March 2025 arXiv papers — page 125

Csilla Bujtás, Pakanun Dokyeesun, Sandi Klavžar, Miloš Stojaković

A predominated graph is a pair $(G,D)$, where $G$ is a graph and the vertices in $D\subseteq V(G)$ are considered already dominated. Maker-Breaker domination game critical (MBD critical) predominated graphs are introduced as the predominated graphs $(G,D)$ on which Staller wins the game, but Dominator wins on $(G, D \cup \{v\})$ for every vertex $v \in V(G)

Ruchika Chavhan, Abhinav Mehrotra, Malcolm Chadwick, Alberto Gil Ramos

Text-to-image synthesis has witnessed remarkable advancements in recent years. Many attempts have been made to adopt text-to-image models to support multiple tasks. However, existing approaches typically require resource-intensive re-training or additional parameters to accommodate for the new tasks, which makes the model inefficient for on-device deployment

Diego Piciocchi, Alexander Dikopoltsev, Ina Heckelmann, Mattias Beck

Optical frequency comb devices have unlocked new capabilities in telecommunications, sensing, and metrology. Yet, precise in situ control of the comb spectral envelope remains extremely challenging. By introducing mode coupling with non-trivial phases, we demonstrate a spectral shaping technique that enables continuous tuning of a dominant spectral lobe acro

Harbir Antil, Alex Kaltenbach, Keegan L. A. Kirk

In this paper, we study an insulation problem that seeks the optimal distribution of a fixed amount $m>0$ of insulating material coating an insulated boundary $\Gamma_I\subseteq \partial\Omega$ of a thermally conducting body $\Omega\subseteq \mathbb{R}^d$, $d\in \mathbb{N}$. The thickness of the thin insulating layer $\Sigma_{I}^{\varepsilon}$ is given local

Jessica Metzger, Sunghan Ro, Julien Tailleur

The "ratchet principle" asserts that non-equilibrium systems which violate parity symmetry generically exhibit steady-state currents. As recently shown, there are exceptions to this principle, due to the existence of hidden time-reversal symmetry or bulk momentum conservation. For underdamped and overdamped Brownian dynamics, we show how thermal fluctuations

Shengkun Cui, Archit Patke, Hung Nguyen, Aditya Ranjan

This study characterizes GPU resilience in Delta, a large-scale AI system that consists of 1,056 A100 and H100 GPUs, with over 1,300 petaflops of peak throughput. We used 2.5 years of operational data (11.7 million GPU hours) on GPU errors. Our major findings include: (i) H100 GPU memory resilience is worse than A100 GPU memory, with 3.2x lower per-GPU MTBE

Lauren Harrell, Christine Kaeser-Chen, Burcu Karagol Ayan, Keith Anderson

Species distribution models (SDMs) are necessary for measuring and predicting occurrences and habitat suitability of species and their relationship with environmental factors. We introduce a novel presence-only SDM with graph neural networks (GNN). In our model, species and locations are treated as two distinct node sets, and the learning task is predicting

Joseph R. Loffredo, Suyeol Yun

The applications of Large Language Models (LLMs) in political science are rapidly expanding. This paper demonstrates how LLMs, when augmented with predefined functions and specialized tools, can serve as dynamic agents capable of streamlining tasks such as data collection, preprocessing, and analysis. Central to this approach is agentic retrieval-augmented g

Da Long, Shandian Zhe, Samuel Williams, Leonid Oliker

Simulating the long-term dynamics of multi-scale and multi-physics systems poses a significant challenge in understanding complex phenomena across science and engineering. The complexity arises from the intricate interactions between scales and the interplay of diverse physical processes, which manifest in PDEs through coupled, nonlinear terms that govern th

Yash Lodha

We show that just infinite quotients of finitely generated subgroups of Richard Thompson's group F are virtually abelian, answering a question of Grigorchuk. We show the same holds for the group of piecewise linear orientation preserving homeomorphisms of the interval, and the group of piecewise projective homeomorphisms of the real line. The latter provides

Merve Tekgurler

Large Language Models (LLMs) have demonstrated remarkable adaptability in performing various tasks, including machine translation (MT), without explicit training. Models such as OpenAI's GPT-4 and Google's Gemini are frequently evaluated on translation benchmarks and utilized as translation tools due to their high performance. This paper examines Gemini's pe

Hilal Asi, Vitaly Feldman, Hannah Keller, Guy N. Rothblum

We revisit the problem of secure aggregation of high-dimensional vectors in a two-server system such as Prio. These systems are typically used to aggregate vectors such as gradients in private federated learning, where the aggregate itself is protected via noise addition to ensure differential privacy. Existing approaches require communication scaling with t

Jennifer Mankoff, Janice Light, James Coughlan, Christian Vogler

We argue that there is a need for Accessibility to be represented in several important domains: - Capitalize on the new capabilities AI provides - Support for open source development of AI, which can allow disabled and disability focused professionals to contribute, including - Development of Accessibility Apps which help realise the promise of AI in accessi

Jingwei Liu

Musical expressivity and coherence are indispensable in music composition and performance, while often neglected in modern AI generative models. In this work, we introduce a listening-based data-processing technique that captures the expressivity in musical performance. This technique derived from Weber's law reflects the human perceptual truth of listening

Bhiman Kumar Baghel, Emma Jordan, Zheyuan Ryan Shi, Xiang Lorraine Li

Large Language Models (LLMs) are widely deployed in downstream tasks, but keeping their knowledge up-to-date via retraining or fine-tuning is often computationally expensive. Model editing provides a more efficient alternative by updating a targeted subset of parameters, which often follows the locate-and-edit paradigm. Despite this efficiency, existing meth

Andrea Green, Gabrielle Polite, Isabelle Hung, Kristen L. Fessele

The use of interactive voice assistants (IVAs) in healthcare provides an avenue to address diverse health needs, such as gaps in the medical recovery period for older adult patients who have recently experienced serious illness. By using a voice-assisted medical recovery curriculum, discharged patients can receive ongoing support as they recover. However, th

Md Abu Bakr Siddique, Vaishnav Ramesh, Junliang Liu, Piyush Singh

The concept of waterbody style transfer remains largely unexplored in the underwater imaging and vision literature. Traditional image style transfer (STx) methods primarily focus on artistic and photorealistic blending, often failing to preserve object and scene geometry in images captured in high-scattering mediums such as underwater. The wavelength-depende

Chengxuan Qian, Shuo Xing, Shawn Li, Yue Zhao

Multimodal representation learning aims to capture both shared and complementary semantic information across multiple modalities. However, the intrinsic heterogeneity of diverse modalities presents substantial challenges to achieve effective cross-modal collaboration and integration. To address this, we introduce DecAlign, a novel hierarchical cross-modal al

Cyrus Malik, Josef Bajada, Joshua Ellul

Identifying reputable Ethereum projects remains a critical challenge within the expanding blockchain ecosystem. The ability to distinguish between legitimate initiatives and potentially fraudulent schemes is non-trivial. This work presents a systematic approach that integrates multiple data sources with advanced analytics to evaluate credibility, transparenc

Katherine A. Koch, Esteban Rojas-Gatjens, Martin Gomez-Dominguez, Juan-Pablo Correa-Baena

Exciton-exciton interactions are fundamental to the light-emitting properties of semiconductors, influencing applications from lasers to quantum light sources. In this study, we investigate the spectroscopic signatures and binding energy of biexcitons in a metal halide two-dimensional Ruddlesden-Popper structure, which is known for hosting distinct excitonic

Oliver C. Gorton, Konstantinos Kravvaris

Background: The nuclear shell model is a powerful framework for predicting nuclear structure observables, but relies on interaction matrix elements fit to experimental data as its inputs. Extending the shell model's applicability, particularly toward dripline nuclei, requires efficient fitting methods and credible uncertainty quantification. Traditional appr

Anthony N. Ciavarella, I. M. Burbano, Christian W. Bauer

Quantum simulations of lattice gauge theories offer the potential to directly study the non-perturbative dynamics of quantum chromodynamics, but naive analyses suggest that they require large computational resources. Large $N_c$ expansions are performed to order 1/$N_c$ to simplify the Hamiltonian of pure SU($N_c$) lattice gauge theories. A reformulation of

Yichao Zhang, Ningyuan Deng, Xinyuan Song, Ziqian Bi

After AlphaFold won the Nobel Prize, protein prediction with deep learning once again became a hot topic. We comprehensively explore advanced deep learning methods applied to protein structure prediction and design. It begins by examining recent innovations in prediction architectures, with detailed discussions on improvements such as diffusion based framewo

Aryan Mehboudi, Shrawan Singhal, S. V. Sreenivasan

Particle-wall interactions play a crucially important role in various applications such as microfluidic devices for cell sorting, particle separation, entire class of hydrodynamic filtration and its derivatives, etc. Yet, accurate implementation of interactions between wall and finite-size particle is not trivial when working with the currently available par

Aki Pulkkinen, Geoffroy Kremer, Vladimir N. Strocov, Frank Weber

We present a combined density functional theory (DFT), one-step model of photoemission, and soft x-ray angle-resolved photoemission spectroscopy (SX-ARPES) study of the electronic structure of the quaternary borocarbide superconductor YNi$_2$B$_2$C. Our analysis reveals the presence of moderate electronic correlations beyond the semilocal DFT within the gene

Dali Cheng, Heming Wang, Charles Roques-Carmes, Janet Zhong

In spaces of three or more dimensions, there exists topological physics of significant richness that has no lower-dimensional counterparts. To experimentally explore high-dimensional physics, it is advantageous to augment the physical space with synthetic dimensions. An emerging approach is to use a single modulated photonic ring resonator to form multiple s

Bhargav Kulkarni, Pavel Panchekha

Recent advances have made numeric debugging tools much faster by using double-double oracles, and numeric analysis tools much more accurate by using condition numbers. But these techniques have downsides: double-double oracles have correlated error so miss floating-point errors while condition numbers cannot cleanly handle over- and under- flow. We combine b

Trevor D. Canham, SaiKiran Tedla, Michael J. Murdoch, Michael S. Brown

While most images shared on the web and social media platforms are encoded in standard dynamic range (SDR), many displays now can accommodate high dynamic range (HDR) content. Additionally, modern cameras can capture images in an HDR format but convert them to SDR to ensure maximum compatibility with existing workflows and legacy displays. To support both SD

Shadab Choudhury, Asha Kumar, Lara J. Martin

Gaps arise between a language model's use of concepts and people's expectations. This gap is critical when LLMs generate text to help people communicate via Augmentative and Alternative Communication (AAC) tools. In this work, we introduce the evaluation task of Representation Alignment for measuring this gap via human judgment. In our study, we expand keywo

Jieming Bian, Lei Wang, Letian Zhang, Jie Xu

Fine-tuning large language models (LLMs) in federated settings enables privacy-preserving adaptation but suffers from cross-client interference due to model aggregation. Existing federated LoRA fine-tuning methods, primarily based on FedAvg, struggle with data heterogeneity, leading to harmful cross-client interference and suboptimal personalization. In this

Oleg Safronov

We consider the Schr\"odinger operator on the quantum graph whose edges connect the points of ${\Bbb Z}$. The numbers of the edges connecting two consecutive points $n$ and $n+1$ are read along the orbits of a shift of finite type. We prove that the Lyapunov exponent is potitive for energies $E$ that do not belong to a discrete subset of $[0,\infty)$. The nu

Valentina Penza, Serena Criscuoli, Raffaele Reda, Luca Bertello

Stellar UV spectra are fundamental diagnostics of physical and magnetic properties of stars. For instance, lines like Mg II at 280 nm serve as valuable indicators of stellar activity, providing insights into the activity levels of Sun-like stars and their potential influence on the atmospheres of orbiting planets. On the other hand, the effective temperature

Linn E. J. Eriksson, Shyam Menon, Daniel Carrera, Wladimir Lyra

Low-mass, metal-enriched stars were likely present as early as cosmic dawn. In this work, we investigate whether these stars could have hosted planets in their protoplanetary disks. If so, these would have been the first planets to form in the Universe, emerging in systems with metallicities much lower than solar. In the core accretion model, planetesimals s

Manav Kohli, Carlos E. Caicedo, Tingjun Chen, Irfan Tamim

Millimeter-wave (mmWave) communication has been widely accepted as an enabler of 6G and other next-generation wireless networks, though high path loss strains link budgets, and difficult channel conditions have limited the deployment of mmWave within the 5G NR radio access network (RAN) primarily to dense urban environments. In this paper, we seek to demysti

Mattéo Sautron, Alexander Eli McEwen, George Younes, Jérôme Pétri

Population synthesis modeling of the observed dynamical and physical properties of a population is a highly effective method for constraining the underlying birth parameters and evolutionary tracks. In this work, we apply a population synthesis model to the canonical magnetar population to gain insight into the parent population. We utilize simulation-based

Anouar El Moumane, René Wittmann, Hartmut Löwen, Michael te Vrugt

Phase field crystal (PFC) models constitute central tools for a microscopic understanding of the dynamics of complex systems in soft matter physics. They have found widespread application in the modeling of the uniaxial orientational ordering of liquid crystals. However, only very limited progress has been made in applying them to the more complex cases of b

Zhuoqi Li, Shuyuan Huyan, Elizabeth C. Thompson, Tyler J. Slade

LaRu$_3$Si$_2$ is of current research interest as a kagome metal with a superconducting transition temperature, $T_c\sim$7 K and higher temperature charge density wave (CDW) orders. Here we report electrical transport and X-ray diffraction measurements on LaRu$_3$Si$_2$ under pressure up to 65 GPa and 35 GPa respectively. The superconducting transition tempe

Atreyee Das, Siham Mohamed, Raquel A. Ribeiro, Tyler J. Slade

$\text{La}_2 \text{Ni}_7$ is an itinerant magnetic system with a small ordered moment of $\sim$ 0.1 $\mu_{B}/\text{Ni}$ and a series of antiferromagnetic (AFM) transitions at $T_1$ = 61.0 K, $T_2$ = 56.5 K and $T_3$ = 42.2 K. $M(H)$, and $\rho(H)$ isotherms as well as constant field $M(T)$ and $\rho(T)$ measurements on single crystalline samples manifest a c

Boštjan Brešar, Csilla Bujtás, Pakanun Dokyeesun, Tanja Dravec

In the $(a,b)$-biased Maker-Breaker domination game, two players alternately select unplayed vertices in a graph $G$ such that Dominator selects $a$ and Staller selects $b$ vertices per move. Dominator wins if the vertices he selected during the game form a dominating set of $G$, while Staller wins if she can prevent Dominator from achieving this goal. Given

Arvind Raghavan, Elias Bareinboim

It is commonly believed that, in a real-world environment, samples can only be drawn from observational and interventional distributions, corresponding to Layers 1 and 2 of the Pearl Causal Hierarchy. Layer 3, representing counterfactual distributions, is believed to be inaccessible by definition. However, Bareinboim, Forney, and Pearl (2015) introduced a pr

Adam Barański, Daniel Murawski, Piotr Nayar, Krzysztof Oleszkiewicz

We derive optimal dimension independent constants in the classical Khintchine inequality between the $p$th and fourth moment for $p\ge 4$. As an application we deduce stability estimates for the Khintchine inequality between the $p$th and second moment for $p \geq 4$.

Zahra Mehraban, Alois Pichler

Accurate approximation of probability measures is essential in numerical applications. This paper explores the quantization of probability measures using the maximum mean discrepancy (MMD) distance as a guiding metric. We first investigate optimal approximations by determining the best weights, followed by addressing the problem of optimal facility locations

Xin Wu, Hossam Elgabarty, Vahideh Alizadeh, Andres Henao

Stimulated by the renewed interest and recent developments in semi-empirical quantum chemical (SQC) methods for noncovalent interactions, we examine the properties of liquid water at ambient conditions by means of molecular dynamics (MD) simulations, both with the conventional NDDO-type (neglect of diatomic differential overlap) methods, e.g. AM1 and PM6, an

Kaiyue He

We introduce a new numerical invariant $\gamma_I(M)$ associated to a finite-length $R$-module $M$ and an ideal $I$ in an Artinian local ring $R$. This invariant measures the ratio between $\lambda(IM)$ and $\lambda(M/IM)$. We establish fundamental relationships between this invariant and the Betti numbers of the module under the assumption of the $\operatorn

Dinmukhammed Akpan, Andrei Oshemkov

In the paper, three-dimensional Nijenhuis operators are studied that have differential singularities, i.e., such points at which the coefficients of the characteristic polynomials are dependent. The case is studied in which the differentials of all invariants of the Nijenhuis operator are proportional, as well as the case when two invariants are functionally

Mahdi Bagheri, Adam Barletta, Jordan Bogdan, Anthony M. Brown

The Trinity Demonstrator is a proof of concept prototype for the Trinity Neutrino Observatory, which is sensitive to astrophysical neutrinos above PeV energies. The Demonstrator is a one-square meter class imaging atmospheric Cherenkov telescope deployed on Frisco Peak, Utah, and remotely operated. The light-collection surface is equipped with 77 mirror face

Yue Ju, Bo Wahlberg, Håkan Hjalmarsson

Empirical Bayes estimators are based on minimizing the average risk with the hyper-parameters in the weighting function being estimated from observed data. The performance of an empirical Bayes estimator is typically evaluated by its mean squared error (MSE). However, the explicit expression for its MSE is generally unavailable for finite sample sizes. To ad

Benjamin Chung, Kazuya Echigo, Behçet Açıkmeşe

We describe a successive convex programming (Sequential Convex Programming (SCP)) based approach for estimate the set of points where a 5-degree of freedom (5-DoF) reusable launch vehicle (RLV) returning to a landing site can transition from aerodynamic to propulsive descent. Determining the set of feasible ignition points that a RLV can use and then safely

Yekta Amirkhalili, Ho Yi Wong

The rapid growth of mobile banking (m-banking), especially after the COVID-19 pandemic, has reshaped the financial sector. This study analyzes consumer reviews of m-banking apps from five major Canadian banks, collected from Google Play and iOS App stores. Sentiment analysis and topic modeling classify reviews as positive, neutral, or negative, highlighting

Dinmukhammed Akpan

The paper is devoted to the study of Nijenhuis operators of arbitrary dimension $n$ in a neighborhood of a point at which the first $n-1$ coefficients of the characteristic polynomial are functionally independent, and the last coefficient (the determinant of the operator) is an arbitrary function. We prove a theorem on the general form of such Nijenhuis oper

Tyler B. Martin, Duncan R. Sutherland, Austin McDannald, A. Gilad Kusne

The pace of soft material formulation (re)development and design is rapidly increasing as both consumers and new legislation demand products that do less harm to the environment while maintaining high standards of performance. To meet this need, we have developed the Autonomous Formulation Lab (AFL), a platform that can automatically prepare and measure the

Ivan Kartáč, Mateusz Lango, Ondřej Dušek

Large Language Models (LLMs) have demonstrated great potential as evaluators of NLG systems, allowing for high-quality, reference-free, and multi-aspect assessments. However, existing LLM-based metrics suffer from two major drawbacks: reliance on proprietary models to generate training data or perform evaluations, and a lack of fine-grained, explanatory feed

Meng Yuan, Adam Burman, Changfu Zou

The proper disposal and repurposing of end-of-life electric vehicle batteries are critical for maximizing their environmental benefits. This study introduces a robust model predictive control (MPC) framework designed to optimize the battery discharging process during pre-treatment, ensuring both efficiency and safety. The proposed method explicitly incorpora

A. Feldmeier-Krause, N. Neumayer, A. Seth, G. van de Ven

The Galactic Centre region contains a dense accumulation of stars, which can be separated into two components: A flattened and dense nuclear star cluster (NSC), and a surrounding, more extended and more flattened, nuclear stellar disc (NSD). Previous studies have collected a few thousand spectra of the inner NSC, and also the outer NSD, and measured line-of-

Jingzong Zhou, Yuhan Zhu, Xiaobin Zhang, Sunil Agrawal

This paper introduces a 3D parallel robot with three identical five-degree-of-freedom chains connected to a circular brace end-effector, aimed to serve as an assistive device for patients with cervical spondylosis. The inverse kinematics of the system is solved analytically, whereas learning-based methods are deployed to solve the forward kinematics. The met

Yue Ju, Bo Wahlberg, Håkan Hjalmarsson

Regularized system identification has become a significant complement to more classical system identification. It has been numerically shown that kernel-based regularized estimators often perform better than the maximum likelihood estimator in terms of minimizing mean squared error (MSE). However, regularized estimators often require hyper-parameter estimati

Dennis Donathan, Mike Nason, Marco Tullney, Julie Shi

Introduction: Scholarly research spans multiple languages, making multilingual metadata crucial for organizing and accessing knowledge across linguistic boundaries. These multilingual metadata already exist and are propagated throughout scholarly publishing infrastructure, but the extent to which they are correctly recorded, or how they affect metadata quali

Dinmukhammed Akpan

A Nijenhuis operator $L$ is a $(1,1)$-tensor field on a smooth manifold $M$ with vanishing Nijenhuis torsion ${ {\mathcal N_L}}$. At each point $x\in M$, the algebraic type of $L(x)$ is characterized by its Jordan normal form. In this paper, we study singularities of a two-dimensional Nijenhuis operator in the case when its trace has a non-zero differential

Jutika Borah, Hidam Kumarjit Singh

Deep learning models have gained increasing adoption in medical image analysis. However, these models often produce overconfident predictions, which can compromise clinical accuracy and reliability. Bridging the gap between high-performance and awareness of uncertainty remains a crucial challenge in biomedical imaging applications. This study focuses on deve

Vitaly Feldman, Audra McMillan, Guy N. Rothblum, Kunal Talwar

Pan-privacy was proposed by Dwork et al. as an approach to designing a private analytics system that retains its privacy properties in the face of intrusions that expose the system's internal state. Motivated by federated telemetry applications, we study local pan-privacy, where privacy should be retained under repeated unannounced intrusions on the local st

Yi Wang, Zhitong Xiong, Chenying Liu, Adam J. Stewart

Advances in Earth observation (EO) foundation models have unlocked the potential of big satellite data to learn generic representations from space, benefiting a wide range of downstream applications crucial to our planet. However, most existing efforts remain limited to fixed spectral sensors, focus solely on the Earth's surface, and overlook valuable metada

Maximiliane Rautenstrauß, Maximilian Schiffer

Minimizing response times to meet legal requirements and serve patients in a timely manner is crucial for Emergency Medical Service (EMS) systems. Achieving this goal necessitates optimizing operational decision-making to efficiently manage ambulances. Against this background, we study a centrally controlled EMS system for which we learn an online ambulance

Stan Wagon

A unibike curve is a track that can be made by either a bicycle or a unicycle. More precisely, the end of a unit tangent vector at any point on a unibike curve lies on the curve (so the bike's front wheel always lies on the track made by the rear wheel). David Finn found such a curve in 2002, but it loops around itself in an extremely complicated way with ma

Alexander Weers, Alexander H. Berger, Laurin Lux, Peter Schüffler

The histopathological analysis of whole-slide images (WSIs) is fundamental to cancer diagnosis but is a time-consuming and expert-driven process. While deep learning methods show promising results, dominant patch-based methods artificially fragment tissue, ignore biological boundaries, and produce black-box predictions. We overcome these limitations with a n

Nirmalya Thakur, Niven Francis Da Guia Fernandes, Madje Tobi Marc'Avent Tchona

Long COVID continues to challenge public health by affecting a considerable number of individuals who have recovered from acute SARS-CoV-2 infection yet endure prolonged and often debilitating symptoms. Social media has emerged as a vital resource for those seeking real-time information, peer support, and validating their health concerns related to Long COVI

Juan Diego Aguilera, Rocío Ranchal, Fernando Gálvez, José Miguel Colino

A Love wave device was designed to generate surface acoustic waves (SAWs) with strong shear-horizontal polarization, interacting with a polycrystalline Fe72Ga28 magnetostrictive layer. The shear strain induced by these waves at a frequency of approximately 160 MHz, coupled with magnetoelastic effects, leads to domain magnetization oscillation, resulting in u

Anna Kazeykina, Zhenjie Ren, Xiaozhen Wang, Yufei Zhang

We study an entropic optimal transport problem in which the transport plan is penalized by a nonlinear convex functional acting on the coupling. We establish existence, uniqueness, and uniform a priori bounds for minimizers, and we show that each minimizer satisfies a fixed-point first-order optimality system associated with an exponentially tilted reference

Halil Alperen Gozeten, M. Emrullah Ildiz, Xuechen Zhang, Mahdi Soltanolkotabi

Test-time training (TTT) methods explicitly update the weights of a model to adapt to the specific test instance, and they have found success in a variety of settings, including most recently language modeling and reasoning. To demystify this success, we investigate a gradient-based TTT algorithm for in-context learning, where we train a transformer model on

Tianwei Lan, Luca Demetrio, Farid Nait-Abdesselam, Yufei Han

Machine Learning (ML) malware detectors rely heavily on crowd-sourced AntiVirus (AV) labels, with platforms like VirusTotal serving as trusted sources of malware annotations. But what if attackers could manipulate these labels to classify benign software as malicious? We introduce label spoofing attacks, a new threat that contaminates crowd-sourced datasets

Kelsey M. Brown, Derek Moran

Tableau switching is a well studied bijection on pairs of skew Young tableaux which swaps their relative positions. This is achieved by successively sliding the entries of the inner tableaux through the outer one via jeu de taquin (JDT) slides. Tableau coswitching is a similar but coplactic operation, meaning it commutes with any sequence of JDT slides. Cosw

Aryan Mehboudi, Shrawan Singhal, S. V. Sreenivasan

Deterministic lateral displacement (DLD) is a popular technique for size-based separation of particles. One of the challenges in design of DLD chips is to eliminate the disturbance of fluid flow patterns caused by channel sidewalls intersecting with the pillars matrix. While there are numerous reports in the literature attempting to mitigate this issue by ad

Ximing Wen, Rezvaneh Rezapour

Sarcasm detection, with its figurative nature, poses unique challenges for affective systems designed to perform sentiment analysis. While these systems typically perform well at identifying direct expressions of emotion, they struggle with sarcasm's inherent contradiction between literal and intended sentiment. Since transformer-based language models (LMs)

K. J. Kwon, Hang Yu, Tejaswi Venumadhav

A neutron star (NS) in a binary system deforms due to the companion's tidal gravitational field. As the binary inspirals due to gravitational wave (GW) emission, the NS's deformation evolves; this evolution is typically modeled as the star's linear response to the companion's time-evolving tidal potential. In principle, the fluid elements' displacements can

Oscar Morris

Feedback is a very important part the learning process. However, it is challenging to make this feedback both timely and accurate when relying on human markers. This is the challenge that Automated Feedback Generation attempts to address. In this paper, a technique to train such a system on a very small dataset with very long sequences is presented. Both of

Haoxin Liu, Harshavardhan Kamarthi, Zhiyuan Zhao, Shangqing Xu

Time series analysis (TSA) is a longstanding research topic in the data mining community and has wide real-world significance. Compared to "richer" modalities such as language and vision, which have recently experienced explosive development and are densely connected, the time-series modality remains relatively underexplored and isolated. We notice that many

Ajesh Kumar, Byungmin Kang, Patrick A. Lee

Recent experiments on the Kagome spin liquid candidate YCOB suggest the presence of Dirac fermionic spinons near the magnetization plateau at 1/9. Theories suggest that the spinons are charge neutral spin-$1/2$ excitations, in a $2\pi/3$ flux which triples the unit cell. Generally a gap is expected, and there is no symmetry protection for the Dirac nodes in

Peiqi Yang, Conglong Xu, Hao Wu

We prove a convergence theorem for stochastic gradient descents on manifolds with adaptive learning rate and apply it to the weighted low-rank approximation problem.

Yihang Chen, Haikang Deng, Kaiqiao Han, Qingyue Zhao

Chain-of-Thought (CoT) reasoning enhances large language models (LLMs) by decomposing complex problems into step-by-step solutions, improving performance on reasoning tasks. However, current CoT disclosure policies vary widely across different models in frontend visibility, API access, and pricing strategies, lacking a unified policy framework. This paper an

Zhenhong Hu, Esra Adiyeke, Ziyuan Guan, Divya Vellanki

Background: Social determinants of health (SDoH) play a crucial role in influencing health outcomes, accounting for nearly 50% of modifiable health factors and bringing to light critical disparities among disadvantaged groups. Despite the significant impact of SDoH, existing data resources often fall short in terms of comprehensiveness, integration, and usab

Chandima Kasun Edirisinghe, Anuradha Wijesinghe, Anjali Rathore, Pradip Adhikari

The incorporation of dilute concentrations of bismuth (Bi) into traditional III V alloys leads to significant reduction in bandgap energy, making InSbBi is a promising candidate for long wavelength infrared photodetection sensors due to its small bandgap (<0.17 eV). Furthermore, InSbBi could serve as a valuable platform for spin dynamics and quantum phenomen

O. Fresse-Colson, S. Guérin, Xi Chen, D. Sugny

We study the time-optimal robust control of a two-level quantum system subjected to field inhomogeneities. We apply the Pontryagin Maximum Principle and we introduce a reduced space onto which the optimal dynamics is projected down. This reduction leads to a complete analytical derivation of the optimal solution in terms of elliptic functions and elliptic in

Jushan Chen, Santiago Paternain

Multi-agent reinforcement learning is a challenging and active field of research due to the inherent nonstationary property and coupling between agents. A popular approach to modeling the multi-agent interactions underlying the multi-agent RL problem is the Markov Game. There is a special type of Markov Game, termed Markov Potential Game, which allows us to

Atefeh Mollabagher, Parinaz Naghizadeh

Recommendation systems underlie a variety of online platforms. These recommendation systems and their users form a feedback loop, wherein the former aims to maximize user engagement through personalization and the promotion of popular content, while the recommendations shape users' opinions or behaviors, potentially influencing future recommendations. These

Chengyan Jiang, Jiamin Fan, Talal Halabi, Israat Haque

The widespread adoption of smartphones and smart wearable devices has led to the widespread use of Centralized Federated Learning (CFL) for training powerful machine learning models while preserving data privacy. However, CFL faces limitations due to its overreliance on a central server, which impacts latency and system robustness. Decentralized Federated Le

Kezia Oketch, John P. Lalor, Yi Yang, Ahmed Abbasi

Closed large language models (LLMs) such as GPT-4 have set state-of-the-art results across a number of NLP tasks and have become central to NLP and machine learning (ML)-driven solutions. Closed LLMs' performance and wide adoption has sparked considerable debate about their accessibility in terms of availability, cost, and transparency. In this study, we per

Ananya Agarwal, Fnu Alusi, Arbie Hsu, Arif Syraj

The mathematics of redistricting is an area of study that has exploded in recent years. In particular, many different research groups and expert witnesses in court cases have used outlier analysis to argue that a proposed map is a gerrymander. This outlier analysis relies on having an ensemble of potential redistricting maps against which the proposed map is

Osama A. Marzouk

The Adiabatic Flame Temperature (AFT) in combustion represents the maximum attainable temperature at which the chemical energy in the reactant fuel is converted into sensible heat in combustion products without heat loss. AFT depends on the fuel, oxidizer, and chemical composition of the products. Computing AFT requires solving either a nonlinear equation or

Subash Adhikari, Yan Yang, William H. Matthaeus

In this study, we revisit the pressure-strain interaction in kinetic turbulence, and in particular we re-examine the decomposition of pressure-strain interaction into compressive and incompressive parts. The pressure dilatation ingredient is clearly due to plasma compressions, but here using kinetic particle-in-cell (PIC) simulations of plasma turbulence, it

Tuomas Jalonen, Mohammad Al-Sa'd, Serkan Kiranyaz, Moncef Gabbouj

Labeled time-series data is often expensive and difficult to obtain, making it challenging to train accurate machine learning models for real-world applications such as anomaly detection or fault diagnosis. The scarcity of labeled samples limits model generalization and leaves valuable unlabeled data underutilized. We propose Dual-Domain Fusion (DDF), a new

Luna Lima Keller, Daniel Jost Brod

Quantum walks in general graphs, or more specifically scattering on graphs, encompass enough complexity to perform universal quantum computation. Any given quantum circuit can be broken down into single- and two-qubit gates, which can then be translated into subgraphs -- gadgets -- that implement such unitaries on the logical qubits, simulated by particles t

Chenglei Hu, Daniela Castro-Camilo

The multivariate generalized Pareto distribution (mGPD) is a common method for modeling extreme threshold exceedance probabilities in environmental and financial risk management. Despite its broad applicability, mGPD faces challenges due to the infinite possible parametrizations of its dependence function, with only a few parametric models available in pract

R. Pablo Arribillaga, Eliana Pepa-Risma

In two-sided matching markets with contracts, quantile (or generalized median) stable mechanisms represent an interesting class that produces stable allocations which can be viewed as compromises between both sides of the market. These mechanisms balance the competing priorities of the parties while maintaining stability. This paper explores obvious manipula

Antonio Lorenzin, Fabio Zanasi

Moralisation and Triangulation are transformations allowing to switch between different ways of factoring a probability distribution into a graphical model. Moralisation allows to view a Bayesian network (a directed model) as a Markov network (an undirected model), whereas triangulation works in the opposite direction. We present a categorical framework wher

Yigit Efe Erginbas, Thomas A. Courtade, Kannan Ramchandran

We consider an assortment selection and pricing problem in which a seller has $N$ different items available for sale. In each round, the seller observes a $d$-dimensional contextual preference information vector for the user, and offers to the user an assortment of $K$ items at prices chosen by the seller. The user selects at most one of the products from th

Simone Franchini

We investigate the properties of the thermodynamic limit in a general bipartite spin network with pairwise interactions. This is done by integrating one of the the spin groups, to transform the bipartite problem into a single group problem with a non-linear Hamiltonian. The transformed model is also relevant due to similarity with the LCVAE architecture.

Daniel Barake, Cameron Franc

We describe families of MLDEs whose solutions are modular forms of level one that converge, $2$-adically, to a Hauptmodul on $\Gamma_0(2)$ by using a theorem of Serre. Then, we apply this to show that the image of the character map on the $2$-adic Heisenberg VOA $S_{1}$ contains the space of $2$-adic overconvergent modular forms $M_{2}^{\dagger}(1/2)$ of wei

Neusha Javidnia, Bita Darvish Rouhani, Farinaz Koushanfar

Large language models (LLMs) have demonstrated exceptional capabilities in generating text, images, and video content. However, as context length grows, the computational cost of attention increases quadratically with the number of tokens, presenting significant efficiency challenges. This paper presents an analysis of various Key-Value (KV) cache compressio

Kendall L. Thomas, Jan Hannig

This paper presents the quantile cube, a novel three-dimensional summary representation designed to analyze external load using GPS-derived movement data. While broadly applicable, we demonstrate its utility through an application to data from elite female soccer athletes across 23 matches. The quantile cube segments athlete movements into discrete quantiles

Juan Cruz-Martinez, Toon Hasenack, Felix Hekhorn, Giacomo Magni

We present NNPDFpol2.0, a new set of collinear helicity parton distribution functions (PDFs) of the proton based on legacy measurements of structure functions in inclusive neutral-current longitudinally polarised deep-inelastic scattering (DIS), and of W -boson, single-inclusive, and di-jet production asymmetries in longitudinally polarised proton-proton col

Lazaros Souvaitzis, Antti Rantala, Thorsten Naab

Star clusters can interact and merge in galactic discs, halos, or centers. We present direct N-body simulations of binary mergers of star clusters with $M_{\star} = 2.7 \times 10^4 \: \mathrm{M_{\odot}}$ each, using the N-body code BIFROST with subsystem regularisation and post-Newtonian dynamics. We include 500 $\mathrm{M_{\odot}}$ massive black holes (MBHs