quantum computers are an ideal platform to study the ground state properties of strongly correlated systems due to the limitation of classical computing techniques particularly for systems exhibiting quantum phase transitions. While the error rates of Noisy Intermediate-Scale Quantum (NISQ) computers are still high, simulating strongly correlated systems on

Andreu Ballus Santacana

We study the free PROP $\mathrm{Syn}(\delta)$ on a single binary generator $\delta:1\to 2$. The ancestry functor $\Pi:\mathrm{Syn}(\delta)\to \mathrm{FinCorel}$, defined by connected components of the underlying undirected string diagram, has image the sub-PROP $\mathrm{FinCorel}^{\circ}$ of finite corelations whose equivalence classes contain exactly one in

Fan Huang

Motivated by the identification $C(\mathbb{T})\cong C_r^*(\mathbb{Z})$ and the wave equation on the circle, we explore the wave equation in the context of reduced group $C^*$-algebras $C_r^*(G)$ for countably infinite, possibly non-abelian groups $G$. Using a one-parameter group of $*$-automorphisms whose infinitesimal generator paves the way to an analogue

Jonathan Brundan, Weiqiang Wang, Ben Webster

We introduce a new family of graded 2-categories generalizing the 2-quantum groups introduced by Khovanov, Lauda and Rouquier. We use them to categorify quasi-split iquantum groups in all symmetric types.

Massimiliano Pronesti, Michela Lorandi, Paul Flanagan, Oisin Redmond

Systematic reviews in medicine play a critical role in evidence-based decision-making by aggregating findings from multiple studies. A central bottleneck in automating this process is extracting numeric evidence and determining study-level conclusions for specific outcomes and comparisons. Prior work has framed this problem as a textual inference task by ret

Robert Marosi, Muhammed Zuboraj, Filippo Capolino

We introduce a slow-wave structure (SWS) for a millimeter-wave sheet-beam traveling-wave tube (TWT) with wide bandwidth. The wideband and stable operation is enabled through the topological properties associated with glide-symmetry that close the bandgap at the $3\pi$-point and also make the on-axis interaction impedance negligible for the backward wave. Thi

Sylvey Lin, Zhi-Yi Cao

We investigate whether synthetic images generated by diffusion models can enhance multi-label classification of protein subcellular localization. Specifically, we implement a simplified class-conditional denoising diffusion probabilistic model (DDPM) to produce label-consistent samples and explore their integration with real data via two hybrid training stra

Andrew N. Jordan, John C. Howell, Nicholas Vamivakas, Ebrahim Karimi

This book chapter gives a selective review of physical implementations and applications of superoscillations and associated phenomena. We introduce the field by reviewing simple examples of superoscillations and showing how their existence naturally follows from the real part of the quantum mechanical weak value, which the parallel phenomena of supergrowth n

Chaitanya Sharma

Retrieval-Augmented Generation (RAG) has emerged as a powerful paradigm to enhance large language models (LLMs) by conditioning generation on external evidence retrieved at inference time. While RAG addresses critical limitations of parametric knowledge storage-such as factual inconsistency and domain inflexibility-it introduces new challenges in retrieval q

Yahui Chai, Alice Di Tucci

We propose a hybrid quantum-classical algorithm for solving QUBO problems using an Imaginary Time Evolution-Mimicking Circuit (ITEMC). The circuit parameters are optimized to closely mimic imaginary time evolution, using only single- and two-qubit expectation values. This significantly reduces the measurement overhead by avoiding full energy evaluation. By u

Anqi Li, Weijie Gan, Ulugbek S. Kamilov

We introduce Blind Plug-and-Play Diffusion Models (Blind-PnPDM) as a novel framework for solving blind inverse problems where both the target image and the measurement operator are unknown. Unlike conventional methods that rely on explicit priors or separate parameter estimation, our approach performs posterior sampling by recasting the problem into an alter

Athanasios Glentis, Jiaxiang Li, Qiulin Shang, Andi Han

Fueled by their remarkable ability to tackle diverse tasks across multiple domains, large language models (LLMs) have grown at an unprecedented rate, with some recent models containing trillions of parameters. This growth is accompanied by substantial computational challenges, particularly regarding the memory and compute resources required for training and

Jerome J. Cuomo, Ian Goodall, C. Richard Guarnieri, Gennaro

This study introduces a catalyst-free, ambient-temperature method for synthesizing nitrogen-based compounds critical to fertilizer production, including ammonia, urea, ammonium salts, and amino acids. The process relies on bubble-burst-induced microenvironments, where gas bubbles undergo rapid growth and collapse, releasing intense localized energy sufficien

Yue Xing, Tao Yang, Yijiashun Qi, Minggu Wei

This paper addresses the limitations of large language models in understanding long-term context. It proposes a model architecture equipped with a long-term memory mechanism to improve the retention and retrieval of semantic information across paragraphs and dialogue turns. The model integrates explicit memory units, gated writing mechanisms, and attention-b

Soham Prajapati, Ali Fakhreddine, Krishnan Mahesh

We develop a three-dimensional Eulerian framework to simulate fluid-structure interaction (FSI) problems on a fixed Cartesian grid using the geometric volume-of-fluid (VOF) method. The coupled problem involves incompressible flow and viscous hyperelastic solids. A VOF-based one-continuum formulation is used to describe the unified momentum conservation equat

Nikita Mehandru, Niloufar Golchini, Namrata Garg, Kathy T. LeSaint

Existing benchmarks for evaluating the clinical reasoning capabilities of large language models (LLMs) often lack a clear definition of "clinical reasoning" as a construct, fail to capture the full breadth of interdependent tasks within a clinical workflow, and rely on stylized vignettes rather than real-world clinical documentation. As a result, recent stud

Ruichen Chen, Keith G. Mills, Liyao Jiang, Chao Gao

Diffusion Transformers (DiT) have become the de-facto model for generating high-quality visual content like videos and images. A huge bottleneck is the attention mechanism where complexity scales quadratically with resolution and video length. One logical way to lessen this burden is sparse attention, where only a subset of tokens or patches are included in

Ammar Jahin, Shi-Zeng Lin

In this work, we study the spontaneous formation of a vortex lattice in two-dimensional valley polarized superconductors due to orbital magnetization. The screening of magnetic field is weak for two-dimension superconductors, allowing for the magnetic flux associated with vortices to penetrate deep into the superconducting region. The Zeeman coupling between

Sulaiman khan, Muhammad Ahmad, Fida Ullah, Carlos Aguilar Ibañez

Cancer is fundamentally a genetic disease characterized by genetic and epigenetic alterations that disrupt normal gene expression, leading to uncontrolled cell growth and metastasis. High-dimensional microarray datasets pose challenges for classification models due to the "small n, large p" problem, resulting in overfitting. This study makes three different

Maksim Kolodiazhnyi, Denis Tarasov, Dmitrii Zhemchuzhnikov, Alexander Nikulin

Computer-Aided Design (CAD) plays a central role in engineering and manufacturing, making it possible to create precise and editable 3D models. Using a variety of sensor or user-provided data as inputs for CAD reconstruction can democratize access to design applications. However, existing methods typically focus on a single input modality, such as point clou

Donghyeon Joo, Helya Hosseini, Ramyad Hadidi, Bahar Asgari

We demonstrate that unstructured sparsity significantly improves KV cache compression for LLMs, enabling sparsity levels up to 70% without compromising accuracy or requiring fine-tuning. We conduct a systematic exploration of pruning strategies and find per-token magnitude-based pruning as highly effective for both Key and Value caches under unstructured spa

Warayut Dokduea, Weerachart Tangchirapat, Sompote Youwai

This paper presents a novel Triple Attention Transformer Architecture for predicting time-dependent concrete creep, addressing fundamental limitations in current approaches that treat time as merely an input parameter rather than modeling the sequential nature of deformation development. By transforming concrete creep prediction into an autoregressive sequen

Minjae Cho, Hiroyasu Tsukamoto, Huy T. Tran

Control contraction metrics (CCMs)-defined by Riemannian metrics under which a closed-loop system is incrementally exponentially stable-offer a constructive framework for synthesizing contracting policies in nonlinear path-tracking problems. However, while the synthesized policies ensure pointwise satisfaction of the CCM conditions, they may not ensure long-

Karla Garnica, Deborah Dultzin, Paola Marziani, Swayamtrupta Panda

Knowledge of the broad-band active galactic nuclei (AGN) spectral energy distribution (SED) that ionizes the gas-rich broad emission line region is key to understanding the various radiative processes at play and their importance that eventually leads to the emission line formation. We modeled a spectral energy distribution for highly accreting quasars, also

Matthew Beveridge, Shree K. Nayar

We introduce a taxonomy of materials for hierarchical recognition from local appearance. Our taxonomy is motivated by vision applications and is arranged according to the physical traits of materials. We contribute a diverse, in-the-wild dataset with images and depth maps of the taxonomy classes. Utilizing the taxonomy and dataset, we present a method for hi

Chahat Raj, Mahika Banerjee, Jinhao Pan, Aylin Caliskan

When a student fails an exam, do we tend to blame their effort or the test's difficulty? Attribution, defined as how reasons are assigned to event outcomes, shapes perceptions, reinforces stereotypes, and influences decisions. Attribution Theory in social psychology explains how humans assign responsibility for events using implicit cognition, attributing ca

Cristian Chica, Yinglong Guo, Gilad Lerman

There is growing experimental evidence that $Q$-learning agents may learn to charge supracompetitive prices. We provide the first theoretical explanation for this behavior in infinite repeated games. Firms update their pricing policies based solely on observed profits, without computing equilibrium strategies. We show that when the game admits both a one-sta

Hao Xu, Xiaolin Wu, Xi Zhang

Current 3DGS compression methods largely forego the neural analysis-synthesis transform, which is a crucial component in learned signal compression systems. As a result, redundancy removal is left solely to the entropy coder, overburdening the entropy coding module and reducing rate-distortion (R-D) performance. To fix this critical omission, we propose a tr

Rachel Katharine Sterken, James Ravi Kirkpatrick

The development of sophisticated artificial intelligence (AI) conversational agents based on large language models raises important questions about the relationship between human norms, values, and practices and AI design and performance. This article explores what it means for AI agents to be conversationally aligned to human communicative norms and practic

Emmanuel Anaya González, Raven Rothkopf, Sorin Lerner, Nadia Polikarpova

While AI programming tools hold the promise of increasing programmers' capabilities and productivity to a remarkable degree, they often exclude users from essential decision-making processes, causing many to effectively "turn off their brains" and over-rely on solutions provided by these systems. These behaviors can have severe consequences in critical domai

Marcin Lawenda, Kyrylo Khloponin, Krzesimir Samborski, Łukasz Szustak

The effectiveness and efficiency of machine learning methodologies are crucial, especially with respect to the quality of results and computational cost. This paper discusses different model optimization techniques, providing a comprehensive analysis of key performance indicators. Several parallelization strategies for image recognition, adapted to different

Youngsoo Choi, Siu Wun Cheung, Youngkyu Kim, Ping-Hsuan Tsai

The widespread success of foundation models in natural language processing and computer vision has inspired researchers to extend the concept to scientific machine learning and computational science. However, this position paper argues that as the term "foundation model" is an evolving concept, its application in computational science is increasingly used wi

Jacob Bedrossian, Alex Blumenthal, Sam Punshon-Smith

We develop a general framework for establishing non-uniqueness of stationary measures for stochastically forced dynamical systems possessing an almost surely invariant submanifold. Our main abstract result provides sufficient conditions for the existence of multiple stationary measures on compact manifolds, though the underlying methodology extends to non-co

Shengyuan Hu, Neil Kale, Pratiksha Thaker, Yiwei Fu

Machine unlearning has the potential to improve the safety of large language models (LLMs) by removing sensitive or harmful information post hoc. A key challenge in unlearning involves balancing between forget quality (effectively unlearning undesirable information) and retain quality (maintaining good performance on other, general tasks). Unfortunately, as

Edison Leguizamón, Carsten Trunk, Mitsuru Wilson, Monika Winklmeier

On a star graph $G$ with $n = n_+ + n_-$ edges of unit length, we study the operator $-\frac{\mathrm{d}^2}{\mathrm{d} x^2}$ on $n_+$ and $\frac{\mathrm{d}^2}{\mathrm{d} x^2}$ on $n_-$ edges equipped with Dirichlet boundary conditions at the outer vertices and a Kirchhoff condition at the central vertex. We study the spectral properties of the corresponding i