March 2025 arXiv papers — page 123

Faizuddin Ahmed, Ahmad Al-Badawi, İzzet Sakallı

In this study, we investigate the geodesic structure, gravitational lensing/mirroring phenomena, and scalar perturbations of deformed AdS-Schwarzschild black holes with global monopoles, incorporating both ordinary and phantom configurations. We introduce a modified black hole metric characterized by a deformation parameter $\alpha$, a control parameter $\be

Michael Zwilich, Jan Wichmann, Carsten Fallnich

Transverse mode-locked (TML) beams exhibit high-speed beam scanning, which motivates a comparison with established beam deflection technologies, such as galvanometer and voice coil scanners. This study explores the hypothesis that TML beams can be regarded as high-speed equivalents of such periodically deflected beams. By analytically modeling the spatiotemp

Ron Shnapp

Lagrangian particles in turbulence separate away from each other faster in the backward in time direction as compared to forward in time. In this work, we show that time irreversibility is kinematically rooted in the fact that, when viewed backward in time, the alignment of particles' relative velocities is better than when viewed forward in time.

Jameel-Un Nabi, Wajeeha Khalid, Abdul Kabir, Syeda Anmol Rida

We use the relativistic mean field (RMF) model to conduct a thorough analysis of the ground-state properties of $^{67\text{--}80}$Ge nuclei. Binding energies and neutron skin thicknesses are computed for a total of 14 neutron-rich Ge isotopes. This study provides a comprehensive overview of the RMF model's explanation of nuclear ground-state properties. Furt

Kirill A. Bronnikov, Milena V. Skvortsova

Alexei Starobinsky is most famous for his great contribution to cosmology, but he has considerable achievements in other branches of gravitational physics and astrophysics, such as the theory of compact objects including black holes and wormholes. In this note, we give a brief review of Alexei's papers devoted to wormhole physics. They mostly concern such is

Gino E. Jansen, Mark J. Schuuring, Berto J. Bouma, Ivana Išgum

This work presents a novel approach to achieving temporally consistent mitral annulus landmark localization in echocardiography videos using sparse annotations. Our method introduces a self-supervised loss term that enforces temporal consistency between neighboring frames, which smooths the position of landmarks and enhances measurement accuracy over time. A

Rui Qian, Chenyangguang Zhang, Yan Di, Guangyao Zhai

Neural Radiance Fields (NeRF) have exhibited highly effective performance for photorealistic novel view synthesis recently. However, the key limitation it meets is the reliance on a hand-crafted frequency annealing strategy to recover 3D scenes with imperfect camera poses. The strategy exploits a temporal low-pass filter to guarantee convergence while decele

Matteo Cercola, Nicola Gatti, Pedro Huertas Leyva, Benedetto Carambia

Effective traffic incident management is essential for ensuring safety, minimizing congestion, and reducing response times in emergency situations. Traditional highway incident management relies heavily on radio room operators, who must make rapid, informed decisions in high-stakes environments. This paper proposes an innovative solution to support and enhan

Omri Isac, Idan Refaeli, Haoze Wu, Clark Barrett

Current Deep Neural Network (DNN) verifiers are typically designed to prioritize scalability over reliability. Reliability can be reinforced through the generation of proofs that are checkable by trusted, external proof checkers. To date, only a handful of verifiers support proof production; and these rely on verifier-specific formats, and balance between sc

Matthew Nicoletti

We analyze asymptotic height function fluctuations in uniformly random domino tiling models on multiply connected Temperleyan domains. Starting from asymptotic formulas derived by Kenyon [arXiv:math-ph/9910002v1], we show that (1) the difference of the centered height function and a harmonic function with boundary values given by the (random) centered hole h

Jose A. Carrillo, Bin Li, Li Xie

This paper is concerned with the Dirichlet initial-boundary value problem of a 2-D parabolic-elliptic system proposed to model the formation of biological transport networks. Even if global weak solutions for this system are known to exist, how to improve the regularity of weak solutions is a challenging problem due to the peculiar cubic nonlinearity and the

Nicola Milano, Michela Ponticorvo, Davide Marocco

In this article we explore the application of Large Language Models (LLMs) in assessing the content validity of psychometric instruments, focusing on the Big Five Questionnaire (BFQ) and Big Five Inventory (BFI). Content validity, a cornerstone of test construction, ensures that psychological measures adequately cover their intended constructs. Using both hu

Yubo Zhang, Zirui Dong, Jun Luo

Heusler alloys, renowned for their multifunctionality and capacity for vast elemental customization, are primarily classified into half-Heusler (XYZ) and full-Heusler (X2YZ) structural types. Typically, the 18-electron half-Heusler and the 24-electron full-Heusler alloys are recognized as semiconductors, following the Slater-Pauling rule. Semiconductors are

Jin Kim, Byunghwee Lee, Taekho You, Jinhyuk Yun

The rise of multimodal generative AI is transforming the intersection of technology and art, offering deeper insights into large-scale artwork. Although its creative capabilities have been widely explored, its potential to represent artwork in latent spaces remains underexamined. We use cutting-edge generative AI, specifically Stable Diffusion, to analyze 50

Hanyuan Jiang, Yuxiang Zhang, Yameng Liu, Jianhua Zhang

Integrated Sensing and Communication (ISAC), as a fundamental technology of 6G, empowers Vehicle-to-Everything (V2X) systems with enhanced sensing capabilities. One of its promising applications is the reliance on constructed maps for vehicle positioning. Traditional positioning methods primarily rely on Line-of-Sight (LOS), but in urban vehicular scenarios,

Zhengrong Yue, Shaobin Zhuang, Kunchang Li, Yanbo Ding

Despite the recent advancement in video stylization, most existing methods struggle to render any video with complex transitions, based on an open style description of user query. To fill this gap, we introduce a generic multi-agent system for video stylization, V-Stylist, by a novel collaboration and reflection paradigm of multi-modal large language models.

N. Bruckmoser, L. Koch, I. Tsitsilin, M. Grammer

Scaling up superconducting quantum processors requires a high routing density for readout and control lines, relying on low-loss interconnects to maintain design flexibility and device performance. We propose and demonstrate a universal subtractive fabrication process for air bridges based on an aluminum hard mask and niobium as the superconducting film. Usi

Yang Li

We develop a structure theory for the limit of $SU(2)$ $G_2$-monopoles (resp. Calabi-Yau monopoles) on a principal $SU(2)$-bundle over an asymptotically conical $G_2$-manifolds (resp. Calabi-Yau 3-folds) as the mass parameter tends to infinity, while the topologial data for the bundle stays fixed. We show how to extract a singular abelian $G_2$-monopole (res

Marco Laudato, Luca Manzari, Khemraj Shukla

Thrombosis involves processes spanning large-scale fluid flow to sub-cellular events such as platelet activation. Traditional CFD approaches often treat blood as a continuum, which can limit their ability to capture these microscale phenomena. In this paper, we introduce a neural operator-based surrogate model to bridge this gap. Our approach employs DeepONe

Thomas G. Bisbas, Zhi-Yu Zhang, Maria-Christina Kyrmanidou, Gan Luo

Understanding the molecular gas content in the interstellar medium (ISM) is crucial for studying star formation and galaxy evolution. The CO-to-H$_2$ ($X_{\rm CO}$) and the [CI]-to-H$_2$ ($X_{\rm CI}$) conversion factors are widely used to estimate the molecular mass content in galaxies. However, these factors depend on many ISM environmental parameters. Thi

Abhilasha Ravichander, Jillian Fisher, Taylor Sorensen, Ximing Lu

High-quality training data has proven crucial for developing performant large language models (LLMs). However, commercial LLM providers disclose few, if any, details about the data used for training. This lack of transparency creates multiple challenges: it limits external oversight and inspection of LLMs for issues such as copyright infringement, it undermi

Raju Kumar Yadav, Prince Philip, Boddepalli SanthiBhushan

While doping and defects are often considered detrimental to material performance, at the nanoscale, modifications are needed to create novel properties beneficial for device applications. In this work, we focus on optimizing graphene as a gas sensor for detecting toxic gases such as nitrogen oxide (NO). The study explores the effects of doping graphene shee

Aniruddha Chakraborty, Md. Fahim F. Chowdhury, Mohamad Niknam, Louis S. Bouchard

We show that Heisenberg exchange interactions between the neighboring spins comprising an ensemble spin qubit (E-qubit) can act as an intrinsic error mitigator, increasing gate fidelity even at high temperatures. As an example, the fidelity of a {\pi} gate applied to E-qubits above 1 K was studied by tuning the ferromagnetic exchange strength to show an exch

Etienne Lein, Cécile Aprili, Jayaditya Deep, Isaac Kazuo Uyehara

This work builds on a rich discourse surrounding the social brain hypothesis as well as the definition and quantification of social complexity. We believe this manuscript provides a new perspective on the topic by pairing a conceptual framework with an evolutionary model. This treatment of the topic allows us to analyse the social brain hypothesis with a foc

Tie-Jun Gao, Jian-Xia Guo

In this work, we study the generation of gravitational waves in the E-model inflation with the scalar field non-minimally coupled to the Gauss-Bonnet term. Considering a wall-crossing behavior in the moduli space, we parameterize the coupling coefficient $\xi$ as a step-like function, then if $V_{,\phi}\xi_{,\phi}>0$, the Gauss-Bonnet term dominate the infla

Wei Lai, Tianyu Ding, ren dongdong, Lei Wang

Dataset distillation synthesizes compact datasets that enable models to achieve performance comparable to training on the original large-scale datasets. However, existing distillation methods overlook the robustness of the model, resulting in models that are vulnerable to adversarial attacks when trained on distilled data. To address this limitation, we intr

Qingchen Tang, Lei Fan, Maurice Pagnucco, Yang Song

Weakly supervised image segmentation with image-level labels has drawn attention due to the high cost of pixel-level annotations. Traditional methods using Class Activation Maps (CAMs) often highlight only the most discriminative regions, leading to incomplete masks. Recent approaches that introduce textual information struggle with histopathological images

Amir M. Mansourian, Rozhan Ahmadi, Masoud Ghafouri, Amir Mohammad Babaei

Deep Neural Networks (DNNs) have achieved notable performance in the fields of computer vision and natural language processing with various applications in both academia and industry. However, with recent advancements in DNNs and transformer models with a tremendous number of parameters, deploying these large models on edge devices causes serious issues such

Yuetong Yu, Ruiyang Ge, Ilker Hacihaliloglu, Alexander Rauscher

Background: Data driven stratification of patients into biologically informed subtypes holds promise for precision neuropsychiatry, yet neuroimaging-based clustering methods often fail to generalize across cohorts. While algorithmic innovations have focused on model complexity, the role of underlying dataset characteristics remains underexplored. We hypothes

Frans Zdyb, Albert Alonso, Julius B. Kirkegaard

Detecting slender, overlapping structures remains a challenge in computational microscopy. While recent coordinate-based approaches improve detection, they often produce less accurate splines than pixel-based methods. We introduce a training-free differentiable rendering approach to spline refinement, achieving both high reliability and sub-pixel accuracy. O

Muhayy Ud Din, Waseem Akram, Ahsan B Bakht, Yihao Dong

Unmanned Surface Vessels (USVs) are essential for various maritime operations. USV mission planning approach offers autonomous solutions for monitoring, surveillance, and logistics. Existing approaches, which are based on static methods, struggle to adapt to dynamic environments, leading to suboptimal performance, higher costs, and increased risk of failure.

Sanjay Shukla, Rahul Pandit

Magnetars, highly magnetized neutron stars, host superconducting and superfluid phases. We develop a minimal model that captures the interplay between neutron superfluidity, proton superconductivity, and electromagnetic fields using the Gross-Pitaevskii-Poisson, Ginzburg-Landau, and Maxwell equations. Our numerical simulations show that strong rotation enhan

Yuqing Yan, Yirui Wu

Cell counting remains a fundamental yet challenging task in medical and biological research due to the diverse morphology of cells, their dense distribution, and variations in image quality. We present DLA-Count, a breakthrough approach to cell counting that introduces three key innovations: (1) K-adjacent Hungarian Matching (KHM), which dramatically improve

Vineet Kumar, Ronald Tony, Darshita Rathore, Vipasha Rana

Data and insights discovery is critical for decision-making in modern organizations. We present Genicious, an LLM-aided interface that enables users to interact with tabular datasets and ask complex queries in natural language. By benchmarking various prompting strategies and language models, we have developed an end-to-end tool that leverages contextual few

Yuqing Yan, Yirui Wu

In recent years, crowd counting and localization have become crucial techniques in computer vision, with applications spanning various domains. The presence of multi-scale crowd distributions within a single image remains a fundamental challenge in crowd counting tasks. To address these challenges, we introduce the Efficient Hybrid Network (EHNet), a novel f

Tom Bachmann, Robert Burklund, Zhouli Xu

We reconstruct (appropriately completed) categories of cellular motivic spectra over fields of small cohomological dimension in terms of only their absolute Galois groups. As our main application, we determine the motivic stable stems (away from the characteristic) of almost all fields.

Begüm Ateşli, Oğul Esen, Serkan Sütlü

This work explores the geometrical/algebraic framework of Lie algebroids, with a specific focus on the decoupling and coupling phenomena within the bicocycle double cross product realization. The bicocycle double cross product theory serves as the most general method for (de)coupling an algebroid into the direct sum of two vector bundles in the presence of m

Chenhao Lin, Chenyang Zhao, Shiwei Wang, Longtian Wang

Backdoor attacks typically place a specific trigger on certain training data, such that the model makes prediction errors on inputs with that trigger during inference. Despite the core role of the trigger, existing studies have commonly believed a perfect match between training-inference triggers is optimal. In this paper, for the first time, we systematical

Saujatya Mandal, Debashish Das

The electromechanical response of PbZr0.52Ti0.48O3 (PZT) near the morphotropic phase boundary (MPB) is strongly influenced by crystallographic texture and residual stress, both of which affect domain switching behavior. While these effects are critical for optimizing sensors, actuators, and MEMS devices, their combined influence remains poorly understood. We

Shenglun Yan, Weiyuan Zou

In this paper, we propose a coupled system describing the interaction between the Relativistic Cucker-Smale model and the incompressible Navier-Stokes equations via a drag force, and establish a global existence theory as well as the time-asymptotic behavior of the proposed model in $\mathbb{T}^3$. It is shown that the coupled system exhibits an exponential

Chuancheng Zhang, Zhenhao Wang, Jiangcheng Wang, Kun Su

Decision-making in long-tail scenarios is pivotal to autonomous-driving development, and realistic and challenging simulations play a crucial role in testing safety-critical situations. However, existing open-source datasets lack systematic coverage of long-tail scenes, and lane-change maneuvers being emblematic, rendering such data exceedingly scarce. To br

Hao-Min Sun, Yong Zhang, Xu-Jia Ouyang, Sheng-Li Qin

A long-standing enigma in observational astronomy is the identification of the so-called 21 $\mu$m feature in a subset of envelopes of post-asymptotic giant branch (post-AGB) stars. Identifying this transient feature is important for understanding the chemical processes during the brief post-AGB phase and the enrichment of the interstellar medium. Understand

Yuhao Zhou, Yuxin Tian, Jindi Lv, Mingjia Shi

In the realm of high-frequency data streams, achieving real-time learning within varying memory constraints is paramount. This paper presents Ferret, a comprehensive framework designed to enhance online accuracy of Online Continual Learning (OCL) algorithms while dynamically adapting to varying memory budgets. Ferret employs a fine-grained pipeline paralleli

Zhiyao Sun, Yu-Hui Wen, Ho-Jui Fang, Sheng Ye

Creating detailed 3D human avatars with fitted garments traditionally requires specialized expertise and labor-intensive workflows. While recent advances in generative AI have enabled text-to-3D human and clothing synthesis, existing methods fall short in offering accessible, integrated pipelines for generating CG-ready 3D avatars with physically compatible

Fan Gao, Cheng Huang, Nyima Tashi, Xiangxiang Wang

Large language models have made tremendous progress in recent years, but low-resource languages, like Tibetan, remain significantly underrepresented in their evaluation. Despite Tibetan being spoken by over seven million people, it has largely been neglected in the development and assessment of large language models. To address this gap, we present a \textbf

Zhang Zhiou, Guo Weian, Zhang Qin, Lin Haibin

Vehicular Ad Hoc Networks (VANETs) are a cornerstone of intelligent transportation systems, facilitating real-time communication between vehicles and infrastructure. However, the dynamic nature of VANETs introduces significant challenges in routing, especially in minimizing communication delay while ensuring route stability. This paper proposes a hierarchica

Yangyijian Liu, Jun Li, Wu-Jun Li

The high memory and computation demand of large language models (LLMs) makes them challenging to be deployed on consumer devices due to limited GPU memory. Offloading can mitigate the memory constraint but often suffers from low GPU utilization, leading to low inference efficiency. In this work, we propose a novel framework, called pipelined offloading (PIPO

Ruijie Lu, Yixin Chen, Yu Liu, Jiaxiang Tang

Humans can infer complete shapes and appearances of objects from limited visual cues, relying on extensive prior knowledge of the physical world. However, completing partially observable objects while ensuring consistency across video frames remains challenging for existing models, especially for unstructured, in-the-wild videos. This paper tackles the task

Daniela S. J. Cordeiro, Ednaldo L. B. Junior, José Tarciso S. S. Junior, Francisco S. N. Lobo

Space-times exhibiting spontaneous Lorentz symmetry-breaking have recently attracted much attention, with Kalb-Ramond (KR) gravity providing a notable example. In this context, we examine the free-fall motion of a test particle toward an electrically charged black hole arising from the coupling of the KR field with the Maxwell one in General Relativity. We i

Hangrui Xu, Zhengxian Wu, Chuanrui Zhang, Zhuohong Chen

Gait recognition has emerged as a robust biometric modality due to its non-intrusive nature. Conventional gait recognition methods mainly rely on silhouettes or skeletons. While effective in controlled laboratory settings, their limited information entropy restricts generalization to real-world scenarios. To overcome this, we propose a novel representation c

Kaining Shi, Cong Ma

This paper introduces a novel theoretical framework for auditing differential privacy (DP) in a black-box setting. Leveraging the concept of $f$-differential privacy, we explicitly define type I and type II errors and propose an auditing mechanism based on conformal inference. Our approach robustly controls the type I error rate under minimal assumptions. Fu

Andreas Kaltenbrunner, Josep Ferrer, David Moreno, Vicenç Gómez

In a scenario of growing usage of park-and-ride facilities, understanding and predicting car park occupancy is becoming increasingly important. This study presents a model that effectively captures the occupancy patterns of park-and-ride car parks for commuters using truncated normal distributions for vehicle arrival and departure times. The objective is to

Ali Raeisdanaei, Juho Kim, Michael Liao, Sparsh Kochhar

In many safety-critical engineering domains, hazard analysis techniques are an essential part of requirement elicitation. Of the methods proposed for this task, STPA (System-Theoretic Process Analysis) represents a relatively recent development in the field. The completion, management, and traceability of this hazard analysis technique present a time-consumi

Zhedong Zhang, Liang Li, Chenggang Yan, Chunshan Liu

Movie dubbing describes the process of transforming a script into speech that aligns temporally and emotionally with a given movie clip while exemplifying the speaker's voice demonstrated in a short reference audio clip. This task demands the model bridge character performances and complicated prosody structures to build a high-quality video-synchronized dub

Hyunsoo Cho, Byungchan Kim, Eunmi Kim, Ae Ja Yee

Recently, Griffin, Ono, and Tsai examined the distribution of the number of $t$-hooks in partitions of $n$, which was later followed by the work of Craig, Ono, and Singh on the distribution of the number of $t$-hooks in self-conjugate partitions of $n$. Motivated by these studies, in this paper, we further investigate the number of $t$-hooks in some subsets

Xiaojian Li, Yongkang Leng, Ruiqing Ding, Hangjie Mo

The human-like reasoning capabilities exhibited by Large Language Models (LLMs) challenge the traditional neural network theory's understanding of the flexibility of fixed-parameter systems. This paper proposes the "Cognitive Activation" theory, revealing the essence of LLMs' reasoning mechanisms from the perspective of dynamic systems: the model's reasoning

Yusuke Fujita, Makoto Iima

A corrugated structure, rather than a smooth surface, is a characteristic feature of insect wings (e.g., dragonfly wings), which enhances their aerodynamic performance at low Reynolds numbers ($Re \simeq O(10^3)$). However, the mechanisms responsible for these improvements remain largely unexplored. Previous studies have shown that a secondary vortex forms o

Yuqi Liu, Jose E. Roman, Meiyue Shao

In this work, we combine Beyn's method and the recently developed recursive integral method (RIM) to propose a contour integral-based, region partitioning eigensolver for nonlinear eigenvalue problems. A new partitioning criterion is employed to eliminate the need for a problem-dependent parameter, making our algorithm much more robust compared to the origin

Hang Ni, Jindong Han, Nengjun Zhu, Hao Liu

Graph Anomaly Detection (GAD) plays a vital role in various data mining applications such as e-commerce fraud prevention and malicious user detection. Recently, Graph Neural Network (GNN) based approach has demonstrated great effectiveness in GAD by first encoding graph data into low-dimensional representations and then identifying anomalies under the guidan

Jianqi Gao, Xizheng Pang, Qi Liu, Yanjie Li

Reinforcement learning-based mapless navigation holds significant potential. However, it faces challenges in indoor environments with local minima area. This paper introduces a safe mapless navigation framework utilizing hierarchical reinforcement learning (HRL) to enhance navigation through such areas. The high-level policy creates a sub-goal to direct the

Zhihao Zhu

Representing a dynamic scene using a structured spatial-temporal scene graph is a novel and particularly challenging task. To tackle this task, it is crucial to learn the temporal interactions between objects in addition to their spatial relations. Due to the lack of explicitly annotated temporal relations in current benchmark datasets, most of the existing

Zhengyuan Peng, Jinpeng Ma, Zhimin Sun, Ran Yi

Generalized Category Discovery (GCD) is a classification task that aims to classify both base and novel classes in unlabeled images, using knowledge from a labeled dataset. In GCD, previous research overlooks scene information or treats it as noise, reducing its impact during model training. However, in this paper, we argue that scene information should be v

Enes Erdogan, Eren Erdal Aksoy, Sanem Sariel

Recognition of human manipulation actions in real-time is essential for safe and effective human-robot interaction and collaboration. The challenge lies in developing a model that is both lightweight enough for real-time execution and capable of generalization. While some existing methods in the literature can run in real-time, they struggle with temporal sc

Jiaying Li, Yuanwei Liu, Hong Xing

With the emergence of simultaneous localization and communication (SLAC), it becomes more and more attractive to perform angle of departure (AoD) estimation at the receiving Internet of Thing (IoT) user end for improved positioning accuracy, flexibility and enhanced user privacy. To address challenges like a large number of real-time measurements required fo

Jong In Han, Jeong-Hoon Ju, Yeongrak Kim

We investigate new lower bounds on the tensor rank of the determinant and the permanent tensors via recursive usage of the Koszul flattening method introduced by Landsberg-Ottaviani and Hauenstein-Oeding-Ottaviani-Sommese. Our lower bounds on $\mathbf{R} (\det_n)$ completely separate the determinant and the permanent tensors by their tensor ranks. Furthermor

Yu-Hang Ma, Nai-Jie Guo, Wei Liu, Xiao-Dong Zeng

Silicon-based semiconductor nanofabrication technology has achieved a remarkable level of sophistication and maturity, and color centers in silicon naturally inherit this advantage. Besides, their emissions appear in telecommunication bands, which makes them play a crucial role in the construction of quantum network. To address the challenge of weak spontane

Zhenxin Li, Shihao Wang, Shiyi Lan, Zhiding Yu

End-to-end autonomous driving research currently faces a critical challenge in bridging the gap between open-loop training and closed-loop deployment. Current approaches are trained to predict trajectories in an open-loop environment, which struggle with quick reactions to other agents in closed-loop environments and risk generating kinematically infeasible

F. Çengel, V. Adanova, S. Tari

The planar ornaments are created by repeating a base unit using a combination of four primitive geometric operations: translation, rotation, reflection, and glide reflection. According to group theory, different combinations of these four geometric operations lead to different symmetry groups. In this work, we select a single challenging ornament, and analyz

Arya Chandran, K Vishnu Namboothiri

An arithmetical function $f$ is said to admit a \emph{Cohen-Ramanujan expansion} $f(n) := \sum\limits_{r}\widehat{f}(r)c_r^s(n)$, if the series on the right hand side converges for suitable complex numbers $\widehat{f}(r)$. Here $c_r^s(n)$ denotes the Cohen-Ramanujan sum defined by E. Cohen. We deduce here a Cohen-Ramanujan expansion for the Jordan totient f

Zihan Zhou, Changrui Dai, Aibo Song, Xiaolin Fang

Self-supervised video correspondence learning depends on the ability to accurately associate pixels between video frames that correspond to the same visual object. However, achieving reliable pixel matching without supervision remains a major challenge. To address this issue, recent research has focused on feature learning techniques that aim to encode uniqu

Hong-Jun Ge, Jack H. Koolen

Tan et al. conjectured that connected co-edge-regular graphs with four distinct eigenvalues and fixed smallest eigenvalue, when having sufficiently large valency, belong to two different families of graphs. In this paper we construct two new infinite families of connected co-edge-regular graphs with four distinct eigenvalues and fixed smallest eigenvalue, th

Byeongjun Park, Hyojun Go, Hyelin Nam, Byung-Hoon Kim

Recent progress in 3D/4D scene generation emphasizes the importance of physical alignment throughout video generation and scene reconstruction. However, existing methods improve the alignment separately at each stage, making it difficult to manage subtle misalignments arising from another stage. Here, we present SteerX, a zero-shot inference-time steering me

Hyunjin Jung, Jiwon Jung, ChoongJae Won, Hae-Ryong Park

The emergence of a pseudogap is a hallmark of anomalous electronic states formed through substantial manybody interaction but the mechanism of the pseudogap formation and its role in related emerging quantum states such as unconventional superconductivity remain largely elusive. Here, we report the emergence of an unusual pseudogap in a representative van de

Youde Wang, Linqin Zhang

In this paper, we use the Saloff-Coste Sobolev inequality and Nash-Moser iteration method to study the local and global behaviors of positive solutions to the nonlinear elliptic equation $\Delta_pv+a(v+b)^q=0$ defined on a complete Riemannian manifold $\left(M,g\right)$ with Ricci lower bound, where $p>1$ is a constant and $\Delta_pv=\mathrm{div}\left(\left|

Piotr Graczyk, Bivas Rana, Aleksandra Trzaskowska, Bipul Kumar Mahato

Surface acoustic waves have emerged as one of the potential candidates for the development of next-generation wave-based information and computing technologies. For practical devices, it is essential to develop the excitation techniques for different types of surface acoustic waves, especially at higher microwave frequencies, and to tailor their frequency ve

Jiafan He, Quanquan Gu

Variance-dependent regret bounds for linear contextual bandits, which improve upon the classical $\tilde{O}(d\sqrt{K})$ regret bound to $\tilde{O}(d\sqrt{\sum_{k=1}^K\sigma_k^2})$, where $d$ is the context dimension, $K$ is the number of rounds, and $\sigma^2_k$ is the noise variance in round $k$, has been widely studied in recent years. However, most existi

Zhe Jin, Tat-Seng Chua

Text-to-Image (T2I) diffusion models (DM) have garnered widespread adoption due to their capability in generating high-fidelity outputs and accessibility to anyone able to put imagination into words. However, DMs are often predisposed to generate unappealing outputs, much like the random images on the internet they were trained on. Existing approaches to add

Sukhendu De, V. Choudhary, R. Chatterjee, W. Horiuchi

This study examines charge-changing cross sections for 12C, 14N, 16O, and 20Ne projectiles on elemental targets (C, Al, Cu) at a beam energy of around 290 MeV/nucleon. The two-stage abrasion-ablation model is used, with the abrasion stage described via the Glauber model, incorporating validated single-nucleon density distributions from proton elastic scatter

Yebo Wu, Chunlin Tian, Jingguang Li, He Sun

Large Language Models (LLMs) have demonstrated impressive success across various tasks. Integrating LLMs with Federated Learning (FL), a paradigm known as FedLLM, offers a promising avenue for collaborative model adaptation while preserving data privacy. This survey provides a systematic and comprehensive review of FedLLM. We begin by tracing the historical

Donglin Yang, Paul Vicol, Xiaojuan Qi, Renjie Liao

Deep learning-based super-resolution (SR) methods often perform pixel-wise computations uniformly across entire images, even in homogeneous regions where high-resolution refinement is redundant. We propose the Quadtree Diffusion Model (QDM), a region-adaptive diffusion framework that leverages a quadtree structure to selectively enhance detail-rich regions w

Shun Zou, Yi Zou, Mingya Zhang, Shipeng Luo

Existing image deraining methods typically rely on single-input, single-output, and single-scale architectures, which overlook the joint multi-scale information between external and internal features. Furthermore, single-domain representations are often too restrictive, limiting their ability to handle the complexities of real-world rain scenarios. To addres

Qingshi Sun, Nathan Justin, Andres Gomez, Phebe Vayanos

Logistic regression models are widely used in the social and behavioral sciences and in high-stakes domains, due to their simplicity and interpretability properties. At the same time, such domains are permeated by distribution shifts, where the distribution generating the data changes between training and deployment. In this paper, we study a distributionall

BoGwang Jeon

This paper is subsequent to [5]. In this paper, we extend the classification of hyperbolic Dehn fillings with sufficiently large coefficients by addressing the remaining case not covered in [5]. Specifically, by considering the case in which the two cusp shapes lie in the same quadratic field, we obtain the complete classification under a mild assumption sat

Qixian Chen, Yuxiong Xu, Sara Mandelli, Sheng Li

In audio spoofing detection, most studies rely on clean datasets, making models susceptible to real-world post-processing attacks, such as channel compression and noise. To overcome this challenge, we propose the Adaptive MixtUre Low-rank ExperTs (AMULET) framework, which enhances resilience by leveraging attack-specific knowledge and dynamically adapting to

Xin Jin, Haisheng Su, Kai Liu, Cong Ma

Recent advances in LiDAR 3D detection have demonstrated the effectiveness of Transformer-based frameworks in capturing the global dependencies from point cloud spaces, which serialize the 3D voxels into the flattened 1D sequence for iterative self-attention. However, the spatial structure of 3D voxels will be inevitably destroyed during the serialization pro

Xiaoyu Wu, Yifei Pang, Terrance Liu, Steven Wu

Tabular data synthesis using diffusion models has gained significant attention for its potential to balance data utility and privacy. However, existing privacy evaluations often rely on heuristic metrics or weak membership inference attacks (MIA), leaving privacy risks inadequately assessed. In this work, we conduct a rigorous MIA study on diffusion-based ta

Chen-Rong Liu, Runxia Tao, Xiang Lv, Ying Dong

Fisher information provides a rigorous theoretical benchmark for evaluating quantum sensor sensitivity; however, a comprehensive framework for quantifying the fundamental limits of Rydberg-atom microwave electrometers remains lacking. In this work, we establish such a framework by deriving the Fisher information for slope detection and establishing its conne

Zhe Shan, Yang Liu, Lei Zhou, Cheng Yan

The availability of large-scale remote sensing video data underscores the importance of high-quality interactive segmentation. However, challenges such as small object sizes, ambiguous features, and limited generalization make it difficult for current methods to achieve this goal. In this work, we propose ROS-SAM, a method designed to achieve high-quality in

Daniyal Munir, Atta Ullah, Danish Mehmood Mughal, Min Young Chung

This paper investigates the use of intelligent reflecting surfaces (IRS) to assist cellular communications and radar sensing operations in a communications and sensing setup. The IRS dynamically allocates reflecting elements to simultaneously localize a target and assist a user's communication. To achieve this, we propose a novel optimization framework that

K. Q. Abbasi, F. L. Carneiro, M. Z. A. Moughal

We investigate the geometry, dynamics, and collision mechanisms in the ergoregion of KerrNewman-AdS black hole influenced by quintessential energy. Particle splittings within the ergoregion are analyzed, demonstrating their role in energy extraction via the Penrose process. Increased spin elongates the ergosphere, while higher quintessential parameters expan

Zhiyuan Liu, Shuhang Zhang, Qingyu Liu, Hongliang Zhang

Fine-grained radio map presents communication parameters of interest, e.g., received signal strength, at every point across a large geographical region. It can be leveraged to improve the efficiency of spectrum utilization for a large area, particularly critical for the unlicensed WiFi spectrum. The problem of fine-grained radio map estimation is to utilize

James Usevitch, Jackson Sahleen

A fundamental and classical problem in mobile autonomous systems is maintaining the safety of autonomous agents during deployment. Prior literature has presented techniques using control barrier functions (CBFs) to achieve this goal. These prior techniques utilize CBFs to keep an isolated point in state space away from the unsafe set. However, various situat

Mark Pustilnik, Antonio Loquercio, Francesco Borrelli

In dynamic games with shared constraints, Generalized Nash Equilibria (GNE) are often computed using the normalized solution concept, which assumes identical Lagrange multipliers for shared constraints across all players. While widely used, this approach excludes other potentially valuable GNE. This paper addresses the limitations of normalized solutions in

Zhennan Pan, Gang Han

Non-commutative Poisson algebras are the algebras having an associative algebra structure and a Lie algebra structure together with the Leibniz law. Let $P$ be a non-commutative Poisson algebra over some algebraically closed field of characteristic zero. For any $z\in P$, there exist four subalgebras of $P$ associated with the inner derivation $ad_z$ on $P$.

Tongxuan Tian, Haoyang Li, Bo Ai, Xiaodi Yuan

Cloth manipulation is challenging due to its highly complex dynamics, near-infinite degrees of freedom, and frequent self-occlusions, which complicate both state estimation and dynamics modeling. Inspired by recent advances in generative models, we hypothesize that these expressive models can effectively capture intricate cloth configurations and deformation

Taehyun Eom, Minki Kim, Eon Lee

Given a point set $S$ in $\mathbb{R}^d$, a family of sets is $S$-intersecting if its members have a point in common in $S$. Recently, Edwards and Sober\'{o}n proved a fractional version of Halman's theorem for axis-parallel boxes, showing that every finite family $F$ of axis-parallel boxes in $\mathbb{R}^d$ with positive density of $S$-intersecting $(d+1)$-t

Mateusz Miotk, Michał Zakrzewski, Paweł Żyliński

We prove that the class of trees with unique minimum edge-vertex dominating sets is equivalent to the class of trees with unique minimum paired dominating sets.

Jun Cui

This study investigates the impact of artificial intelligence (AI) technology on cross-border trade using a qualitative content analysis approach. By synthesizing existing empirical studies, we aim to quantify the overall effect of AI on trade flows and identify the key moderating and mediating variables. Besides, our results show that AI adoption significan

Ruchik Mishra, Laksita Prasanna, Adair Adair, Dan O Popa

In this paper, we compare a manual assembly task communicated to workers using both printed and robot-delivered instructions. The comparison was made using physiological signals (blood volume pulse (BVP) and electrodermal activity (EDA)) collected from individuals during an experimental study. In addition, we also collected responses of individuals using the

Shun Zou, Yi Zou, Mingya Zhang, Shipeng Luo

In recent years, Transformer has witnessed significant progress in food recognition. However, most existing approaches still face two critical challenges in lightweight food recognition: (1) the quadratic complexity and redundant feature representation from interactions with irrelevant tokens; (2) static feature recognition and single-scale representation, w