March 2025 arXiv papers — page 12

J. V. S. Souza, C. B. Vieira, G. D. C. Cavalcanti, R. M. O. Cruz

In recent years, the rise of cyber threats has emphasized the need for robust malware detection systems, especially on mobile devices. Malware, which targets vulnerabilities in devices and user data, represents a substantial security risk. A significant challenge in malware detection is the imbalance in datasets, where most applications are benign, with only

John R. Kitchin

Machine learning and automation are transforming scientific research, yet the implementation of self-driving laboratories (SDLs) remains costly and complex, and it remains difficult to learn how to use these facilities. To address this, we introduce Claude-Light, a lightweight, remotely accessible instrument designed for prototyping automation algorithms and

Susie Lu, John Urschel, Ji Liu

In a paper by Nishikawa and Motter, a quantity called the normalized spread of the Laplacian eigenvalues is used to measure the synchronizability of certain network dynamics. Through simulations, and without theoretical validation, it is conjectured that among all simple directed graphs with a fixed number of vertices and arcs, the optimal value of this quan

Arkaprava Roy, Anindya Roy, Subhashis Ghosal

In multivariate time series analysis, understanding the underlying causal relationships among variables is often of interest for various applications. Directed acyclic graphs (DAGs) provide a powerful framework for representing causal dependencies. This paper proposes a novel Bayesian approach for modeling multivariate time series where conditional independe

Florian Neukart, Reuben Brasher, Eike Marx

We present the Quantum Memory Matrix (QMM) hypothesis, which addresses the longstanding Black Hole Information Paradox rooted in the apparent conflict between Quantum Mechanics (QM) and General Relativity (GR). This paradox raises the question of how information is preserved during black hole formation and evaporation, given that Hawking radiation appears to

Diego Corro

In this survey we present classical results on methods to use group actions to collapse manifolds to the orbit spaces while keeping some control on the curvature, and recent extensions of these constructions to the setting of singular Riemannian foliations.

Niall O'Sullivan, Licio Romao, Kostas Margellos

Conformal prediction and scenario optimization constitute two important classes of statistical learning frameworks to certify decisions made using data. They have found numerous applications in control theory, machine learning and robotics. Despite intense research in both areas, and apparently similar results, a clear connection between these two frameworks

Rae Holcomb, Christopher Lam

Catsteroseismology, or asterocatsmology, is an unexplored area of observational and theoretical research that proposes to use purr-mode oscillations to study the much-beloved but poorly-understood species Felis catus. In this work, we conduct a survey to measure fundamental purrameters of cats and relate them to their purr-modes. Relations between these fund

Wayne A. Morra, Gail W. Hearn, Andrew J. Buck

Bushmeat hunters on Bioko Island, Equatorial Guinea use shotguns and snares to capture wild arboreal and ground animals for sale in the Malabo Bushmeat market. Two tools for the analysis of economic efficiency, the production possibilities frontier and isorevenue line, can be used to explain the post hoc changing spatial distribution of takeoff rates of bush

Piyush Pallav, Purba Bhattacharya, Supratik Mukhopadhyay, Nayana Majumdar

The research focuses on the non-invasive imaging technique using cosmic muon absorption tomography to monitor the internals of archaeological / civil / industrial structures of intermediate size. It integrates experimental measurements and numerical simulations with Geant4, ascertaining the reliability and precision of muon absorption tomography using easily

Cui Hu, Ben G. Li

Although Google is blocked in China, Chinese provinces export significantly more to foreign countries that recently searched for them (up to 12 months prior). This attention premium is found mainly at the extensive margin of exports, larger in products that are relatively homogeneous, substitutable, and upstream in the production process, and more pronounced

L. Alvarez-Ruso, A. M. Ankowski, A. Ashkenazi, J. Barrow

In this document drafted by the Neutrino Scattering Theory Experiment Collaboration (NuSTEC), we provide input on the synergies between theoretical and experimental efforts that can provide critical input to the prediction accuracy needed for the forthcoming high-precision neutrino measurements. These efforts involve a wide range of energies and interaction

Anahita Mobaseri, Satish Kumar, Xiang Cheng

The maximum spreading of an impacting liquid drop is a key metric for characterizing the fundamental fluid process of drop impact. While extensively studied for Newtonian liquids, how far a non-Newtonian drop can spread upon impacting a solid substrate remains an open question. Here, by combining simulations, experiments, and scaling analyses, we establish a

Angel Ballesteros, Ivan Gutierrez-Sagredo, Jose de Ramon, J. Javier Relancio

The symmetric subspace of multi-qubit systems, that is, the space of states invariant under permutations, is commonly encountered in applications in the context of quantum information and communication theory. It is known that the symmetric subspace can be described in terms of irreducible representations of the group $SU(2)$, whose representation spaces for

Anisur Rahaman

In this work, we explore the intriguing phenomenon of acceleration radiation exhibited by an atom falling into a black hole, as previously studied in Phys. Rev. Lett. 121, 071301 (2018) . Our investigation focuses on examining the impact of Lorentz violation within the framework of the bumblebee gravity model on this phenomenon. We observe that the excitatio

Tomohiro Hirano, Joseph E. Stiglitz

This paper analyses the impact of credit expansions arising from decreases in collateral requirements or more expansionary monetary policies on long-term productivity in a model with endogenous growth. Credit expansions associated with relaxation of land collateral financing (capital collateral financing) will be productivity-and growth-retarding (enhancing)

Bang Quan Zheng

Structural Equation Modeling (SEM) or Covariance Structure Analysis (CSA) is a versatile and powerful method in the social and behavioral sciences, providing a framework for modeling complex relationships, testing mediation, accounting for measurement error, and analyzing latent constructs. However, SEM remains underutilized in in political science; its appl

Manel Gil-Sorribes, Alexis Molina

Accurately predicting drug-drug interactions (DDIs) is crucial for pharmaceutical research and clinical safety. Recent deep learning models often suffer from high computational costs and limited generalization across datasets. In this study, we investigate a simpler yet effective approach using molecular representations such as Morgan fingerprints (MFPS), gr

Van Higgs, Doug Pickrell

A linear quantum harmonic oscillator factors into one dimensional oscillators and can be solved using creation and annihilation operators. We consider a spherical analogue. This analogue does not factor. The two dimensional case is critical, and we compute the spectrum and partition function. This is of interest because the spherical oscillator is potentiall

Parveen Kumar, Ankit Kumar

In this paper, the notions of $p$-cyclic $\phi$-contraction and $p$-cyclic Kannan nonexpansive mappings are introduced, and the existence of coupled best proximity points for such mappings is established.

Saif M. Mohammad

Factor analysis studies have shown that the primary dimensions of word meaning are Valence (V), Arousal (A), and Dominance (D) (also referred to in social cognition research as Competence (C)). These dimensions impact various aspects of our lives from social competence and emotion regulation to success in the work place and how we view the world. We present

Kevin C. Smith, Austin G. Nixon, David J. Masiello

In this work, we present a field-theoretic model of strongly coupled photonic molecules composed of interacting dielectric cavities in a closed, perfect-electric-conductor domain. Within this setting, we treat the resulting inter-mode couplings non-perturbatively. We demonstrate the predictive power of this framework by showing that supermode eigenfrequencie

Rebecca M. Crossley, Philip K. Maini, Ruth E. Baker

Collective cell migration plays a crucial role in numerous biological processes, including tumour growth, wound healing, and the immune response. Often, the migrating population consists of cells with various different phenotypes. This study derives a general mathematical framework for modelling cell migration in the local environment, which is coarse-graine

Nat Gopalswamy, Pertti Makela, Hong Xie, Sachiko Akiyama

Solar Cycle (SC) 24 was the weakest in the space age, yet it produced many sustained gamma ray emission (SGRE) events from the Sun. Solar cycle (SC) 25, which is a bit stronger than SC 24 observed only a handful of SGRE events over the first five years. Here we report on the 2024 September 14 SGRE event, which has the longest duration (\~11.29 hrs) as of thi

Andrey Kharitenko, Marta Fochesato, Anastasios Tsiamis, Niklas Schmid

We consider distributionally robust optimization problems where the uncertainty is modeled via a structured Wasserstein ambiguity set. Specifically, the ambiguity is restricted to product measures $P^{\otimes N}$, where $P$ lies within a Wasserstein ball centered at an empirical distribution $\widehat{P}$. This structure reflects the assumption of independen

Xabier de Zuazo, Eva Navas, Ibon Saratxaga, Inma Hernáez Rioja

Automatic speech recognition systems have undoubtedly advanced with the integration of multilingual and multitask models such as Whisper, which have shown a promising ability to understand and process speech across a wide range of languages. Despite their robustness, these models often fall short in handling the linguistic distinctions of minority languages.

T. Battich, M. M. Miller Bertolami, A. Weiss, M. Dorsch

It has been shown that proton ingestion episodes can happen in the formation of hot-subdwarf stars, and that neutron-capture processes are possible in those cases. Moreover, some helium-rich hot subdwarfs display extraordinarily high abundances of heavy elements such as Zr, Yr and Pb on their surfaces. We explore under which conditions neutron-capture proces

Ruilong Yue, Giray Ökten

We introduce a new global sensitivity measure, the global activity scores. The measure is based on finite differences of the underlying function, in contrast to several sensitivity measures in the literature that are based on derivatives of the function. We establish its theoretical connection with Sobol' sensitivity indices and demonstrate its performance t

Nishant Mehrotra, Sandesh Rao Mattu, Robert Calderbank

Waveforms with ideal ambiguity functions are fundamental to integrated sensing and communication, to active sensing (radar), and to uplink multiple access. We describe a general method of constructing waveforms using the discrete Zak transform (DZT) to convert sequences of length $MN$ in the time domain to waveforms in the delay-Doppler (DD) domain, each of

L. M. Robledo

Pfaffian formulas used to compute overlaps necessary to carry out generator coordinate method calculations using a set of Hartree- Fock- Bogoliubov wave functions, is generalized to the case where each of the HFB states are expanded in different arbitrary bases spanning different sub-space of the Hilbert space. The formula obtained is compared with previous

Jiyeon Han, Dahee Kwon, Gayoung Lee, Junho Kim

Recent text-to-image generative models, particularly Stable Diffusion and its distilled variants, have achieved impressive fidelity and strong text-image alignment. However, their creative capability remains constrained, as including `creative' in prompts seldom yields the desired results. This paper introduces C3 (Creative Concept Catalyst), a training-free

Xiaoyang Li, Yixuan Jiang, Junze Zhu, Haotian Tang

In the field of sensor-based Human Activity Recognition (HAR), deep neural networks provide advanced technical support. Many studies have proven that recognition accuracy can be improved by increasing the depth or width of the network. However, for wearable devices, the balance between network performance and resource consumption is crucial. With minimum res

Djordje Miladinovic, Tobias Höppe, Mathieu Chevalley, Andreas Georgiou

Data generated in perturbation experiments link perturbations to the changes they elicit and therefore contain information relevant to numerous biological discovery tasks -- from understanding the relationships between biological entities to developing therapeutics. However, these data encompass diverse perturbations and readouts, and the complex dependence

Rafi Ibn Sultan, Hui Zhu, Chengyin Li, Dongxiao Zhu

Medical image segmentation typically relies solely on visual data, overlooking the rich textual information clinicians use for diagnosis. Vision-language models attempt to bridge this gap, but existing approaches often process visual and textual features independently, resulting in weak cross-modal alignment. Simple fusion techniques fail due to the inherent

Mario Raciti, Simone Di Mauro, Dimitri Van Landuyt, Giampaolo Bella

Digital forensics is a cornerstone of modern crime investigations, yet it raises significant privacy concerns due to the collection, processing, and storage of digital evidence. Despite that, privacy threats in digital forensics crime investigations often remain underexplored, thereby leading to potential gaps in forensic practices and regulatory compliance,

Vasanth Pidaparthy

This article continues the study of moduli spaces of special Lagrangians with boundary in a Calabi--Yau manifold. The moduli space was shown to be a smooth finite-dimensional manifold in the prequel arXiv:2503.6321918. This article investigates geometric structures on the moduli space of special Lagrangians with boundary and constructs a pair of special affi

Xiao-Wei Zheng, Jun-Cong Zheng, Xue-Feng Pan, Pengbo Li

Quantum coherence is critical resource for applications in quantum technology, among which quantum-enhanced sensing represents a typical example.Compared with quantum metrology with entangled states of multiple qubits, bosonic interferometers have the advantage of being hardware-effcient, enabled by exploiting the high-dimensional Hilbert space of a bosonic

Chloé Mas, Julia Roquette, Marc Audard, Mate Madarász

Context: Photometric variability is a defining characteristic of young stellar objects (YSO), which can be traced back to a range of physical processes taking place at different stages of young stars' formation and early evolution. Gaia's third Data Release (GDR3) has provided an unprecedented dataset of photometric time series, including 79375 light curves

Shuhei Tarashima, Xinqi Shu, Norio Tagawa

Source-Free Domain Adaptation (SFDA) aims to adapt a pre-trained source model to a target dataset from a different domain without access to the source data. Conventional SFDA methods are limited by the information encoded in the pre-trained source model and the unlabeled target data. Recently, approaches leveraging auxiliary resources have emerged, yet remai

Magdalena Kołodziej

The major advantage of proton therapy over conventional radiotherapy is the dose deposition pattern, enabling precise coverage of the tumour volume while sparing nearby healthy tissues. However, accurate control of the proton beam range online during patient irradiation is still considered a challenge. Thus, there are extensive efforts to develop a detector

Pedro L. Garrido, Tomasz Komorowski, Joel L. Lebowitz, Stefano Olla

We study the propagation of energy in one-dimensional anharmonic chains subject to a periodic, localized forcing. For the purely harmonic case, forcing frequencies outside the linear spectrum produce exponentially localized responses, preventing equi-distribution of energy per degree of freedom. We extend this result to anharmonic perturbations with bounded

Ruoxu Cen, Jason Li, Debmalya Panigrahi

The network unreliability problem asks for the probability that a given undirected graph gets disconnected when every edge independently fails with a given probability $p$. Valiant (1979) showed that this problem is \#P-hard; therefore, the best we can hope for are approximation algorithms. In a classic result, Karger (1995) obtained the first FPTAS for this

Vasanth Pidaparthy

This article studies the deformation problem for compact special Lagrangians with boundary in a Calabi--Yau manifold, with each boundary component constrained along a given Lagrangian submanifold. The tangent vectors generating such deformations are identified with harmonic 1-forms vanishing on the boundary of the special Lagrangian, and the deformation gene

Jiafeng Chen

This paper clarifies how and why structural demand models (Berry and Haile, 2014, 2024) predict unit-level counterfactual outcomes. We do so by casting structural assumptions equivalently as restrictions on the joint distribution of potential outcomes. Our reformulation highlights a counterfactual homogeneity assumption underlying structural demand models: T

Haochen Liu, Song Wang, Chen Chen, Jundong Li

Large Language Models (LLMs) often struggle with tasks requiring external knowledge, such as knowledge-intensive Multiple Choice Question Answering (MCQA). Integrating Knowledge Graphs (KGs) can enhance reasoning; however, existing methods typically demand costly fine-tuning or retrieve noisy KG information. Recent approaches leverage Graph Neural Networks (

Pedro Paulo A. Oliveira, Isaías P. de Jesus, Gilcenio R. de Sousa-Neto

In this article, we investigate certain theoretical aspects of the hierarchical controllability problem in one-dimensional wave equations within a moving domain using Stackelberg strategy. The controls are applied along a portion of the boundary and establish an equilibrium strategy among them, considering a leader control and a follower. We consider a linea

Maksym Yu. Pyatnytskyy, Ivan L. Andronov

We analyzed period changes of the high-amplitude Delta Scuti variable star CSS_J102714.3+205943 for about 20 years, utilizing data from the automated sky surveys along with our own observations. With the help of the O-C diagram, we found that the period decreased noticeably between JD2454800 and JD2457300. A possible cause of the change could be intrinsic pr

Christian Grussler

Two nested classes of discrete-time linear time-invariant systems, which differ by the set of periodic signals that they leave invariant, are studied. The first class preserves the property of periodic monotonicity (period-wise unimodality). The second class is invariant to signals with at most two sign changes per period, and requires that periodic signals

Haruya Ishikawa, Yoshimitsu Aoki

Semi-supervised semantic segmentation (SS-SS) aims to mitigate the heavy annotation burden of dense pixel labeling by leveraging abundant unlabeled images alongside a small labeled set. While current consistency regularization methods achieve strong results, most do not explicitly model boundaries as a separate learning objective. In this paper, we propose B

Arash Bahari Kordabad, Arabinda Ghosh, Sybert Stroeve, Sadegh Soudjani

In this paper, we propose an intent-aware Model Predictive Control (MPC) approach for the remain-well-clear (RWC) functionality of a multi-agent aircraft detect-and-avoid (DAA) system and compare its performance with the standardized Airborne Collision Avoidance System Xu (ACAS Xu). The aircraft system is modeled as a linear system for horizontal maneuvering

Haylee N. Archer, Deidre A. Hunter, Bruce G. Elmegreen, Leslie K. Hunt

We investigate the stellar populations and molecular gas properties of a star-forming region within the dwarf irregular (dIrr) galaxy WLM. Low-metallicity dIrrs like WLM offer a valuable window into star formation in environments that are unlike those of larger, metal-rich galaxies such as the Milky Way. In these conditions, carbon monoxide (CO), typically u

Natasha S. Sharma, Giordano Tierra

We present a numerical scheme for solving a sixth-order Cahn-Hilliard type equation that captures the dynamics of phase transitions in a ternary mixture consisting of two immiscible fluids and a surface active molecule that is amphiphilic. We show that by considering a suitable midpoint approximation for the nonlinear terms in the differential equation, we o

Alice E. A. Allen, Emily Shinkle, Roxana Bujack, Nicholas Lubbers

The representation of atomic configurations for machine learning models has led to the development of numerous descriptors, often to describe the local environment of atoms. However, many of these representations are incomplete and/or functionally dependent. Incomplete descriptor sets are unable to represent all meaningful changes in the atomic environment.

Siqi Fan, Xiusheng Huang, Yiqun Yao, Xuezhi Fang

Large language models (LLMs) can carry out human-like dialogue, but unlike humans, they are stateless due to the superposition property. However, during multi-turn, multi-agent interactions, LLMs begin to exhibit consistent, character-like behaviors, hinting at a form of emergent lifelong learning. Despite this, existing benchmarks often fail to capture thes

Zhengren Wang, Jiayang Yu, Dongsheng Ma, Zhe Chen

Domain-specific intelligence demands specialized knowledge and sophisticated reasoning for problem-solving, posing significant challenges for large language models (LLMs) that struggle with knowledge hallucination and inadequate reasoning capabilities under constrained parameter budgets. Inspired by Bloom's Taxonomy in educational theory, we propose Retrieva

Qiang Yi, Yangfan He, Jianhui Wang, Xinyuan Song

Large Language Models (LLMs) can generate creative and engaging narratives from user-specified input, but maintaining coherence and emotional depth throughout these AI-generated stories remains a challenge. In this work, we propose SCORE, a framework for Story Coherence and Retrieval Enhancement, designed to detect and resolve narrative inconsistencies. By t

Xingyu Lyu, Ning Wang, Yang Xiao, Shixiong Li

Federated Learning (FL) is a popular paradigm enabling clients to jointly train a global model without sharing raw data. However, FL is known to be vulnerable towards backdoor attacks due to its distributed nature. As participants, attackers can upload model updates that effectively compromise FL. What's worse, existing defenses are mostly designed under ind

Xingyu Lyu, Mengya Zhang, Xiaokuan Zhang, Jianyu Niu

In Ethereum, private transactions, a specialized transaction type employed to evade public Peer-to-Peer (P2P) network broadcasting, remain largely unexplored, particularly in the context of the transition from Proof-of-Work (PoW) to Proof-of-Stake (PoS) consensus mechanisms. To address this gap, we investigate the transaction characteristics, (un)intended us

Tianming Liang, Haichao Jiang, Wei-Shi Zheng, Jian-Fang Hu

Referring Video Object Segmentation (RVOS) aims to segment target objects throughout a video based on a text description. This task has attracted increasing attention in the field of computer vision due to its promising applications in video editing and human-agent interaction. Recently, ReferDINO has demonstrated promising performance in this task by adapti

Yuming Chen, Jiangyan Feng, Haodong Zhang, Lijun Gong

Language-based object detection (LOD) aims to align visual objects with language expressions. A large amount of paired data is utilized to improve LOD model generalizations. During the training process, recent studies leverage vision-language models (VLMs) to automatically generate human-like expressions for visual objects, facilitating training data scaling

Siladittya Manna, Suresh Das, Sayantari Ghosh, Saumik Bhattacharya

Decentralized federated learning enables learning of data representations from multiple sources without compromising the privacy of the clients. In applications like medical image segmentation, where obtaining a large annotated dataset from a single source is a distressing problem, federated self-supervised learning can provide some solace. In this work, we

Aly Abuelmaged, Eric Dohner, Shang-Jie Liou, Herbert F Fotso

We study the nonequilibrium dynamics of a binary disordered alloy when it is subjected to an interaction quench. Our study uses a nonequilibrium embedding scheme (DMFT+CPA) that combines the capacity of DMFT (dynamical mean field theory) to treat strongly correlated systems and the capacity of CPA (coherent potential approximation) to treat disordered system

Chang-Bing Li

The topological entropy dimension is mainly used to distinguish the zero topological entropy systems. Two types of topological entropy dimensions, the classical entropy dimension and the Pesin entropy dimension, are investigated for nonautonomous dynamical systems. Several properties of the entropy dimensions are discussed, such as the power rule, monotonici

Sid Bharthulwar, John Rho, Katrina Brown

We present a framework for optimizing prompts in vision-language models to elicit multimodal reasoning without model retraining. Using an evolutionary algorithm to guide prompt updates downstream of visual tasks, our approach improves upon baseline prompt-updating algorithms, which lack evolution-style "survival of the fittest" iteration. Crucially, we find

Jannik Endres, Oliver Hahn, Charles Corbière, Simone Schaub-Meyer

Omnidirectional depth perception is essential for mobile robotics applications that require scene understanding across a full 360{\deg} field of view. Camera-based setups offer a cost-effective option by using stereo depth estimation to generate dense, high-resolution depth maps without relying on expensive active sensing. However, existing omnidirectional s

Nicola Borri, Denis Chetverikov, Yukun Liu, Aleh Tsyvinski

We show that the higher-order terms and interactions of the common sparse linear factors are significantly priced in the cross-section of equity returns. A higher-order model with only a small number of selected higher-order terms from six widely used factors outperforms traditional benchmarks both in-sample and out-of-sample. It also substantially reduces t

Prem Nigam Kar

We develop an abstract operator-algebraic characterization of robust self-testing for synchronous correlations and games. Specifically, we show that a synchronous correlation is a robust self-test if and only if there is a unique state on an appropriate $C^*$-algebra that "implements" the correlation. Extending this result, we prove that a synchronous game i

Minjia Shi, Lu Wang, Patrick Solé

A criterion for Lehmer's conjecture in terms of the spherical designs held in the shells of the lattice $E_8$ was derived by de La Harpe, Pache and Venkov circa 2005. We check that this criterion is satisfied by combining spherical designs, harmonic polynomials, weighted theta series, and Deligne's bound on the modulus of the $\tau$ function.

Li Yang, Dongbo Wang

The ac4C modification on mRNA has been demonstrated to be associated with various diseases; however, its molecular mechanism remains unclear. The wet lab experiments produced relatively rough data, which lack precise ac4C modification sites, and extracting valuable information from such data remains a challenge. In this study, we integrate linguistics, tradi

Shangyi Shi, Husheng Han, Jianan Mu, Xinyao Zheng

Fully Homomorphic Encryption (FHE) imposes substantial memory bandwidth demands, presenting significant challenges for efficient hardware acceleration. Near-memory Processing (NMP) has emerged as a promising architectural solution to alleviate the memory bottleneck. However, the irregular memory access patterns and flexible dataflows inherent to FHE limit th

Ashim Dahal, Saydul Akbar Murad, Nick Rahimi

Understanding the representation shift on Vision Language Models like CLIP under different augmentations provides valuable insights on Mechanistic Interpretability. In this study, we show the shift on CLIP's embeddings on 9 common augmentation techniques: noise, blur, color jitter, scale and rotate, flip, elastic and perspective transforms, random brightness

Li Yang, Dongbo Wang

Decoding how linear DNA encodes regulatory information remains a central challenge. Existing decoding approaches lack interpretability and struggle to reveal the underlying coding principles. Here, we present the interpretability-first, structural artificial intelligence (AI) framework for DNA (ISAF4DNA), which uses state-aware symbolic encoding and couples

Edward Valachovic

This research introduces a novel extension, called the Extended Kolmogorov-Zurbenko Fourier Transform (EKZFT), to an existing class of band-pass filters first introduced by Kolmogorov and Zurbenko. Their original Kolmogorov-Zurbenko Fourier Transform (KZFT) is a useful tool in time series and spatio-temporal analysis, Fourier analysis, and related statistica

Yoav G. Pollack, Komal Bhattacharyya, Anas Hussin, Emily Klass

Hackathons are intensive innovation-oriented events where participants work in teams to solve problems or create projects in as little as 24 or 48 hours. These events are common in startup culture, open source communities and mainstream industry. Here we examine how hackathons can be ported to academic teaching, specifically in computational biophysics. We p

MingWei Zhou, Xiaobing Pei

Adversarial training (AT) is an effective technique for enhancing adversarial robustness, but it usually comes at the cost of a decline in generalization ability. Recent studies have attempted to use clean training to assist adversarial training, yet there are contradictions among the conclusions. We comprehensively summarize the representative strategies an

Jiaxin Xu, Gang Liu, Ruilan Guo, Meng Jiang

The advancement of polymer informatics has been significantly propelled by the integration of machine learning (ML) techniques, enabling the rapid prediction of polymer properties and expediting the discovery of high-performance polymeric materials. However, the field lacks a standardized workflow that encompasses prediction accuracy, uncertainty quantificat

Peishan Li

The concept of graph toughness was first introduced in 1973. In 1995, scholars first explored the lower bound of the toughness of connected d-regular graphs with respect to d and the second largest eigenvalue of the adjacency matrix. The concept of the variation of toughness was first introduced in 1988. The variation of toughness is defined as tau(G) = min{

Yangbo Wei, Zhen Huang, Huang Li, Wei W. Xing

Hardware design automation faces challenges in generating high-quality Verilog code efficiently. This paper introduces VFlow, an automated framework that optimizes agentic workflows for Verilog code generation. Unlike traditional approaches relying on fixed prompts or manually designed flows, VFlow treats workflow discovery as a search over graph-structured

Erik Adli, Joshua Appleby, Timothy L. Barklow, Marica Biagini

This document expands on the Comprehensive Summary submitted to the EPPSU 2026. It contains details on aspects of the HALHF project that could not be fitted into the Summary. Some sections contain work that is still preliminary and/or status reports on current progress.

Roberto Giorgi, Gianfranco Mariotti

WebRISC-V is a web-based educational tool designed to simulate the pipelined execution of assembly programs according to the RV64IM specifications (64-bit RISC-V processor). The tool allows users to investigate pipeline stalls, understand the internal state of pipeline architectural blocks, and visualize the cycle-by-cycle execution of instructions. WebRISC-

Alexander P. Zubarev

A method for approximating continuous functions $\mathbb{Z}_{p}^{n}\rightarrow\mathbb{Z}_{p}$ by a linear superposition of continuous functions $\mathbb{Z}_{p}\rightarrow\mathbb{Z}_{p}$ is presented and a polynomial regression model is constructed that allows approximating such functions with any degree of accuracy. A physical interpretation of such a model

Irtaza Khalid, Amir Masoud Nourollah, Steven Schockaert

Large Language Models (LLMs) have been found to struggle with systematic reasoning. Even on tasks where they appear to perform well, their performance often depends on shortcuts, rather than on genuine reasoning abilities, leading them to collapse on out-of-distribution (OOD) examples. Post-training strategies based on reinforcement learning and chain-of-tho

Nikil Jayasuriya, Deshan Sumanathilaka

This paper systematically explores the advancements in adaptive trip route planning and travel time estimation (TTE) through Artificial Intelligence (AI). With the increasing complexity of urban transportation systems, traditional navigation methods often struggle to accommodate dynamic user preferences, real-time traffic conditions, and scalability requirem

A. M. Melnik, E. N. Podzolkova

We study the formation of the Hercules stream in the model Galactic disk which includes the outer resonance ring R1R2 located near the Outer Lindblad Resonance (OLR) of the bar. The Hercules region and the anti-Hercules region introduced for calibration were restricted in space by the solar neighborhood, r<0.5 kpc, and on the (VR, VT) plane by ellipses cente

Mahdis Tajdari, Jason Forsyth, Sol Lim

Navigating peripersonal space requires reaching targets in both horizontal (e.g., desks) and vertical (e.g., shelves) layouts with high precision. We developed a haptic glove to aid peri-personal target navigation and investigated the effectiveness of different feedback delivery methods. Twenty-two participants completed target navigation tasks under various

Katrina Brown, Reid McIlroy

Large language models (LLMs) demonstrate remarkable performance on many NLP tasks, yet often exhibit order dependence: simply reordering semantically identical tokens (e.g., answer choices in multiple-choice questions) can lead to inconsistent predictions. Recent work proposes Set-Based Prompting (SBP) as a way to remove order information from designated tok

Faisal Suwayyid, Guo-Wei Wei

Topological data analysis (TDA) has emerged as an effective approach in data science, with its key technique, persistent homology, rooted in algebraic topology. Although alternative approaches based on differential topology, geometric topology, and combinatorial Laplacians have been proposed, combinatorial commutative algebra has hardly been developed for ma

Guoyizhe Wei, Rama Chellappa

Vision Transformers (ViTs) have delivered remarkable progress through global self-attention, yet their quadratic complexity can become prohibitive for high-resolution inputs. In this work, we present ViT-Linearizer, a cross-architecture distillation framework that transfers rich ViT representations into a linear-time, recurrent-style model. Our approach leve

Konstantin L. Metlov, Artem S. Tarasenko, Yulia A. Bezus, Maksim M. Gordei

A generalized micromagnetic model of hopfions in a helimagnet with a two-dimensional (allowing both radial and azimuthal dependence) profile function is considered. Calculations confirm the elliptical stability of hopfions and the previously obtained analytical expression for the upper critical field of their lattice. Dependencies of the hopfion lattice peri

Haofei Kuang, Yue Pan, Xingguang Zhong, Louis Wiesmann

Globally localizing a mobile robot in a known map is often a foundation for enabling robots to navigate and operate autonomously. In indoor environments, traditional Monte Carlo localization based on occupancy grid maps is considered the gold standard, but its accuracy is limited by the representation capabilities of the occupancy grid map. In this paper, we

C. E. Ardila-Gutiérrez, D. Torres-Amaris, Rafael González-Hernández, Aldo. H. Romero

Chirality in crystals arises from the exclusive presence of proper symmetry operations, such as rotations and screw axes, while lacking improper operations like inversion, mirror planes, and roto-inversions. Crystallographic chirality is expected to be coupled with magnetic responses in magnetically active chiral compounds. Therefore, this study investigates

Ivan Anokhin, Rishav Rishav, Matthew Riemer, Stephen Chung

Real-time reinforcement learning (RL) introduces several challenges. First, policies are constrained to a fixed number of actions per second due to hardware limitations. Second, the environment may change while the network is still computing an action, leading to observational delay. The first issue can partly be addressed with pipelining, leading to higher

Tina Vartziotis, Maximilian Schmidt, George Dasoulas, Ippolyti Dellatolas

Due to increased computing use, data centers consume and emit a lot of energy and carbon. These contributions are expected to rise as big data analytics, digitization, and large AI models grow and become major components of daily working routines. To reduce the environmental impact of software development, green (sustainable) coding and claims that AI models

Varsha N. Behrunani, Philipp Heer, Roy S. Smith, John Lygeros

Peer-to-peer(P2P) energy trading may increase efficiency and reduce costs, but introduces significant challenges for network operators such as maintaining grid reliability, accounting for network losses, and redistributing costs equitably. We propose a novel loss-aware pricing strategy for P2P energy markets that addresses these challenges while incentivizin

Janmejoy Sarkar, VN Nived, Soumya Roy, Rushikesh Deogaonkar

The Solar Ultraviolet Imaging Telescope (SUIT) on board the AdityaL1 mission observes the Sun in the 200-400 nm wavelength range. This paper presents the results of various on ground and on board tests and their comparison with the specifications. Moreover, we also present the scheme for data calibration. We demonstrate that the test results are compliant wi

Weijun Wu, Saumya Shivam, Amos Chan

We investigate universal signatures of quantum chaos in the presence of time reversal symmetry (TRS) in generic many body quantum chaotic systems (gMBQCs). We study three classes of minimal models of gMBQCs with TRS, realized through random quantum circuits with (i) local TRS, (ii) global TRS, and (iii) TRS combined with discrete time-translation symmetry. I

Özgün Yavuz, Ihor Pavlov, Abdullah bin Aamir, Sezin Galioğlu

Controlling laser-induced pattern formation remains a long-standing challenge. A key advance was recognising the pivotal role of intrinsic feedback mechanisms in self-organisation, which enabled self-similar patterns with long-range order through nonlinear laser lithography. This concept was recently leveraged to surpass the diffraction limit. However, demon

Yassine Habchi, Hamza Kheddar, Yassine Himeur, Adel Belouchrani

The rapid advancement of deep learning (DL) has transformed healthcare, particularly in cancer detection and diagnosis. DL surpasses traditional machine learning and human accuracy, making it a critical tool for identifying diseases. Despite numerous reviews on DL in healthcare, a comprehensive analysis of its role in cancer detection remains limited. Existi

Taekyun Kim, Dae San Kim

This paper introduces a novel generalization of Stirling and Lah numbers, termed ``heterogeneous Stirling numbers," which smoothly interpolate between these classical combinatorial sequences. Specifically, we define heterogeneous Stirling numbers of the second and first kinds, demonstrating their convergence to standard Stirling numbers for lambda=0 and to (

Guandong Li, Mengxia Ye

Deep neural networks face several challenges in hyperspectral image classification, including insufficient utilization of joint spatial-spectral information, gradient vanishing with increasing depth, and overfitting. To enhance feature extraction efficiency while skipping redundant information, this paper proposes a dynamic attention convolution design based