May 2025 arXiv papers — page 119

Subrata Biswas, Mohammad Nur Hossain Khan, Bashima Islam

Spoken Language Understanding (SLU) systems must balance performance and efficiency, particularly in resource-constrained environments. Existing methods apply distillation and quantization separately, leading to suboptimal compression as distillation ignores quantization constraints. We propose QUADS, a unified framework that optimizes both through multi-sta

João S. Ferreira, Pierre Fromholz, Hari Shaji, James R. Wootton

Reservoir computing is a form of machine learning particularly suited for time series analysis, including forecasting predictions. We take an implementation of \emph{quantum} reservoir computing that was initially designed to generate variants of musical scores and adapt it to create levels of Super Mario Bros. Motivated by our analysis of these levels, we d

B. Hamil

In this work, we construct an exact spherically symmetric black hole solution with a global monopole in the context of four-dimensional noncommutative Einstein-Gauss-Bonnet gravity. We modeled the spacetime noncommutativity via a Lorentzian-smeared mass distribution. Then we study the horizon structure and find that this black hole can have two configuration

Mikhail A. Komarov

Let $K\subset \mathbb{C}$ be a convex compact set, and let $\Pi_n(K)$ be the class of polynomials of exact degree $n$, all of whose zeros lie in $K$. The Tur\'an type inverse Markov factor is defined by $M_n(K)=\inf_{P\in \Pi_n(K)} \left(\|P'\|_{C(K)}/\|P\|_{C(K)}\right)$. A combination of two well-known results due to Levenberg and Poletsky (2002) and R\'ev

Matteo Acclavio, Lutz Strassburger

We present the logic IBV, which is an intuitionistic version of BV, in the sense that its restriction to the MLL connectives is exactly IMLL, the intuitionistic version of MLL. For this logic we give a deep inference proof system and show cut elimination. We also show that the logic obtained from IBV by dropping the associativity of the new non-commutative s

Tianyi Chu, Jonathan B. Estrada, Spencer H. Bryngelson

We develop a computational approach that significantly improves the efficiency of Bayesian optimal experimental design (BOED) using local radial basis functions (RBFs). The presented RBF--BOED method uses the intrinsic ability of RBFs to handle scattered parameter points, a property that aligns naturally with the probabilistic sampling inherent in Bayesian m

Sahil Mishra, Kumar Arjun, Tanmoy Chakraborty

Taxonomies are hierarchical knowledge graphs crucial for recommendation systems, and web applications. As data grows, expanding taxonomies is essential, but existing methods face key challenges: (1) discriminative models struggle with representation limits and generalization, while (2) generative methods either process all candidates at once, introducing noi

Zekun Wang, Sashank Varma

With the rapid improvement of machine learning (ML) models, cognitive scientists are increasingly asking about their alignment with how humans think. Here, we ask this question for computer vision models and human sensitivity to geometric and topological (GT) concepts. Under the core knowledge account, these concepts are innate and supported by dedicated neu

Elias Collaert, Abel Rodríguez, Sander Joos, Lieven Desmet

Despite significant advances in the area, adversarial robustness remains a critical challenge in systems employing machine learning models. The removal of adversarial perturbations at inference time, known as adversarial purification, has emerged as a promising defense strategy. To achieve this, state-of-the-art methods leverage diffusion models that inject

Zhiqiang Yan, Jianhao Jiao, Zhengxue Wang, Gim Hee Lee

Depth completion in dynamic scenes poses significant challenges due to rapid ego-motion and object motion, which can severely degrade the quality of input modalities such as RGB images and LiDAR measurements. Conventional RGB-D sensors often struggle to align precisely and capture reliable depth under such conditions. In contrast, event cameras with their hi

Daniel Weiner, Raj Korpan

Modular construction, involving off-site prefabrication and on-site assembly, offers significant advantages but presents complex coordination challenges for robotic automation. Effective task allocation is critical for leveraging multi-agent systems (MAS) in these structured environments. This paper introduces the Hybrid Voting-Based Task Assignment (HVBTA)

Gabriel Wiest, Niklas Nolzen, Florian Baader, André Bardow

Large uncertainties in the energy transition urge decision-makers to develop low-regret strategies, i.e., strategies that perform well regardless of how the future unfolds. To address this challenge, we introduce a decision-support framework that identifies low-regret strategies in energy system planning under uncertainty. Our framework (i) automatically ide

Sebastian Barzaghi, Arianna Moretti, Ivan Heibi, Silvio Peroni

This paper presents CHAD-KG, a knowledge graph designed to describe bibliographic metadata and digitisation paradata of cultural heritage objects in exhibitions, museums, and collections. It also documents the related data model and materialisation engine. Originally based on two tabular datasets, the data was converted into RDF according to CHAD-AP, an OWL

Pierre-Marc Jodoin, Manon Edde, Gabriel Girard, Félix Dumais

Over the years, ComBAT has become the standard method for harmonizing MRI-derived measurements, with its ability to compensate for site-related additive and multiplicative biases while preserving biological variability. However, ComBAT relies on a set of assumptions that, when violated, can result in flawed harmonization. In this paper, we thoroughly review

Raphaël Sarfati, Haley Moller, Toni J. B. Liu, Nicolas Boullé

Large language models use high-dimensional latent spaces to encode and process textual information. Much work has investigated how the conceptual content of words translates into geometrical relationships between their vector representations. Fewer studies analyze how the cumulative information of an entire prompt becomes condensed into individual embeddings

Anthony Zhou, Amir Barati Farimani

Designing neural networks within a Hamiltonian framework offers a principled way to ensure that conservation laws are respected in physical systems. While promising, these capabilities have been largely limited to discrete, analytically solvable systems. In contrast, many physical phenomena are governed by PDEs, which govern infinite-dimensional fields throu

Andrea Pedicone, Fabrizio Cinque

We study the asymptotic properties, in the weak sense, of regenerative processes and Markov renewal processes. For the latter, we derive both renewal-type results, also concerning the related counting process, and ergodic-type ones, including the so-called phi-mixing property. This theoretical framework permits us to study the weak limit of the integral of a

Alessandro Avellone, Paolo Bartesaghi, Stefano Benati, Christos Charalambous

Modularity and persistence probability are two widely used quality functions for detecting communities in complex networks. In this paper, we introduce a new objective function called null-adjusted persistence, which incorporates features from both modularity and persistence probability, as it implies a comparison of persistence probability with the same nul

Lei Sheng, Shuai-Shuai Xu

Large language models (LLMs) have demonstrated strong capabilities in translating natural language questions about relational databases into SQL queries. In particular, test-time scaling techniques such as Self-Consistency and Self-Correction can enhance SQL generation accuracy by increasing computational effort during inference. However, these methods have

Fabian Ritter-Gutierrez, Yi-Cheng Lin, Jui-Chiang Wei, Jeremy H. M Wong

Despite the progress in self-supervised learning (SSL) for speech and music, existing models treat these domains separately, limiting their capacity for unified audio understanding. A unified model is desirable for applications that require general representations, e.g. audio large language models. Nonetheless, directly training a general model for speech an

Leyang Zhang, Yaoyu Zhang, Tao Luo

This paper investigates the sample dependence of critical points for neural networks. We introduce a sample-independent critical lifting operator that associates a parameter of one network with a set of parameters of another, thus defining sample-dependent and sample-independent lifted critical points. We then show by example that previously studied critical

Y. Y. Huang, Q. Q. Cui, X. H. Wu, S. Q. Zhang

The abundance ratios of radioactive elements U/Th and stable elements Pb/Os from the $r$-process are found to have a strong correlation. This correlation is quite robust with respect to astrophysical conditions. The U/Th-Pb/Os correlation is then applied to provide customized initial abundance ratios U/Th from the observed abundance ratios Pb/Os for six $r$-

Omar R. Malik

Using data from the U.S. Census Bureaus Business Trends and Outlook Survey (BTOS), I examine the adoption of AI among US firms at national, state, industry, and firm size levels. I find that adoption remains overall low (only around 7% of firms currently use AI), but is on a steady upward trajectory with a rising share of firms planning to implement AI. Adop

Livia Qian, Carol Figueroa, Gabriel Skantze

Vocal feedback (e.g., `mhm', `yeah', `okay') is an important component of spoken dialogue and is crucial to ensuring common ground in conversational systems. The exact meaning of such feedback is conveyed through both lexical and prosodic form. In this work, we investigate the perceived prosodic similarity of vocal feedback with the same lexical form, and to

E. F. Ocran, A. R. Taylor, J. M. Stil, M. Vaccari

This study investigates the radio spectral properties of \textit{K}$_{S}$-selected star-forming galaxies (SFGs) in the XMM-LSS field using extensive multiwavelength data. By employing various diagnostics, SFGs are distinguished from quiescent galaxies and AGN across seven redshift bins ($\rm{0.1\leq\,\textit{z}\,\leq\,3.0}$). The broadband radio frequency sp

Yehao Liu, Xiaosu Xu, Zijian Wang, Yiqing Yao

3D Lane detection plays an important role in autonomous driving. Recent advances primarily build Birds-Eye-View (BEV) feature from front-view (FV) images to perceive 3D information of Lane more effectively. However, constructing accurate BEV information from FV image is limited due to the lacking of depth information, causing previous works often rely heavil

Ben Hayes, Srivatsav Kunnawalkam Elayavalli, Leonel Robert

We study selflessness in the general setting of reduced free products of $C^*$-algebras. Towards this end, we develop a suitable theory of rapid decay for filtrations in arbitrary $C^*$-probability spaces. We provide several natural examples and permanence properties of this phenomenon. By using this framework in combination with von Neumann algebraic techni

Carlos Rodriguez-Pardo, Louis Daumas, Leonardo Chiani, Massimo Tavoni

Climate-economic modeling under uncertainty presents significant computational challenges that may limit policymakers' ability to address climate change effectively. This paper explores neural network-based approaches for solving high-dimensional optimal control problems arising from models that incorporate ambiguity aversion in climate mitigation decisions.

Krzysztof Lebioda, Nenad Petrovic, Fengjunjie Pan, Vahid Zolfaghari

Large Language Models (LLMs) are taking many industries by storm. They possess impressive reasoning capabilities and are capable of handling complex problems, as shown by their steadily improving scores on coding and mathematical benchmarks. However, are the models currently available truly capable of addressing real-world challenges, such as those found in

Júlia Martínez-Marín

A K3 surface over a number field has infinitely many rational points over a finite field extension. For K3 surfaces of degree 2, arising as double covers of $\mathbb{P}^2$ branched along a smooth sextic curve, we give a bound for the degree of such an extension. Moreover, using ideas of van Luijk and a surface constructed by Elsenhans and Jahnel, we give an

Mingrui Chen, Haogeng Liu, Hao Liang, Huaibo Huang

In this work, we investigate how explicitly modeling problem's difficulty prior information shapes the effectiveness of reinforcement learning based fine-tuning for multimodal reasoning. Our exploration mainly comprises of following three perspective: First, through offline data curation, we analyze the U-shaped difficulty distribution of two given datasets

Alexander I. Efimov

In this paper we give a different proof of Quillen's D\'evissage theorem using Barwick's theorem of the heart. The key ingredient is a certain short exact sequence of dg categories, which is closely related with the Auslander-type construction for nilpotent extensions which was used in the papers of Kuznetsov-Lunts \cite{KL15}, Land-Tamme \cite{LT19} and the

Tommaso Mario Buonocore, Enea Parimbelli

Content moderation for large language models (LLMs) remains a significant challenge, requiring flexible and adaptable solutions that can quickly respond to emerging threats. This paper introduces Retrieval Augmented Rejection (RAR), a novel approach that leverages a retrieval-augmented generation (RAG) architecture to dynamically reject unsafe user queries w

Tianshi Zheng, Zheye Deng, Hong Ting Tsang, Weiqi Wang

Large Language Models (LLMs) are catalyzing a paradigm shift in scientific discovery, evolving from task-specific automation tools into increasingly autonomous agents and fundamentally redefining research processes and human-AI collaboration. This survey systematically charts this burgeoning field, placing a central focus on the changing roles and escalating

Jingyi Ren, Yekun Xu, Xiaolong Wang, Weitao Li

Retrieval-Augmented Generation (RAG) delivers substantial value in knowledge-intensive applications. However, its generated responses often lack transparent reasoning paths that trace back to source evidence from retrieved documents. This opacity not only compromises the interpretability of the output but also limits the model's ability to fully exploit the

Zilu Tang, Afra Feyza Akyürek, Ekin Akyürek, Derry Wijaya

A prominent issue in aligning language models (LMs) to personalized preferences is underspecification -- the lack of information from users about their preferences. A popular trend of injecting such specification is adding a prefix (e.g. prior relevant conversations) to the current user's conversation to steer preference distribution. Most methods passively

Paul D. Nelson

We give a soft proof of a uniform upper bound for the local factors in the triple product formula, sufficient for deducing effective and general forms of quantum unique ergodicity (QUE) from subconvexity.

Shihan Wu, Xu Luo, Ji Zhang, Junlin Xie

Robotic foundation models, or generalist robot policies, hold immense potential to enable flexible, general-purpose and dexterous robotic systems. Despite their advancements, our empirical experiments reveal that existing robot policies are prone to learning spurious correlations from pre-training trajectories, adversely affecting their generalization capabi

Siran Liu, Yang Ye, Qianchao Zhu, Zane Cao

Autoregressive decoding inherently limits the inference throughput of Large Language Model (LLM) due to its sequential dependency. Speculative decoding mitigates this by verifying multiple predicted tokens in parallel, but its efficiency remains constrained by what we identify as verification heterogeneity -- the uneven difficulty of verifying different spec

Lennart Röstel, Dominik Winkelbauer, Johannes Pitz, Leon Sievers

In-hand manipulation and grasping are fundamental yet often separately addressed tasks in robotics. For deriving in-hand manipulation policies, reinforcement learning has recently shown great success. However, the derived controllers are not yet useful in real-world scenarios because they often require a human operator to place the objects in suitable initia

Rikhil Amonkar, Ceyhun Efe Kayan, Qimei Lai, Ronan Le Bras

Recent work shows superior performance when using large language models (LLMs) as formalizers instead of as end-to-end solvers for symbolic reasoning problems. Given the problem description, the LLM generates a formal program that derives a solution via an external solver. We systematically investigate the formalization capability of LLMs on real-life constr

Sidney Wong

The advent of the internet has been both a blessing and a curse for once marginalised communities. When used well, the internet can be used to connect and establish communities crossing different intersections; however, it can also be used as a tool to alienate people and communities as well as perpetuate hate, misinformation, and disinformation especially o

Hashan K. Weerasooriya, Prateek Chennuri, Weijian Zhang, Istvan Gyongy

Single-Photon Light Detection and Ranging (SP-LiDAR is emerging as a leading technology for long-range, high-precision 3D vision tasks. In SP-LiDAR, timestamps encode two complementary pieces of information: pulse travel time (depth) and the number of photons reflected by the object (reflectivity). Existing SP-LiDAR reconstruction methods typically recover d

Hanzhao Wang, Guanting Chen, Kalyan Talluri, Xiaocheng Li

We build a Generative Pre-trained Transformer (GPT) model from scratch to solve sequential decision making tasks arising in contexts of operations research and management science which we call OMGPT. We first propose a general sequence modeling framework to cover several operational decision making tasks as special cases, such as dynamic pricing, inventory m

Le Vu Anh, Dinh Duc Nha Nguyen, Phi Long Nguyen

Large Language Models (LLMs) have become foundational in modern artificial intelligence, powering a wide range of applications from code generation and virtual assistants to scientific research and enterprise automation. However, concerns about data contamination--where test data overlaps with training data--have raised serious questions about the reliabilit

Yipeng Sun, Linda-Sophie Schneider, Chengze Ye, Mingxuan Gu

Cone-Beam Computed Tomography (CBCT) is essential in medical imaging, and the Feldkamp-Davis-Kress (FDK) algorithm is a popular choice for reconstruction due to its efficiency. However, FDK is susceptible to noise and artifacts. While recent deep learning methods offer improved image quality, they often increase computational complexity and lack the interpre

Naim Shandi, Jason M. Merlo, Jeffrey A. Nanzer

We demonstrate a distributed beamforming and beamsteering from a six-node distributed phased array using fully wireless coordination with decentralized time synchronization. In wireless applications such as distributed beamforming, high-accuracy time synchronization across the array is crucial for high coherent gain. The decentralized time synchronization me

J. Gamboa

We study infrared divergences in quantum electrodynamics using geometric phases and the adiabatic approximation in quantum field theory. In this framework, the asymptotic \textit{in} and \textit{out} states are modified by Berry phases, $e^{i \Delta \alpha_{\text{in}}}$ and $e^{i \Delta \alpha_{\text{out}}}$, which encode the infrared structure non-perturbat

Roberto Pugliese, George Kourousias, Francesco Venier, Grazia Garlatti Costa

Purpose: This paper introduces the concept of "Agentic Publication," a novel LLM-driven framework designed to complement traditional scientific publishing by transforming papers into interactive knowledge systems that address challenges created by exponential growth in scientific literature. Design/methodology/approach: Our architecture integrates structured

Sayan Bhowmik, Abhiraj Sharma, Andrew J. Medford, John E. Pask

We present a first principles investigation of strain-driven vacancy clustering in aluminum. Specifically, we perform Kohn-Sham density functional theory calculations to study the influence of hydrostatic strains on clustering in tri-, quad-, and heptavacancies. We find that compressive strains are a key driving force for vacancy aggregation, particularly fo

Jieying Xue, Phuong Minh Nguyen, Minh Le Nguyen, Xin Liu

With the rapid advancement of global digitalization, users from different countries increasingly rely on social media for information exchange. In this context, multilingual multi-label emotion detection has emerged as a critical research area. This study addresses SemEval-2025 Task 11: Bridging the Gap in Text-Based Emotion Detection. Our paper focuses on t

Wenbin Zhou, Agni Orfanoudaki, Shixiang Zhu

In many operational settings, decision-makers must commit to actions before uncertainty resolves, but existing optimization tools rarely quantify how consistently a chosen decision remains optimal across plausible scenarios. This paper introduces CREDO -- Conformalized Risk Estimation for Decision Optimization, a distribution-free framework that quantifies t

Arghajit Jana, Claudio Ricci, Sophie M. Venselaar, Chin-Shin Chang

The origin of compact millimeter (mm) continuum emission from radio-quiet AGNs (RQAGNs) is still not fully understood. Changing-state AGNs (CSAGNs) display rapid and strong variability, which can allow us to investigate the origin of the mm emission. We present here the results of the first study of the mm continuum variability of a CSAGN using archival ALMA

Yuan-Zheng Lei, Yaobang Gong, Dianwei Chen, Yao Cheng

Physics-informed machine learning (PIML) is crucial in modern traffic flow modeling because it combines the benefits of both physics-based and data-driven approaches. In conventional PIML, physical information is typically incorporated by constructing a hybrid loss function that combines data-driven loss and physics loss through linear scalarization. The goa

Giovanni C. Baiesi Pillastrini

The formation of the giant binary-like system composed by the Corona Borealis and Abell 2142 superclusters is an intriguing conundrum of the formation of large scale structures since, from the observational point of view, it represents a rare peculiarity in the distribution of massive galaxy superclusters. Having a configuration similar to a giant binary sys

Pedro Otero-García, David Pérez-Castro, Manuel Fernández-Veiga, Ana Fernández-Vilas

The advancement of quantum computing threatens classical cryptographic methods, necessitating the development of secure quantum key distribution (QKD) solutions for QKD Networks (QKDN). In this paper, a novel key distribution protocol, Onion Routing Relay (ORR), that integrates onion routing (OR) with post-quantum cryptography (PQC) in a key-relay (KR) model

Christophe Parisel

While data perimeter is ubiquitous in cybersecurity speak, it rarely defines how boundary points are arranged. In this paper we show how Azure s blast radius ultrametric provides the distance, and how solving the Traveling Salesman Problem in this ultrametric space provides the ordering, yielding a true geometric contour: an actionable perimeter measure for

Chih-Kai Yang, Neo Ho, Yen-Ting Piao, Hung-yi Lee

Large audio-language models (LALMs) extend the large language models with multimodal understanding in speech, audio, etc. While their performances on speech and audio-processing tasks are extensively studied, their reasoning abilities remain underexplored. Particularly, their multi-hop reasoning, the ability to recall and integrate multiple facts, lacks syst

Anandh C, Karthik Pandia Durai, Jeena Prakash, Manickavela Arumugam

ASR endpointing (EP) plays a major role in delivering a good user experience in products supporting human or artificial agents in human-human/machine conversations. Transducer-based ASR (T-ASR) is an end-to-end (E2E) ASR modelling technique preferred for streaming. A major limitation of T-ASR is delayed emission of ASR outputs, which could lead to errors or

Joseph Ben Geloun, Arnauld Solente

In Tensor Field Theory (TFT), observables are defined through tensor field contractions that produce unitary invariants for complex-valued tensor fields. Traditionally, these observables are constructed using tensor fields of a fixed order $d$. Here, we propose an extended theoretical framework for TFT that incorporates tensor fields of varying orders $d'$,

Dang Hoai Nam, Huynh Tong Dang Khoa, Vo Nguyen Le Duy

Humans can quickly generalize handwriting styles from a single example by intuitively separating content from style. Machines, however, struggle with this task, especially in low-data settings, often missing subtle spatial and stylistic cues. Motivated by this gap, we introduce WriteViT, a one-shot handwritten text synthesis framework that incorporates Visio

Francesco Galuppi, Giovanni Moreno, Pierpaola Santarsiero

The signature of a path is a sequence of tensors which allows to uniquely reconstruct the path. By employing the geometric theory of nonlinear systems of ordinary differential equations, we find necessary and sufficient algebraic conditions on the signature tensors of a path to be a solution of a given system of ODEs. As an application, we describe in detail

Lincan Cai, Jingxuan Kang, Shuang Li, Wenxuan Ma

Pretrained vision-language models (VLMs), e.g., CLIP, demonstrate impressive zero-shot capabilities on downstream tasks. Prior research highlights the crucial role of visual augmentation techniques, like random cropping, in alignment with fine-grained class descriptions generated by large language models (LLMs), significantly enhancing zero-shot performance

Younghyun Kim, Jongheon Jeong, Sangkyung Kwak, Kyungmin Lee

Learning robust representations from data often requires scale, which has led to the success of recent zero-shot models such as CLIP. However, the obtained robustness can easily be deteriorated when these models are fine-tuned on other downstream tasks (e.g., of smaller scales). Previous works often interpret this phenomenon in the context of domain shift, d

Junyi Chen, Alap Kshirsagar, Frederik Heller, Mario Gómez Andreu

One of the most important object properties that humans and robots perceive through touch is hardness. This paper investigates information-theoretic active sampling strategies for sample-efficient hardness classification with vision-based tactile sensors. We evaluate three probabilistic classifier models and two model-uncertainty-based sampling strategies on

Zhongyi Wang, Mingshuai Chen, Tengjie Lin, Linyu Yang

We launch Parf - a toolkit for adaptively tuning abstraction strategies of static program analyzers in a fully automated manner. Parf models various types of external parameters (encoding abstraction strategies) as random variables subject to probability distributions over latticed parameter spaces. It incrementally refines the probability distributions base

Fernando Abreu de Souza, Maura Barros, Nuno Filipe Castro, Miguel Crispim Romão

The search for physics beyond the Standard Model (BSM) at collider experiments requires model-independent strategies to avoid missing possible discoveries of unexpected signals. Anomaly detection (AD) techniques offer a promising approach by identifying deviations from the Standard Model (SM) and have been extensively studied. The sensitivity of these method

Tianbao Xie, Jiaqi Deng, Xiaochuan Li, Junlin Yang

Graphical user interface (GUI) grounding, the ability to map natural language instructions to specific actions on graphical user interfaces, remains a critical bottleneck in computer use agent development. Current benchmarks oversimplify grounding tasks as short referring expressions, failing to capture the complexity of real-world interactions that require

Yu Guo, Ning Yang

It is known that $\rho^{AB}$ as a bipartite reduced state of the 3-qubit GHZ state is separable, but part $A$ and part $B$ indeed ``share tripartite entanglement'' in the GHZ state. Namely, whether a state can ``share'' more entanglement is dependent on the global system it lives in. Here we explore such kind of entanglement in any $n$-partite system with ar

Manuel de León, Rubén Izquierdo-López, Xavier Rivas

In this paper we introduce a graded bracket of forms on multicontact manifolds. This bracket satisfies a graded Jacobi identity as well as two different versions of the Leibniz rule, one of them being a weak Leibniz rule, extending the well-known notions in contact geometry. In addition, we develop the multisymplectization of multicontact structures to relat

Junqi Tang, Guixian Xu

We prove for the first time that, if a linear inverse problem exhibits a group symmetry structure, gradient-based optimizers can be designed to exploit this structure for faster convergence rates. This theoretical finding demonstrates the existence of a special class of structure-adaptive optimization algorithms which are tailored for symmetry-structured inv

Mikhail Osipov

We study the problem of reducing a task cost functional $W : H^s(M) \to \mathbb{R}$, not assumed continuous or differentiable, defined over Sobolev-class signals $S \in H^s(M) $, in the presence of a global symmetry group $G \subset \mathrm{Diff}(M)$. The group acts on signals by pullback, and the cost $W$ is invariant under this action. Such scenarios arise

BESIII Collaboration, M. Ablikim, M. N. Achasov, P. Adlarson

Based on 12.0 $\mathrm{fb^{-1}}$ of $e^{+}e^{-}$ collision data samples collected by the BESIII detector at center-of-mass energies from 4.1271 to 4.3583 GeV, a partial wave analysis is performed for the process $e^{+}e^{-} \rightarrow \pi^{+}\pi^{-}J/\psi$. The cross sections for the sub processes ${e^{+}e^{-}\rightarrow\pi^{+}Z_{c}(3900)^{-}+c.c.\rightarro

Satyam Mittal

In this study, we introduce a new single point metaheuristic optimization approach suitable for both continuous and discrete domains. The proposed algorithm, entitled Blindfolded Spiderman Optimization, follows a piecewise linear search trajectory where each line segment considers a move to an improved solution point. The trajectory resembles spiderman jumpi

Gennadiy Feldman

Let $L_1$ and $L_2$ be linear forms of real-valued independent random variables. By Heyde's theorem, if the conditional distribution of $L_2$ given $L_1$ is symmetric, then the random variables are Gaussian. A number of papers are devoted to generalisation of Heyde's theorem to the case, where independent random variables take values in a locally compact Abe

Jie Ying, Zihong Chen, Zhefan Wang, Wanli Jiang

Seed science is essential for modern agriculture, directly influencing crop yields and global food security. However, challenges such as interdisciplinary complexity and high costs with limited returns hinder progress, leading to a shortage of experts and insufficient technological support. While large language models (LLMs) have shown promise across various

Jiafu Wu, Yabiao Wang, Jian Li, Jinlong Peng

Diffusion Transformers (DiTs) achieve remarkable performance within image generation via the transformer architecture. Conventionally, DiTs are constructed by stacking serial isotropic global modeling transformers, which face significant quadratic computational cost. However, through empirical analysis, we find that DiTs do not rely as heavily on global info

Marina Estévez-Almenzar, Ricardo Baeza-Yates, Carlos Castillo

Decision support systems enhanced by Artificial Intelligence (AI) are increasingly being used in high-stakes scenarios where errors or biased outcomes can have significant consequences. In this work, we explore the conditions under which AI-based decision support systems affect the decision accuracy of humans involved in face matching tasks. Previous work su

Minbo Gao, Zhengfeng Ji, Qisheng Wang, Wenjun Yu

We formalize and study the Hamiltonian certification problem. Given access to $e^{-\mathrm{i} Ht}$ for an unknown Hamiltonian $H$, the goal of the problem is to determine whether $H$ is $\varepsilon_1$-close to or $\varepsilon_2$-far from a target Hamiltonian $H_0$. While Hamiltonian learning methods have been extensively studied, they often require restrict

Fabrizio Olmeda, Misha Gupta, Onurcan Bektas, Steffen Rulands

DNA methylation is a primary layer of epigenetic modification that plays a pivotal role in the regulation of development, aging, and cancer. The concurrent activity of opposing enzymes that mediate DNA methylation and demethylation gives rise to a biochemical cycle and active turnover of DNA methylation. While the ensuing biochemical oscillations have been i

Seungjun Oh, Younggeun Lee, Hyejin Jeon, Eunbyung Park

Recent advancements in dynamic 3D scene reconstruction have shown promising results, enabling high-fidelity 3D novel view synthesis with improved temporal consistency. Among these, 4D Gaussian Splatting (4DGS) has emerged as an appealing approach due to its ability to model high-fidelity spatial and temporal variations. However, existing methods suffer from

Zeqian Zhang

This paper explores the unique observational signatures of accretion onto a Janis-Newman-Winicour (JNW) strongly naked singularity, particularly in the absence of a photon sphere. The surrounding spacetime of such a singularity exhibits pronounced reflective properties, causing light rays traveling in its vicinity to undergo reflection and produce paired ima

Yanfeng Yang, Kenji Fukumizu

Creating large-scale datasets for training high-performance generative models is often prohibitively expensive, especially when associated attributes or annotations must be provided. As a result, merging existing datasets has become a common strategy. However, the sets of attributes across datasets are often inconsistent, and their naive concatenation typica

Qingling Shu, Sibao Chen, Xiao Wang, Zhihui You

Accurate detection of road and bridge changes is crucial for urban planning and transportation management, yet presents unique challenges for general change detection (CD). Key difficulties arise from maintaining the continuity of roads and bridges as linear structures and disambiguating visually similar land covers (e.g., road construction vs. bare land). E

Sand. ai, Hansi Teng, Hongyu Jia, Lei Sun

We present MAGI-1, a world model that generates videos by autoregressively predicting a sequence of video chunks, defined as fixed-length segments of consecutive frames. Trained to denoise per-chunk noise that increases monotonically over time, MAGI-1 enables causal temporal modeling and naturally supports streaming generation. It achieves strong performance

Xiaocong Du, Haoyu Pei, Haipeng Zhang

Classical Chinese poetry is a vital and enduring part of Chinese literature, conveying profound emotional resonance. Existing studies analyze sentiment based on textual meanings, overlooking the unique rhythmic and visual features inherent in poetry,especially since it is often recited and accompanied by Chinese paintings. In this work, we propose a dialect-

Yubin Kim, Taehan Kim, Wonjune Kang, Eugene Park

Vocal health plays a crucial role in peoples' lives, significantly impacting their communicative abilities and interactions. However, despite the global prevalence of voice disorders, many lack access to convenient diagnosis and treatment. This paper introduces VocalAgent, an audio large language model (LLM) to address these challenges through vocal health d

A. P. Jones

The nature of the Diffuse Interstellar Band (DIB) carriers is perhaps the most studied, longest standing, unresolved problem in astronomy. While four bands have been associated with the fullerene cation (C^+_60) the vast majority (> 550) remain unidentified. This works is an attempt to provide a conceptual framework for the typical energy transitions that ar

Colin Krawchuk, Nikhil Khatri, Neil John Ortega, Dimitri Kartsaklis

Quantum approaches to natural language processing (NLP) are redefining how linguistic information is represented and processed. While traditional hybrid quantum-classical models rely heavily on classical neural networks, recent advancements propose a novel framework, DisCoCirc, capable of directly encoding entire documents as parameterised quantum circuits (

Hillol Biswas, Sayan Choudhury

We propose periodic driving protocols to realize discrete time crystals (DTCs) in a spin-s central spin model. Interestingly, we identify parameter regimes, where eternal period-doubling and higher-order(HO)-DTCs can be realized, even for finite-sized systems. We have determined the dependence of the DTC order on the number of satellite spins and the central

Valentin Kilian, Benjamin Guedj, François Caron

Completely random measures (CRMs) are fundamental to Bayesian nonparametric models, with applications in clustering, feature allocation, and network analysis. A key quantity of interest is the Laplace exponent, whose asymptotic behavior determines how the random structures scale. When the Laplace exponent grows nearly linearly - known as rapid variation - th

Antonios Athanassiadis, Robert Ariniello, Louis Helary, Luke Hendriks

Recently, advancements in high-intensity laser technology have enabled the exploration of non-perturbative Quantum Electrodynamics (QED) in strong-field regimes. Notable aspects include non-linear Compton scattering and Breit-Wheeler pair production, observable when colliding high-intensity laser pulses and relativistic electron beams. The LUXE experiment at

Lingxiao Li, Yihao Wang, Jiacheng Fan, Jing Li

As foundational tools in natural language processing, Large Language Models (LLMs) have immense parameter scales, which makes deployment and inference increasingly prohibitive, especially in resource-constrained devices. Therefore, knowledge distillation for LLMs, i.e., compressing the LLM to a smaller model, is meaningful. With strong parameter representati

Jikai Wang, Zhenxu Tian, Juntao Li, Qingrong Xia

Recent works have revealed the great potential of speculative decoding in accelerating the autoregressive generation process of large language models. The success of these methods relies on the alignment between draft candidates and the sampled outputs of the target model. Existing methods mainly achieve draft-target alignment with training-based methods, e.

Christopher Lang

The topological space of the stack of $G$-zips can be computed using a refinement process. We extend this refinement process to a more general framework and show that in many situations this process can be used to compute the equivalence classes of a certain equivalence relation, which in the case of $G$-zips is precisely the topological space.

Yuzhen Chen, Hojun Son, Arpan Kusari

Determining material properties from camera images can expand the ability to identify complex objects in indoor environments, which is valuable for consumer robotics applications. To support this, we introduce MatPredict, a dataset that combines the high-quality synthetic objects from Replica dataset with MatSynth dataset's material properties classes - to c

Jorge Alencar, Jean-Guy Caputo, Leonardo de Lima, Arnaud Knippel

A graph is called bivalent or trivalent if there exists an eigenvector of the graph Laplacian composed from {-1,1} or {-1,0,1}, respectively. These bivalent and trivalent eigenvectors are important for engineering applications, in particular for vibrating systems. In this article, we determine the structure of bivalent and trivalent graphs in the following p

Felix Selz, Johanna Kölbel, Felix Paries, Georg von Freymann

Spintronic terahertz emitters (STEs) pumped by femtosecond lasers have become a widely used source of broadband terahertz radiation. However, the strength of the emitted field is limited in part by the optical damage threshold at the pump wavelength. Thermal management of STEs can be improved by understanding electron temperature relaxation in the spintronic

Stephen Zhang, Suryanarayana Maddu, Xiaojie Qiu, Victor Chardès

Time-resolved single-cell omics data offers high-throughput, genome-wide measurements of cellular states, which are instrumental to reverse-engineer the processes underpinning cell fate. Such technologies are inherently destructive, allowing only cross-sectional measurements of the underlying stochastic dynamical system. Furthermore, cells may divide or die

Lili Zhang, Haomiaomiao Wang, Long Cheng, Libao Deng

As Large Language Models (LLMs) become increasingly integrated into real-world decision-making systems, understanding their behavioural vulnerabilities remains a critical challenge for AI safety and alignment. While existing evaluation metrics focus primarily on reasoning accuracy or factual correctness, they often overlook whether LLMs are robust to adversa