November 2024 arXiv papers — page 16

Hazem Ibrahim, Farhan Khan, Hend Alabdouli, Maryam Almatrooshi

Social media platforms play a pivotal role in shaping public opinion and amplifying political discourse, particularly during elections. However, the same dynamics that foster democratic engagement can also exacerbate polarization. To better understand these challenges, here, we investigate the ideological positioning of tweets related to the 2024 U.S. Presid

Jiancheng Zhang, Peiran Dong, Yongyong Chen, Yin-Ping Zhao

Denoising Diffusion Probabilistic Models (DDPMs) have gained great attention in adversarial purification. Current diffusion-based works focus on designing effective condition-guided mechanisms while ignoring a fundamental problem, i.e., the original DDPM sampling is intended for stable generation, which may not be the optimal solution for adversarial purific

Hui Li, Mingwang Xu, Yun Zhan, Shan Mu

Recent advancements in visual generation technologies have markedly increased the scale and availability of video datasets, which are crucial for training effective video generation models. However, a significant lack of high-quality, human-centric video datasets presents a challenge to progress in this field. To bridge this gap, we introduce OpenHumanVid, a

Ariful Azad, Afeefa Banu

Papers published in top conferences contribute influential discoveries that are reshaping the landscape of modern Artificial Intelligence (AI). We analyzed 87,137 papers from 11 AI conferences to examine publication trends over the past decade. Our findings reveal a consistent increase in both the number of papers and authors, reflecting the growing interest

Jingyan Li, Yuri Muranov, Jie Wu, Shing-Tung Yau

In this paper we introduce a primitive path homology theory on the category of simple digraphs. On the subcategory of asymmetric digraphs, this theory coincides with the path homology theory which was introduced by Grigor'yan, Lin, Muranov, and Yau, but these theories are different in general case. We study properties of the primitive path homology and descr

Yaomin Wang, Chaolong Ying, Xiaodong Luo, Tianshu Yu

Scalable high-quality MAP inference in arbitrary-order Markov Random Fields (MRFs) remains challenging. Approximate message-passing methods are often efficient but can degrade on dense or high-order instances, while exact solvers such as Toulbar2 become increasingly expensive at scale. We present ReMAP, an instance-wise neural reparameterization framework th

Jisheng Bai, Haohe Liu, Mou Wang, Dongyuan Shi

With the emergence of audio-language models, constructing large-scale paired audio-language datasets has become essential yet challenging for model development, primarily due to the time-intensive and labour-heavy demands involved. While large language models (LLMs) have improved the efficiency of synthetic audio caption generation, current approaches strugg

Henrik Bachmann, Khalef Yaddaden

We provide a proof of a conjecture by Zhao concerning the structure of certain relations among cyclotomic multiple zeta values in weight two. We formulate this conjecture in a broader algebraic setting in which we give a natural equivalence between two schemes attached to a finite abelian group $G$. In particular, when $G$ is the group of roots of unity, the

Samuel Cuthbertson, Glen Wheeler, Valentina Wheeler

In this paper we introduce the target flow -- a specific curve shortening flow with an ambient forcing term -- that, given an embedded (not necessarily convex) target curve, will attempt to evolve a given source curve to that target. The motivation for this flow is to address a question of Yau. Our main result is that the target flow with uniformly normal gr

Tamali Roy, Prasanta Chowdhury, Mohamad Numan, Saurav Giri

The quasi two-dimensional kagome ferrimagnet TbMn$_6$Sn$_6$ is investigated for thermo-remanent magnetization and Hall effects. On cooling under a moderate magnetic field, the sample attains a magnetization value close to the saturation magnetization. Upon heating in a very small magnetic field, the sample continues to maintain the large value of magnetizati

Tianyi Zhou, Ding Ding, Shengyu Wang, Chuhan Shi

In this study, we compare the virtual and real gait parameters to investigate the effect of appearances of embodied avatars and virtual reality experience on gait in physical and virtual environments. We developed a virtual environment simulation and gait detection system for analyzing gait. The system transfers real-life scenarios into a realistic presentat

Xue Tan, Hao Luan, Mingyu Luo, Xiaoyan Sun

Retrieval-Augmented Generation (RAG) enriches the input to LLMs by retrieving information from the relevant knowledge database, enabling them to produce responses that are more accurate and contextually appropriate. It is worth noting that the knowledge database, being sourced from publicly available channels such as Wikipedia, inevitably introduces a new at

Marie Al Ghossein, Emile Contal, Alexandre Robicquet

In-Context Learning (ICL) enables Large Language Models (LLMs) to perform new tasks by conditioning on prompts with relevant information. Retrieval-Augmented Generation (RAG) enhances ICL by incorporating retrieved documents into the LLM's context at query time. However, traditional retrieval methods focus on semantic relevance, treating retrieval as a searc

Frederik vom Ende, Fereshte Shahbeigi

This paper introduces the concept of a generating set for stochastic matrices -- a subset of matrices whose repeated composition generates the entire set. Understanding such generating sets requires specifying the "indivisible elements" and "building blocks" within the set, which serve as fundamental components of the generation process. Expanding upon prior

Jiaqi Yao, Lewis Mitchell

Community detection can reveal the underlying structure and patterns of complex networks, identify sets of nodes with specific functions or similar characteristics, and study the evolution process and development trends of networks. Despite the myriad community detection methods that have been proposed, researchers continue to strive for ways to enhance the

Dhashin Krishna, Mayuri Sathyanarayana Rao

Photons emitted during the formation of primordial hydrogen and helium atoms over the Epoch of Recombination are expected to be preserved as additive distortions to the Cosmic Microwave Background (CMB) spectrum. The 'ripple' like spectral features from Cosmological Recombination Radiation (CRR) have never been detected, and are expected to be 9 orders of ma

Navin Ranjan, Bruno Artacho, Andreas Savakis

We propose the Waterfall Transformer architecture for Pose estimation (WTPose), a single-pass, end-to-end trainable framework designed for multi-person pose estimation. Our framework leverages a transformer-based waterfall module that generates multi-scale feature maps from various backbone stages. The module performs filtering in the cascade architecture to

V. V. Flambaum, A. J. Mansour

Deformed nuclei exhibit enhanced moments that violate time-reversal invariance ($T$) and parity ($P$). This paper focuses on the enhanced nuclear electric dipole moment (EDM) and Schiff moment present in nuclei with octupole deformation (pear-shaped nuclei). These moments, which are proportional to the octupole deformation, have a collective nature and are l

Dhruv Kohli, Jesse He, Chester Holtz, Alexander Cloninger

This paper addresses the problem of detecting boundary points and estimating the sampling density of a dataset derived from a compact manifold with boundary, potentially in the presence of noise. We extend recent advances in doubly stochastic scaling of the Gaussian heat kernel via Sinkhorn iterations to this setting. Our main contributions are: (a) deriving

Hongda Liu, Yunfan Liu, Min Ren, Hao Wang

In skeleton-based action recognition, a key challenge is distinguishing between actions with similar trajectories of joints due to the lack of image-level details in skeletal representations. Recognizing that the differentiation of similar actions relies on subtle motion details in specific body parts, we direct our approach to focus on the fine-grained moti

Jinghui Liu, Anthony Nguyen

Clinical language models are important for many applications in healthcare, but their development depends on access to extensive clinical text for pretraining. However, obtaining clinical notes from electronic health records (EHRs) at scale is challenging due to patient privacy concerns. In this study, we rephrase existing clinical notes using LLMs to genera

Jose A. Terrazas, Piyush Kumar, Arturo Rodriguez, Luis F. Rodriguez

Specializing in high-speed testing, Holloman High-Speed Test Track (HHSTT) uses water braking to stop vehicles on the test track. This method takes advantage of the higher density of water, compared to air, to increase braking capability through momentum exchange by increasing the water content in that section at the end of the track. By studying water braki

Henan Sun, Xunkai Li, Daohan Su, Junyi Han

In recent years, Graph Neural Networks (GNNs) have made significant advances in processing structured data. However, most of them primarily adopted a model-centric approach, which simplifies graphs by converting them into undirected formats and emphasizes model designs. This approach is inherently limited in real-world applications due to the unavoidable inf

Dionne Ibarra, Daniel V. Mathews, Jessica S. Purcell, Jonathan Spreer

We prove that for any knot $K$, there exists a one-vertex triangulation of the $3$-sphere containing an edge forming $K$. The proof is constructive, and based on fully augmented links. We use our method to produce ``complicated'' simplicial triangulations of the $3$-sphere that we show are smallest possible, up to a constant multiplicative factor.

Rishabh Sharma, Siddhartha Solanki, Manohar Lal, Vineet Kumar Agotiya

In this article, we have studied the dissociation temperature of 1S and 2S states of heavy quarkonium in the presence of anisotropy and a strong magnetic field background using the dissociation energy criterion. We utilized the medium-modified form of the Cornell potential, which depends on temperature as well as the anisotropic parameter {\xi} and the magne

Weimin Qiu, Jieke Wang, Meng Tang

Diffusion models achieved unprecedented fidelity and diversity for synthesizing image, video, 3D assets, etc. However, subject mixing is an unresolved issue for diffusion-based image synthesis, particularly for synthesizing multiple similar-looking subjects. We propose Self-Cross Diffusion Guidance to penalize the overlap between cross-attention maps and the

Zongwei Li, Wenkai Li, Xiaoqi Li, Yuqing Zhang

The decentralized exchange (DEX) leverages smart contracts to trade digital assets for users on the blockchain. Developers usually develop several smart contracts into one project, implementing complex logic functions and multiple transaction operations. However, the interaction among these contracts poses challenges for developers analyzing the state logic.

Yue Cao, Yun Xing, Jie Zhang, Di Lin

Large vision-language models (LVLMs) have shown remarkable capabilities in interpreting visual content. While existing works demonstrate these models' vulnerability to deliberately placed adversarial texts, such texts are often easily identifiable as anomalous. In this paper, we present the first approach to generate scene-coherent typographic adversarial at

Caldon T. Whyte, L. H. Quiroga-Nuñez, Manasvi Lingam, Paola Pinilla

With recent observations confirming exoplanets orbiting white dwarfs, there is growing interest in exploring and quantifying the habitability of temperate rocky planets around white dwarfs. In this work, the limits of the habitable zone of an Earth-like planet around a white dwarf are computed based on the incident stellar flux, and these limits are utilized

Yunyang Xiong, Chong Zhou, Xiaoyu Xiang, Lemeng Wu

Segment Anything Model 2 (SAM 2) has emerged as a powerful tool for video object segmentation and tracking anything. Key components of SAM 2 that drive the impressive video object segmentation performance include a large multistage image encoder for frame feature extraction and a memory mechanism that stores memory contexts from past frames to help current f

Rao Fu, Ziyang Luo, Hongzhan Lin, Zhen Ye

Recent advancements in large multimodal models (LMMs) have showcased impressive code generation capabilities, primarily evaluated through image-to-code benchmarks. However, these benchmarks are limited to specific visual programming scenarios where the logic reasoning and the multimodal understanding capacities are split apart. To fill this gap, we propose S

Kart-Leong Lim, Rahul Dutta, Mihai Rotaru

Traditional electrostatic simulation are meshed-based methods which convert partial differential equations into an algebraic system of equations and their solutions are approximated through numerical methods. These methods are time consuming and any changes in their initial or boundary conditions will require solving the numerical problem again. Newer comput

Seong-Eun Hong, Soobin Lim, Juyeong Hwang, Minwook Chang

Generating natural and expressive human motions from textual descriptions is challenging due to the complexity of coordinating full-body dynamics and capturing nuanced motion patterns over extended sequences that accurately reflect the given text. To address this, we introduce BiPO, Bidirectional Partial Occlusion Network for Text-to-Motion Synthesis, a nove

Timothy T. Duignan

Potassium ion channels are critical components of biology. They conduct potassium ions across the cell membrane with remarkable speed and selectivity. Understanding how they do this is crucially important for applications in neuroscience, medicine, and materials science. However, many fundamental questions about the mechanism they use remain unresolved, part

Yinjie Zhao, Heng Zhao, Bihan Wen, Yew-Soon Ong

The rapid development of AI models has led to a growing emphasis on enhancing their capabilities for complex input data such as videos. While large-scale video datasets have been introduced to support this growth, the unique challenges of reducing redundancies in video \textbf{sets} have not been explored. Compared to image datasets or individual videos, vid

Siddharth Malviy, Vipul Kakkar

In this paper, we classify all the finite groups $G$ such that the commuting graph $\Gamma_C(G)$, order-sum graph $\Gamma_{OS}(G)$ and non-inverse graph $\Gamma_{NI}(G)$ are minimally edge connected graphs. We also classify all the finite groups $G$ for that, these graphs are minimally connected. We also classify some groups for that the co-prime graph $\Gam

Sakshi Agarwal, Gabriel Hope, Jimin Heo, Erik B. Sudderth

Diffusion probabilistic models learn to remove noise added during training, generating novel data (e.g., images) from Gaussian noise through sequential denoising. However, conditioning the generative process on corrupted or masked images is challenging. While various methods have been proposed for inpainting masked images with diffusion priors, they often fa

Qixuan Hu, Guoyi Xu, Shuai Zhang

For three dimensional complete Riemannian manifolds with scalar curvature no less than one, we obtain the sharp upper bound of complete stable minimal surfaces' diameter.

Baohua Qi, Yong Liu, Huangchao Shi, Danqi Wang

A front-end readout system with a large dynamic range is required for the high-granularity crystal electromagnetic calorimeter (ECAL) at future Higgs factories. A new commercially available ASIC, MPT2321, which offers a significantly large dynamic range for the readout of the silicon photomultiplier (SiPM), has been tested in the laboratory and at a test bea

Adrian Tormos, Blanca Llauradó, Fernando Núñez, Axel Romero

The scarcity of data in medical domains hinders the performance of Deep Learning models. Data augmentation techniques can alleviate that problem, but they usually rely on functional transformations of the data that do not guarantee to preserve the original tasks. To approximate the distribution of the data using generative models is a way of reducing that pr

Yunke Zhu, Xiuyuan Lu, Jingjing Qiu, Peng Bai

The unique anisotropic properties of colloidal quantum wells (CQWs) make them highly promising as components in nanocrystal-based devices. However, the limited performance of green and blue light-emitting diodes (LEDs) based on CQWs has impeded their practical applications. In this study, we tailored alloy CdZnSe core CQWs with precise compositions via direc

Lui Yoshida

This study investigates the impact of example selection on the performance of au-tomated essay scoring (AES) using few-shot prompting with GPT models. We evaluate the effects of the choice and order of examples in few-shot prompting on several versions of GPT-3.5 and GPT-4 models. Our experiments involve 119 prompts with different examples, and we calculate

Meher Bhardwaj, Hrishikesh Ethari, Dennis Singh Moirangthem

The SQL-to-text generation task traditionally uses template base, Seq2Seq, tree-to-sequence, and graph-to-sequence models. Recent models take advantage of pre-trained generative language models for this task in the Seq2Seq framework. However, treating SQL as a sequence of inputs to the pre-trained models is not optimal. In this work, we put forward a new SQL

Junan Li, Yunxiang Li, Yuren Wang, Xixin Wu

Alzheimer's disease (AD) has become one of the most significant health challenges in an aging society. The use of spoken language-based AD detection methods has gained prevalence due to their scalability due to their scalability. Based on the Cookie Theft picture description task, we devised an explainable and effective feature set that leverages the visual

Yu-Qin Chen, Shi-Xin Zhang

In the pursuit of numerically identifying the ground state of quantum many-body systems, approximate quantum wavefunction ansatzes are commonly employed. This study focuses on the spectral decomposition of these approximate quantum many-body states into exact eigenstates of the target Hamiltonian. The energy spectral decomposition could reflect the intricate

Sergei Agapov

We study Riemannian metrics on 2-surfaces with integrable geodesic flows such that an additional first integral is high-degree polynomial in momenta. This problem reduces to searching for solutions to certain quasi-linear systems of PDEs which turn out to be semi-Hamiltonian. We construct plenty of local explicit and implicit integrable examples with polynom

Yinlin Zhu, Miao Hu, Di Wu

Managing evolving graph data presents substantial challenges in storage and privacy, and training graph neural networks (GNNs) on such data often leads to catastrophic forgetting, impairing performance on earlier tasks. Despite existing continual graph learning (CGL) methods mitigating this to some extent, they rely on centralized architectures and ignore th

Yuke Li, Xinfa Zhu, Hanzhao Li, JiXun Yao

Zero-shot voice conversion (VC) aims to convert the original speaker's timbre to any target speaker while keeping the linguistic content. Current mainstream zero-shot voice conversion approaches depend on pre-trained recognition models to disentangle linguistic content and speaker representation. This results in a timbre residue within the decoupled linguist

Kai-Xing Lu, Yan-Rong Li, Qingwen Wu, Luis C. Ho

Changing-look active galactic nuclei (CL-AGNs), characterized by emerging or disappearing of broad lines accompanied with extreme continuum flux variability, have drawn much attention for their potential of revealing physical processes underlying AGN evolution. We perform seven-season spectroscopic monitoring on Mrk~1018, one of the earliest identified CL-AG

John S. Russo, Thomas A. Shiels, Chin-Hsuan Sophie Lin, Sam E. John

Multiple Sclerosis (MS) is a heterogeneous autoimmune-mediated disorder affecting the central nervous system, commonly manifesting as fatigue and progressive limb impairment. This can significantly impact quality of life due to weakness or paralysis in the upper and lower limbs. A Brain-Computer Interface (BCI) aims to restore quality of life through control

Vishnou Vinayagame, Gregory Senay, Luis Martí

Business documents often contain substantial tabular and textual information with numerical values, requiring mathematical reasoning for effective document understanding. While Small Language Models (SLMs) still struggle at this task, tool-augmented multi-step agents perform better, at the cost of relying on closed-source or larger models, external data, or

D. Goulart, A. M. Sindhwad, H. M. Jackson, N. I. Kowitt

Current and future searches for dark matter axions, based on their resonant conversion to photons in a magnetic field, span many orders of magnitude. A major impediment to designing resonators at the high end of this range, 5 GHz and above, is the proliferation of TE modes, which overwhelm and hybridize with the TM010 mode to which the axion couples, making

Shruti Garg, Thomas Cohn, Russ Tedrake

Optimization based motion planning provides a useful modeling framework through various costs and constraints. Using Graph of Convex Sets (GCS) for trajectory optimization gives guarantees of feasibility and optimality by representing configuration space as the finite union of convex sets. Nonlinear parametrizations can be used to extend this technique to ha

N. V. Volkov, A. A. Lagutin, A. I. Reviakin, R. T. Bizhanov

The paper proposes a new approach for approximating the lateral distribution functions (LDF) of Cherenkov light emitted by the electromagnetic component of extensive air showers (EAS) in the Earth's atmosphere. The information basis of the study is a series of simulations with the CORSIKA code. To approximate the LDF atmospheric Cherenkov light the probabili

Kapil K. Sharma, Rishikant Rajdeepak, Fatih Ozaydin

Munero et. al. developed one parameter family of mixed states $\rho^{l}$, which are more entangled than bipartite Werner state. The similar family of mixed states $\rho^{n}$ are developed by L. Derkacz et. al. with differed approach. Further the author extend $\rho^{n}$ to two parameter family of quantum states $\rho^{m}$ and characterized these states in te

N. Myrzakulov, S. H. Shekh, Anirudh Pradhan

Understanding the accelerating expansion of the universe remains one of the foremost challenges in modern cosmology. This study investigates Barrow Holographic Dark Energy (BHDE), a model inspired by quantum gravitational corrections, within the framework of \(f(Q,C)\) gravity. This extension of symmetric teleparallel gravity incorporates the non-metricity s

Simrandeep Kaur, Harsimran Singh, Kenji Watanabe, Takashi Taniguchi

We present a detailed experimental study of the particle-hole symmetry (PHS) of the fractional quantum Hall (FQH) states about half filling in a multiband system. Specifically, we focus on the lowest Landau level of the monolayer-like band of Bernal stacked trilayer graphene (TLG). In pristine TLG, the excitation energy gaps, Land\'e g-factor, effective mass

Atsunori Sakurai, Shota Takahashi, Tatsuto Mochizuki, Toshiki Sugimoto

Vibrational sum frequency generation (SFG) is a nonlinear spectroscopic technique widely used to study the molecular structure and dynamics of surface systems. However, the spatial resolution achieved by far-field observations is constrained by the diffraction limit, obscuring molecular details in inhomogeneous structures smaller than the wavelength of light

Wataru Kawabe, Yusuke Sugano

Machine learning (ML) models have significantly impacted various domains in our everyday lives. While large language models (LLMs) offer intuitive interfaces and versatility, task-specific ML models remain valuable for their efficiency and focused performance in specialized tasks. However, developing these models requires technical expertise, making it parti

Yuma Iwasaki, Daisuke Ogawa, Masato Kotsugi, Yukiko K. Takahashi

The efficient exploration of expansive material spaces remains a significant challenge in materials science. To address this issue, autonomous material search methods that combine machine learning with ab initio calculations have emerged as a promising solution. These approaches offer a systematic and rapid means of discovering new materials, particularly wh

Abhinav Thorat, Ravi Kolla, Niranjan Pedanekar

Causal effect estimation under observational studies is challenging due to the lack of ground truth data and treatment assignment bias. Though various methods exist in literature for addressing this problem, most of them ignore multi-dimensional treatment information by considering it as scalar, either continuous or discrete. Recently, certain works have dem

Caimei Niu, Lifeng Liu

High-Entropy Alloys (HEAs) exhibit complex atomic interactions, with short-range order (SRO) playing a critical role in determining their properties. Traditional methods, such as Monte Carlo generator of Special Quasirandom Structures within the Alloy Theoretic Automated Toolkit (ATAT-mcsqs), Super-cell Random Approximates (SCRAPs), and hybrid Monte Carlo-Mo

De Li, Haodong Qian, Qiyu Li, Zhou Tan

Federated Graph Learning (FGL) is a distributed machine learning paradigm based on graph neural networks, enabling secure and collaborative modeling of local graph data among clients. However, label noise can degrade the global model's generalization performance. Existing federated label noise learning methods, primarily focused on computer vision, often yie

Yanpeng Li, Yu Li, Jiang-Hua Lu

We present a general framework for constructing polynomial integrable systems on linearizations of Poisson varieties that admit log-canonical systems. Our construction is in particular applicable to Poisson varieties with compatible cluster or generalized cluster structures. As examples, we consider a standard complex semi-simple Poisson Lie group $G$ and a

Chen-Chia Chang, Chia-Tung Ho, Yaguang Li, Yiran Chen

In the advanced technology nodes, the integrated design rule checker (DRC) is often utilized in place and route tools for fast optimization loops for power-performance-area. Implementing integrated DRC checkers to meet the standard of commercial DRC tools demands extensive human expertise to interpret foundry specifications, analyze layouts, and debug code i

Tianyu Zhao

For $i\in \{1,2,3\}$, let $E_i(x)$ denote the error term in each of the three theorems of Mertens on the asymptotic distribution of prime numbers. We show that for $i\in \{1,2\}$ the Riemann hypothesis is equivalent to the condition $\int_2^X E_i(x) \:\mathrm{d}x>0$ for all $X>2$, and we examine assumptions under which the equivalence also holds for $i=3$. I

Guangyao Zheng, Michael A. Jacobs, Vishwa S. Parekh

Self-supervised foundation models have recently been successfully extended to encode three-dimensional (3D) computed tomography (CT) images, with excellent performance across several downstream tasks, such as intracranial hemorrhage detection and lung cancer risk forecasting. However, as self-supervised models learn from complex data distributions, questions

Yu-Tung Liu, Kuan-Chen Wang, Rong Chao, Sabato Marco Siniscalchi

Surface electromyography (sEMG) recordings can be contaminated by electrocardiogram (ECG) signals when the monitored muscle is closed to the heart. Traditional signal processing-based approaches, such as high-pass filtering and template subtraction, have been used to remove ECG interference but are often limited in their effectiveness. Recently, neural netwo

Gaurav Bhandari, S. D. Pathak, Manabendra Sharma, Anzhong Wang

The prediction of a minimal length scale by various quantum gravity candidates (such as string/M theory, Doubly Special Relativity, Loop Quantum Gravity and others) have suggested modification of Heisenberg Uncertainty Principle (HUP), resulting in the Generalized Uncertainty Principle (GUP). In this short review, we investigate the origins of the GUP and ex

Anastassiya Semenova, Eleanor Byrnes

We consider traveling waves on a surface of an ideal fluid of finite depth. The equation describing Stokes waves in conformal variables formulation are referred to as the Babenko equation. We use a Newton-Conjugate-Gradient method to compute Stokes waves for a range of conformal depths from deep to shallow water. In deep water, we compute eigenvalues of the

Bo-Wen Zhang, Xi-Yang Qiu, Yicheng Ma, Qingmei Hu

Carbyne, a one-dimensional (1D) carbon allotrope with alternating triple and single bonds, has the highest known mechanical strength but is unstable to bending, limiting synthesis to short linear chains. Encapsulation within carbon nanotubes (CNTs) stabilizes carbyne, forming confined carbyne (CC), thus enabling further research concerning attractive 1D phys

Mohamed S. H. Alabassy

Creating as-is models from scratch is to this day still a time- and money-consuming task due to its high manual effort. Therefore, projects, especially those with a big spatial extent, could profit from automating the process of creating semantically rich 3D geometries from surveying data such as Point Cloud Data (PCD). An automation can be achieved by using

Matteo Cati, Dmitrii V. Pasechnik

Hadamard matrices of order $n$ are conjectured to exist whenever $n$ is $1$, $2$, or a multiple of $4$; a similar conjecture exists for skew Hadamard matrices. We provide constructions covering orders $\le 1208$ of all known Hadamard and skew Hadamard matrices in the open-source software SageMath. This allowed us to verify the correctness of results given in

Jingxin Liu, Xiang Gao, Yisha Li, Xin Li

In the context of a short video & live stream mixed recommendation scenario, the live stream recommendation system (RS) decides whether to allocate at most one live stream into the video feed for each user request. To maximize long-term user engagement, it is crucial to determine an optimal live stream policy for accurate live stream allocation. The inapprop

Zhiyao Hu, Shilin Wang, Linmu Qiao, Takuya Isogawa

In practical applications like quantum sensing and quantum imaging, there is often a necessity to estimate multiple parameters simultaneously. Although the ultimate precision limits for single-parameter estimation are well established, the precision limit of multi-parameter estimation is much less understood. This is primarily due to the inherent incompatibi

Adam Karvonen, Can Rager, Samuel Marks, Neel Nanda

Sparse Autoencoders (SAEs) are an interpretability technique aimed at decomposing neural network activations into interpretable units. However, a major bottleneck for SAE development has been the lack of high-quality performance metrics, with prior work largely relying on unsupervised proxies. In this work, we introduce a family of evaluations based on SHIFT

Changsheng Lv, Mengshi Qi, Liang Liu, Huadong Ma

Understanding the traffic scenes and then generating high-definition (HD) maps present significant challenges in autonomous driving. In this paper, we defined a novel Traffic Topology Scene Graph, a unified scene graph explicitly modeling the lane, controlled and guided by different road signals (e.g., right turn), and topology relationships among them, whic

Huy Trinh, Khang Tran, Nam Nguyen, Tri Cao

Segmentation has long been essential in computer vision due to its numerous real-world applications. However, most traditional deep learning and machine learning models need help to capture geometric features such as size and convexity of the segmentation targets, resulting in suboptimal outcomes. To resolve this problem, we propose using a CovHuSeg algorith

Majid Ghasemi, Amir Hossein Moosavi, Dariush Ebrahimi

Reinforcement Learning (RL) has emerged as a powerful paradigm in Artificial Intelligence (AI), enabling agents to learn optimal behaviors through interactions with their environments. Drawing from the foundations of trial and error, RL equips agents to make informed decisions through feedback in the form of rewards or penalties. This paper presents a compre

Yu Si, Jingtao Shi

This paper studies a linear-quadratic mean-field game of stochastic large-population system, where the large-population system satisfies a class of $N$ weakly coupled linear backward stochastic differential equation. Different from the fixed-point approach commonly used to address large population problems, we first directly apply the maximum principle and d

Jiangjiang Cheng, Ge Chen, Wenjun Mei, Francesco Bullo

This paper introduces a heterogeneous multidimensional bounded confidence (BC) opinion dynamics with random pairwise interactions, whereby each pair of agents accesses each other's opinions with a specific probability. This revised model is motivated by the observation that the standard Hegselmann-Krause (HK) dynamics requires unrealistic all-to-all interact

Yixuan Yin, Tiantian Wang, Biao Wu

We show that a highly-excited energy eigenfunction $\psi_{nlm}(\vec{r})$ of hydrogen atom can be approximated as an equal-weight superposition of classical elliptic orbits with energy $E_n$ and angular momentum $L=\sqrt{l(l+1)}\hbar$, and $z$ component of angular momentum $L_z=m\hbar$. This correspondence is established by comparing the quantum probability d

Yohei Miki, Toshihiro Hanawa

GPU is the dominant accelerator device due to its high performance and energy efficiency. Directive-based GPU offloading using OpenACC or OpenMP target is a convenient way to port existing codes originally developed for multicore CPUs. Although OpenACC and OpenMP target provide similar features, both methods have pros and cons. OpenACC has better functions a

Hazem Darwish, Abdalrahman Al Malah, Khloud Al Jallad, Nada Ghneim

Brain-Computer-Interface (BCI) aims to support communication-impaired patients by translating neural signals into speech. A notable research topic in BCI involves Electroencephalography (EEG) signals that measure the electrical activity in the brain. While significant advancements have been made in BCI EEG research, a major limitation still exists: the scarc

Teddy Mishura

We present a characterization of Robinsonian $L^p$ graphons for $p > 5$. Each $L^p$ graphon $w$ is the limit object of a sequence of edge density-normalized simple graphs $\{G_n/\|G_n\|_1\}$ under the cut distance $\delta_{\Box}$. A graphon $w$ is Robinson if it satisfies the Robinson property: if $x\leq y\leq z$, then $w(x,z)\leq \min\{w(x,y),w(y,z)\}$, and

Ananjan Nandi, Christopher D. Manning, Shikhar Murty

While compositional accounts of human language understanding are based on a hierarchical tree-like process, neural models like transformers lack a direct inductive bias for such tree structures. Introducing syntactic inductive biases could unlock more robust and data-efficient learning in transformer language models (LMs), but existing methods for incorporat

Jesse Atuhurra, Hidetaka Kamigaito, Hiroki Ouchi, Hiroyuki Shindo

Adapting named entity recognition (NER) methods to new domains poses significant challenges. We introduce RapidNER, a framework designed for the rapid deployment of NER systems through efficient dataset construction. RapidNER operates through three key steps: (1) extracting domain-specific sub-graphs and triples from a general knowledge graph, (2) collecting

Mengya Xu, Wenjin Mo, Guankun Wang, Huxin Gao

Robot-assisted Endoscopic Submucosal Dissection (ESD) improves the surgical procedure by providing a more comprehensive view through advanced robotic instruments and bimanual operation, thereby enhancing dissection efficiency and accuracy. Accurate prediction of dissection trajectories is crucial for better decision-making, reducing intraoperative errors, an

Yuyang Qiu, Farzad Yousefian, Brian Zhang

In noncooperative Nash games, equilibria are often inefficient. This is exemplified by the Prisoner's Dilemma and was first provably shown in the 1980s. Since then, understanding the quality of Nash equilibrium (NE) received considerable attention, leading to the emergence of inefficiency measures characterized by the best or the worst equilibrium. Tradition

Changyu Yao, Yue Yu, Yinyao Shi, Ji-In Jung

Understanding intricate magnetic structures in materials is essential for advancing materials science, spintronics, and geology. Recent developments of quantum-enabled magnetometers, such as nitrogen-vacancy (NV) centers in diamond, have enabled direct imaging of magnetic field distributions across a wide range of magnetic profiles. However, reconstructing t

Silvio Reggiani

The sedenion algebra $\mathbb S$ is a non-commutative, non-associative, $16$-dimensional real algebra with zero divisors. It is obtained from the octonions through the Cayley-Dickson construction. The zero divisors of $\mathbb S$ can be viewed as the submanifold $\mathcal Z(\mathbb S) \subset \mathbb S \times \mathbb S$ of normalized pairs whose product equa

Yan Xing, Qi'ao Xu, Zongyu Guo, Rui Huang

Semi-supervised change detection (SSCD) utilizes partially labeled data and abundant unlabeled data to detect differences between multi-temporal remote sensing images. The mainstream SSCD methods based on consistency regularization have limitations. They perform perturbations mainly at a single level, restricting the utilization of unlabeled data and failing

Yuyao Wang, Andrew Ying, Ronghui Xu

Time-to-event outcomes are often subject to left truncation and right censoring. While many survival analysis methods have been developed to handle truncation and censoring, majority of the past works require strong independence assumptions. We relax these stringent assumptions through leveraging covariate information together with orthogonal learning, and d

Qiumo Yu, Linglong Dai

Reconfigurable intelligent surface (RIS) has emerged as a promising solution to overcome the challenges of high path loss and easy signal blockage in millimeter-wave (mmWave) and terahertz (THz) communication systems. With the increase of RIS aperture and system bandwidth, the near-field beam split effect emerges, which causes beams at different frequencies

Shantia Yarahmadian, Maryam Rahmani, Michael Mazzola

In this paper, we introduce a novel waveletbased algorithm for reconstructing time-domain impulse responses from band-limited scattering parameters (frequencydomain data) with a particular focus on ship hull applications. We establish the algorithm and demonstrate its convergence, as well as its efficiency for a class of functions that can be expanded as exp

Koffka Khan, Wayne Goodridge

This study addresses a critical gap in the literature regarding the use of Swarm Intelligence Optimization (SI) algorithms for client selection in Federated Learning (FL), with a focus on cybersecurity applications. Existing research primarily explores optimization techniques for centralized machine learning, leaving the unique challenges of client diveristy

Mostafa Farrokhabadi

This paper presents a theoretical interpretation and explores possible improvements of a widely adopted rule-based control for residential solar photovoltaics (PV) paired with battery storage systems (BSS). The method is referred to as Occam's control in this paper, given its simplicity and as a tribute to the 14th-century William of Ockham. Using the self-c

Shuyi Miao, Wangjie Qiu, Hongwei Zheng, Qinnan Zhang

The scaled Web 3.0 digital economy, represented by decentralized finance (DeFi), has sparked increasing interest in the past few years, which usually relies on blockchain for token transfer and diverse transaction logic. However, illegal behaviors, such as financial fraud, hacker attacks, and money laundering, are rampant in the blockchain ecosystem and seri

Bing Xie, Yigeng Zhao, Yongqiang Zhao

It is well known that the standard flat torus $\mathbb{T}^2=\mathbb{R}^2/\Z^2$ has arbitrarily large Laplacian-eigenvalue multiplicities. We prove, however, that $24$ is the optimal upper bound for the multiplicities of the nonzero eigenvalues of a $2$-dimensional discrete torus. For general higher dimension discrete tori, we characterize the eigenvalues wit

Yijia Zhang, Zhihong Gou, Shijie Cao, Weigang Feng

Deep Neural Networks (DNNs) have revolutionized various fields, but their deployment on GPUs often leads to significant energy consumption. Unlike existing methods for reducing GPU energy consumption, which are either hardware-inflexible or limited by workload constraints, this paper addresses the problem at the GPU kernel level. We propose a novel search-ba