April 2024 arXiv papers — page 151

Yiyang Ma, Wenhan Yang, Jiaying Liu

The images produced by diffusion models can attain excellent perceptual quality. However, it is challenging for diffusion models to guarantee distortion, hence the integration of diffusion models and image compression models still needs more comprehensive explorations. This paper presents a diffusion-based image compression method that employs a privileged e

Belle II Collaboration, I. Adachi, L. Aggarwal, H. Aihara

We report a measurement of the $e^+e^- \to \pi^+\pi^-\pi^0$ cross section in the energy range from 0.62 to 3.50 GeV using an initial-state radiation technique. We use an $e^+e^-$ data sample corresponding to 191 $\text{fb}^{-1}$ of integrated luminosity, collected at a center-of-mass energy at or near the $\Upsilon{(4S)}$ resonance with the Belle II detector

Cristian Mejía-Cortés, Jesús Muñoz-Muñoz, Mario I. Molina

In this paper, we investigate a two-dimensional photonic array featuring a circular shape and an alternating gain and loss pattern. Our analysis revolves around determining the presence and resilience of optical ring modes with varying vorticity values. This investigation is conducted with respect to both the array's length and the strength of the non-Hermit

Gyeongjin Kang, Younggeun Lee, Seungjun Oh, Eunbyung Park

Neural Radiance Fields (NeRF) have achieved huge success in effectively capturing and representing 3D objects and scenes. However, to establish a ubiquitous presence in everyday media formats, such as images and videos, we need to fulfill three key objectives: 1. fast encoding and decoding time, 2. compact model sizes, and 3. high-quality renderings. Despite

Prashil Wankhede, Nirabhra Mandal, Sonia Martínez, Pavankumar Tallapragada

We propose a continuous-time nonlinear model of opinion dynamics with utility-maximizing agents connected via a social influence network. A distinguishing feature of the proposed model is the inclusion of an opinion-dependent resource-penalty term in the utilities, which limits the agents from holding opinions of large magnitude. This model is applicable in

Jaewoong Heo, Moonjoo Lee

Quantum computer is extensively used in solving financial problems. Quantum amplitude estimation, an algorithm that aims to estimate the amplitude of a given quantum state, can be utilized to determine the expectation value of bonds as the logic introduced in quantum risk analysis. As the number of the evaluation qubit increases, the more accurate the precis

Hou-I Liu, Christine Wu, Jen-Hao Cheng, Wenhao Chai

Monocular 3D object detection (Mono3D) holds noteworthy promise for autonomous driving applications owing to the cost-effectiveness and rich visual context of monocular camera sensors. However, depth ambiguity poses a significant challenge, as it requires extracting precise 3D scene geometry from a single image, resulting in suboptimal performance when trans

Angie Veronica, Thomas H. Reiprich, Florian Pacaud, Jeremy S. Sanders

Methods. We utilized the combined five SRG/eROSITA All-Sky Survey data (eRASS:5) to perform X-ray imaging and spectral analyses of the Centaurus cluster in various directions to large radii. Surface brightness (SB) profiles out to $2R_{200}$ were constructed. We acquired gas temperature, metallicity, and normalization per area profiles out to $R_{200}$. We c

Andi Zhang, Tim Z. Xiao, Weiyang Liu, Robert Bamler

We revisit the likelihood ratio between a pretrained large language model (LLM) and its finetuned variant as a criterion for out-of-distribution (OOD) detection. The intuition behind such a criterion is that, the pretrained LLM has the prior knowledge about OOD data due to its large amount of training data, and once finetuned with the in-distribution data, t

Renlong Wu, Zhilu Zhang, Yu Yang, Wangmeng Zuo

When zooming between dual cameras on a mobile, noticeable jumps in geometric content and image color occur in the preview, inevitably affecting the user's zoom experience. In this work, we introduce a new task, ie, dual-camera smooth zoom (DCSZ) to achieve a smooth zoom preview. The frame interpolation (FI) technique is a potential solution but struggles wit

Anik Kumar Paul, Arun D Mahindrakar, Rachel K Kalaimani

Saddle point problems, ubiquitous in optimization, extend beyond game theory to diverse domains like power networks and reinforcement learning. This paper presents novel approaches to tackle saddle point problem, with a focus on continuous-time contexts. In this paper we propose a continuous time dynamics to tackle saddle point problem utilizing projected dy

Geoffrey B. Campbell

We cover rational and integer solutions for the equations $\mathbf{x^y = y^x}$ and $\mathbf{x^y y^x = v^w w^v}$. The former equation solutions go back to Euler, and the latter equation solutions appear to be new. Another definitely new related topic is application of VPV identities to give transforms of infinite products from our solutions. The present paper

Mengyan Wang, Yuxuan Hu, Shiqing Wu, Weihua Li

While preference-based recommendation algorithms effectively enhance user engagement by recommending personalized content, they often result in the creation of ``filter bubbles''. These bubbles restrict the range of information users interact with, inadvertently reinforcing their existing viewpoints. Previous research has focused on modifying these underlyin

Youran Zhou, Sunil Aryal, Mohamed Reda Bouadjenek

Missing data poses a significant challenge in data science, affecting decision-making processes and outcomes. Understanding what missing data is, how it occurs, and why it is crucial to handle it appropriately is paramount when working with real-world data, especially in tabular data, one of the most commonly used data types in the real world. Three missing

Yuang Li, Min Zhang, Mengxin Ren, Miaomiao Ma

Audio deepfake detection (ADD) is essential for preventing the misuse of synthetic voices that may infringe on personal rights and privacy. Recent zero-shot text-to-speech (TTS) models pose higher risks as they can clone voices with a single utterance. However, the existing ADD datasets are outdated, leading to suboptimal generalization of detection models.

Rohit Agarwal, Arijit Das, Alexander Horsch, Krishna Agarwal

The domain of online learning has experienced multifaceted expansion owing to its prevalence in real-life applications. Nonetheless, this progression operates under the assumption that the input feature space of the streaming data remains constant. In this survey paper, we address the topic of online learning in the context of haphazard inputs, explicitly fo

Xin Pang, Zhucong Li, Jiaxiang Chen, Yuan Cheng

We introduce AI2Apps, a Visual Integrated Development Environment (Visual IDE) with full-cycle capabilities that accelerates developers to build deployable LLM-based AI agent Applications. This Visual IDE prioritizes both the Integrity of its development tools and the Visuality of its components, ensuring a smooth and efficient building experience.On one han

A. Skopenkov

Let $f:S^q\sqcup S^q\to S^m$ be a link (i.e. an embedding). How does (the isotopy class of) the knot $S^q\to S^m$ obtained by embedded connected sum of the components of $f$ depend on $f$? Define a link $\sigma f:S^q\sqcup S^q\to S^m$ as follows. The first component of $\sigma f$ is the `standardly shifted' first component of $f$. The second component of $\s

L. Herrera, A. Di Prisco

The concept of cracking refers to the tendency of a fluid distribution to "split'', once it abandons the equilibrium. In this manuscript we develop a general formalism to describe the occurrence of cracking within a dissipative fluid distribution, in comoving coordinates. The role of dissipative processes in the occurrence of cracking is brought out. Next, w

Jordan Dotzel, Yash Akhauri, Ahmed S. AbouElhamayed, Carly Jiang

Large language models (LLMs) often struggle with strict memory, latency, and power demands. To meet these demands, various forms of dynamic sparsity have been proposed that reduce compute on an input-by-input basis. These methods improve over static methods by exploiting the variance across individual inputs, which has steadily grown with the exponential inc

M. M. Asgari, P. Garg, X. Wang, M. S. Mirmoosa

This tutorial offers a comprehensive overview of photonic time crystals - artificial materials whose electromagnetic properties are periodically modulated in time at scales comparable to the oscillation period of light while remaining spatially uniform. Being the temporal analogs to traditional photonic crystals, photonic time crystals differ in that they ex

Ziheng Liu, Jiayi Zhang, Enyu Shi, Zhilong Liu

Multi-agent reinforcement learning (MARL) has become a fundamental component of next-generation wireless communication systems. Theoretically, although MARL has the advantages of low computational complexity and fast convergence rate, there exist several challenges including partial observability, non-stationary, and scalability. In this article, we investig

Kangzhi Zhou, Jiajun Feng, Ziran Liu, Huiqiu Deng

In body-centered cubic (bcc) metals such as molybdenum, screw dislocations often exhibit non-Schmid behavior, moving in directions unpredicted by the Schmid law. The mobility of these dislocations is notably influenced by the presence of solute atoms within the alloy matrix. In this study, employing first-principles calculations, we delve into the electronic

Jiale Chen

Let $2\leq p<\infty$ and $X$ be a complex infinite-dimensional Banach space. It is proved that if $X$ is $p$-uniformly PL-convex, then there is no nontrivial bounded Volterra operator from the weak Hardy space $\mathscr{H}^{\text{weak}}_p(X)$ to the Hardy space $\mathscr{H}^+_p(X)$ of vector-valued Dirichlet series. To obtain this, a Littlewood--Paley inequa

Luming Yang, Tao Jiang, Ran Cheng

Ant Colony Optimization (ACO) is renowned for its effectiveness in solving Traveling Salesman Problems, yet it faces computational challenges in CPU-based environments, particularly with large-scale instances. In response, we introduce a Tensorized Ant Colony Optimization (TensorACO) to utilize the advancements of GPU acceleration. As the core, TensorACO ful

Sota Narikiyo, Sumiko Miyata, Ken-ichi Baba, Katsunori Yamaoka

In emergencies such as disasters, the number of voice calls (VoIP sessions) increases rapidly for a variety of purposes. Thus, a control server near a disaster area may not be able to connect to VoIP sessions due to congestion. To solve this problem, a Call Admission Control (CAC) is needed to determine whether a VoIP session requesting a connection can be a

Lev D. Beklemishev

We show that the polymodal provability logic GLP, in a language with at least two modalities and one variable, has nullary unification type. More specifically, we show that the formula [1]p does not have maximal unifiers, and exhibit an infinite complete set of unifiers for it. Further, we discuss the algorithmic problem of whether a given formula is unifiab

Christoph Bandt, Michael F. Barnsley

For self-similar sets, there are two important separation properties: the open set condition and the weak separation condition introduced by Zerner, which may be replaced by the formally stronger finite type property of Ngai and Wang. We show that any finite type self-similar set can be represented as a graph-directed construction obeying the open set condit

Fatemeh Asghari, Mohammad Reza Soheili, Faezeh Gholamrezaie

The global fashion industry plays a pivotal role in the global economy, and addressing fundamental issues within the industry is crucial for developing innovative solutions. One of the most pressing challenges in the fashion industry is the mismatch between body shapes and the garments of individuals they purchase. This issue is particularly prevalent among

Jiangnan Tang, Jingya Wang, Kaiyang Ji, Lan Xu

Estimating full-body human motion via sparse tracking signals from head-mounted displays and hand controllers in 3D scenes is crucial to applications in AR/VR. One of the biggest challenges to this task is the one-to-many mapping from sparse observations to dense full-body motions, which endowed inherent ambiguities. To help resolve this ambiguous problem, w

Ewelina Gajewska, Katarzyna Budzynska, Barbara Konat, Marcin Koszowy

Growing polarisation in society caught the attention of the scientific community as well as news media, which devote special issues to this phenomenon. At the same time, digitalisation of social interactions requires to revise concepts from social science regarding establishment of trust, which is a key feature of all human interactions, and group polarisati

Q. N. Li, W. Xu, Y. M. Xiao, L. Ding

We present a detailed theoretical study of the optoelectronic properties of a Na$_3$Bi-based three-dimensional Dirac electronic system (3DDES). The optical conductivity is evaluated using the energy-balance equation derived from a Boltzmann equation, where the electron Hamiltonian is taken from a simplified $\mathbf{k}\cdotp \mathbf{p}$ approach. We find tha

Qingshan Hou, Shuai Cheng, Peng Cao, Jinzhu Yang

Representation learning offers a conduit to elucidate distinctive features within the latent space and interpret the deep models. However, the randomness of lesion distribution and the complexity of low-quality factors in medical images pose great challenges for models to extract key lesion features. Disease diagnosis methods guided by contrastive learning (

Xingyu Su, Xiaojie Zhu, Yang Li, Yong Li

Amidst the surge in deep learning-based password guessing models, challenges of generating high-quality passwords and reducing duplicate passwords persist. To address these challenges, we present PagPassGPT, a password guessing model constructed on Generative Pretrained Transformer (GPT). It can perform pattern guided guessing by incorporating pattern struct

Wenlong Liao, Fernando Porte-Agel, Jiannong Fang, Christian Rehtanz

Machine learning models have made significant progress in load forecasting, but their forecast accuracy is limited in cases where historical load data is scarce. Inspired by the outstanding performance of large language models (LLMs) in computer vision and natural language processing, this paper aims to discuss the potential of large time series models in lo

Huan Zhong, Chen Wu, Ziqi Xiao

Change detection, as a research hotspot in the field of remote sensing, has witnessed continuous development and progress. However, the discrimination of boundary details remains a significant bottleneck due to the complexity of surrounding elements between change areas and backgrounds. Discriminating the boundaries of large change areas results in misalignm

Zihan Liu, Hanyi Wang, Yaoyu Kang, Shilin Wang

Generative models have shown a giant leap in synthesizing photo-realistic images with minimal expertise, sparking concerns about the authenticity of online information. This study aims to develop a universal AI-generated image detector capable of identifying images from diverse sources. Existing methods struggle to generalize across unseen generative models

Zhikai You, Yiyang Zhang, Zhu Fang, Shuiqing Li

Particle resuspension is a ubiquitous phenomenon with pivotal relevance in numerous natural and industrial contexts. In this study, we present findings on the resuspension of individual micro-sized particles, captured through high-speed camera experiments. Our observations reveal a universal behavior whereby a particle undergoes oscillatory motion due to tur

Wen-Qing Guo, Zi-Qing Xia, Xiaoyuan Huang

Axions and axion-like particles (ALPs) appear in many extensions of the Standard Model and are being investigated as promising dark matter (DM) candidates. One viable methodology for their detection involves the investigation of the line-like radio emissions from the dwarf spheroidal galaxy, potentially originating from the radiative decay of ALPs or the con

Butian Xiong, Nanjun Zheng, Junhua Liu, Zhen Li

We introduce a novel, multimodal large-scale scene reconstruction benchmark that utilizes newly developed 3D representation approaches: Gaussian Splatting and Neural Radiance Fields (NeRF). Our expansive U-Scene dataset surpasses any previously existing real large-scale outdoor LiDAR and image dataset in both area and point count. GauU-Scene encompasses over

Jianfang Mao

Mainstream autonomous exploration methods usually perform excessively-repeated explorations for the same region, leading to long exploration time and exploration trajectory in complex scenes. To handle this issue, we propose a novel semantic region aware autonomous exploration method, the core idea of which is considering the information of semantic regions

Wei Fang, Yuxing Tang, Heng Guo, Mingze Yuan

In the realm of medical 3D data, such as CT and MRI images, prevalent anisotropic resolution is characterized by high intra-slice but diminished inter-slice resolution. The lowered resolution between adjacent slices poses challenges, hindering optimal viewing experiences and impeding the development of robust downstream analysis algorithms. Various volumetri

Sanket Doshi, Mihir Gohel, Manish K. Gupta

In the current world due to the huge demand for storage, DNA-based storage solution sounds quite promising because of their longevity, low power consumption, and high capacity. However in real life storing data in the form of DNA is quite expensive, and challenging. Therefore researchers and developers develop such kind of software that helps simulate real-l

Yifan Yang, Dong Liu, Shuhai Zhang, Zeshuai Deng

Reconstructing 3D clothed human involves creating a detailed geometry of individuals in clothing, with applications ranging from virtual try-on, movies, to games. To enable practical and widespread applications, recent advances propose to generate a clothed human from an RGB image. However, they struggle to reconstruct detailed and robust avatars simultaneou

Peng Tu, Xun Zhou, Mingming Wang, Xiaojun Yang

Neural Radiance Fields (NeRF) have emerged as a paradigm-shifting methodology for the photorealistic rendering of objects and environments, enabling the synthesis of novel viewpoints with remarkable fidelity. This is accomplished through the strategic utilization of object-centric camera poses characterized by significant inter-frame overlap. This paper expl

Dahlia Shehata

Despite the advantages of their low-resource settings, traditional sparse retrievers depend on exact matching approaches between high-dimensional bag-of-words (BoW) representations of both the queries and the collection. As a result, retrieval performance is restricted by semantic discrepancies and vocabulary gaps. On the other hand, transformer-based dense

Moshe Eliasof, Eldad Haber

We investigate a link between Graph Neural Networks (GNNs) and Quadratic Unconstrained Binary Optimization (QUBO) problems, laying the groundwork for GNNs to approximate solutions for these computationally challenging tasks. By analyzing the sensitivity of QUBO formulations, we frame the solution of QUBO problems as a heterophilic node classification task. W

A. Naser, R. E. Abdel-Khalek, R. M. Salem, A. M. Hassanein

In this paper, we continue investigation of the directed and undirected irreducible divisor graph concepts $G(x)$ and $\Gamma (x)$ of $x\in D^{\ast} \backslash U(D)$, respectively, which were introduced in [7]. Consequently, we introduce two generalizations of these concepts. The first one is the irreducible divisor simplicial complex $S(x)$ of $x\in D^{\ast

Iraj Safaei, Kayoomars Karami

Here, the quartic inflationary potential $V(ϕ)=\fracλ{4}ϕ^4$ within a non-canonical framework characterized by a power-law Lagrangian is investigated. We demonstrate that the predictions of this model align with the Planck 2018 observational data. We explore how the predictions of the model depend on the non-canonical parameter $α$ and the number of $e$-fold

Yu-Hsi Chen

In the realm of continual learning, the presence of noisy labels within data streams represents a notable obstacle to model reliability and fairness. We focus on the data stream scenario outlined in pertinent literature, characterized by fuzzy task boundaries and noisy labels. To address this challenge, we introduce a novel and intuitive sampling method call

Jaesung Choi, Pilwon Kim

Removing noise from a signal without knowing the characteristics of the noise is a challenging task. This paper introduces a signal-noise separation method based on time series prediction. We use Reservoir Computing (RC) to extract the maximum portion of "predictable information" from a given signal. Reproducing the deterministic component of the signal usin

Yiqun Duan, Qiang Zhang, Renjing Xu

The utilization of Large Language Models (LLMs) within the realm of reinforcement learning, particularly as planners, has garnered a significant degree of attention in recent scholarly literature. However, a substantial proportion of existing research predominantly focuses on planning models for robotics that transmute the outputs derived from perception mod

Xiangsong Cheng, Xin He, Jian Liu

Isomorphism of the two-state system is heuristic in understanding the dynamical or statistical behavior of the simplest yet most quantum system that has no classical counterpart. We use the constraint phase space developed in J. Chem. Phys. 2016, 145, 204105; 2019, 151, 024105 and J. Phys. Chem. Lett. 2021, 12, 2496-2501, non-covariant phase space functions,

Patrick J. Browne, Qëndrim R. Gashi, Padraig Ó Catháin

Motivated by an Erd\H{o}s--Ko--Rado type problem on sets of strongly orthogonal roots in the $A_{\ell}$ root system, we estimate bounds for the size of a family of pairs $(A_{i}, B_{i})$ of $k$-subsets in $\{ 1, 2, \ldots, n\}$ such that $A_{i} \cap B_{j}= \emptyset$ and $|A_{i} \cap A_{j}| + |B_{i} \cap B_{j}| = k$ for all $i \neq j$. This is reminiscent of

Baihua Wu, Xin He, Jian Liu

Nonadiabatic transition dynamics lies at the core of many electron/hole transfer, photoactivated, and vacuum field-coupled processes. About a century after Ehrenfest proposed "Phasenraum" and the Ehrenfest theorem, we report a conceptually novel trajectory-based nonadiabatic dynamics approach, nonadiabatic field (NaF), based on a generalized exact coordinate

Zhen Fang, Yixuan Li, Feng Liu, Bo Han

Supervised learning aims to train a classifier under the assumption that training and test data are from the same distribution. To ease the above assumption, researchers have studied a more realistic setting: out-of-distribution (OOD) detection, where test data may come from classes that are unknown during training (i.e., OOD data). Due to the unavailability

Mingyao Cui, Qunsong Zeng, Kaibin Huang

The advancement of Rydberg atoms in quantum sensing is driving a paradigm shift from classical receivers to atomic receivers. Capitalizing on the extreme sensitivity of Rydberg atoms to external disturbance, atomic receivers can measure radio-waves more precisely than classical receivers to support high-performance wireless communication and sensing. Althoug

Ruiqing Wang, Danyang Yao, Jiuren Zhou, Yang Li

For the first time, the fatigue behavior involving external oxygen in highly Sc-doped AlN ferroelectric film was observed using transmission electron microscope techniques. Despite increasing the Sc composition in AlScN film contributes to reducing the device operation voltage, the inherent affinity of Sc for oxygen introduces instability in device performan

Lingyi Chen, Shitong Wu, Jiachuan Ye, Huihui Wu

Information Bottleneck (IB) is a technique to extract information about one target random variable through another relevant random variable. This technique has garnered significant interest due to its broad applications in information theory and deep learning. Hence, there is a strong motivation to develop efficient numerical methods with high precision and

Muyao Qiu, Jinguang Han

Functional encryption introduces a new paradigm of public key encryption that decryption only reveals the function value of encrypted data. To curb key leakage issues and trace users in FE-IP, a new primitive called traceable functional encryption for inner product (TFE-IP) has been proposed. However, the privacy protection of user's identities has not been

Yuxi Ren, Jie Wu, Yanzuo Lu, Huafeng Kuang

Recent advancements in diffusion-based generative image editing have sparked a profound revolution, reshaping the landscape of image outpainting and inpainting tasks. Despite these strides, the field grapples with inherent challenges, including: i) inferior quality; ii) poor consistency; iii) insufficient instrcution adherence; iv) suboptimal generation effi

Yuqing Li, Tao Luo, Qixuan Zhou

In this paper, we advance the understanding of neural network training dynamics by examining the intricate interplay of various factors introduced by weight parameters in the initialization process. Motivated by the foundational work of Luo et al. (J. Mach. Learn. Res., Vol. 22, Iss. 1, No. 71, pp 3327-3373), we explore the gradient descent dynamics of neura

Tony Lindeberg

This paper presents results of combining (i) theoretical analysis regarding connections between the orientation selectivity and the elongation of receptive fields for the affine Gaussian derivative model with (ii) biological measurements of orientation selectivity in the primary visual cortex, to investigate if (iii) the receptive fields can be regarded as s

Jorge de Heuvel, Tharun Sethuraman, Maren Bennewitz

Preference-aligned robot navigation in human environments is typically achieved through learning-based approaches, utilizing user feedback or demonstrations for personalization. However, personal preferences are subject to change and might even be context-dependent. Yet traditional reinforcement learning (RL) approaches with static reward functions often fal

Chenguang Liu, Chisheng Wang, Feifei Dong, Xiayang Xiao

Edge detection is a long-standing problem in computer vision. Despite the efficiency of existing algorithms, their performance, however, rely heavily on the pre-trained weights of the backbone network on the ImageNet dataset. The use of pre-trained weights in previous methods significantly increases the difficulty to design new models for edge detection with

Chao Han, Wei Wang, Jun Zeng, Jia-Lu Zhang

We present a comprehensive investigation of leading-twist lightcone distribution amplitudes (LCDAs) and quasi distribution amplitudes (quasi-DAs) for light octet and decuplet baryons within large momentum effective theory. In LaMET, LCDAs can be factorized in terms of a hard kernel and quasi-DAs that are defined as spatial correlators and calculable on Latti

David D. Nguyen, David Liebowitz, Surya Nepal, Salil S. Kanhere

Honeyfiles are security assets designed to attract and detect intruders on compromised systems. Honeyfiles are a type of honeypot that mimic real, sensitive documents, creating the illusion of the presence of valuable data. Interaction with a honeyfile reveals the presence of an intruder, and can provide insights into their goals and intentions. Their practi

Dmitry Starkov, Sergey Belan

The population-averaged contact maps generated by the chromosome conformation capture technique provide important information about the average frequency of contact between pairs of chromatin loci as a function of the genetic distance between them. However, these datasets do not tell us anything about the joint statistics of simultaneous contacts between gen

Jorge de Heuvel, Florian Seiler, Maren Bennewitz

To align mobile robot navigation policies with user preferences through reinforcement learning from human feedback (RLHF), reliable and behavior-diverse user queries are required. However, deterministic policies fail to generate a variety of navigation trajectory suggestions for a given navigation task. In this paper, we introduce EnQuery, a query generation

Haoyan Zhang, Jianfeng Guo, Cong Wang, Le Lei

Fe5GeTe2 single crystals can be divided into nonquenched (NQ) and quench-cooled (QC) phases with different magnetic properties. A comprehensive understanding of the magnetic property variations in the NQ and QC phases is imperative for guiding Fe5GeTe2 towards spintronics applications; however, it remains elusive. Here, we report a real-space study on the st

Shaoxiong Ji, Pinzhen Chen

Instruction tuning a large language model with multiple languages can prepare it for multilingual downstream tasks. Nonetheless, it is yet to be determined whether having a handful of languages is sufficient, or whether the benefits increase with the inclusion of more. By fine-tuning large multilingual models on 1 to 52 languages, we present a case study on

Zhilong Wang, Yebo Cao, Peng Liu

Jailbreak attacks on Language Model Models (LLMs) entail crafting prompts aimed at exploiting the models to generate malicious content. Existing jailbreak attacks can successfully deceive the LLMs, however they cannot deceive the human. This paper proposes a new type of jailbreak attacks which can deceive both the LLMs and human (i.e., security analyst). The

Xingtong Ge, Jixiang Luo, Xinjie Zhang, Tongda Xu

Prior research on deep video compression (DVC) for machine tasks typically necessitates training a unique codec for each specific task, mandating a dedicated decoder per task. In contrast, traditional video codecs employ a flexible encoder controller, enabling the adaptation of a single codec to different tasks through mechanisms like mode prediction. Drawin

Ata Atay, Marina Núñez, Tamás Solymosi

This paper studies many-to-one assignment markets, or matching markets with wages. Although it is well-known that the core of this model is non-empty, the structure of the core has not been fully investigated. To the known dissimilarities with the one-to-one assignment game, we add that the bargaining set does not coincide with the core and the kernel may no

Junhong Wu, Yuchen Liu, Chengqing Zong

In the evolving landscape of Neural Machine Translation (NMT), the pretrain-then-finetune paradigm has yielded impressive results. However, the persistent challenge of Catastrophic Forgetting (CF) remains a hurdle. While previous work has introduced Continual Learning (CL) methods to address CF, these approaches grapple with the delicate balance between avoi

Pouya Fallah, Soroush Gooran, Mohammad Jafarinasab, Pouya Sadeghi

Language models, particularly generative models, are susceptible to hallucinations, generating outputs that contradict factual knowledge or the source text. This study explores methods for detecting hallucinations in three SemEval-2024 Task 6 tasks: Machine Translation, Definition Modeling, and Paraphrase Generation. We evaluate two methods: semantic similar

Liangxin Qian, Ping Yang, Jun Zhao, Ze Chen

Wireless communication is rapidly evolving, and future wireless communications (6G and beyond) will be more heterogeneous, multi-layered, and complex, which poses challenges to traditional communications. Adaptive technologies in traditional communication systems respond to environmental changes by modifying system parameters and structures on their own and

Jiyoung Yun, Hojun Rho, Wan Choi

Enhancing high-speed wireless communication in the future relies significantly on harnessing high frequency bands effectively. These bands predominantly operate in line-of-sight (LoS) paths, necessitating well-configured antenna arrays and beamforming techniques for optimal spectrum utilization. Maximizing the potential of LoS multiple-input multiple-output

Han Song, Yihao Wei, Zhongche Qu, Weihan Wang

Bitcoin stands as a groundbreaking development in decentralized exchange throughout human history, enabling transactions without the need for intermediaries. By leveraging cryptographic proof mechanisms, Bitcoin eliminates the reliance on third-party financial institutions. Ethereum, ranking as the second-largest cryptocurrency by market capitalization, buil

Kyle McKee

Consider the motion of a thin layer of electrically conducting fluid, between two closely spaced parallel plates, in a classical Hele-Shaw geometry. Furthermore, let the system be immersed in a uniform external magnetic field (normal to the plates) and let electrical current be driven between conducting probes immersed in the fluid layer. In the present pape

Michael Fu, Jirat Pasuksmit, Chakkrit Tantithamthavorn

DevOps has emerged as one of the most rapidly evolving software development paradigms. With the growing concerns surrounding security in software systems, the DevSecOps paradigm has gained prominence, urging practitioners to incorporate security practices seamlessly into the DevOps workflow. However, integrating security into the DevOps workflow can impact a

Irene Pagliai, Goya van Boven, Tosin Adewumi, Lama Alkhaled

We introduce new large labeled datasets on bias in 3 languages and show in experiments that bias exists in all 10 datasets of 5 languages evaluated, including benchmark datasets on the English GLUE/SuperGLUE leaderboards. The 3 new languages give a total of almost 6 million labeled samples and we benchmark on these datasets using SotA multilingual pretrained

Siyu Qiu, Benjamin Tan, Hammond Pearce

Training new engineers in digital design is a challenge, particularly when it comes to teaching the complex electronic design automation (EDA) tooling used in this domain. Learners will typically deploy designs in the Verilog and VHDL hardware description languages to Field Programmable Gate Arrays (FPGAs) from Altera (Intel) and Xilinx (AMD) via proprietary

Owen Lynch, Kris Brown, James Fairbanks, Evan Patterson

Categories and categorical structures are increasingly recognized as useful abstractions for modeling in science and engineering. To uniformly implement category-theoretic mathematical models in software, we introduce GATlab, a domain-specific language for algebraic specification embedded in a technical programming language. GATlab is based on generalized al

Zhigang Wu, Mengqian Liu, Juanzi Cai

This paper is dedicated to the study of the inviscid liquid-gas two-phase flow model in $\mathbb{R}^d\ (d\geq1)$. We establish the global existence of strong solutions to this system with small initial data in hybrid Besov spaces based on general $L^p$-norms. Additionally, we obtain the decay estimates of solutions rely on the constructed Lyapunov functional

Changqing Luo, Jiao Li, Chuanjie Zheng, Dongdong Liu

Although supernovae is a well-known endpoint of an accreting white dwarf, alternative theoretical possibilities has been discussing broadly, such as the accretion-induced collapse (AIC) event as the endpoint of oxygen-neon (ONe) white dwarfs, either accreting up to or merging to excess the Chandrasekhar limit (the maximum mass of a stable white dwarf). AIC i

B Kereopa-Yorke

The rapid integration of Artificial Intelligence (AI) systems across critical domains necessitates robust security evaluation frameworks. We propose a novel approach that introduces three metrics: System Complexity Index (SCI), Lyapunov Exponent for AI Stability (LEAIS), and Nash Equilibrium Robustness (NER). SCI quantifies the inherent complexity of an AI s

Junda He, Christoph Treude, David Lo

Integrating Large Language Models (LLMs) into autonomous agents marks a significant shift in the research landscape by offering cognitive abilities that are competitive with human planning and reasoning. This paper explores the transformative potential of integrating Large Language Models into Multi-Agent (LMA) systems for addressing complex challenges in so

Binghui Chen, Wenyu Li, Yifeng Geng, Xuansong Xie

With the development of the large-scale diffusion model, Artificial Intelligence Generated Content (AIGC) techniques are popular recently. However, how to truly make it serve our daily lives remains an open question. To this end, in this paper, we focus on employing AIGC techniques in one filed of E-commerce marketing, i.e., generating hyper-realistic advert

Tong Zhao, Xi Lin, Fang He, Hanwen Dai

In the contemporary logistics industry, automation plays a pivotal role in enhancing production efficiency and expanding industrial scale. Autonomous mobile robots, in particular, have become integral to the modernization efforts in warehouses. One noteworthy application in robotic warehousing is the robotic sorting system (RSS), distinguished by its charact

Chengyu Du, Fang He, Xi Lin

We address a dynamic pricing problem for airlines aiming to maximize expected revenue from selling cargo space on a single-leg flight. The cargo shipments' weight and volume are uncertain and their precise values remain unavailable at the booking time. We model this problem as a Markov decision process, and further derive a necessary condition for its optima

Zhongdong Han, Tingxin Li, Long Zhang, Rui-Rui Du

We report a magneto-induced topological phase transition in inverted InAs/GaSb bilayers from a quantum spin Hall insulator to a normal insulator. We utilize a dual-gated Corbino device in which the degree of band inversion, or equivalently the electron and hole densities, can be continuously tuned. We observe a topological phase transition around the magneti

Jichen Bian, Chong Tan, Peiyao Tang, Min Zheng

Wireless sensing technologies become increasingly prevalent due to the ubiquitous nature of wireless signals and their inherent privacy-friendly characteristics. Device-free personnel identity recognition, a prevalent application in wireless sensing, is susceptibly challenged by imbalanced channel state information (CSI) datasets. This letter proposes a nove

Youze Xue, Binghui Chen, Yifeng Geng, Xuansong Xie

Customized generative text-to-image models have the ability to produce images that closely resemble a given subject. However, in the context of generating advertising images for e-commerce scenarios, it is crucial that the generated subject's identity aligns perfectly with the product being advertised. In order to address the need for strictly-ID preserved a

Shuailiang Ge, Yuxin Liu, Jing Shu, Yue Zhao

The recent detection of gravitational waves from a binary merger involving a potential low-mass gap black hole (LMBH) by LIGO-Virgo-KAGRA (LVK) Collaboration motivates investigations into mechanisms beyond conventional stellar evolution theories to account for their existence. We study a mechanism in which dark matter (DM), through its capture and accumulati

Li-Ke Yang, Zheng-Li Wang, Jia-Jun Wu, Bing-Song Zou

Ascribed as $\rho\rho$ and $K^* \bar{K}^*$ molecular states, respectively, iso-scalar $f_0(1500)$ and $f_0(1710)$ states are expected to have iso-vector partners, potentially identified as $a_0(1450)$ and $a_0(1710)$. The predicted dominant decay modes for these two $a_0$ resonances are $a_0(1450) \to \omega\pi\pi$ and $a_0(1710) \to \omega\pi\pi,\,\phi\pi\p

Atoosa Parsa, Corey S. O'Hern, Rebecca Kramer-Bottiglio, Josh Bongard

There is growing interest in engineering unconventional computing devices that leverage the intrinsic dynamics of physical substrates to perform fast and energy-efficient computations. Granular metamaterials are one such substrate that has emerged as a promising platform for building wave-based information processing devices with the potential to integrate s

Muhammad Tanzil Furqon, Mahardhika Pratama, Lin Liu, Habibullah

Remaining Useful Life (RUL) predictions play vital role for asset planning and maintenance leading to many benefits to industries such as reduced downtime, low maintenance costs, etc. Although various efforts have been devoted to study this topic, most existing works are restricted for i.i.d conditions assuming the same condition of the training phase and th

Weijia Li, Haote Yang, Zhenghao Hu, Juepeng Zheng

3D building reconstruction from monocular remote sensing images is an important and challenging research problem that has received increasing attention in recent years, owing to its low cost of data acquisition and availability for large-scale applications. However, existing methods rely on expensive 3D-annotated samples for fully-supervised training, restri

Jacob Coreno, Di Feng

This paper studies multi-object reallocation without monetary transfers, where agents initially own multiple indivisible objects and have strict preferences over bundles (e.g., shift exchange among workers at a firm). Focusing on marginal rules that elicit only rankings over individual objects, we provide axiomatic characterizations of the generalized Top Tr