March 2025 arXiv papers — page 221

Ethan C. McGarrigle, Hector D. Ceniceros, Glenn H. Fredrickson

We introduce a projected complex Langevin (CL) numerical sampling method -- a fictitious Langevin dynamics scheme that uses numerical projection to sample a constrained stationary distribution with highly oscillatory character. Despite the complex-valued degrees of freedom and associated sign-problem, the projected CL method succeeds as a natural extension o

Johann Hartleb, Marie Schmidt, Samuel Wolf, Alexander Wolff

Train timetables can be represented as event graphs, where correspond to a train passing through a location at a certain point in time. A visual representation of an event graph is important for many applications such as dispatching and (the development of) dispatching software. A common way to represent event graphs are time-space diagrams. In such a diagra

Hamish Ivison, Muru Zhang, Faeze Brahman, Pang Wei Koh

Selecting high-quality training data from a larger pool is a crucial step when instruction-tuning language models, as carefully curated datasets often produce models that outperform those trained on much larger, noisier datasets. Automated data selection approaches for instruction-tuning are typically tested by selecting small datasets (roughly 10k samples)

Sk. Selim, Chhapikul Miah, Monoj Kumar Das, Shyamapada Modak

The study of local function in topological spaces is remarkable. Various branches have been developed through this study. In this paper, we further consider the local function and exploring the various properties of the same by considering some generalized open sets. In this situation some of the properties of local function fails to hold due to the finite i

Mohammad Albinhassan, Pranava Madhyastha, Alessandra Russo

Ensuring both syntactic and semantic correctness in Large Language Model (LLM) outputs remains a significant challenge, despite being critical for real-world deployment. In this paper, we introduce $\texttt{SEM-CTRL}$, a unified approach that allows for enforcing rich context-sensitive constraints, and task and instance specific semantics directly on the LLM

Peijun Hou, Nan Cen

Hybrid light fidelity (LiFi) and wireless fidelity (WiFi) indoor networks has been envisioned as a promising technology to alleviate radio frequency spectrum crunch to accommodate the ever-increasing data rate demand in indoor scenarios. The hybrid LiFi/WiFi indoor networks can leverage the advantages of fast data transmission from LiFi and wider coverage of

Yu Zhang, Yihan Liu, C. -Y. Ng, Mallory S. E. Roberts

Pulsar wind nebulae (PWNe) are important sources for understanding galactic high-energy processes, but it is controversial until now about how high-energy particles in PWNe are accelerated and transported. Lacking radio counterparts of X-ray PWNe (the proposed acceleration sites) introduce difficulties to better understandings in multi wavelengths. Our recen

Johannes Freischuetz, Konstantinos Kanellis, Brian Kroth, Shivaram Venkataraman

Autotuning plays a pivotal role in optimizing the performance of systems, particularly in large-scale cloud deployments. One of the main challenges in performing autotuning in the cloud arises from performance variability. We first investigate the extent to which noise slows autotuning and find that as little as $5\%$ noise can lead to a $2.5$x slowdown in c

Yu Deng, Zaher Hani, Xiao Ma

In this paper, we rigorously derive the fundamental PDEs of fluid mechanics, such as the compressible Euler and incompressible Navier-Stokes-Fourier equations, starting from the hard sphere particle systems undergoing elastic collisions. This resolves Hilbert's sixth problem, as it pertains to the program of deriving the fluid equations from Newton's laws by

Peter Zizler

Time independent convolution yields circulant matrices whose eigenvectors are the Fourier exponentials with the eigenvalues being the Fourier transform of the mask. The case of time dependent convolution, the non-stationary case, no longer has this property and two matrices are then introduced, the cyclic convolution matrix and the cyclic combination matrix.

Md. Farhan Shahriyar, Gazi Tanbhir, Abdullah Md Raihan Chy, Mohammed Abdul Al Arafat Tanzin

Phishing URL detection is crucial in cybersecurity as malicious websites disguise themselves to steal sensitive infor mation. Traditional machine learning techniques struggle to per form well in complex real-world scenarios due to large datasets and intricate patterns. Motivated by quantum computing, this paper proposes using Variational Quantum Classifiers

Ayten Koç, Murad Özaydın

Hilbert's Nullstellensatz states that a quotient of the algebra of polynomial functions on an algebraic variety over an algebraically closed field is the algebra of polynomial functions on a (sub)variety if and only if its radical is trivial. We prove a noncommutative analog: A quotient of a Leavitt path algebra over an algebraically closed field is isom

Benjamin D. Woods

The exchange interaction $J$ offers a powerful tool for quantum computation based on semiconductor spin qubits. However, the exchange interaction in two-electron systems in the absence of a magnetic field is usually constrained to be non-negative $J \geq 0$, which inhibits the construction of various dynamically corrected exchange-based gates. In this work,

Carlos Mora-Corral, David Mur-Callizo

We work in a class of Sobolev $W^{1,p}$ maps, with $p > d-1$, from a bounded open set $\Omega \subset \mathbb{R}^{d}$ to $\mathbb{R}^{d}$ that do not exhibit cavitation and whose trace on $\partial \Omega$ is also $W^{1,p}$. Under the assumptions that the Jacobian is positive and the deformation can be approximated on the boundary by injective maps, we show

Junhyun Park, Chanyu Moon, Donghwan Lee, Kyungsu Kim

Contrastive Language-Image Pre-Training (CLIP) has enabled zero-shot classification in radiology, reducing reliance on manual annotations. However, conventional contrastive learning struggles with normal case detection due to its strict intra-sample alignment, which disrupts normal sample clustering and leads to high false positives (FPs) and false negatives

Gary M. Bernstein

The explosion of high-precision astrometric data on main-belt asteroids (MBAs) enables new inferences on gravitational and non-gravitational forces present in this region. We estimate the size of MBA motions caused by mutual gravitational encounters with other MBAs that are either omitted from ephemeris models or have uncertain mass estimates. In other words

Andrei Buliga, Chiara Di Francescomarino, Chiara Ghidini, Marco Montali

Counterfactual explanations are one of the prominent eXplainable Artificial Intelligence (XAI) techniques, and suggest changes to input data that could alter predictions, leading to more favourable outcomes. Existing counterfactual methods do not readily apply to temporal domains, such as that of process mining, where data take the form of traces of activiti

Nadia Dachlythra, Kevin Wolz, Susanna Azzoni, David Alonso

The Simons Observatory (SO) Small Aperture Telescopes (SATs) will observe the Cosmic Microwave Background (CMB) temperature and polarization at six frequency bands. Within these bands, the angular response of the telescope (beam) is convolved with the instrument's spectral response (commonly called bandpass) and the signal from the sky, which leads to the ba

Filippo Spaggiari, Marco Bonatto

We characterize several properties of core quandles in terms of the properties of their underlying groups. Specifically, we characterize connected cores providing an answer to an open question in \cite{saito} and present a standard homogeneous representation for them, which allows us to prove that simple core quandles are primitive.

Chengyi Xing, Hao Li, Yi-Lin Wei, Tian-Ao Ren

Tactile sensing is essential for dexterous manipulation, yet large-scale human demonstration datasets lack tactile feedback, limiting their effectiveness in skill transfer to robots. To address this, we introduce TacCap, a wearable Fiber Bragg Grating (FBG)-based tactile sensor designed for seamless human-to-robot transfer. TacCap is lightweight, durable, an

Daniel Shiu

For a real number $\theta$, let $\Vert\theta\Vert$ denote the distance from $\theta$ to the nearest integer. A set of positive integers $\mathcal H$ is a Heilbronn set if for every $\alpha\in \mathbb R$ and every $\epsilon>0$ there exists $h\in\mathcal H$ such that $\Vert h\alpha\Vert<\epsilon$ (see \cite{montgomery} 2.7). The natural numbers are a Heilbronn

Susanne Hambrusch, Lori Pollock, Mary Hall, Nancy M. Amato

To sustain innovation and safeguard national security, the U.S. must strengthen domestic pathways to computing PhDs by engaging talented undergraduates early - before they are committed to industry - with research experiences, mentorship, and financial support for graduate studies.

Amir Shehata, Peter Groszkowski, Thomas Naughton, Murali Gopalakrishnan Meena

This paper presents a comprehensive software stack architecture for integrating quantum computing (QC) capabilities with High-Performance Computing (HPC) environments. While quantum computers show promise as specialized accelerators for scientific computing, their effective integration with classical HPC systems presents significant technical challenges. We

Abdulrahman Alhaidari, Balaji Palanisamy, Prashant Krishnamurthy

Smart contracts in Decentralized Finance (DeFi) platforms are attractive targets for attacks as their vulnerabilities can lead to massive amounts of financial losses. Flash loan attacks, in particular, pose a major threat to DeFi protocols that hold a Total Value Locked (TVL) exceeding \$106 billion. These attacks use the atomicity property of blockchains to

N. Tripathi, B. Miao, A. Sloss, E. Rockafellow

New techniques for the optical generation of plasma waveguides -- optical fibres for ultra-intense light pulses -- have become vital to the advancement of multi-GeV laser wakefield acceleration. Here, we demonstrate the fabrication and characterization of a transmissive eight-level logarithmic diffractive axicon (LDA) for the generation of meter-scale plasma

Mona Singh, Katie Siek, David Danks, Rayid Ghani

The transformative potential of AI in healthcare - including better diagnostics, treatments, and expanded access - is currently limited by siloed patient data across multiple systems. Federal initiatives are necessary to provide critical infrastructure for health data repositories for data sharing, along with mechanisms to enable access to this data for appr

Ziyu Liu, Zeyi Sun, Yuhang Zang, Xiaoyi Dong

Reinforcement Fine-Tuning (RFT) in Large Reasoning Models like OpenAI o1 learns from feedback on its answers, which is especially useful in applications when fine-tuning data is scarce. Recent open-source work like DeepSeek-R1 demonstrates that reinforcement learning with verifiable reward is one key direction in reproducing o1. While the R1-style model has

Henok Tenaw Moges, Thanos Manos, Ovidiu Racoveanu, Charalampos Skokos

The behavior of the Generalized Alignment Index (GALI) method has been extensively studied and successfully applied for the detection of chaotic motion in conservative Hamiltonian systems, yet its application to non-Hamiltonian dissipative systems remains relatively unexplored. In this work, we fill this gap by investigating the GALI's ability to identify st

Lisa Amini, Henry F. Korth, Nita Patel, Evan Peck

AI's rapid integration into the workplace demands new approaches to workforce education and training and broader AI literacy across disciplines. Coordinated action from government, industry, and educational institutions is necessary to ensure workers can adapt to accelerating technological change.

J-B. Dakeyo, P. Démoulin, A. P. Rouillard, M. Maksimovic

The isopoly bi-fluid approach assumes an isothermal evolution of the solar wind near the Sun up to the radial distance riso, followed by a polytropic evolution constrained by the observed polytropic indices. This approach provides a more accurate model of the interplanetary properties of the solar wind (u, n, Tp, Te) and their radial evolution (Dakeyo et al.

Meghana Rajeev, Rajkumar Ramamurthy, Prapti Trivedi, Vikas Yadav

We investigate the robustness of reasoning models trained for step-by-step problem solving by introducing query-agnostic adversarial triggers - short, irrelevant text that, when appended to math problems, systematically mislead models to output incorrect answers without altering the problem's semantics. We propose CatAttack, an automated iterative attack pip

Pamela Wisniewski, Katie Siek, Kevin Butler, Gabrielle Allen

Technology can pose signicant risks to a wide array of vulnerable populations. However, by addressing the challenges and opportunities in technology design, research, and deployment, we can create systems that benet everyone, fostering a society where even the most vulnerable are empowered and supported.

Andrea Di Giovan Paolo, Jose Higueras

This paper analyzes optimal insurance design when the insurer internalizes the effect of coverage on third-party service prices. A monopolistic insurer contracts with risk-averse agents who have sequential two-dimensional private information and preferences represented by Yaari's dual utility. Insurance contracts shape service demand and, through a market-cl

Brian LaMacchia, Matt Campagna, William Gropp

The development of quantum computing threatens the security of our currently widely deployed cryptographic algorithms. While signicant progress has been made in developing post-quantum cryptography (PQC) standards to protect against future quantum computing threats, the U.S. government's estimated $7.1 billion transition cost for non-National Security System

Nicolas Underwood, Fabien Paillusson

Non-ergodicity impacts statistical inference in a diverse range of disciplines inside and outside of physics. However the concept of ergodicity is used inconsistently, and may refer to several nonequivalent notions. To help address this, we first identify and clarify the relationship between three major interpretations of ergodicity. We then introduce a meth

Claudio Eligio Calosso, Nils Nemitz

The atomic transitions employed in the best of today's optical clocks are a strong foundation for the upcoming redefinition of the SI second. Including multiple transitions in the definition offers increased accuracy, a robust diversity of implementations and would drive continuous performance validation through frequency comparisons. The cost is that such a

Adam Shostack, L. Jean Camp, Yi Ting Chua, Josiah Dykstra

The United States needs national institutions and frameworks to systematically collect cybersecurity data, measure outcomes, and coordinate responses across government and private sectors, similar to how public health systems track and address disease outbreaks.

F. Arrouas, J. Hébraud, N. Ombredane, E. Flament

The Coherent Backscattering (CBS) peak is a well-known interferential signature of weak localization in disordered or chaotic systems. More recently, a second interference feature -- the Coherent Forward Scattering (CFS) peak -- was predicted to emerge in the regime of strong localization. However, it has never been directly observed. Here we report the firs

Tiansheng Wen, Yifei Wang, Zequn Zeng, Zhong Peng

Many large-scale systems rely on high-quality deep representations (embeddings) to facilitate tasks like retrieval, search, and generative modeling. Matryoshka Representation Learning (MRL) recently emerged as a solution for adaptive embedding lengths, but it requires full model retraining and suffers from noticeable performance degradations at short lengths

Allen Alvarez Loya, Daniel A. Serino, J. W. Burby, Qi Tang

Neural ordinary differential equations (NODEs) are an effective approach for data-driven modeling of dynamical systems arising from simulations and experiments. One of the major shortcomings of NODEs, especially when coupled with explicit integrators, is its long-term stability, which impedes their efficiency and robustness when encountering stiff problems.

Jay Zhangjie Wu, Yuxuan Zhang, Haithem Turki, Xuanchi Ren

Neural Radiance Fields and 3D Gaussian Splatting have revolutionized 3D reconstruction and novel-view synthesis task. However, achieving photorealistic rendering from extreme novel viewpoints remains challenging, as artifacts persist across representations. In this work, we introduce Difix3D+, a novel pipeline designed to enhance 3D reconstruction and novel-

Shiqi Chen, Tongyao Zhu, Ruochen Zhou, Jinghan Zhang

Large Vision Language Models (VLMs) have long struggled with spatial reasoning tasks. Surprisingly, even simple spatial reasoning tasks, such as recognizing "under" or "behind" relationships between only two objects, pose significant challenges for current VLMs. In this work, we study the spatial reasoning challenge from the lens of mechanistic interpretabil

Michael J. Probst, Jacob M. Hiesener, Archana Kaushalram, Stephen E. Ralph

We enable density-based topology optimization (TO) to design integrated photonic devices featuring nonvertical sidewall profiles. Specifically, we demonstrate TO for fabrication processes with slanted sidewalls which are often used to enhance vertical coupling efficiency and fabrication processes with angled sidewalls which are a common feature of etching. T

Viktoria Noel, Thomas Gasenzer, Kirill Boguslavski

We investigate the different degrees of freedom underlying far-from-equilibrium scaling behaviour in a relativistic, single-component $\mathrm{O}(1)$ scalar field theory in two and three spatial dimensions. In such a strongly correlated many-body system, identifying the respective roles of nonlinear wave excitations and defect dynamics is a prerequisite for

Amit Kumar, Dharm Veer Singh, Sudhaker Upadhyay

In this paper, we derive the black hole solution in the context of nonlinear electrodynamics (NLED) coupled to a perfect fluid dark matter (PFDM) field. The resulting black hole solution interpolates between the $AdS$ Ay\'{o}n--Beato--Garc\'{i}a (ABG) black hole in the absence of the PFDM field and the Schwarzschild black hole devoid of magnetic monopole cha

Chenning Li, Anton A. Zabreyko, Arash Nasr-Esfahany, Kevin Zhao

Flow-level simulation is widely used to model large-scale data center networks due to its scalability. Unlike packet-level simulators that model individual packets, flow-level simulators abstract traffic as continuous flows with dynamically assigned transmission rates. While this abstraction enables orders-of-magnitude speedup, it is inaccurate by omitting c

Andrew Konya, Luke Thorburn, Wasim Almasri, Oded Adomi Leshem

A growing body of work has shown that AI-assisted methods -- leveraging large language models, social choice methods, and collective dialogues -- can help navigate polarization and surface common ground in controlled lab settings. But what can these approaches contribute in real-world contexts? We present a case study applying these techniques to find common

Heming Fu, Hongkai Chen, Shan Lin, Guoliang Xing

Alzheimer's Disease (AD) has become an increasingly critical global health concern, which necessitates effective monitoring solutions in smart health applications. However, the development of such solutions is significantly hindered by the scarcity of AD-specific activity datasets. To address this challenge, we propose SHADE-AD, a Large Language Model (LLM)

Xiuqi Tommy Zhu, Heidi Cheerman, Minxin Cheng, Sheri Kiami

VR simulation in Health Professions (HP) education demonstrates huge potential, but fixed learning content with little customization limits its application beyond lab environments. To address these limitations in the context of VR for patient communication training, we conducted a user-centered study involving semi-structured interviews with advanced HP stud

Valentio Iverson, Gautam Kamath, Argyris Mouzakis

We provide the first $\widetilde{\mathcal{O}}\left(d\right)$-sample algorithm for sampling from unbounded Gaussian distributions under the constraint of $\left(\varepsilon, \delta\right)$-differential privacy. This is a quadratic improvement over previous results for the same problem, settling an open question of Ghazi, Hu, Kumar, and Manurangsi.

A. Díaz, J. M. Alegre, I. I. Cuesta, E. Martínez-Pañeda

Prediction of hydrogen embrittlement requires a robust modelling approach and this will foster the safe adoption of hydrogen as a clean energy vector. A generalised computational model for hydrogen embrittlement is here presented, based on a phase field description of fracture. In combination with Part I of this work, which describes the process of hydrogen

Yuvaraj Elangovan, B. Satyanarayana, Ravindra Shinde, Mandar Saraf

Primary cosmic rays when interact with our atmosphere, produce a cascade of lighter secondary particles namely pion, kaon, neutrons, muons, electrons, positrons and neutrinos. Muons are one of the most abundant and easily detectable particles at the ground surface using a large variety of particle detectors. Resistive Plate Chambers (RPCs) of 2m x 2m in dime

Zhengliang Shi, Yuhan Wang, Lingyong Yan, Pengjie Ren

Tool learning aims to augment large language models (LLMs) with diverse tools, enabling them to act as agents for solving practical tasks. Due to the limited context length of tool-using LLMs, adopting information retrieval (IR) models to select useful tools from large toolsets is a critical initial step. However, the performance of IR models in tool retriev

Ugo Nzongani, Andrea Simonetto, Giuseppe Di Molfetta

The task of finding an element in an unstructured database is known as spatial search and can be expressed as a quantum walk evolution on a graph. In this article, we modify the usual search problem by adding an extra trapping vertex to the graph, which is only connected to the target element. We study the transfer efficiency of the walker to a trapping site

Steven Seung-Suk Kang, Joseph Bodenheimer, Kayley Morris, Tracey Butler

The claustrum is a thin gray matter structure in each brain hemisphere, characterized by exceptionally high connectivity with nearly all brain regions. Despite extensive animal studies on its anatomy and function and growing evidence of claustral deficits in neuropsychiatric disorders, its specific roles in normal and abnormal human brain function remain lar

Stéphanie Dumont, Jean de Bremond d'Ars, Jean-Baptiste Boulé, Vincent Courtillot

We have explored the temporal variability of the seismicity at global scale over the last 124 years, as well as its potential drivers. To achieve this, we constructed and analyzed an averaged global seismicity curve for earthquakes of magnitude equal or greater than 6 since 1900. Using Singular Spectrum Analysis, we decomposed this curve and compared the ext

Daniel Gilkarov, Ran Dubin

This paper examines the challenges in distributing AI models through model zoos and file transfer mechanisms. Despite advancements in security measures, vulnerabilities persist, necessitating a multi-layered approach to mitigate risks effectively. The physical security of model files is critical, requiring stringent access controls and attack prevention solu

Julia Guse, Stefan Wundrack, Marius Eckert, Peter Richter

In this study the growth process of epitaxial graphene on SiC was investigated systematically. The transition from the initial buffer layer growth to the formation of the first monolayer graphene domains was investigated by various techniques: atomic force microscopy, low energy electron diffraction, low energy electron microscopy, Raman spectroscopy, scanni

Zhuo Cheng, Brandon Lucia

Advancements in nanosatellite technology lead to more Earth-observation satellites in low-Earth orbit. We explore using nanosatellite constellations to achieve low-latency detection for time-critical events, such as forest fires, oil spills, and floods. The detection latency comprises three parts: capture, compute and transmission. Previous solutions reduce

Dariusz C. Lis, William D. Langer, Jorge L. Pineda, Kahaan Gandhi

We extend the survey for organics in the southern hemisphere by observing two cores in the Chamaeleon complex using NASA's Deep Space Network 70-m antenna in Canberra, Australia, over the frequency range of 18 to 25 GHz. We surveyed the class 0 protostar Cha-MMS1 and the prestellar core Cha-C2, which represent two stages in the evolution of dense cores. We d

Guande Wu, Huan Song, Yawei Wang, Qiaojing Yan

Reasoning is increasingly crucial for various tasks. While chain-of-thought prompting enables large language models to leverage reasoning effectively, harnessing the reasoning capabilities of Vision-Language Models (VLMs) remains challenging. To solve this problem, we propose a novel self-distillation framework that enhances the reasoning capabilities of the

Yuchen Xiang, Zhaolu Liu, Monica Emili Garcia-Segura, Daniel Simon

Hyperspectral imaging is a powerful bioimaging tool which can uncover novel insights, thanks to its sensitivity to the intrinsic properties of materials. However, this enhanced contrast comes at the cost of system complexity, constrained by an inherent trade-off between spatial, spectral, and temporal resolution. To overcome this limitation, we present a sel

Quan Mai, Susan Gauch, Douglas Adams

We present Boolean-aware attention, a novel attention mechanism that dynamically adjusts token focus based on Boolean operators (e.g., and, or, not). Our model employs specialized Boolean experts, each tailored to amplify or suppress attention for operator-specific contexts. A predefined gating mechanism activates the corresponding experts based on the detec

Martin Kreuzer, Lorenzo Robbiano

Border basis schemes are open subschemes of the Hilbert scheme of $\mu$ points in an affine space $\mathbb{A}^n$. They have easily describable systems of generators of their vanishing ideals for a natural embedding into a large affine space $\mathbb{A}^{\mu\nu}$. Here we bring together several techniques for re-embedding affine schemes into lower dimensional

Marco Scialanga, Thibault Laugel, Vincent Grari, Marcin Detyniecki

As Large Langue Models have been shown to memorize real-world facts, the need to update this knowledge in a controlled and efficient manner arises. Designed with these constraints in mind, Knowledge Editing (KE) approaches propose to alter specific facts in pretrained models. However, they have been shown to suffer from several limitations, including their l

Nastaran Mansourian, Arash Mohammadi, M. Omair Ahmad, M. N. S. Swamy

Driver emotion recognition plays a crucial role in driver monitoring systems, enhancing human-autonomy interactions and the trustworthiness of Autonomous Driving (AD). Various physiological and behavioural modalities have been explored for this purpose, with Electrocardiogram (ECG) emerging as a standout choice for real-time emotion monitoring, particularly

Eris Rocha Walchek

We construct big generalized Heegner classes by interpolating $p$-adically the generalized Heegner classes associated to quaternionic modular forms along a Coleman (finite slope) family, following the approach introduced by Jetchev--Loeffler--Zerbes.

Alejandro Mata Ali

In this paper, we present a new method for calculating expectation values of operators that can be expressed as a linear combination of unitary (LCU) operators. This method allows to perform this calculation in a single quantum circuit measuring a single qubit, which speeds up the computation process. This method is general for any quantum algorithm and is o

Sam Bowyer, Laurence Aitchison, Desi R. Ivanova

Rigorous statistical evaluations of large language models (LLMs), including valid error bars and significance testing, are essential for meaningful and reliable performance assessment. Currently, when such statistical measures are reported, they typically rely on the Central Limit Theorem (CLT). In this position paper, we argue that while CLT-based methods f

Emmanuel Filiot, Pierre-Alain Reynier, Nathan Lhote

Regular transductions over finite words have linear input-to-output growth. This class of transductions enjoys many characterizations. Recently, regular transductions have been extended by Boja\'nczyk to polyregular transductions, which have polynomial growth, and are characterized by pebble transducers and MSO interpretations. Another class of interest is t

Gaylor Wafflard-Fernandez, Geoffroy Lesur

Recent studies indicate that circumstellar disks exhibit weak turbulence, with their dynamics and evolution being primarily influenced by magnetic winds. However, most numerical studies have focused on planet-disk interactions in turbulent disk models. We aim to explore how wind-driven accretion affects the orbital and eccentricity evolution of a Jovian plan

Linda Kanaan, Karine Amis, Frédéric Guilloud, Rémi Chauvat

Satellites receiving Automatic Identification System (AIS) packets in dense areas are particularly prone to AIS channel overload due to the extensive number of vessels. Thus a failure of detection might be caused by the collisions among AIS messages. To improve the detection capability, we propose to exploit the presence of the cyclic redundancy check (CRC)

Microsoft, :, Abdelrahman Abouelenin, Atabak Ashfaq

We introduce Phi-4-Mini and Phi-4-Multimodal, compact yet highly capable language and multimodal models. Phi-4-Mini is a 3.8-billion-parameter language model trained on high-quality web and synthetic data, significantly outperforming recent open-source models of similar size and matching the performance of models twice its size on math and coding tasks requi

Alberto Purpura, Sahil Wadhwa, Jesse Zymet, Akshay Gupta

The rapid growth of Large Language Models (LLMs) presents significant privacy, security, and ethical concerns. While much research has proposed methods for defending LLM systems against misuse by malicious actors, researchers have recently complemented these efforts with an offensive approach that involves red teaming, i.e., proactively attacking LLMs with t

Chandan Kumar Sheemar, Wali Ullah Khan, Marco di Renzo, Asad Mahmood

Reconfigurable holographic surfaces (RHS) have emerged as a transformative material technology, enabling dynamic control of electromagnetic waves to generate versatile holographic beam patterns. This paper addresses the problem of secrecy rate maximization for an RHS-assisted systems by joint designing the digital beamforming, artificial noise (AN), and the

Xiang-Lei Chen, Chao-Wei Tsai, Daniel Stern, Christopher D. Bochenek

The properties of host galaxies associated with Fast Radio Bursts (FRBs) provide critical information for inferring the progenitors and radiation mechanisms of these bursts. We report on the host galaxy of the repeating FRB 20190520B, a dwarf galaxy at the spectroscopic redshift $z=0.241$ with a stellar mass of $(6.2 \pm 0.8) \times 10^8 \ M_{\odot}$. The em

Wenhao Wang, Yi Yang

Text-to-video generative models convert textual prompts into dynamic visual content, offering wide-ranging applications in film production, gaming, and education. However, their real-world performance often falls short of user expectations. One key reason is that these models have not been trained on videos related to some topics users want to create. In thi

Stergios Koutsioumpas, Hasan Sayginel, Mark Webster, Dan E Browne

We introduce AutDEC, a fast and accurate decoder for quantum error-correcting codes with large automorphism groups. Our decoder employs a set of automorphisms of the quantum code and an ensemble of belief propagation (BP) decoders. Each BP decoder is given a syndrome which is transformed by one of the automorphisms, and is run in parallel. For quantum codes,

Mohammad Rafid Ul Islam, Prasad Tadepalli, Alan Fern

Missing values in multivariate time series data can harm machine learning performance and introduce bias. These gaps arise from sensor malfunctions, blackouts, and human error and are typically addressed by data imputation. Previous work has tackled the imputation of missing data in random, complete blackouts and forecasting scenarios. The current paper addr

A. Díaz, J. M. Alegre, I. I. Cuesta, E. Martínez-Pañeda

Hydrogen threatens the structural integrity of metals and thus predicting hydrogen-material interactions is key to unlocking the role of hydrogen in the energy transition. Quantifying the interplay between material deformation and hydrogen diffusion ahead of cracks and other stress concentrators is key to the prediction and prevention of hydrogen-assisted fa

Grace McKnight, Channyung Lee, Elif Ertekin

Monoclinic $\beta$-Ga$_2$O$_3$, a promising wide band gap semiconducting material, exhibits complex, anisotropic diffusional characteristics and mass transport behavior as a results of its low symmetry crystal structure. From first-principles calculations combined with master diffusion equations, we determine three-dimensional diffusion tensors for neutral (

Alex Aiken, David Jensen, Catherine Gill, William Gropp

The United States risks losing its global leadership in information technology research due to declining basic research funding, challenges in attracting talent, and tensions between research security and openness.

Vito Mengers, Oliver Brock

We introduce a novel gradient-based approach for solving sequential tasks by dynamically adjusting the underlying myopic potential field in response to feedback and the world's regularities. This adjustment implicitly considers subgoals encoded in these regularities, enabling the solution of long sequential tasks, as demonstrated by solving the traditional p

Andrew Harrison-Migochi, Raymond McCulloch

Let $\Lambda\subseteq\mathbb{R}^n$ be a lattice and let $Z\subseteq\mathbb{R}^{m+n}$ be a definable family in an o-minimal expansion of the real field, $\overline{\mathbb{R}}$. A result of Barroero and Widmer gives sharp estimates for the number of lattice points in the fibers $Z_T=\{x\in\mathbb{R}^n:(T,x)\in Z\}$. Here we give an effective version of this r

R. Alexander Glickfield

We discuss an extension to Voiculescu's formula for the quasicentral modulus of a tuple of commuting, self-adjoint operators with spectral measure absolutely continuous with respect to a generalized Hausdorff measure. These Hausdorff measures are defined by gauge functions which are not power functions and are supported on nonself-similar fractals.

Santiago Bou Betran, Alberta Longhini, Miguel Vasco, Yuchong Zhang

Recent progress in robotic manipulation has been fueled by large-scale datasets collected across diverse environments. Training robotic manipulation policies on these datasets is traditionally performed in a centralized manner, raising concerns regarding scalability, adaptability, and data privacy. While federated learning enables decentralized, privacy-pres

Zhe Gao, Jian Huang, Ting Li, Xueqin Wang

Multimodal learning has become a pivotal approach in developing robust learning models with applications spanning multimedia, robotics, large language models, and healthcare. The efficiency of multimodal systems is a critical concern, given the varying costs and resource demands of different modalities. This underscores the necessity for effective modality s

Samuel S. Sohn, Sten Knutsen, Karin Stromswold

Prosody plays a crucial role in speech perception, influencing both human understanding and automatic speech recognition (ASR) systems. Despite its importance, prosodic stress remains under-studied due to the challenge of efficiently analyzing it. This study explores fine-tuning OpenAI's Whisper large-v2 ASR model to recognize phrasal, lexical, and contrasti

José Medina, Amnir Hadachi, Paul Honeine, Abdelaziz Bensrhair

Deep neural networks (DNNs) have remarkably succeeded in various image processing tasks. However, their large size and computational complexity present significant challenges for deploying them in resource-constrained environments. This paper presents an innovative approach for integrating Mamba Architecture within a Progressive Knowledge Distillation (PKD)

Kornelia Nikiel, Sebastian J. Szybka

Halilsoy and Chandrasekhar cylindrical standing gravitational waves correspond to two different classes of solutions to the vacuum Einstein equations. Both families satisfy the definition of standing gravitational waves proposed by Stephani, but only the latter class fulfills the stricter definition introduced by Chandrasekhar. The aim of this research is to

Zitang Zhou, Ke Mei, Yu Lu, Tianyi Wang

This paper introduces HarmonySet, a comprehensive dataset designed to advance video-music understanding. HarmonySet consists of 48,328 diverse video-music pairs, annotated with detailed information on rhythmic synchronization, emotional alignment, thematic coherence, and cultural relevance. We propose a multi-step human-machine collaborative framework for ef

Hernando Gonzalez, Carlos Julio Arizmendi, Beatriz F. Giraldo

The issue of failed weaning is a critical concern in the intensive care unit (ICU) setting. This scenario occurs when a patient experiences difficulty maintaining spontaneous breathing and ensuring a patent airway within the first 48 hours after the withdrawal of mechanical ventilation. Approximately 20 of ICU patients experience this phenomenon, which has s

Ryo Ueda, Tatsuki Kuribayashi, Shunsuke Kando, Kentaro Inui

What is a neural model with minimum architectural complexity that exhibits reasonable language learning capability? To explore such a simple but sufficient neural language model, we revisit a basic reservoir computing (RC) model, Echo State Network (ESN), a restricted class of simple Recurrent Neural Networks. Our experiments showed that ESN with a large hid

Nikolaos Nakis, Niels Raunkjær Holm, Andreas Lyhne Fiehn, Morten Mørup

Low-dimensional embeddings are essential for machine learning tasks involving graphs, such as node classification, link prediction, community detection, network visualization, and network compression. Although recent studies have identified exact low-dimensional embeddings, the limits of the required embedding dimensions remain unclear. We presently prove th

Shishir Adhikari, Sourav Medya, Elena Zheleva

In causal inference, interference refers to the phenomenon in which the actions of peers in a network can influence an individual's outcome. Peer effect refers to the difference in counterfactual outcomes of an individual for different levels of peer exposure, the extent to which an individual is exposed to the treatments, actions, or behaviors of peers. Est

Nico Lorenz, Marc Christian Zimmermann

Let $q$ be a non-degenerate quadratic form defined on an $F$ vector space $V$ and $a \in F$. We consider the Cayley graph on $V$ with generating set $\{x \in V \mid q(x) = a\}$ and study its diameter and girth. In particular, if $F$ is a finite field, we calculate these invariants and the number of cycles of minimal length in these graphs.

Ryien Hosseini, Filippo Simini, Venkatram Vishwanath, Rebecca Willett

Deep generative models have recently achieved significant success in modeling graph data, including dynamic graphs, where topology and features evolve over time. However, unlike in vision and natural language domains, evaluating generative models for dynamic graphs is challenging due to the difficulty of visualizing their output, making quantitative metrics

Olaf Müller

We formulate the Hauptvermutung of Causal Set Theory in two mathematically well-defined but different ways one of which turns out to be wrong and the other one turns out to be true. A further result is that the Hauptvermutung is true if we replace finite by countable sets.

Kevin Burrage, Pamela M. Burrage, Justin N. Kreikemeyer, Adelinde M. Uhrmacher

In this paper, we adapt a two-species agent-based cancer model that describes the interaction between cancer cells and healthy cells on a uniform grid to include the interaction with a third species -- namely immune cells. We run six different scenarios to explore the competition between cancer and immune cells and the initial concentration of the immune cel

Aleksandra Nelson, Peter Wolynes, Evelyn Tang

Complex gene regulatory networks often display emergent simple behavior. Sometimes this simplicity can be traced to a nearly equivalent energy landscape, but not always. Here, we show how a topological theory for stochastic and biochemical networks can predict phase transitions between dynamical regimes, where the simplest landscape paradigm would fail. We d