March 2025 arXiv papers — page 153

Paolo Torrado, Joshua Levin, Markus Grotz, Joshua Smith

Warehouse robotic systems equipped with vacuum grippers must reliably grasp a diverse range of objects from densely packed shelves. However, these environments present significant challenges, including occlusions, diverse object orientations, stacked and obstructed items, and surfaces that are difficult to suction. We introduce \tetra, a novel vacuum-based g

Yong Li, Menglin Liu, Zhen Cui, Yi Ding

Despite the impressive performance of current vision-based facial action unit (AU) detection approaches, they are heavily susceptible to the variations across different domains and the cross-domain AU detection methods are under-explored. In response to this challenge, we propose a decoupled doubly contrastive adaptation (D$^2$CA) approach to learn a purifie

Zirui Gong, Yanjun Zhang, Leo Yu Zhang, Zhaoxi Zhang

Federated Ranking Learning (FRL) is a state-of-the-art FL framework that stands out for its communication efficiency and resilience to poisoning attacks. It diverges from the traditional FL framework in two ways: 1) it leverages discrete rankings instead of gradient updates, significantly reducing communication costs and limiting the potential space for mali

Ruhma Khan, Sumit Gulwani, Vu Le, Arjun Radhakrishna

Program synthesis from input-output examples, also called programming by example (PBE), has had tremendous impact on automating end-user tasks. Large language models (LLMs) have the ability to solve PBE tasks by generating code in different target languages, but they can fail unpredictably. To recover for failure, most approaches, such as self-reflection, us

Maarten Derickx, Wontae Hwang, Daeyeol Jeon, Petar Orlić

We determine all modular curves $X_0(N)$ with density degree $5$, i.e. all curves $X_0(N)$ with infinitely many points of degree $5$ and only finitely many points of degree $d\leq4$. As a consequence, the problem of determining all curves $X_0(N)$ with infinitely many points of degree $5$ remains open for only $30$ levels $N$.

Yong Li, Yi Ren, Xuesong Niu, Yi Ding

Facial Action Units (AUs) are essential for conveying psychological states and emotional expressions. While automatic AU detection systems leveraging deep learning have progressed, they often overfit to specific datasets and individual features, limiting their cross-domain applicability. To overcome these limitations, we propose a doubly adaptive dropout app

Idris Zakariyya, Ferheen Ayaz, Mounia Kharbouche-Harrari, Jeremy Singer

Reducing the memory footprint of Machine Learning (ML) models, especially Deep Neural Networks (DNNs), is imperative to facilitate their deployment on resource-constrained edge devices. However, a notable drawback of DNN models lies in their susceptibility to adversarial attacks, wherein minor input perturbations can deceive them. A primary challenge revolve

Darmindra Arumugam

Out-of-equilibrium Rydberg gases exhibit emergent many-body phases due to mode competition. Sustained limit cycle oscillations (OSC) emerge when driven by B-fields at room-temperature, forming robust Rydberg dissipative time crystals (DTC). These driven-dissipative Rydberg DTC have recently been shown to develop an effective transition centered at the OSC fr

Sara LaPlante, Sofia Triantafillou, Emilija Perković

Covariate adjustment is one method of causal effect identification in non-experimental settings. Prior research provides routes for finding appropriate adjustments sets, but much of this research assumes knowledge of the underlying causal graph. In this paper, we present two routes for finding adjustment sets that do not require knowledge of a graph -- and i

Julian Rene Cuellar Buritica, Vu Dinh, Manjula Burri, Julie Roelandts

Previous studies on echocardiogram segmentation are focused on the left ventricle in parasternal long-axis views. In this study, deep-learning models were evaluated on the segmentation of the ventricles in parasternal short-axis echocardiograms (PSAX-echo). Segmentation of the ventricles in complementary echocardiogram views will allow the computation of imp

Bo Lin, Shangwen Wang, Yihao Qin, Liqian Chen

As software grows in complexity to accommodate diverse features and platforms, software bloating has emerged as a significant challenge, adversely affecting performance and security. However, existing approaches inadequately address the dual objectives of debloating: maintaining functionality by preserving essential features and enhancing security by reducin

Mayank Kabra, Rakesh Nadig, Harshita Gupta, Rahul Bera

Homomorphic encryption (HE) allows secure computation on encrypted data without revealing the original data, providing significant benefits for privacy-sensitive applications. Many cloud computing applications (e.g., DNA read mapping, biometric matching, web search) use exact string matching as a key operation. However, prior string matching algorithms that

Adrien Gregorj, Zeynep Yücel, Francesco Zanlugo, Takayuki Kanda

Gait synchronization in pedestrians is influenced by biomechanical, environmental, and cognitive factors. Studying gait in ecological settings provides insights often missed in controlled experiments. This study tackles the challenges of assessing gait coordination in real-world interactions using a dataset of uninstructed pedestrian trajectories recorded in

Aparna Sasidharan, Xian-He, Jay Lofstead, Scott Klasky

This work describes the design, implementation and performance analysis of a distributed two-tiered storage software. The first tier functions as a distributed software cache implemented using solid-state devices~(NVMes) and the second tier consists of multiple hard disks~(HDDs). We describe an online learning algorithm that manages data movement between the

Khawar Islam, Naveed Akhtar

Generative diffusion models offer a natural choice for data augmentation when training complex vision models. However, ensuring reliability of their generative content as augmentation samples remains an open challenge. Despite a number of techniques utilizing generative images to strengthen model training, it remains unclear how to utilize the combination of

Mouly Dewan, Jiqun Liu, Chirag Shah

In the information retrieval (IR) domain, evaluation plays a crucial role in optimizing search experiences and supporting diverse user intents. In the recent LLM era, research has been conducted to automate document relevance labels, as these labels have traditionally been assigned by crowd-sourced workers - a process that is both time and consuming and cost

Rongxia Tang, Henry Liu, Yueping Shi, Chenming Wang

An edge-coloured path is rainbow if all of its edges have distinct colours. Let $G$ be a connected graph. The rainbow connection number of $G$, denoted by $rc(G)$, is the minimum number of colours in an edge-colouring of $G$ such that, any two vertices are connected by a rainbow path. The strong rainbow connection number of $G$, denoted by $src(G)$, is the m

Sahand Seifnashri, Wilbur Shirley

We clarify the lore that anomaly-free symmetries are either on-site or can be transformed into on-site symmetries. We prove that any finite, internal, anomaly-free symmetry in a 1+1d lattice Hamiltonian system can be disentangled into an on-site symmetry by introducing ancillas and applying conjugation via a finite-depth quantum circuit. We provide an explic

Louis Garénaux, Bastian Hilder

We study convective stability of a two-front superposition in a reaction-diffusion system. Due to the instability of the connecting equilibrium, long-range semi-strong interaction is expected between the two waves. When restricting to the linear dynamic, we indeed identify that convective stability of superposed waves occurs for fewer propagation speeds than

Fynn Bachmann, Daan van der Weijden, Lucien Heitz, Cristina Sarasua

Adaptive questionnaires dynamically select the next question for a survey participant based on their previous answers. Due to digitalisation, they have become a viable alternative to traditional surveys in application areas such as political science. One limitation, however, is their dependency on data to train the model for question selection. Often, such t

Sagar V. Soni, A. C. Khunt, Farook Rahaman, A. H. Hasmani

In this paper, we study the energy conditions of charged traversable wormholes in the framework of $f(R, \mathscr{L}_m)$ modified gravity. In the first case, we derive the shape functions (SFs) for two different choices of the charge function $\mathcal{E}^2$ by considering the Exponential Spheroid (ES) model and analyze the null energy condition (NEC). In th

Josh Jacobson, Michael Bertolacci, Andrew Zammit-Mangion, Andrew Schuh

Contributions from photosynthesis and other natural components of the carbon cycle present the largest uncertainties in our understanding of carbon dioxide (CO$_2$) sources and sinks. While the global spatiotemporal distribution of the net flux (the sum of all contributions) can be inferred from atmospheric CO$_2$ concentrations through flux inversion, attri

Olga Meyer, Marvin Boell, Christoph Legat

The rapid development of Industry 4.0 technologies requires robust and comprehensive standardization to ensure interoperability, safety and efficiency in the Industry of the Future. This paper examines the fundamental role and functionality of standardization, with a particular focus on its importance in Europe's regulatory framework. Based on this, sele

Qiguang Miao, Kang Liu, Zhuoqi Ma, Yunan Li

Radiology reports are crucial for planning treatment strategies and facilitating effective doctor-patient communication. However, the manual creation of these reports places a significant burden on radiologists. While automatic radiology report generation presents a promising solution, existing methods often rely on single-view radiographs, which constrain d

Yuguo Shao, Yong-Chang Li, Fuchuan Wei, Hao Zhan

Quantum error correction is essential for achieving fault-tolerant quantum computation. However, most typical quantum error-correcting codes are designed for generic noise models, which may fail to accurately capture the intricate noise characteristics of real quantum devices, limiting their practical performance. This work introduces a learning-based framew

Benjamin Hackl, Stephan Wagner

Making use of a newly developed package in the computer mathematics system SageMath, we show how to perform a full asymptotic analysis of certain types of sums that occur frequently in combinatorics, including explicit error bounds. We present two applications of the general approach to illustrate its use: the first concerns a classical problem due to Ramanu

Sourav Sanyal, Amogh Joshi, Adarsh Kosta, Kaushik Roy

Neuromorphic vision, inspired by biological neural systems, has recently gained significant attention for its potential in enhancing robotic autonomy. This paper presents a systematic exploration of a proposed Neuromorphic Navigation framework that uses event-based neuromorphic vision to enable efficient, real-time navigation in robotic systems. We discuss t

Yu Wang, Kamalika Das, Xiang Gao, Wendi Cui

In tasks like summarization and open-book question answering (QA), Large Language Models (LLMs) often encounter "contextual hallucination", where they produce irrelevant or incorrect responses despite having access to accurate source information. This typically occurs because these models tend to prioritize self-generated content over the input context, caus

Tomasz Rybotycki, Manish K. Gupta, Piotr Gawron

The emergence of Big Data changed how we approach information systems engineering. Nowadays, when we can use remote sensing techniques for Big Data acquisition, the issues such data introduce are as important as ever. One of those concerns is the processing of the data. Classical methods often fail to address that problem or are incapable of processing the d

GeonU Kim, Kim Youwang, Lee Hyoseok, Tae-Hyun Oh

We present FPGS, a feed-forward photorealistic style transfer method of large-scale radiance fields represented by Gaussian Splatting. FPGS, stylizes large-scale 3D scenes with arbitrary, multiple style reference images without additional optimization while preserving multi-view consistency and real-time rendering speed of 3D Gaussians. Prior arts required t

William Chang, Yuanhao Lu

Single-player contextual bandits are a well-studied problem in reinforcement learning that has seen applications in various fields such as advertising, healthcare, and finance. In light of the recent work on \emph{information asymmetric} bandits \cite{chang2022online, chang2023online}, we propose a novel multiplayer information asymmetric contextual bandit f

Gexin Huang, Zhangsihao Yang, Yalin Wang, Guido Gerig

Structural and appearance changes in brain imaging over time are crucial indicators of neurodevelopment and neurodegeneration. The rapid advancement of large-scale generative models provides a promising backbone for modeling these complex global and local changes in brain images, such as transforming the age of a source image to a target age. However, curren

Joao D. S. Marques, Arlindo L. Oliveira

Pulmonary embolism is a leading cause of out of hospital cardiac arrest that requires fast diagnosis. While computed tomography pulmonary angiography is the standard diagnostic tool, it is not always accessible. Electrocardiography is an essential tool for diagnosing multiple cardiac anomalies, as it is affordable, fast and available in many settings. Howeve

Daniel Lawther, Marianne Vestergaard, Sandra Raimundo, Xiaohui Fan

Mrk 590 is a Changing Look AGN currently in an unusual repeat X-ray and UV flaring state. Here, we report on deep X-ray observations with XMM-Newton, NuSTAR, and NICER, obtained at a range of X-ray flux levels. We detect a prominent soft excess below 2 keV; its flux is tightly correlated with that of both the X-ray and UV continuum, and it persists at the lo

Valeria Ambrosio, Jason Miller, Yizheng Yuan

We define multichordal CLE$_\kappa$ for $\kappa \in (4,8)$ as the conditional law of the remainder of a partially explored CLE$_\kappa$. The strands of a multichordal CLE$_\kappa$ have a random link pattern, and their law conditionally on the linking pattern is a (global) multichordal SLE$_\kappa$. The multichordal CLE$_\kappa$ are the conjectural scaling li

Francesco Marchiori, Mauro Conti

Advancements in battery technology have accelerated the adoption of Electric Vehicles (EVs) due to their environmental benefits. However, their growing sophistication introduces security and privacy challenges. Often seen as mere operational data, battery consumption patterns can unintentionally reveal critical information exploitable for malicious purposes.

Nadir Matringe, Vincent Sécherre, Shaun Stevens, Miyu Suzuki

For a non-Archimedean locally compact field $F$ of odd residue characteristic and characteristic $0$, we prove a conjecture of D. Prasad predicting that, for an integer $n \geq 1$ and a non-split quaternionic $F$-algebra $D$, a discrete series representation of ${\rm GL}_n(D)$ has a symplectic period if and only if it is cuspidal and its Jacquet--Langlands t

Sewade Ogun, Vincent Colotte, Emmanuel Vincent

Augmenting the training data of automatic speech recognition (ASR) systems with synthetic data generated by text-to-speech (TTS) or voice conversion (VC) has gained popularity in recent years. Several works have demonstrated improvements in ASR performance using this augmentation approach. However, because of the lower diversity of synthetic speech, naively

Yifan Tang, Mostafa Rahmani Dehaghani, G. Gary Wang

Digital twin (DT) has emerged as a powerful tool to facilitate monitoring, control, and other decision-making tasks in real-world engineering systems. Online update methods have been proposed to update DT models. Considering the degradation behavior in the system lifecycle, these methods fail to enable DT models to predict the system responses affected by th

Abdolvahhab Rostamijavanani, Shanwu Li, Yongchao Yang

To fully understand, analyze, and determine the behavior of dynamical systems, it is crucial to identify their intrinsic modal coordinates. In nonlinear dynamical systems, this task is challenging as the modal transformation based on the superposition principle that works well for linear systems is no longer applicable. To understand the nonlinear dynamics o

Claudio Casentini, Francesco Verrecchia, Marco Tavani, Maura Pilia

Fast Radio Bursts are millisecond-duration bursts originating from distant sources. They are classified into two categories: non-repeating FRBs, which manifest as singular events, and repeating FRBs, which emit multiple bursts over time In this work, we report a search for X- and Gamma-ray counterparts to a selected sample of R-FRBs using data from the Agile

Rujia Yang, Geng Chen, Chuan Wen, Yang Gao

Following its success in natural language processing and computer vision, foundation models that are pre-trained on large-scale multi-task datasets have also shown great potential in robotics. However, most existing robot foundation models rely solely on 2D image observations, ignoring 3D geometric information, which is essential for robots to perceive and r

Yudhishthira Kundu, Manroop Kaur, Tripty Wig, Kriti Kumar

Cerebras' wafer-scale engine (WSE) technology merges multiple dies on a single wafer. It addresses the challenges of memory bandwidth, latency, and scalability, making it suitable for artificial intelligence. This work evaluates the WSE-3 architecture and compares it with leading GPU-based AI accelerators, notably Nvidia's H100 and B200. The work highlights

Amol Aggarwal, Patrick Lopatto

We pinpoint the spectral decomposition for the Anderson tight-binding model with an unbounded random potential on the Bethe lattice of sufficiently large degree. We prove that there exist a finite number of mobility edges separating intervals of pure-point spectrum from intervals of absolutely continuous spectrum, confirming a prediction of Abou-Chacra, Thou

Ivan Landjev, Konstantin Vorobev

Let $C$ be a binary code of length $n$ with distances $0<d_1<\cdots<d_s\le n$. In this note we prove a general upper bound on the size of $C$ without any restriction on the distances $d_i$. The bound is asymptotically optimal.

Domingo Gallegos, Carlos Málaga

An application of the Newton-Cartan framework to the study of membranes is presented. Specifically, for membranes of co-dimension one in hydrostatic equilibrium embedded in a flat ambient Newton-Cartan spacetime. For such membranes, the corresponding equilibrium partition function at second order in the hydrodynamic derivative expansion is shown. Equilibrium

Andrew Osterhout, Ganesh Gopalakrishnan

It is often difficult to write code that you can ensure will be executed in the right order when programing for parallel compute tasks. Due to the way that today's parallel compute hardware, primarily Graphical Processing Units (GPUs), allows you to write code. It is easy to write code that may result in one thread reading or modifying data before it should,

Ryota Takamido, Jun Ota, Hiroki Nakamoto

This study developed a new explainable artificial intelligence algorithm called PassAI, which classifies successful or failed passes in a soccer game and explains its rationale using both tracking and passer's seasonal stats information. This study aimed to address two primary challenges faced by artificial intelligence and machine learning algorithms in the

Henry F. Legg

We consider the 'parity readout' of a (topological) superconductor claimed in Nature 638, 651-655 (2025). A prerequisite for this claim is the existence of a superconducting gap in the nanowire device. However, to determine the presence of a gap, Nature 638, 651-655 (2025) relied on the so-called topological gap protocol (TGP). Here, we show that the TGP can

D. L. C. Agapito Fernando, A. Bañón Navarro, D. Carralero, A. Alonso

This paper presents the validation of the $\texttt{GENE-KNOSOS-Tango}$ framework for recovering both the steady-state plasma profiles in the considered radial domain and selected turbulence trends in a stellarator. This framework couples the gyrokinetic turbulence code $\texttt{GENE}$, the neoclassical transport code $\texttt{KNOSOS}$, and the transport solv

Runlong Zhou, Maryam Fazel, Simon S. Du

Reinforcement learning from human feedback (RLHF) has become essential for improving language model capabilities, but traditional approaches rely on the assumption that human preferences follow a transitive Bradley-Terry model. This assumption fails to capture the non-transitive nature of populational human preferences. Nash learning from human feedback (NLH

Afaak Lakouader, Abdelilah Lahmar, Spela Kunej, Daoud Mezzane

Ba0.85Ca0.15Zr0.1Ti0.9O3/La0.8Sr0.2MnO3/Ba0.85Ca0.15Zr0.1Ti0.9O3 (BCZT/LSMO/BCZT) sandwich films were elaborated using the sol-gel spin coating process. The dielectric properties displayed excellent thermal stability with the temperature coefficient of capacitance, TCC, remaining within 10% between -50 C and 300 C. The high energy storage density, Wrec, of 1

B. Magacho

Coherent structures (CS) are known to be part of the foundations of turbulent flow dynamics. For a long time, their appearance was believed to be chaotic and unorganized. However, it has been demonstrated through numerical simulations and experiments that a high degree of organization of CS could be attributed to the constitution of a turbulent state. Unders

Anzhe Cheng, Chenzhong Yin, Yu Chang, Heng Ping

Low-resolution image segmentation is crucial in real-world applications such as robotics, augmented reality, and large-scale scene understanding, where high-resolution data is often unavailable due to computational constraints. To address this challenge, we propose MaskAttn-UNet, a novel segmentation framework that enhances the traditional U-Net architecture

Jorge Luiz dos Santos Canuto, Linnyer Beatrys Ruiz Aylon, Rodrigo Clemente Thom de Souza

Due to their effective performance, Convolutional Neural Network (CNN) and Vision Transformer (ViT) architectures have become the standard for solving computer vision tasks. Such architectures require large data sets and rely on convolution and self-attention operations. In 2021, MLP-Mixer emerged, an architecture that relies only on Multilayer Perceptron (M

Kobra Rabiei, Jeffrey R. Petrella, Suzanne Lenhart, Chun Liu

Alzheimer's disease (AD) is driven by the accumulation of amyloid-beta (Abeta) proteins in the brain, leading to memory loss and cognitive decline. While monoclonal antibodies targeting Abetahave been approved, optimizing their use to maximize benefits while minimizing side effects remains a challenge. This study develops a mathematical model to describe Abe

Mohammad Farzanullah, Han Zhang, Akram Bin Sediq, Ali Afana

Sixth generation (6G) wireless technology is anticipated to introduce Integrated Sensing and Communication (ISAC) as a transformative paradigm. ISAC unifies wireless communication and RADAR or other forms of sensing to optimize spectral and hardware resources. This paper presents a pioneering framework that leverages ISAC sensing data to enhance beam selecti

Luca Pennati, Måns I. Andersson, Klaus Steiniger, Rene Widera

This paper presents the design, implementation, and performance analysis of a parallel and GPU-accelerated Poisson solver based on the Preconditioned Bi-Conjugate Gradient Stabilized (Bi-CGSTAB) method. The implementation utilizes the MPI standard for distributed-memory parallelism, while on-node computation is handled using the alpaka framework: this ensure

Zilong Deng, Simon Khan, Shaofeng Zou

In this work, we study the sample complexity problem of risk-sensitive Reinforcement Learning (RL) with a generative model, where we aim to maximize the Conditional Value at Risk (CVaR) with risk tolerance level $\tau$ at each step, a criterion we refer to as Iterated CVaR. We first build a connection between Iterated CVaR RL and $(s, a)$-rectangular distrib

Zhangyu Jin, Andrew Feng, Ankur Chemburkar, Celso M. De Melo

We present PromptGAR, a novel framework for Group Activity Recognition (GAR) that offering both input flexibility and high recognition accuracy. The existing approaches suffer from limited real-world applicability due to their reliance on full prompt annotations, fixed number of frames and instances, and the lack of actor consistency. To bridge the gap, we p

Hongming Li, Yizirui Fang, Shan Zhang, Seiyon M. Lee

Integrating Large Language Models (LLMs) in educational technology presents unprecedented opportunities to improve instructional design (ID), yet existing approaches often prioritize automation over pedagogical rigor and human agency. This paper introduces ARCHED (AI for Responsible, Collaborative, Human-centered Education Instructional Design), a structured

Tianxiang Lin, Mohamad Qadri, Kevin Zhang, Adithya Pediredla

We consider the problem of optimizing neural implicit surfaces for 3D reconstruction using acoustic images collected with drifting sensor poses. The accuracy of current state-of-the-art 3D acoustic modeling algorithms is highly dependent on accurate pose estimation; small errors in sensor pose can lead to severe reconstruction artifacts. In this paper, we pr

Hrishikesh Viswanath, Md Ashiqur Rahman, Chi Lin, Damon Conover

Accurate and efficient 3D mapping of large-scale outdoor environments from LiDAR measurements is a fundamental challenge in robotics, particularly towards ensuring smooth and artifact-free surface reconstructions. Although the state-of-the-art methods focus on memory-efficient neural representations for high-fidelity surface generation, they often fail to pr

Dibri Nsofor, Ben Greenman

Gradually-typed languages feature a dynamic type that supports implicit coercions, greatly weakening the type system but making types easier to adopt. Understanding how developers use this dynamic type is a critical question for the design of useful and usable type systems. This paper reports on an in-progress corpus study of the dynamic type in Python, targ

Alessandro Scagliotti, Federico Scagliotti, Laura Deborah Locati, Federico Sottotetti

In this paper, we explore the application of ensemble optimal control to derive enhanced strategies for pharmacological cancer treatment, and we tackle the problem of the long-term management of the disease, i.e., when the complete eradication of the tumor is not achievable. In particular, we focus on moving beyond the classical clinical approach of giving t

Teran Bukenberger, Brent Davis

Eye tracking has been found to be useful in various tasks including diagnostic and screening tools. However, traditional eye trackers had a complicated setup and operated at a higher frequency to measure eye movements. The use of more commonly available eye trackers such as those in head-mounted virtual reality (VR) headsets greatly expands the utility of th

Samuele Anni, Gaetan Bisson, Annamaria Iezzi, Elisa Lorenzo García

We study endomorphism rings of principally polarized abelian surfaces over finite fields from a computational viewpoint with a focus on exhaustiveness. In particular, we address the cases of non-ordinary and non-simple varieties. For each possible surface type, we survey known results and, whenever possible, provide improvements and missing results.

Laureano Gonzalez-Vega, Jorge Caravantes, Gema M. Diaz-Toca, Mario Fioravanti

This article introduces efficient and user-friendly tools for analyzing the intersection curve between a ringed torus and an irreducible quadric surface. Without loose of generality, it is assumed that the torus is centered at the origin, and its axis of revolution coincides with the $z$-axis. The paper primarily focuses on examining the curve's projection o

Anand Menon, Samit S Miftah, Shamik Kundu, Souvik Kundu

Hardware verification is crucial in modern SoC design, consuming around 70% of development time. SystemVerilog assertions ensure correct functionality. However, existing industrial practices rely on manual efforts for assertion generation, which becomes increasingly untenable as hardware systems become complex. Recent research shows that Large Language Model

Erol Barut, Viktor L. Ginzburg

We continue investigating the connection between the dynamics of a Hamiltonian system and the barcode growth of the associated Floer or symplectic homology persistence module, focusing now on completely integrable systems. We show that for convex/concave or real analytic toric domains and convex/concave or real analytic completely integrable Hamiltonians on

Hoomaan Maskan, Yikun Hou, Suvrit Sra, Alp Yurtsever

We introduce a new projection-free (Frank-Wolfe) method for optimizing structured nonconvex functions that are expressed as a difference of two convex functions. This problem class subsumes smooth nonconvex minimization, positioning our method as a promising alternative to the classical Frank-Wolfe algorithm. DC decompositions are not unique; by carefully se

Kawon Han, Kaitao Meng, Christos Masouros

A distributed integrated sensing and communication (D-ISAC) system offers significant cooperative gains for both sensing and communication performance. These gains, however, can only be fully realized when the distributed nodes are perfectly synchronized, which is a challenge that remains largely unaddressed in current ISAC research. In this paper, we propos

Bilgehan Sel, Dingcheng Li, Phillip Wallis, Vaishakh Keshava

Large language models (LLMs) have demonstrated remarkable capabilities across various tasks, but ensuring their safety and alignment with human values remains crucial. Current safety alignment methods, such as supervised fine-tuning and reinforcement learning-based approaches, can exhibit vulnerabilities to adversarial attacks and often result in shallow saf

Ankur Singha, Elia Cellini, Kim A. Nicoli, Karl Jansen

Investigating critical phenomena or phase transitions is of high interest in physics and chemistry, for which Monte Carlo (MC) simulations, a crucial tool for numerically analyzing macroscopic properties of given systems, are often hindered by an emerging divergence of correlation length -- known as scale invariance at criticality (SIC) in the renormalizatio

Brandon Runnels, Vinamra Agrawal, Maycon Meier

Alamo is a high-performance scientific code that uses block-structured adaptive mesh refinement to solve such problems as: the ignition and burn of solid rocket propellant, plasticity, damage and fracture in materials undergoing loading, and the interaction of compressible flow with eroding solid materials. Alamo is powered by AMReX, and provides a set of un

Yue Sun, Ryan Weightman, Anye Shi, Timur Dogan

Urbanization is rapidly increasing, with urban populations expected to grow significantly by 2050, particularly in developing regions. This expansion brings challenges related to chronic stresses and acute shocks, such as the COVID-19 pandemic, which has underscored the critical role of urban form in a city's capacity to manage public health crises. Despite

Dandan Zhao, Hongpeng Yin, Jintang Bian, Han Zhou

Traditional fault diagnosis methods struggle to handle fault data, with complex data characteristics such as high dimensions and large noise. Deep learning is a promising solution, which typically works well only when labeled fault data are available. To address these problems, a robust unsupervised fault diagnosis using machine learning is proposed in this

Matthieu Terris, Samuel Hurault, Maxime Song, Julian Tachella

Most existing learning-based methods for solving imaging inverse problems can be roughly divided into two classes: iterative algorithms, such as plug-and-play and diffusion methods leveraging pretrained denoisers, and unrolled architectures that are trained end-to-end for specific imaging problems. Iterative methods in the first class are computationally cos

Gengrui Zhang, Shiquan Zhang, Michail Bachras, Yuqiu Zhang

Conventional consensus algorithms, such as Paxos and Raft, encounter inefficiencies when applied to large-scale distributed systems due to the requirement of waiting for replies from a majority of nodes. To address these challenges, we propose Cabinet, a novel consensus algorithm that introduces dynamically weighted consensus, allocating distinct weights to

Georges Sfeir, Filipe Rodrigues, Ravi Seshadri, Carlos Lima Azevedo

This study evaluates path sets generation for route choice models in multimodal public transportation networks, using both conventional (network algorithms) and empirical (smart card data driven) methods. While the empirical approach can present limitations with a short observation period, it improves substantially with more data, offering a computational ef

Lijie Ding, Chi-Huan Tung, Bobby G. Sumpter, Wei-Ren Chen

We present a deep learning approach for analyzing two-dimensional scattering data of semiflexible polymers under external forces. In our framework, scattering functions are compressed into a three-dimensional latent space using a Variational Autoencoder (VAE), and two converter networks establish a bidirectional mapping between the polymer parameters (bendin

Ardalan Kahak, Moustafa Sayed Ahmed, Nahid Kalantaryardebily, Hrishikesh Kulkarni

Morphing surfaces provide a versatile tool to advance the functionalities of high-performance aircraft, soft robots, biomedical devices, and human-machine interfaces. However, achieving precise shape transformation and mechanical property control remains challenging due to nonlinearity, design constraints, and the difficulty of coordinating multiple constitu

Sara C. Billey, Stark Ryan

The Bruhat order on permutations arises out of the study of Schubert varieties in Grassmannians and flag varieties, which have been important for over 100 years. The purpose of this paper is to study variations on this theme related to subvarieties of the spanning line configurations $X_{n,k}$ as defined by Pawlowski and Rhoades. These subvarieties are index

Miguel A. Cardona, Diego A. Mejía, Andrés F. Uribe-Zapata

In this article, we conduct a detailed study of \emph{finitely additive measures} (fams) in the context of Boolean algebras, focusing on three specific topics: freeness and approximation, existence and extension criteria, and integration theory. In the first topic, we present a classification of \emph{free} finitely additive measures, that is, those for whic

Seyed Sina Ziaee, Farhad Maleki, Katie Ovens

Accurate and reliable tumor segmentation is essential in medical imaging analysis for improving diagnosis, treatment planning, and monitoring. However, existing segmentation models often lack robust mechanisms for quantifying the uncertainty associated with their predictions, which is essential for informed clinical decision-making. This study presents a nov

Francesco Baldassarri

We work in the category $\mathcal{CLM}^u_k$ of [5] of separated complete bounded $k$-linearly topologized modules over a complete linearly topologized ring $k$ and discuss duality on certain exact subcategories. We study topological and uniform structures on locally compact paracompact $0$-dimensional topological spaces $X$, named $td$-spaces in [11] and [17

Itay Yona, Ilia Shumailov, Jamie Hayes, Federico Barbero

Large Language Models (LLMs), despite their impressive capabilities, often fail to accurately repeat a single word when prompted to, and instead output unrelated text. This unexplained failure mode represents a vulnerability, allowing even end-users to diverge models away from their intended behavior. We aim to explain the causes for this phenomenon and link

Rodgers Iradukunda, Francisco Rowe, Elisabetta Pietrostefani

Nearly 110 million people are forcibly displaced people worldwide. However, estimating the scale and patterns of internally displaced persons in real time, and developing appropriate policy responses, remain hindered by traditional data streams. They are infrequently updated, costly and slow. Mobile phone location data can overcome these limitations, but onl

Stefano Riva, Andrea Missaglia, Carolina Introini, J. Nathan Kutz

The Shallow Recurrent Decoder networks are a novel paradigm recently introduced for state estimation, combining sparse observations with high-dimensional model data. This architecture features important advantages compared to standard data-driven methods including: the ability to use only three sensors (even randomly selected) for reconstructing the entire d

Xiwen Chen, Wenhui Zhu, Peijie Qiu, Hao Wang

Vision-language models (VLMs) such as CLIP demonstrate strong performance but struggle when adapted to downstream tasks. Prompt learning has emerged as an efficient and effective strategy to adapt VLMs while preserving their pre-trained knowledge. However, existing methods still lead to overfitting and degrade zero-shot generalization. To address this challe

Ing-Guey Jiang, Li-Chin Yeh, Billy Edwards, Ming Yang

Sub-Neptunes have been found to be one of the most common types of exoplanets, yet their physical parameters and properties are poorly determined and in need of further investigation. In order to improve the mass measurement and parameter determination of two sub-Neptunes, K2-266 d and K2-266 e, we present new transit observations obtained with CHaracterisin

Stefano Riva, Carolina Introini, J. Nathan Kutz, Antonio Cammi

The recent developments in data-driven methods have paved the way to new methodologies to provide accurate state reconstruction of engineering systems; nuclear reactors represent particularly challenging applications for this task due to the complexity of the strongly coupled physics involved and the extremely harsh and hostile environments, especially for n

CMS Collaboration

Incoherent J/$\psi$ photoproduction in heavy ion ultraperipheral collisions (UPCs) provides a sensitive probe of localized, fluctuating gluonic structures within heavy nuclei. This study reports the first measurement of the photon-nucleon center-of-mass energy ($W_{\gamma\mathrm{N}}$) dependence of this process in PbPb UPCs at a nucleon-nucleon center-of-mas

Forough Fazeliasl, Michael Minyi Zhang, Bei Jiang, Linglong Kong

Mutual Information (MI) is a crucial measure for capturing dependencies between variables, but exact computation is challenging in high dimensions with intractable likelihoods, impacting accuracy and robustness. One idea is to use an auxiliary neural network to train an MI estimator; however, methods based on the empirical distribution function (EDF) can int

Silvia Neri, Walter Metzner, Dirk Manske

Time-reversal symmetry breaking (TRSB) superconductors show a rich collective mode spectrum. In general, collective excitations in superconductors can provide crucial information on the symmetry of the broken phase, in particular, serving as a fingerprint for determining the groundstate gap symmetry. In this work, we consider several even parity two-dimensio

Charudatta Phatak, John Fullerton, Hanu Arava

Imaging the magnetic domain wall behavior in patterned thin films under external stimuli can enable understanding the underlying energy landscape as well as the role of local microstructure and defects. We present an automated workflow for in-situ Lorentz transmission electron microscopy to image magnetic domain walls at the nanometer length scale and at a t

María Chara, Ricardo Podestá, Luciane Quoos, Ricardo Toledano

We present a simple method to establish the existence of asymptotically good sequences of iso-dual AG-codes. A key advantage of our approach, beyond its simplicity, is its flexibility, allowing it to be applied to a wide range of towers of function fields. As a result, we present a novel example of an asymptotically good sequence of iso-dual AG-codes over a

Miguel Moreira

This paper concerns the intersection numbers of tautological classes on moduli spaces of parabolic bundles on a smooth projective curve. We show that such intersection numbers are completely determined by wall-crossing formulas, Hecke isomorphisms, and flag bundle structures and resulting Weyl symmetry. As applications of these ideas, we prove the Newstead--

Geneviève Bélanger, Sreemanti Chakraborti, Cédric Delaunay, Margaux Jomain

Velocity-independent (s-wave) annihilation of thermal Dark Matter is ruled out by CMB data for masses below 10GeV, effectively ruling out the possibility of indirectly detecting it in this mass range. We demonstrate in a model-independent framework that Breit-Wigner effects from very narrow resonances can circumvent CMB constraints, thereby reviving the pote

B. Lemkalli, K. K. Dudek, M. Kadic, Q. Ji

We investigate a composite elastic meta-slab with exceptional transmission properties, particularly the presence of a W-shaped bandgap. A comprehensive study, utilizing experimental measurements, the finite element method, and an analytical approach, identifies this specific bandgap. The meta-slab design involves cutting an array of composite materials arran