November 2024 arXiv papers — page 33

Xiaochen Wang, Yuxuan Wu, Feng Zhang, Jin Wang

The neocortex, a complex system driving multi-region interactions, remains a core puzzle in neuroscience. Despite quantitative insights across brain scales, understanding the mechanisms underlying neural activities is challenging. Advances from Hopfield networks to large-scale cortical models have deepened neural network theory, yet these models often fall s

Shad Ali

Black hole (BH) mergers are natural sources of gravitational waves (GWs) and are possibly associated with electromagnetic events. Such events from a charged rotating BH with an accretion on to it could be more energetic and ultra-short-lived if the magnetic force dominates the accretion process because the attraction of ionized fluid with a strong magnetic f

Ratnesh Kumar Joshi, Priyanshu Priya, Vishesh Desai, Saurav Dudhate

Given the advancements in conversational artificial intelligence, the evaluation and assessment of Large Language Models (LLMs) play a crucial role in ensuring optimal performance across various conversational tasks. In this paper, we present a comprehensive study that thoroughly evaluates the capabilities and limitations of five prevalent LLMs: Llama, OPT,

Fabio Marco Monetti, Adam Lundström, Antonio Maffei

This study investigates the barriers to integrating Design for Assembly (DFA) principles within modular product architectures established using the Modular Function Deployment (MFD) method -- a critical stage for deploying mass customization production while reducing costs. Despite the potential benefits of DFA, its application in modular architectures devel

Paul Friedrich, Alicia Durrer, Julia Wolleb, Philippe C. Cattin

This paper contributes to the "BraTS 2024 Brain MR Image Synthesis Challenge" and presents a conditional Wavelet Diffusion Model (cWDM) for directly solving a paired image-to-image translation task on high-resolution volumes. While deep learning-based brain tumor segmentation models have demonstrated clear clinical utility, they typically require MR scans fr

M. Alviggi, M. Biglietti, M. T. Camerlingo, M. Della Pietra

The aim of the presented work is the development of single-stage amplification resistive Micro Pattern Gas Detectors (MPGD) based on Micromegas technology with the following characteristics: ability to efficiently operate up to 10 MHz/cm$^2$ counting rate; scalability to large areas; fine granularity readout with small pads of the order of mm$^2$; good spati

Lorraine Vanel, Ariel R. Ramos Vela, Alya Yacoubi, Chloé Clavel

Conversational systems are now capable of producing impressive and generally relevant responses. However, we have no visibility nor control of the socio-emotional strategies behind state-of-the-art Large Language Models (LLMs), which poses a problem in terms of their transparency and thus their trustworthiness for critical applications. Another issue is that

Hengyu Fu, Zihao Wang, Eshaan Nichani, Jason D. Lee

In deep learning theory, a critical question is to understand how neural networks learn hierarchical features. In this work, we study the learning of hierarchical polynomials of \textit{multiple nonlinear features} using three-layer neural networks. We examine a broad class of functions of the form $f^{\star}=g^{\star}\circ \bp$, where $\bp:\mathbb{R}^{d} \r

Sébastien Mattenet, Tim Van der Linden, Raphaël M. Jungers

A well-known, but often ignored issue in Yoneda-style definitions of cohomology objects via collections of $n$-step extensions (i.e., equivalence classes of exact sequences of a given length $n$ between two given objects, usually subject to further criteria, and equipped with some algebraic structure) is, whether such a collection of extensions forms a set.

Kaixin Deng, Senping Luo

Consider the energy per particle on the lattice given by $\min_{ \Lambda }\sum_{ \mathbb{P}\in \Lambda} \left|\mathbb{P}\right|^4 e^{-\pi \alpha \left|\mathbb{P}\right|^2 }$, where $\alpha >0$ and $\Lambda$ is a two dimensional lattice. We prove that for $\alpha\geq\frac{3}{2}$, among two dimensional lattices with unit density, such energy minimum is attaine

Daniel Malagon, Gabriel Torrens, Jaume Segura, Sebastia A. Bota

We present experimental results of the cross-section related to cosmic-ray irradiation at ground level for minimum-sized six-transistors (6T) and eight-transistors (8T) bit-cells SRAM memories implemented on a 65 nm CMOS standard technology. Results were obtained from accelerated irradiation tests performed in the mixed-field irradiation facility of the CERN

Zhao-Ming Wang, S. L. Wu, Mark S. Byrd, Lian-Ao Wu

The precise characterization of dynamics in open quantum systems often presents significant challenges, leading to the introduction of various approximations to simplify a model. One commonly used strategy involves Markovian approximations, assuming a memoryless environment. In this study, such approximations are not used and an analytical dynamical depictio

Yaowei Jin, Qi Huang, Ziyang Song, Mingyue Zheng

Biological processes, functions, and properties are intricately linked to the ensemble of protein conformations, rather than being solely determined by a single stable conformation. In this study, we have developed P2DFlow, a generative model based on SE(3) flow matching, to predict the structural ensembles of proteins. We specifically designed a valuable pr

Haoyu Zhang, Yang Liu, Yimu Jiang, Weiyang Lin

Visual servoing techniques guide robotic motion using visual information to accomplish manipulation tasks, requiring high precision and robustness against noise. Traditional methods often require prior knowledge and are susceptible to external disturbances. Learning-driven alternatives, while promising, frequently struggle with the scarcity of training data

Di Mo, Keyi Liu, Qi Tian, Dengyun Li

This study explores the application of Artificial Intelligence Generated Content (AIGC) technology in urban planning and design, with a particular focus on its impact on placemaking and public participation. By utilizing natural language pro-cessing and image generation models such as Stable Diffusion, AIGC enables efficient transformation from textual descr

Ergen Bao, Jinbin Zhao, Qiang Gao, Ijaz Shahid

Nitriding introduces nitrides into the surface of steels, significantly enhancing the surface me-chanical properties. By combining the variable composition evolutionary algorithm and first-principles calculations based on density functional theory, 50 thermodynamically stable or metastable Fe-N compounds with various stoichiometric ratios were identified, ex

Erfei Yue

In 1965, Bollob\'as proved that for a Bollob\'as set-pair system $\{(A_i,B_i)\mid i\in[m]\}$, the maximum value of $\sum_{i=1}^m\binom{|A_i|+|B_i|}{A_i}^{-1}$ is $1$. Heged\"{u}s and Frankl recently extended the concept of Bollob\'as systems to $d$-tuples, conjecturing that for a Bollob\'as system of $d$-tuples, $\{(A_i^{(1)},\ldots,A_i^{(d)})\mid i\in[m]\}$

Naoki Matsumura, Yuta Yoshimoto, Tamio Yamazaki, Tomohito Amano

Neural network potentials (NNPs) enable large-scale molecular dynamics (MD) simulations of systems containing >10,000 atoms with the accuracy comparable to ab initio methods and play a crucial role in material studies. Although NNPs are valuable for short-duration MD simulations, maintaining the stability of long-duration MD simulations remains challenging d

Gyeongjin Kang, Jisang Yoo, Jihyeon Park, Seungtae Nam

We propose SelfSplat, a novel 3D Gaussian Splatting model designed to perform pose-free and 3D prior-free generalizable 3D reconstruction from unposed multi-view images. These settings are inherently ill-posed due to the lack of ground-truth data, learned geometric information, and the need to achieve accurate 3D reconstruction without finetuning, making it

Xiyang Tan, Ying Jiang, Xuan Li, Zeshun Zong

We introduce PhysMotion, a novel framework that leverages principled physics-based simulations to guide intermediate 3D representations generated from a single image and input conditions (e.g., applied force and torque), producing high-quality, physically plausible video generation. By utilizing continuum mechanics-based simulations as a prior knowledge, our

Dongping Chen, Ruoxi Chen, Shu Pu, Zhaoyi Liu

Many real-world user queries (e.g. "How do to make egg fried rice?") could benefit from systems capable of generating responses with both textual steps with accompanying images, similar to a cookbook. Models designed to generate interleaved text and images face challenges in ensuring consistency within and across these modalities. To address these challenges

Hodaka Kurokawa, Shintaro Nakazato, Toshiharu Makino, Hiromitsu Kato

A neutral nitrogen-vacancy center (NV$^0$) is promising for realizing strong coupling with a single microwave photon due to its large electric field sensitivity, although it is susceptible to environmental phonon noise at 5 K. Decreasing the temperature to 15 mK results in a tenfold increase in orbital relaxation time compared to that at 5 K. Dynamical decou

Wenyi Wang, Sousuke Noda, Taishi Katsuragawa

The scalar-tensor theory of gravity, a modified gravity theory, introduces a fundamental scalar field that can serve as dynamical dark energy, driving the late-time accelerated expansion of the Universe. In this work, we analyze electromagnetic (EM) radiations arising from scalar fields and compare these features with those induced by axion-like particles (A

Hao-Ran Hu, Xiangang Wan, Wei Chen

A novel collinear magnetic phase, termed ``altermagnetism,'' has recently been uncovered, characterized by zero net magnetization and momentum-dependent collinear spin-splitting. To understand the intriguing physical effects of altermagnets and explore their potential applications, it is crucial to analyze both the geometric and spin configurations of alterm

Marco Casagrande, Riccardo Cestaro, Eleonora Losiouk, Mauro Conti

Battery-powered embedded systems (BESs) have become ubiquitous. Their internals include a battery management system (BMS), a radio interface, and a motor controller. Despite their associated risk, there is little research on BES internal attack surfaces. To fill this gap, we present the first security and privacy assessment of e-scooters internals. We cover

Johan Linåker, Elizabeth Bjarnason, Fabian Fagerholm

[Background] The game industry faces fierce competition and games are developed on short deadlines and tight budgets. Continuously testing and experimenting with new ideas and features is essential in validating and guiding development toward market viability and success. Such continuous experimentation (CE) requires user data, which is often limited in earl

Yunzhe Hu, Difan Zou, Dong Xu

Deep neural networks have long been criticized for being black-box. To unveil the inner workings of modern neural architectures, a recent work \cite{yu2024white} proposed an information-theoretic objective function called Sparse Rate Reduction (SRR) and interpreted its unrolled optimization as a Transformer-like model called Coding Rate Reduction Transformer

Fahim Morshed, Md. Abdur Rahman, Sumon Ahmed

Extractive Text Summarization is the process of selecting the most representative parts of a larger text without losing any key information. Recent attempts at extractive text summarization in Bengali, either relied on statistical techniques like TF-IDF or used naive sentence similarity measures like the word averaging technique. All of these strategies suff

Sylvain Sardy, Maxime van Cutsem, Xiaoyu Ma

The growing environmental footprint of artificial intelligence (AI), especially in terms of storage and computation, calls for more frugal and interpretable models. Sparse models (e.g., linear, neural networks) offer a promising solution by selecting only the most relevant features, reducing complexity, preventing over-fitting and enabling interpretation-mar

Ghorbanali Haghighatdoost

In this paper, we introduce right-invariant Poisson-Nijenhuis Structures on Lie groupoids and their infinitesimal counterparts as called (Poisson bivector, Nijenhuis operator) structures. Also, we present a one-to-one correspondence between (Poisson bivector, Nijenhuis operator) structures on Lie algebroids with (Poisson, Nijenhuis) structures on their Lie g

Rui Xie, Tianchen Zhao, Zhihang Yuan, Rui Wan

Visual Autoregressive (VAR) has emerged as a promising approach in image generation, offering competitive potential and performance comparable to diffusion-based models. However, current AR-based visual generation models require substantial computational resources, limiting their applicability on resource-constrained devices. To address this issue, we conduc

Changfu Shi, Xinyi Che, Zeyu Huang, Yi-Ming Hu

Space-based gravitational wave detectors have the capability to detect signals from very high redshifts. It is interesting to know if such capability can be used to study the global structure of the cosmic space. In this paper, we focus on one particular question: if there exists a reflective cosmic boundary at the high redshift ($z>15$), is it possible to f

Chengyou Jia, Changliang Xia, Zhuohang Dang, Weijia Wu

Despite the significant advancements in text-to-image (T2I) generative models, users often face a trial-and-error challenge in practical scenarios. This challenge arises from the complexity and uncertainty of tedious steps such as crafting suitable prompts, selecting appropriate models, and configuring specific arguments, making users resort to labor-intensi

Yoshikazu Giga, Michael Gösswein, Sho Katayama

We consider a surface diffusion flow of the form $V=\partial_s^2f(-\kappa)$ with a strictly increasing smooth function $f$ typically, $f(r)=e^r$, for a curve with arc-length parameter $s$, where $\kappa$ denotes the curvature and $V$ denotes the normal velocity. The conventional surface diffusion flow corresponds to the case when $f(r)=r$. We consider this e

Xin Liu, Shibei Xue, Dezong Zhao, Shan Ma

6D object pose estimation is crucial for robotic perception and precise manipulation. Occlusion and incomplete object visibility are common challenges in this task, but existing pose refinement methods often struggle to handle these issues effectively. To tackle this problem, we propose a global motion-guided recurrent flow estimation method called GMFlow fo

Mukesh Singh Bisht, Prateek Sharma, Alankar Dutta, Biman B. Nath

The circumgalactic medium (CGM) is the largest baryon reservoir around galaxies, but its extent, mass, and temperature distribution remain uncertain. We propose that cool gas ($\sim 10^4$ K) in the CGM resides in clumpy structures referred to as cloud complexes (CCs) rather than uniformly filling the entire CGM volume. Each CC contains a mist of tiny cool cl

Supun B. Attanayake, Minh Dang Nguyen, Amit Chanda, Javier Alonso

Iron oxide (e.g., Fe$_3$O$_4$ or Fe$_2$O$_3$) nanoparticles are promising candidates for a variety of biomedical applications ranging from magnetic hyperthermia therapy to drug delivery and bio-detection, due to their superparamagnetism, non-toxicity, and biodegradability. While particles of small size (below a critical size, ~20 nm) display superparamagneti

T. A. Movsessian, T. Yu. Magakian, A. V. Moiseev

We continue to study the structure and kinematics of HH flows. Herbig-Haro (HH) flows exhibit large variety of morphological and kinematical structures. Both proper motion (PM) and radial velocity investigations are essential to understand the physical nature of such structures. We investigate the kinematics and PM of spectrally separated structures in the P

Zhengrui Ma, Yang Feng, Min Zhang

Streaming generation models are utilized across fields, with the Transducer architecture being popular in industrial applications. However, its input-synchronous decoding mechanism presents challenges in tasks requiring non-monotonic alignments, such as simultaneous translation. In this research, we address this issue by integrating Transducer's decoding wit

V. M. Tripathi

In this paper, we study the multiplicity of nonnegative solutions for mixed local and non-local problem involving critical nonlinearity with sign changing weight. Using Nehari manifold method and fibering map analysis, we have shown existence of two solutions.

Francesco Maltese

In this article we introduced algebraic sieves, i.e. selection procedures on a given finite set to extract a particular subset. Such procedures are performed by finite groups acting on the set. They are called sieves because there are certain sets of numbers which, with appropriate groups, can select, for example, a set of primes, think of the famous Eratost

Juefei He, Yuexing Peng, Wei Li, Junchuan Yu

Relic landslide, formed over a long period, possess the potential for reactivation, making them a hazardous geological phenomenon. While reliable relic landslide detection benefits the effective monitoring and prevention of landslide disaster, semantic segmentation using high-resolution remote sensing images for relic landslides faces many challenges, includ

Peng Cui, Guande He, Dan Zhang, Zhijie Deng

Datasets collected from the open world unavoidably suffer from various forms of randomness or noiseness, leading to the ubiquity of aleatoric (data) uncertainty. Quantifying such uncertainty is particularly pivotal for object detection, where images contain multi-scale objects with occlusion, obscureness, and even noisy annotations, in contrast to images wit

Max Sun Zhou

We analyze the Brown measure the non-normal operators $X = p + i q$, where $p$ and $q$ are Hermitian, freely independent, and have spectra consisting of finitely many atoms. We use the Quaternionic Green's function, an analogue of the operator-valued $R$-transform in the physics literature, to understand the support and the boundary of the Brown measure of $

Arpan Chakraborty, Siddhartha Chattopadhyay

This paper extends the New Keynesian Dynamic Stochastic General Equilibrium (DSGE) framework by incorporating behavioral expectations to analyze the moments of India's output gap and inflation rate, with a particular focus on the impacts of COVID-19 and vaccination programs. While DSGE models traditionally rely on rational expectations, we demonstrate that b

Mohammad Amin Nabian, Chang Liu, Rishikesh Ranade, Sanjay Choudhry

Graph Neural Networks (GNNs) have gained significant traction for simulating complex physical systems, with models like MeshGraphNet demonstrating strong performance on unstructured simulation meshes. However, these models face several limitations, including scalability issues, requirement for meshing at inference, and challenges in handling long-range inter

Jingkai Wang, Jue Gong, Lin Zhang, Zheng Chen

Diffusion models have demonstrated impressive performance in face restoration. Yet, their multi-step inference process remains computationally intensive, limiting their applicability in real-world scenarios. Moreover, existing methods often struggle to generate face images that are harmonious, realistic, and consistent with the subject's identity. In this wo

Kun Dong, Yihao Lin, Xiaoqiang Liu, Jiechao Feng

A recursive extension of the hybrid tetrahedron method for Brillouin-zone integration is proposed, allowing iterative tetrahedron refinement and significantly reducing the error from the linear tetrahedron method. The Brillouin-zone integral is expressed as a weighted sum on the initial grid, with integral weights collected recursively from the finest grid.

Peijin Jia, Ziang Luo, Tuopu Wen, Mengmeng Yang

In autonomous driving, recent advances in lane segment perception provide autonomous vehicles with a comprehensive understanding of driving scenarios. Moreover, incorporating prior information input into such perception model represents an effective approach to ensure the robustness and accuracy. However, utilizing diverse sources of prior information still

Van Thang Nguyen

Typical deep neural video compression networks usually follow the hybrid approach of classical video coding that contains two separate modules: motion coding and residual coding. In addition, a symmetric auto-encoder is often used as a normal architecture for both motion and residual coding. In this paper, we propose a novel approach that handles the drawbac

Max Sun Zhou

We consider the random matrix model $X_n = P_n + i Q_n$, where $P_n$ and $Q_n$ are independently Haar-unitary rotated Hermitian matrices with at most $2$ atoms in their spectra. Let $(M, \tau)$ be a tracial von Neumann algebra and let $p, q \in (M, \tau)$, where $p$ and $q$ are Hermitian and freely independent. Our main result is the following convergence re

Rongrong Zhang, Shengjie Wan, Jiarui Xiong, Lei Ni

Natural biological systems process environmental information through both amplitude and frequency-modulated signals, yet engineered biological circuits have largely relied on amplitude-based regulation alone. Despite the prevalence of frequency-encoded signals in natural systems, fundamental challenges in designing and implementing frequency-responsive gene

Qiufang Yu, Xingyu Lan

Powered by large language models, the conversational capabilities of AI have seen significant improvements. In this context, a series of role-playing AI chatbots have emerged, exhibiting a strong tendency toward anthropomorphism, such as conversing like humans, possessing personalities, and fulfilling social and companionship functions. Informed by media dep

Nan Zhang, Daifeng Tu, Ding Li, Kaixin Tang

In Landau's celebrated Fermi liquid theory, electrons in a metal obey the Wiedemann--Franz law at the lowest temperatures. This law states that electron heat and charge transport are linked by a constant $L_0$, i.e., the Sommerfeld value of the Lorenz number ($L$). Such relation can be violated at elevated temperatures where the abundant inelastic scattering

Rodrigue de Schaetzen, Alexander Botros, Ninghan Zhong, Kevin Murrant

Ice conditions often require ships to reduce speed and deviate from their main course to avoid damage to the ship. In addition, broken ice fields are becoming the dominant ice conditions encountered in the Arctic, where the effects of collisions with ice are highly dependent on where contact occurs and on the particular features of the ice floes. In this pap

Avinash Amballa, Durga Sandeep Saluru, Gayathri Akkinapalli, Abhishek Sureddy

Large language models (LLMs) have demonstrated remarkable capabilities in complex reasoning and text generation. However, these models can inadvertently generate unsafe or biased responses when prompted with problematic inputs, raising significant ethical and practical concerns for real-world deployment. This research addresses the critical challenge of deve

Kevin Yuanbo Wu, Jin Li, Aaron Esser-Kahn, Ishanu Chattopadhyay

Despite having triggered devastating pandemics in the past, our ability to quantitatively assess the emergence potential of individual strains of animal influenza viruses remains limited. This study introduces Emergenet, a tool to infer a digital twin of sequence evolution to chart how new variants might emerge in the wild. Our predictions based on Emergenet

Tzu-Ting Yang, Hsin-Wei Wang, Yi-Cheng Wang, Berlin Chen

Code-switching-where multilingual speakers alternately switch between languages during conversations-still poses significant challenges to end-to-end (E2E) automatic speech recognition (ASR) systems due to phenomena of both acoustic and semantic confusion. This issue arises because ASR systems struggle to handle the rapid alternation of languages effectively

Sicheng Liu, Tao Luo

This manuscript concerns the dynamics of non-isentropic compressible Euler equations in a physical vacuum. We establish the Hadamard-style local well-posedness in low-regularity weighted Sobolev spaces, where the gas-vacuum interface is allowed to have unbounded curvature, demonstrating existence, uniqueness, and continuous dependence on initial data. Additi

Zhixiong Nan, Yilong Chen, Tianfei Zhou, Tao Xiang

This paper addresses the problem of on-road object importance estimation, which utilizes video sequences captured from the driver's perspective as the input. Although this problem is significant for safer and smarter driving systems, the exploration of this problem remains limited. On one hand, publicly-available large-scale datasets are scarce in the commun

Kayumov Abduaziz, Min Sik Kim, Ji Sun Shin

Hierarchical Density-Based Spatial Clustering of Applications with Noise (HDBSCAN) finds meaningful patterns in spatial data by considering density and spatial proximity. As the clustering algorithm is inherently designed for static applications, so have recent studies focused on accelerating the algorithm for static applications using approximate or paralle

Li Lin, Yanjie Zhang, Ao Zhang

We study the random attractors associated with the stochastic fractional Schr\"odinger equation on $\mathbb{R}^n$. Utilizing the stochastic Strichartz estimates for the damped fractional Schr\"odinger equation with Gaussian noise, we show the existence and uniqueness of a global solution to the damped stochastic fractional nonlinear Schr\"odinger equation in

Justin Jiang

The rise of advanced AI models like Generative Adversarial Networks (GANs) and diffusion models such as Stable Diffusion has made the creation of highly realistic images accessible, posing risks of misuse in misinformation and manipulation. This study evaluates the effectiveness of convolutional neural networks (CNNs), as well as DenseNet architectures, for

Chanyoung Kim, Dayun Ju, Woojung Han, Ming-Hsuan Yang

Open-Vocabulary Semantic Segmentation (OVSS) has advanced with recent vision-language models (VLMs), enabling segmentation beyond predefined categories through various learning schemes. Notably, training-free methods offer scalable, easily deployable solutions for handling unseen data, a key goal of OVSS. Yet, a critical issue persists: lack of object-level

Priyanka Kommagouni, Vamshiraghusimha Narasinga, Purva Barche, Sai Akarsh C

Speech disfluencies in spontaneous communication can be categorized as either typical or atypical. Typical disfluencies, such as hesitations and repetitions, are natural occurrences in everyday speech, while atypical disfluencies are indicative of pathological disorders like stuttering. Distinguishing between these categories is crucial for improving voice a

Aditya Mahadevan, Daniel S. Fisher

Feedbacks between evolution and ecology are ubiquitous, with ecological interactions determining which mutants are successful, and these mutants in turn modifying community structure. We study the evolutionary dynamics of several ecological models with overlapping niches, including consumer resource and Lotka-Volterra models. Evolution is assumed slow and ex

Beomjong Kwak, Soonsik Kwon

In this paper, we study the local well-posedness of nonlinear Schr\"odinger equations on tori $\mathbb{T}^{d}$ at the critical regularity. We focus on cases where the nonlinearity $|u|^{a}u$ is non-algebraic with small $a>0$. We prove the local well-posedness for a wide range covering the mass-supercritical regime. Moreover, we supplementarily investigate th

Jinke Yang, Yong Xie, Wenlin Tang, Xindong Liang

In the detection of gravitational waves in space, during the science phase of the mission, the point ahead angle mechanism (PAAM) serves to steer a laser beam to compensate for the angle generated by the relative motion of the two spacecrafts (SCs) during the approximately 10 seconds of flight time a laser beam will take from one SC to reach a distant SC of

Amber E. Gentle, Daniel Horsley, Ian M. Wanless

A sequence covering array, denoted \textsf{SCA}$(N;t,v)$, is a set of $N$ permutations of $\{0, \dots, v-1 \}$ such that each sequence of $t$ distinct elements of $\{0, \dots, v-1\}$ reads left to right in at least one permutation. The minimum number of permutations such a sequence covering array can have is $t!$ and Levenshtein conjectured that if a sequenc

Andrei Grekov, Nikita Nekrasov

We prove the noncommutative analogue of Jacobi triple product identity. As an application we organizing the q-characters of circular quiver gauge theories into an infinite product. We conjecture the gauge origami theory interpretation of the Jacobi identity.

JérŔemy Blanc

We prove that the group $\mathrm{SAut}_{\mathrm{k}}(\mathbb{A}^2)$ is simple as an algebraic group of infinite dimension, over any infinite field $\mathrm{k}$, by proving that any closed normal subgroup is either trivial or the whole group. In higher dimension, we show that closed normal subgroups contain all tame automorphisms. The case of finite fields, ve

Dijia Zhong, Duo Qi, Jaehyung Ju

Mechanical couplings with symmetry breaking open up novel applications such as robotic metamaterials and directional mechanical signal guidance. However, most studies on 3D mechanical couplings have been limited to ad-hoc axial-twist designs due to a lack of comprehensive understanding of 3D non-centrosymmetry and chirality. Few theoretical methods exist to

Xu Zheng, Haiwei Xue, Jialei Chen, Yibo Yan

Simultaneously using multimodal inputs from multiple sensors to train segmentors is intuitively advantageous but practically challenging. A key challenge is unimodal bias, where multimodal segmentors over rely on certain modalities, causing performance drops when others are missing, common in real world applications. To this end, we develop the first framewo

Feng Ding

Crack detection plays a pivotal role in the maintenance and safety of infrastructure, including roads, bridges, and buildings, as timely identification of structural damage can prevent accidents and reduce costly repairs. Traditionally, manual inspection has been the norm, but it is labor-intensive, subjective, and hazardous. This paper introduces an advance

Jing-Yang Wei, Hao Huang, Xin Zhang, De-Mao Ye

To provide a lightweight and cost-effective solution for the long-wave infrared imaging using a singlet, we develop a camera by integrating a High-Frequency-Enhancing Cycle-GAN neural network into a metalens imaging system. The High-Frequency-Enhancing Cycle-GAN improves the quality of the original metalens images by addressing inherent frequency loss introd

Jiaxun Li, Yonghou He, Zhefan Dong, Li Tao

Identifying influential spreaders in complex networks is a critical challenge in network science, with broad applications in disease control, information dissemination, and influence analysis in social networks. The gravity model, a distinctive approach for identifying influential spreaders, has attracted significant attention due to its ability to integrate

Tianle Liu, Zhixiang Wang, Yongwei Zhang, Ziwei Wang

This paper proposes a distributed on-orbit spacecraft assembly algorithm, where future spacecraft can assemble modules with different functions on orbit to form a spacecraft structure with specific functions. This form of spacecraft organization has the advantages of reconfigurability, fast mission response and easy maintenance. Reasonable and efficient on-o

Yujeong Lee, Sangwoo Shin, Wei-Jin Park, Honguk Woo

Employing large language models (LLMs) to enable embodied agents has become popular, yet it presents several limitations in practice. In this work, rather than using LLMs directly as agents, we explore their use as tools for embodied agent learning. Specifically, to train separate agents via offline reinforcement learning (RL), an LLM is used to provide dens

Minho Oh, Byeongho Yu, I Made Aswin Nahrendra, Seoyeon Jang

Accurate traversability estimation using an online dense terrain map is crucial for safe navigation in challenging environments like construction and disaster areas. However, traversability estimation for legged robots on rough terrains faces substantial challenges owing to limited terrain information caused by restricted field-of-view, and data occlusion an

Ravi Shankar

We show removability of half-line singularities for viscosity solutions of fully nonlinear elliptic PDEs which have classical density and a Jacobi inequality. An example of such a PDE is the Monge-Amp\`ere equation, and the original proof follows from Caffarelli 1990. Other examples are the minimal surface and special Lagrangian equations. The present paper'

Hoang-Chau Luong, Quang-Thuc Nguyen, Dat Ba Tran, Minh-Triet Tran

Sharpness-Aware Minimization (SAM) was introduced to improve generalization by seeking flat minima, yet it also exhibits robustness to label noise, a phenomenon that remains only partially understood. Prior work has mainly attributed this effect to SAM's tendency to prolong the learning of clean samples. In this work, we provide a complementary explanation b

Riccardo Fantoni

I propose a possible way to introduce the effect of temperature (defined through the virial theorem) into Einstein's theory of general relativity. This requires the computation of a path integral on a 10-dimensional flat space in a four dimensional spacetime lattice. Standard path integral Monte Carlo methods can be used to compute it.

Yuan-Ming Li, An-Lan Wang, Kun-Yu Lin, Yu-Ming Tang

To guide a learner in mastering action skills, it is crucial for a coach to 1) reason through the learner's action execution and technical points (TechPoints), and 2) provide detailed, comprehensible feedback on what is done well and what can be improved. However, existing score-based action assessment methods are still far from reaching this practical scena

Robert C. Sargent

We present an algorithm for creating contiguous cartograms using meshes. We use numerical optimization to minimize cartographic error and distortion by transforming the mesh vertices. The vertices can either be optimized in the plane or optimized on the unit sphere and subsequently projected to the plane. We also present a hybrid "best of both worlds" method

M. Tanveer, Anushka Tiwari, Mushir Akhtar, C. T. Lin

In real-world applications, class-imbalanced datasets pose significant challenges for machine learning algorithms, such as support vector machines (SVMs), particularly in effectively managing imbalance, noise, and outliers. Fuzzy support vector machines (FSVMs) address class imbalance by assigning varying fuzzy memberships to samples; however, their sensitiv

Zile Hui

This work establishes a definition that is more basic than the previous ones, for the Stirling numbers of first kind, which is a sufficient but not necessary condition for the previous definition. Based on this definition and a combinatorial problem, we discover C sequential optimization numbers, where C is a k+1-tuple vector. For C= (0,1), we prove that C s

Yuncong Yang, Xiao Han, Yidong Chai, Reza Ebrahimi

Recent privacy regulations (e.g., GDPR) grant data subjects the `Right to Be Forgotten' (RTBF) and mandate companies to fulfill data erasure requests from data subjects. However, companies encounter great challenges in complying with the RTBF regulations, particularly when asked to erase specific training data from their well-trained predictive models. While

Yinan Zhou, Yuxin Chen, Haokun Lin, Yichen Wu

With recent advances in Multimodal Large Language Models (MLLMs), grounding and referring capabilities have gained increasing attention for achieving detailed understanding and flexible user interaction. However, these capabilities still remain underdeveloped in visual document understanding due to the scarcity of fine-grained datasets and comprehensive benc

Zhang Guozhen, Yu Zihan, Li Nian, Yu Fudan

In recent years, human behavior simulation has drawn increasing attention from both academia and industry. The reasons fall into two aspects. First, simulation serves as a critical tool for understanding human behaviors, which has become one of the most important research topics in the history. Second, researchers have gradually reached a consensus that simu

Xing Wang, Liam Horrigan, Josh Pinskier, Ge Shi

The ability of robotic grippers to not only grasp but also re-position and re-orient objects in-hand is crucial for achieving versatile, general-purpose manipulation. While recent advances in soft robotic grasping has greatly improved grasp quality and stability, their manipulation capabilities remain under-explored. This paper presents the DexGrip, a multi-

Nouar AlDahoul, Myles Joshua Toledo Tan, Harishwar Reddy Kasireddy, Yasir Zaki

The widespread dissemination of hate speech, harassment, harmful and sexual content, and violence across websites and media platforms presents substantial challenges and provokes widespread concern among different sectors of society. Governments, educators, and parents are often at odds with media platforms about how to regulate, control, and limit the sprea

Adam Johnson

In this paper, using the approximate particular solutions of Helmholtz equations, we solve the boundary value problems of Helmholtz equations by combining the methods of fundamental solutions (MFS) with the methods of particular solutions (MPS). Then the initial boundary value problems of the time dependent diffusion and wave equations are discretized numeri

Yuqiang Li, Junzhi Wang, Juan Li, Prathap Rayalacheruvu

Deuteration is sensitive to environmental conditions in star-forming regions. To investigate NH$_2$D chemistry, we compared the spatial distribution of ortho-NH$_2$D $1_{11}^s-1_{01}^a$, NH$_3$(1,1) and NH$_3$(2,2) in 12 late-stage massive star-forming regions. By averaging several pixels along the spatial slices of ortho-NH$_2$D $1_{11}^s-1_{01}^a$, we obta

P. Popesso, I. Marini, K. Dolag, G. Lamer

This study examines the average X-ray properties of massive halos at z< 0.2, covering the largest halo mass range to date, from Milky Way-like halos to massive clusters. The analysis is based on stacking in the eFEDS area of the GAMA galaxy group sample, validated with synthetic data that mimic observed eROSITA X-ray and GAMA optical data using Magneticum li

Zhaohu Nie, C. Xavier Parent

We give explicit sets of right coset representatives for the congruence subgroups $Γ_0(N)$, $Γ_1(N)$ and $Γ(N)$, and prove that the corresponding unions of standard modular triangles are connected fundamental domains. The construction is based on a study of the projective line ${\mathbb P}^1({\mathbb Z}/N{\mathbb Z})$. For every residue class $j\in{\mathbb Z

Ian P. Kemp, Nickolas M. Pingel, Rowan Worth, Justin Wake

Modern radio telescopes generate large amounts of data, with the next generation Very Large Array (ngVLA) and the Square Kilometre Array (SKA) expected to feed up to 292 GB of visibilities per second to the science data processor (SDP). However, the continued exponential growth in the power of the world's largest supercomputers suggests that for the foreseea

Snigdha Ghosh, Nilanjan Chaudhuri, Sourav Sarkar, Pradip Roy

The electrical conductivity of hot and dense quark matter is calculated using the 2-flavour gauged Nambu-Jona--Lasinio (NJL) model in the presence of a chiral imbalance quantified in terms of a chiral chemical potential (CCP). To this end, the in-medium spectral function corresponding to the vector current correlator is evaluated employing the real time form

Shantanu Acharya, Fei Jia, Boris Ginsburg

Inference with Transformer-based Large Language Models (LLMs) on long sequences is both costly and slow due to the quadratic complexity of the self-attention mechanism. We introduce Star Attention, a two-phase block-sparse approximation that improves computational efficiency by sharding attention across multiple hosts while minimizing communication overhead.

Sosuke Inui, Faezeh Ahangar, Wei Guo

The morphology of rotating viscous classical liquid droplets has been extensively studied and is well understood. However, our understanding of rotating superfluid droplets remains limited. For instance, superfluid $^4$He (He II) can carry angular momentum through two distinct mechanisms: the formation of an array of quantized vortex lines, which induce flow

Wataru Ooyama, Riouhei Nakatani, Takashi Hosokawa, Hiroto Mitani

While protoplanetary disks (PPDs) are generally thought to disperse within several million years, recent observations have revealed gas in their older counterparts, debris disks. The origin of this gas remains uncertain, with one possibility being the unexpectedly long survival of PPDs (the primordial-origin scenario). To explore the plausibility of this sce