March 2025 arXiv papers — page 86

Jun Zhao, Bo Hou, Xin Zhou

In this paper, we study the theory of non-abelian extensions of a Leibniz conformal algebra $R$ by a Leibniz conformal algebra $H$ and prove that all the non-abelian extensions are classified by non-abelian $2$nd cohomology $H^2_{nab}(R,H)$ in the sense of equivalence. Then we introduce a differential graded Lie algebra $\mathfrak{L}$ and show that the set o

Gaole Dai, Shiqi Jiang, Ting Cao, Yuanchun Li

We propose V-Droid, a mobile GUI task automation agent. Unlike previous mobile agents that utilize Large Language Models (LLMs) as generators to directly generate actions at each step, V-Droid employs LLMs as verifiers to evaluate candidate actions before making final decisions. To realize this novel paradigm, we introduce a comprehensive framework for const

Zixuan Guo, Yuekai Shi, Tiantian Ye, Tingjie Wan

Virtual reality (VR) users often encounter interruptions, posing challenges to maintaining real-world awareness during immersive experiences. The Passthrough feature in VR headsets allows users to view their physical surroundings without removing the headset. However, when interruptions come from the rear, users need to turn their heads to see the real world

Soyun Jeon, Jungsoon Choi

Elevated levels of PM10 are known to cause severe respiratory and cardiovascular diseases, and, in extreme cases, cancer and mortality. Despite various reduction policies implemented across different sectors, PM10 concentrations in South Korea continue to exceed the annual recommended limits set by the World Health Organization. Spatio-temporal PM10 concentr

Hongda Liu, Longguang Wang, Ye Zhang, Ziru Yu

Global effective receptive field plays a crucial role for image style transfer (ST) to obtain high-quality stylized results. However, existing ST backbones (e.g., CNNs and Transformers) suffer huge computational complexity to achieve global receptive fields. Recently, the State Space Model (SSM), especially the improved variant Mamba, has shown great potenti

Christian Saugbjerg Lange, Thomas Hansen, Lars Bojer Madsen

The strong-field induced generation of nonclassical states of light is not only a subject of fundamental research but also has potential usage in quantum information science and technology. The emerging field of strong-field quantum optics has developed ways of generating nonclassical states of light from the process of high-harmonic generation (HHG) at much

Sidi Yang, Binxiao Huang, Yulun Zhang, Dahai Yu

While deep neural networks have revolutionized image denoising capabilities, their deployment on edge devices remains challenging due to substantial computational and memory requirements. To this end, we present DnLUT, an ultra-efficient lookup table-based framework that achieves high-quality color image denoising with minimal resource consumption. Our key i

Samer Al-Gharabli, Nafisah Al-Rifai, Simona Jurevičiūte, Aivaras Kareiva

Membrane distillation (MD) stands at the forefront of desalination technology, harnessing the power of phase change to separate water vapor from saline using minimal energy resources efficiently. In response to this challenge, membranes with tuned pores morphology and surface chemistry with biomimetic 3D pine-like structures with improved affinity to water (

Payel Patra, Daniele Di Pompeo, Antinisca Di Marco

Open science represents a transformative research approach essential for enhancing sustainability and impact. Data generation encompasses various methods, from automated processes to human-driven inputs, creating a rich and diverse landscape. Embracing the FAIR principles -- making data and, in general, artifacts (such as code, configurations, documentation,

Philipp Wagner, Tobias Nagel, Philipp Leube, Marco F. Huber

Correctly setting the parameters of a production machine is essential to improve product quality, increase efficiency, and reduce production costs while also supporting sustainability goals. Identifying optimal parameters involves an iterative process of producing an object and evaluating its quality. Minimizing the number of iterations is, therefore, desira

Hui Zhang, Tingwei Gao, Jie Shao, Zuxuan Wu

Diffusion models have demonstrated impressive generation capabilities, particularly with recent advancements leveraging transformer architectures to improve both visual and artistic quality. However, Diffusion Transformers (DiTs) continue to encounter challenges related to low inference speed, primarily due to the iterative denoising process. To address this

Laurits Dixen, Stefan Heinrich, Paolo Burelli

Neural decoding is an important method in cognitive neuroscience that aims to decode brain representations from recorded neural activity using a multivariate machine learning model. The THINGS initiative provides a large EEG dataset of 46 subjects watching rapidly shown images. Here, we test the feasibility of using this method for decoding high-level object

Meisam J. Seikavandi, Maria J. Barrett, Paolo Burelli

Face Emotion Recognition (FER) is essential for social interactions and understanding others' mental states. Utilizing eye tracking to investigate FER has yielded insights into cognitive processes. In this study, we utilized an instructionless paradigm to collect eye movement data from 21 participants, examining two FER processes: free viewing and grounded F

Nadav Goshen, Yarden Mazor

Bianisotropic metasurfaces enable advanced electromagnetic wave manipulation through magnetoelectric coupling. Here, we demonstrate how knot-particles enable single-layer bianisotropic control using their inherent topology. Leveraging their geometric properties, we examine 3D wire configurations characterized by the knot winding numbers (p,q), generating bal

Peiyi Lin, Fukai Zhang, Kai Niu, Hao Fu

Continual instruction tuning enables large language models (LLMs) to learn incrementally while retaining past knowledge, whereas existing methods primarily focus on how to retain old knowledge rather than on selecting which new knowledge to learn. In domain-specific contexts, maintaining data quality and managing system constraints remain key challenges. To

Yin-Jie Ma, Zhi-Qiang Jiang, Fanshu Fang, Charo I. del Genio

In distributed systems, knowledge of the network structure of the connections among the unitary components is often a requirement for an accurate prediction of the emerging collective dynamics. However, in many real-world situations, one has, at best, access to partial connectivity data, and therefore the entire graph structure needs to be reconstructed from

Fatima-Zahrae El-Boukkouri, Josselin Garnier, Olivier Roustant

In this paper, we consider the reproducing property in Reproducing Kernel Hilbert Spaces (RKHS). We establish a reproducing property for the closure of the class of combinations of composition operators under minimal conditions. This allows to revisit the sufficient conditions for the reproducing property to hold for the derivative operator, as well as for t

Fahao Chen, Peng Li, Tom H. Luan, Zhou Su

Speculative decoding has been shown as an effective way to accelerate Large Language Model (LLM) inference by using a Small Speculative Model (SSM) to generate candidate tokens in a so-called speculation phase, which are subsequently verified by the LLM in a verification phase. However, current state-of-the-art speculative decoding approaches have three key

Sahil Gehlawat

Let $\mathcal{F}$ be a singular Riemann surface foliation on a complex manifold $M$, such that the singular set $E \subset M$ is non-discrete. We study the behavior of the foliation near the singular set $E$, particularly focusing on singular points that admit invariant submanifolds (locally) passing through them. Our primary focus is on the singular points

Viktor Abramov

We extend the concepts of the associator and commutator from algebras with a binary multiplication law to algebras with a ternary multiplication law using cube roots of unity. By analogy with the Jacobi identity for the binary commutator, we derive an identity for the proposed ternary commutator. While the Jacobi identity is based on the cyclic permutation g

Jie Zhang, Zheng Yuan, Zhongqi Wang, Bei Yan

The rapid evolution of Large Vision-Language Models (LVLMs) has highlighted the necessity for comprehensive evaluation frameworks that assess these models across diverse dimensions. While existing benchmarks focus on specific aspects such as perceptual abilities, cognitive capabilities, and safety against adversarial attacks, they often lack the breadth and

Macheng Shen, Jishen Peng, Zefang Huang

A fundamental challenge in imitation learning is the \emph{covariate shift} problem. Existing methods to mitigate covariate shift often require additional expert interactions, access to environment dynamics, or complex adversarial training, which may not be practical in real-world applications. In this paper, we propose a simple yet effective method (DeCIL)

Beilei Cui, Long Bai, Mobarakol Islam, An Wang

Accurate 3D scene reconstruction is essential for numerous medical tasks. Given the challenges in obtaining ground truth data, there has been an increasing focus on self-supervised learning (SSL) for endoscopic depth estimation as a basis for scene reconstruction. While foundation models have shown remarkable progress in visual tasks, their direct applicatio

Fangxin Liu, Haomin Li, Zongwu Wang, Bo Zhang

Modular arithmetic, particularly modular reduction, is widely used in cryptographic applications such as homomorphic encryption (HE) and zero-knowledge proofs (ZKP). High-bit-width operations are crucial for enhancing security; however, they are computationally intensive due to the large number of modular operations required. The lookup-table-based (LUT-base

Wenbo Li, Xianlong Mai, Ying Li, Weihua Li

Hand exoskeletons have significant potential in labor-intensive fields by mitigating hand grip fatigue, enhancing hand strength, and preventing injuries. However, most of the traditional hand exoskeletons are driven by motors, whose output force is limited in the constrained installation conditions. Besides, they also come with the disadvantages of high powe

Jiayi He, Xu Wang, Ruobei Zhang, Shengeng Tang

We introduce the hfut-lmc team's solution to the SLRTP Sign Production Challenge. The challenge aims to generate semantically aligned sign language pose sequences from text inputs. To this end, we propose a Text-driven Diffusion Model (TDM) framework. During the training phase, TDM utilizes an encoder to encode text sequences and incorporates them into the d

Z. Śniadecki, M. Werwiński, A. Szajek, U. K. Rößler

Intermetallic YCo$_2$ compound is a Pauli exchange-enhanced paramagnet. Structural and magnetic properties of rapidly quenched YCo$_2$ and YCo$_2$ alloyed with Nb or Ti are presented. Samples produced by melt spinning have been characterized by X-ray diffraction (XRD) and vibrating sample magnetometry (VSM). The samples crystallize in MgCu$_2$-type phase wit

Natalia Pierunek, Zbigniew Śniadecki, Mirosław Werwiński, Bartosz Wasilewski

Magnetic and magnetocaloric properties of Y$_{1-x}$Gd$_{x}$Co$_{2}$ compounds, where x = 0.2, 0.4, 0.6, 0.8 and 1.0, were investigated experimentally and theoretically. Crystal structures were characterized by X-ray diffraction (Rietveld analysis) and investigated samples possess the MgCu$_{2}$-type single phase with Fd-3m space group. Melt-spinning process

Z. Śniadecki, N. Pierunek, B. Idzikowski, B. Wasilewski

In this paper, the changes of magnetic properties with increasing disorder in the exchange enhanced Pauli paramagnet YCo$_2$ are discussed. The structural disorder is initially introduced by rapid quenching, while further changes on micro-/nanoscale are caused by a high pressure torsion (HPT). Values of the magnetic moment determined for the plastically defo

Junsung Park, Hwijeong Lee, Inha Kang, Hyunjung Shim

Existing domain generalization methods for LiDAR semantic segmentation under adverse weather struggle to accurately predict "things" categories compared to "stuff" categories. In typical driving scenes, "things" categories can be dynamic and associated with higher collision risks, making them crucial for safe navigation and planning. Recognizing the importan

Ankita, Arpita Chatterjee

In this work, we investigate the performance of non-Gaussian entangled resources in continuous-variable quantum teleportation within a realistic setting. We describe the characteristic functions of three distinct entangled resources, a two-mode squeezed vacuum state, a two-mode photon-subtracted squeezed state, and a two-mode photon-added squeezed state. We

Jiatong Xia, Libo Sun, Lingqiao Liu

Recent methods, such as Neural Radiance Fields (NeRF) and 3D Gaussian Splatting (3DGS), have demonstrated remarkable capabilities in novel view synthesis. However, despite their success in producing high-quality images for viewpoints similar to those seen during training, they struggle when generating detailed images from viewpoints that significantly deviat

Hanzhang Wang, Shuai Liu, Han Wang, Hongyu Chen

Context. Population III star clusters are predicted to form in unenriched dark matter halos. Direct N-body simulation of Pop III clusters implies the possible formation and merger of intermediate-mass binary black holes (IMBBHs). The gravitational wave signals could be detected by space-borne gravitational wave detectors like TianQin. Aims. This study evalua

Yanbin Zhu, Xiaomeng Jiang, Yong Li

In this paper, we compute the Onsager-Machlup functional for distribution dependent SDEs driven by fractional Brownian motions with Hurst parameter $H\in (\frac{1}{4},1)$. In the case $ \frac{1}{4} < H < \frac{1}{2} $, the norm can be either the supremum norm or H\"older norms of order $ \beta $ with $ 0 < \beta < H - \frac{1}{4} $. In the case $\frac{1}{2}

Jiyuan Wang, Chunyu Lin, Cheng Guan, Lang Nie

In this paper, we propose Jasmine, the first Stable Diffusion (SD)-based self-supervised framework for monocular depth estimation, which effectively harnesses SD's visual priors to enhance the sharpness and generalization of unsupervised prediction. Previous SD-based methods are all supervised since adapting diffusion models for dense prediction requires hig

Evan Chen, Run-Jun Zhan, Yan-Bai Lin, Hung-Hsuan Chen

Large Language Models (LLMs) have revolutionized natural language processing, yet concerns persist regarding their tendency to reflect or amplify social biases. This study introduces a novel evaluation framework to uncover gender biases in LLMs: using free-form storytelling to surface biases embedded within the models. A systematic analysis of ten prominent

Kirill Vishniakov, Boulbaba Ben Amor, Engin Tekin, Nancy A. ElNaker

We introduce Gene42, a novel family of Genomic Foundation Models (GFMs) designed to manage context lengths of up to 192,000 base pairs (bp) at a single-nucleotide resolution. Gene42 models utilize a decoder-only (LLaMA-style) architecture with a dense self-attention mechanism. Initially trained on fixed-length sequences of 4,096 bp, our models underwent cont

Abdullah Guvendi, Semra Gurtas Dogan

In this study, we investigate the relativistic dynamics of vector bosons within the context of rotating frames of negative curvature wormholes. We seek exact solutions for the fully-covariant vector boson equation, derived as an excited state of zitterbewegung. This equation encompasses a symmetric rank-two spinor, enabling the derivation of a non-perturbati

Aahan Singh, Engin Tekin, Maryam Nadeem, Nancy A. ElNaker

Revolutionizing drug discovery demands more than just understanding molecular interactions - it requires generative models that can design novel ligands tailored to specific biological targets. While chemical Language Models (cLMs) have made strides in learning molecular properties, most fail to incorporate target-specific insights, restricting their ability

Theodosios Chatzistergos, Natalie A. Krivova, Sami K. Solanki, Kok Leng Yeo

Accurate information on long-term variations in solar irradiance, important for understanding the solar influence on Earth's climate, cannot be derived from direct irradiance measurements due to the comparatively short lifetimes of space-borne experiments. Models using measurements of the solar photospheric magnetic field as input can provide an independent

Jose Lara-Rangel, Clare Heinbaugh

Brain connectomes offer detailed maps of neural connections within the brain. Recent studies have proposed novel connectome graph datasets and attempted to improve connectome classification by using graph deep learning. With recent advances demonstrating transformers' ability to model intricate relationships and outperform in various domains, this work explo

Amit Kumar Mondal, Nafisha Aslam, Prasenjit Maji, Hemanta Kumar Mondal

The potential for catastrophic collision makes near-Earth asteroids (NEAs) a serious concern. Planetary defense depends on accurately classifying potentially hazardous asteroids (PHAs), however the complexity of the data hampers conventional techniques. This work offers a sophisticated method for accurately predicting hazards by combining machine learning, d

Jingtian Shi, Jennifer Cano, Nicolás Morales-Durán

We investigate the many-body ground states in a family of fractionally-filled bands where the Berry curvature fluctuations can be tuned while maintaining ideal quantum geometry. We numerically find that the neutral gap of the fractional Chern insulator (FCI) ground state decreases as the Berry curvature becomes less homogeneous, ultimately driving an instabi

Zheng-Yan Guo, Ze-Chun Hu, Run-Yu Wang

Let $B(n,p)$ denote a binomial random variable with parameters $n$ and $p$. Chv\'{a}tal's theorem says that for any fixed $n\geq 2$, as $m$ ranges over $\{0,1,\ldots,n\}$, the probability $q_m:=P(B(n,m/n)\leq m)$ is the smallest when $m$ is closest to $2n/3$. Let $\mathcal{R}$ be the family of random variables of the form $X=\sum^n_{k=1}a_k\varepsilon_k$, wh

Boran Wen, Dingbang Huang, Zichen Zhang, Jiahong Zhou

Reconstructing human-object interactions (HOI) from single images is fundamental in computer vision. Existing methods are primarily trained and tested on indoor scenes due to the lack of 3D data, particularly constrained by the object variety, making it challenging to generalize to real-world scenes with a wide range of objects. The limitations of previous 3

Junho Kim, Gwangtak Bae, Eun Sun Lee, Young Min Kim

Understanding scene contexts is crucial for machines to perform tasks and adapt prior knowledge in unseen or noisy 3D environments. As data-driven learning is intractable to comprehensively encapsulate diverse ranges of layouts and open spaces, we propose teaching machines to identify relational commonalities in 3D spaces. Instead of focusing on point-wise o

Arthur Capozzi, Salvatore Vilella, Dario Moncalvo, Marco Fornasiero

In recent years, the digitization and automation of anti-financial crime (AFC) investigative processes have faced significant challenges, particularly the need for interpretability of AI model results and the lack of labeled data for training. Network analysis has emerged as a valuable approach in this context. In this paper, we present WeirdFlows, a top-dow

Maria Makarova, Qian Liu, Dzmitry Tsetserukou

This paper investigates whether sequential context improves goal-conditioned Reinforcement Learning in sparse-reward manipulation tasks. While Hindsight Experience Replay (HER) addresses reward sparsity through goal relabeling, its operation on isolated transitions limits its ability to capture temporal dependencies inherent in joint-space control. We hypoth

Ling Feng, SK Yang

In Rectified Flow, by obtaining the rectified flow several times, the mapping relationship between distributions can be distilled into a neural network, and the target distribution can be directly predicted by the straight lines of the flow. However, during the pairing process of the mapping relationship, a large amount of error accumulation will occur, resu

Muneya Matsui, Thomas Mikosch

We consider a borderline case: the central limit theorem for a strictly stationary time series with infinite variance but a Gaussian limit. In the iid case a well-known sufficient condition for this central limit theorem is regular variation of the marginal distribution with tail index $\alpha=2$. In the dependent case we assume the stronger condition of seq

Jiawei Wang, Kai Hu, Qiang Huo

Document structure analysis, aka document layout analysis, is crucial for understanding both the physical layout and logical structure of documents, serving information retrieval, document summarization, knowledge extraction, etc. Hierarchical Document Structure Analysis (HDSA) specifically aims to restore the hierarchical structure of documents created usin

Haiyang Yu, Siyang Yi, Ke Niu, Minghan Zhuo

With the rapid advancement of deep learning, particularly in the field of medical image analysis, an increasing number of Vision-Language Models (VLMs) are being widely applied to solve complex health and biomedical challenges. However, existing research has primarily focused on specific tasks or single modalities, which limits their applicability and genera

Rui Wang, Yiming Jiao, Xiaowei Zhao, Chong Huang

The coronal heating problem remains one of the most challenging questions in solar physics. The energy driving coronal heating is widely understood to be associated with convective motions below the photosphere. Recent high-resolution observations reveal that photospheric magnetic fields in the quiet Sun undergo complex and rapid evolution. These photospheri

Yoav Wald, Mark Goldstein, Yonathan Efroni, Wouter A. C. van Amsterdam

Problems in fields such as healthcare, robotics, and finance requires reasoning about the value both of what decision or action to take and when to take it. The prevailing hope is that artificial intelligence will support such decisions by estimating the causal effect of policies such as how to treat patients or how to allocate resources over time. However,

Cynthia Dong, Hong Jia, Young D. Kwon, Georgios Rizos

While there are many advantages to deploying machine learning models on edge devices, the resource constraints of mobile platforms, the dynamic nature of the environment, and differences between the distribution of training versus in-the-wild data make such deployments challenging. Current test-time adaptation methods are often memory-intensive and not desig

Baolong Bi, Shenghua Liu, Yiwei Wang, Yilong Xu

Retrieval-Augmented Generation (RAG) mitigates hallucinations in Large Language Models (LLMs) by integrating external knowledge. However, conflicts between parametric knowledge and retrieved context pose challenges, particularly when retrieved information is unreliable or the model's internal knowledge is outdated. In such cases, LLMs struggle to determine w

Haochen Wang, Kai Hu, Liangcai Gao

Remote work and online courses have become important methods of knowledge dissemination, leading to a large number of document-based instructional videos. Unlike traditional video datasets, these videos mainly feature rich-text images and audio that are densely packed with information closely tied to the visual content, requiring advanced multimodal understa

Hui Liu, Wenya Wang, Kecheng Chen, Jie Liu

In zero-shot image recognition tasks, humans demonstrate remarkable flexibility in classifying unseen categories by composing known simpler concepts. However, existing vision-language models (VLMs), despite achieving significant progress through large-scale natural language supervision, often underperform in real-world applications because of sub-optimal pro

Hyunjae Suh, Mahan Tafreshipour, Sam Malek, Iftekhar Ahmed

Web accessibility is essential for inclusive digital experiences, yet the accessibility of LLM-generated code remains underexplored. This paper presents an empirical study comparing the accessibility of web code generated by GPT-4o and Qwen2.5-Coder-32B-Instruct-AWQ against human-written code. Results show that LLMs often produce more accessible code, especi

Ibrahim Al Azher, Miftahul Jannat Mokarrama, Zhishuai Guo, Sagnik Ray Choudhury

The Future Work section of a scientific article outlines potential research directions by identifying gaps and limitations of a current study. This section serves as a valuable resource for early-career researchers seeking unexplored areas and experienced researchers looking for new projects or collaborations. In this study, we generate future work suggestio

Kwok-Kun Kwong, Yong Wei

We derive a number of sharp upper bounds for the deficit in the Alexandrov-Fenchel inequality using a weighted Minkowski integral formula and an integral formula for the deficit in Jensen's inequality. Our estimates yield results under weaker convexity assumptions compared to approaches based on inverse curvature flows. The use of weighted formulas provides

Satyapreet Singh Yadav, Bikram Pradhan, Kenil Rajendrabhai Ajudiya, T. S. Kumar

To deepen our understanding of optical astronomy, we must advance imaging technology to overcome conventional frame-based cameras' limited dynamic range and temporal resolution. Our Perspective paper examines how neuromorphic cameras can effectively address these challenges. Drawing inspiration from the human retina, neuromorphic cameras excel in speed and h

Zhiren He, Prathap Kumar Jharapla, Nicolas Leconte, Jeil Jung

In this theoretical work, we propose an all-optical method for fast, precise manipulation of two-dimensional multilayers by transferring orbital angular momentum from phase-structured light (e.g. vortex beams) to a 2D material flake. We model the light-matter interaction, analyze the twist dynamics, and develop a phase diagram for optical twists by mapping t

Ahmed Sharuvan, Ahmed Naufal Abdul Hadee

Data redundancy techniques have been tested in several different applications to provide fault tolerance and performance gains. The use of these techniques is mostly seen at the hardware, device driver, or file system level. In practice, the use of data integrity techniques with logical data has largely been limited to verifying the integrity of transferred

Yunan Wang, Jijie Li, Bo-Wen Zhang, Liangdong Wang

Direct Preference Optimization (DPO) optimizes language models to align with human preferences. Utilizing on-policy samples, generated directly by the policy model, typically results in better performance due to its distribution consistency with the model compared to off-policy samples. This paper identifies the quality of candidate preference samples as ano

DongGeon Lee, Ahjeong Park, Hyeri Lee, Hyeonseo Nam

Addressing non-factoid question answering (NFQA) remains challenging due to its open-ended nature, diverse user intents, and need for multi-aspect reasoning. These characteristics often reveal the limitations of conventional retrieval-augmented generation (RAG) approaches. To overcome these challenges, we propose Typed-RAG, a framework for type-aware decompo

Jiaqi Leng, Yufan Zheng, Zhiyuan Jia, Lei Fan

Non-smooth optimization models play a fundamental role in various disciplines, including engineering, science, management, and finance. However, classical algorithms for solving such models often struggle with convergence speed, scalability, and parameter tuning, particularly in high-dimensional and non-convex settings. In this paper, we explore how quantum

Tiange Xiang, Kai Li, Chengjiang Long, Christian Häne

Recent advances in text-to-image diffusion models have been driven by the increasing availability of paired 2D data. However, the development of 3D diffusion models has been hindered by the scarcity of high-quality 3D data, resulting in less competitive performance compared to their 2D counterparts. To address this challenge, we propose repurposing pre-train

Kai Chen, Zebing Sun

This paper introduces DeepPsy-Agent, an innovative psychological support system that combines the three-stage helping theory in psychology with deep learning techniques. The system consists of two core components: (1) a multi-stage response-capable dialogue model (\textit{deeppsy-chat}), which enhances reasoning capabilities through stage-awareness and deep-

Haiguang Wang, Daqi Liu, Hongwei Xie, Haisong Liu

In recent years, data-driven techniques have greatly advanced autonomous driving systems, but the need for rare and diverse training data remains a challenge, requiring significant investment in equipment and labor. World models, which predict and generate future environmental states, offer a promising solution by synthesizing annotated video data for traini

Etsuko Itou, Akira Matsumoto, Yu Nakayama, Toshiki Onagi

Aharony and Fisher showed that non-local dipolar effects in magnetism destabilize the Heisenberg fixed point in real ferromagnets, leading to a new fixed point, called the dipolar fixed point. The non-perturbative nature of the new fixed point, however, has not been uncovered for many decades. Inspired by the recent understanding that the dipolar fixed point

Elena M. Stetco, Traian Petrisor, Ovidiu A. Pop, Mohamed Belmeguenai

Research on current-induced domain wall (DW) motion in heavy metal/ferromagnet structures is crucial for advancing memory, logic, and computing devices. Here, we demonstrate that adjusting the angle between the DW conduit and the current direction provides an additional degree of control over the current-induced DW motion. A DW conduit with a 45{\deg} sectio

Kobi Kremnizer, David Ssevviiri

Grothendieck constructed a Cousin complex for abelian sheaves on an arbitrary topological space. In a special setting, its dual called the BGG resolution is applicable in representation theory. Arkhipov proposed a complex whose dual is only suitable for representation theory of quantum groups at roots of unity of prime order. It is desirable to get one which

Kyungho Bae, Jinhyung Kim, Sihaeng Lee, Soonyoung Lee

In this work, we tackle action-scene hallucination in Video Large Language Models (Video-LLMs), where models incorrectly predict actions based on the scene context or scenes based on observed actions. We observe that existing Video-LLMs often suffer from action-scene hallucination due to two main factors. First, existing Video-LLMs intermingle spatial and te

Yuxin Miao, Xinyuan Yang, Hongda Fan, Yichun Li

Gastric cancer is one of the most commonly diagnosed cancers and has a high mortality rate. Due to limited medical resources, developing machine learning models for gastric cancer recognition provides an efficient solution for medical institutions. However, such models typically require large sample sizes for training and testing, which can challenge patient

Jing Wang, Yuetao Chen, Shaoyan Gao

The recent investigation into the phenomena of refraction and reflection at temporal boundaries, conducted through the lens of spacetime duality, has attracted considerable scholarly interest. This duality unveils insights into the propagation behaviors of beams at the temporal boundaries of perturbed systems. We have delineated a temporal boundary effect am

Debabrata Mandal, Soumitri Chattopadhyay, Guansen Tong, Praneeth Chakravarthula

Image restoration is essential for enhancing degraded images across computer vision tasks. However, most existing methods address only a single type of degradation (e.g., blur, noise, or haze) at a time, limiting their real-world applicability where multiple degradations often occur simultaneously. In this paper, we propose UniCoRN, a unified image restorati

Rohit Kundu, Shan Jia, Vishal Mohanty, Athula Balachandran

Detecting DeepFakes has become a crucial research area as the widespread use of AI image generators enables the effortless creation of face-manipulated and fully synthetic content, while existing methods are often limited to binary classification (real vs. fake) and lack interpretability. To address these challenges, we propose TruthLens, a novel, unified, a

Dincy R Arikkat, Vinod P., Rafidha Rehiman K. A., Serena Nicolazzo

The widespread adoption of Android devices for sensitive operations like banking and communication has made them prime targets for cyber threats, particularly Advanced Persistent Threats (APT) and sophisticated malware attacks. Traditional malware detection methods rely on binary classification, failing to provide insights into adversarial Tactics, Technique

Jong-Hyun Jeong, Hongki Jo, Qiang Zhou, Tahsin Afroz Hoque Nishat

Wireless sensor networks (WSNs) have become a promising solution for structural health monitoring (SHM), especially in hard-to-reach or remote locations. Battery-powered WSNs offer various advantages over wired systems, however limited battery life has always been one of the biggest obstacles in practical use of the WSNs, regardless of energy harvesting meth

Yuxuan Zhang, Xiaojun Chang, Lin Chen

Consider the Neumann problem: \begin{eqnarray*} \begin{cases} &-\Delta u-\frac{\mu}{|x|^2}u +\lambda u =|u|^{q-2}u+|u|^{p-2}u ~~~\mbox{in}~~\mathbb{R}_+^N,~N\ge3, &\frac{\partial u}{\partial \nu}=0 ~~ \mbox{on}~~ \partial\mathbb{R}_+^N \end{cases} \end{eqnarray*} with the prescribed mass: \begin{equation*} \int_{\mathbb{R}_+^N}|u|^2 dx=a>0, \end{equation*} w

Jonathon L. Yuly

Long ago, life discovered how to efficiently push electrons thermodynamically uphill to lower potential by harnessing energy released by an equal number of electrons moving downhill. Known as electron bifurcation, this form of energy transduction has never been observed in the absence of natural enzymes. To successfully bifurcate electrons, a system must blo

Junguang He, Wei-Ting Lin, J. A. Sauls

We develop a field theory formulation for the interaction of an ensemble of two-level tunneling systems (TLS) with the electronic states of a superconductor. Predictions for the impact of two-level tunneling systems on superconductivity are presented, including $T_c$ and spectrum of quasiparticle states for conventional BCS superconductors. We show that non-

Jie Gan, Zhuonan Liang, Jianan Fan, Lisa Mcguire

The intrapartum ultrasound guideline established by ISUOG highlights the Angle of Progression (AoP) and Head Symphysis Distance (HSD) as pivotal metrics for assessing fetal head descent and predicting delivery outcomes. Accurate measurement of the AoP and HSD requires a structured process. This begins with identifying standardized ultrasound planes, followed

Qu Cao, Fan Zhu

In this letter, we generalize the recursion methods based on cut equations arXiv:2412.21027, originally developed for scalar theories, to gluons in pure Yang-Mills theory. In gauge theories, planar loop integrands are subtle to defined and obtained due to the existence of scaleless integrals. A critical challenge arises when constructing higher-loop integran

Ali Raza, F M Mahomed, F D Zaman, A H Kara

We study the nonlinear wave equation for arbitrary function with fourth order dissipation. A special case that is analysed exclusively is the model of nerve membranes; we consider this model, both, in the presence and absence of the fourth order dissipation. The equivalence transformations, Lie symmetries and a complete classification is presented. We also d

Xiaojun Chang, Vicenţiu D. Rădulescu, Yuxuan Zhang

In this paper, we investigate the following nonlinear Schr\"odinger equation with Neumann boundary conditions: \begin{equation*} \begin{cases} -\Delta u+ \lambda u= f(u) & {\rm in} \,~ \Omega,\\ \displaystyle\frac{\partial u}{\partial \nu}=0 \, &{\rm on}\,~\partial \Omega \end{cases} \end{equation*} coupled with a constraint condition: \begin{equation*} \int

Anthony Pisani

We employ the recently developed hybrid and mmgroup computational models for groups to calculate the character table of $N(\rm{2B}^5) \cong 2^{5+10+20}.( \rm{S}_3 \times \rm{L}_5 {2} )$, a maximal subgroup of the Monster sporadic simple group. This completes the list of the character tables of maximal subgroups of the Monster. Our approach illustrates how th

Emil Albrychiewicz, Andrés Franco Valiente

We study anisotropic scaling limits of topological field theories using tropical geometry. The resulting topological field theories are characterized by foliated geometries and are invariant under foliation-preserving gauge transformations. We demonstrate the tropicalization for the 2D BF theory and generalize the prescription to topological Yang-Mills and C

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

We propose VideoRFSplat, a direct text-to-3D model leveraging a video generation model to generate realistic 3D Gaussian Splatting (3DGS) for unbounded real-world scenes. To generate diverse camera poses and unbounded spatial extent of real-world scenes, while ensuring generalization to arbitrary text prompts, previous methods fine-tune 2D generative models

Durdiev Durdimurod Kalandarovich

In this paper, we consider two linear inverse problems for the time-fractional wave equation, assuming that its right-hand side takes the separable form $f(t)h(x)$, where $t \geq 0$ and $x \in \Omega \subset R^N $. The objective is to determine the unknown function $f(t)$ (Inverse Problem 1) and $h(x)$ (Inverse Problem 2), given that the other function is kn

Ashkan Dehghan, Paweł Prałat, François Théberge

Many real-world and artificial systems and processes can be represented as graphs. Some examples of such systems include social networks, financial transactions, supply chains, and molecular structures. In many of these cases, one needs to consider a collection of graphs, rather than a single network. This could be a collection of distinct but related graphs

Zhenglin Zhou, Fan Ma, Hehe Fan, Tat-Seng Chua

Animatable head avatar generation typically requires extensive data for training. To reduce the data requirements, a natural solution is to leverage existing data-free static avatar generation methods, such as pre-trained diffusion models with score distillation sampling (SDS), which align avatars with pseudo ground-truth outputs from the diffusion model. Ho

Xiaoou Liu, Tiejin Chen, Longchao Da, Chacha Chen

Large Language Models (LLMs) excel in text generation, reasoning, and decision-making, enabling their adoption in high-stakes domains such as healthcare, law, and transportation. However, their reliability is a major concern, as they often produce plausible but incorrect responses. Uncertainty quantification (UQ) enhances trustworthiness by estimating confid

S. Liu, M. Roppongi, M. Kimata, K. Ishihara

The kagome metal CsV$_3$Sb$_5$ exhibits an unusual charge-density-wave (CDW) order, where the emergence of loop current order that breaks time-reversal symmetry (TRS) has been proposed. A key feature of this CDW phase is a non-monotonic Hall effect at low fields, often attributed to TRS breaking. However, its origin remains unclear. Here, we conduct comprehe

Jinghan Zhang, Xiting Wang, Fengran Mo, Yeyang Zhou

Multi-step processes via large language models (LLMs) have proven effective for solving complex reasoning tasks. However, the depth of exploration of the reasoning procedure can significantly affect the task performance. Existing methods to automatically decide the depth often lead to high cost and a lack of flexibility. To address these issues, we propose E

Shuli Zeng, Sijia Zhang, Shaoang Li, Feng Wu

In mixed-integer programming (MIP) solvers, cutting planes are essential for Branch-and-Cut (B&C) algorithms as they reduce the search space and accelerate the solving process. Traditional methods rely on hard-coded heuristics for cut plane selection but fail to leverage problem-specific structural features. Recent machine learning approaches use neural netw

Qishen Zhou, Yifan Zhang, Michail A. Makridis, Anastasios Kouvelas

Network-wide Traffic State Estimation (TSE), which aims to infer a complete image of network traffic states with sparsely deployed sensors, plays a vital role in intelligent transportation systems. With the development of data-driven methods, traffic dynamics modeling has advanced significantly. However, TSE poses fundamental challenges for data-driven appro

Mantas Zilinskas, Christiaan van Buchem, Sebastian Zieba, Yamila Miguel

Recent JWST observations with NIRCam and MIRI of the ultra-short-period super-Earth 55 Cancri e indicate a possible volatile atmosphere surrounding the planet. Previous analysis of the NIRCam spectra suggested potential absorption features from CO2 or CO and significant sub-weekly variability. The MIRI low-resolution spectrum does not contain substantial fea

Tianyi Hao, Amanda Xu, Swamit Tannu

Quantum error correction is essential for achieving practical quantum computing but has a significant computational overhead. Among fault-tolerant (FT) gate operations, non-Clifford gates, such as $T$, are particularly expensive due to their reliance on magic state distillation. These costly $T$ gates appear frequently in FT circuits as many quantum algorith