February 2025 arXiv papers — page 13

Glenn Barnich, Thomas Smoes

Motivated by group-theoretical questions that arise in the context of asymptotic symmetries in gravity, we study model spaces and their quantization from the viewpoint of constrained Hamiltonian systems. More precisely, we propose that a central building block in the construction of the model space for a generic Lie group $G$ is the symplectic submanifold of

Kohei Hattori, Daisuke Taniguchi, Takuji Tsujimoto, Noriyuki Matsunaga

We report the discovery of a metal-rich red giant star, WINERED-HVS1, which is a candidate for a hyper-velocity star (HVS). Its past trajectory suggests that this star may have been ejected by the Galactic supermassive black hole (SMBH; Sgr A*), with a modest ejection velocity of at least $500\; \mathrm{km\;s^{-1}}$. Since WINERED-HVS1 is gravitationally bou

Esteban Bedoya Rodriguez, Leon Escobar Diaz, Sebastian Trujillo Hernandez

Employing the mean-field renormalization group (MFRG) method, we analyzed the $Fe_p Mn_{0.6-p}Al_{0.4}$ and $Fe_{p}Al_{1-p}$ alloys, incorporating second-neighbor interactions in the latter for the first time within this framework. Our analysis utilized neural network fitting, yielding promising results in both the phase diagram adjustments and the estimatio

Tai Nguyen, Phong Le, André Biedenkapp, Carola Doerr

Dynamic Algorithm Configuration (DAC) has garnered significant attention in recent years, particularly in the prevalence of machine learning and deep learning algorithms. Numerous studies have leveraged the robustness of decision-making in Reinforcement Learning (RL) to address the optimization challenges associated with algorithm configuration. However, mak

Stephan Eckstein, Aziz Lakhal

Motivated by the success of Sinkhorn's algorithm for entropic optimal transport, we study convergence properties of iterative proportional fitting procedures (IPFP) used to solve more general information projection problems. We establish exponential convergence guarantees for the IPFP whenever the set of probability measures which is projected onto is define

Mouna Dhaouadi, Bentley James Oakes, Michalis Famelis

In collaborative open-source development, the rationale for code changes is often captured in commit messages, making them a rich source of valuable information. However, research on rationale in commit messages remains limited. In this paper, we present CoMRAT, a tool for analyzing decision and rationale sentences rationale in commit messages. CoMRAT enable

Rongzhen Zhao, Vivienne Wang, Juho Kannala, Joni Pajarinen

Object-Centric Learning (OCL) aggregates image or video feature maps into object-level feature vectors, termed \textit{slots}. It's self-supervision of reconstructing the input from slots struggles with complex object textures, thus Vision Foundation Model (VFM) representations are used as the aggregation input and reconstruction target. Existing methods lev

Asaf Cohen, Ethan Huffman

We consider a system of $N$ particles interacting through their empirical distribution on a finite state space in continuous time. In the formal limit as $N\to\infty$, the system takes the form of a nonlinear (McKean--Vlasov) Markov chain. This paper rigorously establishes this limit. Specifically, under the assumption that the mean field system has a unique

Andrew Grieder, Marcos Calegari Andrade, Hiroyuki Takenaka, Tadashi Ogitsu

Halide perovskites are known for their rich phase diagram and superior performance in diverse optoelectronics applications. The latter property is often attributed to the long electron-hole recombination time, whose underlying physical mechanism has been a long-standing controversy. In this Letter, we investigate the transport and localization properties of

Hao-Run Cai, Han-Jia Ye

Deep tabular models have demonstrated remarkable success on i.i.d. data, excelling in a variety of structured data tasks. However, their performance often deteriorates under temporal distribution shifts, where trends and periodic patterns are present in the evolving data distribution over time. In this paper, we explore the underlying reasons for this failur

J. Li, K. Wu, Q. Xu

Given a Hilbert module $H$ over a $C^*$-algebra, let $\mathcal{L}(H)$ be the set of all adjointable operators on $H$. For each $T\in\mathcal{L}(H)$, its numerical radius is defined by $w(T)=\sup\big\{\|\langle Tx, x \rangle\|: x\in H, \|x\|=1\big\}$. It is proved that $w(T)=\|T\|$ whenever $T$ is normal. Examples are constructed to show that there exist Hilb

Amr Mohamed, Mingmeng Geng, Michalis Vazirgiannis, Guokan Shang

As large language models are increasingly responsible for online content, concerns arise about the impact of repeatedly processing their own outputs. Inspired by the "broken telephone" effect in chained human communication, this study investigates whether LLMs similarly distort information through iterative generation. Through translation-based experiments,

Adrián Franco-Rubio, Arkadiusz Bochniak, J. Ignacio Cirac

In this work, we examine the consequences of the existence of a finite group of matrix product unitary (MPU) symmetries for matrix product states (MPS). We generalize the well-understood picture of onsite unitary symmetries, which give rise to virtual symmetry defects given by insertions of operators in the bonds of the MPS. In the MPU case, we can define an

Yancheng Cai, Fei Yin, Dounia Hammou, Rafal Mantiuk

Computer vision foundation models, such as DINO or OpenCLIP, are trained in a self-supervised manner on large image datasets. Analogously, substantial evidence suggests that the human visual system (HVS) is influenced by the statistical distribution of colors and patterns in the natural world, characteristics also present in the training data of foundation m

Yonah Borns-Weil, Di Fang, Jiaqi Zhang

Motivated by various applications, unbounded Hamiltonian simulation has recently garnered great attention. Quantum Magnus algorithms, designed to achieve commutator scaling for time-dependent Hamiltonian simulation, have been found to be particularly efficient for such applications. When applied to unbounded Hamiltonian simulation in the interaction picture,

Hong-Yu Wang, Xiong-Jun Liu

Parafermions, which can be viewed as a fractionalized version of Majorana modes, exhibit profound non-Abelian statistics and emerge in topologically ordered systems, while their realization in experiment has been challenging. Here we propose a novel experimental scheme for the quantum simulation of parafermions and their non-Abelian braiding statistics in su

Greta Panova

Littlewood-Richardson, Kronecker and plethysm coefficients are fundamental multiplicities of interest in Representation Theory and Algebraic Combinatorics. Determining a combinatorial interpretation for the Kronecker and plethysm coefficients is a major open problem, and prompts the consideration of their computational complexity. Recently it was shown that

Nicola Biagi, Saverio Francesconi, Alessandro Zavatta, Marco Bellini

The ability to manipulate light at the level of single photons, its elementary excitation quanta, has recently made it possible to produce a rich variety of tailor-made quantum states and arbitrary quantum operations, of high interest for fundamental science and applications. Here we present a concise review of the progress made over the last few decades in

Alex S. Arvanitakis, Leron Borsten, Dimitri Kanakaris, Hyungrok Kim

Manin theories are a class of non-topological deformations of Chern-Simons theories that naturally realise the third-way mechanism and furthermore admit localisation despite not being supersymmetric in the usual sense. In this paper, we extend this construction to higher dimensions, thereby producing a large class of examples of third-way-type theories. Furt

Akshayveer Akshayveer, Federico C Buroni, Roderick Melnik, Luis Rodriguez-Tembleque

Piezoelectric materials are widely used in several industries, including power sources, energy harvesting, biomedical, electronics, haptic, photostrictive, and sensor/actuator technologies. Conventional piezoelectric materials, such lead zirconate titanate (PZT), pose significant environmental and health risks due to lead content. Recent years have seen a gr

Pierre Vuillecard, Jean-Marc Odobez

Accurate 3D gaze estimation in unconstrained real-world environments remains a significant challenge due to variations in appearance, head pose, occlusion, and the limited availability of in-the-wild 3D gaze datasets. To address these challenges, we introduce a novel Self-Training Weakly-Supervised Gaze Estimation framework (ST-WSGE). This two-stage learning

Jonathan Colen, Malachi Schram, Kishansingh Rajput, Armen Kasparian

We present a reinforcement learning (RL) framework for controlling particle accelerator experiments that builds explainable physics-based constraints on agent behavior. The goal is to increase transparency and trust by letting users verify that the agent's decision-making process incorporates suitable physics. Our algorithm uses a learnable surrogate functio

Chi-Chien Tsai, Chia-Mu Yu, Ying-Dar Lin, Yu-Sung Wu

The increasing adoption of large language models (LLMs) for code-related tasks has raised concerns about the security of their training datasets. One critical threat is dead code poisoning, where syntactically valid but functionally redundant code is injected into training data to manipulate model behavior. Such attacks can degrade the performance of neural

Abdelrahman Abdallah, Jamshid Mozafari, Bhawna Piryani, Mohammed Ali

Knowledge-intensive tasks, particularly open-domain question answering (ODQA), document reranking, and retrieval-augmented language modeling, require a balance between retrieval accuracy and generative flexibility. Traditional retrieval models such as BM25 and Dense Passage Retrieval (DPR), efficiently retrieve from large corpora but often lack semantic dept

Jose L. Bonilla, Krzysztof M. Graczyk, Artur M. Ankowski, Rwik Dharmapal Banerjee

We propose a new approach to simulate neutrino scattering events as an alternative to the standard Monte Carlo generator approach. Generative adversarial neural network (GAN) models are developed to simulate charged current neutrino-carbon collisions in the few-GeV energy range. We consider a simplified framework to generate muon kinematic variables, specifi

Julian Rosenberger, Sophie Kuhlemann, Verena Tiefenbeck, Mathias Kraus

Artificial Intelligence (AI) systems are frequently employed in online services to provide personalized experiences to users based on large collections of data. However, AI systems can be designed in different ways, with black-box AI systems appearing as complex data-processing engines and white-box AI systems appearing as fully transparent data-processors.

Chao Feng, Alberto Huertas Celdrán, Xi Cheng, Gérôme Bovet

Decentralized Federated Learning (DFL) is an emerging paradigm that enables collaborative model training without centralized data and model aggregation, enhancing privacy and resilience. However, its sustainability remains underexplored, as energy consumption and carbon emissions vary across different system configurations. Understanding the environmental im

Álvaro G. Iñesta, Bethany Davies, Sounak Kar, Stephanie Wehner

Entanglement buffers are systems that maintain high-quality entanglement, ensuring it is readily available for consumption when needed. In this work, we study the performance of a two-node buffer, where each node has one long-lived quantum memory for storing entanglement and multiple short-lived memories for generating fresh entanglement. Newly generated ent

Christian Rose

Results regarding off-diagonal Gaussian upper heat kernel bounds on discrete weighted graphs with possibly unbounded geometry are summarized and related. After reviewing uniform upper heat kernel bounds obtained by Carlen, Kusuoka, and Stroock, the universal Gaussian term on graphs found by Davies is addressed and related to corresponding results in terms of

Guizhen Chen, Weiwen Xu, Hao Zhang, Hou Pong Chan

Many challenging reasoning tasks require not just rapid, intuitive responses, but a more deliberate, multi-step approach. Recent progress in large language models (LLMs) highlights an important shift from the "System 1" way of quick reactions to the "System 2" style of reflection-and-correction problem solving. However, current benchmarks heavily rely on the

Gianluca Bencomo, Max Gupta, Ioana Marinescu, R. Thomas McCoy

Artificial neural networks can acquire many aspects of human knowledge from data, making them promising as models of human learning. But what those networks can learn depends upon their inductive biases -- the factors other than the data that influence the solutions they discover -- and the inductive biases of neural networks remain poorly understood, limiti

Alexander Usvyatsov

We prove that in a countable theory T fully stable over a predicate P, any complete set A has the existence property. This means that A can be extended to a model of T without changing the P-part. In particular, T has the Gaifman property: any model of P occurs as the P-part of some model of T. This generalizes results of Lachlan (on stable theories), Hodges

Yang Zhou, Xu Gao, Zichong Chen, Hui Huang

Recent advances in generative diffusion models have shown a notable inherent understanding of image style and semantics. In this paper, we leverage the self-attention features from pretrained diffusion networks to transfer the visual characteristics from a reference to generated images. Unlike previous work that uses these features as plug-and-play attribute

Daniele Lain, Yoshimichi Nakatsuka, Kari Kostiainen, Gene Tsudik

The most widespread type of phishing attack involves email messages with links pointing to malicious content. Despite user training and the use of detection techniques, these attacks are still highly effective. Recent studies show that it is user inattentiveness, rather than lack of education, that is one of the key factors in successful phishing attacks. To

Daniela Böhm, Georg Gottlob, Matthias Lanzinger, Davide Longo

Query optimization has played a central role in database research for decades. However, more often than not, the proposed optimization techniques lead to a performance improvement in some, but not in all, situations. Therefore, we urgently need a methodology for designing a decision procedure that decides for a given query whether the optimization technique

Zhehua Zhang, Zeyan Zhang, Shaoxing Han, Yuqing Zhang

Nonequilibrium dynamics are closely related to various fields of research, in which vastly different phases emerge when parameters are changed. However, it is difficult to construct nonequilibrium systems that have sufficiently tunable controllable parameters. Using microwave field coupling induced optical bistability, Rydberg gases exhibit a range of signif

Clare Grogan, Jackie Kay, María Pérez-Ortiz

While existing studies have recognised explicit biases in generative models, including occupational gender biases, the nuances of gender stereotypes and expectations of relationships between users and AI companions remain underexplored. In the meantime, AI companions have become increasingly popular as friends or gendered romantic partners to their users. Th

Nathaniel Warren, Chloe Skidmore, Katherine J. Harmon, Wonsuk Cha

Of current importance for alloy design is controlling chemical ordering through processing routes to optimize an alloy's mechanical properties for a desired application. However, characterization of chemical ordering remains an ongoing challenge, particularly when nondestructive characterization is needed. In this study, Bragg coherent diffraction imaging is

Eshwar Ram Arunachaleswaran, Natalie Collina, Yishay Mansour, Mehryar Mohri

Swap regret is a notion that has proven itself to be central to the study of general-sum normal-form games, with swap-regret minimization leading to convergence to the set of correlated equilibria and guaranteeing non-manipulability against a self-interested opponent. However, the situation for more general classes of games -- such as Bayesian games and exte

Julius Natrup, Qun Wang, Yuchen Wang

We show that there exist an upper bound and a lower bound for the number of non-degenerate central configurations of the n-body problem in the plane with a homogeneous potential. In particular, both bounds are independent of the homogeneous degree of the potential under consideration.

Yang Lu, Wei Sun

This paper investigates conditional specifications for multivariate count variables. Recently, the spatial count data literature has proposed several conditional models such that the conditional expectations are linear in the conditioning variables. These models are much easier to estimate than existing spatial count models based on Gaussian random field. Ho

N. K. Patra, Tuhin Malik, Helena Pais, Kai Zhou

We have conducted an extensive study using a diverse set of equations of state (EoSs) to uncover strong relationships between neutron star (NS) observables and the underlying EoS parameters using symbolic regression method. These EoS models, derived from a mix of agnostic and physics-based approaches, considered neutron stars composed of nucleons, hyperons,

Maya Vilenko

Inflation prediction guides decisions on interest rates, investments, and wages, playing a key role in economic stability. Yet accurate forecasting is challenging due to dynamic factors and the layered structure of the Consumer Price Index, which organizes goods and services into multiple categories. We propose the Bi-directional Hierarchical Recurrent Neura

Lam Pham, Dat Tran, Phat Lam, Florian Skopik

In this paper, we propose a deep neural network approach for deepfake speech detection (DSD) based on a lowcomplexity Depthwise-Inception Network (DIN) trained with a contrastive training strategy (CTS). In this framework, input audio recordings are first transformed into spectrograms using Short-Time Fourier Transform (STFT) and Linear Filter (LF), which ar

Wei Yang, Yiran Zhu, Jiayu Shen, Yuhan Tang

Diabetic Macular Edema (DME), a prevalent complication among diabetic patients, constitutes a major cause of visual impairment and blindness. Although deep learning has achieved remarkable progress in medical image analysis, traditional DME diagnosis still relies on extensive annotated data and subjective ophthalmologist assessments, limiting practical appli

Mingqiang Han, Chunlin Yi

To maximize palm oil yield and quality, it is essential to harvest palm fruit at the optimal maturity stage. This project aims to develop an automated computer vision system capable of accurately classifying palm fruit images into five ripeness levels. We employ deep Convolutional Neural Networks (CNNs) to classify palm fruit images based on their maturity s

Ramtin Tabatabaei, Vassilis Kostakos, Wafa Johal

Detecting robot failures during collaborative tasks is crucial for maintaining trust in human-robot interactions. This study investigates user gaze behaviour as an indicator of robot failures, utilising machine learning models to distinguish between non-failure and two types of failures: executional and decisional. Eye-tracking data were collected from 26 pa

Francisco S. Guzman

We study the stability properties of multi-state configurations of the Schr\"odinger-Poisson system without self-interaction, with monopolar and first dipolar components $(1,0,0)$+$(2,1,0)$. We show these configurations studied are stable using numerical simulations, and using criteria of stationarity, unitarity and time dependence consistency. The study cov

Tomoaki Okayama

Stenger and Rashidinia--Zarebnia independently proposed two Sinc-collocation methods for Volterra integral equations of the second kind, but the relation between the methods has not been clarified. This study reformulates Stenger's method for general two-variable kernels and rigorously establishes its applicability and convergence. We prove that the appr

Tobias Kirschstein, Javier Romero, Artem Sevastopolsky, Matthias Nießner

Traditionally, creating photo-realistic 3D head avatars requires a studio-level multi-view capture setup and expensive optimization during test-time, limiting the use of digital human doubles to the VFX industry or offline renderings. To address this shortcoming, we present Avat3r, which regresses a high-quality and animatable 3D head avatar from just a few

Everardo Rivera-Oliva

In this study, a recursive solution technique in conjunction with generalized integrating factors is presented and applied to address first and second order linear differential equations. This approach demonstrates practical utility in classical differential equations encountered in physics, inclusive of equations with variable coefficients, particularly whe

Slavisa Tomic, Marko Beko, Dejan Vukobratovic, Srdjan Krco

This work aspires to provide a trustworthy solution for target localization in adverse environments, where malicious nodes, capable of manipulating distance measurements (i.e., performing spoofing attacks), are present, thus hindering accurate localization. Besides localization, its other goal is to identify (detect) which of the nodes participating in the p

Jimmy Chiun, Shizhe Zhang, Yizhuo Wang, Yuhong Cao

In multi-robot exploration, a team of mobile robot is tasked with efficiently mapping an unknown environments. While most exploration planners assume omnidirectional sensors like LiDAR, this is impractical for small robots such as drones, where lightweight, directional sensors like cameras may be the only option due to payload constraints. These sensors have

Loukas Ilias, Dimitris Askounis

Amyotrophic Lateral Sclerosis (ALS) constitutes a progressive neurodegenerative disease with varying symptoms, including decline in speech intelligibility. Existing studies, which recognize dysarthria in ALS patients by predicting the clinical standard ALSFRS-R, rely on feature extraction strategies and the design of customized convolutional neural networks

Daniel Kapla, Efstathia Bura

We consider supervised learning (regression/classification) problems with tensor-valued input. We derive multi-linear sufficient reductions for the regression or classification problem by modeling the conditional distribution of the predictors given the response as a member of the quadratic exponential family. We develop estimation procedures of sufficient r

Mattéo Clémot, Julie Digne, Julien Tierny

This paper presents a novel topology-aware dimensionality reduction approach aiming at accurately visualizing the cyclic patterns present in high dimensional data. To that end, we build on the Topological Autoencoders (TopoAE) formulation. First, we provide a novel theoretical analysis of its associated loss and show that a zero loss indeed induces identical

Sergio Cerezo-Roquebrún, Aleix Bou-Comas, Jan T. Schneider, Esperanza López

The study of many-body quantum systems out of equilibrium remains a significant challenge with complexity barriers arising in both state and operator-based representations. In this work, we review recent approaches based on finding better contraction strategies for the full spatio-temporal tensor networks that encode the path integral of the dynamics, as wel

Loukas Ilias, Dimitris Askounis

Depression is a mental disorder and can cause a variety of symptoms, including psychological, physical, and social. Speech has been proved an objective marker for the early recognition of depression. For this reason, many studies have been developed aiming to recognize depression through speech. However, existing methods rely on the usage of only the spontan

Xupeng Cheng, Lijin Wang, Yanzhao Cao, Chen Chen

In this paper, we introduces a Pseudo-Symplectic Neural Network (PSNN) for learning general Hamiltonian systems (both separable and non-separable) from data. To address the limitations of existing structure-preserving methods (e.g., implicit symplectic integrators restricted to separable systems or explicit approximations requiring high computational costs),

Sebastian Fürst

Radiocarbon dating poses a challenge in many archaeological contexts due to the limited precision of conventional calibration methods. In this study, we introduce a novel approach to fine-dating that is based on the repeated application of OxCal's R_Simulate function. By constructing extensive reference tables and aggregating measures of central tendency (me

Kamil Kaleta, René L. Schilling, Paweł Sztonyk

We study the spatial decay behaviour of resolvent kernels for a large class of non-local L\'evy operators and bound states of the corresponding Schr\"odinger operators. Our findings naturally lead us to proving results for L\'evy measures, which have subexponential or exponential decay, respectively. This leads to sharp transitions in the the decay rates of

Luis Marquez-Carpintero, Sergio Suescun-Ferrandiz, Carolina Lorenzo Álvarez, Jorge Fernandez-Herrero

In this paper, a novel dataset is introduced, designed to assess student attention within in-person classroom settings. This dataset encompasses RGB camera data, featuring multiple cameras per student to capture both posture and facial expressions, in addition to smartwatch sensor data for each individual. This dataset allows machine learning algorithms to b

Lu Sang, Zehranaz Canfes, Dongliang Cao, Riccardo Marin

Generating realistic intermediate shapes between non-rigidly deformed shapes is a challenging task in computer vision, especially with unstructured data (e.g., point clouds) where temporal consistency across frames is lacking, and topologies are changing. Most interpolation methods are designed for structured data (i.e., meshes) and do not apply to real-worl

Massimo Cafaro, Aneglo Coluccia, Italo Epicoco, Marco Pulimeno

Fast and accurate estimation of quantiles on data streams coming from communication networks, Internet of Things (IoT), and alike, is at the heart of important data processing applications including statistical analysis, latency monitoring, query optimization for parallel database management systems, and more. Indeed, quantiles are more robust indicators for

Ousmane Coulibaly, Harouna Sangaré

In this paper we extend the classical Glivenko-Cantelli theorem to real-valued empirical functions under dependence structures characterised by $\alpha$-mixing and $\beta$-mixing conditions. We investigate sufficient conditions ensuring that families of real-valued functions exhibit the Glivenko-Cantelli (GC) property in these dependence settings. Our analys

Ivan Alvarez-Rios, Francisco S. Guzman

In this paper we study the behavior of test particles on top of a galactic-type of Fuzzy Dark Matter (FDM) structure, characterized by the core-halo density profile found in simulations. Our workhorse structure is an anisotropic, time-dependent, virialized core-tail FDM clump resulting from a multicore merger. For our analysis we allow this structure to keep

Parul Awasthy, Aashka Trivedi, Yulong Li, Mihaela Bornea

We introduce the Granite Embedding models, a family of encoder-based embedding models designed for retrieval tasks, spanning dense-retrieval and sparse retrieval architectures, with both English and Multilingual capabilities. This report provides the technical details of training these highly effective 12 layer embedding models, along with their efficient 6

Suryanarayana Sankagiri, Bruce Hajek

A payment channel network is a blockchain-based overlay mechanism that allows parties to transact more efficiently than directly using the blockchain. These networks are composed of payment channels that carry transactions between pairs of users. Due to its design, a payment channel cannot sustain a net flow of money in either direction indefinitely. Therefo

Son T. Nguyen

Effective field theory (EFT) provides a powerful model-independent theoretical framework for illuminating complicated interactions across a wide range of physics areas and subfields. In this work, we consider the low-energy deuteron-Helium-4, triton-Helium-4, and helion-Helium-4 systems at low energies in cluster EFT. In particular, we focus on the deuteron

Nino Bašić, Ivan Damnjanović

A nut graph is a nontrivial graph whose adjacency matrix has a one-dimensional null space spanned by a vector without zero entries. Recently, it was shown that a nut graph has more edge orbits than vertex orbits. It was also shown that for any even $r \ge 2$ and any $k \ge r + 1$, there exist infinitely many nut graphs with $r$ vertex orbits and $k$ edge orb

ALICE Collaboration

The interaction between pions and nucleons plays a crucial role in hadron physics. It represents a fundamental building block of the low-energy QCD dynamics and is subject to several resonance excitations. This work studies the p-$\pi^{\pm}$ dynamics using femtoscopic correlations in high-multiplicity pp collisions at $\sqrt{s} = 13$ TeV measured by ALICE at

Xinwu Ye, Chengfan Li, Siming Chen, Wei Wei

Recent advances in large language models (LLMs) and vision-language models (LVLMs) have shown promise across many tasks, yet their scientific reasoning capabilities remain untested, particularly in multimodal settings. We present MMSciBench, a benchmark for evaluating mathematical and physical reasoning through text-only and text-image formats, with human-an

J. -T. Bu, Lei Zhang, Zhan Yu, Jing-Bo Wang

Quantum signal processing (QSP), which enables systematic polynomial transformations on quantum data through sequences of qubit rotations, has emerged as a fundamental building block for quantum algorithms and data re-uploading quantum neural networks. While recent experiments have demonstrated the feasibility of shallow QSP circuits, the inherent limitation

V. S. Lebedev, A. D. Kondorskiy

We review the studies of a wide range of optical phenomena resulting from near-field coupling between excitons and localized surface plasmon-polaritons in hybrid nanostructures. Modern physical approaches and theoretical models reported here for the description of light absorption, scattering, and extinction spectra are appropriate for interpreting physical

Martin Schoeberl

In computer architecture courses, we usually teach RISC processors using a five-stage pipeline, neglecting alternative organizations. This design choice, rooted in the 1980s technology, may not be optimal today, and it is certainly not the easiest pipeline for education. This paper examines more straightforward pipeline organizations for RISC processors that

Haibin Chen, Kangtao Lv, Chengwei Hu, Yanshi Li

With the increasing use of Large Language Models (LLMs) in fields such as e-commerce, domain-specific concept evaluation benchmarks are crucial for assessing their domain capabilities. Existing LLMs may generate factually incorrect information within the complex e-commerce applications. Therefore, it is necessary to build an e-commerce concept benchmark. Exi

Benjamin Delarue, Daniel Monclair, Andrew Sanders

Given a non-compact semisimple real Lie group $G$ and an Anosov subgroup $\Gamma$, we utilize the correspondence between $\mathbb R$-valued additive characters on Levi subgroups $L$ of $G$ and $\mathbb R$-affine homogeneous line bundles over $G/L$ to systematically construct families of non-empty domains of proper discontinuity for the $\Gamma$-action. If $\

Julian Koch, Sergii Sologub, Dorothee Sylvia Boesler, Chitran Ghosal

Proximity coupling is an effective approach for the functionalization of graphene. However, graphene's inertness inhibits the adsorption of closed films, thus favoring island growth, whose inhomogeneity might be reflected in the induced properties. In order to study the homogeneity of the doping profile induced by an inhomogeneous coverage and the spin orbit

Rajab Aghamov, Christel Baier, Toghrul Karimov, Rupak Majumdar

Standpoint linear temporal logic ($SLTL$) is a recently introduced extension of classical linear temporal logic ($LTL$) with standpoint modalities. Intuitively, these modalities allow to express that, from agent $a$'s standpoint, it is conceivable that a given formula holds. Besides the standard interpretation of the standpoint modalities we introduce four n

Olai B. Mykland, Zhao Zhang

In this article we present analytical results on the exact tensor network representations and correlation functions of the first examples of 2D ground states with quantum phase transitions between area law and extensive entanglement entropy. The tensor networks constructed are one dimension higher than the lattices of the physical systems, allowing entangled

Manuel Krannich, Alexander Lytchak, Marco Radeschi

We prove that there are only finitely many isoparametrically foliated closed connected Riemannian manifolds with bounded geometry, fixed dimension $n\neq5$, and finite fundamental group, up to foliated diffeomorphism. In addition, we construct various infinite families of isoparametric foliations that are mutually not foliated diffeomorphic, for instance on

Zhouyu He, Peng Qiao, Rongchun Li, Yong Dou

As the demands for superior agents grow, the training complexity of Deep Reinforcement Learning (DRL) becomes higher. Thus, accelerating training of DRL has become a major research focus. Dividing the DRL training process into subtasks and using parallel computation can effectively reduce training costs. However, current DRL training systems lack sufficient

Laura Pecorari, Guido Pupillo

Identifying the best families of quantum error correction (QEC) codes for near-term experiments is key to enabling fault-tolerant quantum computing. Ideally, such codes should have low overhead in qubit number, high physical error thresholds, and moderate requirements on qubit connectivity to simplify experiments, while allowing for high logical error suppre

Pedro Sousa, Cláudio Klautau Mello, Frank B. Morte, Luis F. Solis Navarro

Question-answering tasks in the telecom domain are still reasonably unexplored in the literature, primarily due to the field's rapid changes and evolving standards. This work presents a novel Retrieval-Augmented Generation framework explicitly designed for the telecommunication domain, focusing on datasets composed of 3GPP documents. The framework introduces

Francesco Fazzini

Effective dust collapse inspired by loop quantum gravity predicts two main features for general realistic initial profiles: a quantum gravitational bounce of the stellar core, when the energy density becomes planckian, and shell-crossing singularities, arising within almost a planckian time after the bounce. The aim of this work is to study the mathematical

Yan-Lun Chen, Yi-Ru Wei, Chia-Yi Hsu, Chia-Mu Yu

Large language models (LLMs) demonstrate strong task-specific capabilities through fine-tuning, but merging multiple fine-tuned models often leads to degraded performance due to overlapping instruction-following components. Task Arithmetic (TA), which combines task vectors derived from fine-tuning, enables multi-task learning and task forgetting but struggle

Pablo A. Cano

We obtain the full set of tidal Love numbers of non-rotating black holes in an effective field theory extension of general relativity. We achieve our results using a recently introduced modified Teukolsky equation that describes the perturbations of black holes in this theory. We show how to identify the Love numbers and their beta functions in a systematic

Shouwei Hu, Xi Li, Banyao Ruan, Zhihao Liu

The classical-quantum transfer learning (CQTL) method is introduced to address the challenge of training large-scale, high-resolution image data on a limited number of qubits (ranging from tens to hundreds) in the current Noisy Intermediate-Scale quantum (NISQ) era. existing CQTL frameworks have been demonstrate quantum advantages with a small number of para

Zeyi Ren, Qingfeng Lin, Jingreng Lei, Yang Li

In the realm of activity detection for massive machine-type communications, intelligent reflecting surfaces (IRS) have shown significant potential in enhancing coverage for devices lacking direct connections to the base station (BS). However, traditional activity detection methods are typically designed for a single type of channel model, which does not refl

Tara Abrishami, Jadwiga Czyżewska, Kacper Kluk, Marcin Pilipczuk

It is known that there is a linear dependence between the treewidth of a graph and its balanced separator number: the smallest integer $k$ such that for every weighing of the vertices, the graph admits a balanced separator of size at most $k$. We investigate whether this connection can be lifted to the setting of coarse graph theory, where both the bags of t

Wenkang Jiang, Jiaxin Han, Fuyu Dong, Feihong He

In the $\Lambda$CDM universe, structure formation is generally not a self-similar process, while some self-similarity remains in certain statistics which can greatly simplify our description and understanding of the cosmic structures. In this work, we show that the merger tree of dark matter halos is approximately self-similar by investigating the universali

Yunhan Mou, Haitao Pan, Yu Jiang, Yuan Huang

Composite endpoints are widely used in cardiovascular clinical trials. In recent years, hierarchical composite endpoints-particularly the win ratio approach and its predecessor, the Finkelstein-Schoenfeld (FS) test, also known as the unmatched win ratio test-have gained popularity. These methods involve comparing individuals across multiple endpoints, ranked

Arthur M. Faria, Marcus V. S. Bonanca, Eric Lutz

Non-Gaussian noise is omnipresent in systems where the central-limit theorem is inapplicable. We here investigate the stochastic thermodynamics of small systems that are described by a general Kramers-Moyal equation that includes both Gaussian and non-Gaussian white noise contributions. We obtain detailed and integral fluctuation relations for the nonequilib

Zhaoxuan Wang, Yang Li, Jie Zhang, Xingshuo Han

Unmanned aerial vehicles (UAVs) are increasingly employed to perform high-risk tasks that require minimal human intervention. However, UAVs face escalating cybersecurity threats, particularly from GNSS spoofing attacks. While previous studies have extensively investigated the impacts of GNSS spoofing on UAVs, few have focused on its effects on specific tasks

Antonio Forcina

The paper derives new results on the marginal likelihood of a two-way table which clarify the conditions under which Ecological inference is possible and lead to an efficient algorithm for maximizing the exact multinomial likelihood. The first part generalizes the work of Placket(1977} on the marginal likelihood of a 2 x 2 table to a general R x C table. In

Xinran Liu, Zhenhua Feng, Diptesh Kanojia, Wenwu Wang

In music-driven dance motion generation, most existing methods use hand-crafted features and neglect that music foundation models have profoundly impacted cross-modal content generation. To bridge this gap, we propose a diffusion-based method that generates dance movements conditioned on text and music. Our approach extracts music features by combining high-

Kaustubh Vyas, Damien Graux, Sébastien Montella, Pavlos Vougiouklis

In recent advancements, large language models (LLMs) have exhibited proficiency in code generation and chain-of-thought reasoning, laying the groundwork for tackling automatic formal planning tasks. This study evaluates the potential of LLMs to understand and generate Planning Domain Definition Language (PDDL), an essential representation in artificial intel

Arthur M. Faria, Marcus V. S. Bonanca, Eric Lutz

We consider a microscopic model of an inhomogeneous environment where an arbitrary quantum system is locally coupled to a harmonic bath via a finite-range interaction. We show that in the overdamped regime the position distribution obeys a classical Kramers-Moyal equation that involves an infinite number of higher derivatives, implying that the finite bath c

Pardeep Kumar, Patricio I. Rosen Esquivel

We investigate the phase equilibrium problem for multicomponent mixtures under specified internal energy (U), volume (V), and mole numbers (N1,N2, . . . ,Nn), commonly known as the UVN-flash problem. While conventional phase equilibrium calculations typically use pressure-temperature-mole number (PTN) specifications, the UVN formulation is essential for dyna

Liang Chen, Shuai Bai, Wenhao Chai, Weichu Xie

The field of advanced text-to-image generation is witnessing the emergence of unified frameworks that integrate powerful text encoders, such as CLIP and T5, with Diffusion Transformer backbones. Although there have been efforts to control output images with additional conditions, like canny and depth map, a comprehensive framework for arbitrary text-image in