March 2026 arXiv papers — page 68

Jieting Yuan, Songhan Zhao, Ye Xue, Yu Zhao

This paper focuses on secure communications in UAV-assisted wireless networks, which comprise multiple legitimate UAVs (LE-UAVs) and an intelligent eavesdropping UAV (EA-UAV). The intelligent EA-UAV can observe the LE-UAVs'transmission strategies and adaptively adjust its trajectory to maximize information interception. To counter this threat, we propose a m

Pronob Kumar Barman, Pronoy Kumar Barman

Automated classification of clinical transcriptions into medical specialties is essential for routing, coding, and clinical decision support, yet prior work on the widely used MTSamples benchmark suffers from severe data leakage caused by applying SMOTE oversampling before train test splitting. We first document this methodological flaw and establish a leaka

Tao Huang, Chen Hou, Guosen Wu, Jiayang Meng

Privacy leakage in LLM agents is often studied through individual storage or execution components, such as memory modules, retrieval pipelines, or tool-mediated artifacts. However, these settings are typically analyzed in isolation, making it difficult to compare how private internal dependence becomes externally recoverable across heterogeneous agent pipeli

Simon D. Nguyen, Hayden McTavish, Kentaro Hoffman, Cynthia Rudin

Active learning reduces labeling costs by selecting samples that maximize information gain. A dominant framework, Query-by-Committee (QBC), typically relies on perturbation-based diversity by inducing model disagreement through random feature subsetting or data blinding. While this approximates one notion of epistemic uncertainty, it sacrifices direct charac

Shuhei Naganuma, Jiro Kitagawa

AI-driven inorganic materials research has garnered significant attention due to its ability to reduce the time, labor, and cost associated with experiments. An AI model known as GNoME, recently developed by Google DeepMind, is particularly fascinating because it is integrated with the Materials Project open database. The experimental verification of compoun

Yu. E. Geints, I. V. Minin, O. V. Minin

A concept of an innovative optical trap based on the retro-reflected standing-wave photon nanojet (SWOT) is presented. An open resonance cavity is formed between two coaxial microparticles of different geometries (sphere, cylinder, ring, truncated cone) with one particle docked to a plain mirror. Numerical simulations have shown the achievement of a record-h

Bo Li, He-Ran Wang, Fei Song

We report a novel mechanism of boundary-sensitive PT symmetry breaking in one-dimensional Floquet systems. By designing a time-periodic driving protocol, we realize a Floquet Hamiltonian that is Hermitian under periodic boundary conditions yet acquires non-Hermitian boundary terms under open boundary conditions due to the non-commutativity of driving Hamilto

Ramakrishna Pasupuleti

We present the KR excitation regulation framework, a coupled ordinary differential equation system that produces Critical Slowing Down (CSD) indicators from rolling earthquake magnitude windows, and demonstrate independent cross catalog replication of a pre seismic CSD quiescence signal across two subduction zone settings. In the Japan USGS catalog (Mc >= 4.

Grigory Ivanov

Qualitatively, a no-dimensional Helly-type theorem says that if every small subfamily of convex sets has a common point in a bounded region, then suitable neighborhoods of all the sets in the whole family have a common point. Quantitative bounds, when available, depend on the ambient metric. We say that a Banach space has the Helly approximation property if

Yongheng Han, Bing Wang

The Calder\'on-Zygmund inequality is a cornerstone of harmonic analysis and partial differential equations. In this article, we establish various Calder\'on-Zygmund inequalities on evolving Riemannian manifolds with bounded curvature. We also provide concrete applications of such inequalities.

Matthew S. Zhang, Jason M. Altschuler, Sinho Chewi

Generating samples from a continuous probability density is a central algorithmic problem across statistics, engineering, and the sciences. For high-dimensional settings, Hamiltonian Monte Carlo (HMC) is the default algorithm across mainstream software packages. However, despite the extensive line of work on HMC and its widespread empirical success, it remai

Yusuke Ikeda, Takuya Kawada, Yuki Shiomi

We demonstrate a pronounced decrease in the electrical resistance of highly disordered palladium (Pd) films deposited under a high working Ar pressure using a compact film coating system. The resulting resistance change ratio of up to $1/335$ is predominant among those reported previously. Film characterization suggests two primary mechanisms responsible for

Jan Oellerich, Takayuki Yamada

This paper proposes a variational framework for multi-objective level set topology optimization. The approach interprets the level set function as a generalized coordinate of a fictitious material and derives its equation of motion from Hamilton's principle, resulting in a damped wave equation governing the optimization process. The objective functionals are

Dimitrios Sinodinos, Bahareh Nikpour, Jack Yi Wei, Sushant Sinha

Accurate prediction of mechanical properties of steel during hot rolling processes, such as Thin Slab Direct Rolling (TSDR), remains challenging due to complex interactions among chemical compositions, processing parameters, and resultant microstructures. Traditional empirical and experimental methodologies, while effective, are often resource-intensive and

Zhi-Xuan Li, Yin-Kai Yu, Zi-Xiang Li, Shuai Yin

Symmetric mass generation (SMG) transitions defy the conventional Landau-Ginzburg-Wilson paradigm by opening a many-body gap without spontaneous symmetry breaking or topological order, attracting intense interest across particle physics and condensed matter physics. Here, we utilize unbiased quantum Monte Carlo simulations to investigate the equilibrium and

Le Bin Ho, Vu Xuan Tung Duong, Nozomu Takahashi, Hiroaki Matsueda

Interference provides a fundamental mechanism for generating and manipulating entanglement in many-body quantum systems. Here, we develop an interference framework in which the nonlinear dynamics of collective spin-$\tfrac{1}{2}$ ensembles are mapped onto phase accumulation and self-interference in phase space, providing a direct and physically transparent d

Tao Chen, Bingjia Xiao, Xin Qian, Puqing Jiang

Thermal interface materials (TIMs) critically regulate heat dissipation from electronic chips to heat spreaders, yet their thermal conductivity (k), volumetric heat capacity (C), and interfacial thermal resistance (ITR) evolve with mechanical pressure and cannot be determined simultaneously using existing steady-state or transient techniques. As a result, th

Khanh Binh Nguyen, Chae Jung Park

Large-scale pre-trained image-text models exhibit robust multimodal representations, yet applying the Contrastive Language-Image Pre-training (CLIP) model to audio-visual localization remains challenging. Replacing the classification token ([CLS]) with an audio-embedded token ([V_A]) struggles to capture semantic cues, and the prompt "a photo of a [V_A]" fai

Jiachen Li, Shihao Li, Jian Chu, Wei Li

Autonomous mobile robot fleets must coordinate task allocation and charging under limited shared resources, yet most battery aware planning methods address only a single robot. This paper extends degradation cost aware task planning to a multi robot setting by jointly optimizing task assignment, service sequencing, optional charging decisions, charging mode

Mohammad Elayan, Wissam Kontar

Driver heterogeneity is often reduced to labels or discrete regimes, compressing what is inherently dynamic into static categories. We introduce quantum-inspired representation that models each driver as an evolving latent state, presented as a density matrix with structured mathematical properties. Behavioral observations are embedded via non-linear Random

Heinrich Dinkel, Jiahao Zhou, Guanbo Wang, Yadong Niu

This paper presents the Interspeech 2026 Audio Encoder Capability Challenge, a benchmark specifically designed to evaluate and advance the performance of pre-trained audio encoders as front-end modules for Large Audio Language Models (LALMs). While LALMs have shown remarkable understanding of complex acoustic scenes, their performance depends on the semantic

Shun Kashiwa, Ayla Kurdak, Savitha Ravi, Ridhi Srikanth

The quality of scientific code is a critical concern for the research community. Poorly written code can result in irreproducible results, incorrect findings, and slower scientific progress. In this study, we evaluate scientific code quality across three dimensions: reproducibility, readability, and reusability. We curated a corpus of 518 code repositories b

Wenqiang Yang, Wenyuan Wu, Yong Feng, Changbo Chen

Simulation and modeling are essential in product development, integrated into the design and manufacturing process to enhance efficiency and quality. They are typically represented as complex nonlinear differential algebraic equations. The growing diversity of product requirements demands multi-task optimization, a key challenge in simulation modeling resear

Tao Chen, Puqing Jiang

Understanding heat transfer across solid-liquid interfaces is central to thermal management and energy technologies, yet whether the interfacial thermal conductance (ITC) depends on the timescale of heating remains unclear. Here we use square-pulsed source thermoreflectance, which combines time-resolved detection with broadband modulation, to probe Al-water

Sangmin Jo, Wootaek Jeong, Da-Woon Heo, Yoohwan Hwang

Recent progress in artificial intelligence has encouraged numerous attempts to understand and decode human visual system from brain signals. These prior works typically align neural activity independently with semantic and perceptual features extracted from images using pre-trained vision models. However, they fail to account for two key challenges: (1) the

Xingguo Chen, Pingshou Xiong, Zhenyu Luo, Mengfei Hu

The efficiency of game engines and policy optimization algorithms is crucial for training reinforcement learning (RL) agents in complex sequential decision-making tasks, such as Tetris. Existing Tetris implementations suffer from low simulation speeds, suboptimal state evaluation, and inefficient training paradigms, limiting their utility for large-scale RL

Wenlang He, Ping Zhou, Bingqiu Chen

Magnetars are highly magnetized neutron stars (NSs) whose evolution and radiation are governed by the decay and/or reconfiguration of their magnetic fields. The origin of magnetars remains an open question, with proposed progenitor scenarios including core-collapse (CC) of very massive stars ($\ge 25~M_\odot$) or non-very massive stars ($8<M_*<25~M_\odot$),

Zerui Guo, Jianbin Tan, Hui Huang

Integrative analysis of multivariate functional time series (MFTS) is both critical and challenging across many scientific domains. Such data often exhibit complex multi-way dependencies arising from within-curve structures, temporal correlations across curves, and cross-subject interactions, underscoring the need for efficient methods that can jointly captu

Peihan Lei, You Huang, Zhi Cheng, Fazhan Shi

Solid-state spins in diamond are promising building blocks for quantum computing and quantum sensing, both of which require precise nanoscale addressing of individual spins. To explore the resolution limit of this approach, we demonstrate Fourier magnetic imaging of nitrogen-vacancy centers in diamond under state-of-the-art conditions. We constructed a highl

Bushra Sabir, Shigang Liu, Seung Ick Jang, Sharif Abuadbba

Automatically generating source code from natural language using large language models (LLMs) is becoming common, yet security vulnerabilities persist despite advances in fine tuning and prompting. In this work, we systematically evaluate whether multi LLM ensembles and collaborative strategies can meaningfully improve secure code generation. We present MULT

Sumin Yu, Juhyeon Park, Taesup Moon

We present PopResume, a population-representative resume dataset for causal fairness auditing of LLM- and VLM-based resume screening systems. Unlike existing benchmarks that rely on manually injected demographic information and outcome-level disparities, PopResume is grounded in population statistics and preserves natural attribute relationships, enabling pa

R. A. Bailey, Rahul Mukerjee

We investigate block designs, under the A- and MV-criteria, when each treatment can have only one or two replications due to resource constraints, as can happen, for example, in early generation varietal trials. While these are commonly known as partially replicated designs, a key new feature of the present work is that no restriction about a constant block

Guangpu Wu, Shibei Xue, Yuting Zhu, Guofeng Zhang

In waveguide quantum electrodynamics (QED) systems, a giant cavity can be engineered to interact with quantum fields by multiple distant coupling points so that its non-Markovian dynamics are quite different from traditional quantum optical cavity systems. Towards feedback control this system, this paper designs an optimal filter for the giant cavity systems

Naohiro Tawara, Samuele Cornell, Alexander Polok, Marc Delcroix

Conversational automatic speech recognition remains challenging due to overlapping speech, far-field noise, and varying speaker counts. While recent LLM-based systems perform well on single-speaker benchmarks, their robustness in multi-speaker settings is unclear. We systematically compare LLM-based and modular pipeline approaches along four axes: overlap ro

Xinyi Zhang, Tiantian Chen, Zhentao Han, Zhaoyan Hong

Modern database management systems (DBMSs) expose hundreds of configuration knobs that critically influence performance. Existing automated tuning methods either adopt a data-driven paradigm, which incurs substantial overhead, or rely on manual-driven heuristics extracted from database documentation, which are often limited and overly generic. Motivated by t

Pranav Shetty, Mirazul Haque, Zhiqiang Ma, Xiaomo Liu

As large language models (LLMs) are trained on increasingly vast and opaque text corpora, determining which data contributed to training has become essential for copyright enforcement, compliance auditing, and user trust. While prior work focuses on detecting whether a dataset was used in training (membership inference), the complementary problem -- verifyin

Wentao Lu, Alexander Senchenko, Alan Sayle, Abram Hindle

Video-based quality assurance (QA) for long-form gameplay video is labor-intensive and error-prone, yet valuable for assessing game stability and visual correctness over extended play sessions. Vision language models (VLMs) promise general-purpose visual reasoning capabilities and thus appear attractive for detecting visual bugs directly from video frames. R

Emiko Shishido, Seiko Miyata, Tetsuya Yamamoto, Masaki Fukunaga

Background: Autism spectrum disorder (ASD) is characterized by significant clinical and biological heterogeneity. Conventional group-mean analyses of eye movements often mask individual atypicalities, potentially overlooking critical pathological signatures. This study aimed to identify idiosyncratic oculomotor patterns in ASD using an "outlier analysis" of

Baihan Li, Bingrui Jin, Kunyao Lan, Ming Wang

Patient simulation is essential for developing and evaluating mental health dialogue systems. As most existing approaches rely on snapshot-style prompts with limited profile information, homogeneous behaviors and incoherent disease progression in multi-turn interactions have become key chellenges. In this work, we propose DEPROFILE, a data-grounded patient s

Yifan Sun, Yiyuan Pan, Shangtao Li, Caiwu Ding

Emergency stop (E-stop) mechanisms are the de facto standard for robot safety. However, for humanoid robots, abruptly cutting power can itself cause catastrophic failures; instead, an emergency stop must execute a predefined fallback controller that preserves balance and drives the robot toward a minimum-risk condition. This raises a critical question: from

Yumou Fei

We initiate the study of distribution testing for probability distributions over the edges of a graph, motivated by the closely related question of ``edge-distribution-free&#39;&#39; graph property testing. The main results of this paper are nearly-tight bounds on testing bipartiteness, triangle-freeness and square-freeness of edge distributions, whose sampl

Teer Song, Yue Zhang, Yu Tian, Ziyang Wang

Recent progress in face restoration has shifted from visual fidelity to identity fidelity, driving a transition from reference-free to reference-based paradigms that condition restoration on reference images of the same person. However, these methods assume the reference and degraded input are age-aligned. When only cross-age references are available, as in

Tetsuro Tsuchino, Motoki Shiga

In solving partial differential equations (PDEs), machine learning utilizing physical laws has received considerable attention owing to advantages such as mesh-free solutions, unsupervised learning, and feasibility for solving high-dimensional problems. An effective approach is based on physics-informed neural networks (PINNs), which are based on deep neural

Sabyasachi Chattopadhyay, Matthew A. Bershady, Michael P. Smith, Antoine Mahoro

Integral field spectroscopy (IFS) has been added as a new observation mode to the Robert Stobie Spectrograph (RSS), the workhorse multi-mode instrument on the Southern African Large Telescope. RSS operates as an imaging spectrograph covering 320-900 nm with a spectral resolution--slit-width product of 6600 arcsec. Using fiber optics and prismatic fold mirror

Kyle Linn, Megan Goh, Sachin Vaidya, Christina Jörg

Photonic systems provide a highly tunable platform for emulating quantum Hall physics. This tunability enables probing of the interplay between strong disorder and robust topological transport that remains difficult to access in solid-state systems. Here we realize a photonic version of the Harper-Hofstadter and Aubry-Andr\'e models using a one-dimensional m

Takumu Shinkai, Satoshi Iihama, Kensuke Hayashi, Takahiro Moriyama

Spin-wave-based physical reservoir computing (RC) is a promising candidate for energy-efficient physical implementations of artificial intelligence because of its potential for nanoscale integration with low power consumption. Most of the previous studies on spin-wave RC have utilized spin waves excited in a single-layer ferromagnet. In this study, we focuse

Zhibin Su, Junjian Mi, Shaohua Yan, Jiade Li

Excitonic band structure is critical for investigating exciton dynamics. Theoretically, quantum effects from exchange scattering between electron-hole pairs significantly modulate exciton dispersion. Here, we report the direct observation of dimensionality-dependent exciton dispersion in a single-band Mott insulator Nb3Cl8 through high-resolution electron en

Cameron Kemp

This is a continuation of the previous paper (arXiv:2508.01944) in this series. We recontextualise Cirio and Martins' work to motivate our fundamental conjecture that the Drinfeld-Kohno (Lie) 2-algebra has trivial cohomology. It is then shown that this conjecture implies the following: given a coherent totally symmetric infinitesimal 2-braiding $t$, every mo

Shulan Yan, Qingzheng Yu, Taotao Fang, Chuan He

Atomic hydrogen (HI) plays a fundamental role in fueling star formation in galaxies. However, the behavior of HI gas in interacting systems, particularly galaxy pairs, remains elusive. In this work, we investigate the HI content of major mergers by cross-matching the extragalactic HI catalog from the FAST All-Sky HI Survey (FASHI) with a previously establish

Mohammad Salmanpour, Shahram Taeb, Ali Fathi Jouzdani, Mohammad Ayazi

Artificial intelligence based radiomics models for thyroid ultrasound (US) often achieve strong diagnostic performance but remain difficult to interpret, limiting clinical trust and adoption. We developed and validated an interpretable radiomic feature (RF) framework for thyroid nodule classification by linking quantitative US features to the Thyroid Imaging

Tianfang Xie

Fully provisioned Message Passing Interface (MPI) parallelism achieves near-optimal wall-clock time for Computational Fluid Dynamics (CFD) solvers. This work addresses a complementary question for shared, cloud-managed clusters: can fine-grained CPU provisioning reduce resource reservation of low-load subdomains, improving cluster packing efficiency without

Tzu-Ti Wei, Chu-Yu Huang, Yu-Chee Tseng, Jen-Jee Chen

Privacy-preserving semantic understanding of human activities is important for indoor sensing, yet existing Wi-Fi CSI-based systems mainly focus on pose estimation or predefined action classification rather than fine-grained language generation. Mapping CSI to natural-language descriptions remains challenging because of the semantic gap between wireless sign

Zhanjie Wen, Jingqiao Guo

Corporate AI-washing-the strategic misrepresentation of AI capabilities via exaggerated or fabricated cross-channel disclosures-has emerged as a systemic threat to capital market information integrity with the widespread adoption of generative AI. Existing detection methods rely on single-modal text frequency analysis, suffering from vulnerability to adversa

Mingrui Chen, Hexiong Yang, Haogeng Liu, Huaibo Huang

In this paper, we present a holistic multimodal benchmark that evaluates the reasoning capabilities of MLLMs with an explicit focus on reasoning width, a complementary dimension to the more commonly studied reasoning depth. Specifically, reasoning depth measures the model's ability to carry out long-chain, sequential reasoning in which each step is tightly a

Vadim E. Levit, Eugen Mandrescu

It was proved in (Levit and Mandrescu, 2022) that both $(V(G), Crown(G))$ and $(V(G), CritIndep(G))$ are augmentoids, established partial augmentation phenomena for the family $\Psi(G)$ of local maximum independent sets, and asked in Problem~5.5 to characterize the graphs whose family $\Psi(G)$ is an augmentoid. We prove that the answer is positive in full g

Jiayin Sun, Caixia Sun, Boyu Yang, Hailin Li

Multimodal Large Language Models (MLLMs) have recently demonstrated remarkable perceptual and reasoning abilities. However, they struggle to perceive fine-grained geometric structures, constraining their ability of geometric understanding and visual reasoning. To address this, we propose GeoTikzBridge, a framework that enhances local geometric perception and

Zhaoxiang Liu, Samuel Judson, Raj Dutta, Mark Santolucito

We present BlindMarket, an end-to-end zero-trust distribution framework for hardware IP cores. BlindMarket allows two parties, the IP user and the IP vendor, to complete an IP trading process with strong guarantees of verifiability and confidentiality before the transaction, and then traceability after. We propose verification heuristics and adapt the cone o

A. Elgailani, D. Vandembroucq, C. E. Maloney

We study a mesoscopic elasto-plastic model of amorphous matter with varying dimensionless compression modulus, $K/\mu$, where $K$ and $\mu$ are the compression and shear moduli. We study both cyclic shear with amplitude $\Gamma$ and forward steady shear. In cyclic shear, the terminal behavior is, in order of increasing $\Gamma$: i) trivially elastic, ii) hys

Edward Y. Chang

We reduce the Collatz conjecture to a fixed-modulus, one-bit orbit-mixing problem. Working with the compressed odd-to-odd Collatz map, we prove exact low-depth decomposition formulas at depths K = 3, 4, 5, reducing block-discrepancy terms to explicit run statistics. We then prove a Map Balance Theorem: among the 2^(K-3), 1 burst residues modulo 2^K that init

Tsogtgerel Gantumur

The family of Tibetan lunisolar calendars operates on a shared arithmetic axiom (67 lunar months = 65 solar months) that provides a rigid structure but causes observable seasonal drift. This study deconstructs the calendar through a progressive analytical sequence, first presenting it as an explicit computational procedure, then isolating its structural core

Dariush Amirkhani, Junfeng Zhang

We propose a novel iterative process to establish the minimum separation between two ellipsoids. The method maintains one point on each surface and updates their locations in the theta-phi parametric space. The tension along the connecting segment between the two surface points serves as the guidance for the sliding direction, and the distance between them d

Zhenning Wang, Ni Lu, Dan Liu, Xiaosen Yang

We introduce the non-Hermitian mosaic Maryland model, where a discrete modulation period and a non-Hermitian phase are incorporated into the potential, rendering the originally exactly solvable system generally non-integrable. This model provides a unique platform to investigate how structural modulation governs localization in complex quasiperiodic potentia

Gianni Boschetti, Miguel Campiglia

Using matching properties of the gravitational field at timelike and spatial infinity, together with universal formulas for gravitational wave tails, we obtain exact expressions for the peeling-violating components of the Weyl tensor at future and past null infinity. The coefficients depend solely on incoming scattering data and reduce, in the Newtonian limi

Md Nurul Anwar, Michael Pan, Ashleigh V. Morrice-West, Fatemeh Malekipour

Musculoskeletal injuries (MSI) in Thoroughbred racehorses are a leading cause of death and premature retirement in racehorses and are heavily influenced by training practices. Greater distances of high-speed galloping accumulated during racing campaigns are associated with MSI. Bone injury is the most common MSI, and understanding how training practices infl

Dun Liang, Bin Xu, Wenyan Yang

We study the asymptotic behaviour of the character summation part of the Frobenius formula for three conjugacy classes of the symmetric groups. When two conjugacy classes contain fixed points of order $\sim H_i\sqrt{n}$ for $i=1,2$ and all other cycles are long, the summation converges uniformly to $2e^{-H_1H_2}$; the exponential factor coincides with the no

Ruofan Jiang, Ananth N. Shankar, Ziquan Yang

Let X/C be a non iso-trivial family of K3 surfaces over a curve C defined over characteristic p > 2 field. We show that if X avoids a necessary and structural obstruction coming from Frobenius, and satisfies a big monodromy condition, then there are infinitely may geometric fibers that have larger Picard rank than the geometric generic fiber.

Di Zhu, Zixuan Li

Distributional metrics such as Fr\'echet Audio Distance cannot score individual music clips and correlate poorly with human judgments, while the only per-sample learned metric achieving high human correlation is closed-source. We introduce MUQ-EVAL, an open-source per-sample quality metric for AIgenerated music built by training lightweight prediction heads

Ashif Khan, Chetan D. Pahlajani

The principal aim of the present work is to explore limit theorems for small random perturbations of dynamical systems with periodic impulse effects, in the limit of vanishing noise intensity. We start with a system whose time evolution is governed by a nonlinear ordinary differential equation in between impulses, and a nonlinear resetting map at impulses; t

Sakib Mostafa, Tarik Massoud, Maximilian Diehn, Lei Xing

Tabular data are central to biomedical research, from liquid biopsy and bulk and single-cell transcriptomics to electronic health records and phenotypic profiling. Unlike images or sequences, however, tabular datasets lack intrinsic spatial organization: features are treated as unordered dimensions, and their relationships must be inferred implicitly by the

Boxuan Ma, Baofeng Ren, Huiyong Li, Gen Li

Generative AI tools are increasingly used for coursework help, shifting much of students' help-seeking and reasoning into student-AI chats that are largely invisible to instructors. This loss of visibility can weaken instructors' ability to understand students' difficulties, ensure alignment with course goals, and uphold course policies. Yet transcript-level

Boxuan Ma, Yinjie Xie, Huiyong Li, Gen Li

AI-powered coding assistants can support students in programming courses by providing on-demand explanations and debugging help. However, existing research often focuses on individual tools, leaving a gap in evidence-based design recommendations that reflect both educator and student perspectives in education settings. To ground the design of learning-orient

Boxuan Ma, Huiyong Li, Gen Li, Li Chen

Generative AI (GenAI) tools such as ChatGPT now provide novice programmers with instant, personalized support and are reshaping computing education. While a growing body of work examines AI's immediate impacts, longitudinal evidence remains limited on how students' awareness, student-AI interaction patterns, and course outcomes evolve as AI becomes routine i

Sebastian V. Pios, Maxim F. Gelin, Wolfgang Domcke, Lipeng Chen

In multi-dimensional time-resolved spectroscopic experiments, multiple (more than two) short laser pulses with variable pulse delay times are employed for the time-resolved exploration of the photoinduced dynamics of molecular chromophores. In the present work, the quasi-classical doorway-window (DW) methodology recently developed for transient absorption pu

K. Hermansen, W. -J. Ong, H. Schatz, J. Browne

Understanding the thermal structure of the outer crust of accreting neutron stars is important to interpret astronomical X-ray observations. Ground-state to ground-state $\beta$-decay transitions of neutron-rich nuclei comprising the crust enable Urca neutrino cooling processes that affect this thermal structure. Here we constrain the ground-state to ground-

Ye Li, Yewei Huang, Wenlong GaoZhang, Alberto Quattrini Li

Autonomous exploration by unmanned surface vehicles (USVs) in near-shore waters requires reliable localisation and consistent mapping over extended areas, but this is challenged by GNSS degradation, environment-induced localisation uncertainty, and limited on-board computation. Virtual map-based methods explicitly model localisation and mapping uncertainty b

Fereshteh Yousefirizi, Jean-Mathieu Beauregard, Arman Rahmim

Quantitative molecular imaging is central to treatment response assessment in oncology, yet clinical practice remains largely dominated by patient-level or limited target-lesion criteria that ignore inter-lesion heterogeneity. This limitation is particularly important in prostate cancer, where PSMA PET/CT can reveal extensive skeletal and nodal metastatic di

Itay Glazer, Dan Mikulincer

We begin with the observation, based on previous results, that dimension-free lower bounds on the variance of a polynomial under a log-concave measure yield dimension-free small-ball and Fourier decay estimates. Motivated by this, we establish variance bounds for polynomials on log-concave random vectors beyond the classical setting of product measures. Firs

Vu Thi Hai Yen, Duc V. Nguyen, Cao Anh Minh Huy, Truong Thu Huong

Short-form videos have become one of the most popular user-generated content formats nowadays. Popular short-video platforms use a simple streaming approach that preloads one or more videos in the recommendation list in advance. However, this approach results in significant data wastage, as a large portion of the downloaded video data is not used due to the

Pavel Kornilovitch

We propose an explanation of the observed dependence of the maximal critical temperature $T_{c,max}$ on the number of conducting layers $n$ in layered copper-oxide superconductors within the preformed pair mechanism. Copper-oxygen planes fine-tune the lattice anisotropy and regulate the balance between the attractive and kinetic energies of carrier holes. To

Mohamed Bahi Yahiaoui, Geoffrey Daniel, Loïc Giraldi, Jérémie Bruyelle

Out-of-distribution (OOD) detection aims to identify inputs that differ from the training distribution in order to reduce unreliable predictions by deep neural networks. Among post-hoc feature-space approaches, OOD detection is commonly performed by approximating the in-distribution support in the representation space of a pretrained network. Existing method

Marc Fersztand, Jan Jendrysiak

We develop the first algorithms for computing the Skyscraper Invariant [FJNT24]. This is a filtration of the classical rank invariant for multiparameter persistence modules defined by the Harder-Narasimhan filtrations along every central charge supported at a single parameter value. Cheng's algorithm [Cheng24] can be used to compute HN filtrations of arbitra

Mattia Gatti, Alberto Mariani, Ignazio Gallo, Fabiano Monti

Accurate change detection from satellite imagery is essential for monitoring rapid mass-movement hazards such as snow avalanches, which increasingly threaten human life, infrastructure, and ecosystems due to their rising frequency and intensity. This study presents a systematic investigation of large-scale avalanche mapping through bi-temporal change detecti

Sanchit Sabhlok, Shelley A. Wright, Andrey Vayner, Norman Murray

We present Ly$\alpha$, He II and C IV observations of 7 redshift ~ 2 radio-loud quasars observed using the Keck Cosmic Web Imager (KCWI) and compare it to observed radio jet emission using archival VLA and ALMA radio observations. We detect 80-120 kpc diameter Ly$\alpha$ and 10-40 kpc He II and C IV emission around the targets. We find the Ly$\alpha$ emissio

S. Cordero, E. Nahmad-Achar, O. Castaños, R. López-Peña

Quantum information measures are used to study the quantum phase diagrams of the two-level Dicke model including the atomic dipole-dipole interaction, for a finite number of particles, with and without the rotating-wave approximation, which yields the conservation of the total number of excitations in the first case and its parity in the general case. We sho

Rhea Ghosal, Ronok Ghosal, Eileen Lou

Portable CT scanners may support earlier stroke assessment, but reduced photon counts introduce noise that affects image quality and may alter automated classification. We compared direct classification of simulated low-dose slices with residual U-Net denoising followed by the same fixed classifier. Poisson noise was generated at photon-count scaling factors

Yiming Wang, Zhengnan Zhang, Genghe Zhang, Jiawen Dan

Learning system dynamics from observations is a critical problem in many applications over various real-world complex systems, e.g., climate, ecology, and fluid systems. Recently, neural dynamics modeling method have become a prevalent solution that embeds the object's observations into a latent space before learning dynamics using neural methods such as neu

Bo Wang, Miroslav Krstic

We develop an optimization-free framework for safe stabilization of single-input control-affine nonlinear systems with a given control Lyapunov function (CLF) and a given control barrier function (CBF), where the desired equilibrium lies in the interior of the safe set. An explicit compatibility condition is derived that is necessary and sufficient for the p

Yingjie Mi, Zihao Ren, Lei Wang, Daniel E. Quevedo

This paper studies quantum-encrypted explicit MPC for constrained discrete-time linear systems in a cloud-based architecture. A finite-horizon quadratic MPC problem is solved offline to obtain a piecewise-affine controller. Shared quantum keys generated from Bell pairs and protected by quantum key distribution are used to encrypt the online control evaluatio

Luca Avena, Conrado da Costa

Transport in disordered media is a central theme in probability and statistical physics, where randomness in the underlying medium produces phenomena such as localization, anomalous scaling, and slow relaxation. A paradigmatic model for transport in disordered media is that of Random Walks in Random Environments (RWRE), which has been extensively studied sin

Siddhant Kulkarni, Yukta Kulkarni

The adoption of large language models (LLMs) for structured information extraction from financial documents has accelerated rapidly, yet production deployments face fundamental architectural decisions with limited empirical guidance. We present a systematic benchmark comparing four multi-agent orchestration architectures: sequential pipeline, parallel fan-ou

Lican Huang

This paper introduces Calligraphy Writing Score Representation (CWSR) and proposes Shu Dao as a framework that interprets East Asian calligraphy as a performative art rather than a static visual artifact. Inspired by traditions such as Japanese Shodō and embodied cultural practices such as Chadao , the framework models calligraphy as a structured performance

Pierre Halftermeyer

We prove that Cypher 25, the graph query language of Neo4j, is Turing-complete. The proof shows that a single RETURN statement using reduce(), CASE expressions, and list comprehensions can simulate any 2-counter machine (Minsky 1967). We address the bounded-step objection via two complementary resolutions and present a third graph-native simulation using qua

Ruichen Zheng, Biao Zhang, Michael Birsak, Mikhail Skopenkov

We introduce Patchwork, a new general-purpose shape representation capable of modeling 2D and 3D geometry with a small number of parameters. Patchwork is grounded in a rigorous mathematical framework, providing provable complexity bounds and the ability to approximate arbitrary shapes with arbitrary precision in any dimension. We propose an efficient gradien

Ashwin Aravind

The safety of autonomous AI agents is increasingly recognized as a critical open problem. As agents transition from passive text generators to active actors capable of executing shell commands, modifying files, calling APIs, and browsing the web, the consequences of unsafe or adversarially manipulated behavior become immediate and tangible. Existing AI safet

Hiroshi Matsubara, Shingo Matsugaya, Taichi Aoki, Masaki Hashimoto

This study investigates the applicability of authorship attribution based on stylistic features to support actor analysis in threat intelligence. As a foundational step toward future application to dark web forums, we conducted experiments using Japanese review data from clear web sources. We constructed datasets from Rakuten Ichiba reviews and compared four

Eugene Lee, Yu-Chi Lin, Jiajie Diao

Multimodal Large Language Models (MLLMs) adapt to visual tasks via in-context learning (ICL), which relies heavily on demonstration quality. The dominant demonstration selection strategy is unsupervised k-Nearest Neighbor (kNN) search. While simple, this similarity-first approach is sub-optimal for complex factual regression tasks; it selects redundant examp

David J. Hoffman, Douglas Garratt, Matthew Bain, Christina Y. Hampton

The chemRIXS instrument at the Linac Coherent Light Source offers new opportunities for studying solution-phase systems with time-resolved soft X-ray spectroscopy through the recently commissioned high-repetition-rate LCLS-II X-ray free electron laser. The orders-of-magnitude X-ray flux improvement provided by the superconducting accelerator, combined with c

Harun Tolasa, Volkan Patoglu

During human motor skill training and physical rehabilitation, there is an inherent trade-off between task difficulty and user performance. Characterizing this trade-off is crucial for evaluating user performance, designing assist-as-needed (AAN) protocols, and assessing the efficacy of training protocols. In this study, we propose a novel human-in-the-loop

Ram Rachum, Yotam Amitai, Yonatan Nakar, Reuth Mirsky

A major challenge of Reinforcement Learning is that agents often learn undesired behaviors that seem to defy the reward structure they were given. Explainable Reinforcement Learning (XRL) methods can answer queries such as &#34;explain this specific action&#34;, &#34;explain this specific trajectory&#34;, and &#34;explain the entire policy&#34;. However, XRL

L. Pantoni, M. Baes, L. Decin, P. Guillard

Polycyclic aromatic hydrocarbons (PAHs) are key dust components in galaxies and play a fundamental role in the physics of the interstellar medium (ISM), yet their response to AGN feedback remains debated. We present a spatially resolved analysis of PAHs in the central $7^{\prime\prime}\times12^{\prime\prime}$ ($\sim100\times200$ pc$^2$) of Centaurus A. We us