May 2025 arXiv papers — page 31

Marianne Akian, Stéphane Gaubert, Loïc Marchesini, Ian Morris

The competitive spectral radius extends the notion of joint spectral radius to the two-player case: two players alternatively select matrices in prescribed compact sets, resulting in an infinite matrix product; one player wishes to maximize the growth rate of this product, whereas the other player wishes to minimize it. We show that when the matrices represe

Jiaxi Yang, Mengqi Zhang, Yiqiao Jin, Hao Chen

Large Language Model-based Multi-Agent Systems (MASs) have emerged as a powerful paradigm for tackling complex tasks through collaborative intelligence. However, the topology of these systems--how agents in MASs should be configured, connected, and coordinated--remains largely unexplored. In this position paper, we call for a paradigm shift toward \emph{topo

Timothy J. Burke, Xiaoyang Shi, Jasmine Sinanan-Singh, Isaac L. Chuang

Quantum logic gates performed via two-photon stimulated-Raman transitions in ions and atoms are fundamentally limited by spontaneous scattering errors. Recent theoretical treatment of these scattering processes has predicted no lower bound on the error rate of such gates when implemented with far-detuned lasers, while also providing an extension to metastabl

Zobia Batool, Huseyin Ozkan, Erchan Aptoula

Although Alzheimer's disease detection via MRIs has advanced significantly thanks to contemporary deep learning models, challenges such as class imbalance, protocol variations, and limited dataset diversity often hinder their generalization capacity. To address this issue, this article focuses on the single domain generalization setting, where given the data

Jonas Knoerr

Continuous dually epi-translation invariant valuations on convex functions are characterized in terms of the Fourier-Laplace transform of the associated Goodey-Weil distributions. This description is used to obtain integral representations of the smooth vectors of the natural representation of the group of translations on the space of these valuations. As an

Wenhan Dong, Tianyi Hu, Jingyi Zheng, Zhen Sun

Multi-round incomplete information tasks are crucial for evaluating the lateral thinking capabilities of large language models (LLMs). Currently, research primarily relies on multiple benchmarks and automated evaluation metrics to assess these abilities. However, our study reveals novel insights into the limitations of existing methods, as they often yield m

Koshy George, A. Boselli, J. -C. Cuillandre, M. Kümmel

Euclid is delivering optical and near-infrared imaging data over 14,000 deg$^2$ on the sky at spatial resolution and surface brightness levels that can be used to understand the morphological transformation of galaxies within groups and clusters. Using the Early Release Observations (ERO) of the Perseus cluster, we demonstrate the capability offered by Eucli

Somnath Jana, Ronny Knut, Dima Afanasiev, Niko Pontius

Using time-resolved X-ray magnetic linear dichroism in reflection, we provide a direct probe of the N\'eel vector dynamics in TmFeO$_3$ on a ultrafast timescale. Our measurements reveal that, following optical excitation, the N\'eel vector undergoes a spin reorientation transition primarily within the a-c plane, completing a full 90{\deg} rotation within app

Qiucheng Yu, Yuan Xie, Xin Tan

3D occupancy prediction has attracted much attention in the field of autonomous driving due to its powerful geometric perception and object recognition capabilities. However, existing methods have not explored the most essential distribution patterns of voxels, resulting in unsatisfactory results. This paper first explores the inter-class distribution and ge

Lukas Vogelhuber, Ihor Korolov, Mate Vass, Katharina Noesges

This work investigates the transitions between ohmic mode and Penning-Gamma mode in a capacitively coupled radio frequency micro atmospheric pressure plasma jets (CCRF $\mu$APPJ) operated in He/O$_2$ mixtures by comparing phase-resolved optical emission spectroscopy (PROES) measurements of helium excitation with numerical simulations. The simulations employ

Subhankar Bhadra, Minh Tang, Srijan Sengupta

Blockmodels are a foundational tool for modeling community structure in networks, with the stochastic blockmodel (SBM), degree-corrected blockmodel (DCBM), and popularity-adjusted blockmodel (PABM) forming a natural hierarchy of increasing generality. While community detection under these models has been extensively studied, much less attention has been paid

Seun-An Choe, Keon-Hee Park, Jinwoo Choi, Gyeong-Moon Park

Unsupervised domain adaptation for semantic segmentation (UDA-SS) aims to transfer knowledge from labeled source data to unlabeled target data. However, traditional UDA-SS methods assume that category settings between source and target domains are known, which is unrealistic in real-world scenarios. This leads to performance degradation if private classes ex

Haonan Wang, Hongfu Liu, Xiangyan Liu, Chao Du

Next-token prediction serves as the foundational learning task enabling reasoning in LLMs. But what should the learning task be when aiming to equip MLLMs with temporal reasoning capabilities over video inputs? Existing tasks such as video question answering often rely on annotations from humans or much stronger MLLMs, while video captioning tends to entangl

Roni Blushtein-Livnon, Tal Svoray, Itay Ficshhendler, Havatzelet Yahel

Understanding the dynamics of renewable energy adoption is essential for designing strategies that accelerate its spread - an urgent priority for advancing climate goals and improving well-being, especially in off-grid regions facing energy poverty. This study introduces a time-series-based analytical framework that quantifies and classifies adoption behavio

Frédéric Berthommier

The synthesis of Ohman's VCV sequences with intervocalic plosive consonants was first achieved 30 years ago using the DRM model. However, this approach remains primarily acoustic and lacks articulatory constraints. In this study, the same 75 VCVs are analyzed, but generated with the Maeda model, using trajectory planning that differentiates vowel-to-vowel tr

Mark Danza, Sonia Lopez Alarcon, Cory Merkel

Under the nearing error-corrected era of quantum computing, it is necessary to understand the suitability of certain post-NISQ algorithms for practical problems. One of the most promising, applicable and yet difficult to implement in practical terms is the Harrow, Hassidim and Lloyd (HHL) algorithm for linear systems of equations. An enormous number of probl

Lai Wei, Yuting Li, Chen Wang, Yue Wang

Improving Multi-modal Large Language Models (MLLMs) in the post-training stage typically relies on supervised fine-tuning (SFT) or reinforcement learning (RL), which require expensive and manually annotated multi-modal data--an ultimately unsustainable resource. This limitation has motivated a growing interest in unsupervised paradigms as a third stage of po

Luca Fresta, Giovanna Marcelli

We investigate spin transport in a class of time-reversal symmetric insulators on the honeycomb structure, the Kane--Mele model being an emblematic example in this class. We derive the spin conductivity by the linear response \`a la Kubo and show that it is well-defined and independent of the choice of the spin current. For models that do not conserve the sp

Yuanhang Liu, Yanxing Huang, Yanqiao Wang, Peng Li

Large Reasoning Models (LRMs) have made significant progress in mathematical capabilities in recent times. However, these successes have been primarily confined to competition-level problems. In this work, we propose AI Mathematician (AIM) framework, which harnesses the reasoning strength of LRMs to support frontier mathematical research. We have identified

Ossi Räisä, Boris van Breugel, Mihaela van der Schaar

Any method's development and practical application is limited by our ability to measure its reliability. The popularity of generative modeling emphasizes the importance of good synthetic data metrics. Unfortunately, previous works have found many failure cases in current metrics, for example lack of outlier robustness and unclear lower and upper bounds. We p

Joel Daniel Andersson, Lukas Retschmeier, Boel Nelson, Rasmus Pagh

Koufogiannis et al. (2016) showed a $\textit{gradual release}$ result for Laplace noise-based differentially private mechanisms: given an $\varepsilon$-DP release, a new release with privacy parameter $\varepsilon' > \varepsilon$ can be computed such that the combined privacy loss of both releases is at most $\varepsilon'$ and the distribution of the latter

Andrew S. Toms, Hao Wan

We consider the variety of spectral measures that are induced by quasitraces on the spectrum of a self-adjoint operator in a simple separable unital and Z-stable C$^*$-algebra. This amounts to a continuous map from the simplex of quasitraces of the C$^*$-algebra into regular Borel probability measures on the spectrum of the operator under consideration. In t

Sizai Hou, Songze Li, Baturalp Buyukates

Prompt learning is a crucial technique for adapting pre-trained multimodal language models (MLLMs) to user tasks. Federated prompt personalization (FPP) is further developed to address data heterogeneity and local overfitting, however, it exposes personalized prompts - valuable intellectual assets - to privacy risks like prompt stealing or membership inferen

Qing-Wu Wang, Xiao-Fu Ł\textü, Hua-Zhong Guo

We present a novel framework for analyzing unstable composite particles using Green's functions and dispersion relations. As an illustrative example, we explore the $\rho$ vector meson decay process $\rho\to\pi\pi$ within the Nambu -- Jona - Lasinio (NJL) model. Our approach addresses a key limitation of the four-quark interaction description, which adequate

Liyao Tang, Zhe Chen, Dacheng Tao

The emergence of large-scale pre-trained point cloud models has significantly advanced 3D scene understanding, but adapting these models to specific downstream tasks typically demands full fine-tuning, incurring high computational and storage costs. Parameter-efficient fine-tuning (PEFT) techniques, successful in natural language processing and 2D vision tas

Selina Cheggour, Valeria Loscri

As sixth-generation (6G) networks continue to evolve, AI-driven solutions are playing a crucial role in enabling more efficient and adaptive resource management in wireless communication. One of the key innovations in 6G is user-centric cell-free massive Multiple-Input Multiple-Output (UC-CFmMIMO), a paradigm that eliminates traditional cell boundaries and e

Chaitanya Amballa, Sattwik Basu, Yu-Lin Wei, Zhijian Yang

Neural Radiance Fields (NeRFs) have been remarkably successful at synthesizing novel views of 3D scenes by optimizing a volumetric scene function. This scene function models how optical rays bring color information from a 3D object to the camera pixels. Radio frequency (RF) or audio signals can also be viewed as a vehicle for delivering information about the

Khan Masood Parvez, Sk Md Abidar Rahaman, Ali Shiri Sichani

The rapid evolution of wireless technologies necessitates automated design frameworks to address antenna miniaturization and performance optimization within constrained development cycles. This study demonstrates a machine learning enhanced workflow integrating Quantum-Behaved Dynamic Particle Swarm Optimization (QDPSO) with ANSYS HFSS simulations to acceler

Márcio Batista, Marcos P. Cavalcante, Abraão Mendes, Ivaldo Nunes

In this paper, we investigate the spectral properties of the Jacobi operator for immersed surfaces with nonpositive Euler characteristic, extending previous results in the field. We first prove a sharp upper bound for the second eigenvalue of the Jacobi operator for compact surfaces with nonpositive Euler characteristic that are fully immersed in the Euclide

Zijian Liang, Kai Niu, Changshuo Wang, Jin Xu

Recent contributions of semantic information theory reveal the set-element relationship between semantic and syntactic information, represented as synonymous relationships. In this paper, we propose a synonymous variational inference (SVI) method based on this synonymity viewpoint to re-analyze the perceptual image compression problem. It takes perceptual si

S. Ganjizadeh, Alireza Amani, M. A. Ramzanpour

In this paper, we explore the particle creation scenario in the context of $f(\mathcal{G})$ gravity in flat-FLRW metric. For this purpose, from the perspective of thermodynamics and considering an adiabatic universe, we obtain the modified continuity equation in terms of the dynamic number of particles $N$. On the other hand, we obtain Friedmann's equations

Lucas Butsch, Vicky Fasen-Hartmann

For multivariate regularly random vectors of dimension $d$, the dependence structure of the extremes is modeled by the so-called angular measure. When the dimension $d$ is high, estimating the angular measure is challenging because of its complexity. In this paper, we use Principal Component Analysis (PCA) as a method for dimension reduction and estimate the

Arunava Nag, Floris van Breugel

The development of robust odor navigation strategies for automated environmental monitoring applications requires realistic simulations of odor time series for agents moving across large spatial scales. Traditional approaches that rely on computational fluid dynamics (CFD) methods can capture the spatiotemporal dynamics of odor plumes, but are impractical fo

Victor Jüttner, Erik Buchmann

Due to the increasing presence of networked devices in everyday life, not only cybersecurity specialists but also end users benefit from security applications such as firewalls, vulnerability scanners, and intrusion detection systems. Recent approaches use large language models (LLMs) to rewrite brief, technical security alerts into intuitive language and su

Zobia Batool, Huseyin Ozkan, Erchan Aptoula

Alzheimer's detection efforts aim to develop accurate models for early disease diagnosis. Significant advances have been achieved with convolutional neural networks and vision transformer based approaches. However, medical datasets suffer heavily from class imbalance, variations in imaging protocols, and limited dataset diversity, which hinder model generali

Philip Caesar M. Flores, Stefanos Carlström, Serguei Patchkovskii, Misha Ivanov

Chirality describes the asymmetry between an object and its mirror image and underlies diverse functionalities across molecular, mesoscopic and bulk matter. A particularly intriguing example is chirality-induced spin selectivity (CISS), where chiral structures generate enantio-sensitive spin polarization. Despite extensive research, its microscopic origin an

Grigorios P. Zouros, Konstantinos Delimaris, Carsten Rockstuhl, Georgios D. Kolezas

In this work, we develop a full-wave vectorial solution for the 2.5-dimensional (2.5-D), i.e., at oblique plane wave incidence, electromagnetic (EM) multiple scattering (MS) by a collection of gyrotropic cylinders. All cylinders are infinitely long and share a common $z$-axis. However, each cylinder can have a different cross-section with an arbitrary shape

Mina Akhyani, Luca Zanini, Henrik Rønnow

Brightness is a critical metric for optimizing the design of neutron sources and beamlines, yet there is no direct way to calculate brightness within most Monte Carlo packages used for neutron source simulation. In this paper, we present Brightify, an open-source Python-based tool designed to calculate brightness from Monte Carlo Particle List (MCPL) files,

Kun Li, Yunxiang Li, Tianhua Zhang, Hongyin Luo

Robust evaluation is critical for deploying trustworthy retrieval-augmented generation (RAG) systems. However, current LLM-based evaluation frameworks predominantly rely on directly prompting resource-intensive models with complex multi-stage prompts, underutilizing models' reasoning capabilities and introducing significant computational cost. In this paper,

Rong Li, Shijie Li, Lingdong Kong, Xulei Yang

3D Visual Grounding (3DVG) seeks to locate target objects in 3D scenes using natural language descriptions, enabling downstream applications such as augmented reality and robotics. Existing approaches typically rely on labeled 3D data and predefined categories, limiting scalability to open-world settings. We present SeeGround, a zero-shot 3DVG framework that

Ya-Fang Lin, Xiaotian Li, Wan-Hsuan Huang, Charan Pushpanathan Prabavathi

Couples often experience a decrease in closeness as they cope with the demands of parenthood. Existing technologies have supported parenting and parental collaboration. However, these technologies do not adequately support closeness in co-parenting. We use scenarios and design probes to brainstorm with 10 new parent couples to explore and envision possibilit

Van-Tin Luu, Yon-Lin Cai, Vu-Hoang Tran, Wei-Chen Chiu

This paper presents a groundbreaking approach - the first online automatic geometric calibration method for radar and camera systems. Given the significant data sparsity and measurement uncertainty in radar height data, achieving automatic calibration during system operation has long been a challenge. To address the sparsity issue, we propose a Dual-Perspect

R. S. Sidhu, F. Casaburo, E. Masha, M. Aliotta

In the nova temperature range, 0.1 GK $< T <$ 0.4 GK, several low-energy resonances dominate the $^{21}$Ne(p,$\gamma$)$^{22}$Na reaction rate, which is currently affected by large uncertainties. We present a high-precision study of the resonances at $E^{\rm{lab}}_{\rm{r}}$ = 127.3, 271.4, 272.3, 291.5, and 352.6 keV, measured directly at the Laboratory for U

Xueliang Zhao, Wei Wu, Lingpeng Kong

Large language models (LLMs) have made significant advances in complex reasoning tasks, yet they remain bottlenecked by two core challenges: architectural inefficiency due to reliance on Transformers, and a lack of structured fine-tuning for high-difficulty domains. We introduce \ourmodel, an attention-free language model that addresses both issues through a

Jingxi Lu, Wenhao Li, Jianxiong Guo, Xingjian Ding

With the rapid growth of IoT devices and their diverse workloads, container-based microservices deployed at edge nodes have become a lightweight and scalable solution. However, existing microservice scheduling algorithms often assume static resource availability, which is unrealistic when multiple containers are assigned to an edge node. Besides, containers

Jonathan B. Hill

We derive so-called weak and strong \textit{max-laws of large numbers} for $% \max_{1\leq i\leq k_{n}}|1/n\sum_{t=1}^{n}x_{i,n,t}|$ for zero mean stochastic triangular arrays $\{x_{i,n,t}$ $:$ $1$ $\leq $ $t$ $\leq n\}_{n\geq 1}$, with dimension counter $i$ $=$ $1,...,k_{n}$ and dimension $% k_{n}$ $\rightarrow $ $\infty $. Rates of convergence are also anal

Raphael Boleslavsky, Silvana Krasteva

This paper examines competitive information disclosure in search markets with a mix of savvy consumers, who search costlessly, and inexperienced consumers, who face positive search costs. Savvy consumers incentivize truthful disclosure; inexperienced consumers, concealment. With both types, equilibrium features partial disclosure, which persists despite inte

Václav Voráček, Francesco Orabona

The construction of confidence intervals for the mean of a bounded random variable is a classical problem in statistics with numerous applications in machine learning and virtually all scientific fields. In particular, obtaining the tightest possible confidence intervals is vital every time the sampling of the random variables is expensive. The current state

Anthony Chen, Wenzhao Zheng, Yida Wang, Xueyang Zhang

Recent advancements in world models have revolutionized dynamic environment simulation, allowing systems to foresee future states and assess potential actions. In autonomous driving, these capabilities help vehicles anticipate the behavior of other road users, perform risk-aware planning, accelerate training in simulation, and adapt to novel scenarios, there

Hairu Wang, Yuan Feng, Yukun Cao, Xike Xie

Large language models excel at many tasks but often incur high inference costs during deployment. To mitigate hallucination, many systems use a knowledge graph to enhance retrieval-augmented generation (KG-RAG). However, the large amount of retrieved knowledge contexts increase these inference costs further. A promising solution to balance performance and co

Chuqiao Zhang, Sarath Chandra Dantu, Debarghya Mitra, Dalia Chakrabarty

Identification of critical residues of a protein is actively pursued, since such residues are essential for protein function. We present three ways of recognising critical residues of an example protein, the evolution of which is tracked via molecular dynamical simulations. Our methods are based on learning a Random Geometric Graph (RGG) variable, where the

Alexander Sattler, Maria Daghofer

Both the Haldane spin-$1$ chain and dimerized chains of spin-$1/2$ exhibit topologically protected edge states that are robust against specific perturbations. Recently, such spin chains have been specifically assembled on surfaces and we investigate here the robustness of these edge states against coupling to the surface. Since no physical system can be cons

Ezra Alexander, Alexandra Alexiu, Matthias Kick, Troy Van Voorhis

Non-toxic III-V quantum dots (QDs) are plagued with a higher density of performance-limiting trap states than II-VI and IV-VI QDs. Such trap states are generally understood to arise from under-coordinated atoms on the QD surface. Here, we present computational evidence for, and an exploration of, trap states in InP and GaP QDs that arise from fully-coordinat

Jeongwon Jo, He Zhang, Jie Cai, Nitesh Goyal

Supplemental Nutrition Assistance Program (SNAP) is an essential benefit support system provided by the US administration to 41 million federally determined low-income applicants. Through interviews with such applicants across a diverse set of experiences with the SNAP system, our findings reveal that new AI technologies like LLMs can alleviate traditional b

Xinbing Kong, Bin Wu, Wuyi Ye

This paper introduces a high-dimensional binary variate model that accommodates nonstationary covariates and factors, and studies their asymptotic theory. This framework encompasses scenarios where single indices are nonstationary or cointegrated. For nonstationary single indices, the maximum likelihood estimator (MLE) of the coefficients has dual convergenc

Sihun Cha, Serin Yoon, Kwanggyoon Seo, Junyong Noh

Accurately retargeting facial expressions to a face mesh while enabling manipulation is a key challenge in facial animation retargeting. Recent deep-learning methods address this by encoding facial expressions into a global latent code, but they often fail to capture fine-grained details in local regions. While some methods improve local accuracy by transfer

J. B. B. Chen, E. Adli, P. Drobniak, O. G. Finnerud

We introduce ABEL, the Adaptable Beginning-to-End Linac simulation framework developed for agile design studies of plasma-based accelerators and colliders. ABEL's modular architecture allows users to simulate particle acceleration across various beamline components. The framework supports specialised codes such as HiPACE++, Wake-T, ELEGANT, GUINEA-PIG, CLICo

Md Ehtesham-Ul-Haque, Syed Masum Billah

This paper examines how the coding strategies of sighted and blind programmers differ when working with audio feedback alone. The goal is to identify challenges in mixed-ability collaboration, particularly when sighted programmers work with blind peers or teach programming to blind students. To overcome limitations of traditional blindness simulation studies

Romeo Brunetti, Klaus Fredenhagen, Nicola Pinamonti

The thermodynamics of Dirac fields under the influence of external electromagnetic fields is studied. For perturbations which act only for finite time, the influence of the perturbation can be described by an automorphism which can be unitarily implemented in the GNS representations of KMS states, a result long known for the Fock representation. For time-ind

Yu-Ting Rong, Ping-Mo Liu, Dan Yang, Rong An

Charge radii are sensitive indicators to identify the nuclear structure phenomena throughout the whole nuclide chart. In particular, the shrunken trend of changes of charge radii along a long isotopic chain is intimately associated with the shell quenching effect. In this work, the systematic evolution of charge radii along the proton numbers $Z=8$, $10$, $1

Yao Huang, Huanran Chen, Shouwei Ruan, Yichi Zhang

Recent advances in Large Reasoning Models (LRMs) have demonstrated remarkable capabilities in solving complex tasks such as mathematics and coding. However, these models frequently exhibit a phenomenon known as overthinking during inference, characterized by excessive validation loops and redundant deliberation, leading to substantial computational overheads

Cornelius Brand, Radu Curticapean, Baitian Li, Kevin Pratt

Given two vectors $u,v \in \mathbb{Q}^D$ over a finite domain $D$ and a function $f : D\times D\to D$, the convolution problem asks to compute the vector $w \in \mathbb{Q}^D$ whose entries are defined by $w(d) = \sum_{\substack{x,y \in D \\ f(x,y)=d}} u(x)v(y).$ In parameterized and exponential-time algorithms, convolutions on product domains are particularl

Shuai Peng, Sijia Peng, Lijun Ren, Shaokun Liu

The magnetic dipolar splitting of a p-wave Feshbach resonance is governed by the spin-orbital configuration of the valence electrons in the triplet molecular state. We perform high-resolution trap loss spectroscopy on ultracold 6Li atoms to resolve this splitting with sub-milligauss precision. By comparing spin-polarized (|mS| = 1) and spin-mixture (mS = 0)

Victor Enescu, Hichem Sahbi

Continual or incremental learning holds tremendous potential in deep learning with different challenges including catastrophic forgetting. The advent of powerful foundation and generative models has propelled this paradigm even further, making it one of the most viable solution to train these models. However, one of the persisting issues lies in the increasi

Jiadong Pan, Zhiyuan Ma, Kaiyan Zhang, Ning Ding

Diffusion models have recently demonstrated exceptional performance in image generation task. However, existing image generation methods still significantly suffer from the dilemma of image reasoning, especially in logic-centered image generation tasks. Inspired by the success of Chain of Thought (CoT) and Reinforcement Learning (RL) in LLMs, we propose SRRL

Gerhard Heinzel, Javier Álvarez-Vizoso, Miguel Dovale-Álvarez

The Laser Interferometer Space Antenna (LISA) will enable direct observations of low-frequency gravitational waves, offering unprecedented insight into astrophysical and cosmological phenomena. LISA's heterodyne interferometric measurement system requires phase-locking five of its six onboard lasers with tunable frequency offsets to ensure that all beatnotes

Jevgēnijs Vihrovs

We show a simple generalization of the quantum walk algorithm for search in backtracking trees by Montanaro (ToC 2018) to the case where vertices can have different times of computation. If a vertex $v$ in the tree of depth $D$ is computed in $t_v$ steps from its parent, then we show that detection of a marked vertex requires $\text{O}(\sqrt{TD})$ queries to

Stef Cuyckens, Xiaoling Yi, Nitish Satya Murthy, Chao Fang

Autonomous robots require efficient on-device learning to adapt to new environments without cloud dependency. For this edge training, Microscaling (MX) data types offer a promising solution by combining integer and floating-point representations with shared exponents, reducing energy consumption while maintaining accuracy. However, the state-of-the-art conti

Luca Fantin, Marco Antonelli, Margherita Cesetti, Daniele Irto

Assessing the quality of public transportation services requires the analysis of large quantities of data on the scheduled and actual trips and documents listing the quality constraints each service needs to meet. Interrogating such datasets with SQL queries, organizing and visualizing the data can be quite complex for most users. This paper presents a chatb

Vladimir Shpilrain

We describe an alternative way of computing Alexander polynomials of knots/links, based on the Artin representation of the corresponding braids by automorphisms of a free group. Then we apply the same method to other representations of braid groups discovered by Wada and compare the corresponding isotopic invariants to Alexander polynomials.

Roopshree Banchode, Surajit Das, Shampa Raghunathan, Raghunathan Ramakrishnan

Solvent environments play a central role in determining molecular structure, energetics, reactivity, and interfacial phenomena. However, modeling solvation from first principles remains difficult due to the complex interplay of interactions and unfavorable computational scaling of first-principles treatment with system size. Machine-learned potentials (MLPs)

Richard Guest, Eva Lievens, Martin Sas, Elena Botoeva

Automated facial age assessment systems operate in either estimation mode - predicting age based on facial traits, or verification mode - confirming a claimed age. These systems support access control to age-restricted goods, services, and content, and can be used in areas like e-commerce, social media, forensics, and refugee support. They may also personali

Zehao Li, Hao Jiang, Yujun Cai, Jianing Chen

Although dynamic scene reconstruction has long been a fundamental challenge in 3D vision, the recent emergence of 3D Gaussian Splatting (3DGS) offers a promising direction by enabling high-quality, real-time rendering through explicit Gaussian primitives. However, existing 3DGS-based methods for dynamic reconstruction often suffer from \textit{spatio-tempora

Damola Ajeyemi, Yiting Chen, Antonin Colot, Jorge Cortes

This paper focuses on an AC optimal power flow (OPF) problem for distribution feeders equipped with controllable distributed energy resources (DERs). We consider a solution method that is based on a continuous approximation of the projected gradient flow - referred to as the safe gradient flow - that incorporates voltage and current information obtained eith

S. Illésová, V. Novák, T. Bezděk, C. Possel

The prevalence of variational methods in near-term quantum computing makes optimizer choice critical, yet selection is frequently intuition-based. We therefore present a systematic benchmark of eight classical optimization algorithms for variational quantum chemistry using the truncated Variational Hamiltonian Ansatz. Performance is evaluated on H$_2$, H$_4$

Christoph Brunken, Olivier Peltre, Heloise Chomet, Lucien Walewski

Machine Learning Interatomic Potentials (MLIP) are a novel in silico approach for molecular property prediction, creating an alternative to disrupt the accuracy/speed trade-off of empirical force fields and density functional theory (DFT). In this white paper, we present our MLIP library which was created with two core aims: (1) provide to industry experts w

Xudong Li, Mengdan Zhang, Peixian Chen, Xiawu Zheng

Multi-modal Large Language Models (MLLMs) excel at single-image tasks but struggle with multi-image understanding due to cross-modal misalignment, leading to hallucinations (context omission, conflation, and misinterpretation). Existing methods using Direct Preference Optimization (DPO) constrain optimization to a solitary image reference within the input se

Asghar Ullah, Özgür E. Müstecaplıoğlu, Matteo G. A. Paris

We study the performance of quantum sensors composed of four qubits arranged in different geometries for magnetometry and thermometry. The qubits interact via the transverse-field Ising model with both ferromagnetic and antiferromagnetic couplings, maintained in thermal equilibrium with a heat bath under an external magnetic field. Using quantum Fisher infor

Chetan lodha, Manak Parmar, Ajay Kumar Rai

Motivated by the discovery of several charm states exhibiting tetraquark-like characteristics at BESIII and LHCb, this study investigates the spectroscopy and decay properties of D mesons and tetraquark states with quark content $Cq\bar{q}\bar{q}$ within the diquark - antidiquark framework. The analysis is performed using a potential model based on the Corne

San-Dong Guo, Liguo Zhang, Xiao-Shu Guo, Gangqiang Zhu

Using the valley degree of freedom as a carrier of information for storage and processing, valley polarization plays a crucial role. A variety of mechanisms for valley polarization have been proposed, among which the valley-layer coupling mechanism involves the induction of valley polarization by an out-of-plane electric field. Here, through first-principles

Daojin Fan, Guoding Liu, Shaowei Li, Ming Gong

Benchmarking large-scale quantum gates, typically involving multiple native two-qubit and singlequbit gates, is crucial in quantum computing. Global fidelity, encompassing information about intergate correlations, offers a comprehensive metric for evaluating and optimizing gate performance, unlike the fidelities of individual local native gates. In this work

Haomiao Qiu, Miao Zhang, Ziyue Qiao, Liqiang Nie

Continual Learning (CL) aims to enable models to continuously acquire new knowledge from a sequence of tasks with avoiding the forgetting of learned information. However, existing CL methods only rely on the parameters of the most recent task for inference, which makes them susceptible to catastrophic forgetting. Inspired by the recent success of model mergi

Zhentao Shi, Jin Xi, Haitian Xie

This paper investigates the use of synthetic control methods for causal inference in macroeconomic settings when dealing with possibly nonstationary data. While the synthetic control approach has gained popularity for estimating counterfactual outcomes, we caution researchers against assuming a common nonstationary trend factor across units for macroeconomic

Jaehyun Choi, Gyojin Han, Dong-Jae Lee, Sunghyun Baek

Dataset Condensation (DC) has emerged as a promising solution to mitigate the computational and storage burdens associated with training deep learning models. However, existing DC methods largely overlook the multi-domain nature of modern datasets, which are increasingly composed of heterogeneous images spanning multiple domains. In this paper, we extend DC

Dinesha V. Hegde, Tae K. Kim, Nikolai V. Pogorelov, Shaela I. Jones

The solar wind (SW) is a vital component of space weather, providing a background for solar transients such as coronal mass ejections, stream interaction regions, and energetic particles propagating toward Earth. Accurate prediction of space weather events requires a precise description and thorough understanding of physical processes occurring in the ambien

Samaira Tibrewal, Soumyajit Seth

Nonlinear oscillators are commonly encountered in a wide range of physical and engineering systems, exhibiting rich and complex dynamics. Among these, the Van der Pol oscillator is well known for its self-sustained limit cycle behavior. However, when subjected to external sinusoidal forcing, its dynamics can deviate significantly from this regular behavior.

Foivos Fioravantes, Harmender Gahlawat, Nikolaos Melissinos

Imagine we want to split a group of agents into teams in the most \emph{efficient} way, considering that each agent has their own preferences about their teammates. This scenario is modeled by the extensively studied \textsc{Coalition Formation} problem. Here, we study a version of this problem where each team must additionally be of bounded size. We conduct

M. Carrasco-H, E. Contreras, E. Fuenmayor, P. León

In this work, we study self-gravitating objects that obey a polytropic equation of state in hyperbolic symmetry. Specifically, we describe in detail the steps to derive the Lane-Emden equation from the structure equations of the system. To integrate the equations numerically, we propose the Cosenza-Herrera-Esculpi-Witten anisotropy and study the cases $\gamm

Noam D. Elkies, Jean Kieffer

We describe an algorithm to numerically evaluate Riemann theta functions in any dimension in quasi-linear time in terms of the required precision, uniformly on reduced input. This algorithm is implemented in the FLINT number theory library and vastly outperforms existing software. As an application, we evaluate the theta constants attached to certain special

Lukas Kirchdorfer, Konrad Özdemir, Stjepan Kusenic, Han van der Aa

Business Process Simulation (BPS) is a critical tool for analyzing and improving organizational processes by estimating the impact of process changes. A key component of BPS is the case-arrival model, which determines the pattern of new case entries into a process. Although accurate case-arrival modeling is essential for reliable simulations, as it influence

Michel van Garrel, Helge Ruddat, Bernd Siebert

Let $E$ be a smooth cubic in the projective plane $\mathbb{P}^2$. Nobuyoshi Takahashi formulated a conjecture that expresses counts of rational curves of varying degree in $\mathbb{P}^2\setminus E$ as the Taylor coefficients of a particular period integral of a pencil of affine plane cubics after reparametrizing the pencil using the exponential of a second p

Souradip Chattopadhyay, Zihao Yu, Y. Sungtaek Ju, Hangjie Ji

Water vapor capture through free surface flows plays a crucial role in various industrial applications, such as liquid desiccant air conditioning systems, water harvesting, and dewatering. This paper studies the dynamics of a silicone liquid sorbent (also known as water-absorbing silicone oil) flowing down a vertical cylindrical fibre while absorbing water v

Michael Hertneck, David Meister, Frank Allgöwer

The defining characteristic of event-based control is that feedback loops are only closed when indicated by a triggering condition that takes recent information about the system into account. This stands in contrast to periodic control where the feedback loop is closed periodically. Benefits of event-based control arise when sampling comes at a cost, which o

Na Xue, Minghua Chen

Fractional physics-informed neural networks (fPINNs) have been successfully introduced in [Pang, Lu and Karniadakis, SIAM J. Sci. Comput. 41 (2019) A2603-A2626], which observe relative errors of $10^{-3} \, \sim \, 10^{-4}$ for the subdiffusion equations. However their high-precision (multiprecision) numerical solution remains challenging, due to the limited

Jiseung Hong, Grace Byun, Seungone Kim, Kai Shu

Large Language Models (LLMs) are expected to provide helpful and harmless responses, yet they often exhibit sycophancy--conforming to user beliefs regardless of factual accuracy or ethical soundness. Prior research on sycophancy has primarily focused on single-turn factual correctness, overlooking the dynamics of real-world interactions. In this work, we int

Başak Küçük

Both the Klein-Williams invariant $\ell_G(f)$ from \cite{KW2} and the generalized equivariant Lefschetz invariant $\lambda_G(f)$ from \cite{weber07} serve as complete obstructions to the fixed point problem in the equivariant setting. The latter is functorial in the sense of Definition \ref{functorial}. The first part of this paper aims to demonstrate that $

Hanting Chen, Yasheng Wang, Kai Han, Dong Li

This work presents Pangu Embedded, an efficient Large Language Model (LLM) reasoner developed on Ascend Neural Processing Units (NPUs), featuring flexible fast and slow thinking capabilities. Pangu Embedded addresses the significant computational costs and inference latency challenges prevalent in existing reasoning-optimized LLMs. We propose a two-stage tra

Nicolas Pfitzer, Yifan Zhou, Marco Poggensee, Defne Kurtulus

Over 43 million people worldwide live with severe visual impairment, facing significant challenges in navigating unfamiliar environments. We present MR.NAVI, a mixed reality system that enhances spatial awareness for visually impaired users through real-time scene understanding and intuitive audio feedback. Our system combines computer vision algorithms for

E. V. Nikitenko, Yu. G. Nikonorov

Let us consider the set $\Omega (\triangle ABC)$ of all tetrahedra $ABCD$ with a given non-degenerate base $ABC$ in $\mathbb{E}^3$ and $D$ lying outside the plane $ABC$. Let us denote by $\Sigma(\triangle ABC)$ the set $\left\{\Bigl(\cos \overline{\alpha},\cos \overline{\beta},\cos \overline{\gamma} \Bigr)\in \mathbb{R}^3\,|\, ABCD \in \Omega (\triangle ABC)

Denis S. Goldobin, Maria V. Ageeva, Matteo di Volo, Ferdinand Tixidre

Neural dynamics is determined by the transmission of discrete synaptic pulses (synaptic shot-noise) among neurons. However, the neural responses are usually obtained within the diffusion approximation modeling synaptic inputs as continuous Gaussian noise. Here, we present a rigorous mean-field theory that encompasses synaptic shot-noise for sparse balanced i