November 2025 arXiv papers — page 56

Mirza Raquib, Munazer Montasir Akash, Tawhid Ahmed, Saydul Akbar Murad

In our daily lives, newspapers are an essential information source that impacts how the public talks about present-day issues. However, effectively navigating the vast amount of news content from different newspapers and online news portals can be challenging. Newspaper headlines with sentiment analysis tell us what the news is about (e.g., politics, sports)

Litian Gong, Fatemeh Bahrani, Yutai Zhou, Amin Banayeeanzade

AutoFocus-IL is a simple yet effective method to improve data efficiency and generalization in visual imitation learning by guiding policies to attend to task-relevant features rather than distractors and spurious correlations. Although saliency regularization has emerged as a promising way to achieve this, existing approaches typically require costly superv

Joseph Malone, Rachith Aiyappa, Byunghwee Lee, Haewoon Kwak

Beliefs shape how people reason, communicate, and behave. Rather than existing in isolation, they exhibit a rich correlational structure--some connected through logical dependencies, others through indirect associations or social processes. As usage of large language models (LLMs) becomes more ubiquitous in our society, LLMs' ability to understand and reason

Jiawei Hu, Javier A. Barria

Label shift, a prevalent challenge in supervised learning, arises when the class prior distribution of test data differs from that of training data, leading to significant degradation in classifier performance. To accurately estimate the test priors and enhance classification accuracy, we propose a Bayesian framework for label shift estimation, termed Full M

Carlo von der Osten, Sabrina Aufiero, Pierpaolo Vivo, Fabio Caccioli

Debt recycling is a leveraged equity management strategy in which homeowners use accumulated home equity to finance investments, applying the resulting returns to accelerate mortgage repayment. We propose a novel framework to model equity and mortgage dynamics in presence of mortgage interest rates, borrowing costs on equity-backed credit lines, and tax shie

O. V. Kompaniiets, I. B. Vavilova, O. M. Kukhar, D. V. Dobrycheva

The environmental density of galaxies within the cosmic web constrains their 3D locations in filaments, voids, groups, and clusters. It traces the distribution of baryons and the influence of dark-matter halos on galaxy evolution, and helps diagnose external processes such as supernova and AGN feedback, tidal interactions, ram-pressure stripping, and large-s

Mengdi Wang, Efe Bozkir, Enkelejda Kasneci

Split learning emerges as a promising paradigm for collaborative distributed model training, akin to federated learning, by partitioning neural networks between clients and a server without raw data exchange. However, sequential split learning suffers from poor scalability, while parallel variants like parallel split learning and split federated learning oft

Ferhat Bayar, Haci Ilhan, Erdogan Aydin

Reconfigurable intelligent surfaces (RISs) are gaining traction for their ability to reshape wireless environments with low energy consumption. However, prior studies primarily explore single-RIS deployments with static or semi-static reflection control. In this paper, we propose a novel dual-RIS-assisted architecture for smart indoor wireless signal routing

David Krakauer, Gülce Kardeş, Joshua Grochow

Progress in understanding expert performance is limited by the scarcity of quantitative data on long-term knowledge acquisition and deployment. Here we use the Rubik's Cube as a cognitive model system existing at the intersection of puzzle solving, skill learning, expert knowledge, cultural transmission, and group theory. By studying competitive cube communi

Jiangrui Zheng, Yingming Zhou, Ali Abdullah Ahmad, Hanqing Yao

Bug bounty platforms (e.g., HackerOne, BugCrowd) leverage crowd-sourced vulnerability discovery to improve continuous coverage, reduce the cost of discovery, and serve as an integral complement to internal red teams. With the rise of AI-generated bug reports, little work exists to help bug hunters understand why these reports are labeled as invalid. To impro

Bo-Eun Choi, Jessica K. Werk, Kirill Tchernyshyov, Mary E. Putman

The Magellanic Stream (MS) is a vast gaseous structure in the Milky Way halo, containing most of its mass in ionized form and tracing the interaction between the Large and Small Magellanic Clouds and the Galaxy. Using HST/COS G160M spectra from the Plane Quasar Survey, we detect C IV absorbers likely associated with the MS, extending to the northern side of

Subramanyam Sahoo, Aman Chadha, Vinija Jain, Divya Chaudhary

Reinforcement Learning from Human Feedback (RLHF) is widely used for aligning large language models, yet practitioners face a persistent puzzle: improving safety often reduces fairness, scaling to diverse populations becomes computationally intractable, and making systems robust often amplifies majority biases. We formalize this tension as the Alignment Tril

Yanxi Li, Ruocheng Shan

Large language models are increasingly used for text classification tasks such as sentiment analysis, yet their reliance on natural language prompts exposes them to prompt injection attacks. In particular, class-directive injections exploit knowledge of the model's label set (e.g., positive vs. negative) to override its intended behavior through adversarial

Kensuke Nakamura, Arun L. Bishop, Steven Man, Aaron M. Johnson

Latent safety filters extend Hamilton-Jacobi (HJ) reachability to operate on latent state representations and dynamics learned directly from high-dimensional observations, enabling safe visuomotor control under hard-to-model constraints. However, existing methods implement "least-restrictive" filtering that discretely switch between nominal and safety polici

Annapurna Banik

We provide a necessary and sufficient condition for the existence of H\"{o}lder continuous solutions to the complex Monge--Amp\`{e}re equation on bounded domains in $\mathbb{C}^n$. This condition is motivated by a paper by S.-Y. Li. We also prove a result on the regularity of solutions to the complex Monge--Amp\`{e}re equation on general $B$-regular domains

Lucas Heublein, Thorsten Nowak, Tobias Feigl, Jaspar Pahl

Jamming devices disrupt signals from the global navigation satellite system (GNSS) and pose a significant threat by compromising the reliability of accurate positioning. Consequently, the detection and localization of these interference signals are essential to achieve situational awareness, mitigating their impact, and implementing effective countermeasures

Hannah Lee, James D. Motes, Marco Morales, Nancy M. Amato

This study informs the design of future multi-agent pathfinding (MAPF) and multi-robot motion planning (MRMP) algorithms by guiding choices based on constraint classification for constraint-based search algorithms. We categorize constraints as conservative or aggressive and provide insights into their search behavior, focusing specifically on vanilla Conflic

Neon Srinivasu, Amit Shivam, Nobin Paul

This paper develops a new guidance law for powered descent landing of a rocket-powered vehicle. The proposed law derives the acceleration command for a point mass model of the vehicle by expressing velocity as a dynamical system undergoing supercritical transcritical bifurcation with three bifurcation parameters. The parameters are designed such that the sta

Guilherme Grancho D. Fernandes, Duarte Alexandrino, Eduardo Silva, João Matias

High harmonic generation (HHG) is a nonlinear process that enables table-top generation of tunable, high-energy, coherent, ultrashort radiation pulses in the extreme ultraviolet (EUV) to soft X-ray range. These pulses find applications in photoemission spectroscopy in condensed matter physics, pump-probe spectroscopy for high-energy-density plasmas, and atto

Silouanos Brazitikos, Dimitris-Marios Liakopoulos

We investigate integral-geometric quantities arising from harmonic analysis which measure visibility and transversality. Motivated by their applications in multilinear Kakeya problems and affine-invariant measures on surfaces, we derive exact lower and upper bounds employing geometric and functional inequalities of convex geometry.

Wenchao Ma, Dario Kneubuehler, Maurice Chu, Ian Sachs

In this paper, we present RigAnyFace (RAF), a scalable neural auto-rigging framework for facial meshes of diverse topologies, including those with multiple disconnected components. RAF deforms a static neutral facial mesh into industry-standard FACS poses to form an expressive blendshape rig. Deformations are predicted by a triangulation-agnostic surface lea

Hong Li, Chongjie Ye, Houyuan Chen, Weiqing Xiao

Neural asset authoring and neural rendering have traditionally evolved as disjoint paradigms: one generates digital assets for fixed graphics pipelines, while the other maps conventional assets to images. However, treating them as independent entities limits the potential for end-to-end optimization in fidelity and consistency. In this paper, we bridge this

Eric A. Bai, Minling Zhou, Ricardo Henao, Kyle M. Schwing

Narratives drive human behavior and lay at the core of geopolitics, but have eluded quantification that would permit measurement of their overlap and evolution. We present an interpretable model that integrates an established bag-of-words (BoW) topical representation and a novel LLM-based question answering (Q&A) narrative model, which share a latent Reprodu

Mohsen Soltanifar

The sequence space of all real-valued sequences, denoted $Seq(\mathbb{R})$, is typically investigated through the lens of infinite-dimensional vector spaces, utilizing Banach space norms or Schauder bases. This work proposes a complementary, constructive classification based instead on the asymptotic limit profile encoded by the pair $(\liminf a_n, \limsup a

Srijaya Nandi, Dipanjan Roy, Aesha Lahiri, Anamitra Roy

The urban habitat provides a landscape that increases the chances of human-animal interactions, which can lead to increased human-animal conflict, but also coexistence. Some species show high levels of socio-cognitive abilities that enable them to perceive communicational gestures of humans and use them for their own benefit. This study investigated the abil

H. M. Shadman Tabib, Jaber Ahmed Deedar

Large Language Models (LLMs) have demonstrated impressive capabilities in natural language and code generation, and are increasingly deployed as automatic judges of model outputs and learning activities. Yet, their behavior on structured tasks such as predicting the difficulty of competitive programming problems remains under-explored. We conduct a systemati

Onofre Rojas

We study the coupled twin-diamond chain, a decorated one-dimensional Ising model motivated by the magnetic structure of $\mathrm{Cu}_{2}(\mathrm{TeO}_{3})_{2}\mathrm{Br}_{2}$. By applying an exact mapping to an effective Ising chain, we obtain the full thermodynamic description of the system through a compact transfer-matrix formulation. The ground-state ana

Wenhao Guo, Golrokh Mirzaei

Differentiating true tumor progression (TP) from treatment-related pseudoprogression (PsP) in glioblastoma remains challenging, especially at early follow-up. We present the first stage-specific, cross-sectional benchmarking of deep learning models for follow-up MRI using the Burdenko GBM Progression cohort (n = 180). We analyze different post-RT scans indep

Ahmad A. Aziz El-Banna, Octavia A. Dobre

Integrated sensing and communication and millimeter wave (mmWave) have emerged as pivotal technologies for 6G networks. However, the narrow nature of mmWave beams requires precise alignments that typically necessitate large training overhead. This overhead can be reduced by incorporating the position information with beam adjustments. This letter proposes a

Milad Siami

Graph Diffusion Models (GDMs) optimize for statistical likelihood, implicitly acting as \textbf{frequency filters} that favor abundant substructures over spectrally critical ones. We term this phenomenon \textbf{Generative Myopia}. In combinatorial tasks like graph sparsification, this leads to the catastrophic removal of ``rare bridges,'' edges that are str

Wei Dong, Han Zhou, Junwei Lin, Jun Chen

Real-world dark images commonly exhibit not only low visibility and contrast but also complex noise and blur, posing significant restoration challenges. Existing methods often rely on paired data or fail to model dynamic illumination and blur characteristics, leading to poor generalization. To tackle this, we propose a generative framework based on visual au

Michael Trusov, Minha Hwang, Zainab Jamal, Swarup Chandra

Organizations are rapidly adopting Large Language Models (LLMs) to transform their operations, yet they lack clear guidance on key decisions for adoption and implementation. While LLMs offer powerful capabilities in content generation, assisted coding, and process automation, businesses face critical challenges in data security, LLM solution development appr

Souvik Das, Avishek Ghosh, Debasish Chatterjee

This article introduces an anomaly detection based algorithm (AD-CPS) to detect false data injection attacks that fall under the category of data deception/integrity attacks, but with arbitrary information structure, in cyber-physical systems (CPSs) modeled as stochastic linear time-invariant systems. The core idea of this data-driven algorithm is based on t

Jonathan Brundan, Alistair Savage

We develop a general framework for studying Abelian categories arising in isomeric representation theory, that is, representation theory broadly related to the supergroup Q(n). In this first part, we introduce notions of isomeric Heisenberg categorification and isomeric Kac-Moody categorication, and explain how to pass from the former to the latter. This is

Abigail N. Poteshman, Francesco Ricci, Jeffrey B. Neaton

Electric polarization in the absence of an externally applied electric field is a key property of polar materials, but the standard interpolation-based ab initio approach to compute polarization differences within the modern theory of polarization presents challenges for automated high-throughput calculations. Berry flux diagonalization [J. Bonini et. al, Ph

Mihai Marciu

We explore a novel cosmological model based on coupled fields in the framework of scalar tensor theories, considering the specific interplay between gravity and scalar fields. The model further extends a recent axion-dilaton system by introducing viable couplings with the space--time geometry encoded into the scalar curvature. After briefly introducing the a

Peter Anninos, Matthew Portman, Scott Carmichael, Robert Hoffman

We investigate nuclear reactions and feedback in hyperaccreting neutron star environments, considering accretion rates in the range 0.3 - $3\times10^4$ $M_\odot$ yr$^{-1}$, typical of short-period compact object binaries in common envelopes. Our mode ls account for weak reactions, neutrino energy loss, nuclear energy release, pair production, degenerate equa

Valentin Roth, Marco Avella-Medina

Dependent data underlies many statistical studies in the social and health sciences, which often involve sensitive or private information. Differential privacy (DP) and in particular \textit{user-level} DP provide a natural formalization of privacy requirements for processing dependent data where each individual provides multiple observations to the dataset.

Md Shahabub Alam, Md Asifuzzaman Jishan, Ayan Kumar Ghosh

Heat pump systems are critical components in modern energy-efficient buildings, yet their operational stress detection remains challenging due to complex thermodynamic interactions and limited real-world data. This paper presents a novel Physics-Guided Deep Neural Network (PG-DNN) approach for heat pump stress classification using the When2Heat dataset, cont

David Almog

Concerns about how workers are perceived can deter effective collaboration with artificial intelligence (AI). In a field experiment on a large online labor market, I hired 450 U.S.-based remote workers to complete an image-categorization job assisted by AI recommendations. Workers were incentivized by the prospect of a contract extension based on an HR evalu

Yanting Wang, Runpeng Geng, Jinghui Chen, Minhao Cheng

Many recent studies showed that LLMs are vulnerable to jailbreak attacks, where an attacker can perturb the input of an LLM to induce it to generate an output for a harmful question. In general, existing jailbreak techniques either optimize a semantic template intended to induce the LLM to produce harmful outputs or optimize a suffix that leads the LLM to in

Christopher Hojny, Mathieu Besançon, Ksenia Bestuzheva, Sander Borst

The SCIP Optimization Suite provides a collection of software packages for mathematical optimization, centered around the constraint integer programming (CIP) framework SCIP. This report discusses the enhancements and extensions included in SCIP Optimization Suite 10.0. The updates in SCIP 10.0 include a new solving mode for exactly solving rational mixed-in

Kooktae Lee, Ethan Brook

Multi-agent systems play a central role in area coverage tasks across search-and-rescue, environmental monitoring, and precision agriculture. Achieving non-uniform coverage, where spatial priorities vary across the domain, requires coordinating agents while respecting dynamic and communication constraints. Density-driven approaches can distribute agents acco

Eghbal Rahimikia, Hao Ni, Weiguan Wang

Financial time series forecasting is central to trading, portfolio optimization, and risk management, yet it remains challenging due to noisy, non-stationary, and heterogeneous data. Recent advances in time series foundation models (TSFMs), inspired by large language models, offer a new paradigm for learning generalizable temporal representations from large

Mehmet Ali Erturk, Kubra Duran, Ahmed Al-Dubai, Berk Canberk

Digital Twins are being used as an enabling technology in 6G applications across various domains, valued for their data-driven insights and real-time decision-making capabilities. However, integrating Digital Twins into 6G environments presents challenges in maintaining consistent network services under adverse conditions such as including denial-of-service

Ayobami Paul Abolade, Ibrahim Olanrewaju Lawal, Kamoru Lanre Akanbi, Ahmed Orilonise Salami

Access to finance is vital for improving food security, particularly in developing nations where agricultural production is crucial. Despite several financial interventions targeted at increasing agricultural production, smallholder farmers continue to lack access to reasonable, timely, and sufficient financing, limiting their ability to invest in improved t

Leonid Bedratyuk

We consider the action of the projective group $PGL(3,\mathbb{R})$ on the $n$-fold first-order jet space of point configurations on the plane. Using the method of moving frames, we construct an explicit complete generating set for the field of absolute first-order joint projective differential invariants $\mathcal{I}_{n,0}$ for any $n \ge 3$. This approach p

Yiying He, Zhiqiang Zuo, Yianni Alissandratos, Penny Willson

Expired functional drinks have great valorisation potential due to the high concentration of organic molecules present. However, detailed information of the resources in these expired functional drinks is limited, hindering the rational design of a recovery system. To address this gap, we present here a study that comprehensively characterises the chemical c

Mathias Hedegaard Kristensen, Paweł Piotr Cielecki, Esben Skovsen

We investigate how feature selection algorithms can enable accurate, reference-free classification of materials using sparse-frequency terahertz (THz) reflection spectroscopy. Three classes of feature selection strategies are evaluated. Namely, the filter-based mRMR (minimum Redundancy Maximum Relevance), the embedded LASSO (Least Absolute Shrinkage and Sele

Jiazhen Hong, Geoffrey Mackellar, Soheila Ghane

Long-sequence electroencephalogram (EEG) modeling is essential for developing generalizable EEG representation models. This need arises from the high sampling rate of EEG data and the long recording durations required to capture extended neurological patterns in brain activity. Transformer-based models have shown promise in modeling short sequences of a few

Samarth Chopra, Jing Liang, Gershom Seneviratne, Dinesh Manocha

Understanding physical properties such as friction, stiffness, hardness, and material composition is essential for enabling robots to interact safely and effectively with their surroundings. However, existing 3D reconstruction methods focus on geometry and appearance and cannot infer these underlying physical properties. We present PhysGS, a Bayesian-inferre

Gabriella Pinzari, Lei Zhao

The validity of Kepler Laws for the {\it spherical Kepler problem} -- namely, the problem of the motion of a particle on the unit sphere {in $\mathbb R^3$} undergoing an attraction by another particle in the sphere, tangent to the geodesic line between the two and inversely proportional to its squared length -- prompted geometers to try to interpret such sys

Arya Shah, Vaibhav Tripathi

The Forward-Forward (FF) algorithm offers a biologically plausible alternative to backpropagation, enabling neural networks to learn through local updates. However, FF's efficacy relies heavily on the definition of "goodness", which is a scalar measure of neural activity. While current implementations predominantly utilize a simple sum-of-squares metric, it

KM3NeT Collaboration, O. Adriani, A. Albert, A. R. Alhebsi

Core-collapse supernovae mark the end of life of massive stars. However, despite their importance in astrophysics, their underlying mechanisms remain unclear. Neutrinos that emerge from the dense core of the star offer a promising way to study supernova dynamics. A strategy is presented to improve the potential of the KM3NeT neutrino telescope to detect core

Siddharth Singh, Carrie Vuyovich, Ana P. Barros

This study presents a coupled physical statistical framework for retrieving snow water equivalent (SWE) in forested areas using dual frequency X and Ku band SAR observations. The method combines a multilayer snow hydrology model (MSHM) with microwave propagation and backscatter models, and includes a canopy parameterization based on a modified Water Cloud Mo

Cem Bilaloglu, Tobias Löw, Sylvain Calinon

Curved objects pose a fundamental challenge for skill transfer in robotics: unlike planar surfaces, they do not admit a global reference frame. As a result, task-relevant directions such as "toward" or "along" the surface vary with position and geometry, making object-centric tasks difficult to transfer across shapes. To address this, we introduce an approac

Xunlei Qian, Yue Xing

Conformal prediction (CP) provides distribution-free, finite-sample coverage guarantees but critically relies on exchangeability, a condition often violated under distribution shift. We study the robustness of split conformal prediction under adversarial perturbations at test time, focusing on both coverage validity and the resulting prediction set size. Our

Zitian Peng, Shiyao Zhang, Shanliang Yao, Xiaohui Zhu

Unmanned Surface Vehicles (USVs) are increasingly utilised for diverse applications, ranging from environmental monitoring to security patrols. While USV technology is progressing, it remains clear that full autonomy is not achievable in all scenarios, and remote human intervention is still crucial, particularly in dynamic or complex environments. This conti

Kevin Lee, Russell Spiewak, James Walsh

Scientific reasoning through Large Language Models in heliophysics involves more than just recalling facts: it requires incorporating physical assumptions, maintaining consistent units, and providing clear scientific formats through coordinated approaches. To address these challenges, we present Reasoning With a Star, a newly contributed heliophysics dataset

Mehedi Hasan, Khayrul Islam, Michael T. Kio, AKM Masud

Carbon nanotube (CNT)-polymer composites are widely engineered using surface coatings and chemical treatments to improve interfacial bonding and load transfer. It has been suggested in the nanocomposite literature that nanotube curvature, in conjunction with surface functionalization such as polydopamine (PDA) coating, could serve as an additional control kn

Kuan Wei Huang, Brandon Li, Bharath Hariharan, Noah Snavely

Geometric models like DUSt3R have shown great advances in understanding the geometry of a scene from pairs of photos. However, they fail when the inputs are from vastly different viewpoints (e.g., aerial vs. ground) or modalities (e.g., photos vs. abstract drawings) compared to what was observed during training. This paper addresses a challenging version of

Genoveva Vargas-Solar

As Artificial Intelligence (AI) and Data Science (DS) become pervasive, addressing gender disparities and diversity gaps in their workforce is urgent. These rapidly evolving fields have been further impacted by the COVID-19 pandemic, which disproportionately affected women and minorities, exposing deep-seated inequalities. Both academia and industry shape th

Sathyanarayan Narayan, N. Uday Kiran

In this paper, we introduce integer sequences satisfying new congruence properties inspired by the Euler and Gauss congruences, which we call Euler--Gauss sequences. Noting that every Gauss sequence is an Euler--Gauss sequence, we compare them with certain generalisations of Gauss sequences and provide several counterexamples. Unlike Gauss sequences, Euler--

Yacouba Diarra, Nouhoum Souleymane Coulibaly, Panga Azazia Kamaté, Madani Amadou Tall

Creating speech datasets, models, and evaluation frameworks for low-resource languages remains challenging given the lack of a broad base of pertinent experience to draw from. This paper reports on the field collection of 612 hours of spontaneous speech in Bambara, a low-resource West African language; the semi-automated annotation of that dataset with trans

Asaf Katz, Thomas Aloysius O'Hare

We prove effective equidistribution theorems for (weighted) packets of closed periodic orbits for Anosov flows. In particular, for the case of contact Anosov flows on three-dimensional manifolds, we show that the Bowen packets equidistribute at an exponential rate.

Alexander W. Hsu, Ike Griss Salas, Jacob M. Stevens-Haas, J. Nathan Kutz

We develop an all-at-once modeling framework for learning systems of ordinary differential equations (ODE) from scarce, partial, and noisy observations of the states. The proposed methodology amounts to a combination of sparse recovery strategies for the ODE over a function library combined with techniques from reproducing kernel Hilbert space (RKHS) theory

Adam Lechowicz, Nicolas Christianson, Mohammad Hajiesmaili, Adam Wierman

We introduce and study a class of online problems called online smoothed demand management $(\texttt{OSDM})$, motivated by paradigm shifts in grid integration and energy storage for large energy consumers such as data centers. In $\texttt{OSDM}$, an operator makes two decisions at each time step: an amount of energy to be purchased, and an amount of energy t

András Czégel, Dávid Sipos, Boglárka G. -Tóth

We propose a complete quantum-classical hybrid branch-and-bound algorithm (QCBB) to solve binary linear programs with equality constraints. That includes bound calculation, convergence metrics and optimality guarantee to the quantum optimization based algorithm, which makes our method directly comparable to classical methods. Key aspects of the proposed algo

Han Li, Yutong Lou, Dunhui Xiao

Accurately predicting turbulent flows remains a central challenge in fluid dynamics due to their high dimensionality and intrinsic nonlinearity. Recent developments in quantum algorithms and machine learning offer new opportunities for overcoming the computational barriers inherent in turbulence modeling. Here we present a new hybrid quantum-classical framew

Ingyu Jang, Leila J. Bridgeman

In this paper, the L2 stability of switched networks is studied based on the QSR-dissipativity of each agent. While the integration of dissipativity with switched systems has received considerable attention, most previous studies have focused on passivity, internal stability, or feedback networks involving only two agents. This work makes two contributions:

Kerr Maxwell

We introduce the notion of geometric pseudo-quantisation based on geometric quantisation with a weakened curvature condition. We show how such a structure arises naturally from simple deformations of the symplectic structure and pullbacks of prequantum data by non-symplectic diffeomorphisms. Our main result is deriving the equations of motion for some simple

Maria Stella Albarelli

In recent years, online shopping has grown rapidly, especially during the COVID-19 period. However, it still lacks elements typical of physical stores, such as empathic support and personalised advice from a sales assistant. This study explores how an emotion-aware Conversational Agent (CA) can improve the online shopping experience by responding to user emo

Faraz Farhadi, Shadi A. Esfahani, Fereshteh Yousefirizi, Monica Luo

The field of Clinical-Computational Nuclear Medicine is rapidly advancing, fueled by AI, tracer kinetic modeling, radiomics, and integrated informatics. These technologies improve imaging quality, automate lesion detection, and enable personalized radiopharmaceutical therapy through physiologically based pharmacokinetic (PBPK) modeling and voxel-level dosime

Siyue Liu, Victor Reis

Given positive integers $m, n$, a fractional assignment $x \in [0,1]^{m \times n}$ and weights $d \in \mathbb{R}^n_{>0}$, we show that there exists an assignment $y \in \{0,1\}^{m \times n}$ so that for every $i\in[m]$ and $t\in [n]$, \[ \Big|\sum_{j \in [t]} d_j (x_{ij} - y_{ij}) \Big| < \max_{j \in [n]} d_j. \] This generalizes a result of Tijdeman (1973)

Luca Amata, Francesco Oliveri, Emanuele Sgroi

The complete optimal systems of subalgebras of all nonisomorphic three- and four-dimensional real Lie algebras are analyzed by the program \symbolie running in the computer algebra system \emph{Wolfram Mathematica}\texttrademark. The approach uses the definition of $p$-families of Lie subalgebras whose set can be partitioned by introducing a binary relation

Veronica Poda, Veronica Vinciotti, Ernst C. Wit

Gossiping is a widespread social phenomenon that shapes relationships and information flow in communities. From a network theoretic point of view, gossiping can be seen as a higher-order interaction, as it involves at least two persons talking about a non-present third. The mechanism of gossiping is complex: it is most likely dynamic, as its intensity change

Chengwei Zhou, Gourav Datta

Spiking neural networks (SNNs) exhibit temporal, sparse, and event-driven dynamics that make them appealing for efficient inference. However, extending these models to self-supervised regimes remains challenging because the discontinuities introduced by spikes break the cross-view gradient correspondences required by contrastive and consistency-driven object

Amir Subba, Subhaditya Bhattacharya, Abhik Sarkar

We investigate the sensitivity of a future high-luminosity $e^-e^+$ collider operating at $\sqrt{s}=250~\text{GeV}$ to CP-violating and CP-conserving anomalous $hVV$ interactions via the Higgsstrahlung process. The effects of new physics are parameterized in the Standard Model Effective Field Theory~(SMEFT) framework through six dimension-6 operators modifyi

Lingyu Jiang, Lingyu Xu, Peiran Li, Dengzhe Hou

We propose TimePre, a simple framework that unifies the efficiency of Multilayer Perceptron (MLP)-based models with the distributional flexibility of Multiple Choice Learning (MCL) for Probabilistic Time-Series Forecasting (PTSF). Stabilized Instance Normalization (SIN), the core of TimePre, is a normalization layer that explicitly addresses the trade-off am

Jian Yang, Xianglong Liu, Weifeng Lv, Ken Deng

Large language models (LLMs) have fundamentally transformed automated software development by enabling direct translation of natural language descriptions into functional code, driving commercial adoption through tools like Github Copilot (Microsoft), Cursor (Anysphere), Trae (ByteDance), and Claude Code (Anthropic). While the field has evolved dramatically

Tuomas Varanka, Juan Luis Gonzalez, Hyeongwoo Kim, Pablo Garrido

Existing video deraining methods are often trained on paired datasets, either synthetic, which limits their ability to generalize to real-world rain, or captured by static cameras, which restricts their effectiveness in dynamic scenes with background and camera motion. Furthermore, recent works in fine-tuning diffusion models have shown promising results, bu

Dallas Albritton, Rajendra Beekie

We consider the advection-diffusion equation describing the evolution of a passive scalar in a background shear flow. We prove the optimal uniform-in-diffusivity mixing rate $\| f \|_{H^{-1}} \lesssim \langle t \rangle^{-1/(N+1)}$, $t \geq 0$, where $N$ is the maximal order of vanishing of the derivative $b'(y)$ of the shear profile, e.g., $N=1$ for plane Po

Pengcheng Fang, Hongli Chen, Guangzhen Yao, Jian Shi

Reconstructing high-fidelity MR images from undersampled k-space data requires recovering high-frequency details while maintaining anatomical coherence. We present HiFi-MambaV2, a hierarchical shared-routed Mixture-of-Experts (MoE) Mamba architecture that couples frequency decomposition with content-adaptive computation. The model comprises two core componen

Md Mizanur Rahman Mustakim, Jianwu Li, Sumya Bhuiyan, Mohammad Mehedi Hasan

Accurate segmentation of individual teeth from panoramic radiographs remains a challenging task due to anatomical variations, irregular tooth shapes, and overlapping structures. These complexities often limit the performance of conventional deep learning models. To address this, we propose DE-KAN, a novel Dual Encoder Kolmogorov Arnold Network, which enhance

Di Feng, Kaixin Ma, Feng Nan, Haofeng Chen

Multimodal large language models (MLLMs) are increasingly deployed in real-world, agentic settings where outputs must not only be correct, but also conform to predefined data schemas. Despite recent progress in structured generation in textual domain, there is still no benchmark that systematically evaluates schema-grounded information extraction and reasoni

Affan Khadir, Erik Osinga, Wonki Lee, David McConnell

The line-of-sight magnetic field of galaxy clusters can be probed using Faraday rotation measure (RM) data. However, our understanding of cluster magnetism is limited due to the scarcity of polarized background radio sources, with most previous studies being constrained to $\sim 10$ sources per cluster. Leveraging the increased source density of the POlarisa

Akashdeep Saha, Zeng Wang, Prithwish Basu Roy, Johann Knechtel

Despite rapid progress in logic locking (LL), reproducibility remains a challenge as codes are rarely made public. We present LockForge, a first-of-its-kind, multi-agent large language model (LLM) framework that turns LL descriptions in papers into executable and tested code. LockForge provides a carefully crafted pipeline realizing forethought, implementati

Kaidi Wang, Yi He, Wenhao Guan, Weijie Wu

Video dubbing aims to generate high-fidelity speech that is precisely temporally aligned with the visual content. Existing methods still suffer from limitations in speech naturalness and audio-visual synchronization, and are limited to monolingual settings. To address these challenges, we propose SyncVoice, a vision-augmented video dubbing framework built up

Alexander G. Reisach, Olivier Collier, Alex Luedtke, Antoine Chambaz

We propose a way of transforming the problem of conditional density estimation into a single nonparametric regression task via the introduction of auxiliary samples. This allows leveraging regression methods that work well in high dimensions, such as neural networks and decision trees. Our main theoretical result characterizes and establishes the convergence

Farhad Hakimi, Hosain Hakimi

Gravitational wave detection has transformed astrophysics, granting us direct access to black hole mergers, neutron star collisions, and the cataclysms of stellar death. Yet the great observatories of today, LIGO, Virgo, KAGRA, and the planned Einstein Telescope, rest on Michelson interferometers that, despite their triumphs, confront fundamental barriers of

Tianyi Zhou

This note is an introductory survey of non-Hausdorff separation axioms. The main focus is to study properties that are between $T_0$ and $T_1$, properties between $T_1$ and Hausdorff and how the $T_0$-quotient change them and the relation between them.

D. A. Kukusta, L. V. Bekenov, P. F. Perndorfer, D. V. Vyalikh

The single-layered perovskite Ca$_2$RuO$_4$, characterized by a 4$d^4$ electron configuration, has been studied from first principles using density functional theory (DFT) using the generalized gradient approximation, with inclusion of strong on-site Coulomb interactions and spin-orbit coupling (GGA+SO+$U$), in the framework of the fully relativistic, spin-p

Samarth Chopra, Jing Liang, Gershom Seneviratne, Yonghan Lee

We present Splatblox, a real-time system for autonomous navigation in outdoor environments with dense vegetation, irregular obstacles, and complex terrain. Our method fuses segmented RGB images and LiDAR point clouds using Gaussian Splatting to construct a traversability-aware Euclidean Signed Distance Field (ESDF) that jointly encodes geometry and semantics

Joseph Ngatchou-Wandji, Echarif Elharfaoui, Michel Harel

We study multiple change-points detection using multi-samples tests based on U-statistics for absolutely regular observations. Our results extend those of Ngatchou-Wandji et al. (2022) concerned with the study of one single changepoint. The asymptotic distributions of the test statistics under the null hypothesis and under a sequence of local alternatives ar

Hou Hei Lam, Jiangjie Qiu, Xiuyuan Hu, Wentao Li

Organic photovoltaic (OPV) materials offer a promising path toward sustainable energy generation, but their development is limited by the difficulty of identifying high performance donor and acceptor pairs with strong power conversion efficiencies (PCEs). Existing design strategies typically focus on either the donor or the acceptor alone, rather than using

Biagio Ricceri

Using as a main tool our recent result on the strict minimax inequality proved in [5], in this note we establish a multiplicity theorem for a problem of the type $$\cases{-K\left(\int_{\Omega}|\nabla u(x)|^2dx\right)\Delta u = h(x,u) & in $\Omega$\cr & \cr {{\partial u}\over {\partial\nu}}=0 & on $\partial\Omega$.\cr}$$

Oleksandr Kravchenko

We present a framework for non-singular bouncing cosmology in a closed ($k=+1$) universe with a two-field sigma model whose regularized hyperbolic field-space metric $g^S_{\chi\chi}(\phi) = (1 + e^{-2\alpha\phi/M_{\mathrm{Pl}}})^{-1}$ is derived from three physical boundary conditions: (i) kinetic suppression during contraction enabling the bounce, (ii) cano

Core Francisco Park, Manuel Perez-Carrasco, Caroline Nowlan, Cecilia Garraffo

Geostationary hyperspectral satellites generate terabytes of data daily, creating critical challenges for storage, transmission, and distribution to the scientific community. We present a variational autoencoder (VAE) approach that achieves x514 compression of NASA's TEMPO satellite hyperspectral observations (1028 channels, 290-490nm) with reconstruction er

Zhe Li, Fangyang Zhan, Haijun Cao, Jingjing Cao

Floquet engineering severs as a forceful technique for uncovering high Chern numbers of quantum anomalous Hall (QAH) states with feasible tunability in high-order topologically insulating plumbene, which is readily accessible for experimental investigations. Under the irradiation of righthanded circularly polarized light, we predict a three-stage topological

Xinlin Zhuang, Yichen Li, Xiwei Liu, Haolin Yang

Adapting CLIP to vertical domains is typically approached by novel fine-tuning strategies or by continual pre-training (CPT) on large domain-specific datasets. Yet, data itself remains an underexplored factor in this process. We revisit this task from a data-centric perspective: Can effective data selection substitute for large-scale datasets in CPT? We intr