November 2025 arXiv papers — page 173

Hanzheng Lyu, Shaokang Xie, Jianyu Niu, Mohammad Sadoghi

Multi-Byzantine Fault Tolerant (Multi-BFT) consensus, which runs multiple BFT instances in parallel, has recently emerged as a promising approach to overcome the leader bottleneck in classical BFT protocols. However, existing designs rely on a global ordering layer to serialize blocks across instances, an intuitive yet costly mechanism that constrains scalab

Nan Qiao, Wangcheng Li, Jingxiao Zhang, Canyi Chen

Synthesizing information from multiple data sources is crucial for constructing accurate individualized treatment rules (ITRs). However, privacy concerns often present significant barriers to the integrative analysis of such multi-source data. Classical meta-learning, which averages local estimates to derive the final ITR, is frequently suboptimal due to bia

Changqing Gong, Huafeng Qin, Mounim A. El-Yacoubi

Alzheimer's disease is a prevalent neurodegenerative disorder for which early detection is critical. Handwriting-often disrupted in prodromal AD-provides a non-invasive and cost-effective window into subtle motor and cognitive decline. Existing handwriting-based AD studies, mostly relying on online trajectories and hand-crafted features, have not systematica

Zhanyi Jiao, Qiuqi Wang, Yimiao Zhao

Backtesting risk measures is a central task in financial regulation. While standard backtests evaluate whether a forecasting model is statistically consistent with observed losses, regulatory practice often requires assessing the performance of an internal model relative to benchmark models. We develop a non-parametric sequential framework for comparative ba

Qiu-Ju Huang, Bao Wang, Jun-Jie Wei, Xue-Feng Wu

Axions or axion-like particles (ALPs) are well-motivated dark matter (DM) candidates whose coupling to photons induces periodic oscillations in the polarization angle of astrophysical light. This work reports the first search for such a signature using ten years of optical polarimetric monitoring of the blazar 1ES 1959+650. No statistically significant perio

Laasya Koduru, Sylee Beltiukov, Jaber Daneshamooz, Eugene Vuong

Poor broadband access undermines civic and economic life, a challenge exacerbated by the fact that millions of Americans still lack reliable high-speed connectivity. Federal broadband funding initiatives aim to address these gaps, but their success depends on accurate availability and affordability data. Existing data, often based on self-reported ISP inform

Michael Lesnick, Matthew Wright

The fibered barcode $\mathcal{F}(M)$ of a bipersistence module $M$ is the map sending each non-negatively sloped affine line $\ell \subset \mathbb{R}^2$ to the barcode of the restriction of $M$ along $\ell$. The simplicity, computability, and stability of $\mathcal{F}(M)$ make it a natural choice of invariant for data analysis applications. In an earlier pre

Yui Tatsumi, Ziyue Zeng, Hiroshi Watanabe

Image Coding for Machines (ICM) has become increasingly important with the rapid integration of computer vision technology into real-world applications. However, most neural network-based ICM frameworks operate at a fixed rate, thus requiring individual training for each target bitrate. This limitation may restrict their practical usage. Existing variable ra

Wei-Wei Zhang, Chao Chen, Jizhou Wu

Biphoton states have shown promising applications in quantum information processing, including quantum communications, quantum metrology, and quantum imaging. The generation and manipulation of biphoton entanglement in topologically distinct modes paves the way in this direction. Here we present a comprehensive method for regulating the topological propertie

Christian Ellington, Paramahansa Pramanik, Haley K. Robinson

The popularity of electronic games has grown steadily in recent years, attracting a broad audience across age groups. With this growth comes a large volume of related data, prompting efforts like the PlayMyData to compile and share structured datasets for academic use. This study utilizes such a dataset to compare user review ratings across four current-gene

Xinshan Jiao, Yuxin Luo, Yilin Bi, Tao Zhou

Link prediction is a fundamental problem in network science, aiming to infer potential or missing links based on observed network structures. With the increasing adoption of parameterized models, the rigor of evaluation protocols has become critically important. However, a previously common practice of using the test set during hyperparameter tuning has led

Taixi Chen, Yiu-ming Cheung

Remote photoplethysmography (rPPG) can remotely extract physiological signals from RGB video, which has many advantages in detecting heart rate, such as low cost and no invasion to patients. The existing rPPG model is usually based on the transformer module, which has low computation efficiency. Recently, the Mamba model has garnered increasing attention due

Yunge Li, Lanyu Xu

The quadratic compute and memory costs of global self-attention severely limit its use in high-resolution images. Local attention reduces complexity by restricting attention to neighborhoods. Block-sparse kernels can further improve the efficiency of local attention, but conventional local attention patterns often fail to deliver significant speedups because

Zhen Zhang, Yuantao Ding, David Cesar, Feng Zhou

Beam halo formation poses a critical challenge for high-repetition-rate continuous-wave (CW) free-electron lasers (FELs), directly affecting beam quality and machine protection, as observed during the LCLS-II commissioning. We identify and experimentally validate a previously unrecognized three-step mechanism for halo generation in the photoinjector, arising

Gunhee Cho, Myungsin Cho

We prove that the Green--Griffiths--Demailly (GGD) hyperbolicity thresholds are structurally invariant. In other words, the minimal jet order and asymptotic growth rate at which invariant jet differentials appear remain unchanged when passing from a compact complex manifold to any compact smooth analytic Deligne--Mumford stack (orbifold) with the same coarse

Somnath Dutta, Chin-Fei Lee, Naomi Hirano, Doug Johnstone

Protostellar jets provide valuable insight into the evolutionary stage and formation history of star-forming cores in their earliest phases. We investigated the inner envelope structures of three extremely young protostars, selected for having the shortest dynamical timescales in their outflows and jets. Our analysis is based on Atacama Large Millimeter/subm

Zhiguan Niu, Xiaochao Zhou, Hao Xiong

Missiles pose a major threat to aircraft in modern air combat. Advances in technology make them increasingly difficult to detect until they are close to the target and highly resistant to jamming. The evasion maneuver is the last line of defense for an aircraft. However, conventional rule-based evasion strategies are limited by computational demands and aero

Stephen Brockerhoff, Brittany Corn-Agostini

The ability to preserve multipartite entanglement in noisy environments is central to advancing quantum information processing. In this work, we develop a semiclassical theoretical model of three entangled qubits exposed to local Markov noise environments with tunable statistical correlations between noise sources. We show that such correlations can signific

Taixi Chen, Yiu-ming Cheung, Yiqun Zhang

An appropriate distance metric is crucial for categorical data clustering, as the distance between categorical data cannot be directly calculated. However, the distances between attribute values usually vary in different clusters induced by their different distributions, which has not been taken into account, thus leading to unreasonable distance measurement

Boyang Liu

In order to meet the needs of students' programming debugging ability training, this paper designs and implements a data acquisition and analysis system for programming debugging process based on VS Code plug-in, which aims to solve the limitation of traditional assessment methods that are difficult to fully evaluate students' debugging ability. The system s

Chaiyong Ragkhitwetsagul, Morakot Choetkiertikul, Srisupa Palakvangsa-Na-Ayudhya, Thanwadee Sunetnanta

The COVID-19 pandemic impacted the way of working, including software development. During the pandemic, software companies were forced to work remotely, and many companies have been using such work arrangements. There are prior studies showing the benefits and drawbacks of remote work in software development during COVID-19. However, there is no study that t

Yihang Qiu, Zengrong Huang, Simin Tao, Hongda Zhang

Recent research has demonstrated that artificial intelligence (AI) can assist electronic design automation (EDA) in improving both the quality and efficiency of chip design. But current AI for EDA (AI-EDA) infrastructures remain fragmented, lacking comprehensive solutions for the entire data pipeline from design execution to AI integration. Key challenges in

Sayak Mukherjee, Ramij R. Hossain, Kaustav Chatterjee, Sameer Nekkalapu

This paper explores the development of learning-based tunable control gains using EMT-in-the-loop simulation framework (e.g., PSCAD interfaced with Python-based learning modules) to address critical sub-synchronous oscillations. Since sub-synchronous control interactions (SSCI) arise from the mis-tuning of control gains under specific grid configurations, ef

Rujiphart Charatvaraphan, Bunradar Chatchaiyadech, Thitirat Sukijprasert, Chaiyong Ragkhitwetsagul

Assessing developer proficiency in open-source software (OSS) projects is essential for understanding project dynamics, especially for expertise. This paper presents PyGress, a web-based tool designed to automatically evaluate and visualize Python code proficiency using pycefr, a Python code proficiency analyzer. By submitting a GitHub repository link, the s

Nina Lutz, Benjamin Olsen, Weishung Liu, E. Glen Weyl

Previous work has found a lack of research in HCI on religion, partly driven by misunderstandings of values and practices between religious and technical communities. To bridge this divide in an empirically rigorous way, we conducted an interview study with 48 religious people and/or experts from 11 faiths, and we document how religious people experience, un

Yuchen Su, Zhineng Chen, Yongkun Du, Zuxuan Wu

End-to-end text spotting aims to jointly optimize text detection and recognition within a unified framework. Despite significant progress, designing an accurate and efficient end-to-end text spotter for arbitrary-shaped text remains challenging. We identify the primary bottleneck as the lack of a reliable and efficient text detection method. To address this,

Weiyan Shi, Sunaya Upadhyay, Geraldine Quek, Kenny Tsu Wei Choo

Sketching is a widely used medium for generating and exploring early-stage design concepts. While generative AI (GenAI) chatbots are increasingly used for idea generation, designers often struggle to craft effective prompts and find it difficult to express evolving visual concepts through text alone. In the formative study (N=6), we examined how designers us

Taku Okawara, Ryo Nishibe, Mao Kasano, Kentaro Uno

Limbed climbing robots are designed to explore challenging vertical walls, such as the skylights of the Moon and Mars. In such robots, the primary role of a hand-eye camera is to accurately estimate 3D positions of graspable points (i.e., convex terrain surfaces) thanks to its close-up views. While conventional climbing robots often employ RGB-D cameras as h

Ji Cheng, Bo Xue, Qingfu Zhang

Parametric multi-objective optimization (PMO) addresses the challenge of solving an infinite family of multi-objective optimization problems, where optimal solutions must adapt to varying parameters. Traditional methods require re-execution for each parameter configuration, leading to prohibitive costs when objective evaluations are computationally expensive

Shuning Lin, Yifan He, Yitong Chen

In today's landscape, Mixture of Experts (MoE) is a crucial architecture that has been used by many of the most advanced models. One of the major challenges of MoE models is that they usually require much more memory than their dense counterparts due to their unique architecture, and hence are harder to deploy in environments with limited GPU memory, such as

In-on Wiratsin, Chaiyong Ragkhitwetsagul, Matheus Paixao, Denis De Sousa

Suboptimal code is prevalent in software systems. Developers often write low-quality code due to factors like technical knowledge gaps, insufficient experience, time pressure, management decisions, or personal factors. Once integrated, the accumulation of this suboptimal code leads to significant maintenance costs and technical debt. Developers frequently co

Addison Kalanther, Daniel Bostwick, Chinmay Maheshwari, Shankar Sastry

We consider a scenario where a team of two unmanned aerial vehicles (UAVs) pursue an evader UAV within an urban environment. Each agent has a limited view of their environment where buildings can occlude their field-of-view. Additionally, the pursuer team is agnostic about the evader in terms of its initial and final location, and the behavior of the evader.

Yu Zhang, Hui-Ling Zhen, Mingxuan Yuan, Bei Yu

Training large language models with FP8 formats offers significant efficiency gains. However, the reduced numerical precision of FP8 poses challenges for stable and accurate training. Current frameworks preserve training performance using mixed-granularity quantization, i.e., applying per-group quantization for activations and per-tensor/block quantization f

Bowen Xu, Xinyue Zeng, Jiazhen Hu, Tuo Wang

Building trustworthy clinical AI systems requires not only accurate predictions but also transparent, biologically grounded explanations. We present \texttt{DiagnoLLM}, a hybrid framework that integrates Bayesian deconvolution, eQTL-guided deep learning, and LLM-based narrative generation for interpretable disease diagnosis. DiagnoLLM begins with GP-unmix, a

Yu Chen, Botao He, Yuemin Mao, Arthur Jakobsson

For many complex tasks, multi-finger robot hands are poised to revolutionize how we interact with the world, but reliably grasping objects remains a significant challenge. We focus on the problem of synthesizing grasps for multi-finger robot hands that, given a target object's geometry and pose, computes a hand configuration. Existing approaches often strugg

Cheng Li, Yong Xu, Suhua Tang, Wenqiang Lin

In e-commerce service recommendation, utilizing auxiliary behaviors to alleviate data sparsity often relies on the flawed assumption that auxiliary behaviors that fail to trigger target actions are negative samples. This approach is fundamentally flawed as it ignores false negatives where users actually harbor latent intent or interest but have not yet conve

The NOvA Collaboration

We present a high-statistics measurement of muon antineutrino-induced charged-current neutral pion production on a hydrocarbon target using the NOvA Near Detector. The differential cross sections as functions of the momenta and angles of the outgoing pion and muon, the squared four-momentum transfer, and the invariant mass of the hadronic system at an averag

Caleb McKinley Shor

In this paper, we extend recent results about the distribution of even and odd gaps of a numerical semigroup. We find that, for any numerical semigroup, the distribution can be computed in terms of the numbers of or the sums of odd and even elements in a corresponding Ap\'ery set. With free numerical semigroups specifically, we show that there are always at

Winston Chen, Michael W. Sjoding, Jenna Wiens

AI models are often evaluated based on their ability to predict the outcome of interest. However, in many AI for social impact applications, the presence of an intervention that affects the outcome can bias the evaluation. Randomized controlled trials (RCTs) randomly assign interventions, allowing data from the control group to be used for unbiased model eva

Valentin Noël

Agentic language models compose multi step reasoning chains, yet intermediate steps can be corrupted by inconsistent context, retrieval errors, or adversarial inputs, which makes post hoc evaluation too late because errors propagate before detection. We introduce a diagnostic that requires no additional training and uses only the forward pass to emit a binar

Lalit Maurya, Honghai Liu, Reyer Zwiggelaar

Medical image segmentation faces challenges due to variations in anatomical structures. While convolutional neural networks (CNNs) effectively capture local features, they struggle with modeling long-range dependencies. Transformers mitigate this issue with self-attention mechanisms but lack the ability to preserve local contextual information. State-of-the-

Bo Xue, Yuanyu Wan, Zhichao Lu, Qingfu Zhang

In multi-objective decision-making with hierarchical preferences, lexicographic bandits provide a natural framework for optimizing multiple objectives in a prioritized order. In this setting, a learner repeatedly selects arms and observes reward vectors, aiming to maximize the reward for the highest-priority objective, then the next, and so on. While previou

Yue Fang, Geert Ridder

In cluster randomized controlled trials (CRCT) with a finite populations, the exact design-based variance of the Horvitz-Thompson (HT) estimator for the average treatment effect (ATE) depends on the joint distribution of unobserved cluster-aggregated potential outcomes and is therefore not point-identifiable. We study a common two-stage sampling design-rando

Ziyue Yu, Xinyu Si, Lei Fang

Turbulence -- ubiquitous in nature and engineering alike [1-5] -- is traditionally viewed as an intrinsically inertial phenomenon, emerging only when the Reynolds number (Re), which quantifies the ratio of inertial to dissipative forces [6], far exceeds unity [7, 8]. Here, we demonstrate that strong energy flux between different length scales of motion -- a

Venus Keus, Stephen F. King

Following the recent Atacama Cosmology Telescope (ACT) results, we revisit chaotic inflation based on a single complex scalar field with mass term $M^2 |Φ|^2$, which usually predicts a spectra index $n_s\approx 0.96$ but a too-large tensor to scalar ratio $r\approx 0.16$. With radiative corrections, the potential $M^2 |Φ|^2 \ln \left( |Φ|^2/Λ^2 \right)$ indu

William R. Johnson, Patrick Meng, Nelson Chen, Luca Cimatti

Tensegrity robots, composed of rigid struts and elastic tendons, provide impact resistance, low mass, and adaptability to unstructured terrain. Their compliance and complex, coupled dynamics, however, present modeling and control challenges, hindering path planning and obstacle avoidance. This paper presents a complete, open-source, and reproducible system t

Yigitcan Kaya, Anton Landerer, Stijn Pletinckx, Michelle Zimmermann

Prompt injection attacks pose a critical threat to large language models (LLMs), with prior work focusing on cutting-edge LLM applications like personal copilots. In contrast, simpler LLM applications, such as customer service chatbots, are widespread on the web, yet their security posture and exposure to such attacks remain poorly understood. These applicat

Yong Huang, Ruihao Li, Mingyang Chen, Feiyang Zhao

The open nature of wireless communications renders unmanned aerial vehicle (UAV) communications vulnerable to impersonation attacks, under which malicious UAVs can impersonate authorized ones with stolen digital certificates. Traditional fingerprint-based UAV authentication approaches rely on a single modality of sensory data gathered from a single layer of

Xuqing Geng, Lei Su, Zhongwei Bian, Zewen Sun

Magnetic Particle Imaging (MPI) is a novel medical imaging modality. One of the established methods for MPI reconstruction is based on the System Matrix (SM). However, the calibration of the SM is often time-consuming and requires repeated measurements whenever the system parameters change. Current methodologies utilize deep learning-based super-resolution (

Jong-Kwon Ha, Seong Ho Kim, Seung Kyu Min

We propose mixed quantum-classical equations of motion that unify electronic coherence and phase evolution simultaneously within the exact factorization framework. Our derivation shows that incorporating the second-order electron-nuclear correlation terms from the exact coupled time-dependent Schr\"odinger equations is essential to recover both correct phase

David Salas

Lecture Notes based on the course given at Toulouse School of Economics, on Fall 2024. It contains a quick introduction to the field of bilevel optimization, following a perspective from Variational Analysis.

Chi-Zhuo Wang, Xue-Bing Wu, Yuanzhe Jiang, Qinchun Ma

In our previous work, we applied the ICCF-Cut method to the continuum reverberation mapping (CRM) of six active galactic nuclei (AGNs) based on the published Swift data. Extending this work, we perform a systematic AGN CRM study utilizing the Swift archive. We enlarge our sample with eight additional AGNs at $z<0.05$ with high-cadence ($<3$ days) and multiba

Manav Kulshrestha, S. Talha Bukhari, Damon Conover, Aniket Bera

Robotic grasping is a fundamental capability for enabling autonomous manipulation, with usually infinite solutions. State-of-the-art approaches for grasping rely on learning from large-scale datasets comprising expert annotations of feasible grasps. Curating such datasets is challenging, and hence, learning-based methods are limited by the solution coverage

Xiao-Cheng Liao, Yi Mei, Mengjie Zhang

Deep Reinforcement Learning have achieved significant success in automatically devising effective traffic signal control (TSC) policies. Neural policies, however, tend to be over-parameterized and non-transparent, hindering their interpretability and deployability on resource-limited edge devices. This work presents SymLight, a priority function search frame

Shanhao Zhan, Zhang Liu, Lianfen Huang, Shaowei Shen

The convergence of the Internet of vehicles (IoV) and 6G networks is driving the evolution of next-generation intelligent transportation systems. However, IoV networks face persistent challenges, including low spectral efficiency in vehicular communications, difficulty in achieving dynamic and adaptive resource optimization, and the need for long-term stabil

Chang He, Bo Jiang, Yuntian Jiang, Chuwen Zhang

In this paper, we develop a new adaptive regularization method for minimizing a composite function, which is the sum of a $p$th-order ($p \ge 1$) Lipschitz continuous function and a simple, convex, and possibly nonsmooth function. We use a history of local Lipschitz estimates to adaptively select the current regularization parameter, an approach we shall ter

Ki-Seok Kim, Arpita Mitra, Debangshu Mukherjee, Seung-Jong Yoo

We investigate the relationship between the functional renormalization group (RG) and the dual holography framework in the path integral formulation, highlighting how each can be understood as a manifestation of the other. Rather than employing the conventional functional RG formalism, we consider a functional RG equation for the probability distribution fun

Lianhao Yin, Haiping Yu, Pascal Spino, Daniela Rus

Biological swarms, such as ant colonies, achieve collective goals through decentralized and stochastic individual behaviors. Similarly, physical systems composed of gases, liquids, and solids exhibit random particle motion governed by entropy maximization, yet do not achieve collective objectives. Despite this analogy, no unified framework exists to explain

Yaxuan Wang, Chris Yuhao Liu, Quan Liu, Jinglong Pang

Unlearning in Large Language Models (LLMs) is crucial for protecting private data and removing harmful knowledge. Most existing approaches rely on fine-tuning to balance unlearning efficiency with general language capabilities. However, these methods typically require training or access to retain data, which is often unavailable in real world scenarios. Alth

Steven Atkinson

This work demonstrates "slimmable Neural Amp Models", whose size and computational cost can be changed without additional training and with negligible computational overhead, enabling musicians to easily trade off between the accuracy and compute of the models they are using. The method's performance is quantified against commonly-used baselines, and a real-

Lingyang Liu

This paper addresses the stability of a class of parabolic equations in non-cylindrical domains. We investigate the $L^\infty$-stability of systems for both nondegenerate and degenerate cases. Unlike in cylindrical domains, solutions to such problems may not exhibit exponential decay. An interesting phenomenon observed is that degeneracy has a positive impac

Lalit Maurya, Honghai Liu, Reyer Zwiggelaar

Unsupervised domain adaptation (UDA) for medical image segmentation remains challenging due to substantial domain shifts across imaging modalities, such as CT and MRI. Although recent vision-language representation learning methods have shown promise in medical image analysis, their role in cross-modality UDA segmentation remains underexplored. To address th

Ignacio Gonzalez Oliva, Sebastian Tillack, Fabio Caruso, Pasquale Pavone

By means of first-principles calculations, we investigate the role of electron-phonon interaction in the electronic structure of hybrid interfaces, formed by MoS$_2$ and monolayers of the organic molecules pyrene and pyridine, respectively. Quasiparticle energies are initially obtained within the $G_0W_0$ approximation and subsequently used to evaluate the e

Zhao Guo

We investigate the small, quasi-periodic modulations seen in the gravity-mode period spacings of pulsating stars. These ``wiggles'' are produced by buoyancy glitches -- sharp features in the buoyancy frequency ($N$) caused by composition transitions and the convective-radiative interface. Our method takes the Fourier transform of the period-spacing series, $

Ahmed Saad Al-Karsani, Maryam Khanbaghi

The transition towards clean energy and the introduction of Inverter-Based Resources (IBRs) are leading to the formation of Microgrids (MGs) and Networks of MGs (NMGs). MGs and NMGs can operate autonomously in islanded mode, which requires Grid-Forming (GFM) IBRs that can perform black start, synchronization, restoration and regulation. However, such IBRs ca

Aleksandra Urbańczyk, Bogumiła Papiernik, Piotr Magiera, Piotr Urbańczyk

This paper presents the application of socio-cognitive mutation operators inspired by the TOPSIS method to the Low Autocorrelation Binary Sequence (LABS) problem. Traditional evolutionary algorithms, while effective, often suffer from premature convergence and poor exploration-exploitation balance. To address these challenges, we introduce socio-cognitive mu

Jianqing Jia, Ashley Prater-Bennette, Lixin Shen

Sparse signal recovery has been a cornerstone of advancements in data processing and imaging. Recently, the squared ratio of $\ell_1$ to $\ell_2$ norms, $(\ell_1/\ell_2)^2$, has been introduced as a sparsity-prompting function, showing superior performance compared to traditional $\ell_1$ minimization, particularly in challenging scenarios with high coherenc

Susanne C. Brenner, José C. Garay, Li-yeng Sung

We present a multiscale finite element method for a diffusion problem with rough and high contrast coefficients. The construction of the multiscale finite element space is based on the localized orthogonal decomposition methodology and it involves solutions of local finite element eigenvalue problems. We show that the performance of the multiscale finite ele

Li-Yu Lin, Benjamin Perseghetti, James Goppert

Most of the rigid-body systems which evolve on nonlinear Lie groups where Euclidean control designs lose geometric meaning. In this paper, we introduce a log-linear backstepping control law on SE2(3) that preserves full rotational-translational coupling. Leveraging a class of mixed-invariant system, which is a group-affine dynamic model, we derive exact loga

James Siene

The classical Bach tensor in four dimensions can be expressed as a linear combination of two independent, symmetric, divergence-free, quadratic-in-curvature tensors U and V. Several classification results for gradient-shrinking Ricci solitons have been obtained under the assumption that the Bach tensor vanishes. We define a Bach-like tensor to be any other l

Zishuai Liu, Weihang You, Jin Lu, Fei Dou

Ambient sensor-based human activity recognition (HAR) in smart homes remains challenging due to the need for real-time inference, spatially grounded reasoning, and context-aware temporal modeling. Existing approaches often rely on pre-segmented, within-activity data and overlook the physical layout of the environment, limiting their robustness in continuous,

B. Mederos, J. Mejía, A. Medina-Reyes, Y. Espinosa-Almeyda

This work presents an approach for recognizing isolated sign language gestures using skeleton-based pose data extracted from video sequences. A Graph-GRU temporal network is proposed to model both spatial and temporal dependencies between frames, enabling accurate classification. The model is trained and evaluated on the AUTSL (Ankara university Turkish sign

Seyed Alireza Javid, Nuria González-Prelcic

Accurate multiple-input multiple-output (MIMO) channel estimation is critical for next-generation wireless systems, enabling enhanced communication and sensing performance. Traditional model-based channel estimation methods suffer, however, from performance degradation in complex environments with a limited number of pilots, while purely data-driven approach

Ruihai Wang, Qianhao Zhao, Zhixuan Hong, Qiong Ma

Achieving gigapixel space-bandwidth products (SBP) at video rates represents a fundamental challenge in imaging science. Here we demonstrate video-rate ptychography that overcomes this barrier by exploiting spatiotemporal correlations through neural field representations. Our approach factorizes the space-time volume into low-rank spatial and temporal featur

Nir Gutman, Eliya Blumenthal, Shay Hacohen-Gourgy, Ariel Orda

We introduce a family of bosonic quantum error-correcting codes built as a rotation-symmetric superposition of squeezed vacuum states, which promise protection against both loss and dephasing noise channels. The robustness of these &#34;squeezed-vacuum codes&#34; arises from being arranged at evenly spaced angles in phase-space, and simultaneously in evenly

Luis Marquez-Carpintero, Alberto Lopez-Sellers, Miguel Cazorla

Simulated Students offer a valuable methodological framework for evaluating pedagogical approaches and modelling diverse learner profiles, tasks which are otherwise challenging to undertake systematically in real-world settings. Recent research has increasingly focused on developing such simulated agents to capture a range of learning styles, cognitive devel

Elena-Mirela Babalic, Calin-Iuliu Lazaroiu

We discuss the strong version of the consistency conditions for SRRT inflation in general two-field cosmological models. In the fiducial case, this condition is a geometric PDE which relates the scalar field metric and scalar potential of such models. When supplemented by appropriate boundary conditions, this equation determines the scalar field metric in te

Tong Zhou, Yubing Li

This paper investigates how to recover parameters of a linear time invariant system from values and derivatives of its transfer function matrix, along several particular directions at a prescribed set of points in the complex plane, in which system matrices depend on these parameters through a linear fractional transformation. A necessary and sufficient cond

Xuanchi Zhou, Xiaohui Yao, Xiaomei Qiao

Thermally activated abrupt switching between localized and itinerant electronic states during the insulator-metal transition (IMT) in correlated oxide systems serves as a powerful platform for exploring exotic physical phenomena and device functionality. One ongoing focal challenge lies in the realization of the broadly tunable IMT property in correlated sys

Tri-Thien Nguyen, Lorenz A. Kapsner, Tobias Hepp, Shirin Heidarikahkesh

Background: Magnetic resonance imaging (MRI) has high sensitivity for breast cancer detection, but interpretation is time-consuming. Artificial intelligence may aid in pre-screening. Purpose: To evaluate the DINOv2-based Medical Slice Transformer (MST) for ruling out significant findings (Breast Imaging Reporting and Data System [BI-RADS] >=4) in contrast-en

Mohd Ruhul Ameen, Akif Islam

Social platforms connect billions of people, yet their engagement-first algorithms often work on users rather than with them, amplifying stress, misinformation, and a loss of control. We propose Human-Layer AI (HL-AI)--user-owned, explainable intermediaries that sit in the browser between platform logic and the interface. HL-AI gives people practical, moment

Nguyen Gia Hien Vu, Yifan Tang, Rey Lim, Yifan Yang

Tabular Prior-Data Fitted Network (TabPFN) is a foundation model designed for small to medium-sized tabular data, which has attracted much attention recently. This paper investigates the application of TabPFN in Combinatorial Optimization (CO) problems. The aim is to lessen challenges in time and data-intensive training requirements often observed in using t

Alexander E. Patkowski

In this paper we find the smallest part function related to the $4$-th symmetrized crank function, corresponding to the one obtained in Patkowski [11] for the $4$-th symmetrized rank function. This provides us with a direct relationship with Garvan&#39;s second order smallest part function. We obtain some congruences for these $spt$ functions, as well as asy

Hao Yang, Wei Wang, Gerson J. Ferreira, Ning Hao

The spin-orbit (SO) coupling is conventionally known to manifest as \emph{odd} functions of momentum. Here, through both model calculations and symmetry analysis along with the method of invariants, we reveal that, in ordinary semiconductor heterostructures, a \emph{quadratic} Dresselhaus SO term -- inheriting from its bulk crystal form -- emerges via the in

Neelam Kandhil, Alessandro Languasco, Pieter Moree

Assuming the Generalized Riemann Hypothesis and a pair correlation conjecture for the zeros of Dirichlet $L$-functions, we establish the truth of a conjecture of Montgomery (in its corrected form stated by Friedlander and Granville) on the magnitude of the error term in the prime number theorem in arithmetic progressions. As a consequence, we obtain that, un

Zhijing Ye, Sheng Di, Jiamin Wang, Zhiqing Zhong

Federated learning (FL) enables collaborative model training without exposing clients' private data, but its deployment is often constrained by the communication cost of transmitting gradients between clients and the central server, especially under system heterogeneity where low-bandwidth clients bottleneck overall performance. Lossy compression of gradient

Kathleen W. Guan, Sarthak Giri, Mohammed Amara, Bernard J. Jansen

Youth increasingly turn to large language models (LLMs) for mental well-being support, yet current personalization in LLMs can overlook the heterogeneous lived experiences shaping their needs. We conducted a participatory study with youth, parents, and youth care workers (N=38), using co-created youth personas as scaffolds, to elicit community perspectives o

Andrew Snowden, Noah Snyder

In recent joint work with Harman, we studied a pre-Tannakian category called the Delannoy category, and showed that it had numerous special properties. One of these is that the Adams operations on its Grothendieck group are trivial. In this paper, we prove three theorems inspired by this. Theorem A states that the Delannoy category is the unique semi-simple

Artem E. Shitikov, Alina N. Golodukhina, Nikita Yu. Dmitriev, Darya M. Sokol

Silicon nitride microresonators have become an ubiquitous platform for cutting-edge photonics applications. Improvement in silicon nitride fabrication techniques, providing ultra-high quality-factor values up to $10^7$, has opened up new possibilities for nonlinear effects realizations in such structures. Here we report for the first time to our knowledge on

Felipe Valencia-Clavijo

Large language models (LLMs) are increasingly examined as both behavioral subjects and decision systems, yet it remains unclear whether observed cognitive biases reflect surface imitation or deeper probability shifts. Anchoring bias, a classic human judgment bias, offers a critical test case. While prior work shows LLMs exhibit anchoring, most evidence relie

Qiuhao Zeng

In few-shot learning, classifiers are expected to generalize to unseen classes given only a small number of instances of each new class. One of the popular solutions to few-shot learning is metric-based meta-learning. However, it highly depends on the deep metric learned on seen classes, which may overfit to seen classes and fail to generalize well on unseen

A. V. Tsiganov

One of the oldest methods for constructing integrable Hamiltonian systems, proposed by Jacobi, recently is being presented as a novel St\"{a}ckel lift construction related with Haantjes geometry. It may cause some confusion.

Samvrit Srinath, Annapurna Vadaparty, David H. Smith, Leo Porter

Problem decomposition--the ability to break down a large task into smaller, well-defined components--is a critical skill for effectively designing and creating large programs, but it is often not included in introductory computer science curricula. With the rise of generative AI (GenAI), students even at the introductory level are able to generate large quan

Ruihuan Fang, Cuiju Yu, Youqiang Huang, Tosson Elalaily

Tunable superconductors provide a versatile platform for advancing next-generation quantum technologies. Here, we demonstrate controllable superconductivity in suspended NbSe2 thin layers, achieved through local strain and thermal modulation of the superconducting state. Our results show that suspended NbSe2 structures enable strain modulation of the critica

Diana Cohen, Roy Friedman, Rana Shahout

Sketches are commonly used in computer systems and network monitoring tools to provide efficient query executions while maintaining a compact data representation. Switches and routers maintain sketches to track statistical characteristics of network traffic. The availability of such data is essential for the network analysis as a whole. Consequently, being a

Danilo F. Rocha, Marcelo Emilio, Jonathan Labadie-Bartz, Coralie Neiner

Although triple systems are common, their orbital dynamics and stellar evolution remain poorly understood. We investigated the V1371 Tau system using TESS photometry, multi-epoch spectroscopy, and recent interferometric data, confirming it as a rare triple system consisting of an eclipsing binary orbited by a classical Be star, with a spectral classification

Mateo Ortiz, Juan Olmos, Fabio Martínez

The detection of clinically significant prostate cancer lesions (csPCa) from biparametric magnetic resonance imaging (bp-MRI) has emerged as a noninvasive imaging technique for improving accurate diagnosis. Nevertheless, the analysis of such images remains highly dependent on the subjective expert interpretation. Deep learning approaches have been proposed f

Marcelo Arenas, Pablo Barceló, Luis Cofré, Alexander Kozachinskiy

Kleinberg and Mullainathan showed that language generation in the limit is always possible at the level of computability: given enough positive examples, a learner can eventually generate data indistinguishable from a target language. However, such existence results do not address feasibility. We study the sample complexity of language generation in the limi

Anirudh Satheesh, Sooraj Sathish, Swetha Ganesh, Keenan Powell

In this work, we study the problem of finding robust and safe policies in Robust Constrained Average-Cost Markov Decision Processes (RCMDPs). A key challenge in this setting is the lack of strong duality, which prevents the direct use of standard primal-dual methods for constrained RL. Additional difficulties arise from the average-cost setting, where the Ro

Hassan Iqbal, Xingjian Li, Tyler Ingebrand, Adam Thorpe

We introduce a differentiable framework for zero-shot adaptive control over parametric families of nonlinear dynamical systems. Our approach integrates a function encoder-based neural ODE (FE-NODE) for modeling system dynamics with a differentiable predictive control (DPC) for offline self-supervised learning of explicit control policies. The FE-NODE capture