November 2025 arXiv papers — page 84

Nils Hertl, Connor L. Box, Reinhard J. Maurer

Electron-phonon coupling in atoms and molecules adsorbed at metal surfaces gives rise to finite vibrational linewidths in infrared or electron energy loss spectra. When it is the dominant contribution to the vibrational lifetime, it manifests itself in the form of a Fano line shape. Here, we report the linewidths of vibrational modes of chemisorbed hydrogen

Vincent Astier, Thomas Unger

In this paper we continue our investigation of signatures of hermitian forms over Azumaya algebras with involution over commutative rings. We show that the approach used in an earlier paper for central simple algebras can be extended to Azumaya algebras and leads to a natural way of choosing the signature of a hermitian form at a given ordering, producing to

Michel Tokic, Slobodan Djukanović, Anja von Beuningen, Cheng Feng

We introduce a classification method based on in-context learning using time-series foundation models (TSFMs). We demonstrate how data not included in the TSFM training can be classified without fine-tuning the foundation model or training a traditional classification model. Examples are represented as targets (class labels) and covariates (data matrices) wi

Alexej Brauer, Mario V. Wüthrich

The Gini score is a popular tool in statistical modeling and machine learning for model validation and model selection. It is a purely rank based score that allows one to assess risk rankings. The Gini score for statistical modeling has mainly been used in a binary context, in which it has many equivalent reformulations such as the receiver operating charact

Victorita Dolean, Daria Hrebenshchykova, Stéphane Lanteri, Victor Michel-Dansac

Accurately simulating wave propagation is crucial in fields such as acoustics, electromagnetism, and seismic analysis. Traditional numerical methods, like finite difference and finite element approaches, are widely used to solve governing partial differential equations (PDEs) such as the Helmholtz equation. However, these methods face significant computation

Jiajun He, Xidong Mu, Hien Quoc Ngo, Michail Matthaiou

This paper proposes a novel localization framework underpinned by a pinching-antenna (PA) system, in which the target location is estimated using received signal strength (RSS) measurements obtained from downlink signals transmitted by the PAs. To develop a comprehensive analytical framework, we employ stochastic geometry to model the spatial distribution of

Ao Xie, Jiahui Chen, Quanzhi Zhu, Xiaoze Jiang

Dense retrieval has become a foundational paradigm in modern search systems, especially on short-video platforms. However, most industrial systems adopt a self-reinforcing training pipeline that relies on historically exposed user interactions for supervision. This paradigm inevitably leads to a filter bubble effect, where potentially relevant but previously

Shao-Feng Ge, Chui-Fan Kong, João Paulo Pinheiro

The JUNO first data and the recent neutrino global fit results are implemented in the sum rule from the residual $\mathbb Z^s_2$ and $\overline{\mathbb Z}^s_2$ symmetries to make prediction of the leptonic Dirac CP phase $\delta_D$. Without involving model parameters, the probability distribution of $\delta_D$ can be readily obtained from the experimental me

Michele Aleandri, Marco Dall'Aglio

We extend the coopetition index introduced by Aleandri and Dall'Aglio (2025) for simple games to the broader class of monotone transferable utility (TU) games and to all non-empty coalitions, including singletons. The new formulation allows us to define an absolute coopetition index with a universal range in [-1,1], facilitating meaningful comparisons across

Johannes C. Bauer, Paul Geng, Stephan Trattnig, Petr Dokládal

Remanufacturing describes a process where worn products are restored to like-new condition and it offers vast ecological and economic potentials. A key step is the quality inspection of disassembled components, which is mostly done manually due to the high variety of parts and defect patterns. Deep neural networks show great potential to automate such visual

Pei-Yao Song, Jin-Lei Wu, Weibin Li, Shi-Lei Su

We propose a topological transport platform for microwave-to-optical conversion at the single-photon level in a Rydberg atom-cavity setting. This setting leverages a hybrid dual-mode Jaynes-Cummings (JC) configuration, where a microwave resonator couples an optical cavity mediated by a Rydberg atom ensemble. Our scheme uniquely enables the formation of Fock-

Yong-Yong Liu, Jing-Rui Zhang, Ming-Hui Du, He-Shan Liu

In the space-based gravitational wave detections, the axion-like dark matter would alter the polarization state of the laser link between spacecrafts due to the birefringence effect. However, current designs of space-based laser interferometer are insensitive to variations in the polarization angle. Thus, the additional wave plates are employed to enable the

Thanadet Chuangsuwanich, Patipol Tiyajamorn, Yibo Chen, Michael Dattilo

Purpose: To develop the first 3D optic nerve head (ONH) atlas using AI-based registration and evaluate its use in: (1) atlas-adjusted retinal nerve fiber layer (RNFL) analysis for glaucoma diagnosis, and (2) strain-based assessment of glaucoma severity. Methods: Large-scale OCT datasets were registered using REFLECTIVITY-generated tissue segmentations. A hea

Fabrizio Canfora, Cristóbal Corral, Borja Diez, Luis Guajardo

Motivated by the recent interest in conformal and duality invariant nonlinear electrodynamics, we study the non-Abelian extension of ModMax electrodynamics. The theory is parameterized by a single dimensionless constant, and it is continuously connected to Yang-Mills theory in its vanishing limit. We show that the theory admits (anti-)self-dual instantons, d

Soumajit Dey, Sudip Kumar Acharyya, Dhananjoy Mandal

Let $\mathcal{M}(X,\mathcal{A},\mu)$ be the ring of all real-valued measurable functions constructed over a measure space $(X,\mathcal{A},\mu)$. A topology on $\mathcal{M}(X,\mathcal{A},\mu)$, called the {$F_\mu$-topology} weaker than the { $U_\mu$-topology} is introduced. It is realized that the {component}, the {quasi component} and the {path component }in

Linyin Luo, Yujuan Ding, Yunshan Ma, Wenqi Fan

Advanced multimodal Retrieval-Augmented Generation (MRAG) techniques have been widely applied to enhance the capabilities of Large Multimodal Models (LMMs), but they also bring along novel safety issues. Existing adversarial research has revealed the vulnerability of MRAG systems to knowledge poisoning attacks, which fool the retriever into recalling injecte

Georg Goldenits, Philip Koenig, Sebastian Raubitzek, Andreas Ekelhart

Phishing websites pose a major cybersecurity threat, exploiting unsuspecting users and causing significant financial and organisational harm. Traditional machine learning approaches for phishing detection often require extensive feature engineering, continuous retraining, and costly infrastructure maintenance. At the same time, proprietary large language mod

YiKang Shao, Tao Shi

Multimodal object detection has attracted significant attention in both academia and industry for its enhanced robustness. Although numerous studies have focused on improving modality fusion strategies, most neglect fusion degradation, and none provide a theoretical analysis of its underlying causes. To fill this gap, this paper presents a systematic theoret

Amir Rezaei Balef, Mykhailo Koshil, Katharina Eggensperger

Despite the architectural similarities between tabular in-context learning (ICL) models and large language models (LLMs), little is known about how individual layers contribute to tabular prediction. In this paper, we investigate how the latent spaces evolve across layers in tabular ICL models, identify potential redundant layers, and compare these dynamics

Yongtao Li, Hong Liu, Shengtong Zhang

A classical result of Nosal asserts that every $m$-edge graph with spectral radius $\lambda (G)> \sqrt{m}$ contains a triangle. A celebrated extension of Nikiforov [35] states that if $G$ is an $m$-edge graph with $\lambda (G)> \sqrt{(1- {1}/{r})2m}$, then $G$ contains a clique $K_{r+1}$. This result implies the Tur\'{a}n theorem and Wilf theorem, and offers

Marc-Emmanuel Coupvent des Graviers, Hejer Ammar, Christophe Guettier, Yann Dumortier

We introduce WarNav, a novel real-world dataset constructed from images of the open-source DATTALION repository, specifically tailored to enable the development and benchmarking of semantic segmentation models for autonomous ground vehicle navigation in unstructured, conflict-affected environments. This dataset addresses a critical gap between conventional u

Junyoung Heo, Yubin Lee

In this article, we study the optimization of resource distributions in a one-dimensional logistic diffusive model. The goal is to determine a distribution on a bounded one-dimensional domain that maximizes the total population at equilibrium. Previous works have shown that optimal resources are bang-bang, and in one dimension, a sufficiently large dispersal

Martin Schäfer, Tino Ullrich

Tchakaloff's theorem from 1957 asserts the existence of exact quadrature rules with non-negative weights for any polynomial space of finite degree on $\mathbb{R}^d$ if the underlying measure is positive, compactly supported, and absolutely continuous with respect to the Lebesgue measure. This classical result coined the term Tchakaloff quadrature for quadrat

Yuanjie Zhu, Liangwei Yang, Ke Xu, Weizhi Zhang

Large Language Models (LLMs) are reshaping unsupervised learning by offering an unprecedented ability to perform text clustering based on their deep semantic understanding. However, their direct application is fundamentally limited by a lack of stateful memory for iterative refinement and the difficulty of managing cluster granularity. As a result, existing

Giridhari Deogharia, Madhurima Pandey, Ashadul Halder

We investigate the cosmological dynamics of a universe described by Lyra's geometry in the presence of dark energy (DE) and dark matter (DM). Dark energy is modeled as a quintessence scalar field with bulk viscosity, while dark matter is allowed to interact with the scalar sector. The displacement vector field, arising naturally in Lyra's manifold, provides

Ethan Hicks, Joseph Oglio, Mikhail Nesterenko, Gokarna Sharma

We study financial transaction confirmation finality in Bitcoin as a function of transaction amount and user risk tolerance. A transaction is recorded in a block on a blockchain. However, a transaction may be revoked due to a fork in the blockchain, the odds of which decrease over time but never reach zero. Therefore, a transaction is considered confirmed if

Giorgio Scordo, Kostas Kanellopulos, Surangrat Thongkorn, Samuel Tavares da Silva Maraschin

Optogenetics has emerged as a powerful tool for disease modeling, enabling precise control of cellular activities through light stimulation and providing a valuable insights into disease mechanisms and therapeutic possibilities. Innovative materials and technologies such as micro-LEDs, optical fibers and micro/nano probes have been developed to allow precise

Arjun Gangwar, Kaousheik Jayakumar, S. Umesh

This paper describes the systems developed by SPRING Lab, Indian Institute of Technology Madras, for the ASRU MADASR 2.0 challenge. The systems developed focuses on adapting ASR systems to improve in predicting the language and dialect of the utterance among 8 languages across 33 dialects. We participated in Track 1 and Track 2, which restricts the use of ad

Ward Struyve

While Einstein was guided by the principle of general covariance in formulating general relativity, Kretschmann later argued that this principle lacks physical significance, since any space-time theory can be reformulated in a generally covariant form. This critique has prompted an ongoing debate over how to distinguish substantive general covariance from me

Davide Tornielli Bellini, Dario Tagliaferri, Pietro Grassi, Davide Scazzoli

Sensing in non-line-of-sight (NLOS) is one of the major challenges for integrated sensing and communication systems. Existing countermeasures for NLOS either use prior knowledge on the environment to characterize all the multiple bounces or deploy anomalous reflectors in the environment to enable communication infrastructure to ''\textit{see behind the corne

F. La Barbera, A. Vazdekis, F. Matteucci, E. Spitoni

We present radial trends of metallicity ([Fe/H]) and abundance ratios ([X/Fe]) for several chemical elements -- including C, N, Na, and the so-called alpha-elements (O, Mg, Si, Ca, and Ti) -- in the bulge of M31, out to ~0.6kpc from the center. We estimated abundances using full-spectrum fitting, full-index fitting, and line-strength analysis, in combination

Mingyang Feng, Shaoyuan Li, Xiang Yin

We investigate the sampling-based optimal path planning problem for robotics in complex and dynamic environments. Most existing sampling-based algorithms neglect environmental information or the information from previous samples. Yet, these pieces of information are highly informative, as leveraging them can provide better heuristics when sampling the next s

Zi-Qi Zeng, Jian Wang, Xiu-Bin Liu, Xu-Jie Wang

Entanglement is a central resource in quantum technologies, and the realization of photonic entanglement necessarily relies on interaction with matter. Resonance fluorescence (RF), originating from the coherent interaction between a driving field and a two-level system, plays a pivotal role in quantum optics. Here, we demonstrate a novel route to entanglemen

Yibing Wan, Zhengxiong Guan, Chaoli Zhang, Xiaoyang Li

In the user growth scenario, Internet companies invest heavily in paid acquisition channels to acquire new users. But sustainable growth depends on acquired users' generating lifetime value (LTV) exceeding customer acquisition cost (CAC). In order to maximize LTV/CAC ratio, it is crucial to predict channel-level LTV in an early stage for further optimization

Evgenii Vinogradov, Aymen Fakhreddine, Abdul Saboor, Sergi Abadal

We present an approach for spatially-consistent semi-deterministic Air-to-Ground (A2G) channel modeling in Unmanned Aerial Vehicle-assisted networks. We use efficient 3D building shadow projections to determine Line-of-Sight (LOS) regions, enabling fast generation of LOS maps. By integrating LOS-aware deterministic path loss with stochastic shadow fading, th

Wenlun Zhang, Yunshan Zhong, Zihao Ding, Xinyu Li

Data-Free Quantization (DFQ) offers a practical solution for model compression without requiring access to real data, making it particularly attractive in privacy-sensitive scenarios. While DFQ has shown promise for unimodal models, its extension to Vision-Language Models such as Contrastive Language-Image Pre-training (CLIP) models remains underexplored. In

Jan Paseka, Thomas Vetterlein

We axiomatise the dagger category of complex Hilbert spaces and bounded linear maps, using exclusively purely categorical conditions. Our axioms are chosen with the aim of an easy interpretability: two of them describe the composition of objecs, two further ones deal with the decomposition of objects, and a final axiom expresses a symmetry property. The cate

Luca Mossina, Corentin Friedrich

Reliable semantic segmentation is essential for clinical decision making, yet deep models rarely provide explicit statistical guarantees on their errors. We introduce a simple post-hoc framework that constructs confidence masks with distribution-free, image-level control of false-positive predictions. Given any pretrained segmentation model, we define a nest

Meimei Liu, Yuqi Zhu, Giorgio Gratta

Off-axis parabolic mirrors (OAPs) are occasionally desirable for specialized applications, but are known to introduce field-dependent astigmatic aberrations. In an experiment where optical tweezers are formed by OAPs, another OAP is added to form a relay configuration with an optional microscope, resulting in near diffraction-limited performance, with a reso

Zizhen Zhou, Ying-Chang Liang, Yanyu Cheng, Wei Yang Bryan Lim

Deploying foundation models (FMs) on uncrewed aerial vehicles (UAVs) promises broad ``low-altitude economy'' applications. Split federated learning (SFL)-based fine-tuning leverages distributed data while keeping raw data local and reduces client-side burden by partitioning the model between client and server. However, the per-round training latency is domin

Isabel Amaral, Alexandra Mendes, José Campos

In verification-aware languages, such as Dafny, despite their critical role, specifications are as prone to error as implementations. Flaws in specifications can result in formally verified programs that deviate from the intended behavior. In this paper, we explore the use of mutation testing to reveal weaknesses in formal specifications written in Dafny. We

Robert Alicki

The previously proposed modification of the standard (flat) inflationary $\Lambda CDM$ model in which the inflaton field(s) and ``dark energy" are replaced by the vacum in expanding Friedmann-Lema\^itre-Robertson-Walker Universe is studied. The expanding joint vacuum of the all ingrediences of matter, including Standard Model particles and a dark matter sect

Suo-Ning Wang, Bin-Bin Zhang, Rubén García-Benito

We present a systematic analysis of transient astrophysical events -- including supernovae (SNe), gamma-ray bursts (GRBs), and fast radio bursts (FRBs) -- in void and non-void galaxies within the local universe ($0.005 < z < 0.05$). Cosmic voids, defined by low galaxy densities and characterized by minimal environmental interactions, offer a natural laborato

Chukwuebuka J. Ugwua, James O. Chibuezea, Willice Obonyoa, Mavis Seidu

This work explored the spatial distribution of C$^{17}$O, SiO, HC$_{3}$N and SO$_{2}$ molecules, as well as the energetics of outflows in G358.46$-$0.39 proto-cluster using ALMA band 7 archival data, with the aim of providing an improved understanding of its protostellar nature, gas kinematics and dynamics. G358.46$-$0.39 is previously known to consist of 4

Malek Telfah, Abdalla Obeidat

We present an exact analytical solution for the one-dimensional Ising model in the presence of an external magnetic field applied periodically to every $k$-th site. The problem is handled using the symmetrized transfer matrix approach, we derive a compact closed-form expression for the system's eigenvalues for arbitrary period $k$. From the resulting free en

Manos Kamarianakis, Antonis Protopsaltis, George Papagiannakis

This position paper delves into the transformative role of Geometric Algebra (GA) in advancing specific areas of Computer Graphics (CG) and Extended Reality (XR), particularly in character animation, rendering, rigging, neural rendering, and generative AI-driven scene editing. Common CG algorithms require handling rotations, translations, and dilations (unif

Cong Wang, Zexin Fu, Jiayi Huang, Shanshi Huang

Vision Transformers (ViTs) have established new performance benchmarks in vision tasks such as image recognition and object detection. However, these advancements come with significant demands for memory and computational resources, presenting challenges for hardware deployment. Heterogeneous compute-in-memory (CIM) accelerators have emerged as a promising s

Can Lei, Nuno Gracias, Rafael Garcia, Hayat Rajani

Side-scan sonar mosaicking plays a crucial role in large-scale seabed mapping but is challenged by complex non-linear, spatially varying distortions due to diverse sonar acquisition conditions. Existing rigid or affine registration methods fail to model such complex deformations, whereas traditional non-rigid techniques tend to overfit and lack robustness in

Simon Boeder, Fabian Gigengack, Simon Roesler, Holger Caesar

Recent progress in self- and weakly supervised occupancy estimation has largely relied on 2D projection or rendering-based supervision, which suffers from geometric inconsistencies and severe depth bleeding. We thus introduce ShelfOcc, a vision-only method that overcomes these limitations without relying on LiDAR. ShelfOcc brings supervision into native 3D s

Ludovico Bruni Bruno, Stefano Serra-Capizzano

Histopolation is the approximation procedure that associates a degree $ d-1 $ polynomial $ p_{d-1} \in \mathscr{P}_{d-1} (I) $ with a locally integrable function $ f $ imposing that the integral (or, equivalently, the average) of $p$ coincides with that of $f$ on a collection of $ d $ distinct segments $s_i$. In this work we discuss unisolvence and condition

Kunyu Zhang, Mingxuan Wang, Xiangjie Shi, Haoxing Xu

The brain age is a key indicator of brain health. While electroencephalography (EEG) is a practical tool for this task, existing models struggle with the common challenge of imperfect medical data, such as learning a ``normal'' baseline from weakly supervised, healthy-only cohorts. This is a critical anomaly detection task for identifying disease, but standa

Sirui Chen, Mengshi Zhao, Lei Xu, Yuying Zhao

Recent advances in large language models (LLMs) have greatly improved their reasoning and decision-making abilities when deployed as agents. Richer reasoning, however, often comes at the cost of longer chain of thought (CoT), hampering interaction efficiency in real-world scenarios. Nevertheless, there still lacks systematic definition of LLM agent efficienc

Shao-Feng Ge, Chui-Fan Kong, Manfred Lindner, João Paulo Pinheiro

The first results from the JUNO reactor neutrino oscillation experiment improve our knowledge of neutrino masses and mixing parameters, especially the solar angle $\theta_s \equiv \theta_{12}$ and the solar mass squared difference $\Delta m^2_s \equiv \Delta m^2_{21}$. We discuss the implications of these results on neutrinoless double beta decay by itself a

Suyu Chen, Yimeng Bai, Yulong Huang, Xiaoyan Zhao

Large Language Models (LLMs) are increasingly integrated into users' daily lives, driving a growing demand for personalized outputs. Prior work has primarily leveraged a user's own history, often overlooking inter-user differences that are critical for effective personalization. While recent methods have attempted to model such differences, their feature ext

Lucianna Kiffer, Lioba Heimbach, Dennis Trautwein, Yann Vonlanthen

Blockchain technologies underpin an expanding ecosystem of decentralized applications, financial systems, and infrastructure. However, the fundamental networking layer that sustains these systems, the peer-to-peer layer, of all but the top few ecosystems remains largely opaque. In this paper, we present the first longitudinal, cross-network measurement study

Xiao-Wu Chen

We give a detailed proof of the following fundamental result: the singularity category of a ring is triangle equivalent to the stabilization of its stable module category. The result yields singular equivalences between rings of different nature. We use Leavitt rings to describe singularity categories of artinian rings.

Damjan Dagbjartsson, Simon Banks, Björgvin Hjörvarsson

We present a rare example of a one-dimensional system with short-range range interactions for which a self-stabilizing long-range ordered phase persists to finite temperatures. Our model offers a new perspective on the origins of shape anisotropy on the mesoscopic scale in magnetic metamaterials. Specifically, we show how the combination of a physically real

Mengyao Chen, Jipeng Cheng, Jinbiao Wang

The 2-component BKP (2-BKP) hierarchy is an important integrable system corresponding to the infinite dimensional Lie algebras $b_{\infty}$ and $d_{\infty}$, which contains Novikov-Veselov equation and can be used to describe the total descendent potential of D type singularity. Here we firstly introduce the projections of the mixed pseudo-differential opera

Byungho Jo

Aircraft Maintenance Technicians (AMTs) spend up to 30% of work time searching manuals, a documented efficiency bottleneck in MRO operations where every procedure must be traceable to certified sources. We present a compliance-preserving retrieval system that adapts LLM reranking and semantic search to aviation MRO environments by operating alongside, rather

Yi Zhang, Yushen Long, Liping Huang, Yicheng Zhang

This paper presents a closed-loop framework for conflict-free routing and scheduling of multi-aircraft in Terminal Manoeuvring Areas (TMA), aimed at reducing congestion and enhancing landing efficiency. Leveraging data-driven arrival inputs (either historical or predicted), we formulate a mixed-integer optimization model for real-time control, incorporating

Vanja Nikolić, Belkacem Said-Houari

Optimal control of nonlinear acoustic waves is relevant in many medical ultrasound technologies, ranging from cancer therapy to targeted drug delivery, where it can help guide the precise deposition of acoustic energy. In this work, we study Neumann boundary and distributed control problems for tracking a prescribed pressure field governed by the Westervelt

Baoliang Tian, Yuxuan Si, Jilong Wang, Lingyao Li

Multimodal Large Language Models are primarily trained and evaluated on aligned image-text pairs, which leaves their ability to detect and resolve real-world inconsistencies largely unexplored. In open-domain applications visual and textual cues often conflict, requiring models to perform structured reasoning beyond surface-level alignment. We introduce Cros

Iris Bree, Federico Gasparotto, Antonela Matijašić, Pouria Mazloumi

In this paper, we give a detailed account of the algorithm outlined in [1] for Feynman integral reduction and $\varepsilon$-factorised differential equations. The algorithm consists of two steps. In the first step, we use a new geometric order relation in the integration-by-parts reduction to obtain a basis of master integrals, whose differential equations o

Matas Tartėnas, Kastytis Zubovas, Eimantas Skuodas

The tight empirical $M-\sigma$ relation between the mass of a SMBH) and the velocity dispersion of the host galaxy bulge is often interpreted as the result of self-regulation by AGN feedback. This picture is motivated by analytical and semi-analytical models in which momentum-driven AGN winds can expel the gas once the SMBH reaches a critical mass. However,

Yunjiao Zhou, Xinyan Chen, Junlang Qian, Lihua Xie

Understanding complex human activities demands the ability to decompose motion into fine-grained, semantic-aligned sub-actions. This motion grounding process is crucial for behavior analysis, embodied AI and virtual reality. Yet, most existing methods rely on dense supervision with predefined action classes, which are infeasible in open-vocabulary, real-worl

Trevor McInroe

We introduce Terra Nova, a new comprehensive challenge environment (CCE) for reinforcement learning (RL) research inspired by Civilization V. A CCE is a single environment in which multiple canonical RL challenges (e.g., partial observability, credit assignment, representation learning, enormous action spaces, etc.) arise simultaneously. Mastery therefore de

Simon Klüttermann

In this paper, we study the problem of finding the global minima of a given function. Specifically, we consider complicated functions with numerous local minima, as is often the case for real-world data mining losses. We do so by applying a model from theoretical physics to create an Ising model-based evolutionary optimization algorithm. Our algorithm create

Shuolei Wang, Zimeng Xiao, Jinjing Shi, Heyuan Shi

Quantum neural networks (QNNs) are an important model for implementing quantum machine learning (QML), while they demonstrate a high degree of vulnerability to backdoor attacks similar to classical networks. To address this issue, a quantum backdoor attack detection framework called QSentry is proposed, in which a quantum Measurement Clustering method is int

Lingxiang Wang, Hainan Zhang, Zhiming Zheng

Domain-specific post-training often causes catastrophic forgetting, making foundation models lose their general reasoning ability and limiting their adaptability to dynamic real-world environments. Preserving general capabilities while acquiring downstream domain knowledge is a central challenge for large language and multimodal models. Traditional continual

Ruifen Dai, Xin Zheng, Fang Wang, Lei Guo

The investigation of legal judgment prediction (LJP), such as sentencing prediction, has attracted broad attention for its potential to promote judicial fairness, making the accuracy and reliability of its computation result an increasingly critical concern. In view of this, we present a new sentencing model that shares both legal logic interpretability and

Eitan Greenshtein

We consider a Nonparametric Empirical Bayes (NPEB) framework. Let $Y_i$ be random variables, $Y_i \sim f(y|\theta_i)$, $i=1,...,n$, where $\theta_i \sim G$, and $\theta_i \in \Theta$ are independent. The variables $Y_i $ are conditionally independent given $\theta_i, \; i=1,...,n$. The mixing distribution $G$ is unknown and assumed to belong to a nonparametr

Sam Adriaensen, Peter Sziklai, Zsuzsa Weiner

In a 2022, Bartoli, Cossidente, Marino, and Pavese proved that in the projective space ${\rm PG}(3,q^3)$, one can find three $\mathbb F_q$-subgeometries such that the union of their point sets is a strong blocking set. This proves the existence of linear minimal codes with parameters $[3(q^2+1)(q+1),4]_{q^3}$ for every prime power $q$. We give a short proof

Eddie Conti, Álvaro Parafita, Axel Brando

Assessing the importance of individual features in Machine Learning is critical to understand the model's decision-making process. While numerous methods exist, the lack of a definitive ground truth for comparison highlights the need for alternative, well-founded measures. This paper introduces a novel post-hoc local feature importance method called Counterf

Guoqiang Liang, Jingqian Gong, Mengxuan Li, Gege Lin

Large language models (LLMs) have exhibited exceptional capabilities in natural language understanding and generation, image recognition, and multimodal tasks, charting a course towards AGI and emerging as a central issue in the global technological race. This manuscript conducts a comprehensive review of the core technologies that support LLMs from a user s

Gihwan Kim, Jemin Lee, Hyungshin Kim

Previous Quantization-Aware Training (QAT) methods for vision transformers rely on expensive retraining to recover accuracy loss in non-linear layer quantization, limiting their use in resource-constrained environments. In contrast, existing Post-Training Quantization (PTQ) methods either partially quantize non-linear functions or adjust activation distribut

B. Martínez-Haya, N. Morillo, A. Cuetos

Aggregation processes in systems of planar macromolecules and colloids drive a broad range of phenomena in natural systems and soft materials. Depending on chemical architecture, intermolecular interactions in these systems may favor different relative pair orientations, such as stacking face-face or percolating edge-edge arrangements. In this work, we emplo

Xin Yang, Xin Fan, Zengshi Wang, Jun Han

Deep Neural Networks (DNNs) are widely applied across domains and have shown strong effectiveness. As DNN workloads increasingly run on CPUs, dedicated Matrix Processing Units (MPUs) and Matrix Instruction Set Architectures (ISAs) have been introduced. At the same time, sparsity techniques are widely adopted in algorithms to reduce computational cost. Despit

Ao Huang, Christian Röver, Tim Friede

Random-effects meta-analyses are widely used for evidence synthesis in medical research. However, conventional methods based on large-sample approximations often exhibit poor performance in case of very few studies (e.g., 2 to 4), which is very common in practice. Existing methods aiming to improve small-sample performance either still suffer from poor estim

Tushar Waghmare

Standard measures of quantum non-Markovianity are usually defined in terms of dynamical maps on a preferred time foliation and therefore do not extend straightforwardly to curved spacetimes, where no global time coordinate exists and causal structure is primary. We develop a covariant framework for open quantum dynamics along arbitrary timelike worldlines by

Ke Wu, Baozhong Yang, Zhenkun Ying, Dexin Zhou

We show that while anonymization effectively obscures firm identity, it significantly reduces the power of textual understanding, thereby diminishing models' ability to extract meaningful economic signals from financial texts. This information loss is particularly severe when numerical and object entities are removed from texts and is amplified in texts char

Yuhu Lu, Jinjing Shi

Efficiently encoding classical visual data into quantum states is essential for realizing practical quantum neural networks (QNNs). However, existing encoding schemes often discard spatial and semantic information when adapting high-dimensional images to the limited qubits of Noisy Intermediate-Scale Quantum (NISQ) devices. We propose a Fidelity-Preserving Q

R. V. Romanik, O. A. Dobush, M. P. Kozlovskii, I. V. Pylyuk

In this paper we present results for the pair distribution function for the cell fluid model with Curie-Weiss interaction. As a supplementary result, one- and two-particle densities are calculated.

Zhi-Qiang Chen, Jun-Jie Jin, Wei-Jian Guo, Sheng-Xiu Sun

We present a systematic search for changing-look (CL) quasars at high redshift z > 0.9 by cross-matching the spectroscopic datasets from the Dark Energy Spectroscopic Instrument Data Release 1 and Sloan Digital Sky Survey Data Release 18. We identify 97 CL quasars showing significant variability in high-ionization broad emission lines, including 45 turn-on a

Gabriele Calzolari, Vidya Sumathy, Christoforos Kanellakis, George Nikolakopoulos

Autonomous exploration of obstacle-rich spaces requires strategies that ensure efficiency while guaranteeing safety against collisions with obstacles. This paper investigates a novel platform-agnostic reinforcement learning framework that integrates a graph neural network-based policy for next-waypoint selection, with a safety filter ensuring safe mobility.

Yinan Yu, Falk Dippel, Christina E. Lundberg, Martin Lindgren

Objective: Machine learning (ML) predictive models are often developed without considering downstream value trade-offs and clinical interpretability. This paper introduces a cost-aware prediction (CAP) framework that combines cost-benefit analysis assisted by large language model (LLM) agents to communicate the trade-offs involved in applying ML predictions.

Amandip Sangha

We develop an information-theoretic approach to isoperimetric inequalities based on entropy dissipation under heat flow. By viewing diffusion as a noisy information channel, we measure how mutual information about set membership decays over time. This decay rate is shown to be determined by the boundary measure of the set, leading to a new proof of the Eucli

Alexis Correa-Guillén, Carlos Gómez-Rodríguez, David Vilares

We introduce HEAD-QA v2, an expanded and updated version of a Spanish/English healthcare multiple-choice reasoning dataset originally released by Vilares and G\'omez-Rodr\'iguez (2019). The update responds to the growing need for high-quality datasets that capture the linguistic and conceptual complexity of healthcare reasoning. We extend the dataset to over

Hanyue Wang, Daniel J. Eisenstein, Jessica Nicole Aguilar, Steven Ahlen

We present an efficient estimator for higher-order galaxy clustering using small groups of nearby galaxies, or multiplets. Using the Luminous Red Galaxy (LRG) sample from the Dark Energy Spectroscopic Instrument (DESI) Data Release 2, we identify galaxy multiplets as discrete objects and measure their cross-correlations with the general galaxy field. Our res

Zi-Rui Hao, Gong-Tao Fan, Qian-Kun Sun, Hong-Wei Wang

The Giant Dipole Resonance (GDR) in $^{51}$V has been a long-term conflicting interpretation, with existing photoneutron cross section data suggesting either a single peak or a pronounced splitting, leading to opposite conclusions on nuclear deformation. A new measurement of the $^{51}$V($\gamma$,1n) cross section, performed at the Shanghai Laser Electron Ga

Lennart Luettgau, Vanessa Cheung, Magda Dubois, Keno Juechems

People increasingly seek personal advice from large language models (LLMs), yet whether humans follow their advice, and its consequences for their well-being, remains unknown. In a longitudinal randomised controlled trial with a representative UK sample (N = 6,474), we found that up to 79% of participants who had a 20-minute discussion with one of three AI c

Yifu Guo, Zishan Xu, Zhiyuan Yao, Yuquan Lu

Existing multimodal reasoning models and frameworks suffer from fundamental architectural limitations: most lack the human-like ability to autonomously explore diverse reasoning pathways-whether in direct inference, tool-driven visual exploration, programmatic visual manipulation, or intrinsic visual imagination. Consequently, they struggle to adapt to dynam

Nathanael Bosch, Oleksandr Shchur, Nick Erickson, Michael Bohlke-Schneider

Ensembling is a powerful technique for improving the accuracy of machine learning models, with methods like stacking achieving strong results in tabular tasks. In time series forecasting, however, ensemble methods remain underutilized, with simple linear combinations still considered state-of-the-art. In this paper, we systematically explore ensembling strat

Aman Kumar, Arkaprabha Sarangi

We model the formation of dust in the ejecta of Type Iax supernovae (SNe), which is a low-luminosity subclass of Type Ia SNe. A non-equilibrium chemical kinetic approach is adopted to trace the synthesis of molecules, molecular clusters, and dust grains in the ejecta of thermonuclear SNe. We find that Type Iax SNe provide conditions conducive to the formatio

Cornelis van der Mee

In this article we develop the direct and inverse scattering theory of the Ablowitz-Kaup-Newell-Segur (AKNS) system $\bv_x=(ik\zS+\CQ(x))\bv$, where $\zS$ is a diagonal $n\times n$ matrix with diagonal entries $1$ and $-1$ and a single zero diagonal entry and $\CQ(x)$ is an $n\times n$ potential anticommuting with $\zS$ with entries in $L^1(\R)$. We derive t

Hao Qian, Pu You, Lin Zeng, Jingyuan Zhou

Glioblastoma (GBM) remains the most aggressive tumor, urgently requiring novel therapeutic strategies. Here, we present a dry-to-wet framework combining generative modeling and experimental validation to optimize peptides targeting ATP5A, a potential peptide-binding protein for GBM. Our framework introduces the first lead-conditioned generative model, which

Koushik Sen, Mathieu Renzo, Harim Jin, Norbert Langer

Understanding the transfer of mass and angular momentum in binary interactions is crucial for modelling the evolution of any interacting binary after the first mass transfer phase. Mass transfer physics assumptions shape the predictions for later stages of binary evolution, such as the immediate progenitors of stripped-envelope supernovae and gravitational w

A. Chakraborty, B. K. Sahoo

By employing singles, doubles, and triples excitations within the relativistic coupled-cluster framework, we perform comprehensive calculations of a wide range of atomic properties for the Th$^{3+}$ ion. These properties are essential for advancing nuclear clock technology and probing fundamental physics. Combining our isotope shift parameters with experimen

Alessandra Crippa, Julien Coatléven, Daniele A. Di Pietro, Nicolas Guy

In this work, we introduce a new Hybrid High-Order method for the numerical simulation of fracture propagation based on phase-field models. The proposed method supports general meshes made of polygonal/polyhedral elements, which provides great flexibility in mesh design and adaptation, and can accommodate large variations of both the displacement and damage

Stefan Dawydiak

Let $W_{\mathrm{aff}}$ be an extended affine Weyl group and $\mathbf{H}$ and $J$ be the corresponding affine and asymptotic Hecke algebras with standard bases $\{T_x\}$ and $\{t_w\}$, respectively. Viewing $J$ as a subalgebra of the $\mathbf{q}^{-\frac{1}{2}}$-adic completion of $\mathbf{H}$, we give formulas for the coefficient of $T_x$ in $t_w$ for various

Spyridon Loukovitis, Vasileios Karampinis, Athanasios Voulodimos

Autonomous navigation in complex scenes requires reliable perception across scenarios that the model did not encounter during its training. Along its route, an autonomous framework encounters objects it was trained to recognize, obstacles it has never seen, and background structures that resemble objects. Each of the three must be handled differently. To tac