December 2025 arXiv papers — page 16

Jackie Baek, Will Ma, Dmitry Mitrofanov

Partnering with a large online retailer, we consider the problem of sending daily personalized promotions to a userbase of over 20 million customers. We propose an efficient policy for determining, every day, the promotion that each customer should receive (10%, 12%, 15%, 17%, or 20% off), while respecting global allocation constraints. This policy was succe

Shengyi Hua, Jianfeng Wu, Tianle Shen, Kangzhe Hu

Recent pathological foundation models have substantially advanced visual representation learning and multimodal interaction. However, most models still rely on a static inference paradigm in which whole-slide images are processed once to produce predictions, without reassessment or targeted evidence acquisition under ambiguous diagnoses. This contrasts with

Alan Dow, István Juhász

The main result of this paper is the proof of the simultaneous consistency, modulo a weakly compact cardinal, of the equality $2^{< \mathfrak{c}} = \mathfrak{c}$ with the following property (*) of partitions of pairs of $\mathfrak{c}$: \smallskip (*) For any coloring (or partition) $k : [\mathfrak{c}]^2 \rightarrow 2$ either there is a homogeneous set of siz

Lucas Gonzalez-Rivas, Leonardo Krapp, Ximena Ramos, Pablo Benitez-Llambay

Surviving rapid inward orbital migration is a crucial aspect of formation models for the Jupiter's Galilean moons. The primary aim of this study is to investigate the orbital migration of the Galilean moons by incorporating self-consistent solid dynamics in circumjovian disk models. We perform two-fluid simulations using the FARGO3D code on a 2D polar grid.

Pengfei Zhou, Liliang Chen, Shengcong Chen, Di Chen

Specifying robotic manipulation tasks in a manner that is both expressive and precise remains a central challenge. While visual goals provide a compact and unambiguous task specification, existing goal-conditioned policies often struggle with long-horizon manipulation due to their reliance on single-step action prediction without explicit modeling of task pr

Mehdi Hamzehnejad, Abbas Salemi

Gauss--Christoffel quadrature is a fundamental method for numerical integration, and its convergence analysis is closely related to the decay of Chebyshev expansion coefficients. Classical estimates, including those due to Trefethen, are based on weighted bounded variation assumptions involving the singular weight $(1-x^{2})^{-1/2}$, which may be too restric

Oleg Antipin, Jahmall Bersini, Jacob Hafjall, Giulia Muco

We present a novel semiclassical framework tailored to determine the scaling dimensions of heavy neutral composite operators in conformal field theories (CFTs) which are inaccessible with other current methodologies. It utilizes the state-operator correspondence to map the desired scaling dimensions to the semiclassical energy spectrum of periodic homogeneou

J. Sumaya-Martinez, J. Mulia-Rodriguez

Structured optical beams possess rich spatial features that are commonly characterized using entropic measures of field complexity. However, such measures do not directly quantify the operational usefulness of optical structure for parameter estimation and sensing. Here we introduce Fisher information as an operational metric to assess the metrological conte

Binhe Yu, Zhen Wang, Kexin Li, Yuqian Yuan

Multi-subject customization aims to synthesize multiple user-specified subjects into a coherent image. To address issues such as subjects missing or conflicts, recent works incorporate layout guidance to provide explicit spatial constraints. However, existing methods still struggle to balance three critical objectives: text alignment, subject identity preser

I. V. Tokatly, Y. Lu, F. S. Bergeret

We show that nonequilibrium spin injection into a superconductor can generate an anomalous supercurrent or induce a phase gradient, even for spin voltages below the superconducting gap. Our mechanism does not require breaking time-reversal symmetry in the effective superconducting Hamiltonian, but instead relies on nonequilibrium spin injection. We further d

Eduardo Salazar

This paper presents a private transfer architecture for the Internet Computer (ICP) that decouples deposit and retrieval through two short-lived intermediaries, with sealed storage and attested teardown by an ephemeral witness. The protocol uses a non-interactive RDMPF-based encapsulation to derive per-transfer transport keys. A public notice hint is compute

Andrés Chirre, Markus Valås Hagen

Assuming the Riemann Hypothesis, we prove that for all $x\geq 2$, there exists at least one even integer within the interval $(x, x+123\log^2x]$, that can be expressed as the sum of two primes. This result is an improvement over the recent work of Cully-Hugill and Dudek, who obtained the constant $9696$ instead of $123$.

Alexia Nix, Evangelos Tsolakidis

We employ the massive gravity approach to stress-tensor deformations in a variety of scenarios, obtaining novel results and establishing new connections. Starting with perturbation theory, we show that the addition of $\text{tr} T+\Lambda_{2}$ to $T\overline{T}$ can be recovered and we construct the deformed action of an interacting non-abelian spin-1 along

Hexin Zhang, Dong Li, Jie Huang, Bingzhou Wang

Diffusion models have become a leading paradigm for image super-resolution (SR), but existing methods struggle to guarantee both the high-frequency perceptual quality and the low-frequency structural fidelity of generated images. Although inference-time scaling can theoretically improve this trade-off by allocating more computation, existing strategies remai

Marianne Bauer, William Bialek

In many biological networks the responses of individual elements are ambiguous. We consider a scenario in which many sensors respond to a shared signal, each with limited information capacity, and ask that the outputs together convey as much information as possible about an underlying relevant variable. In a low noise limit where we can make analytic progres

S. W. Ellingson, A. J. Yip

In past work, we described the use of a reconfigurable intelligent surface (RIS) mounted on the rim of an axisymmetric prime focus-fed reflector to create nulls in the close-in sidelobes. In this paper, we show that similar performance is possible in an offset Gregorian reflector system using a RIS on the rim of the subreflector. Applications include radio a

Ali Hossary, Laura Crosara, Stefano Tomasin

Jamming attacks pose a critical threat to wireless networks, particularly in cell-free massive MIMO systems, where distributed access points and user equipment (UE) create complex, time-varying topologies. This paper proposes a novel jamming detection framework leveraging dynamic graphs and graph convolutional neural networks (GCN) to address this challenge.

Barbara Fiedorowicz, Amitabh Basu

Given a resistive electrical network, we would like to determine whether all the resistances (edges) in the network are working, and if not, identify which edge (or edges) are faulty. To make this determination, we are allowed to measure the effective resistance between certain pairs of nodes (which can be done by measuring the amount of current when one uni

Maryam Mirzaei, Farzaneh Shayegh, Hamed Narimani

Accurate recognition of human emotional states is critical for effective human-machine interaction. Electroencephalography (EEG) offers a reliable source for emotion recognition due to its high temporal resolution and its direct reflection of neural activity. Nevertheless, variations across recording sessions present a major challenge for model generalizatio

Chen Tan, Yong-Qiang Wang

This paper investigates the structure and properties of neutron stars in four-dimensional non-polynomial gravities. Solving the modified Tolman-Oppenheimer-Volkoff equations for three different equations of state (BSk19, SLy4, AP4), we confirm that neutron star solutions remain in existence. As the modification parameter $\alpha$ increases, neutron stars gro

Zhuo Huang

Machine Learning (ML) has been a foundational topic in artificial intelligence (AI), providing both theoretical groundwork and practical tools for its exciting advancements. From ResNet for visual recognition to Transformer for vision-language alignment, the AI models have achieved superior capability to humans. Furthermore, the scaling law has enabled AI to

Timo Aukusti Laine

We investigate the structure of Large Language Model (LLM) embedding spaces using mathematical concepts, particularly linear algebra and the Hamiltonian formalism, drawing inspiration from analogies with quantum mechanical systems. Motivated by the observation that LLM embeddings exhibit distinct states, suggesting discrete semantic representations, we explo

Alejandro Corvalan

Economists often define the middle class based on income distribution, yet selecting which segment constitutes the `middle' is essentially arbitrary. This paper proposes a definition of the middle class based solely on the properties of income distribution. It argues that for a collection of unequal societies, the poor and rich extremes of the distribution u

Stefan Tutić

We consider the space $\mathscr{H}_L ^{s,r} (O)$ consisting of all local Sobolev distributions of order $s$ on an open set $O$ whose Sobolev wave front set of order $r$ is contained in the closed conic set $L\subseteq O\times(\mathbb{R}^m\backslash\{0\})$. We introduce a locally convex topology on $\mathscr{H}_L ^{s,r} (O)$ and show that the ordinary product

Chun-Yang Yuan, Tzu-Kan Hsiao

Germanium hole spin qubits based on strained Ge/SiGe quantum well have attracted much research attention due to the strong spin-orbit coupling. In particular, the strain dependence of the heavy-hole--light-hole mixing and thus the $g$-tensor anisotropy offer unique opportunities for acoustic driving and spin-phonon coupling. In this work we numerically simul

Donghao Zhou, Jingyu Lin, Guibao Shen, Quande Liu

Recent visual generative models enable story generation with consistent characters from text, but human-centric story generation faces additional challenges, such as maintaining detailed and diverse human face consistency and coordinating multiple characters across different images. This paper presents IdentityStory, a framework for human-centric story gener

Kio M. Lovric

Despite nearly a century of use in tailless and flying-wing aircraft, the NACA five-digit reflex camber-line family lacks published closed-form expressions for the governing design integrals; practitioners have instead relied on numerical quadrature and tabulated constants available only for a limited set of standard configurations. This paper addresses that

Hazel Kim, Philip Torr

Large language models (LLMs) are highly vulnerable to input confirmation bias. When a prompt implies a preferred answer, models often reinforce that bias rather than explore alternatives. This phenomenon remains underexplored, yet it is already harmful in base models and poses an even greater risk in multi-agent debate, where echo chambers reinforce bias ins

Zeusu Sato

It is widely claimed in investment education and practice that extending the investment horizon reduces risk, and that diversifying investment timing, for example through dollar-cost averaging (DCA), further mitigates investment risk. Although such claims are intuitively appealing, they are often stated without precise definitions of risk or a clear separati

S. V. Mironov, A. I. Buzdin, O. B. Zuev, M. V. Kovalenko

In this paper we review the recent progress in theoretical understanding of the peculiarities of photogalvanic phenomena, photon drag and inverse Faraday effects in superconductors and hybrid superconducting structures. Our study is based on the time-dependent Ginzburg-Landau (TDGL) theory with a complex relaxation constant which provides the simplest descri

Siqi Song, Xuanbing Xie, Zonglin Li, Yuqiang Li

Multi-robot collaboration tasks often require heterogeneous robots to work together over long horizons under spatial constraints and environmental uncertainties. Although Large Language Models (LLMs) excel at reasoning and planning, their potential for coordinated control has not been fully explored. Inspired by human teamwork, we present CLiMRS (Cooperative

Zuoyou Jiang, Li Zhao, Rui Sun, Ruohan Sun

Signal decay and regime shifts pose recurring challenges for data-driven investment strategies in non-stationary markets. Conventional time-series and machine learning approaches, which rely primarily on historical correlations, often struggle to generalize when the economic environment changes. While large language models (LLMs) offer strong capabilities fo

Sergey Turtaev, Tomáš Tyc, Ulf Poßner, Tina Eschrich

The remarkable advancements in our capacity to synthesise structured light have facilitated the generation of any desired optical landscapes and even controlling the spatial distribution of light propagating through optically complex media such as multimode fibres. The availability of precisely defined structured light at the extremity of an exceedingly narr

Siqi Li, Benjamin A. Spencer, Yiran Wang, Yasser G. Abdelhafez

Bone marrow (BM) metabolic quantification with 18F-fluorodeoxyglucose (FDG) positron emission tomography (PET) is of broad clinical significance for accurate assessment of BM at staging and follow-up, especially when immunotherapy is involved. However, current methods of quantifying BM may be inaccurate because the volume defined to measure bone marrow may a

Fengjiao Chen, Minhao Jing, Weitao Lu, Yan Feng

Vision-language large models are moving toward the unification of visual understanding and visual generation tasks. However, whether generation can enhance understanding is still under-explored on large data scale. In this work, we analysis the unified structure with a concise model, UniHetero, under large-scale pretraining (>200M samples). Our key observati

Aayush Kumar

Hallucinations hinder reliable question answering, especially in resource-constrained deployments where frontier-scale models or retrieval pipelines may be impractical. We present EdgeJury, a lightweight ensemble framework that improves truthfulness and robustness using only small instruction-tuned language models (3B-8B) suitable for serverless edge inferen

Xinyi Zheng, Ningke Li, Xiaokun Luan, Kailong Wang

Large Language Models (LLMs) have demonstrated impressive reasoning capabilities, leading to their adoption in high-stakes domains such as healthcare, law, and scientific research. However, their reasoning often contains subtle logical errors masked by fluent language, posing significant risks for critical applications. While existing approaches like fact-ch

Zun-Xian Huang, Peng-Cheng Li

We revisit the quasinormal-mode/greybody factor correspondence for Kerr black holes in the eikonal limit and develop a systematic WKB-based formulation by recasting the radial Teukolsky equation into a Schr\"odinger-type equation with a short-range potential. Building on earlier studies of the correspondence in rotating backgrounds, we extend the analysis to

Mikhail Rybakov

This work investigates the algorithmic complexity of non-classical logics, focusing on superintuitionistic and modal systems. It is shown that propositional logics are usually polynomial-time reducible to their fragments with at most two variables (often to the one-variable or even variable-free fragments). Also, it is proved that predicate logics are usuall

Alessio Benavoli, Alessandro Facchini, Marco Zaffalon

How can we ensure that AI systems are aligned with human values and remain safe? We can study this problem through the frameworks of the AI assistance and the AI shutdown games. The AI assistance problem concerns designing an AI agent that helps a human to maximise their utility function(s). However, only the human knows these function(s); the AI assistant m

Shuai Tang

Argumentation frameworks ($AF$s) have been extensively developed, but existing higher-order bipolar $AF$s suffer from critical limitations: attackers and supporters are restricted to arguments, multi-valued and fuzzy semantics lack unified generalization, and encodings often rely on complex logics with poor interoperability. To address these gaps, this paper

Denesa Zyberaj, Pascal Hirmer, Marco Aiello, Stefan Wagner

The automotive domain is shifting to software-centric development to meet regulation, market pressure, and feature velocity. This shift increases embedded systems' complexity and strains testing capacity. Despite relevant standards, a coherent system-testing methodology that spans heterogeneous, legacy-constrained toolchains remains elusive, and practice oft

Shengsong Luo, Ruilin Wu, Chongbin Xu, Junjie Ma

This paper considers recovering a continuous angular power spectrum (APS) from the channel covariance. Building on the projection-onto-linear-variety (PLV) algorithm, an affine-projection approach introduced by Miretti \emph{et. al.}, we analyze PLV in a well-defined \emph{weighted} Fourier-domain to emphasize its geometric interpretability. This yields an e

Mehdi Heydari Shahna

Today's heavy-duty mobile machines (HDMMs) face two transitions: from diesel-hydraulic actuation to clean electric systems driven by climate goals, and from human supervision toward greater autonomy. Diesel-hydraulic systems have long dominated, so full electrification, via direct replacement or redesign, raises major technical and economic challenges. Altho

Hadar Miller, Tsvi Kuflik, Moshe Lavee

This paper presents ACT (Allocate Connections between Texts), a novel three-stage algorithm for the automatic detection of biblical quotations in Rabbinic literature. Unlike existing text reuse frameworks that struggle with short, paraphrased, or structurally embedded quotations, ACT combines a morphology-aware alignment algorithm with a context-sensitive en

Alex Hansen, Sauro Succi

We discuss whether science is in the process of being transformed from a quest for causality to a quest for correlation in light of the recent development in artificial intelligence. We observe that while a blind trust in the most seductive promises of AI is surely to be avoided, a judicious combination of computer simulation based on physical insight and th

Md Arafat Habib, Medhat Elsayed, Majid Bavand, Pedro Enrique Iturria Rivera

Radio Access Network (RAN) slicing enables multiple logical networks to exist on top of the same physical infrastructure by allocating resources to distinct service groups, where radio resource scheduling plays a key role in ensuring compliance with slice-specific Service-Level Agreements (SLAs). Existing configuration-based or intent-driven Reinforcement Le

Kang Xiang, Yueyuan Wang, Shi Huang, Hongyuan Song

Using quasi-simultaneous synchrotron X-ray diffraction and tomography techniques, we have studied in-situ and in real-time the nucleation and co-growth dynamics of the peritectic structures in an Al-Mn alloy during solidification. We collected ~30 TB 4D datasets which allow us to elucidate the phases' co-growth dynamics and their spatial, crystallographic an

Debasish Biswasray, Yogendra Singh, Amar Jyoti Biswal, Bala Murali Krishna Mariserla

Controllable quasiparticle radiation in two-dimensional (2D) semiconductors is essential for efficient carrier recombination, tunable emission, and modulation of valley polarization which are strongly determined by both the density and nature of underlying excitonic species. Conventional chemical doping techniques, however, often hinder the reversibility and

Brice Arléon Zemtsop Ndadji, Simon Bliudze, Clément Quinton

Modern cloud architectures demand self-adaptive capabilities to manage dynamic operational conditions. Yet, existing solutions often impose centralized control models ill-suited to microservices decentralized nature. This paper presents AdaptiFlow, a framework that leverages well-established principles of autonomous computing to provide abstraction layers fo

Henrique De Medeiros, Denisse Muñante, Sophie Chabridon, César Perdigão Batista

[Context and Motivation] Global energy consumption has been steadily increasing in recent years, with data centers emerging as major contributors. This growth is largely driven by the widespread migration of applications to the Cloud, alongside a rising number of users consuming digital content. Dynamic adaptation (or self-adaptive) approaches appear as a wa

Giuseppe De Palma, Saverio Giallorenzo, Ivan Lanese, Gianluigi Zavattaro

The Adaptable TeaStore has recently been proposed as a reference model for adaptable microservice architectures. It includes different configurations, as well as scenarios requiring to transition between them. We describe an implementation of the Adaptable TeaStore based on AIOCJ, a choreographic language that allows one to program multiparty systems that ca

Anna Gallone, Simon Bliudze, Sophie Cerf, Olga Kouchnarenko

When designing new web applications, developers must cope with different kinds of constraints relative to the resources they rely on: software, hardware, network, online micro-services, or any combination of the mentioned entities. Together, these entities form a complex system of communicating interdependent processes, physical or logical. It is very desira

Eddy Truyen

The Adaptable TeaStore specification provides a microservice-based case study for implementing self-adaptation through a control loop. We argue that implementations of this specification should be informed by key properties of self-adaptation: system-wide consistency (coordinated adaptations across replicas), planning (executing an adaptation until appropria

Eddy Truyen, Wouter Joosen

This paper presents how an existing framework for offline performance optimization can be applied to microservice applications during the Release phase of the DevOps life cycle. Optimization of resource allocation configuration parameters for CPU and memory during the Release phase remains a largely unexplored problem as most research has focused on intellig

Wen G. Gong

With hundreds of multilingual embedding models available, practitioners lack clear guidance on which provide genuine cross-lingual semantic alignment versus task performance through language-specific patterns. Task-driven benchmarks (MTEB) may mask fundamental alignment shortcomings. We introduce Semantic Affinity (SA), a bounded (between 0 and 1) metric mea

Wei Gao, Paul Zheng, Peng Wu, Yulin Hu

In this article, we consider an industrial internet of things (IIoT) network supporting multi-device dynamic ultra-reliable low-latency communication (URLLC) while the channel state information (CSI) is imperfect. A joint link adaptation (LA) and device scheduling (including the order) design is provided, aiming at maximizing the total transmission rate unde

Christophe Paul, Ignaz Rutter

To date, the best circle graph recognition algorithm runs in almost linear time as it relies on a split decomposition algorithm that uses the union-find data-structure. We show that in the case of circle graphs, the PC-tree data-structure allows one to avoid the union-find data-structure to compute the split decomposition in linear time. As a consequence, we

Zhiyu Li, Klaas De Kinder, Xikui Ma, Christophe Caloz

Scattering at interluminal modulation interfaces, where a sharp space-time perturbation moves at a velocity lying between the wave velocities of the two surrounding media, has remained an open problem for decades. This regime is somewhat reminiscent of the Cherenkov regime, in which the velocity of a charged particle exceeds the phase velocity of light in a

Dimitrios Karapiperis, George Papadakis, Vassilios Verykios

Entity Resolution (ER) is a critical data cleaning task for identifying records that refer to the same real-world entity. In the era of Big Data, traditional batch ER is often infeasible due to volume and velocity constraints, necessitating Progressive ER methods that maximize recall within a limited computational budget. However, existing progressive approa

A. R. Offringa, R. J. van Weeren

Context. Processing radio interferometric data often requires storing forward-predicted model data. In direction-dependent calibration, these data may have a volume an order of magnitude larger than the original data. Existing lossy compression techniques work well for observed, noisy data, but cause issues in calibration when applied to forward-predicted mo

Haoyu Pei, Zhongyang Liu, Xiangyi Xiao, Xiaocong Du

Most venture capital (VC) investments fail, while a few deliver outsized returns. Accurately predicting startup success requires synthesizing complex relational evidence, including company disclosures, investor track records, and investment network structures, through explicit reasoning to form coherent, interpretable investment theses. Traditional machine l

Maria Spichkova

In this paper, we share our lessons learned from more than a decade of teaching software quality aspects within Software Engineering (SE) courses, where the focus is on Agile/Scrum settings: final year software development projects and the course on SE Project Management. Based on the lessons learned, we also provide a number of recommendations on embedding

Vassilis Digalakis, Ramayya Krishnan, Gonzalo Martin Fernandez, Agni Orfanoudaki

We study how organizations should select among competing AI models when user utility, deployment costs, and compliance requirements jointly matter. Widely used capability leaderboards do not translate directly into deployment decisions, creating a capability -- deployment gap; to bridge it, we take a systems-level view in which model choice is tied to applic

Mingyuan Jiu, Hailong Zhu, Wenchuan Wei, Hichem Sahbi

Context modeling is crucial for visual recognition, enabling highly discriminative image representations by integrating both intrinsic and extrinsic relationships between objects and labels in images. A limitation in current approaches is their focus on basic geometric relationships or localized features, often neglecting cross-scale contextual interactions

Guoan Wan, Tianyu Chen, Fangzheng Feng, Haoyi Zhou

Parameter-efficient fine-tuning (PEFT) methods have emerged as a practical solution for adapting large foundation models to downstream tasks, reducing computational and memory costs by updating only a small subset of parameters. Among them, approaches like LoRA aim to strike a balance between efficiency and expressiveness, but often suffer from slow converge

Ido Fridman, Shemuel Sternklar, Eliran Talker

We investigated experimentally and theoretically a cavity-free microwave field that couples the two ground states of a {\Lambda}-type atomic system, thereby forming a closed {\Delta} configuration. In this regime, the absence of cavity-imposed phase matching leads to a strong sensitivity of the ground-state coherence to the microwave field parameters. We obs

Zongsheng Cao, Yangfan He, Anran Liu, Feng Chen

Large Video Language Models (LVLMs) have rapidly emerged as the focus of multimedia AI research. Nonetheless, when confronted with lengthy videos, these models struggle: their temporal windows are narrow, and they fail to notice fine-grained semantic shifts that unfold over extended durations. Moreover, mainstream text-based retrieval pipelines, which rely c

Marie S. Bauer, Julia Gachot, Matthias Kerzel, Cornelius Weber

Within the context of human-robot interaction (HRI), Theory of Mind (ToM) is intended to serve as a user-friendly backend to the interface of robotic systems, enabling robots to infer and respond to human mental states. When integrated into robots, ToM allows them to adapt their internal models to users' behaviors, enhancing the interpretability and predicta

Toqeer Ali Syed, Mohammad Riyaz Belgaum, Salman Jan, Asadullah Abdullah Khan

The software supply chain attacks are becoming more and more focused on trusted development and delivery procedures, so the conventional post-build integrity mechanisms cannot be used anymore. The available frameworks like SLSA, SBOM and in toto are majorly used to offer provenance and traceability but do not have the capabilities of actively identifying and

Irene Spelta, Carolina Tamborini

We study the algebraic monodromy of families of cyclic Galois coverings of curves. Under a condition on the $G$-decomposition of the associated variation of Hodge structures, we prove a criterion for the maximality of the monodromy. The proof combines the genus-zero case with a degeneration argument involving Prym varieties of certain admissible coverings. A

Aleksei Ilin, Leonid Rybnikov

We study the family of commutative subspaces in the trigonometric holonomy Lie algebra $t^{\mathrm{trig}}_{\Phi}$, introduced by Toledano Laredo, for an arbitrary root system $\Phi$. We call these subspaces \emph{Bethe subspaces} because they can be regarded as quadratic components of \emph{Bethe subalgebras} in the Yangian corresponding to the root system $

Wen G Gong

We introduce a multi-level analysis framework for examining semantic geometry in multilingual embeddings, implemented through Semanscope (a visualization tool that applies PHATE manifold learning across four linguistic levels). Analysis of diverse datasets spanning sub-character components, alphabetic systems, semantic domains, and numerical concepts reveals

Zbigniew Was, Ananya Tapadar, J. M. John, S. Banerjee

From the perspective of low energy tau decays and radiative corrections in decays, not much has changed since the last, Tau23 conference. Also, TAUOLA, tau decay library, and PHOTOS for radiative corrections in decays have not changed much, neither for QED nor for New Physics processes application. Progress was in the domain of flexibilities for applications

Junyu Chen, Pratik Nag, Huixia Judy-Wang, Ying Sun

Accurate spatial interpolation of the air quality index (AQI), computed from concentrations of multiple air pollutants, is essential for regulatory decision-making, yet AQI fields are inherently non-Gaussian and often exhibit complex nonlinear spatial structure. Classical spatial prediction methods such as kriging are linear and rely on Gaussian assumptions,

Shuyuan Lin, Hailiang Liao, Qiang Qi, Junjie Huang

Recent research has focused on using convolutional neural networks (CNNs) as the backbones in two-view correspondence learning, demonstrating significant superiority over methods based on multilayer perceptrons. However, CNN backbones that are not tailored to specific tasks may fail to effectively aggregate global context and oversmooth dense motion fields i

Shuyuan Lin, Wenwu Peng, Junjie Huang, Qiang Qi

Robust and discriminative feature learning is critical for high-quality point cloud registration. However, existing deep learning-based methods typically rely on Euclidean neighborhood-based strategies for feature extraction, which struggle to effectively capture the implicit semantics and structural consistency in point clouds. To address these issues, we p

Thomas Haschka, Joseph Bakarji

Recent advances in large language models enable documents to be represented as dense semantic embeddings, supporting similarity-based operations over large text collections. However, many web-scale systems still rely on flat clustering or predefined taxonomies, limiting insight into hierarchical topic relationships. In this paper we operationalize hierarchic

Wenjun Jiang, Xiaojun Yuan, Chenchen Liu, Boyu Teng

Channel knowledge map (CKM) is a promising paradigm for environment-aware communications by establishing a deterministic mapping between physical locations and channel parameters. Existing CKM construction methods focus on quasi-static propagation environment. This paper develops a dynamic CKM construction method for multiple-input multiple-output orthogonal

M. Haider Akbar, Özgür E. Müstecaplıoğlu

Quantum annealing offers a promising strategy for solving complex optimization problems by encoding the solution into the ground state of a problem Hamiltonian. While most implementations rely on spin-$1/2$ systems, we explore the performance of quantum annealing on a spin-$1$ system where the problem Hamiltonian includes a single ion anisotropy term of the

Aryan Agarwala, Nithin Varma

In this paper, we present efficient pseudodeterministic algorithms for both the global minimum cut and minimum s-t cut problems. The running time of our algorithm for the global minimum cut problem is asymptotically better than the fastest sequential deterministic global minimum cut algorithm (Henzinger, Li, Rao, Wang; SODA 2024). Furthermore, we implement o

Hajime Ogawa, Shonosuke Sugasawa

Gaussian process-based models are attractive for estimating heterogeneous treatment effects (HTE), but their computational cost limits scalability in causal inference settings. In this work, we address this challenge by extending Patchwork Kriging into the causal inference framework. Our proposed method partitions the data according to the estimated propensi

Kai-Heng Xiao, Shi-Lei Su, Xiang Ni, Yi-Ke Sun

Adiabatic topological pumping enables robust transport of energy and information, yet its operational speed is fundamentally constrained by the instantaneous adiabatic condition, which necessitates prohibitively slow parameter variations. Here, we propose a paradigm shift from instantaneous to global adiabaticity. We derive a global adiabatic criterion (GAC)

P. Adlarson, W. Augustyniak, W. Bardan, M. Bashkanov

The differential cross section for the $^{1}$H$(d,pp)n$ breakup reaction at deuteron beam energy of 380 MeV has been determined with high precision for 189 angular configurations of outgoing protons in the region of forward laboratory angles. The cross section data were compared to theoretical predictions based on the state-of-the-art nucleon-nucleon potenti

Yuxin Wen, Qing Shuai, Di Kang, Jing Li

We present HY-Motion 1.0, a series of state-of-the-art, large-scale, motion generation models capable of generating 3D human motions from textual descriptions. HY-Motion 1.0 represents the first successful attempt to scale up Diffusion Transformer (DiT)-based flow matching models to the billion-parameter scale within the motion generation domain, delivering

Bohan Xiao, Peiyong Wang, Qisheng He, Ming Dong

Image-to-Image (I2I) translation involves converting an image from one domain to another. Deterministic I2I translation, such as in image super-resolution, extends this concept by guaranteeing that each input generates a consistent and predictable output, closely matching the ground truth (GT) with high fidelity. In this paper, we propose a denoising Brownia

Manu, Yi Guo, Kanchana Thilakarathna, Nirhoshan Sivaroopan

Large Language Models (LLMs) can be driven into over-generation, emitting thousands of tokens before producing an end-of-sequence (EOS) token. This degrades answer quality, inflates latency and cost, and can be weaponized as a denial-of-service (DoS) attack. Recent work has begun to study DoS-style prompt attacks, but typically focuses on a single attack alg

Andrzej Czarnecki, Jishnu Khanna

We briefly review false-vacuum decay and examine a recent proposal by Frampton to model the origin of the first single-celled organism (SCO) as a phase transition between no-life and life vacua. In his calculation the exponent $n$ entering the probability $P_{\rm SCO}\sim 10^{-n}$ has dimensions of inverse time: it is an energy barrier divided by the Planck

Alexander L. Gavrilyuk, Sho Suda

It is known that a Delsarte $t$-design in a $Q$-polynomial association scheme has degree at least $\left \lceil{\frac{t}{2}}\right \rceil $. Following Ionin and Shrikhande who studied combinatorial $(2s-1)$-designs (i.e., Delsarte designs in Johnson association schemes) having exactly $s$ block intersection numbers, we call a Delsarte $(2s-1)$-design with de

Zijian Ling, Man Zhou, Hongda Zhai, Yating Huang

In recent years, drone delivery, which utilizes unmanned aerial vehicles (UAVs) for package delivery and pickup, has gradually emerged as a crucial method in logistics. Since delivery drones are expensive and may carry valuable packages, they must maintain a safe distance from individuals until user-drone mutual authentication is confirmed. Despite numerous

Kongcheng Zhang, Qi Yao, Shunyu Liu, Wenjian Zhang

Reinforcement Learning (RL) has shown promise for aligning Large Language Models (LLMs) to follow instructions with various constraints. Despite the encouraging results, RL improvement inevitably relies on sampling successful, high-quality responses; however, the initial model often struggles to generate responses that satisfy all constraints due to its limi

Kayathri Vigneswaran, Hugo Retief, Jai Clifford Holmes, Mariangel Garcia Andarcia

Accurate and continuous monitoring of river water levels is essential for flood forecasting, water resource management, and ecological protection. Traditional hydrological observation methods are often limited by manual measurement errors and environmental constraints. This study presents a hybrid framework integrating vision based waterline detection, YOLOv

Kanishka Hewageegana, Janani Harischandra, Nipuna Senanayake, Gihan Danansuriya

This study investigates fraud detection in ride hailing platforms through Graph Neural Networks (GNNs),focusing on the effectiveness of various models. By analyzing prevalent fraudulent activities, the research highlights and compares the existing work related to fraud detection which can be useful when addressing fraudulent incidents within the online ride

Zongsheng Cao, Yangfan He, Anran Liu, Jun Xie

Large Vision-Language Models (LVLMs) have achieved impressive progress in multi-modal understanding and generation. However, they still tend to produce hallucinated content that is inconsistent with the visual input, which limits their reliability in real-world applications. We propose \textbf{CoFi-Dec}, a training-free decoding framework that mitigates hall

Anna Bartkiewicz, Olga Bayandina, Alberto Sanna, Marian Szymczak

High-mass protostars are deeply embedded in dust inside their natal cores and are not easily detectable. However, maser emission at centimeter wavelengths, owing to its high brightness, enables us to study gas kinematics in protostars' circumstellar regions. We aim to understand the origin of the ring-like structures outlined by the 6.7 GHz methanol maser em

E. Malik, P. Kaigorodov, D. Kovaleva, O. Malkov

Using Gaia DR3 data, binary star catalogs have been created containing information on a total of more than 2.6 million pairs. This increases by more than an order of magnitude the ensemble of binary stars with known characteristics, which previously numbered about 140 thousand pairs. To perform statistical analysis of the complete ensemble of binary stars, i

Yuanchao Lou, Takaaki Nomura, Xinran Xu, Kei Yagyu

We investigate the phenomenology of 2 Higgs doublet models (2HDMs) with a new $U(1)$ gauge symmetry, $U(1)_X$, by which flavor changing neutral currents are forbidden at tree level. As an important consequence of the spontaneous breaking of both the $U(1)_X$ and electroweak symmetries by electroweak vacuum expectation values, upper limits appear on masses of

Zichang Lin, Wenjie Chen, Yitao Lin, Xinxin Zhang

Theoretical simulation is helpful for accurate interpretation of experimental X-ray absorption near-edge structure (XANES) spectra that contain rich atomic and electronic structure information of materials. However, current simulation methods are usually too complex to give the needed accuracy and timeliness when a large amount of data need to be analyzed, s

Vladimer Khasia

Mixture of Experts (MoE) models scale capacity but often suffer from representation collapse and gradient instability. We propose Dynamic Subspace Composition (DSC), a framework that approximates context-dependent weights via a state-dependent, sparse expansion of a shared basis bank. Formally, DSC models the weight update as a residual trajectory within a S

Ang Lv, Jin Ma, Yiyuan Ma, Siyuan Qiao

Mixture-of-Experts (MoE) models lack explicit constraints to ensure the router's decisions align well with the experts' capabilities, which ultimately limits model performance. To address this, we propose expert-router coupling (ERC) loss, a lightweight auxiliary loss that tightly couples the router's decisions with expert capabilities. Our approach treats e