November 2025 arXiv papers — page 125

Lise Waldek, Brian Ballsun-Stanton, Muhammad Iqbal, David Kernot

Ethical conduct in digital research is full of grey areas. Disciplinary, institutional and individual norms and conventions developed to support research are challenged, often leaving scholars with a sense of unease or lack of clarity. The growing availability of hacked data is one area. Discussions and debates around the use of these datasets in research ar

Feilong Wang, Fuqiang Liu

The integration of large language models (LLMs) into automated driving systems has opened new possibilities for reasoning and decision-making by transforming complex driving contexts into language-understandable representations. Recent studies demonstrate that fine-tuned LLMs can accurately predict vehicle trajectories and lane-change intentions by gathering

Zhixia He, Chen Zhao, Minglai Shao, Xintao Wu

Out-of-distribution (OOD) detection is committed to delineating the classification boundaries between in-distribution (ID) and OOD images. Recent advances in vision-language models (VLMs) have demonstrated remarkable OOD detection performance by integrating both visual and textual modalities. In this context, negative prompts are introduced to emphasize the

Shuaiyu Xie, Hanbin He, Jian Wang, Bing Li

Root cause analysis in microservice systems typically involves two core tasks: root cause localization (RCL) and failure type identification (FTI). Despite substantial research efforts, conventional diagnostic approaches still face two key challenges. First, these methods predominantly adopt a joint learning paradigm for RCL and FTI to exploit shared informa

Ming Zhong, Zhemin Zhang, Xiangyu Ren, Chenghong Zhu

Mid-circuit measurement (MCM) provides the capability for qubit reuse and dynamic control in quantum processors, enabling more resource-efficient algorithms and supporting error-correction procedures. However, MCM introduces several sources of error, including measurement-induced crosstalk, idling-qubit decoherence, and reset infidelity, and these errors exh

Ahmad Al-Badawi, Faizuddin Ahmed, İzzet Sakallı

In a recent article, the author of Ref. [EPJC 85 (2025) 1256] constructed an exact solution describing a static, spherically symmetric black hole embedded in a Dehnen-($\alpha, \beta, \gamma$) type dark matter halo with the parameter choice $\alpha=1,\,\beta=4,\,\gamma=1/2$. The author first derived the density profile, which was subsequently integrated to o

Jialei Liu, Jun Liao, Kuangnan Fang

Positive-Unlabeled (PU) learning presents unique challenges due to the lack of explicitly labeled negative samples, particularly in high-stakes domains such as fraud detection and medical diagnosis. To address data scarcity and privacy constraints, we propose a novel transfer learning with model averaging framework that integrates information from heterogene

Arian Nadjimzadah

We prove that in all dimensions at least 3 and for any Hörmander-type phase function satisfying Bourgain's condition, the sticky case of the corresponding curved Kakeya conjecture reduces to the sticky case of the classical Kakeya conjecture. This supports a conjecture of Guo--Wang--Zhang that an oscillatory integral operator satisfies the same $L^p$ bou

Qiuping Wang, Lu Pan, Ting Yan

When the number of subjects, $n$, is large, paired comparisons are often sparse. Here, we study statistical inference in a class of paired comparison models parameterized by a set of merit parameters, under an Erd\"{o}s--R\'{e}nyi comparison graph, where the sparsity is measured by a probability $p_n$ tending to zero. We use the moment estimation base on the

Long Wei, Liheng Hao

This paper studies the validity and discourse reasoning of non-trivial generalized syllogisms involving the quantifiers in Square{most} and Square{all} from the perspective of knowledge reasoning. Firstly, this paper presents knowledge representations for these syllogisms and formally proves the validity of generalized syllogism AMI-1. Subsequently, 19 non-t

Guanxiong He, Jie Wang, Liaoyuan Tang, Zheng Wang

Federated clustering addresses the critical challenge of extracting patterns from decentralized, unlabeled data. However, it is hampered by the flaw that current approaches are forced to accept a compromise between performance and privacy: \textit{transmitting embedding representations risks sensitive data leakage, while sharing only abstract cluster prototy

Zihan Gu, Ruoyu Chen, Junchi Zhang, Yue Hu

Attribution is essential for interpreting object-level foundation models. Recent methods based on submodular subset selection have achieved high faithfulness, but their efficiency limitations hinder practical deployment in real-world scenarios. To address this, we propose PhaseWin, a novel phase-window search algorithm that enables faithful region attributio

Guangke Chen, Yuhui Wang, Shouling Ji, Xiapu Luo

Modern text-to-speech (TTS) systems, particularly those built on Large Audio-Language Models (LALMs), generate high-fidelity speech that faithfully reproduces input text and mimics specified speaker identities. While prior misuse studies have focused on speaker impersonation, this work explores a distinct content-centric threat: exploiting TTS systems to pro

Ben Zelenskiy, Saibilila Abudukelimu, George Flint, Kevin Zhu

We introduce the Sumudu Neural Operator (SNO), a neural operator rooted in the properties of the Sumudu Transform. We leverage the relationship between the polynomial expansions of transform pairs to decompose the input space as coefficients, which are then transformed into the Sumudu Space, where the neural operator is parameterized. We evaluate the operato

Arsh Gupta, Ajay Narayanan Sridhar, Bonam Mingole, Amulya Yadav

Large language models (LLMs) have demonstrated capabilities across diverse domains, yet their performance on rare disease diagnosis from narrative medical cases remains underexplored. We introduce a novel dataset of 176 symptom-diagnosis pairs extracted from House M.D., a medical television series validated for teaching rare disease recognition in medical ed

Andy Wu, Sanjiva K. Lele

Large Eddy Simulations (LES) are becoming increasingly viable due to the growth in computational power the last few decades, and subgrid stress modelling plays a large role in the accuracy of LES. A new class of neural network models, S4 and S4ND models, allow for learning a continuous representation of the discrete dataset, which facilitates a principled ap

Peichen Xie, Shuotao Xu, Yang Wang, Fan Yang

Modern AI accelerators rely on matrix multiply-accumulate units (MMAUs), such as NVIDIA Tensor Cores and AMD Matrix Cores, to accelerate deep neural network workloads. MMAUs expose only instruction-level or API-level interfaces of matrix multiply-accumulate (MMA) operations, while leaving internal floating-point arithmetic behaviors undocumented. Consequentl

Weichen Liu, Qiyao Xue, Haoming Wang, Xiangyu Yin

Spatial reasoning, which requires ability to perceive and manipulate spatial relationships in the 3D world, is a fundamental aspect of human intelligence, yet remains a persistent challenge for Multimodal large language models (MLLMs). While existing surveys often categorize recent progress based on input modality (e.g., text, image, video, or 3D), we argue

Leonard Susskind

A question arises in the holographic description of the static patch of de Sitter space: Where does the entropy reside? The answer of course is in the stretched horizon, but how far from the mathematical horizon is the stretched horizon? In recent papers and lectures I argued that the entropy in DSSYK/JT-de Sitter resides at a string distance from the horizo

Zesen Huang, Marco Velli, Olga Panasenco, Richard J. Morton

The Sun's surface vibrates in characteristic 5-minute oscillations, known as p-modes, generated by sound waves trapped within the convection zone. Although these oscillations have long been hypothesized to reach into the solar wind, direct in situ evidence has remained elusive, even during previous close encounters by Parker Solar Probe (PSP). Here, we prese

Hyun-Ki Jung

Object detection using images or videos captured by drones is a promising technology with significant potential across various industries. However, a major challenge is that drone images are typically taken from high altitudes, making object identification difficult. This paper proposes an effective solution to address this issue. The base model used in the

Mac Lee, Stefan Llewellyn Smith

Stability analysis is performed on surface quasigeostrophic systems subjected to a Kolmogorov-type "shear force" on the boundaries using linear and nonlinear approaches. For a SQG system of semi-infinite depth forced on the upper boundary, the most linearly unstable mode is 2.74 the energy injection length scale. This is contrary to two-dimensional fluid sys

Ramya Kumar, Dhruv Gulwani, Sonit Singh

This paper explores the automatic classification of exam questions and learning outcomes according to Bloom's Taxonomy. A small dataset of 600 sentences labeled with six cognitive categories - Knowledge, Comprehension, Application, Analysis, Synthesis, and Evaluation - was processed using traditional machine learning (ML) models (Naive Bayes, Logistic Regres

Mengze Hong, Di Jiang, Weiwei Zhao, Yawen Li

While large language models (LLMs) offer promising capabilities for automating academic workflows, existing systems for academic peer review remain constrained by text-only inputs, limited contextual grounding, and a lack of actionable feedback. In this work, we present an interactive web-based system for multimodal, community-aware peer review simulation to

Deniz Kerimoglu, Nicholas D. Naclerio, Sean Chu, Andrew Krohn

Most engineered pilings require substantially more force to be driven into the ground than they can resist during extraction. This requires relatively heavy equipment for insertion, which is problematic for anchoring in hard-to-access sites, including in extraterrestrial locations. In contrast, for tree roots, the external reaction force required to extract

Yongfu Xue

Reward models are pivotal for aligning Large Language Models (LLMs) with human preferences. Existing approaches face two key limitations: Discriminative reward models require large-scale annotated data, as they cannot exploit the preference instruction-following capability of LLMs available to generative reward models. Moreover, reward models are particularl

Xueren Ge, Sahil Murtaza, Anthony Cortez, Homa Alemzadeh

Large language models (LLMs) have shown promise in medical question answering, yet they often overlook the domain-specific expertise that professionals depend on, such as the clinical subject areas (e.g., trauma, airway) and the certification level (e.g., EMT, Paramedic). Existing approaches typically apply general-purpose prompting or retrieval strategies w

Farima Fatahi Bayat, Pouya Pezeshkpour, Estevam Hruschka

Tool-augmented Language Models (TaLMs) can invoke external tools to solve problems beyond their parametric capacity. However, it remains unclear whether these tool-enabled gains reflect trustworthy reasoning. Focusing on the Code Interpreter tool, we show that even when tools are selected and executed correctly, TaLMs treat tool outputs as substitutes for re

Chenghao Duan, Chuanyi Ji

Natural disasters such as hurricanes, wildfires, and winter storms have induced large-scale power outages in the U.S., resulting in tremendous economic and societal impacts. Accurately predicting power outage recovery and impact is key to resilience of power grid. Recent advances in machine learning offer viable frameworks for estimating power outage duratio

Xianxin Song, Xianghao Yu, Jie Xu, Derrick Wing Kwan Ng

Integrated sensing and communications (ISAC) is a disruptive technology enabling future sixth-generation (6G) networks. This paper investigates target detection in a bistatic ISAC system, in which the base station (BS) transmits superimposed ISAC signals comprising both Gaussian information-bearing and deterministic sensing components to simultaneously provi

Lihua Jian, Jiabo Liu, Shaowu Wu, Lihui Chen

Despite remarkable advancements in supervised pansharpening neural networks, these methods face domain adaptation challenges of resolution due to the intrinsic disparity between simulated reduced-resolution training data and real-world full-resolution scenarios.To bridge this gap, we propose an unsupervised pansharpening framework, CLIPPan, that enables mode

Irena Penev

This is the second in a series of two papers dealing with $(2P_3,C_4,C_6)$-free graphs, or equivalently, $(2P_3,\text{even hole})$-free graphs. In this two-paper series, we give a full structural description of $(2P_3,C_4,C_6)$-free graphs that contain no simplicial vertices, and we show that such graphs have bounded clique-width. This implies that Graph Col

Luciano Araujo Dourado Filho, Almir Moreira da Silva Neto, Anthony Miyaguchi, Rodrigo Pereira David

This paper proposes a competitive and computationally efficient approach to probabilistic rainfall nowcasting. A video projector (V-JEPA Vision Transformer) associated to a lightweight probabilistic head is attached to a pre-trained satellite vision encoder (DINOv3-SAT493M) to map encoder tokens into a discrete empirical CDF (eCDF) over 4-hour accumulated ra

Wonjin Jung, Yongseok Choi

We address polymer property prediction with a multi-view design that exploits complementary representations. Our system integrates four families: (i) tabular RDKit/Morgan descriptors, (ii) graph neural networks, (iii) 3D-informed representations, and (iv) pretrained SMILES language models, and averages per-property predictions via a uniform ensemble. Models

Feng Li, Ke Wu, Yongwei Li

Multimodal emotion recognition plays a key role in many domains, including mental health monitoring, educational interaction, and human-computer interaction. However, existing methods often face three major challenges: unbalanced category distribution, the complexity of dynamic facial action unit time modeling, and the difficulty of feature fusion due to mod

Tiantian He, An Zhao, Elinor Thompson, Anna Schroder

Understanding the interactions between biomarkers among brain regions during neurodegenerative disease is essential for unravelling the mechanisms underlying disease progression. For example, pathophysiological models of Alzheimer's Disease (AD) typically describe how variables, such as regional levels of toxic proteins, interact spatiotemporally within a dy

Irena Penev

This is the first in a series of two papers dealing with $(2P_3,C_4,C_6)$-free graphs, or equivalently, $(2P_3,\text{even hole})$-free graphs. In this two-paper series, we give a full structural description of $(2P_3,C_4,C_6)$-free graphs that contain no simplicial vertices, and we show that such graphs have bounded clique-width. This implies that Graph Colo

Soogand Alavi, Salar Nozari, Andrea Luangrath

Recent advances in large language models (LLMs) have dramatically improved performance on a wide range of tasks, driving rapid enterprise adoption. Yet, the cost of adopting these AI services is understudied. Unlike traditional software licensing in which costs are predictable before usage, commercial LLM services charge per token of input text in addition t

Bolin Liu, Abicumaran Uthamacumaran, Alexander Fulton, Hector Zenil

Understanding disease relationships through blood biomarkers offers a pathway toward data-driven taxonomy and precision medicine. In this study, we constructed a digital blood twin, a computational model derived from 103 disease signatures comprising longitudinal hematological and biochemical analytes. Profiles were standardized into a unified disease-analyt

A. Ahmad, S. Dai, E. Lenc, M. D. Filipović

It has been proposed that radio pulsars can be distinguished from other point-like radio sources in continuum images by their unique interstellar scintillation signatures. Using data from the Australian Square Kilometre Array Pathfinder (ASKAP) Evolutionary Map of the Universe (EMU) survey, we conducted a pilot survey of radio pulsars at high Galactic latitu

I. E. Ochs, E. J. Kolmes, A. S. Glasser, N. J. Fisch

Proton-Boron 11 (pB11) fusion is relatively safe and clean, but difficult to use for net power production, since bremsstrahlung radiation tends to radiate away power more quickly than it can be generated by fusion power, particularly once poisoning by alpha particles is taken into account. While in magnetic confinement fusion (MCF), this problem can be addre

Raymond Chu, Matt Jacobs

In this paper, we study higher-order-accurate-in-time minimizing movements schemes for Wasserstein gradient flows. We introduce a novel accelerated second-order scheme, leveraging the differential structure of the Wasserstein space in both Eulerian and Lagrangian coordinates. For sufficiently smooth energy functionals, we show that our scheme provably achiev

Abdullah Al Shiam, Md. Khademul Islam Molla, Abu Saleh Musa Miah, Md. Abdus Samad Kamal

A Brain Computer Interface (BCI) connects the human brain to the outside world, providing a direct communication channel. Electroencephalography (EEG) signals are commonly used in BCIs to reflect cognitive patterns related to motor function activities. However, due to the multichannel nature of EEG signals, explicit information processing is crucial to lesse

Hanamantagouda P. Sankappanavar

This paper is inspired by 1892 paper of Johnson, where he has given an axiomatization for the variety of Boolean algebras (equivalently, for classical propositional calculus). The fact that the axioms of Johnson include the associative law, the most well-known identity of associative type of length 3, led us naturally to the question as to whether there are

Parashmoni Kashyap, Liton Majumdar, Edwin A. Bergin, Geoffrey A. Blake

The physical and chemical conditions within a protoplanetary disk play a crucial role in determining its chemical composition, which is subsequently inherited by any forming planets. To probe these conditions, high-resolution molecular line observations, coupled with modelling, are essential. In this study, we investigate the chemistry of the nearby, massive

Jongyoon Song, Sangwon Yu, Sungroh Yoon

Negative bias refers to the tendency of large language models (LLMs) to excessively generate negative responses in binary decision tasks (e.g., yes-no question answering). Previous research has focused on detecting and addressing negative attention heads that induce negative bias. However, the underlying detailed factors influencing negative bias remain unde

Momchil E. Molnar, Richard Morton, Alin Paraschiv, Chris Gilly

The plasma of the solar corona harbors a multitude of coronal wave modes, some of which could be dissipated to provide the required energy and momentum to heat the corona and accelerate the solar wind. We present observations of the corona acquired with the newly commissioned infrared slit spectropolarimeter Cryo-NIRSP at the Daniel K. Inouye Solar Telescope

Dennis Wei, Ronny Luss, Xiaomeng Hu, Lucas Monteiro Paes

Large Language Models (LLMs) have become ubiquitous in everyday life and are entering higher-stakes applications ranging from summarizing meeting transcripts to answering doctors' questions. As was the case with earlier predictive models, it is crucial that we develop tools for explaining the output of LLMs, be it a summary, list, response to a question, etc

Shuhao Ma, Zeyi Huang, Yu Cao, Wesley Doorsamy

Time-efficient estimation of muscle activations and forces across multi-joint systems is critical for clinical assessment and assistive device control. However, conventional approaches are computationally expensive and lack a high-quality labeled dataset for multi-joint applications. To address these challenges, we propose a physics-informed deep learning fr

Veikka Piispa, Dilshanie Prasikala, Joonas Lahtinen, Alexandra Koulouri

Kalman filtering has proven to be effective for estimating brain activity using EEG recordings. In particular, the introduced post hoc standardization step of the algorithm, inspired by the sLORETA time-invariant method, reduces the depth bias and thus allows the estimation to appear at the correct depth from the electrode surface. In the current work, we pr

Francesco Vitale, Francesco Flammini, Mauro Caporuscio, Nicola Mazzocca

Context: Ensuring high levels of dependability in modern computer-based systems has become increasingly challenging due to their complexity. Although systems are validated at design time, their behavior can be different at runtime, possibly showing control-flow anomalies due to ``unknown unknowns''. Objective: We aim to detect control-flow anomalies through

Felicia Servina Djuang, Arizka Yuliana, Widi Bagaskara, Yeni Susanti

This study investigates the properties of the 3-token graph derived from path graphs, with a particular focus on its structural characteristics and key attributes. We analyze how the 3-token graph is constructed from path graphs and explore fundamental properties such as connectivity, diameter, and chromatic number. Furthermore, we extend our analysis to the

Yorai Shaoul, Zhe Chen, Mohamed Naveed Gul Mohamed, Federico Pecora

Coordinating a team of robots to reposition multiple objects in cluttered environments requires reasoning jointly about where robots should establish contact, how to manipulate objects once contact is made, and how to navigate safely and efficiently at scale. Prior approaches typically fall into two extremes -- either learning the entire task or relying on p

Sebastian Fernández, Divine Mbachu, Manchen Hu, Han Cui

Metal halide perovskites possess desirable optical, material, and electrical properties which have had substantial impact on next-generation optoelectronics. However, given the toxicity of lead, alternative lead-free perovskite semiconductors are needed. By fully replacing lead with rare-earth elements, one can simultaneously address toxicity concerns and ac

Shuyuan Zhang, Zihan Wang, Xiao-Wen Chang, Doina Precup

The integration of graphs with Goal-conditioned Hierarchical Reinforcement Learning (GCHRL) has recently gained attention, as intermediate goals (subgoals) can be effectively sampled from graphs that naturally represent the overall task structure in most RL tasks. However, existing approaches typically rely on domain-specific knowledge to construct these gra

Ksenia Apolonskaya, Oleg R. Musin

This paper studies the minimal number of vertices $\lambda(n,d)$ required in a triangulation of the $n$-sphere to admit a simplicial map to the boundary of a $(n+1)$-simplex with a given degree $d$. We establish upper bounds for $\lambda(n,d)$ in dimensions $n \geq 3$. Furthermore, we provide exact formulas for small values of $d$, showing that $\lambda(n,d)

Juncheng Yi, Kaiwen Zhou, James Jiang

The Infinite Monkey Theorem states that if one monkey randomly hits the keys in front of a typewriter keyboard during an infinite amount of time, any works written by William Shakespeare will almost surely be typed out at the end of the total text. Due to the seemingly low chance of typing the exact literature works, our group are motivated to find out the e

Tamanna Zakia, Ayeman Nahin, Dunji Yu, Jacob Pustelnik

The strength of body-centered cubic materials is traditionally known to be governed by screw dislocations. However, recent findings reveal that in certain refractory complex concentrated alloys, edge dislocations can instead control strength. This work integrates high-temperature mechanical testing, in-situ neutron scattering during heating and tension, scan

Marko Lela

We prove a \(\Gamma\)-convergence result for a diffeomorphism-natural discrete MDL-type functional to the Einstein-Hilbert action with the Gibbons-Hawking-York boundary term. On boundary-fitted, shape-regular meshes we establish interior and boundary blow-ups, identify the Carath\'eodory densities \(f_{\mathrm{in}}=\alpha_0+\alpha_1 R\) and \(f_{\mathrm{bdry

Seoik Jung, Taekyung Song, Yangro Lee, Sungjun Lee

This paper proposes a Short-Window Sliding Learning framework for real-time violence detection in CCTV footages. Unlike conventional long-video training approaches, the proposed method divides videos into 1-2 second clips and applies Large Language Model (LLM)-based auto-caption labeling to construct fine-grained datasets. Each short clip fully utilizes all

Sherry Shi, Renyao Wei, Michele Tufano, José Cambronero

Reliable evaluation is crucial for advancing Automated Program Repair (APR), but prevailing benchmarks rely on execution-based evaluation methods (unit test pass@k), which fail to capture true patch validity. Determining validity can require costly manual annotation. To reduce this cost, we introduce a human-in-the-loop approach to LLM-based patch validity j

Sarthak Chakraborty, Suman Nath, Xuchao Zhang, Chetan Bansal

Large Language Models (LLMs) are increasingly being used to plan, reason, and execute tasks across diverse scenarios. In use cases like repeatable workflows and agentic settings, prompts are often reused with minor variations while having a similar structure for recurring tasks. This opens up opportunities for caching. However, exact prompt matching fails on

Jose Vasquez, Xuping Zhang

Quantifying greenhouse-gases (GHG) in wetlands is critical for climate modeling and restoration assessment, yet manual sampling is labor-intensive, and time demanding. We present WetExplorer, an autonomous tracked robot that automates the full GHG-sampling workflow. The robot system integrates low-ground-pressure locomotion, centimeter-accurate lift placemen

Yiping Ma, Yue Guo, Harish Karthikeyan, Antigoni Polychroniadou

This paper presents a secure aggregation system Armadillo that has disruptive resistance against adversarial clients, such that any coalition of malicious clients (within the tolerated threshold) can affect the aggregation result only by misreporting their private inputs in a pre-defined legitimate range. Armadillo is designed for federated learning setting,

H. M. Murtuza, Md Oliullah

Newsrooms and journalists across the world are adopting Generative AI (GenAI). Drawing on in-depth interviews with 23 journalists, this study identifies Bangladeshi journalists' awareness, acceptance, usage patterns, and their media organizations' stance toward GenAI. This study finds Bangladeshi journalists' high reliance on GenAI like their Western colleag

Jorge Olivares, Daniel Posada-Buriticá

We study foliations $\mathscr{F}$ on projective complete intersection K3 surfaces $X \hookrightarrow \mathbb{P}^n$, where $\mathscr{F}$ has isolated singularities and it is the restriction of a foliation of degree $d$ on $\mathbb{P}^n$ that leaves $X$ invariant. We compute the values of the degrees $d$ for which $\mathscr{F}$ is uniquely determined by its si

Andrea Brunello, Luca Geatti, Michele Mignani, Angelo Montanari

Due to its expressiveness and unambiguous nature, First-Order Logic (FOL) is a powerful formalism for representing concepts expressed in natural language (NL). This is useful, e.g., for specifying and verifying desired system properties. While translating FOL into human-readable English is relatively straightforward, the inverse problem, converting NL to FOL

A. Maiti, M. Amato, V. S. Stolyarov, H. Aubin

Due to the Coulomb blockade effect, electrons rarely bunch during transport, a phenomenon observed only in a few specially engineered mesoscopic configurations. In this work, we introduce an atomically resolved shot-noise study to demonstrate the possibility of electron bunching through vibrational coupling which takes place in an atomically sized nano-elect

Nicolas Cerardi, Sambit K. Giri, Michele Bianco, Davide Piras

The Epoch of Reionization (EoR), when the first luminous sources ionised the intergalactic medium, represents a new frontier in cosmology. The Square Kilometre Array Observatory (SKAO) will offer unprecedented insights into this era through observations of the redshifted 21-cm signal, enabling constraints on the Universe's reionization history. We invest

Giuseppe Meneghini, Maja Löwe, Raul Perea-Causin, Jan Philipp Bange

Angle-resolved photoemission spectroscopy (ARPES) has recently emerged as a direct probe of excitonic correlations in two-dimensional semiconductors, resolving their dispersion and dynamics in energy-momentum space, including dark exciton states inaccessible to optical techniques. However, the ARPES fingerprint of charged excitons (trions), which plays a key

Petter Persson, Nils Henriksson, Nicolò Maccaferri

Deep learning has emerged as a key tool for designing nanophotonic structures that manipulate light at sub-wavelength scales. We investigate how to inversely design plasmonic nanostructures using conditional generative adversarial networks. Although a conventional approach of measuring the optical properties of a given nanostructure is conceptually straightf

Anton Talkachov, Paul Leask, Egor Babaev

A fundamental characteristic of a superconducting state is the coherence length $ξ$. Multicomponent superconductors, particularly ones breaking multiple symmetries, are characterized by multiple coherence lengths. Here we show that even, nominally $single$-component superconductors under certain conditions are characterized by multiple coherence lengths. We

Gerard Pons, Besim Bilalli, Anna Queralt

Many Knowledege Graphs (KGs) are frequently updated, forcing their Knowledge Graph Embeddings (KGEs) to adapt to these changes. To address this problem, continual learning techniques for KGEs incorporate embeddings for new entities while updating the old ones. One necessary step in these methods is the initialization of the embeddings, as an input to the KGE

Redwan Hussain, Mizanur Rahman, Prithwiraj Bhattacharjee

Artificial intelligence (AI) in media has advanced rapidly over the last decade. The introduction of Generative Adversarial Networks (GANs) improved the quality of photorealistic image generation. Diffusion models later brought a new era of generative media. These advances made it difficult to separate real and synthetic content. The rise of deepfakes demons

Andrea Brunello, Luca Geatti, Angelo Montanari, Nicola Saccomanno

In runtime verification, monitoring consists of analyzing the current execution of a system and determining, on the basis of the observed finite trace, whether all its possible continuations satisfy or violate a given specification. This is typically done by synthesizing a monitor--often a Deterministic Finite State Automaton (DFA)--from logical specificatio

Özgür Akgün, Mun See Chang, Ian P. Gent, Christopher Jefferson

In constraint programming and related paradigms, a modeller specifies their problem in a modelling language for a solver to search and return its solution(s). Using high-level modelling languages such as Essence, a modeller may express their problems in terms of abstract structures. These are structures not natively supported by the solvers, and so they have

Gunho Jung, Heejo Kong, Seong-Whan Lee

Dynamic facial expression recognition (DFER) aims to identify emotional states by modeling the temporal changes in facial movements across video sequences. A key challenge in DFER is the many-to-one labeling problem, where a video composed of numerous frames is assigned a single emotion label. A common strategy to mitigate this issue is to formulate DFER as

Wu Wen, Jing Li, Yunquan Liu

We study tunable nanoscale femtosecond coherent radiation based on a coupled nanowire pair (CNP) structure that is excited by a strong laser. The structure functions as a nanoscale undulator (NU): the electrons moving through the nanogap are driven by a spatially periodic, transverse optical near-field. We show that the transverse near-field can actively sha

Baoshan Zhang, Sean M. O'Brien, Yuan Wu, Laine E. Thomas

Propensity score (PS) methods are widely used to estimate treatment effects in non-randomized studies. Variance is typically estimated using sandwich or bootstrap methods, which can either treat the PS as estimated or fixed. The latter is thought to be conservative. Comparisons between the sandwich and bootstrap estimators have been compared in moderate to l

Indranil Rudra, Md. Moktadir Billah Tahmid, Jahid Emon, Mohammad Jane Alam Khan

Graphene's exceptional mechanical properties are crucial for its integration into advanced technological applications. However, real-world synthesis and functionalization processes introduce structural modifications that can compromise its mechanical integrity. Nitrogen doping, while beneficial for electronic property tuning, often results in atomic clus

Thales Bezerra, Emanoel Thyago, Kelvin Cunha, Rodrigo Abreu

AI-based dermatology adoption remains limited by biased datasets, variable image quality, and limited validation. We introduce DermAI, a lightweight, smartphone-based application that enables real-time capture, annotation, and classification of skin lesions during routine consultations. Unlike prior dermoscopy-focused tools, DermAI performs on-device quality

Abdulmumin Sa'ad, Sulaimon Oyeniyi Adebayo

The global waste crisis is escalating, with solid waste generation expected to increase tremendously in the coming years. Traditional waste collection methods, particularly in remote or harsh environments like deserts, are labor-intensive, inefficient, and often hazardous. Recent advances in computer vision and deep learning have opened the door to automated

Quanyu Tang, Shu Zhang

Let $\|\!\cdot\!\|_p$ denote the Schatten $p$-norm of matrices and $\|\!\cdot\!\|_F$ the Frobenius norm. For a square matrix $X$, let $|X|$ denote its absolute value. In 2010, Eun-Young Lee posed the problem of determining the smallest constant $c_p$ such that $\|A+B\|_p \le c_p\|\,|A|+|B|\,\|_p$ for all complex matrices $A,B$. The Frobenius case $(p=2)$ con

Yijie Li, Weichong Ling, Taiting Lu, Bao Dao

Many real-world sensing tasks require co-located, multi-modal measurements at a single site, typically a bundle of two to five sensors, for example, in plant stress sensing and blood pressure estimation. RF-backscatter devices have emerged as a low-power solution for sensing, yet existing backscatter tags support a single sensor. Placing several single-senso

José Balduque, Rafael Sánchez

Nanoscale conductors are interesting for thermoelectrics because of their particular spectral features connecting separated heat and particle currents. Multiterminal devices in the quantum regime benefit from phase-coherent phenomena, which turns the thermoelectric effect nonlocal, and from tunable single-particle interactions. This way one can define quantu

Yatin Dandi, Luca Pesce, Lenka Zdeborová, Florent Krzakala

Understanding the advantages of deep neural networks trained by gradient descent (GD) compared to shallow models remains an open theoretical challenge. In this paper, we introduce a class of target functions (single and multi-index Gaussian hierarchical targets) that incorporate a hierarchy of latent subspace dimensionalities. This framework enables us to an

Michael Bate, Brent Everitt, Sam Ford, Eric Ramos

We compute the homology of the matching complex $M(Γ)$, where $Γ$ is the complete hypergraph on $n\geq 2$ vertices, and analyse the $S_n$-representations carried by this homology. These results are achieved using standard techniques in combinatorial topology, such as the theory of shellings. We then broaden the scope to the larger class of fibre-closed famil

Pieter Belmans, Shinnosuke Okawa, Andrea T. Ricolfi

To a smooth and proper morphism $\mathcal{X}\to U$ with quasicompact semiseparated target we associate a sheaf in the étale topology, which takes an affine $U$-scheme $V$ to the set of $V$-linear semiorthogonal decompositions (of fixed length) of the category $\operatorname{Perf}\mathcal{X}_V$. We use Artin's criterion to prove that, when $U$ is excellen

Rabimba Karanjai, Lei Xu, Weidong Shi

Unit testing in High-Performance Computing (HPC) is critical but challenged by parallelism, complex algorithms, and diverse hardware. Traditional methods often fail to address non-deterministic behavior and synchronization issues in HPC applications. This paper introduces HPCAgentTester, a novel multi-agent Large Language Model (LLM) framework designed to au

Xuchen Gong, Tian Li

One of the major bottlenecks for deploying popular first-order differentially private (DP) machine learning algorithms (e.g., DP-SGD) lies in their high computation and memory cost, despite the existence of optimized implementations. Zeroth-order methods have promise in mitigating the overhead, as they leverage function evaluations to approximate the gradien

Dimitria Silveria, Kleber Cabral, Peter Jardine, Sidney Givigi

This paper presents a novel decentralized approach for achieving emergent behavior in multi-agent systems with minimal information sharing. Based on prior work in simple orbits, our method produces a broad class of stable, periodic trajectories by stabilizing the system around a Lie group-based geometric embedding. Employing the Lie group SO(3), we generate

Seyedeh Mobina Noorani, Shangde Gao, Changjie Chen, Karla Saldana Ochoa

Conventional planning units or urban regions, such as census tracts, zip codes, or neighborhoods, often do not capture the specific demands of local communities and lack the flexibility to implement effective strategies for hazard prevention or response. To support the creation of dynamic planning units, we introduce a planning support system with agentic AI

Nikolaos Kidonakis

I present calculations of higher-order corrections to top-quark production via three different processes: the production of a top-antitop pair and a $Z$ boson; single-top quark production in the $s$-channel; and top-antitop production with a Higgs boson. It is shown that the contributions from soft-gluon corrections are numerically dominant and large in all

Tom Yuviler, Dana Drachsler-Cohen

Despite recent advances in LLMs, the task of code generation is still challenging. To cope, code selection algorithms select the best program from multiple programs generated by an LLM. However, existing algorithms can fail to identify the correct program, either because they can misidentify nonequivalent programs or because they rely on an LLM and assume it

Vade Shah, Jason R. Marden

As electricity consumption grows, reducing peak demand--the maximum load on the grid--has become critical for preventing infrastructure strain and blackouts. Pricing mechanisms that incentivize consumers with flexible loads to shift consumption away from high-demand periods have emerged as effective tools, yet different mechanisms are used in practice with u

Gerui Xu, Boyou Chen, Huizhong Guo, Dave LeBlanc

Traffic collision reconstruction traditionally relies on human expertise and can be accurate, but pre-crash reconstruction is more challenging. This study develops a multi-agent AI framework that reconstructs pre-crash scenarios and infers vehicle behaviors from fragmented collision data. We propose a two-phase collaborative framework with reconstruction and

Chris Umans, Siki Wang

The fastest known algorithm for factoring a degree $n$ univariate polynomial over a finite field $\mathbb{F}_q$ runs in time $O(n^{3/2 + o(1)}\text{polylog } q)$, and there is a reason to believe that the $3/2$ exponent represents a ''barrier'' inherent in algorithms that employ a so-called baby-steps-giant-steps strategy. In this paper, we propose a new str

Stefan Horoi, Sangwoo Cho, Supriyo Chakraborty, Shi-Xiong Zhang

Task arithmetic is a powerful technique for transferring skills between Large Language Models (LLMs), but it often suffers from negative interference when models have diverged during training. We address this limitation by first aligning the models' parameter spaces, leveraging the inherent permutation, rotation, and scaling symmetries of Transformer archite

Aias Asteris, Thai-Son Nguyen, Chuan F. C. Chang, Chandrashekhar Savant

Aluminum scandium nitride (AlScN) is a promising barrier material for gallium nitride (GaN)-based transistors for the next generation of radio-frequency electronic devices. In this work, we examine the transport properties of two dimensional electron gases (2DEGs) in single- and multi-channel AlScN/GaN heterostructures grown by molecular beam epitaxy, and de

Brad Shook, Abby Turner, Jieshi Chen, Michał Wiliński

Time series foundation models (TSFMs) pretrained on data from multiple domains have shown strong performance on diverse modeling tasks. Various efforts have been made to develop foundation models specific to electroencephalography (EEG) data, which records brain electrical activity as time series. However, no comparative analysis of EEG-specific foundation m