February 2024 arXiv papers — page 62

Andrei V. Nikolaev

Given an undirected graph $G = (V,E)$, the cut polytope $\mathrm{CUT}(G)$ is defined as the convex hull of the incidence vectors of all cuts in $G$. The 1-skeleton of $\mathrm{CUT}(G)$ is a graph whose vertex set is the vertex set of the polytope, and the edge set is the set of geometric edges or one-dimensional faces of the polytope. We study the diameter a

Ziyi Guan, Hantao Huang, Yupeng Su, Hong Huang

Large Language Models (LLMs) have greatly advanced the natural language processing paradigm. However, the high computational load and huge model sizes pose a grand challenge for deployment on edge devices. To this end, we propose APTQ (Attention-aware Post-Training Mixed-Precision Quantization) for LLMs, which considers not only the second-order information

Chenxiao Zhao, Gonçalo Catarina, Jin-Jiang Zhang, João C. G. Henriques

Unlocking the potential of topological order within many-body spin systems has long been a central pursuit in the realm of quantum materials. Despite extensive efforts, the quest for a versatile platform enabling site-selective spin manipulation, essential for tuning and probing diverse topological phases, has persisted. Here, we utilize on-surface synthesis

Yuanyuan Liu, Ke Wang, Lin Wei, Jingying Chen

Affective computing, which aims to recognize, interpret, and understand human emotions, provides benefits in healthcare, such as improving patient care and enhancing doctor-patient communication. However, there is a noticeable absence of a comprehensive summary of recent advancements in affective computing for healthcare, which could pose difficulties for re

Liqiu Dong, Marta Zagorowska, Tong Liu, Alex Durkin

Physics informed neural networks (PINNs) have recently been proposed as surrogate models for solving process optimization problems. However, in an active learning setting collecting enough data for reliably training PINNs poses a challenge. This study proposes a broadly applicable method for incorporating physics information into existing machine learning (M

Yunxin Li, Baotian Hu, Wenhan Luo, Lin Ma

In this paper, we propose a new setting for generating product descriptions from images, augmented by marketing keywords. It leverages the combined power of visual and textual information to create descriptions that are more tailored to the unique features of products. For this setting, previous methods utilize visual and textual encoders to encode the image

Kirti Gupta, Subham Sahoo, Bijaya Ketan Panigrahi

In power electronic systems (PES), attacks on data availability such as latency attacks, data dropouts, and time-synchronization attacks (TSAs) continue to pose significant threats to both the communication network and the control system performance. As per the conventional norms of communication engineering, PES still rely on time synchronized sampling, whi

Lian-Yun Song, Zhi-Jia Tian

Binary stars ubiquitous throughout the universe are important. Contact binaries (CBs) possessing Period-Luminosity (PL) relations could be adopted as distance tracers. The PL relations of CBs are influenced by metallicity abundance and color index, which are connected to both the radius and luminosity of stars. Here we propose fine relations of Period-Lumino

Woojeong Jin, Tejas Srinivasan, Jesse Thomason, Xiang Ren

Humans perceive and comprehend different visual properties of an object based on specific contexts. For instance, we know that a banana turns brown ``when it becomes rotten,'' whereas it appears green ``when it is unripe.'' Previous studies on probing visual commonsense knowledge have primarily focused on examining language models' understanding of typical p

Kai Lv, Xiaoran Liu, Qipeng Guo, Hang Yan

The quality of training data are crucial for enhancing the long-text capabilities of foundation models. Despite existing efforts to refine data quality through heuristic rules and evaluations based on data diversity and difficulty, there's a lack of systematic approaches specifically tailored for assessing long texts. Addressing this gap, our work systematic

Yifan Yanggong, Hao Pan, Lei Wang

Games are a simplified model of reality and often serve as a favored platform for Artificial Intelligence (AI) research. Much of the research is concerned with game-playing agents and their decision making processes. The game of Guandan (literally, "throwing eggs") is a challenging game where even professional human players struggle to make the right decisio

Athira Divakaran, Tijo James, Sandi Klavžar, Latha S Nair

In the Maker-Breaker domination game, Dominator and Staller play on a graph $G$ by taking turns in which each player selects a not yet played vertex of $G$. Dominator's goal is to select all the vertices in a dominating set, while Staller aims to prevent this from happening. In this paper, the game is investigated on corona products of graphs. Its outcome is

J. Sedaghat, B. Eslam Panah, R. Moradi, S. M. Zebarjad

We have investigated the structural properties of strange quark stars (SQSs) in a modified theory of gravity known as massive gravity. In order to obtain the equation of state (EOS) of strange quark matter, we have employed a modified version of the Nambu-Jona-Lasinio model (MNJL) which includes a combination of NJL Lagrangian and its Fierz transformation by

Siyang Xiong

Traditionally, mechanism design focuses on simultaneous-move games (e.g., Myerson (1981)). In this paper, we study mechanism design with sequential-move games, and provide two results on revelation principles for general solution concepts (e.g., perfect Bayesian equilibrium, obvious dominance, strong-obvious dominance). First, if a solution concept is additi

Chen Shenglun, Zhang Hong, Ma XinZhu, Wang Zhihui

Depth completion is a long-standing challenge in computer vision, where classification-based methods have made tremendous progress in recent years. However, most existing classification-based methods rely on pre-defined pixel-shared and discrete depth values as depth categories. This representation fails to capture the continuous depth values that conform to

Binglu Wang, Chenxi Guo, Yang Jin, Haisheng Xia

Gaze object prediction aims to predict the location and category of the object that is watched by a human. Previous gaze object prediction works use CNN-based object detectors to predict the object's location. However, we find that Transformer-based object detectors can predict more accurate object location for dense objects in retail scenarios. Moreover, th

Xueliang Zhao, Xinting Huang, Tingchen Fu, Qintong Li

Multimodal reasoning stands as a pivotal capability for large vision-language models (LVLMs). The integration with Domain-Specific Languages (DSL), offering precise visual representations, equips these models with the opportunity to execute more accurate reasoning in complex and professional domains. However, the vanilla Chain-of-Thought (CoT) prompting meth

Danyang Hou, Liang Pang, Huawei Shen, Xueqi Cheng

Video Corpus Moment Retrieval (VCMR) is a new video retrieval task aimed at retrieving a relevant moment from a large corpus of untrimmed videos using a text query. The relevance between the video and query is partial, mainly evident in two aspects:~(1)~Scope: The untrimmed video contains many frames, but not all are relevant to the query. Strong relevance i

Yang Li, Wenyi Tan, Tingrui Wang, Xinkai Liang

This study introduces a novel approach to neural rendering, specifically tailored for adversarial camouflage, within an extensive 3D rendering framework. Our method, named FPA, goes beyond traditional techniques by faithfully simulating lighting conditions and material variations, ensuring a nuanced and realistic representation of textures on a 3D target. To

Kai Yan

The famous Drazin inverse and generalized Drazin inverse were introduced by Drazin in 1958 and Koliha in 1996, respectively. In the present paper, the author introduces the concepts of left and right (generalized) Drazin inverses, which are the one-sided versions of classical (generalized) Drazin inverses, in Banach algebras. Several characterizations of one

Ethan Smith, Nayan Saxena, Aninda Saha

Attention mechanism has been crucial for image diffusion models, however, their quadratic computational complexity limits the sizes of images we can process within reasonable time and memory constraints. This paper investigates the importance of dense attention in generative image models, which often contain redundant features, making them suitable for spars

Liang Chen, Yichi Zhang, Shuhuai Ren, Haozhe Zhao

We present PCA-Bench, a multimodal decision-making benchmark for evaluating the integrated capabilities of Multimodal Large Language Models (MLLMs). Departing from previous benchmarks focusing on simplistic tasks and individual model capability, PCA-Bench introduces three complex scenarios: autonomous driving, domestic robotics, and open-world games. Given t

Yihang Gao, Chuanyang Zheng, Enze Xie, Han Shi

Besides natural language processing, transformers exhibit extraordinary performance in solving broader applications, including scientific computing and computer vision. Previous works try to explain this from the expressive power and capability perspectives that standard transformers are capable of performing some algorithms. To empower transformers with alg

Ritwik Mishra, Pooja Desur, Rajiv Ratn Shah, Ponnurangam Kumaraguru

Coreference resolution involves the task of identifying text spans within a discourse that pertain to the same real-world entity. While this task has been extensively explored in the English language, there has been a notable scarcity of publicly accessible resources and models for coreference resolution in South Asian languages. We introduce a Translated da

Gao-Feng Wei, Yu-Liang Zhao

Within an isospin- and momentum-dependent transport model by including the kaon reaction channels, we study the kaon prodution in heavy-ion collisions (HICs) at SIS (Darmstadt Schwerionen Synchrotron, GSI) energies. Based on simulations of a centrality of 0-40% Au + Au collision at $\sqrt{s_{NN}}=2.4$ GeV, a typical reaction that has been carried out by the

Michael C Dallaston

We find solutions that describe the levelling of a thin fluid film, comprising a non-Newtonian power-law fluid, that coats a substrate and evolves under the influence of surface tension. We consider the evolution from both periodic and localised initial conditions as separate cases. Particular (similarity) solutions in each of these two cases exhibit the gen

Chenze Dong, Khee-Gan Lee, Romeel Davé, Weiguang Cui

The intergalactic medium (IGM) in the vicinity of galaxy protoclusters are interesting testbeds to study complex baryonic effects such as gravitational shocks and feedback. Here, we utilize hydrodynamical simulations from the SIMBA and The Three Hundred suites to study the mechanisms influencing large-scale Lyman-$\alpha$ transmission in $2<z<2.5$ protoclust

Shengwei Xu, Yichi Zhang, Paul Resnick, Grant Schoenebeck

Because high-quality data is like oxygen for AI systems, effectively eliciting information from crowdsourcing workers has become a first-order problem for developing high-performance machine learning algorithms. Two prevalent paradigms, spot-checking and peer prediction, enable the design of mechanisms to evaluate and incentivize high-quality data from human

Danyang Hou, Liang Pang, Huawei Shen, Xueqi Cheng

Video Corpus Moment Retrieval (VCMR) is a practical video retrieval task focused on identifying a specific moment within a vast corpus of untrimmed videos using the natural language query. Existing methods for VCMR typically rely on frame-aware video retrieval, calculating similarities between the query and video frames to rank videos based on maximum frame

Won-Kwang Park

We consider the application of a subspace migration (SM) algorithm to quickly identify small objects in microwave imaging. In various problems, it is easy to measure the diagonal elements of the scattering matrix if the location of the transmitter and the receiver is the same. To address this issue, several studies have been conducted by setting the diagonal

C. Abinash Bhuyan, Kishore K. Madapu, K. Prabakar, K. Ganesan

We fabricated the 1D nanoscrolled monolayer MoS2 (1L-MoS2) with superior characteristics from 1L-MoS2 film in a facile route, using a suitable organic solvent with optimum surface tension, evaporation rate and dielectric constant, which facilitates the controlled scroll formation. These nanoscrolls behave as multilayers in morphology and monolayer electronic

Kaijie Zhu, Jindong Wang, Qinlin Zhao, Ruochen Xu

Evaluation of large language models (LLMs) has raised great concerns in the community due to the issue of data contamination. Existing work designed evaluation protocols using well-defined algorithms for specific tasks, which cannot be easily extended to diverse scenarios. Moreover, current evaluation benchmarks can only provide the overall benchmark results

Keiya Ishiguro, Tatsuo Kobayashi, Satsuki Nishimura, Hajime Otsuka

We study the modular symmetry in heterotic string theory on Calabi-Yau threefolds. In particular, we examine whether moduli-dependent holomorphic Yukawa couplings are described by modular forms in the context of heterotic string theory with standard embedding. We find that $SL(2,\mathbb{Z})$ modular symmetry emerges in asymptotic regions of the Calabi-Yau mo

Alaa Alomari, Hossam Faris, Pedro A. Castillo

The Covid-19 pandemic has led to an increase in the awareness of and demand for telemedicine services, resulting in a need for automating the process and relying on machine learning (ML) to reduce the operational load. This research proposes a specialty detection classifier based on a machine learning model to automate the process of detecting the correct sp

Seong Hoon Lim, Taejun Yun, Jinhyeon Kim, Jihun Choi

The successful adaptation of multilingual language models (LMs) to a specific language-task pair critically depends on the availability of data tailored for that condition. While cross-lingual transfer (XLT) methods have contributed to addressing this data scarcity problem, there still exists ongoing debate about the mechanisms behind their effectiveness. In

Yunxin Li, Xinyu Chen, Baotian Hu, Haoyuan Shi

Evaluating and Rethinking the current landscape of Large Multimodal Models (LMMs), we observe that widely-used visual-language projection approaches (e.g., Q-former or MLP) focus on the alignment of image-text descriptions yet ignore the visual knowledge-dimension alignment, i.e., connecting visuals to their relevant knowledge. Visual knowledge plays a signi

Sungjoo Lim, Seunghyun Baek, Jacob Whitlow, Marissa D'Onofrio

We present the design and characterization of individual addressing optics based on a multi-channel acousto-optic modulator (AOM) for trapped ytterbium-171 ions. The design parameters of the individual addressing system were determined based on the tradeoff between the expected crosstalk and the required numerical aperture of the projection objective lens. T

Shuang Du

Continuous gravitational waves (GWs) of neutrons stars haven't been detected directly until now. One possible way to indirectly identify their signatures is via the correlation between magnetars and gamma-ray bursts (GRBs), since, under this magnetar scenario of GRBs, GW radiation can affect the evolution of GRB X-ray light curves. Nevertheless, relevant stu

Gholam Hossein Bordbar, Mohammad Mazhari, Ahmad Poostforush

With regard to the coupling constant and the strong magnetic field of neutron stars, we have studied these stars in the 4D Einstein Gauss Bonnet (4D EGB) gravity model in order to grasp a better understanding of these objects. In this paper, we have shown that the neutron star properties are considerably affected by the coupling constant and magnetic field.

Huangxing Li

The Nab collaboration will study free neutron beta decay at the Spallation Neutron Source at Oak Ridge National Lab. A neutron decays into a proton, an electron and an anti-neutrino in this process, where the energy of the outgoing protons and electrons are collected to determine (1) the electron-antineutrino correlation co-efficient $a$ to the precision of

Surbhi Khetrapal, Emil Tore Mærsk Pedersen

We consider a chain of spin-half particles of a finite length, evolved with the mixed-field Ising Hamiltonian and impose open boundary condition. We simulate the time evolution of entanglement entropy and mutual information following quench from the N\'eel state in this system using tensor networks. We find that the entanglement entropy for non-integrable sy

X. Yu, J. -C. Weeber, L. Markey, J. Arocas

Integrated quantum photonic circuits require the efficient coupling of photon sources to photonic waveguides. Hybrid plasmonic/photonic platforms are a promising approach, taking advantage of both plasmon modal confinement for efficient coupling to a nearby emitter and photonic circuitry for optical data transfer and processing. In this work, we established

Yuchen Yan, Peiyan Zhang, Zheng Fang, Qingqing Long

The "Graph pre-training and fine-tuning" paradigm has significantly improved Graph Neural Networks(GNNs) by capturing general knowledge without manual annotations for downstream tasks. However, due to the immense gap of data and tasks between the pre-training and fine-tuning stages, the model performance is still limited. Inspired by prompt fine-tuning in Na

Moutaz Alazab, Ruba Abu Khurma, Pedro A. Castillo, Bilal Abu-Salih

This paper proposes an Intrusion Detection System (IDS) employing the Harris Hawks Optimization algorithm (HHO) to optimize Multilayer Perceptron learning by optimizing bias and weight parameters. HHO-MLP aims to select optimal parameters in its learning process to minimize intrusion detection errors in networks. HHO-MLP has been implemented using EvoloPy NN

Xiangzhe Kong, Yinjun Jia, Wenbing Huang, Yang Liu

Peptide design plays a pivotal role in therapeutics, allowing brand new possibility to leverage target binding sites that are previously undruggable. Most existing methods are either inefficient or only concerned with the target-agnostic design of 1D sequences. In this paper, we propose a generative model for full-atom \textbf{Pep}tide design with \textbf{G}

Thang V. Nguyen, Thanh V. Pham, Anh T. Pham, Dang T. Ngoc

Free-space optics (FSO)-based satellite communication systems have recently received considerable attention due to their enhanced capacity compared to their radio frequency (RF) counterparts. This paper analyzes the performance of physical layer security of space-to-ground intensity modulation/direct detection FSO satellite links under the effect of atmosphe

Changyuan Zhao, Hongyang Du, Dusit Niyato, Jiawen Kang

Generative Artificial Intelligence (GAI) stands at the forefront of AI innovation, demonstrating rapid advancement and unparalleled proficiency in generating diverse content. Beyond content creation, GAI has significant analytical abilities to learn complex data distribution, offering numerous opportunities to resolve security issues. In the realm of securit

Jonas Schöpf, Fabian Mitterwallner, Aart Middeldorp

We show that (local) confluence of terminating locally constrained rewrite systems is undecidable, even when the underlying theory is decidable. Several confluence criteria for logically constrained rewrite systems are known. These were obtained by replaying existing proofs for plain term rewrite systems in a constrained setting, involving a non-trivial effo

Liyan Xu, Jiangnan Li, Mo Yu, Jie Zhou

This work introduces an original and practical paradigm for narrative comprehension, stemming from the characteristics that individual passages within narratives tend to be more cohesively related than isolated. Complementary to the common end-to-end paradigm, we propose a fine-grained modeling of narrative context, by formulating a graph dubbed NarCo, which

Duc M. T. Hoang, Thanh V. Pham, Anh T. Pham, Chuyen T Nguyen

There has been an increasing interest in physical layer security (PLS), which, compared with conventional cryptography, offers a unique approach to guaranteeing information confidentiality against eavesdroppers. In this paper, we study a joint design of adaptive $M$-ary pulse amplitude modulation (PAM) and precoding, which aims to optimize wiretap visible-li

Siyang Li, Hui Xiong, Yize Chen

Due to the vast electric vehicle (EV) penetration to distribution grid, charging load forecasting is essential to promote charging station operation and demand-side management.However, the stochastic charging behaviors and associated exogenous factors render future charging load patterns quite volatile and hard to predict. Accordingly, we devise a novel Diff

Zhipeng Xu, Zhenghao Liu, Yukun Yan, Shuo Wang

Large Language Models (LLMs) have demonstrated strong performance across a wide range of NLP tasks. However, they often exhibit suboptimal behaviors and inconsistencies when exposed to unfamiliar external information, underscoring their limitations in effectively leveraging such knowledge. Inspired by constructivist learning theory, we propose ThinkNote, a n

Yunxin Li, Xinyu Chen, Baotain Hu, Min Zhang

Long video understanding is a significant and ongoing challenge in the intersection of multimedia and artificial intelligence. Employing large language models (LLMs) for comprehending video becomes an emerging and promising method. However, this approach incurs high computational costs due to the extensive array of video tokens, experiences reduced visual cl

Haoqi He

This paper explores the application of Quadratic Unconstrained Binary Optimization (QUBO) models in solving the Travelling Salesman Problem (TSP) through Quantum Annealing algorithms and Graph Neural Networks. Quantum Annealing (QA), a quantum-inspired optimization method that exploits quantum tunneling to escape local minima, is used to solve QUBO formulati

Guandong Li, Xian Yang, Wenpin Ma

Document tamper detection has always been an important aspect of tamper detection. Before the advent of deep learning, document tamper detection was difficult. We have made some explorations in the field of text tamper detection based on deep learning. Our Ps tamper detection method includes three steps: feature assistance, audit point positioning, and tampe

Gavin S. Hartnett

A high-altitude nuclear blast can produce an electromagnetic pulse (EMP) capable of disrupting electronics on Earth. The basic phenomenology of the initial (E1) phase of the EMP was initially worked out in the 1960s by Longmire, Karzas, and Latter, and although more accurate and sophisticated EMP models have since been devised, the Karzas-Latter model is par

Nghia Tran, Tuan Nguyen, Jay Black, Tuan Ngo

In this research, the ambient cured one part alkali activated material (AAM) containing graphene nanoplatelets (GNPs), fly ash, slag and silica fume has been investigated after high temperature exposure to 200 to 800oC. Their compressive strength, thermal properties, microstructure, pore structure were characterised through visual observation, isothermal cal

Lingxi Zhang, Yue Yu, Kuan Wang, Chao Zhang

Retrieval-augmented generation enhances large language models (LLMs) by incorporating relevant information from external knowledge sources. This enables LLMs to adapt to specific domains and mitigate hallucinations in knowledge-intensive tasks. However, existing retrievers are often misaligned with LLMs due to their separate training processes and the black-

Akio Kawasaki, Takumi Kobayashi, Akiko Nishiyama, Takehiko Tanabe

Measurements of isotope shifts have recently been attracting considerable attention due to their potentials in searching for new forces. We report on the isotope shifts of the $4f^{14}6s^{2}~^1S_0- 4f^{13}5d6s^{2}(J=2)$ transition at 431 nm in Yb, based on absolute frequency measurements with an accuracy of $\sim10$ kHz. With these data, the hyperfine consta

Martin M. Roth

In the process of transforming science cases into a viable and affordable design for a novel instrument, there is the problem of how to gauge their scientific impact, especially when they end up in competing top level requirements that can be incompatible with each other. This research note presents a case study for scientific impact of the integral field sp

Junfeng Li, Changxing Miao, Ankang Yu

In this paper, we studied the space-time estimates for the solution to the Schr\"odinger equation. By polynomial partitioning, induction arguments, bilinear to linear arguments and broad norm estimates, we set up several maximal estimates for the Schr\"odinger equation with high-frequency input data. By these maximal estimates, we obtain the sharp global spa

Stephen Jon Quiton, Hamlin Wu, Xin Xing, Lin Lin

In periodic systems, the Hartree-Fock (HF) exchange energy exhibits the slowest convergence of all HF energy components as the system size approaches the thermodynamic limit. We demonstrate that the recently proposed staggered mesh method for Fock exchange energy [Xing, Li, and Lin, Math. Comp., 2024], which is specifically designed to sidestep certain singu

Zhendong Xiao, Changhao Chen, Shan Yang, Wu Wei

Camera relocalization is pivotal in computer vision, with applications in AR, drones, robotics, and autonomous driving. It estimates 3D camera position and orientation (6-DoF) from images. Unlike traditional methods like SLAM, recent strides use deep learning for direct end-to-end pose estimation. We propose EffLoc, a novel efficient Vision Transformer for s

Jordan Dotzel, Bahaa Kotb, James Dotzel, Mohamed Abdelfattah

Traditional methods, such as JPEG, perform image compression by operating on structural information, such as pixel values or frequency content. These methods are effective to bitrates around one bit per pixel (bpp) and higher at standard image sizes. In contrast, text-based semantic compression directly stores concepts and their relationships using natural l

Kim V. Berghaus, Matthew Forslund, Mark Vincent Guevarra

We propose the first model of warm inflation in which the particle production emerges directly from coupling the inflaton to Standard Model particles. Warm inflation, an early epoch of sustained accelerated expansion at finite temperature, is a compelling alternative to cold inflation, with distinct predictions for inflationary observables such as the amplit

Xuemei Tang, Jun Wang, Qi Su, Chu-ren Huang

Sequence labeling models often benefit from incorporating external knowledge. However, this practice introduces data heterogeneity and complicates the model with additional modules, leading to increased expenses for training a high-performing model. To address this challenge, we propose a two-stage curriculum learning (TCL) framework specifically designed fo

Xiao-Yang Liu, Jie Zhang, Guoxuan Wang, Weiqing Tong

Large language models (LLMs) are computationally intensive. The computation workload and the memory footprint grow quadratically with the dimension (layer width). Most of LLMs' parameters come from the linear layers of the transformer structure and are highly redundant. These linear layers contribute more than 80% of the computation workload and 99% of the m

Quanyu Long, Yue Deng, LeiLei Gan, Wenya Wang

Dense retrieval systems have been widely used in various NLP applications. However, their vulnerabilities to potential attacks have been underexplored. This paper investigates a novel attack scenario where the attackers aim to mislead the retrieval system into retrieving the attacker-specified contents. Those contents, injected into the retrieval corpus by a

Gavin Brown, Krishnamurthy Dvijotham, Georgina Evans, Daogao Liu

We provide an improved analysis of standard differentially private gradient descent for linear regression under the squared error loss. Under modest assumptions on the input, we characterize the distribution of the iterate at each time step. Our analysis leads to new results on the algorithm's accuracy: for a proper fixed choice of hyperparameters, the sampl

Lin An, Andrew A. Li, Benjamin Moseley, Gabriel Visotsky

Online decision-makers often obtain predictions on future variables, such as arrivals, demands, inventories, and so on. These predictions can be generated from simple forecasting algorithms for univariate time-series, all the way to state-of-the-art machine learning models that leverage multiple time-series and additional feature information. However, the pr

Run Yang, Hui He, Weizhe Zhang

Mobile edge computing (MEC) pushes computing resources to the edge of the network and distributes them at the edge of the mobile network. Offloading computing tasks to the edge instead of the cloud can reduce computing latency and backhaul load simultaneously. However, new challenges incurred by user mobility and limited coverage of MEC server service arise.

Md Towhidul Absar Chowdhury, Soumyajit Datta, Naveen Sharma, Ashiqur R. KhudaBukhsh

Current research concentrates on studying discussions on social media related to structural failures to improve disaster response strategies. However, detecting social web posts discussing concerns about anticipatory failures is under-explored. If such concerns are channeled to the appropriate authorities, it can aid in the prevention and mitigation of poten

Toshitaka Aoki, Yuya Mizuno

In this paper, we introduce a new generating function called $d$-polynomial for the dimensions of $\tau$-tilting modules over a given finite dimensional algebra. Firstly, we study basic properties of $d$-polynomials and show that it can be realized as a certain sum of the $f$-polynomials of the simplicial complexes arising from $\tau$-rigid pairs. Secondly,

Hyeon-dong Han, Parada T. P. Hutauruk, Seung-il Nam

In this present investigation, we explore the elastic scattering of pions with nuclei ($\pi$-$A$), primarily influenced by the $\Delta$(1232) resonance, within the Eikonal-Glauber model. The medium effects are incorporated by considering nuclear-density ($\rho_A$) dependent masses of baryons and strong coupling constants. These dependencies are computed and

Hongtao Huang, Xiaojun Chang, Wen Hu, Lina Yao

Recent years have seen the explosion of edge intelligence with powerful Deep Neural Networks (DNNs). One popular scheme is training DNNs on powerful cloud servers and subsequently porting them to mobile devices after being lightweight. Conventional approaches manually specialized DNNs for various edge platforms and retrain them with real-world data. However,

Yang Liu, Meng Xu, Shuo Wang, Liner Yang

Modern large language models (LLMs) should generally benefit individuals from various cultural backgrounds around the world. However, most recent advanced generative evaluation benchmarks tailed for LLMs mainly focus on English. To this end, we introduce OMGEval, the first Open-source Multilingual Generative test set that can assess the capability of LLMs in

Siyu Wang, Bo Yang, Zhiwen Yu, Xuelin Cao

In this paper, we investigate a multi-user offloading problem in the overlapping domain of a multi-server mobile edge computing system. We divide the original problem into two stages: the offloading decision making stage and the request scheduling stage. To prevent the terminal from going out of service area during offloading, we consider the mobility parame

Stephan Goerttler, Fei He, Min Wu

The prospect of future treatment warrants the development of cost-effective screening for Alzheimer's disease (AD). A promising candidate in this regard is electroencephalography (EEG), as it is one of the most economic imaging modalities. Recent efforts in EEG analysis have shifted towards leveraging spatial information, employing novel frameworks such as g

Takashi Kodama, Hirokazu Kiyomaru, Yin Jou Huang, Sadao Kurohashi

Humans pay careful attention to the interlocutor's internal state in dialogues. For example, in recommendation dialogues, we make recommendations while estimating the seeker's internal state, such as his/her level of knowledge and interest. Since there are no existing annotated resources for the analysis, we constructed RecMind, a Japanese movie recommendati

Dawei Gao, Zitao Li, Xuchen Pan, Weirui Kuang

With the rapid advancement of Large Language Models (LLMs), significant progress has been made in multi-agent applications. However, the complexities in coordinating agents' cooperation and LLMs' erratic performance pose notable challenges in developing robust and efficient multi-agent applications. To tackle these challenges, we propose AgentScope, a develo

Noble Saji Mathews, Meiyappan Nagappan

Recent Large Language Models (LLMs) have demonstrated significant capabilities in generating code snippets directly from problem statements. This increasingly automated process mirrors traditional human-led software development, where code is often written in response to a requirement. Historically, Test-Driven Development (TDD) has proven its merit, requiri

Jingyi Gao, Mitchell Newberry

Trees in works of art have stirred emotions in viewers for millennia. Leonardo da Vinci described geometric proportions in trees to provide both guidelines for painting and insights into tree form and function. Da Vinci's Rule of trees further implies fractal branching with a particular scaling exponent $\alpha = 2$ governing both proportions between the dia

Zhanpeng Fu, Roderich Moessner, Hongzheng Zhao, Marin Bukov

The capacity to custom tailor the properties of quantum matter and materials is a central requirement for enlarging their range of possible functionalities. A particularly promising route is the use of driving protocols to engineer specific desired properties with a high degree of control and flexibility. Here, we present such a program for the tunable gener

Mingxuan Xiao, Yan Xiao, Hai Dong, Shunhui Ji

The dependence of Natural Language Processing (NLP) intelligent software on Large Language Models (LLMs) is increasingly prominent, underscoring the necessity for robustness testing. Current testing methods focus solely on the robustness of LLM-based software to prompts. Given the complexity and diversity of real-world inputs, studying the robustness of LLMb

Canaan Yung, Hadi Mohaghegh Dolatabadi, Sarah Erfani, Christopher Leckie

Large language models (LLMs) are susceptible to social-engineered attacks that are human-interpretable but require a high level of comprehension for LLMs to counteract. Existing defensive measures can only mitigate less than half of these attacks at most. To address this issue, we propose the Round Trip Translation (RTT) method, the first algorithm specifica

Chenyang Song, Xu Han, Zhengyan Zhang, Shengding Hu

Activation sparsity refers to the existence of considerable weakly-contributed elements among activation outputs. As a prevalent property of the models using the ReLU activation function, activation sparsity has been proven a promising paradigm to boost model inference efficiency. Nevertheless, most large language models (LLMs) adopt activation functions wit

Piotr Majek, Ireneusz Weymann

In this work we investigate the spin-dependent transport through a double quantum dot embedded in a ferromagnetic tunnel junction and side attached to a topological superconducting nanowire hosting Majorana zero-energy modes. We focus on the transport regime when the Majorana mode leaks into the double quantum dot competing with the two-stage Kondo effect an

Hongru Wang, Boyang Xue, Baohang Zhou, Tianhua Zhang

Previous research has typically concentrated on leveraging the internal knowledge of Large Language Models (LLMs) to answer known questions (i.e., \textit{internal reasoning such as generate-then-read}). In contrast, for questions that fall outside their known scope, these models rely on external knowledge retrieval to provide accurate responses (i.e., \text

Iulian Brumar, Rodrigo Rocha, Alex Bernat, Devashree Tripathy

Designing accelerators for resource- and power-constrained applications is a daunting task. High-level Synthesis (HLS) addresses these constraints through resource sharing, an optimization at the HLS binding stage that maps multiple operations to the same functional unit. However, resource sharing is often limited to reusing instructions within a basic block

M. Emrullah Ildiz, Yixiao Huang, Yingcong Li, Ankit Singh Rawat

Modern language models rely on the transformer architecture and attention mechanism to perform language understanding and text generation. In this work, we study learning a 1-layer self-attention model from a set of prompts and associated output data sampled from the model. We first establish a precise mapping between the self-attention mechanism and Markov

Rui Zhou, Xian Li, Ying Fang, Xiaofei Li

In this work, we propose Mel-FullSubNet, a single-channel Mel-spectrogram denoising and dereverberation network for improving both speech quality and automatic speech recognition (ASR) performance. Mel-FullSubNet takes as input the noisy and reverberant Mel-spectrogram and predicts the corresponding clean Mel-spectrogram. The enhanced Mel-spectrogram can be

Zhentao Huang, Yukun Shi, Neil Bruce, Minglun Gong

The widespread adoption of implicit neural representations, especially Neural Radiance Fields (NeRF), highlights a growing need for editing capabilities in implicit 3D models, essential for tasks like scene post-processing and 3D content creation. Despite previous efforts in NeRF editing, challenges remain due to limitations in editing flexibility and qualit

Haruki Kawai, Divesh Lala, Koji Inoue, Keiko Ochi

The handling of communication breakdowns and loss of engagement is an important aspect of spoken dialogue systems, particularly for chatting systems such as attentive listening, where the user is mostly speaking. We presume that a human is best equipped to handle this task and rescue the flow of conversation. To this end, we propose a semi-autonomous system,

Liguo Chen, Hongyang Hua, Xinyue Luo, Guoli Xu

Ocean warming significantly affects the fishing industry, with species like Scottish herring and mackerel migrating northwards. Our research, a fusion of artificial intelligence, data science, and operations research, addresses this crisis. Using Long Short Term Memory networks, we forecast sea surface temperatures (SST) and model fish migratory patterns wit

Xiurui Zhao, Francesca Civano, Christopher N. A. Willmer, Silvia Bonoli

We present the second NuSTAR and XMM-Newton extragalactic survey of the JWST North Ecliptic Pole (NEP) Time-Domain Field (TDF). The first NuSTAR NEP-TDF survey (Zhao et al. 2021) had 681 ks total exposure time executed in NuSTAR cycle 5, in 2019 and 2020. This second survey, acquired from 2020 to 2022 in cycle 6, adds 880 ks of NuSTAR exposure time. The over

Ronghao Deng, Meizhu Li, Qi Zhang

Structural analysis in network science is finding the information hidden from the topology structure of complex networks. Many methods have already been proposed in the research on the structural analysis of complex networks to find the different structural information of networks. In this work, the sum of nodes' betweenness centrality (SBC) is used as a new

Luwei Cai, Fu Song, Taolue Chen

Timing side-channel attacks exploit secret-dependent execution time to fully or partially recover secrets of cryptographic implementations, posing a severe threat to software security. Constant-time programming discipline is an effective software-based countermeasure against timing side-channel attacks, but developing constant-time implementations turns out

Chaoqun Du, Yizeng Han, Gao Huang

Recent advancements in semi-supervised learning have focused on a more realistic yet challenging task: addressing imbalances in labeled data while the class distribution of unlabeled data remains both unknown and potentially mismatched. Current approaches in this sphere often presuppose rigid assumptions regarding the class distribution of unlabeled data, th

Qi Liu, Xingyu Li, Ke Sun, Yufeng Li

Scalable service-Oriented Middleware over IP (SOME/IP) is an Ethernet communication standard protocol in the Automotive Open System Architecture (AUTOSAR), promoting ECU-to-ECU communication over the IP stack. However, SOME/IP lacks a robust security architecture, making it susceptible to potential attacks. Besides, random hardware failure of ECU will disrup

R. Kurihara, Y. Sato, A. Miyake, M. Akaki

We performed high-magnetic-field magnetization, polarization, and ultrasonic measurements in Ba$_2$CuGe$_2$O$_7$ to investigate field-induced multiferroic properties arising from a cross-correlation between electric dipoles and electric quadrupoles in addition to cross-correlation between magnetic dipoles and electric dipoles. Magnetization $M$ shows saturat