November 2025 arXiv papers — page 23

Dorothee Schueth

Let $(M,g)$ be a two-dimensional Riemannian manifold of finite diameter with a conical singularity. Under the assumption that the metric near the cone point $C$ is rotationally invariant, but not necessarily flat, we give an explicit formula for the coefficient $b_{1/2}(C)$ in the heat trace expansion $\operatorname{tr}(\operatorname{exp}(-t\Delta_g))\sim_{t

Yeonsung Jung, Trilok Padhi, Sina Shaham, Dipika Khullar

The rapid progress of large foundation models has accelerated the development of task-specialized agents across diverse domains. However, the effectiveness of agents remains tightly coupled with the quality of training data, while curating task-specific datasets remains costly and often infeasible in real-world scenarios. Recent work has explored self-improv

Hoang-Bao Le, Allie Tran, Binh T. Nguyen, Liting Zhou

Image-Guided Retrieval with Optional Text (IGROT) is a general retrieval setting where a query consists of an anchor image, with or without accompanying text, aiming to retrieve semantically relevant target images. This formulation unifies two major tasks: Composed Image Retrieval (CIR) and Sketch-Based Image Retrieval (SBIR). In this work, we address IGROT

Vincent Fromion, Philippe Robert, Jana Zaherddine

This paper analyses of a stochastic model of a chemical reaction network with three types of chemical species ${\cal R}$, ${\cal M}$ and ${\cal U}$ that interact to transform a flow of external resources, the chemical species ${\cal Q}$, to produce a product, the chemical species ${\cal P}_r$. A regulation mechanism involving the sequestration of the chemica

Zhiyi Jiang, Yifu Wang, Xuelian Cheng, Zongyuan Ge

Estimating 3D geometry from monocular colonoscopy images is challenging due to non-Lambertian surfaces, moving light sources, and large textureless regions. While recent 3D geometric foundation models eliminate the need for multi-stage pipelines, their performance deteriorates in clinical scenes. These models are primarily trained on natural scene datasets a

Bolin Lai, Xudong Wang, Saketh Rambhatla, James M. Rehg

Latent diffusion has become the default paradigm for visual generation, yet we observe a persistent reconstruction-generation trade-off as latent dimensionality increases: higher-capacity autoencoders improve reconstruction fidelity but generation quality eventually declines. We trace this gap to the different behaviors in high-frequency encoding and decodin

Cheng Zhang, Mauro Cirio, Xin-Qi Li, Pengfei Liang

Continuous monitoring of open quantum-optical systems offers a promising route towards quantum-enhanced estimation precision. In such continuous-measurement-based sensing protocols, the ultimate precision limit is dictated, through the quantum Cram\'er-Rao bound, by the global quantum Fisher information associated with the joint system-environment state. Rea

Hoang-Bao Le, Allie Tran, Binh T. Nguyen, Liting Zhou

Image-Guided Retrieval with Optional Text (IGROT) unifies visual retrieval (without text) and composed retrieval (with text). Despite its relevance in applications like Google Image and Bing, progress has been limited by the lack of an accessible benchmark and methods that balance performance across subtasks. Large-scale datasets such as MagicLens are compre

Seoyun Yang, Gihoon Kim, Taesup Kim

Text-to-image diffusion models have achieved remarkable progress in generating diverse and realistic images from textual descriptions. However, they still struggle with personalization, which requires adapting a pretrained model to depict user-specific subjects from only a few reference images. The key challenge lies in learning a new visual concept from a l

Maoyuan Ma, Wangyi Guo, Lei Yang, Zhanbo Xu

This paper addresses the joint scheduling problem of stochastic workloads and a hydrogen-enabled distributed energy system in a low-carbon Internet data centers (IDC). Although such workloads can be shifted over temporal and spatial horizons, it poses challenges when they cannot be accurately predicted, resulting in significant efficiency degradation and hig

Constantin Payerne, William d'Assignies, Christophe Yèche, Hendrik Hildebrandt

Local Primordial non-Gaussianities (PNGs), characterized by $f_{\rm NL}^{\rm loc}$, provide a powerful window into the physics of inflation. Cross-correlating high-redshift tracer samples with the CMB lensing potential offers a particularly robust probe of PNGs, mitigating imaging systematics that typically affect large-scale measurements from tracer auto-sp

Qingtao Yu, Changlin Song, Minghao Sun, Zhengyang Yu

The performance of text-to-image diffusion models may be improved at test-time by scaling computation to search for a generated image that maximizes a given reward function. While existing trajectory level exploration methods improve the effectiveness of test-time scaling for standard diffusion models, they are largely incompatible with modern flow matching

A. T. Borlakov, D. I. Kazakov

We consider the 4-fermion scattering amplitude in massless Fermi theory. Based on the Bogolyubov-Parasyuk theorem, which guarantees locality of the counter terms, we derive the recurrence relations for ultraviolet divergences of diagrams that establish a connection between successive orders of perturbation theory. We check their validity up to three loops co

Philip Zhong, Kent Chen, Don Wang

We evaluate the performance of various text embedding models and pipeline configurations for AI-driven search systems. We compare sentence-transformer and generative embedding models (e.g., All-MPNet, BGE, GTE, and Qwen) at different dimensions, indexing methods (Milvus HNSW/IVF), and chunking strategies. A custom evaluation dataset of 11,975 query-chunk pai

Somnath Mondal, Tinkal Mondal, Soumajit Pramanik, Rukmankesh Mehra

The interaction between proteins and nucleic acids is crucial for processes that sustain cellular function, including DNA maintenance and the regulation of gene expression and translation. Amino acid mutations in protein-nucleic acid complexes often lead to vital diseases. Experimental techniques have their own specific limitations in predicting mutational e

Ryosuke Ofuchi, Yuichiro Toda, Naoki Masuyama, Takayuki Matsuno

This paper addresses the problem of building global topological maps from 3D LiDAR point clouds for autonomous mobile robots operating in large-scale, dynamic, and unknown environments. Adaptive Resonance Theory-based Topological Clustering with Different Topologies (ATC-DT) builds global topological maps represented as graphs while mitigating catastrophic f

Qi Chen, Mihai Anitescu

We present a Fourier-enhanced recurrent neural network (RNN) for downscaling electrical loads. The model combines (i) a recurrent backbone driven by low-resolution inputs, (ii) explicit Fourier seasonal embeddings fused in latent space, and (iii) a self-attention layer that captures dependencies among high-resolution components within each period. Across fou

Qi Song, Ziyuan Luo, Renjie Wan

AIGC-based image editing technology has greatly simplified the realistic-level image modification, causing serious potential risks of image forgery. This paper introduces a new approach to tampering detection using the Segment Anything Model (SAM). Instead of training SAM to identify tampered areas, we propose a novel strategy. The entire image is transforme

Simon Püttmann, Jonathan Jair Sànchez Contreras, Lennart Kowitz, Peter Lampen

Accurate 3D microscopy image segmentation is critical for quantitative bioimage analysis but even state-of-the-art foundation models yield error-prone results. Therefore, manual curation is still widely used for either preparing high-quality training data or fixing errors before analysis. We present VessQC, an open-source tool for uncertainty-guided curation

Zehao Deng, Tianjie Ju, Zheng Wu, Zhuosheng Zhang

The rapid development of large vision-language model (VLM) has greatly promoted the research of GUI agent. However, GUI agents still face significant challenges in handling long-horizon tasks. First, single-agent models struggle to balance high-level capabilities and low-level execution capability, facing prevalent issues of responsibility coupling and capab

Chunpeng Du, Fei Fang, Alfonso de Miguel-Arribas, Yikang Lu

Understanding how cooperation emerges and persists is a central challenge in the evolutionary dynamics of social and biological systems. Most prior studies have examined cooperation through pairwise interactions, yet real-world interactions often involve groups and higher-order structures. Reputation is a key mechanism for guiding strategic behavior in such

Xiang Feng, Tieshi Zhong, Shuo Chang, Weiliu Wang

Reconstructing high-resolution (HR) 3D Gaussian Splatting (3DGS) models from low-resolution (LR) inputs remains challenging due to the lack of fine-grained textures and geometry. Existing methods typically rely on pre-trained 2D super-resolution (2DSR) models to enhance textures, but suffer from 3D Gaussian ambiguity arising from cross-view inconsistencies a

Zhen Chen, Yihang Fu, Gabriel Madera, Mauro Giuffre

Multi-modal large language models (MLLMs) have shown promise in advancing healthcare. However, most existing models remain confined to single-image understanding, which greatly limits their applicability in clinical workflows. In practice, medical diagnosis and progression often require synthesizing information across multiple images from different modalitie

Liangwei Dong, Dongshuai Liu, Boris A. Malomed

We report the existence of quasi-stable ring-shaped (necklace-shaped) clusters built, in the free space, of 3D quantum droplets (QDs) in a binary Bose-Einstein condensate, modeled by the Gross-Pitaevskii equations with the Lee-Huang-Yang corrections. The QD clusters exhibit diverse dynamical behaviors, including contraction, oscillations, and expansion, depe

Bao-Yun Dong, Ying Liang, Stefano Chesi, Xue-Feng Zhang

We study the properties of a generalized Dicke-Ising model realized with an array of Rydberg atoms, driven by microwave electric fields and coupled to an optical cavity. As this platform allows for a precisely tunable anisotropy parameter, the model exhibits a rich landscape of phase transitions and critical phenomena, induced by the interplay of rotating-wa

Miguel A. Mendez, Jan van Den Berghe, Manuel Ratz, Matilde Fiore

This chapter provides three tutorial exercises on physics-constrained regression. These are implemented as toy problems that seek to mimic grand challenges in (1) the super-resolution and data assimilation of the velocity field in image velocimetry, (2) data-driven turbulence modeling, and (3) system identification and digital twinning for forecasting and co

Miguel A. Mendez

This chapter opens with a review of classic tools for regression, a subset of machine learning that seeks to find relationships between variables. With the advent of scientific machine learning this field has moved from a purely data-driven (statistical) formalism to a constrained or ``physics-informed'' formalism, which integrates physical knowledge and met

Yuyue Wang, Xin Cheng, Yihan Wu, Xihua Wang

The task of Visual Text-to-Speech (VisualTTS), also known as video dubbing, aims to generate speech synchronized with the lip movements in an input video, in additional to being consistent with the content of input text and cloning the timbre of a reference speech. Existing VisualTTS models typically adopt lightweight architectures and design specialized mod

Josef Bengtson, David Nilsson, Dong In Lee, Yaroslava Lochman

Recent advancements in diffusion and flow models have greatly improved text-based image editing, yet methods that edit images independently often produce geometrically and photometrically inconsistent results across different views of the same scene. Such inconsistencies are particularly problematic for editing of 3D representations such as NeRFs or Gaussian

M. A. Mardyban, V. O. Nesterenko

Low-lying $K^{\pi}=0^+$ and isomeric $8^-$ states in even-even isotopes $^{250-260}$No are explored within the Quasiparticle Random-Phase Approximation (QRPA) method with Skyrme parametrization SLy4. The deformations, single-particle (s-p) spectra and pairing in the isotopes are inspected. The calculations predict a pronounced minimum in the neutron pairing

Ifeanyi Okala

This work examines how three different image-based methods, VGG16, ViT-B/16, and CoAtNet-Tiny, perform in identifying depression, schizophrenia, and healthy controls using daily actigraphy records. Wrist-worn activity signals from the Psykose and Depresjon datasets were converted into 30 by 48 images and evaluated through a three-fold subject-wise split. Alt

Gabriel Aguirre, Simay Atasoy Bingöl, Heiko Hamann, Jonas Kuckling

Effective collective decision-making in swarm robotics often requires balancing exploration, communication and individual uncertainty estimation, especially in hazardous environments where direct measurements are limited or costly. We propose a decentralized Bayesian framework that enables a swarm of simple robots to identify the safer of two areas, each cha

Qi Yang, Kai Liu, Jingjing Zhao, Kaiquan Cai

This paper investigates the rate-energy trade-off for pinching-antenna systems (PASS)-assisted simultaneous wireless information and power transfer (SWIPT) systems. Both the single information user (IU)/energy user (EU) and multiple IUs/EUs scenarios are considered.1) For the single IU/EU scenario, a pinching beamforming optimization problem is formulated fo

Keunbaik Lee, Eun Jin Jang, Dipak Dey

To analyze longitudinal zero-inflated count data, we extend existing models by introducing marginalized zero-inflated Poisson (MZIP) models with random effects, which explicitly capture the marginal effect of covariates and address limitations of previous methods. These models provide a clearer interpretation of the overall mean effect of covariates on zero-

K. O. Parfenov, D. I. Novikov, A. O. Mihalchenko

We present a Gaussianity test of the cosmic microwave background (CMB) polarization by analyzing the statistics of unpolarized points in the sky, classified into three distinct types: saddles, comets, and beaks. This classification of singular points where both Stokes parameters $Q$ and $U$ vanish stems from the fact that linear polarization is described by

Paola F. Antonietti, Silvia Bertoluzza, Fabio Credali

We present a non-nested W-cycle multigrid scheme for the lowest order Virtual Element Method on polygonal meshes. To avoid the implicit definition of the Virtual Element space, which poses several issues in the computation of intergrid operators that underpin multigrid methods, the proposed scheme uses a fully-conforming auxiliary space constructed by cheapl

Md Ashiqur Rahman, Mustofa Tanbir Kuhel, Clara Novoa

The risk of oil spills in the Alaskan Arctic has become an urgent environmental and logistical concern as maritime traffic increases under climate driven sea ice retreat. Traditional deterministic response planning models fail to represent key uncertainties, including variable spill magnitudes, changing environmental sensitivity, and infrastructure limitatio

Yuxuan Chen, Rongpeng Li, Xianfu Chen, Celimuge Wu

Large language model (LLM) agents at the network edge offer low-latency execution for routine queries. In contrast, complex requests often require the superior capability of cloud models, incurring higher latency and cost. To navigate this quality-cost trade-off under dynamic network conditions, we propose a cloud-edge synergy for NetGPT that integrates netw

Yu. D. Panov

For one-dimensional spin and pseudospin models that allow mapping to a Markov chain, the free energy of the system at a finite temperature can be expressed in terms of bond concentrations. Minimizing the free energy function makes it possible to obtain an exact solution of a statistical model. A dilute Ising chain with interacting impurities is considered as

Hao Wang, Yingshuo Wang, Junang Gan, Yanan Cheng

Online pornography and gambling have consistently posed regulatory challenges for governments, threatening both personal assets and privacy. Therefore, it is imperative to research the classification of the newly registered Pornographic and Gambling Domain Names (PGDN). However, scholarly investigation into this topic is limited. Previous efforts in PGDN cla

Ahmad Al-Badawi, Faizuddin Ahmed, İzzet Sakallı

We investigate a static, spherically symmetric black hole solution surrounded by a cloud of strings and immersed in an electromagnetic universe. By deriving the event horizon from the lapse function, we demonstrate that both the string cloud parameter and the electromagnetic background parameter significantly modify the horizon radius compared to the Schwarz

Chih-Huan Chang

We prove a motivic version of the Donaldson--Thomas/Pandharipande--Thomas (DT/PT) correspondence on Calabi--Yau threefolds. The proof combines Toda's wall crossing framework and the motivic integral identity recently proved by Bu. This provides a refinement of the numerical DT/PT correspondence.

Itai Boneh, Estéban Gabory, Paweł Gawrychowski, Adam Górkiewicz

We consider building, given a straight-line program (SLP) consisting of $g$ productions deriving a two-dimensional string $T$ of size $N\times N$, a structure capable of providing random access to any character of $T$. For one-dimensional strings, it is now known how to build a structure of size $\mathcal{O}(g)$ that provides random access in $\mathcal{O}(\l

Avinash Agarwal, Peeyush Agarwal, Manisha J. Nene

As Artificial Intelligence becomes increasingly embedded in critical telecommunications infrastructure, existing legal frameworks remain ill-equipped to address the distinct risks this development introduces. This paper proposes the AI Regulatory Readiness Index (ARRI), a reproducible instrument for doctrinally assessing the legal preparedness of national fr

Dimitrios Voulanas, Eduardo Gildin

Fast and reliable surrogate models are critical for optimization, control and uncertainty analysis in geological carbon-storage projects, yet high-fidelity multiphase simulators remain too expensive. Dynamic Mode Decomposition (DMD) offers an attractive data-driven reduction framework, but its operators are trained for a single set of reservoir properties. W

Junsung Park

Offline inverse reinforcement learning (IRL) aims to recover a reward function that explains expert behavior using only fixed demonstration data, without any additional online interaction. We propose BiCQL-ML, a policy-free offline IRL algorithm that jointly optimizes a reward function and a conservative Q-function in a bi-level framework, thereby avoiding e

Kamil Wrzos, Magdalena Birowska, Milosz Rybak

We explore symmetry-breaking phenomena in MnPS3/TMDC (MoS2, WS2, MoSe2, WSe2) heterostructures using first-principles calculations, considering two high-symmetry stacking configurations, S1 and S2, which differ not only by their interfacial registry but also by a 30{\deg} twist between the layers. Depending on the stacking geometry, the systems exhibit two d

M. A. Gameiro

This study introduces a significant architectural advancement in feature fusion for lyrical content classification by integrating auxiliary structural features directly into the self-attention mechanism of a pre-trained Transformer. I propose the SFL Transformer, a novel deep learning model that utilizes a Contextual Gating mechanism (an Intermediate SFL) to

Shunta Yamazaki, Tomomi Matsui

In this research, we discuss a problem of calculating the Shapley value in bankruptcy games. We show that the decision problem of computing the Shapley value in bankruptcy games is NP-complete. We also investigate the relationship between the Shapley value of bankruptcy games and the Shapley-Shubik index in weighted voting games. The relation naturally impli

Debargha Banerjee, Dron Airon, Pranjal Vishwakarma, Ronit Debnath

We describe an algorithmic method to determine the image of restriction maps for Siegel modular forms with \textit{arbitrary} characters and arbitrary weight. A program has been implemented in the mathematical software \texttt{Java} to compute the Fourier expansion of the image of these restriction maps for Siegel modular forms of genus two. This approach al

Martin Campos Pinto, Frederik Schnack

This article introduces a novel approach for broken-FEEC (Finite Element Exterior Calculus), extending its application to locally refined spline spaces with non-matching interfaces. Traditional broken-FEEC allows for discontinuous discretizations at patch interfaces, preserving the de Rham structure and offering computational benefits. However, local refinem

Chen Wang, Yong Tan, Yongzheng Wen, Shiqiang Zhao

Active control of optical nonlinearity is essential for advancing next-generation electronics and photonics, including high-speed wireless communications, optical information processing, and nonlinear signal manipulation. However, achieving tunable nonlinearity at terahertz (THz) frequencies faces significant challenges due to the lack of materials that comb

Jia-Heng Ji, Zhi-Yan Shao, Yu-Bo Liu, Fan Yang

Magnetism is a fundamental research area in which the recently proposed altermagnetism (AM) has become an emergent frontier. Very recently, the quasicrystal (QC) was proposed as a possible platform to realize AM. However, the existence of AM in QCs still lacks vigorous evidence. In this work, we adopt the sign-problem-free projector quantum Monte Carlo (PQMC

Loup-Noe Levy, Guillaume Guerard, Sonia Djebali, Soufian Ben Amor

This article presents a novel pretopology-based algorithm designed to address the challenges of clustering mixed data without the need for dimensionality reduction. Leveraging Disjunctive Normal Form, our approach formulates customizable logical rules and adjustable hyperparameters that allow for user-defined hierarchical cluster construction and facilitate

Guillaume Guerard, Sonia Djebali

The advent of the big data paradigm has transformed how industries manage and analyze information, ushering in an era of unprecedented data volume, velocity, and variety. Within this landscape, mixed-data clustering has become a critical challenge, requiring innovative methods that can effectively exploit heterogeneous data types, including numerical and cat

Loup-Noe Levy, Jeremie Bosom, Guillaume Guerard, Soufian Ben Amor

This article attempts answering the following problematic: How to model and classify energy consumption profiles over a large distributed territory to optimize the management of buildings' consumption? Doing case-by-case in depth auditing of thousands of buildings would require a massive amount of time and money as well as a significant number of qualified p

Mengying Hu, Quanshui Wu

We construct a family of connected Hopf algebras with finite Gelfand-Kirillov dimension, none of which is an iterated Hopf Ore extension of the universal enveloping algebra of its primitive part. This provides a negative answer to a question posed by Li and Zhou. It is also demonstrated that these connected Hopf algebras can be formulated as an iterated cros

Hossein Abedi, Mohammadsadegh Khazali, Klaus Mølmer

Quantum computing algorithms can be decomposed into a universal set of elementary one- and two-qubit gates. Different physical implementations of quantum computing, however, employ interactions that permit direct conditional dynamics on multiple qubits in a single step. In this work, we leverage quantum optimal control techniques to design single continuous

Ruohai Guo, Jiang Zhu, Chengjie Yu, Zhigang Wang

In modern radar systems, target detection and parameter estimation face significant challenges when confronted with mainlobe jamming. This paper presents a Diffusion-based Model and Data Dual-driven (DMDD) approach to estimate and detect multitargets and suppress structured jamming. In DMDD, the jamming prior is modeled through a score-based diffusion proces

Yin Wang, Lin-Feng Wu, Yi-Bo Shao, Zhe Wang

Significant challenges exist in the nonlinear extensional rheology of entangled polymers. With simulations, we show that the key to understanding this problem is to recognize the existence and importance of a strain-induced crossover from the entangled state to a packed state. This crossover, following the saturation of primitive chain stretch, takes place w

Sejeong Jang, Joo Heung Yoon, Hyo Kyung Lee

Intensive care unit (ICU) data are highly irregular, heterogeneous, and temporally fragmented, posing challenges for generalizable clinical prediction. We present PULSE-ICU, a self-supervised foundation model that learns event-level ICU representations from large-scale EHR sequences without resampling or manual feature engineering. A unified embedding module

Yuri Prokhorov

The goal of these lecture notes is to present the modern point of view on the classification of Fano threefolds. We tried to offer a self-consistent treatment of the topics covered. \par\medskip\noindent These notes have been published in two versions: a Russian edition in \textit{Lektsionnye Kursy NOTs} \textbf{31}. Steklov Inst. Math., Moscow (ISBN 978-5-9

Kevin Hendrey, David R. Wood

We prove that several natural graph classes have tree-decompositions with minimum width such that each bag has bounded treewidth. For example, every planar graph has a tree-decomposition with minimum width such that each bag has treewidth at most 3. This treewidth bound is best possible. More generally, every graph of Euler genus $g$ has a tree-decomposition

Zhiyang Liu, Ruiteng Zhao, Lei Zhou, Chengran Yuan

This paper presents a novel approach for affordance-informed robotic manipulation by introducing 3D keypoints to enhance the understanding of object parts' functionality. The proposed approach provides direct information about what the potential use of objects is, as well as guidance on where and how a manipulator should engage, whereas conventional methods

Jaeseok Lee, Jaekoo Lee

Recently, the impressive generative capabilities of diffusion models have been demonstrated, producing images with remarkable fidelity. Particularly, existing methods for the 3D object generation tasks, which is one of the fastest-growing segments in computer vision, pre-dominantly use text-to-image diffusion models with textual inversion which train a pseud

Jie Qi, Wenbin Fei, Guillermo A. Narsilio

Understanding how pore structure influences flow and transport behaviour in granular materials is essential for addressing a wide range of geotechnical, hydraulic, and environmental challenges. These processes are largely shaped by the microscopic arrangement of particles and interconnections between pores within the material. However, detailed insights cons

Chijul B. Tripathy

We introduce a fun problem that can be considered as a variant of the classic birthday problem, the Bottleneck Birthday Problem (BBP). It is stated as: what is the maximum number of people we have to choose so that no day of the year has more than r >= 1 birthdays incident on it with probability at least 1/2? We provide a survey of techniques used in the lit

Kaplan Desbouis, Adrien Richou

After proving existence and uniqueness of ergodic distribution dependent backward stochastic differential equations (BSDEs) under strong and weak dissipativity regimes for the underlying McKean--Vlasov SDE, we leverage this new framework to investigate the long-time behavior of distribution dependent BSDEs on a finite-time horizon. Finally, we apply our resu

Shin-ichi Inage

This study proposes a time-symmetric framework for quantum measurement that restores microscopic reversibility at the level of the dynamical description while remaining compatible with causality and thermodynamic consistency. Instead of invoking a stochastic wavefunction collapse, the measurement process is modeled as a bidirectional informational update bet

Aswathy Menon Kavumpadikkal Radhakrishnan, Suraj Prasad, Neelkamal Mallick, Raghunath Sahoo

The production of heavy quarks (charm and beauty) in high-energy hadronic and nuclear collisions provides an excellent testing ground for the theory of strong interactions and validates models based on quantum chromodynamics (QCD). In this work, prompt and nonprompt production of $\rm{J/}\psi$ in $pp$ collisions at $\sqrt{s}=13$ TeV are studied as a function

Matthias Pfister, Giovanni Apruzzese, Irdin Pekaric

Many cyberattacks succeed because they exploit flaws at the human level. To address this problem, organizations rely on security awareness programs, which aim to make employees more resilient against social engineering. While some works have suggested that such programs should account for contextual relevance, the common praxis in research is to adopt a "gen

Yan Li, Yong Zhao, Xiaohan Xia, Dongmei Jiang

The key to facial expression recognition is to learn discriminative spatial-temporal representations that embed facial expression dynamics. Previous studies predominantly rely on pre-trained Convolutional Neural Networks (CNNs) to learn facial appearance representations, overlooking the relationships between facial regions. To address this issue, this paper

Qiang Li, Yingwenqi Jiang, Tuoxi Li, Duyu Chen

Realistic and controllable simulation is critical for advancing end-to-end autonomous driving, yet existing approaches often struggle to support novel view synthesis under large viewpoint changes or to ensure geometric consistency. We introduce HybridWorldSim, a hybrid simulation framework that integrates multi-traversal neural reconstruction for static back

Chayanid Termphaiboon, Raula Gaikovina Kula, Youmei Fan, Morakot Choetkiertikul

Security policies, such as SECURITY.md files, are now common in open-source projects. They help guide responsible vulnerability reporting and build trust among users and contributors. Despite their growing use, it is still unclear how these policies influence the structure and evolution of software dependencies. Software dependencies are external packages or

Ruize Gao, Feng Xiao, Jinpu Li, Shaoze Cui

Reasonable pricing of data products enables data trading platforms to maximize revenue and foster the growth of the data trading market. The textual semantics of data products are vital for pricing and contain significant value that remains largely underexplored. Therefore, to investigate how textual features influence data product pricing, we employ five pr

Daniel Sungho Jung, Kyoung Mu Lee

Foot contact plays a critical role in human interaction with the world, and thus exploring foot contact can advance our understanding of human movement and physical interaction. Despite its importance, existing methods often approximate foot contact using a zero-velocity constraint and focus on joint-level contact, failing to capture the detailed interaction

Hongwei Zhu, Xuantai Wu, Jingjie Lv, Qinshan Zhang

Low density parity check (LDPC) codes, initially discovered by Gallager, exhibit excellent performance in iterative decoding, approaching the Shannon limit. MDS array codes, with favorable algebraic structures, are codes suitable for decoding large burst errors. The Blaum-Roth (BR) code, an MDS array code similar to the Reed-Solomon (RS) code but has a parit

Ruairi Baker, Maria Pervez, Angus Hawkey, Nobuya Sakai

Colour conversion offers the clearest path to achieve RGB colours in high resolution microdisplays for AR/VR. With resolutions beyond 5000 ppi (i.e. RGB pitch of 5 um), the thickness of the conversion layers is critical for efficiency and manufacturing. Perovskites outperform other conversion materials (quantum dots or phosphors) with their high absorption c

Cui-Cui Ding, Qin Zhou, B. A. Malomed

We report analytical solutions for diverse multi-pole (MP) soliton and breather states in spatially inhomogeneous binary Bose-Einstein condensates (BECs) with the helicoidally shaped spin-orbit coupling (SOC), including MP stripe solitons on zero background, MP beating stripe solitons on a nonzero plane-wave background, as well as MP beating stripe solitons

Maitrayee Keskar, Mohan Trivedi, Ross Greer

We present a method for trajectory planning for autonomous driving, learning image-based context embeddings that align with motion prediction frameworks and planning-based intention input. Within our method, a ViT encoder takes raw images and past kinematic state as input and is trained to produce context embeddings, drawing inspiration from those generated

Bo Li, Xicong Pang, Guangrui Wei, Haiwang Zhong

Coordinated optimization dispatch (COD) of transmission system operator (TSO) and distribution system operator (DSO) can effectively ensure system security and efficiency under high-penetration distributed energy resource (DER) integration. Researches of large-scale COD problem can be categorized into iterative approaches that allow DSO to dispatch independe

Adnan Ferdous Ashrafi, Hasanul Kabir

ASD is a complicated neurodevelopmental disorder marked by variation in symptom presentation and neurological underpinnings, making early and objective diagnosis extremely problematic. This paper presents a Graph Convolutional Network (GCN) model, incorporating Chebyshev Spectral Graph Convolution and Graph Attention Networks (GAT), to increase the classific

Peiyu Yu, Suraj Kothawade, Sirui Xie, Ying Nian Wu

Most post-training methods for text-to-image samplers focus on model weights: either fine-tuning the backbone for alignment or distilling it for few-step efficiency. We take a different route: rescheduling the sampling timeline of a frozen sampler. Instead of a fixed, global schedule, we learn instance-level (prompt- and noise-conditioned) schedules through

Lukas Struppek, Dominik Hintersdorf, Hannah Struppek, Daniel Neider

Recent large language models achieve strong reasoning performance by generating detailed chain-of-thought traces, but this often leads to excessive token use and high inference latency. Existing efficiency approaches typically focus on model-centric interventions, such as reinforcement learning or supervised fine-tuning, to reduce verbosity. In contrast, we

Nicoleta Tantalaki, Sophia Vei, Athena Vakali

The growing influence of Artificial Intelligence (AI) systems on decision-making in critical domains has exposed their potential to cause significant harms, often rooted in biases embedded across the AI lifecycle. While existing frameworks and taxonomies document bias or harms in isolation, they rarely establish systematic links between specific bias types a

Jinlei Wu, Wei Huang, Ahsan Ali, Yuhong Chen

Monolayer MoS$_2$ is a direct band gap semiconductor with potential applications in optoelectronics and photonics. MoS$_2$ also has a large optical nonlinearity. However, the atomic thickness of the monolayer limits the strength of the measured functional signals, such as the photocurrent or photoluminescence, in optoelectronic devices. Here, we show that ph

Difu Feng, Qianqian Xu, Zitai Wang, Cong Hua

Nowadays, recommendation systems have become crucial to online platforms, shaping user exposure by accurate preference modeling. However, such an exposure strategy can also reinforce users' existing preferences, leading to a notorious phenomenon named filter bubbles. Given its negative effects, such as group polarization, increasing attention has been paid t

Tim S. Lyon

We introduce and study single-conclusioned nested sequent calculi for a broad class of intuitionistic multi-modal logics known as "intuitionistic grammar logics (IGLs)." These logics serve as the intuitionistic counterparts of classical grammar logics, and subsume standard intuitionistic modal and tense logics, including IK and IKt extended with combinations

Young-Jun Lee, Seungone Kim, Byung-Kwan Lee, Minkyeong Moon

Can language models (LMs) self-refine their own responses? This question is increasingly relevant as a wide range of real-world user interactions involve refinement requests. However, prior studies have largely tested LMs' refinement abilities on verifiable tasks such as competition math or symbolic reasoning with simplified scaffolds, whereas users often po

Zhaoyang Wei, Wenchao Ding, Yanchao Hao, Xi Chen

Models capable of "thinking with images" by dynamically grounding their reasoning in visual evidence represent a major leap in multimodal AI. However, replicating and advancing this ability is non-trivial, with current methods often trapped between the instability of end-to-end reinforcement learning (RL) and the rigidity of supervised fine-tuning (SFT). Thi

Pu Li, Wenhao Zhang, Weize Quan, Biao Zhang

Boundary representation (B-rep) is the de facto standard for CAD model representation in modern industrial design. The intricate coupling between geometric and topological elements in B-rep structures has forced existing generative methods to rely on cascaded multi-stage networks, resulting in error accumulation and computational inefficiency. We present Bre

Delong Zhao, Qiang Huang, Di Yan, Yiqun Sun

Concept Bottleneck Models (CBMs) enhance interpretability by introducing a layer of human-understandable concepts between inputs and predictions. While recent methods automate concept generation using Large Language Models (LLMs) and Vision-Language Models (VLMs), they still face three fundamental challenges: poor visual grounding, concept redundancy, and th

Inha Kang, Eunki Kim, Wonjeong Ryu, Jaeyo Shin

Accurate long horizon forecasting of particulate matter (PM) concentration fields is essential for operational public health decisions. However, achieving reliable forecasts remains challenging in regions with complex terrain and strong atmospheric dynamics such as East Asia. While foundation models such as Aurora offer global generality, they often miss reg

Bo Chen, Tao Liu, Qi Chen, Xie Chen

Talking face generation aims to synthesize realistic speaking portraits from a single image, yet existing methods often rely on explicit optical flow and local warping, which fail to model complex global motions and cause identity drift. We present IMTalker, a novel framework that achieves efficient and high-fidelity talking face generation through implicit

Shuai Dong, Junyi Yang, Ye Ke, Hongyang Shang

Convolutional neural networks (CNNs) are computationally intensive and often accelerated using crossbar-based in-memory computing (IMC) architectures. However, large convolutional layers must be partitioned across multiple crossbars, generating numerous partial sums (psums) that require additional buffer, transfer, and accumulation, thus introducing signific

Zhijie Fan, Bo He, Xiaofeng Wang, Zhicheng Zeng

In this paper we initiate the study of absolute summability for big and little Hankel operators $ H_f^\beta,h_f^\beta:A_\alpha^p(\mathbb{B}_n)\to L^q(\mathbb{B}_n,dv_\beta), $ acting between weighted Bergman and weighted Lebesgue spaces on the unit ball, for possibly different integrability exponents $p$ and $q$. We characterize those symbols $f$ for which t

Piotr Nowak, Sanaz Pooya, Sven Raum, Adam Skalski

For every Hecke C*-algebra of right-angled, hyperbolic type, we construct a smooth subalgebra to which traces associated with arbitrary conjugacy classes in the associated Coxeter group extend. We calculate the pairing with K-theory of the delocalised traces arising this way, showing that it is faithful on K-theory.

Jahidul Islam, Md Ataullha, Saiful Azad

LLMs excel at code generation from English prompts, but this progress has not extended to low-resource languages. We address Bangla-to-Python code generation by introducing BanglaCodeAct, an agent-based framework that leverages multi-agent prompting and iterative self-correction. Unlike prior approaches relying on task-specific fine-tuning, BanglaCodeAct emp

Jingyuan Xu, Zhentian Zhang, Jian Dang, Hao Jiang

Fluid antenna systems encompass a broad class of reconfigurable antenna technologies that offer substantial spatial diversity for various optimization objectives and communication tasks. Their capability to enhance spatial resolution within a fixed physical aperture makes fluid antennas particularly attractive for next-generation wireless deployments. In thi

Gabriella Montano, Cheukyu Edward Tong, Dan Marrone

This paper presents the design and testing of a millimeter-wave frequency comb generator developed for the Black Hole Explorer (BHEX) mission, a space Very-Long-Baseline Interferometry (VLBI) mission concept. The heart of BHEX is a dual-band receiver, centered at 90 and 270 GHz. This novel comb generator is based on a microwave phase modulator producing phas