March 2025 arXiv papers — page 213

Huiyuan Lai, Xiao Zhang, Malvina Nissim

While Large language models (LLMs) have proved able to address some complex reasoning tasks, we also know that they are highly sensitive to input variation, which can lead to different solution paths and final answers. Answer consistency across input variations can thus be taken as a sign of stronger confidence. Leveraging this insight, we introduce a framew

Zeng-bao Wu, Tao Chen, Quan-guo Zhang, Yue Zeng

This paper considers a new nonlocal fractional differential quasi-variational inequality (NFDQVI) comprising a fractional differential equation with a nonlocal condition and a time-dependent quasi-variational inequality in Hilbert spaces. Qualitative properties of the solution for the time-dependent parameterized quasi-variational inequality are investigated

Emma Schwartzman, Paula Fudolig, Tracy Clarke, Krisina Nyland

Multiple active galactic nuclei (multi-AGN) are a known result of galaxy mergers. Therefore, they are an important tool for studying the formation and dynamical evolution of galaxies and supermassive black holes (SMBHs). A novel method for the selection of multi-AGN leverages the exquisite positional accuracy of Gaia to detect astrometrically-variable quasar

Hai-Long Shi, Li Gan, Kun Zhang, Xiao-Hui Wang

Collective quantum batteries (QBs) demonstrate remarkable acceleration in charging dynamics compared to their individual counterparts, underscoring the pivotal contribution of quantum correlations to advanced energy storage paradigms. A fundamental challenge lies in identifying QBs that exhibit genuine quantum advantages derived from multipartite entanglemen

Ghafran Khan, Patryk Mach

We construct general-relativistic kinetic models of stationary finite accretion disks in the Kerr spacetime. Our analysis generalizes a previous model of razor-thin accretion disks of collisionless gas in the Kerr spacetime, extending to infinity in the equatorial plane. We investigate monoenergetic configurations, as well as models characterized by Maxwell-

Philip Dinenis, Vishwas Rao, Mihai Anitescu

Dynamic downscaling typically involves using numerical weather prediction (NWP) solvers to refine coarse data to higher spatial resolutions. Data-driven models such as FourCastNet have emerged as a promising alternative to the traditional NWP models for forecasting. Once these models are trained, they are capable of delivering forecasts in a few seconds, tho

Johannes Buchner

In this paper we study oscillatory Bianchi models of class A and are able to show that for admissible periodic heteroclinic chains in Bianchi IX there exisist $C^{1}$- stable - manifolds of orbits that follow these chains towards the big bang. A detailed study of Takens Linearization Theorem and the Non-Resonance-Conditions leads us to this new result in Bia

David Serena, William J Buchanan

Working with generating functions, the combinatorics of a recurrence relation can be expressed in a way that allows for more efficient calculation of the quantity. This is true of the Catalan numbers for an ordered binary tree \cite{abboud2018subtree}. Binary tree isomorphism is an important problem in computer science. The enumeration of the number of non-i

Biqiao Xin, Qianchen Mao, Bingshu Wang, Jiangbin Zheng

The widespread deployment of Infrared Small-Target Detection (IRSTD) algorithms on edge devices necessitates the exploration of model compression techniques. Binarized neural networks (BNNs) are distinguished by their exceptional efficiency in model compression. However, the small size of infrared targets introduces stringent precision requirements for the I

Guilherme Machado, Diogo Pacheco, Ronaldo Menezes, Gareth Baxter

We study the effect of the number of users on the activity of communities within the online content sharing and discussion platform Reddit, called subreddits. We found that comment activity on Reddit has a heavy-tailed distribution, where a large fraction of the comments are made by a small set of users. Furthermore, as subreddits grow in size, this behavior

Isaac Aguirre, Ivan Sipiran, Gabriel Montañana

In this paper, we explore a self-supervised model that learns to detect the symmetry of a single object without requiring a dataset-relying solely on the input object itself. We hypothesize that the symmetry of an object can be determined by its intrinsic features, eliminating the need for large datasets during training. Additionally, we design a self-superv

Pengwei Tang, Xiaolin Hu, Yong Liu, Lizhong Ding

Low-Rank Adaptation (LoRA) is the leading parameter-efficient fine-tuning method for Large Language Models (LLMs), but it still suffers from catastrophic forgetting. Recent work has shown that specialized LoRA initialization can alleviate catastrophic forgetting. There are currently two approaches to LoRA initialization aimed at preventing knowledge forgetti

Inge S. Helland

It is argued from several points of view that quantum probabilities might play a role in statistical settings. New approaches toward quantum foundations have postulates that appear to be equally valid in macroscopic settings. One such approach is described here in detail, while one other is briefly sketched. In particular, arguments behind the Born rule, whi

Catherine Zucker, Andrew K. Saydjari, Joshua S. Speagle, Edward F. Schlafly

We present a deep, high-angular resolution 3D dust map of the southern Galactic plane over $239^\circ < \ell < 6^\circ$ and $|b| < 10^\circ$ built on photometry from the DECaPS2 survey, in combination with photometry from VVV, 2MASS, and unWISE and parallaxes from Gaia DR3 where available. To construct the map, we first infer the distance, extinction, and st

Paul Suganthan, Fedor Moiseev, Le Yan, Junru Wu

Decoder-based transformers, while revolutionizing language modeling and scaling to immense sizes, have not completely overtaken encoder-heavy architectures in natural language processing. Specifically, encoder-only models remain dominant in tasks like classification, regression, and ranking. This is primarily due to the inherent structure of decoder-based mo

Hexiang Wan, Jie Xiong

This paper develops a comparison theorem for viscosity solutions of a new class of Hamilton-Jacobi-Bellman (HJB) equations, which is used to solve the separated problem governed by the K-S equation in the Wasserstein space. A distinctive feature of these HJB equations is the simultaneous presence of variational and Lions derivatives, an inevitable consequenc

LiangLiang Shang, Yuanping Wang, Xiaokang Du, Bingfang Yang

The supersymmetric custodial triplet model (SCTM), which is a fully-super\-symmetric generalization of the Georgi-Machacek (GM) model, is constructed by extending the Higgs sector of the minimal supersymmetric standard model by three triplet chiral superfields with hypercharge $Y=0,\pm 1$, in order to maintain the holomorphy of the superpotential and satisfy

Valery Ashu, Zhisong Liu, Heikki Haario, Andreas Rupp

Cellular automata (CA) models are widely used to simulate complex systems with emergent behaviors, but identifying hidden parameters that govern their dynamics remains a significant challenge. This study explores the use of Convolutional Neural Networks (CNN) to identify jump parameters in a two-dimensional CA model. We propose a custom CNN architecture trai

Casper Lassenius, Torgeir Dingsøyr

Agile development relies on self-organizing teams having a high degree of autonomy. For single-team development, more autonomy is generally considered better. In large-scale agile development, where several teams collaborate on the same software with technical and social dependencies, finding the right balance between autonomy and organizational control beco

William Brach, Kristián Košťál, Michal Ries

The exponential growth of unstructured text data presents a fundamental challenge in modern data management and information retrieval. While Large Language Models (LLMs) have shown remarkable capabilities in natural language processing, their potential to transform unstructured text into standardized, structured formats remains largely unexplored - a capabil

Jiehao Chen, Kaidong Zhao, Zihan Liu, YanJie Li

This paper proposes a learning-based passive fault-tolerant control (PFTC) method for quadrotor capable of handling arbitrary single-rotor failures, including conditions ranging from fault-free to complete rotor failure, without requiring any rotor fault information or controller switching. Unlike existing methods that treat rotor faults as disturbances and

Arpan Akash Ray, Boris Škorić

We propose the first continuous-variable (CV) unclonable encryption scheme, extending the paradigm of quantum encryption of classical messages (QECM) to CV systems. In our construction, a classical message is first encrypted classically and then encoded using an errorcorrecting code. Each bit of the codeword is mapped to a CV mode by creating a coherent stat

Zhiyuan Yu, Hong Ren, Cunhua Pan, Gui Zhou

Uplink integrated sensing and communication (ISAC) systems have recently emerged as a promising research direction, enabling simultaneous uplink signal detection and target sensing. {In this paper, we propose the flexible projection (FP)-type receiver that unifies the projection-type receiver and the successive interference cancellation (SIC)-type receiver b

Wei Zhang, Kun Chen, Su Yi, Tao Shi

We investigate the finite-temperature phase diagram of polar molecules shielded by dual microwave fields using the path integral Monte Carlo method combined with the worm algorithm. We determine the critical temperature $T_c$ for Bose-Einstein condensations (BECs) and identify two distinct phases below $T_c$: the expanding gas (EG) phase and the self-bound g

Timo Gierlich, Andreas Baumbach, Akos F. Kungl, Kevin Max

In both machine learning and in computational neuroscience, plasticity in functional neural networks is frequently expressed as gradient descent on a cost. Often, this imposes symmetry constraints that are difficult to reconcile with local computation, as is required for biological networks or neuromorphic hardware. For example, wake-sleep learning in networ

Gaoping Long, Cong Zhang, Hongguang Liu

In this article, the quantum representation of the algebra among reduced twisted geometries (with respect to the Gauss constraint) is constructed in the gauge invariant Hilbert space of loop quantum gravity. It is shown that the reduced twisted geometric variables not only describe the spatial discrete geometry more clearly, but also form a simple Poisson al

Sambhu Radhakrishnan, Flip de Jong, Estelle Becquevort, Olivier Deschaume

Nucleation, the pivotal first step of crystallization, governs essential characteristics of crystallization products, including size distribution, morphology, and polymorphism. While understanding this process is paramount to the design of chemical, pharmaceutical and industrial production processes, major knowledge gaps remain, especially with respect to th

Yunfan Zhou, Xiwen Cai, Qiming Shi, Yanwei Huang

Data analysts frequently employ code completion tools in writing custom scripts to tackle complex tabular data wrangling tasks. However, existing tools do not sufficiently link the data contexts such as schemas and values with the code being edited. This not only leads to poor code suggestions, but also frequent interruptions in coding processes as users nee

Marius Paicu, Tianyuan Yu, Ning Zhu

In this paper, we study the hydrostatic approximation for the 3D Oldroyd-B model. Firstly, we derive the hydrostatic approximate system for this model and prove the global well-posedness of the limit system with small analytic initial data in horizontal variable. Then we justify the hydrostatic limit strictly from the re-scaled Oldroyd-B model to the hydrost

Laura Koesten, Antonia Saske, Sandra Starchenko, Kathleen Gregory

Crisis maps are regarded as crucial tools in crisis communication, as demonstrated during the COVID-19 pandemic and climate change crises. However, there is limited understanding of how public audiences engage with these maps and extract essential information. Our study investigates the sensemaking of young, digitally native viewers as they interact with cri

Sarif Khan, Hyun Min Lee

We propose a novel $U(1)_{B-L}$ model with singlet dark matter fermions composed of WIMP and FIMP, which is anomaly-free without a need for introducing right-handed neutrinos. Fermion dark matter masses are generated after the $U(1)_{B-L}$ is broken spontaneously, so the Yukawa couplings for WIMP and FIMP components can be distinguished by the hierarchical v

Haiwen Wu, Bayu Jayawardhana, Dabo Xu

This paper addresses the problem of task-space robust regulation of robot manipulators subject to external disturbances. A velocity-free control law is proposed by combining the internal model principle and the passivity-based output-feedback control approach. The resulting controller not only ensures asymptotic convergence of the regulation error but also r

George M. Ferguson, Run Xiao, Anthony R. Richardella, Austin Kaczmarek

The creation of topologically non-trivial matter across electronic, mechanical, cold-atom, and photonic platforms is advancing rapidly, yet understanding the breakdown of topological protection remains a major challenge. In this work, we use magnetic imaging combined with global electrical transport measurements to visualize the current-induced breakdown of

Max Weissenbacher, Herbert Koch, Roland Donninger

The wave maps equation in three spatial dimensions with a spherical target admits an explicit blow-up solution. Numerical studies suggest this solution captures the generic blow-up behaviour in the backward light cone of the singularity. In this work, we establish the mode stability of this blow-up solution in the backward light cone of the blow-up point wit

Haotian Li, Yun Wang, Huamin Qu

Human-AI collaborative tools attract attentions from the data storytelling community to lower the expertise barrier and streamline the workflow. The recent advance in large-scale generative AI techniques, e.g., large language models (LLMs) and text-to-image models, has the potential to enhance data storytelling with their power in visual and narration genera

Mingyu Pu, Songhao Wang, Haowei Wang, Szu Hui Ng

Gaussian Process (GP) models are widely utilized as surrogate models in scientific and engineering fields. However, standard GP models are limited to continuous variables due to the difficulties in establishing correlation structures for categorical variables. To overcome this limitati on, we introduce WEighted Euclidean distance matrices Gaussian Process (W

Qian Chen, Yanlin Bai, Xiaohan Wang, Peipei Peng

In this Letter, we employ the complex screen method to investigate the dynamic evolution of partially coherent pulses with specified properties as they propagate through a nonlinear Kerr medium. Our results reveal that partially coherent pulses can retain stable pulse characteristics and exhibit enhanced robustness when the source coherence is reduced. Impor

Wenxuan Liu, Zixuan Li, Long Bai, Yuxin Zuo

Developing a general-purpose extraction system that can extract events with massive types is a long-standing target in Event Extraction (EE). In doing so, the challenge comes from two aspects: 1) The absence of an efficient and effective annotation method. 2) The absence of a powerful extraction method can handle massive types. For the first challenge, we pr

Gabriel Mastrilli

We consider the hyperuniform model of d-dimensional integer lattice perturbed by independent random variables and we investigate the large scale asymptotic fluctuations of smoothed versions of the usual counting statistics, specifically of linear statistics associated to a smooth function with rapid decay at infinity. We highlight three distinct classes of l

Han Gil Choi, Pavel Petrov, Masahide Yamaguchi

We present a minimal setup within the framework of Horndeski gravity that can describe a nonpathological Genesis scenario. Our setup allows for a fully stable transition to the kination epoch, during which General Relativity (GR) is restored. This Genesis scenario circumvents the no-go theorem at the cost of encountering the risk of strong coupling in the pa

Kai-Robin Lange, Niklas Benner, Lars Grönberg, Aymane Hachcham

Text data is inherently temporal. The meaning of words and phrases changes over time, and the context in which they are used is constantly evolving. This is not just true for social media data, where the language used is rapidly influenced by current events, memes and trends, but also for journalistic, economic or political text data. Most NLP techniques how

Yang Li, Shijie Yuan, Yuan Chang, Xiaolong Chen

Most reinforcement learning (RL) approaches for the decision-making of autonomous driving consider safety as a reward instead of a cost, which makes it hard to balance the tradeoff between safety and other objectives. Human risk preference has also rarely been incorporated, and the trained policy might be either conservative or aggressive for users. To this

David Bani-Harouni, Chantal Pellegrini, Paul Stangel, Ege Özsoy

A safe and trustworthy use of Large Language Models (LLMs) requires an accurate expression of confidence in their answers. We propose a novel Reinforcement Learning approach that allows to directly fine-tune LLMs to express calibrated confidence estimates alongside their answers to factual questions. Our method optimizes a reward based on the logarithmic sco

Amirmohammad Chegeni, Fatemeh Fazel Hesar, Mojtaba Raouf, Bernard Foing

Distinguishing galaxies as either fast or slow rotators plays a vital role in understanding the processes behind galaxy formation and evolution. Standard techniques, which are based on the $\lambda_R$-spin parameter obtained from stellar kinematics, frequently face difficulties to classify fast and slow rotators accurately. These challenges arise particularl

Adrian Atienza, Gouthamaan Manimaran, Sadasivan Puthusserypady, Helena Dominguez

The integration of Artificial Intelligence (AI) into clinical research has great potential to reveal patterns that are difficult for humans to detect, creating impactful connections between inputs and clinical outcomes. However, these methods often require large amounts of labeled data, which can be difficult to obtain in healthcare due to strict privacy law

Stijn N. J. Schepers, Jeroen A. van Oijen

Ultra-lean premixed hydrogen combustion is a possible solution to decarbonize industry, while limiting flame temperatures and thus nitrous oxide emissions. These lean hydrogen/air flames experience strong preferential diffusion effects, which result in thermo-diffusive (TD) instabilities. To efficiently and accurately model lean premixed hydrogen flames, it

Tao Yang, Yang Hu, Feihong Lu, Ziwei Zhang

Social bots have become widely known by users of social platforms. To prevent social bots from spreading harmful speech, many novel bot detections are proposed. However, with the evolution of social bots, detection methods struggle to give high-confidence answers for samples. This motivates us to quantify the uncertainty of the outputs, informing the confide

Xiaoyu Zheng, Xu Chen, Shaogang Gong, Xavier Griffin

Compared to single view medical image classification, using multiple views can significantly enhance predictive accuracy as it can account for the complementarity of each view while leveraging correlations between views. Existing multi-view approaches typically employ separate convolutional or transformer branches combined with simplistic feature fusion stra

Jan-Matthis Lueckmann, Alexander Immer, Alex Bo-Yuan Chen, Peter H. Li

Data-driven benchmarks have led to significant progress in key scientific modeling domains including weather and structural biology. Here, we introduce the Zebrafish Activity Prediction Benchmark (ZAPBench) to measure progress on the problem of predicting cellular-resolution neural activity throughout an entire vertebrate brain. The benchmark is based on a n

Ronald L. Westra

Ion channels selectively transport ions, yet the underlying mechanisms remain elusive. We propose a physical model based on the Driven Damped Harmonic Oscillator (DDHO), where self-organizing turbulent structures in the ionic flow generate oscillating pressure waves and toroidal vortices. These structures drive aqua-ions into resonance, facilitating the shed

Tsvetomila Mihaylova, Stefan Reitmann, Elin A. Topp, Ville Kyrki

Simulation of conflict situations for autonomous driving research is crucial for understanding and managing interactions between Automated Vehicles (AVs) and human drivers. This paper presents a set of exemplary conflict scenarios in CARLA that arise in shared autonomy settings, where both AVs and human drivers must navigate complex traffic environments. We

Zirun Guo, Tao Jin

Test-Time Adaptation (TTA) aims to tackle distribution shifts using unlabeled test data without access to the source data. In the context of multimodal data, there are more complex noise patterns than unimodal data such as simultaneous corruptions for multiple modalities and missing modalities. Besides, in real-world applications, corruptions from different

Pintu Bhunia

Suppose $\mathcal{H}_1, \mathcal{H}_2, \ldots, \mathcal{H}_n$ are arbitrary complex Hilbert spaces, and ${\bf A}=[A_{ij}]$ is an $n\times n$ operator matrix with $A_{ij}\in \mathcal{B}(\mathcal{H}_j, \mathcal{H}_i).$ We show that $w({\bf A}) \leq w\left(\begin{bmatrix} a_{ij} \end{bmatrix}_{i,j=1}^n \right),$ where $w(\cdot)$ denotes the numerical radius and

Yiyan Xu, Jinghao Zhang, Alireza Salemi, Xinting Hu

In the era of large models, content generation is gradually shifting to Personalized Generation (PGen), tailoring content to individual preferences and needs. This paper presents the first comprehensive survey on PGen, investigating existing research in this rapidly growing field. We conceptualize PGen from a unified perspective, systematically formalizing i

Shiri Artstein-Avidan, Arnon Chor, Dan Florentin

Complementing our previous results, we give a classification of all isometries (not necessarily surjective) of the metric space consisting of ball-bodies, endowed with the Hausdorff metric. "Ball bodies" are convex bodies which are intersections of translates of the Euclidean unit ball. We show that any such isometry is either a rigid motion, or a rigid moti

Wuzhou Sun, Siyi Li, Qingxiang Zou, Zixing Liao

In some game scenarios, due to the uncertainty of the number of enemy units and the priority of various attributes, the evaluation of the threat level of enemy units as well as the screening has been a challenging research topic, and the core difficulty lies in how to reasonably set the priority of different attributes in order to achieve quantitative evalua

Parker Knight, Ndey Isatou Jobe, Rui Duan

Statistical integration of diverse data sources is an essential step in the building of generalizable prediction tools, especially in precision health. The invariant features model is a new paradigm for multi-source data integration which posits that a small number of covariates affect the outcome identically across all possible environments. Existing method

Jadson Santos, Daniel Alencar da Costa, Uirá Kulesza

In this paper, we study the benefits and challenges of monitoring Continuous Integration (CI) practices in software development. Our aim is to evaluate the impact of monitoring seven CI practices in industry using three organizations in Brazil as case studies. We developed a tool for monitoring CI practices and conducted a multiple case study, applying a mix

Tianyu Jia, Zongxia Xie, Yanru Sun, Dilfira Kudrat

Non-stationarity is an intrinsic property of real-world time series and plays a crucial role in time series forecasting. Previous studies primarily adopt instance normalization to attenuate the non-stationarity of original series for better predictability. However, instance normalization that directly removes the inherent non-stationarity can lead to three i

Marcello Traiola, Nazar Misyats, Silviu-Ioan Filip, Remi Garcia

Error-tolerant applications, such as multimedia processing, machine learning, signal processing, and scientific computing, can produce satisfactory outputs even when approximate computations are performed. Approximate computing (AxC) is nowadays a well-established design and computing paradigm that produces more efficient computation systems by judiciously r

Andrew L. Miller

Various theories of dark matter predict distinctive astrophysical signatures in gravitational-wave sources that could be observed by ground- and space-based laser interferometers. Different candidates-including axions, dark photons, macroscopic dark matter, WIMPs, and dark-matter spikes-may appear in interferometer data via their coupling to gravity or the S

Adam Hartshorne, Allen Paul, Tony Shardlow, Neill D. F. Campbell

This work presents a unified framework for the unsupervised prediction of physically plausible interpolations between two 3D articulated shapes and the automatic estimation of dense correspondence between them. Interpolation is modelled as a diffeomorphic transformation using a smooth, time-varying flow field governed by Neural Ordinary Differential Equation

Yongjie Deng, Xu-Hui Jiang, Tianbo Liu, Bin Yan

Measurements of $b\to c\tau^-\bar\nu_\tau$ transitions at colliders are highly motivated for testing lepton flavor universality (LFU), a cornerstone hypothesis of the Standard Model (SM). Potential observations of LFU violation could provide significant evidence for physics beyond the SM (BSM). The substantial production of $b$-hadrons at the Electron-Ion Co

Dimitrios Gazoulis

In this work we study the level sets of entire solutions of the Allen-Cahn equation and we prove minimality of the zero level set with respect to a certain perimeter functional with density. This provides a direct relationship between phase transition type problems and minimal surfaces with some weight. In addition, we obtain that the zero level set of entir

Yuyan Ni, Shikun Feng, Haohan Chi, Bowen Zheng

Diffusion-based models have shown great promise in molecular generation but often require a large number of sampling steps to generate valid samples. In this paper, we introduce a novel Straight-Line Diffusion Model (SLDM) to tackle this problem, by formulating the diffusion process to follow a linear trajectory. The proposed process aligns well with the noi

Yulong Hui, Yihao Liu, Yao Lu, Huanchen Zhang

Large Language Models (LLMs) encounter challenges in efficiently processing long-text queries, as seen in applications like enterprise document analysis and financial report comprehension. While conventional solutions employ long-context processing or Retrieval-Augmented Generation (RAG), they suffer from prohibitive input expenses or incomplete information.

Huijun Hou, Qingguo Li

Inspired by Zhao and Xu's study on which a dcpo can be determined by its Scott closed subsets lattice, we further investigate whether a poset (or dcpo) $P$ is able to be determined by the family $\mathcal Q(P)$ of its Scott compact saturated subsets, in the sense that the isomorphism between $(\mathcal Q(P), \supseteq)$ and $(\mathcal Q(M), \supseteq)$ impli

Adam Parkosidis, Dimitris Stamatellos, Basmah Riaz

There is evidence that stars and browns dwarfs grow through episodic rather than continuous gas accretion. However, the role of episodic accretion in the formation of brown dwarfs remains mostly unexplored. We investigate the role of episodic accretion, triggered by the magnetorotational instability in the inner disk regions, resulting in episodic outbursts

Yizhou Huang, Fan Yang, Guoliang Zhu, Gen Li

Affordance refers to the functional properties that an agent perceives and utilizes from its environment, and is key perceptual information required for robots to perform actions. This information is rich and multimodal in nature. Existing multimodal affordance methods face limitations in extracting useful information, mainly due to simple structural designs

Shiri Artstein-Avidan, Arnon Chor, Dan Florentin

We characterize the surjective isometries, with respect to the Hausdorff distance, of the class of bodies given by intersections of Euclidean unit balls. We show that any such isometry is given by the composition of a rigid motion with either the identity or the c-duality mapping, which maps a body in this class to the intersection of Euclidean unit balls ce

Yi Cheng, Shuo Tian, Bin Li, Yejing Dai

Since 2022, large apparent strains (>1%) with highly asymmetrical strain-electric field (S-E) curves have been reported in various thin piezoceramic materials, attributed to a bidirectional electric-field-induced bending (electrobending) deformation, which consistently produces convex bending along the negative electric field direction. In this study, we rep

Enrico Pasqualetto, Janne Taipalus

We investigate the first-order differential calculus over extended metric-topological measure spaces. The latter are quartets $\mathbb X=(X,\tau,{\sf d},\mathfrak m)$, given by an extended metric space $(X,{\sf d})$ together with a weaker topology $\tau$ (satisfying suitable compatibility conditions) and a finite Radon measure $\mathfrak m$ on $(X,\tau)$. Th

Zhaoxing Gan, Mengtian Li, Ruhua Chen, Zhongxia Ji

In this work, we introduce StageDesigner, the first comprehensive framework for artistic stage generation using large language models combined with layout-controlled diffusion models. Given the professional requirements of stage scenography, StageDesigner simulates the workflows of seasoned artists to generate immersive 3D stage scenes. Specifically, our app

Dipranjan Pal, Kumarjit Saha

We investigate the $\ell_{\infty}$ \textit{ directed spanning forest} (DSF), a directed forest whose vertex set is given by a homogeneous Poisson point process, in which each Poisson point connects to the nearest Poisson point, measured in $\ell_{\infty}$ distance, with a strictly larger $y$- coordinate. In this paper, we prove that the two-dimensional $\ell

Yanlong Xu, Haoxuan Qu, Jun Liu, Wenxiao Zhang

The goal of point cloud localization based on linguistic description is to identify a 3D position using textual description in large urban environments, which has potential applications in various fields, such as determining the location for vehicle pickup or goods delivery. Ideally, for a textual description and its corresponding 3D location, the objects ar

Paul Duetting, Michal Feldman, Yarden Rashti

Real-world contracts are often ambiguous. While recent work by D\"utting, Feldman, Peretz, and Samuelson (EC 2023, Econometrica 2024) demonstrates that ambiguous contracts can yield large gains for the principal, their optimal solutions often require deploying an impractically large menu of contracts. This paper investigates \emph{succinct} ambiguous contrac

Salvatore Macis, Annalisa DArco, Eugenio Del Re, Lorenzo Mosesso

Hyperbolic materials exhibit a very peculiar optical anisotropy with simultaneously different signs of the dielectric tensor components. This anisotropy allows the propagation of exotic surface-wave excitations like hyperbolic phonons and plasmon polaritons. While hyperbolic materials hold promise for applications in subwavelength photonics and enhanced ligh

ZhongYi Zhang, ChenYang Xu, LiXuan Zhao, HuiRang Hou

Electroencephalogram (EEG) is a non-invasive tool for real-time neural monitoring,widely used in depression detection via deep learning. However, existing models primarily focus on binary classification (depression/normal), lacking granularity for severity assessment. To address this, we proposed the DepL-GCN, i.e., Depression Level classification based on G

Felipe W. Cruz, César J. Niche, Cilon F. Perusato, Marko Rojas-Medar

We establish new asymptotic results for the solutions of the second-grade fluids equations and characterize their decay rate in terms of the behavior of the initial data. Moreover, assuming more regularity for the initial data, we study the large-time behavior of these solutions by comparing them to the solutions of the linearized equations. As a consequence

Caiyu Hu, Yikai Zhang, Tinghui Zhu, Yiwei Ye

Multimodal Large Language Models (MLLMs) have advanced in integrating diverse modalities but frequently suffer from hallucination. A promising solution to mitigate this issue is to generate text with citations, providing a transparent chain for verification. However, existing work primarily focuses on generating citations for text-only content, leaving the c

Yuanyang Yu, Butian Zhang, Shengxin Lin, Jianping Liang

For space-borne gravitational wave detectors,such as LISA and TianQin ,the disturbance caused by the coupling of test masses and the external magnetic fields is one of the main sources of the residual acceleration noise. Although the detection frequency band is from 0.1 mHz to 1 Hz, magnetic fields with frequencies higher than 1 Hz can still contribute to th

Piotr Koczy, Michael C. Welle, Danica Kragic

We present a framework for learning dexterous in-hand manipulation with multifingered hands using visuomotor diffusion policies. Our system enables complex in-hand manipulation tasks, such as unscrewing a bottle lid with one hand, by leveraging a fast and responsive teleoperation setup for the four-fingered Allegro Hand. We collect high-quality expert demons

Nour Alnajjarine, Michel Lavrauw

An $\mathbb{F}_q$-linear code of minimum distance $d$ is called complete if it is not contained in a larger $\mathbb{F}_q$-linear code of minimum distance $d$. In this paper, we classify $\mathbb{F}_q$-linear complete symmetric rank-distance (CSRD) codes in $M_{3\times 3}(\mathbb{F}_q)$ up to equivalence. This includes the classification of $\mathbb{F}_q$-li

Patryk Marszałek, Maciej Rut, Piotr Kawa, Przemysław Spurek

Implicit neural representations (INR) have gained prominence for efficiently encoding multimedia data, yet their applications in audio signals remain limited. This study introduces the Kolmogorov-Arnold Network (KAN), a novel architecture using learnable activation functions, as an effective INR model for audio representation. KAN demonstrates superior perce

Rui Ma, Mengfang Liu, Boliang Li, Xinghui Li

In view of the problems that visual simultaneous localization and mapping (VSLAM) are susceptible to environmental light interference and luminosity inconsistency, the visual simultaneous localization and mapping technology based on near infrared perception (NIR-VSLAM) is proposed. In order to avoid ambient light interference, the near infrared light is inno

Paweł Teisseyre, Jan Mielniczuk

In many practical applications of machine learning, a discrepancy often arises between a source distribution from which labeled training examples are drawn and a target distribution for which only unlabeled data is observed. Traditionally, two main scenarios have been considered to address this issue: covariate shift (CS), where only the marginal distributio

Hassan S. Al Khatib, Subash Neupane, Sudip Mittal, Shahram Rahimi

The healthcare industry is moving towards a patient-centric paradigm that requires advanced methods for managing and representing patient data. This paper presents a Patient Journey Ontology (PJO), a framework that aims to capture the entirety of a patient's healthcare encounters. Utilizing ontologies, the PJO integrates different patient data sources like m

Alicia Vidler, Toby Walsh

Playing games has a long history of describing intricate interactions in simplified forms. In this paper we explore if large language models (LLMs) can play games, investigating their capabilities for randomisation and strategic adaptation through both simultaneous and sequential game interactions. We focus on GPT-4o-Mini-2024-08-17 and test two games betwee

Jiayi Zhao, Fei Teng, Kai Luo, Guoqiang Zhao

The perception capability of robotic systems relies on the richness of the dataset. Although Segment Anything Model 2 (SAM2), trained on large datasets, demonstrates strong perception potential in perception tasks, its inherent training paradigm prevents it from being suitable for RGB-T tasks. To address these challenges, we propose SHIFNet, a novel SAM2-dri

F. Eppel, M. Krumpe, P. Limaye, N. Intrarat

We report on multiwavelength observations of FRB 20240114A, a nearby (z=0.13), hyperactive, repeating fast radio burst that was discovered in January 2024. We performed simultaneous observations of the source with the Effelsberg 100-m radio telescope, the Thai National Radio Telescope, the Astropeiler Stockert, and the X-ray satellite XMM-Newton in May 2024.

Ege Özsoy, Chantal Pellegrini, Tobias Czempiel, Felix Tristram

Operating rooms (ORs) are complex, high-stakes environments requiring precise understanding of interactions among medical staff, tools, and equipment for enhancing surgical assistance, situational awareness, and patient safety. Current datasets fall short in scale, realism and do not capture the multimodal nature of OR scenes, limiting progress in OR modelin

Xinying Hong, Siyu Li, Kang Zeng, Hao Shi

Bird's Eye View (BEV) perception technology is crucial for autonomous driving, as it generates top-down 2D maps for environment perception, navigation, and decision-making. Nevertheless, the majority of current BEV map generation studies focusing on visual map generation lack depth-aware reasoning capabilities. They exhibit limited efficacy in managing occlu

Boseong Jeon

This paper presents a test-time guidance method to improve the output quality of the human motion diffusion models without requiring additional training. To have negative guidance, Smooth Perturbation Guidance (SPG) builds a weak model by temporally smoothing the motion in the denoising steps. Compared to model-agnostic methods originating from the image gen

G. T. Adamashvili

Two different versions of cubic sixth-order generalised Boussinesq-type wave equations are considered in this study. A generalised perturbation reduction method is used to solve these equations, which allows the reduction of considered equations to coupled nonlinear Schrodinger equations. Two-component nonlinear wave solutions are obtained. The profiles and

Maria J. Esteban

In this paper we describe the work of Luis Caffarelli in the area of fluid mechanics and related topics. Not only has his work on fluid mechanics been very influential, but many of his contributions that do not directly relate to fluid mechanics, such as his important results on the fractional Laplacian or the regularity of solutions to linear parabolic equa

Zi-Chang Zhang, Hai-Chao Yuan, Yong Tang

The distribution of dark matter at the galactic center, crucial for indirect searches, remains uncertain. In particular, in the vicinity of the massive black hole in the center of a galaxy where indirect signals may be stronger, the density of a dark matter spike may undergo redistribution. Here we calculate the density surrounding Schwarzschild black holes

Valerii Serpiva, Artem Lykov, Artyom Myshlyaev, Muhammad Haris Khan

RaceVLA presents an innovative approach for autonomous racing drone navigation by leveraging Visual-Language-Action (VLA) to emulate human-like behavior. This research explores the integration of advanced algorithms that enable drones to adapt their navigation strategies based on real-time environmental feedback, mimicking the decision-making processes of hu

Hannah Selder, Florian Fischer, Per Ola Kristensson, Arthur Fleig

Biomechanical models allow for diverse simulations of user movements in interaction. Their performance depends critically on the careful design of reward functions, yet the interplay between reward components and emergent behaviours remains poorly understood. We investigate what makes a model "breathe" by systematically analysing the impact of rewarding effo

Masahiro Ikeda, César J. Niche, Gabriela Planas

We study the long-time behaviour of solutions to the Hardy-Sobolev parabolic equation in critical function spaces for any spatial dimension $d \geq 5$. By employing the Fourier splitting method, we establish precise decay rates for dissipative solutions, meaning those whose critical norm vanishes as time approaches infinity. Our findings offer a deeper under

R. M. Zaalberg, L. B. Andersen, S. J. Noel, A. J. Buitenhuis

Black soldier fly (Hermetia illucens) larvae are emerging as a sustainable protein source for animal feed and human nutrition. Ensuring consistent amino acid composition is crucial for quality control, necessitating rapid, non-destructive assessment methods, particularly for selective breeding. This study validates near-infrared (NIR) spectroscopy with parti