December 2023 arXiv papers — page 74

Tong Qi, Vince Lyzinski

In this paper, we explore the capability of both the Adjacency Spectral Embedding (ASE) and the Graph Encoder Embedding (GEE) for capturing an embedded pseudo-clique structure in the random dot product graph setting. In both theory and experiments, we demonstrate that, in the absence of additional clean (i.e., without the implanted pseudo-clique) network dat

Jianhao Chen, Junyang Ren, Wentao Ding, Haoyuan Ouyang

Temporal facts, which are used to describe events that occur during specific time periods, have become a topic of increased interest in the field of knowledge graph (KG) research. In terms of quality management, the introduction of time restrictions brings new challenges to maintaining the temporal consistency of KGs. Previous studies rely on manually enumer

Caroline L. Wormell

Equilibrium measures are special invariant measures of chaotic dynamical systems and iterated function systems, commonly studied as salient examples of fractal measures. While useful analytic expressions are rare, computational exploration of these measures can yield useful insight, in particular in studying their Fourier decay. In this note we present simpl

Shihao Feng, Pengpeng Liang, Jin Gao, Erkang Cheng

Point cloud-based 3D object tracking is an important task in autonomous driving. Though great advances regarding Siamese-based 3D tracking have been made recently, it remains challenging to learn the correlation between the template and search branches effectively with the sparse LIDAR point cloud data. Instead of performing correlation of the two branches a

Raffaele Resta

The nonlocal potential contributes an extra term to the velocity operator; I show here that such term affects the formal expression of the Drude weight in a nontrivial way. Notably, the present main result fixes a disturbing discrepancy in the Dreyer-Coh-Stengel sum rule [Phys. Rev. Lett. {\bf 128}, 095901 (2022)].

Nils Strodthoff, Juan Miguel Lopez Alcaraz, Wilhelm Haverkamp

Current deep learning algorithms designed for automatic ECG analysis have exhibited notable accuracy. However, akin to traditional electrocardiography, they tend to be narrowly focused and typically address a singular diagnostic condition. In this exploratory study, we specifically investigate the capability of a single model to predict a diverse range of bo

P. M. Stevenson

In their defence of "maximum conformality" methods, Brodsky et al make the astonishing claim that any RG transformation a'=a(1+V_1 a + ...) in QCD must have V_1 proportional to b=(33-2n_f)/6. It is well known that this is not true. I emphasize again the correctness and central importance of the Celmaster-Gonsalves relation for the prescription dependence of

Xianshun Ming

In this paper, we first establish a Quasinormal Mode (QNM) solver for open resonators made of materials with general dispersion which can be modeled by partial fractions, and develop the corresponding analytical QNM expansion method (QNMEM) for both discrete and periodic resonant structures. When the response of the resonators is dominant by several leading

Federico Camia

We prove a formula, first obtained by Kleban, Simmons and Ziff using conformal field theory methods, for the (renormalized) density of a critical percolation cluster in the upper half-plane "anchored" to a point on the real line. The proof is inspired by the method of images. We also show that more general bulk-boundary connection probabilities have well-def

Jianyao Xu, Qingshan Xu, Xinyao Liao, Wanjuan Su

Neural surfaces learning has shown impressive performance in multi-view surface reconstruction. However, most existing methods use large multilayer perceptrons (MLPs) to train their models from scratch, resulting in hours of training for a single scene. Recently, how to accelerate the neural surfaces learning has received a lot of attention and remains an op

Akey Sungheetha, Rajesh Sharma R, Chinnaiyan R

In the virtual elements of immersive learning, the use of Google Expedition and touch-screen-based emotion are examined. The objective is to investigate possible ways to combine these technologies to enhance virtual learning environments and learners emotional engagement. Pedagogical application, affordances, and cognitive load are the corresponding measures

Ian M. Musson

Let $\mathtt{k}$ be an algebraically closed field of characteristic zero. Let ${\stackrel{{\rm o}}{{\mathfrak{g}}}}$ be the Lie superalgebra ${\mathfrak{sl}}(n|m)$ and let $\mathfrak{W}$ be the Weyl groupoid introduced by Sergeev and Veselov using the root system of ${\stackrel{{\rm o}}{{\mathfrak{g}}}}$. An important subgroupoid $\mathfrak T_{iso}$ of ${\ma

Chenyu Hu, Liang Xu, Ben Wang, Zhiwen Li

Improving three-dimensional (3D) localization precision is of paramount importance for super-resolution imaging. By properly engineering the point spread function (PSF), such as utilizing Laguerre-Gaussian (LG) modes and their superposition, the ultimate limits of 3D localization precision can be enhanced. However, achieving these limits is challenging, as i

Yang Fan, Xiangping Wu, Qingcai Chen, Heng Li

The diversity of tables makes table detection a great challenge, leading to existing models becoming more tedious and complex. Despite achieving high performance, they often overfit to the table style in training set, and suffer from significant performance degradation when encountering out-of-distribution tables in other domains. To tackle this problem, we

Je-Woo Jang, Thai-Hoang Nguyen, Joon-Sung Yang

Resistive Random Access Memory (ReRAM) based Processing In Memory (PIM) Accelerator has emerged as a promising computing architecture for memory intensive applications, such as Deep Neural Networks (DNNs). However, due to its immaturity, ReRAM devices often suffer from various reliability issues, which hinder the practicality of the PIM architecture and lead

Farsane Tabataba-Vakili, Huy P. G. Nguyen, Anna Rupp, Kseniia Mosina

Magnetism in two-dimensional materials reveals phenomena distinct from bulk magnetic crystals, with sensitivity to charge doping and electric fields in monolayer and bilayer van der Waals magnet CrI3. Within the class of layered magnets, semiconducting CrSBr stands out by featuring stability under ambient conditions, correlating excitons with magnetic order

M. Frosini, W. Ryssens, K. Sieja

The low-energy enhancement observed recently in the deexcitation gamma-ray strength functions, suggested to arise due to the magnetic dipole radiation, motivates theoretical efforts to improve the description of M1 strength in available nuclear structure models. Reliable theoretical predictions of nuclear dipole excitations are of interest for different nucl

Nir Lev, Anton Tselishchev

We construct a uniformly discrete sequence $\{\lambda_1 < \lambda_2 < \cdots\} \subset \mathbb{R}$ and functions $g$ and $\{g_n^*\}$ in $L^2(\mathbb{R})$, such that every $f \in L^2(\mathbb{R})$ admits a series expansion \[ f(x) = \sum_{n=1}^{\infty} \langle f, g_n^* \rangle \, g(x-\lambda_n) \] convergent in the $L^2(\mathbb{R})$ norm.

Tianjie Dai, Ruipeng Zhang, Feng Hong, Jiangchao Yao

Vision-Language Pre-training (VLP) that utilizes the multi-modal information to promote the training efficiency and effectiveness, has achieved great success in vision recognition of natural domains and shown promise in medical imaging diagnosis for the Chest X-Rays (CXRs). However, current works mainly pay attention to the exploration on single dataset of C

Guo Pu, Peng-Shuai Wang, Zhouhui Lian

Single-image novel view synthesis is a challenging and ongoing problem that aims to generate an infinite number of consistent views from a single input image. Although significant efforts have been made to advance the quality of generated novel views, less attention has been paid to the expansion of the underlying scene representation, which is crucial to th

Xiaoxi Li, Yujia Zhou, Zhicheng Dou

Generative information retrieval, encompassing two major tasks of Generative Document Retrieval (GDR) and Grounded Answer Generation (GAR), has gained significant attention in the area of information retrieval and natural language processing. Existing methods for GDR and GAR rely on separate retrieval and reader modules, which hinder simultaneous optimizatio

Yanting Zhang, Shuanghong Wang, Qingxiang Wang, Cairong Yan

Ensuring driving safety for autonomous vehicles has become increasingly crucial, highlighting the need for systematic tracking of on-road pedestrians. Most vehicles are equipped with visual sensors, however, the large-scale visual data has not been well studied yet. Multi-target multi-camera (MTMC) tracking systems are composed of two modules: single-camera

Chongjie Si, Xuehui Wang, Yan Wang, Xiaokang Yang

In partial label learning (PLL), each instance is associated with a set of candidate labels among which only one is ground-truth. The majority of the existing works focuses on constructing robust classifiers to estimate the labeling confidence of candidate labels in order to identify the correct one. However, these methods usually struggle to identify and re

Akira Inomata, Georg Junker

Power duality in Feynman's path integral formulation of quantum mechanics is investigated. The power duality transformation consists of a change in coordinate and time variables, an exchange of energy and coupling, and a classical angular momentum replacement. Two physical systems connected by the transformation form a power-dual pair. The propagator (Feynma

Shuai Zhou, Hao Fu, Haodong He, Wei Liu

Robot crowd navigation has been gaining increasing attention and popularity in various practical applications. In existing research, deep reinforcement learning has been applied to robot crowd navigation by training policies in an online mode. However, this inevitably leads to unsafe exploration, and consequently causes low sampling efficiency during pedestr

Hyunmin Choi, Simon Woo, Hyoungshick Kim

Fingerprint authentication is a popular security mechanism for smartphones and laptops. However, its adoption in web and cloud environments has been limited due to privacy concerns over storing and processing biometric data on servers. This paper introduces Blind-Touch, a novel machine learning-based fingerprint authentication system leveraging homomorphic e

Joshua Pritchard, Tara Murphy, George Heald, Michael S. Wheatland

The population of radio-loud stars has to date been studied primarily through either targeted observations of a small number of highly active stars or widefield, single-epoch surveys that cannot easily distinguish stellar emission from background extra-Galactic sources. As a result it has been difficult to constrain population statistics such as the surface

Kun Chen, Lei Bai, Fenghua Ling, Peng Ye

The weather forecasting system is important for science and society, and significant achievements have been made in applying artificial intelligence (AI) to medium-range weather forecasting. However, existing AI-based weather forecasting models rely on analysis or reanalysis products from traditional numerical weather prediction (NWP) systems as initial cond

Valtteri Haavisto, Marcin Mińkowski, Lasse Laurson

Predicting the future behaviour of complex systems exhibiting critical-like dynamics is often considered to be an intrinsically hard task. Here, we study the predictability of the depinning dynamics of elastic interfaces in random media driven by a slowly increasing external force, a paradigmatic complex system exhibiting critical avalanche dynamics linked t

Reginald Frank, Micah Murray, Chawinphat Tankuranand, Junseo Yoo

Replicated state machines (RSMs) cannot communicate effectively today as there is no formal framework or efficient protocol to do so. To address this issue, we introduce a new primitive, Cross-Cluster Consistent Broadcast (C3B) and present PICSOU, a practical implementation of the C3B primitive. PICSOU draws inspiration from networking and TCP to allow two R

José Armando Hernández, Miguel Colom

With the recognized crisis of credibility in scientific research, there is a growth of reproducibility studies in computer science, and although existing surveys have reviewed reproducibility from various perspectives, especially very specific technological issues, they do not address the author-publisher relationship in the publication of reproducible compu

Jingqing Ruan, Kaishen Wang, Qingyang Zhang, Dengpeng Xing

Many complicated real-world tasks can be broken down into smaller, more manageable parts, and planning with prior knowledge extracted from these simplified pieces is crucial for humans to make accurate decisions. However, replicating this process remains a challenge for AI agents and naturally raises two questions: How to extract discriminative knowledge rep

Wei Wan, Yuxuan Ning, Shengshan Hu, Lulu Xue

\textit{Federated learning} (FL) and \textit{split learning} (SL) are prevailing distributed paradigms in recent years. They both enable shared global model training while keeping data localized on users' devices. The former excels in parallel execution capabilities, while the latter enjoys low dependence on edge computing resources and strong privacy protec

Athanasios Karagounis

This manuscript explores the complexities of multi-objective path planning, aiming to optimize routes against a backdrop of conflicting performance criteria. The study integrates the cell mapping approach as its foundational concept. A two-pronged search strategy is introduced; initially, the cell mapping technique is utilized to develop a comprehensive data

Tianyao He, Huabin Liu, Yuxi Li, Xiao Ma

Video Correlation Learning (VCL), which aims to analyze the relationships between videos, has been widely studied and applied in various general video tasks. However, applying VCL to instructional videos is still quite challenging due to their intrinsic procedural temporal structure. Specifically, procedural knowledge is critical for accurate correlation ana

An Lao, Qi Zhang, Chongyang Shi, Longbing Cao

Multimodal content, such as mixing text with images, presents significant challenges to rumor detection in social media. Existing multimodal rumor detection has focused on mixing tokens among spatial and sequential locations for unimodal representation or fusing clues of rumor veracity across modalities. However, they suffer from less discriminative unimodal

Armin Nurkanović, Anton Pozharskiy, Moritz Diehl

This paper examines solution methods for mathematical programs with complementarity constraints (MPCC) obtained from the time-discretization of optimal control problems (OCPs) subject to nonsmooth dynamical systems. The MPCC theory and stationarity concepts are reviewed and summarized. The focus is on relaxation-based methods for MPCCs, which solve a (finite

Minghui Yang, Zhaojie Zhou

In this paper we study the mixed virtual element approximation to an elliptic optimal control problem with boundary observations. The objective functional of this type of optimal control problem contains the outward normal derivatives of the state variable on the boundary, which reduces the regularity of solutions to the optimal control problems. We construc

Philipp Seeberger, Tobias Bocklet, Korbinian Riedhammer

User-generated information content has become an important information source in crisis situations. However, classification models suffer from noise and event-related biases which still poses a challenging task and requires sophisticated task-adaptation. To address these challenges, we propose the use of contrastive task-specialized sentence encoders for dow

Grzegorz Ficht, Sven Behnke

We introduce novel methods for state estimation, feedforward and feedback control, which specifically target humanoid robots with hardware limitations. Our method combines a five-mass model with approximate dynamics of each mass. It enables acquiring an accurate assessment of the centroidal state and Center of Pressure, even when direct forms of force or con

Yang Li, Kangbo Liu, Yaoxin Wu, Zhaoxuan Wang

Bundle recommendations strive to offer users a set of items as a package named bundle, enhancing convenience and contributing to the seller's revenue. While previous approaches have demonstrated notable performance, we argue that they may compromise the ternary relationship among users, items, and bundles. This compromise can result in information loss, ulti

Chin Wa Lau, Chandra Nair

Ruzsa's equivalence theorem provided a framework for converting certain families of inequalities in additive combinatorics to entropic inequalities (which sometimes did not possess stand-alone entropic proofs). In this work, we first establish formal equivalences between some families (different from Ruzsa) of inequalities in additive combinatorics and entro

Yvan Martel

For the Schr\"odinger equation with a cubic-quintic, focusing-focusing nonlinearity in one space dimension, this article proves the local asymptotic completeness of the family of small standing solitary waves under even perturbations in the energy space. For this model, perturbative of the integrable cubic Schr\"odinger equation for small solutions, the line

Zhenhuan Liu, Shuai Liu, Jie Yang, Wei Liu

Novel view synthesis for dynamic scenes is one of the spotlights in computer vision. The key to efficient dynamic view synthesis is to find a compact representation to store the information across time. Though existing methods achieve fast dynamic view synthesis by tensor decomposition or hash grid feature concatenation, their mixed representations ignore th

Rathi Aparna, Dhal Biswabhusan, S S Sarath, Kalon Gopinadhan

In the osmotic power generation field, reaching the industrial benchmark has been challenging because of the need for capillaries close to the sizes of ions and molecules. Here, we fabricated well-controlled 'along-the-capillary' membranes of Na-vermiculite with a capillary size of ~5 Angstrom. They exhibit 1600 times enhanced conductivity compared to common

Zhiyuan Pan, Xing Hu, Xin Xia, Xian Zhan

The number of vulnerabilities reported in open source software has increased substantially in recent years. Security patches provide the necessary measures to protect software from attacks and vulnerabilities. In practice, it is difficult to identify whether patches have been integrated into software, especially if we only have binary files. Therefore, the a

Luca Erhart, Yuichiro Yoshida, Viktor Khinevich, Wataru Mizukami

Introducing an active space approximation is inevitable for the quantum computations of chemical systems. However, this approximation ignores the electron correlations related to non-active orbitals. Here, we propose a computational method for correcting quantum computing results using a well-established classical theory called coupled cluster theory. Our ap

Quan Nguyen, Huy Pham, Dung Dao

In this technical report, we present VinaLLaMA, an open-weight, state-of-the-art (SOTA) Large Language Model for the Vietnamese language, built upon LLaMA-2 with an additional 800 billion trained tokens. VinaLLaMA not only demonstrates fluency in Vietnamese but also exhibits a profound understanding of Vietnamese culture, making it a truly indigenous model.

Shobha Kumari, Sabyasachi Pal

We identify a horseshoe-shaped ring (HSR) of diffuse emission in J1407+0453 from the Faint Images of Radio Sky at Twenty-cm (FIRST) survey using the Very Large Array telescope. An optical galaxy SDSSJ140709.01+045302.1 is present near the limb of the HSR of J1407+0453, with a spectroscopic redshift of $z=0.13360$. The total extent of the source, including th

Antonio Rafael Ramos-Rodriguez, Salustiano Martinez-Fierro, Jose Aurelio Medina-Garrido, Jose Ruiz-Navarro

In the past 15 years, two international observatories have been intensively studying entrepreneurship using empirical studies with different methodologies: GEM and PSED. Both projects have generated a considerable volume of scientific production, and their intellectual structures are worth analyzing. The current work is an exploratory study of the knowledge

Fangzhi Wang, Hua Liao, Richard S. J. Tol, Changjing Ji

Carbon abatement decisions are usually based on the implausible assumption of constant social preference. This paper focuses on a specific case of market and non-market goods, and investigates the optimal climate policy when social preference for them is also changed by climate policy in the DICE model. The relative price of non-market goods grows over time

Matteo Bresciani, Mattia Dalla Brida, Leonardo Giusti, Michele Pepe

We present first non-perturbative results for the renormalization constants of the QCD energy-momentum tensor, based on the framework of thermal QCD with shifted and twisted (for quarks only) boundary conditions in the compact direction. We also show preliminary results for the entropy density obtained with the very same numerical strategy. This opens the wa

Hanqing Guo, Ye Zheng, Yin Zhang, Zhi Gao

Visual detection of micro aerial vehicles (MAVs) has received increasing research attention in recent years due to its importance in many applications. However, the existing approaches based on either appearance or motion features of MAVs still face challenges when the background is complex, the MAV target is small, or the computation resource is limited. In

T. Fujii, O. Janson, H. Yasuoka, H. Rosner

The local electric field gradients and magnetic dynamics of TaSb$_2$ have been studied using $^{121}$Sb, $^{123}$Sb, and $^{181}$Ta nuclear quadrupole resonance (NQR) with density functional theory (DFT) calculations using XRD-determined crystal structures. By measuring all structurally expected thirteen NQR lines, the nuclear quadrupole coupling constant ($

Fang-Mei Yang, Fu-Quan Dou

Quantum batteries (QBs) are energy storage and transfer microdevices that open up new possibilities in energy technology. Here, we derive a resonator-qutrits quantum battery (QB) model consisting of a multi-modes resonator and $N$ superconducting transmon qutrits. We investigate the charging and self-discharging performance of the QB and discuss the roles of

Jasper Müller, Gabriele Di Rosa, Tobias Fehenberger, Mario Wenning

Flexible-grid Elastic Optical Networks (EONs) have been widely deployed in recent years to support the growing demand for bandwidth-intensive applications. To address this cost-efficiently, optimized utilization of EONs is required. Next-generation bandwidth-variable transceivers (BVTs) will offer increased adaptivity in symbol rate as well as modulation thr

Fuqiang Chen, Matteo Ravasi, David Keyes

This paper presents a novel factorization-based, low-rank regularization method for solving multidimensional deconvolution problems in the frequency domain. In this approach, each frequency component of the unknown wavefield is represented as a complex-valued square matrix and approximated using the product of one rectangular matrix and its transpose. The be

Younggeun Kim, Junu Jeong, SungWoo Youn, Sungjae Bae

The axion has emerged as the most attractive solution to two fundamental questions in modern physics related to the charge-parity invariance in strong interactions and the invisible matter component of our universe. Over the past decade, there have been many theoretical efforts to constrain the axion mass based on various cosmological assumptions. Interestin

Tatsuya Suzuki, Yuji Hanada, Masanori Nagao, Yuki Maruyama

Sb-substituted CeOBiS2 single crystals with 0.2-1.0 mm size have been successfully grown using CsCl/KCl flux. Sb substitution strongly suppressed the superconductivity in CeOBiS2, and the substitution of more than approximately 4 atomic percent in Bi-site disappeared the superconductivity at a measurement range of above 0.36 K. Furthermore, the drastic chang

Xinshuai Dong, Biwei Huang, Ignavier Ng, Xiangchen Song

Most existing causal discovery methods rely on the assumption of no latent confounders, limiting their applicability in solving real-life problems. In this paper, we introduce a novel, versatile framework for causal discovery that accommodates the presence of causally-related hidden variables almost everywhere in the causal network (for instance, they can be

Lei Niu, Yi Wang, Xizhuang Xie

The current series of two papers focus on a 3-dimensional Lotka-Volterra competition model of differential equations with seasonal succession, which exhibits that populations experience an external periodically forced environment. We are devoted to providing a delicate global dynamical description for the model. In the first part of the series, we first use

Rishiraj Bhattacharyya, Sourav Chakraborty, Yash Pote, Uddalok Sarkar

Samplers are the backbone of the implementations of any randomised algorithm. Unfortunately, obtaining an efficient algorithm to test the correctness of samplers is very hard to find. Recently, in a series of works, testers like $\mathsf{Barbarik}$, $\mathsf{Teq}$, $\mathsf{Flash}$ for testing of some particular kinds of samplers, like CNF-samplers and Horn-

Jia-Hao Wu, Fu-Jen Tsai, Yan-Tsung Peng, Chung-Chi Tsai

Image deblurring aims to remove undesired blurs from an image captured in a dynamic scene. Much research has been dedicated to improving deblurring performance through model architectural designs. However, there is little work on data augmentation for image deblurring. Since continuous motion causes blurred artifacts during image exposure, we aspire to devel

Yunfan Gao, Yun Xiong, Xinyu Gao, Kangxiang Jia

Large Language Models (LLMs) showcase impressive capabilities but encounter challenges like hallucination, outdated knowledge, and non-transparent, untraceable reasoning processes. Retrieval-Augmented Generation (RAG) has emerged as a promising solution by incorporating knowledge from external databases. This enhances the accuracy and credibility of the gene

Willi Grossmann, Sebastian Eilermann, Tim Rensmeyer, Artur Liebert

Traditional design cycles for new materials and assemblies have two fundamental drawbacks. The underlying physical relationships are often too complex to be precisely calculated and described. Aside from that, many unknown uncertainties, such as exact manufacturing parameters or materials composition, dominate the real assembly behavior. Machine learning (ML

Xu Fang, Xiaolei Li, Lihua Xie

This paper studies 3-D distributed network localization using mixed types of local relative measurements. Each node holds a local coordinate frame without a common orientation and can only measure one type of information (relative position, distance, relative bearing, angle, or ratio-of-distance measurements) about its neighboring nodes in its local coordina

Anton Nedelin

In this contribution we summarize our recent progress in understanding the relation between ${\cal N} = 1$ superconformal indices and relativistic elliptic integrable models. We start briefly reviewing the emergence of such models in computations of the index in presence of surface defect. Next we give an example of such relation considering $4d$ theories ob

Zeping Ren, Shaoli Huang, Xiu Li

We introduce the Cross Human Motion Diffusion Model (CrossDiff), a novel approach for generating high-quality human motion based on textual descriptions. Our method integrates 3D and 2D information using a shared transformer network within the training of the diffusion model, unifying motion noise into a single feature space. This enables cross-decoding of f

Zahra Ghasemi, Mehdi Neshat, Chris Aldrich, John Karageorgos

In mineral processing plants, grinding is a crucial step, accounting for approximately 50 percent of the total mineral processing costs. Semi-autogenous grinding mills are extensively employed in the grinding circuit of mineral processing plants. Maximizing SAG mill throughput is of significant importance considering its profound financial outcomes. However,

Arushi Singh, Vikram Mahamiya, Alok Shukla

Recently, an atomic-scale two-dimensional silicon carbide monolayer has been synthesized {[}Polley \emph{et al., }Phys. Rev. Lett. \textbf{130},076203 (2023){]} which opens up new possibilities for developing next-generation electronic and optoelectronic devices. Our study predicts the pristine SiC monolayer to have an ``indirect'' band gap of 3.38 eV $(K\ri

Shabnam Abutalebi B. A., Seyed Mahmoud Ashrafi, Hassan RanjbarAskari, Alireza Bahrampour

We propose a novel integrated structure for single photon generation at room temperature based on a molecular optomechanics system in a hybrid photonic-plasmonic cavity. The proposed structure comprises a single molecule within a plasmonic cavity, coupled to a 2D photonic crystal resonator. In this paper, we theoretically identify the ability of the scheme t

Xu Fang, Lihua Xie, Xiaolei Li

Different from most existing distributed localization approaches in static networks where the agents in a network are static, this paper addresses the distributed localization problem in dynamic networks where the positions of the agents are time-varying. Firstly, complex constraints for the positions of the agents are constructed based on local relative pos

Tianrui Jia, Haoyang Li, Cheng Yang, Tao Tao

Graph neural networks (GNNs) have been demonstrated to perform well in graph representation learning, but always lacking in generalization capability when tackling out-of-distribution (OOD) data. Graph invariant learning methods, backed by the invariance principle among defined multiple environments, have shown effectiveness in dealing with this issue. Howev

Congchi Yin, Qian Yu, Zhiwei Fang, Changping Peng

Recent major milestones have successfully reconstructed natural language from non-invasive brain signals (e.g. functional Magnetic Resonance Imaging (fMRI) and Electroencephalogram (EEG)) across subjects. However, we find current dataset splitting strategies for cross-subject brain-to-text decoding are wrong. Specifically, we first demonstrate that all curre

Christopher Thron

Quantum field theory currently has a single standard mathematical characterization (the Standard Model), but no single accepted conceptual framework to interpret the mathematics. Many of these conceptualizations rely on intuitive concepts carried over from classical physics (such as "particle" and "causality"). In this paper, instead of relying on classical

Yechi Ma, Neehar Peri, Achal Dave, Wei Hua

Contemporary autonomous vehicle (AV) benchmarks have advanced techniques for training 3D detectors. While class labels naturally follow a long-tailed distribution in the real world, existing benchmarks only focus on a few common classes (e.g., pedestrian and car) and neglect many rare but crucial classes (e.g., emergency vehicle and stroller). However, AVs m

Jing Wang, Ligong Wang, Xiaogang Liu

In this paper, we investigate the existence of pretty good fractional revival on Cayley graphs over dicyclic groups. We first give a necessary and sufficient description for Cayley graphs over dicyclic groups admitting pretty good fractional revival. By this description, we give some sufficient conditions for Cayley graphs over dicyclic groups admitting or n

David Hason Rudd, Huan Huo, Guandong Xu

Financial literacy (FL) represents a person's ability to turn assets into income, and understanding digital currencies has been added to the modern definition. FL can be predicted by exploiting unlabelled recorded data in financial networks via semi-supervised learning (SSL). Measuring and predicting FL has not been widely studied, resulting in limited under

Jinxiang Lai, Wenlong Wu, Bin-Bin Gao, Jun Liu

Image matching and object detection are two fundamental and challenging tasks, while many related applications consider them two individual tasks (i.e. task-individual). In this paper, a collaborative framework called MatchDet (i.e. task-collaborative) is proposed for image matching and object detection to obtain mutual improvements. To achieve the collabora

Aysan Esmradi, Daniel Wankit Yip, Chun Fai Chan

Ensuring the security of large language models (LLMs) is an ongoing challenge despite their widespread popularity. Developers work to enhance LLMs security, but vulnerabilities persist, even in advanced versions like GPT-4. Attackers exploit these weaknesses, highlighting the need for proactive cybersecurity measures in AI model development. This article exp

Diane Donovan, Tara Kemp, James Lefevre

For an integer partition $h_1 + \dots + h_n = N$, a 2-realization of this partition is a latin square of order $N$ with disjoint subsquares of orders $h_1,\dots,h_n$. The existence of 2-realizations is a partially solved problem posed by Fuchs. In this paper, we extend Fuchs' problem to $m$-ary quasigroups, or, equivalently, latin hypercubes. We construct la

Kalpak Bansod, Yanshan Wan, Yugesh Rai

This paper presents a comprehensive solution to address the critical challenge of liquid leaks in the oil and gas industry, leveraging advanced computer vision and deep learning methodologies. Employing You Only Look Once (YOLO) and Real-Time Detection Transformer (RT DETR) models, our project focuses on enhancing early identification of liquid leaks in key

Shulin He, Jinjiang liu, Hao Li, Yang Yang

Target speaker extraction (TSE) aims to isolate a specific voice from multiple mixed speakers relying on a registerd sample. Since voiceprint features usually vary greatly, current end-to-end neural networks require large model parameters which are computational intensive and impractical for real-time applications, espetially on resource-constrained platform

Yousuf Babiker M. Osman, Cheng Li, Weijian Huang, Shanshan Wang

Background: Deep learning has presented great potential in accurate MR image segmentation when enough labeled data are provided for network optimization. However, manually annotating 3D MR images is tedious and time-consuming, requiring experts with rich domain knowledge and experience. Purpose: To build a deep learning method exploring sparse annotations, n

Liam Hockley, Waseem Kamleh, Derek Leinweber, Anthony Thomas

We present a lattice QCD analysis of the $ \Delta $-baryon spectrum, with the goal of finding the position of the $ 2s $ radial excitation of the $ \Delta(1232) $ ground state. Using smeared three-quark operators in a correlation matrix analysis, we report masses for the ground, first and second excited states of the $ J^P = 3/2^+ $ spectrum across a broad r

Zhihao Yu, Chaohe Zhang, Yasha Wang, Wen Tang

Predicting health risks from electronic health records (EHR) is a topic of recent interest. Deep learning models have achieved success by modeling temporal and feature interaction. However, these methods learn insufficient representations and lead to poor performance when it comes to patients with few visits or sparse records. Inspired by the fact that docto

Anton Dochtermann, Takahiro Matsushita

In his work on molecular spaces, Ivashchenko introduced the notion of an $\mathfrak{I}$-contractible transformation on a graph $G$, a family of addition/deletion operations on its vertices and edges. Chen, Yau, and Yeh used these operations to define the $\mathfrak{I}$-homotopy type of a graph, and showed that $\mathfrak{I}$-contractible transformations pres

Seungjun Lee, Taeil Oh

Solving partial differential equations (PDEs) by learning the solution operators has emerged as an attractive alternative to traditional numerical methods. However, implementing such architectures presents two main challenges: flexibility in handling irregular and arbitrary input and output formats and scalability to large discretizations. Most existing arch

Chang Yang

Let $G/H$ be a $p$-adic symmetric space. We compute explicitly the higher relative extension groups for all discrete series representations of $G$ in two examples: the symplectic case and the linear case. The results have immediate applications to the computation of the Euler-Poincar\'e pairing, the alternating sum of the dimensions of the Ext-groups. In the

Cristian S. Calude, Karl Svozil

All quantum random number generators based on measuring value indefinite observables are at least three-dimensional because the Kochen-Specker Theorem and the Located Kochen-Specker Theorem are false in dimension two. In this article, we construct quantum random number generators based on measuring a three-dimensional value indefinite observable that generat

Sukanya Mitra

The causal-stable Muller-Israel-Stewart (MIS) theory is known to have a finite number of out of equilibrium derivative order corrections but requires treating the viscosity tensor as a separate degree of freedom with its own equations of motion, apart from the fundamental fluid degrees of freedom like velocity and temperature. In this work, I will show that

Salvatore De Vincenzo

Theoretically, in (1+1) dimensions, one can have Klein-Fock-Gordon-Majorana (KFGM) particles. More precisely, these are one-dimensional (1D) Klein-Fock-Gordon (KFG) and Majorana particles at the same time. In principle, the wave equations considered to describe such first-quantized particles are the standard 1D KFG equation and/or the 1D Feshbach-Villars (FV

Jing Yang, Rui-Hui Lin, Chao-Jun Feng, Xiang-Hua Zhai

We investigate the cosmological implications of a phantom dark energy model with bulk viscosity. We explore this model as a possible way to resolve the big rip singularity problem that plagues the phantom models. We use the latest type Ia supernova and Hubble parameter data to constrain the model parameters and find that the data favor a significant bulk vis

Kotaro Hisa

We consider necessary conditions and sufficient conditions on the solvability of the Cauchy--Dirichlet problem for a fractional semilinear heat equation in open sets (possibly unbounded and disconnected) with a smooth boundary. Our conditions enable us to identify the optimal strength of the admissible singularity of initial data for the local-in-time solvab

Cheng Feng

While new and effective methods for anomaly detection are frequently introduced, many studies prioritize the detection task without considering the need for explainability. Yet, in real-world applications, anomaly explanation, which aims to provide explanation of why specific data instances are identified as anomalies, is an equally important task. In this w

Zhangchi Qiu, Ye Tao, Shirui Pan, Alan Wee-Chung Liew

Conversational recommender systems (CRS) utilize natural language interactions and dialogue history to infer user preferences and provide accurate recommendations. Due to the limited conversation context and background knowledge, existing CRSs rely on external sources such as knowledge graphs to enrich the context and model entities based on their inter-rela

Satish Mulleti, Timur Zirtiloglu, Arman Tan, Rabia Tugce Yazicigil

Analog-to-digital converters (ADCs) facilitate the conversion of analog signals into a digital format. While the specific designs and settings of ADCs can vary depending on their applications, it is crucial in many modern applications to minimize their power consumption. The significance of low-power ADCs is particularly evident in fields like mobile and han

Niccolò Bigagli, Weijun Yuan, Siwei Zhang, Boris Bulatovic

Ensembles of particles governed by quantum mechanical laws exhibit fascinating emergent behavior. Atomic quantum gases, liquid helium, and electrons in quantum materials all show distinct properties due to their composition and interactions. Quantum degenerate samples of bosonic dipolar molecules promise the realization of novel phases of matter with tunable

Peng Shen, Xuguang Lu, Hisashi Kawai

Effective extraction and application of linguistic features are central to the enhancement of spoken Language IDentification (LID) performance. With the success of recent large models, such as GPT and Whisper, the potential to leverage such pre-trained models for extracting linguistic features for LID tasks has become a promising area of research. In this pa

Franck Sueur

Inspired by Gromov's partial differential relations, we introduce a notion of differential transmutation, which allows to transfer some local properties of solutions of a PDE to solutions of another PDE, in particular local solvability, hypoellipticity, weak and strong unique continuation properties and the Runge property. The latest refers to the possibilit