May 2024 arXiv papers — page 144

P. R. Mishra, Yogesh Kumar, Susanta Samanta, Atul Gaur

The notion of branch numbers of a linear transformation is crucial for both linear and differential cryptanalysis. The number of non-zero elements in a state difference or linear mask directly correlates with the active S-Boxes. The differential or linear branch number indicates the minimum number of active S-Boxes in two consecutive rounds of an SPN cipher,

Pinjun Zheng, Anas Chaaban, Md. Jahangir Hossain, Tareq Y. Al-Naffouri

Non-terrestrial networks (NTNs) present significant challenges for reliable communication due to the dynamic nature of their channels. Studying channel coherence time is crucial, since it directly impacts the design of robust transmission schemes (e.g., channel estimation and precoding strategies). This paper evaluates the coherence time of non-terrestrial c

Zhixiong Zhuang, Maria-Irina Nicolae, Mario Fritz

Deep reinforcement learning policies, which are integral to modern control systems, represent valuable intellectual property. The development of these policies demands considerable resources, such as domain expertise, simulation fidelity, and real-world validation. These policies are potentially vulnerable to model stealing attacks, which aim to replicate th

Arghyajit Datta, Soumen Kumar Manna, Arunansu Sil

A derivative coupling of an axion like particle (ALP) with a B-L current may lead to the baryon asymmetry of the universe via spontaneous leptogenesis provided a lepton number breaking interaction prevails in thermal equilibrium. Conventionally, such scenario works only for heavy ALPs and high reheating temperature due to the fact that the same lepton number

Martin Kreuzer, Florian Walsh

For a commutative finite $\mathbb{Z}$-algebra, i.e., for a commutative ring $R$ whose additive group is finitely generated, it is known that the group of units of $R$ is finitely generated, as well. Our main results are algorithms to compute generators and the structure of this group. This is achieved by reducing the task first to the case of reduced rings,

Yifan Wu, Lutao Yan, Leixian Shen, Yunhai Wang

Chart question answering (ChartQA) tasks play a critical role in interpreting and extracting insights from visualization charts. While recent advancements in multimodal large language models (MLLMs) like GPT-4o have shown promise in high-level ChartQA tasks, such as chart captioning, their effectiveness in low-level ChartQA tasks (e.g., identifying correlati

Yairon Cid-Ruiz, Claudia Polini, Bernd Ulrich

We study the behavior of multidegrees in families and the existence of numerical criteria to detect integral dependence. We show that mixed multiplicities of modules are upper semicontinuous functions when taking fibers and that projective degrees of rational maps are lower semicontinuous under specialization. We investigate various aspects of the polar mult

Renyou Xie, Xin Yin, Chaojie Li, Guo Chen

Distribution system state estimation (DSSE) plays a crucial role in the real-time monitoring, control, and operation of distribution networks. Besides intensive computational requirements, conventional DSSE methods need high-quality measurements to obtain accurate states, whereas missing values often occur due to sensor failures or communication delays. To a

Robert L. Bryant

A symmetric quadratic form $g$ on a surface~$M$ is said to be locally Hessianizable if each $p\in M$ has an open neighborhood~$U$ on which there exists a local coordinate chart $(x^1,x^2):U\to\mathbb{R}^2$ and a function $f:U\to\mathbb{R}$ such that, on $U$, we have $$ g = \frac{\partial^2 f}{\partial x^i\partial x^j}\,\mathrm{d} x^i\circ\mathrm{d} x^j. $$ I

Bora Yalçıner, Ahmet Oğuz Akyüz

We propose a path-guiding algorithm to be incorporated into the wavefront style of path tracers (WFPTs). As WFPTs are primarily implemented on graphics processing units (GPUs), the proposed method aims to leverage the capabilities of the GPUs and reduce the hierarchical data structure and memory usage typically required for such techniques. To achieve this,

Shucheng Zhu, Weikang Wang, Ying Liu

While nationality is a pivotal demographic element that enhances the performance of language models, it has received far less scrutiny regarding inherent biases. This study investigates nationality bias in ChatGPT (GPT-3.5), a large language model (LLM) designed for text generation. The research covers 195 countries, 4 temperature settings, and 3 distinct pr

Aman Priyanshu, Supriti Vijay

As Large Language Models (LLMs) ascend in popularity, offering information with unprecedented convenience compared to traditional search engines, we delve into the intriguing possibility that a new, singular perspective is being propagated. We call this the "Silent Curriculum," where our focus shifts towards a particularly impressionable demographic: childre

Sofia Casarin, Oswald Lanz, Sergio Escalera

Neural Architecture Search (NAS) methods have shown to output networks that largely outperform human-designed networks. However, conventional NAS methods have mostly tackled the single dataset scenario, incuring in a large computational cost as the procedure has to be run from scratch for every new dataset. In this work, we focus on predictor-based algorithm

Yumeng Shao, Jun Li, Long Shi, Kang Wei

Conventional synchronous federated learning (SFL) frameworks suffer from performance degradation in heterogeneous systems due to imbalanced local data size and diverse computing power on the client side. To address this problem, asynchronous FL (AFL) and semi-asynchronous FL have been proposed to recover the performance loss by allowing asynchronous aggregat

Wankang Zhai

Survival prediction is an important branch of cancer prognosis analysis. The model that predicts survival risk through TCGA genomics data can discover genes related to cancer and provide diagnosis and treatment recommendations based on patient characteristics. We found that deep learning models based on Cox proportional hazards often suffer from overfitting

Constantin Schempp, Christian Friedrich

We address the problem of robot guided assembly tasks, by using a learning-based approach to identify contact model parameters for known and novel parts. First, a Variational Autoencoder (VAE) is used to extract geometric features of assembly parts. Then, we combine the extracted features with physical knowledge to derive the parameters of a contact model us

Julien Baglio, Francisco Campanario, Tinghua Chen, Heiko Dietrich-Siebert

VBFNLO is a flexible parton level Monte Carlo program for the simulation of vector boson fusion (VBF), QCD-induced single and double vector boson production plus two jets, and double and triple vector boson production (plus jet) in hadronic collisions at next-to-leading order (NLO) in the strong coupling constant, as well as Higgs boson plus two and three je

Shubham Singh, Anoop Jain

Nonuniform motion constraints are ubiquitous in robotic applications. Geofencing control is one such paradigm where the motion of a robot must be constrained within a predefined boundary. This paper addresses the problem of stabilizing a unicycle robot around a desired circular orbit while confining its motion within a nonconcentric external circular boundar

Yongqian Zhu, Jia-Tao Sun, Jinbo Pan, Jun Deng

The anomalous valley Hall effect (AVHE) is a pivotal phenomenon that allows for the exploitation of the valley degree of freedom in materials. A general strategy for its realization and manipulation is crucial for valleytronics. Here, by considering all possible symmetries, we propose general rules for the realization and manipulation of AVHE in two-dimensio

M. M. Bogdan, O. V. Charkina

Static soliton bound states in nonlinear systems are investigated analytically and numerically in the framework of the parametrically driven, damped nonlinear Schr\"odinger equation. We find that the ordinary differential equations, which determine bound soliton solutions, can be transformed into the form resembling the Schr\"odinger-like equations for eigen

Kexin Jiang, Chuhan Wu, Yaoran Chen

Time series prediction is a fundamental problem in scientific exploration and artificial intelligence (AI) technologies have substantially bolstered its efficiency and accuracy. A well-established paradigm in AI-driven time series prediction is injecting physical knowledge into neural networks through signal decomposition methods, and sustaining progress in

Anningzhe Gao, Shan Dai

Temporal Point Processes (TPPs), especially Hawkes Process are commonly used for modeling asynchronous event sequences data such as financial transactions and user behaviors in social networks. Due to the strong fitting ability of neural networks, various neural Temporal Point Processes are proposed, among which the Neural Hawkes Processes based on self-atte

Jinwei Lin

Front-following is more technically difficult to implement than the other two human following technologies, but front-following technology is more practical and can be applied in more areas to solve more practical problems. The design of sensors structure is an important part of robot detection system. In this paper, we will discuss basic and significant pri

Piaoran Liang, Y. Sophia Dai, Jia-Sheng Huang, Cheng Cheng

We present morphological analysis of the 16$\mu$m flux-density-limited galaxy sample at 0.8$<z<$1.3 from arXiv:2103.04585. At the targeted redshift, the 16$\mu$m emission corresponds to the Polycyclic aromatic hydrocarbon (PAH) feature from intense star formation, or dust heated by AGN (Active galactic nuclei). Our sample of 479 galaxies are dominated by Lum

Muhammad Umar Farooq Qaisar, Weijie Yuan, Paolo Bellavista, Guangjie Han

As IoT-based wireless sensor networks (WSNs) become more prevalent, the issue of energy shortages becomes more pressing. One potential solution is the use of wireless power transfer (WPT) technology, which is the key to building a new shape of wireless rechargeable sensor networks (WRSNs). However, efficient charging and scheduling are critical for WRSNs to

Stephen Bigelow, Jules Martel

We reconstruct a quantum group associated with any Lie algebra together with its representation theory from twisted homologies of generalized configuration spaces of disks. Along the way it brings new combinatorics to the theory, but our diagrams represent true submanifolds of configuration spaces and combinatorial relations between them translate actual twi

Mahmoud Salhab, Faisal Abu-Khzam

Spelling correction is the task of identifying spelling mistakes, typos, and grammatical mistakes in a given text and correcting them according to their context and grammatical structure. This work introduces "AraSpell," a framework for Arabic spelling correction using different seq2seq model architectures such as Recurrent Neural Network (RNN) and Transform

C. I. Ugwu, S. Casarin, O. Lanz

Anomaly detection is crucial in large-scale industrial manufacturing as it helps detect and localise defective parts. Pre-training feature extractors on large-scale datasets is a popular approach for this task. Stringent data security and privacy regulations and high costs and acquisition time hinder the availability and creation of such large datasets. Whil

Yang Yang, Nan Jiang, Yi Xu, De-Chuan Zhan

Open-set Semi-supervised Learning (OSSL) holds a realistic setting that unlabeled data may come from classes unseen in the labeled set, i.e., out-of-distribution (OOD) data, which could cause performance degradation in conventional SSL models. To handle this issue, except for the traditional in-distribution (ID) classifier, some existing OSSL approaches empl

Yuan Guo, Christodoulos Skouroumounis, Symeon Chatzinotas, Ioannis Krikidis

In this paper, we investigate the performance of large-scale heterogeneous low Earth orbit (LEO) satellite networks in the context of three association schemes. In contrast to existing studies, where single-tier LEO satellite-based network deployments are considered, the developed framework captures the heterogeneous nature of real-world satellite network de

Francesco Bacchiocchi, Matteo Bollini, Matteo Castiglioni, Alberto Marchesi

Stackelberg games (SGs) constitute the most fundamental and acclaimed models of strategic interactions involving some form of commitment. Moreover, they form the basis of more elaborate models of this kind, such as, e.g., Bayesian persuasion and principal-agent problems. Addressing learning tasks in SGs and related models is crucial to operationalize them in

Minami Taniguchi

Blair, Campisi, Taylor, and Tomova defined the L-invariant L(F) of a knotted surface F, using pants complexes of trisection surfaces of bridge trisections of F. After that, Aranda, Pongtanapaisan, and Zhang introduced the L*-invariant L*(F) using dual curve complexes instead of pants complexes. In this paper, we determine both of L-invariant and L*-invariant

QingGuo Qi, Hongyang Chen, Minhao Cheng, Han Liu

In recent years, there has been a surge in research on dynamic graph representation learning, primarily focusing on modeling the evolution of temporal-spatial patterns in real-world applications. However, within the domain of discrete-time dynamic graphs, the exploration of temporal edges remains underexplored. Existing approaches often rely on additional se

Thomas Larsen, John R. Royer, Fraser H. J. Laidlaw, Wilson C. K. Poon

A unique bistable transition has been identified in granular/colloidal gel-composites, resulting from shear-induced phase separation of the gel phase into dense blobs. In energy applications, it is critical to understand how this transition influences electrical performance. Mixing conductive colloids with conductive inclusions, we find the conductivity and

Kai Sauerwald, Juha Kontinen

This paper considers KLM-style preferential non-monotonic reasoning in the setting of propositional team semantics. We show that team-based propositional logics naturally give rise to cumulative non-monotonic entailment relations. Motivated by the non-classical interpretation of disjunction in team semantics, we give a precise characterization for preferenti

Louis Rustenholz, Maximiliano Klemen, Miguel Ángel Carreira-Perpiñán, Pedro López-García

Automatic static cost analysis infers information about the resources used by programs without actually running them with concrete data, and presents such information as functions of input data sizes. Most of the analysis tools for logic programs (and many for other languages), as CiaoPP, are based on setting up recurrence relations representing (bounds on)

Zhongye Xia, Weibin Li, Zhichao Liang, Kexin Lou

This paper addresses the problem of controlling the temporal dynamics of complex nonlinear network-coupled dynamical systems, specifically in terms of neurodynamics. Based on the Lyapunov direct method, we derive a control strategy with theoretical guarantees of controllability. To verify the performance of the derived control strategy, we perform numerical

Bibhabasu De

The Standard Model (SM), if augmented with a light SM-singlet scalar $\phi$ and a TeV-scale scalar leptoquark (LQ) $S_1$, 2-body charged lepton flavor violating (CLFV) decay channels can be accessed with $\phi$ as one of the final states, where the leading order effective interactions between $\phi$ and the SM fields arise at one-loop level. Further, in the

Watcharakiete Wongcharoenbhorn, Yotsanan Meemark

The well-known result states that the square-free counting function up to $N$ is $N/\zeta(2)+O(N^{1/2})$. This corresponds to the identity polynomial $\text{Id}(x)$. It is expected that the error term in question is $O_\varepsilon(N^{\frac{1}{4}+\varepsilon})$ for arbitrarily small $\varepsilon>0$. Usually, it is more difficult to obtain a similar order of e

Watcharakiete Wongcharoenbhorn, Yotsanan Meemark

A $\textit{square-full}$ number is a positive integer for which all its prime divisors divide itself at least twice. The counting function of square-full integers of the form $f(n)$ for $n\leqslant N$ is denoted by $S^{{\mathstrut\hspace{0.05em}\blacksquare}}_f(N)$. We have known that for a relatively prime pair $(a,b)\in\mathbb N\times \mathbb N\cup\{0\}$ w

Jialong Lu, Rujing Xiong, Tiebin Mi, Ke Yin

The existing methods for reconfigurable intelligent surface (RIS) beamforming in wireless communications are typically limited to uniform phase quantization. However, in practical applications, engineering challenges and design requirements often lead to non-uniform phase and bit resolution of RIS units, which limits the performance potential of these method

Vasilij Natsik, Yurii Semerenko, Viktor Zoryansky

An analysis of the processes of plastic deformation and acoustic relaxation in a high-entropy alloy Al$_{0.5}$CoCrCuFeNi was carried out. The following have been established: dominant dislocation defects; types of barriers that prevent the movement of dislocations; mechanisms of thermally activated movement of various elements of dislocation lines through ba

Zhao-Rong Lai, Xiaotian Wu, Liangda Fang, Ziliang Chen

The extended Weber location problem is a classical optimization problem that has inspired some new works in several machine learning scenarios recently. However, most existing algorithms may get stuck due to the singularity at the data points when the power of the cost function $1\leqslant q<2$, such as the widely-used iterative Weiszfeld approach. In this p

Claudio Novelli, Philipp Hacker, Jessica Morley, Jarle Trondal

Regulation is nothing without enforcement. This particularly holds for the dynamic field of emerging technologies. Hence, this article has two ambitions. First, it explains how the EU's new Artificial Intelligence Act (AIA) will be implemented and enforced by various institutional bodies, thus clarifying the governance framework of the AIA. Second, it propos

Zhixuan Xu, Chongkai Gao, Zixuan Liu, Gang Yang

To substantially enhance robot intelligence, there is a pressing need to develop a large model that enables general-purpose robots to proficiently undertake a broad spectrum of manipulation tasks, akin to the versatile task-planning ability exhibited by LLMs. The vast diversity in objects, robots, and manipulation tasks presents huge challenges. Our work int

Sela Fried, Toufik Mansour

Generalizing previous results, we introduce and study a new statistic on words, that we call rectangle capacity. For two fixed positive integers $r$ and $s$, this statistic counts the number of occurrences of a rectangle of size $r\times s$ in the bargraph representation of a word. We find the bivariate generating function for the distribution on words of th

George Barmpalias, Xiaoyan Zhang

Arranging the bits of a random string or real into k columns of a two-dimensional array or higher dimensional structure is typically accompanied with loss in the Kolmogorov complexity of the columns, which depends on k. We quantify and characterize this phenomenon for arrays and trees and its relationship to negligible classes.

Saeid Ansari, Alireza Akbari, R. Jafari

We explore the dynamics of l_1-norm of steered quantum coherence (SQC), steered quantum relative entropy (SQRE), and magic resource quantifier (QRM) in the one-dimensional XY spin chain in the presence of time dependent transverse magnetic field. We find that the system's response is highly sensitive to the initial state and magnetic field strength. % We sho

Xingxu Li, Nan Ma, Yiheng Han, Shun Yang

To address the limitations inherent to conventional automated harvesting robots specifically their suboptimal success rates and risk of crop damage, we design a novel bot named AHPPEBot which is capable of autonomous harvesting based on crop phenotyping and pose estimation. Specifically, In phenotyping, the detection, association, and maturity estimation of

Niu Su, Hua-Xing Chen, Philipp Gubler, Atsushi Hosaka

We study the recently observed $\Omega(2012)$ baryon in QCD sum rules. We construct the $P$-wave $\Omega$ baryon currents with a covariant derivative, and perform spin projection to obtain the currents with total spin 1/2 and 3/2. We then apply the parity-projected QCD sum rules to separate the contributions of the positive and negative parity states. We ext

Aline Werro, Christian Nitzl, Uwe M. Borghoff

Business wargaming is a central tool for developing sustaining strategies. It transfers the benefits of traditional wargaming to the business environment. However, building wargames that support the process of decision-making for strategy require respective intelligence. This paper investigates the role of intelligence in the process of developing strategic

Yuk-Kei Kong, Youngman Kim, Masayasu Harada

In this review, we summarize recent studies on nuclear matter and finite nuclei based on parity doublet models. We first construct a parity doublet model (PDM), which includes the chiral invariant mass $m_0$ of nucleons together with the mass generated by the spontaneous chiral symmetry breaking. We then study the density dependence of the symmetry energy in

Alessandro Pigati, Tristan Rivière

We study a new notion of critical point for the area of surfaces under the Legendrian constraint, called parametrized Hamiltonian stationary Legendrian varifolds (PHSLVs). We establish several fundamental properties of these objects, including their sequential compactness and an optimal regularity result, showing that they are smooth immersions away from a l

Ernest Scheiber

The subject of the paper is to verify the convergence conditions for the parareal algorithm using Gander and Hairer's theorem . The analysis is conducted in the case where the coarse integrator is the Euler method and the high-accuracy integrator is an explicit Runge-Kutta type method.

Wenyi Cai, Kunlang Bu, Lingyan Zha, Jingjin Zhang

Plant factory with artificial light (PFAL) is a promising technology for relieving the food crisis, especially in urban areas or arid regions endowed with abundant resources. However, lighting and HVAC (heating, ventilation, and air conditioning) systems of PFAL have led to much greater energy consumption than open-field and greenhouse farming, limiting the

S. Choe, T. Emil Rivera-Thorsen, H. Dahle, K. Sharon

"Godzilla" is a peculiar object within the gravitationally lensed Sunburst Arc at $z=2.37$. Despite being very bright, it appears in only one of the twelve lensed images of the source galaxy, and shows exotic spectroscopic properties not found in any other clumps. We use JWST's unique combination of spatial resolution and spectroscopic sensitivity to provide

Vanessa Trapp

Excursion sets of Poisson shot noise processes are a prominent class of random sets. We consider a specific class of Poisson shot noise processes whose excursion sets within compact convex observation windows are almost surely polyconvex. This class contains, for example, the Boolean model. In this paper, we analyse the behaviour of geometric functionals suc

Haozhe Wang, Beixiong Zheng, Xiaodan Shao, Rui Zhang

Electronic countermeasure (ECM) technology plays a critical role in modern electronic warfare, which can interfere with enemy radar detection systems by noise or deceptive signals. However, the conventional active jamming strategy incurs additional hardware and power costs and has the potential threat of exposing the target itself. To tackle the above challe

N. Medvedev, D. I. Zainutdinov, A. E. Volkov

We present a model of non-ionizing scattering of electrons on atomic ensemble in matter, applicable in a wide electron energy range from ~eV up to relativistic ones. The approach based on the dynamic-structure factor formalism considers collective response of the atomic and electronic systems of a target. It accounts for dynamical screening of atomic nuclei

Danyang Su, N. A. Liedienov, V. M. Kalita, I. V. Fesych

The properties of nanoparticles are determined by their size and structure. When exposed to external pressure P, their structural properties can change. The improvement or degradation of the properties of the samples depending on time is particularly interesting. The knowledge of the influence of structural-size effect, aging time, and pressure on the behavi

Shengyuan Liu, Bo Wang, Ye Ma, Te Yang

Existing subject-driven text-to-image generation models suffer from tedious fine-tuning steps and struggle to maintain both text-image alignment and subject fidelity. For generating compositional subjects, it often encounters problems such as object missing and attribute mixing, where some subjects in the input prompt are not generated or their attributes ar

Jinghui Wang, Wei Lv

This paper proposes a Galton board approximation method to analyze the potential walking preferences of pedestrians. We employ the binomial distribution to estimate the walking preferences of pedestrians in dynamic crowds. Estimating the probability of the right-side preference ($p$) based on observed data poses the challenge, as statistical measures such as

Qihao Peng, Hong Ren, Cunhua Pan, Maged Elkashlan

In this paper, to tackle the blockage issue in massive multiple-input-multiple-output (mMIMO) systems, a reconfigurable intelligent surface (RIS) is seamlessly deployed to support devices with ultra-reliable and low-latency communications (URLLC). The transmission power of the base station and the phase shifts of the RIS are jointly devised to maximize the w

Chenxu Jiang, Mingyuan Lin, Chi Zhang, Zhenghai Wang

Depth from Focus estimates depth by determining the moment of maximum focus from multiple shots at different focal distances, i.e. the Focal Stack. However, the limited sampling rate of conventional optical cameras makes it difficult to obtain sufficient focus cues during the focal sweep. Inspired by biological vision, the event camera records intensity chan

Manjie Xu, Chenxing Li, Duzhen zhang, Dan Su

Audio editing involves the arbitrary manipulation of audio content through precise control. Although text-guided diffusion models have made significant advancements in text-to-audio generation, they still face challenges in finding a flexible and precise way to modify target events within an audio track. We present a novel approach, referred to as PPAE, whic

Cui Kaiyuan, Gong Fuzhou

In this paper, we introduce new reference observables to establish a scaling formula in the renormalization group equation. Using the transfer matrix method, we calculate the two point observables of the one dimensional Ising model without an external field under general boundary conditions. The results indicate that the two point observables exhibit exponen

Noemi David, Filippo Santambrogio, Markus Schmidtchen

We present regularity results for nonlinear drift-diffusion equations of porous medium type (together with their incompressible limit). We relax the assumptions imposed on the drift term with respect to previous results and additionally study the effect of linear diffusion on our regularity result (a scenario of particular interest in the incompressible case

Keyi Yin, Xiang Fang, Yunong Shi, Travis Humble

In this paper, we introduce Surf-Deformer, a code deformation framework that seamlessly integrates adaptive defect mitigation functionality into the current surface code workflow. It crafts several basic deformation instructions based on fundamental gauge transformations, which can be combined to explore a larger design space than previous methods. This enab

Zhongzhong Luo, Zhihao Yu, Xiangqian Lu, Wei Niu

Two-dimensional (2D) materials are promising candidates for spintronic applications. Maintaining their atomically smooth interfaces during integration of ferromagnetic (FM) electrodes is crucial since conventional metal deposition tends to induce defects at the interfaces. Meanwhile, the difficulties in picking up FM metals with strong adhesion and in achiev

Jianqing Fan, Yingying Li, Ningning Xia, Xinghua Zheng

We establish central limit theorems for principal eigenvalues and eigenvectors under a large factor model setting, and develop two-sample tests of both principal eigenvalues and principal eigenvectors. One important application is to detect structural breaks in large factor models. Compared with existing methods for detecting structural breaks, our tests pro

Shuaishuai Lu, Xue Yang, Yong Li

This paper examines Poisson stable (including stationary, periodic, almost periodic, Levitan almost periodic, Bohr almost automorphic, pseudo-periodic, Birkhoff recurrent, pseudo-recurrent, etc.) measures and limit theorems for stochastic functional partial differential equations(SFPDEs) with monotone coefficients. We first show the existence and uniqueness

Yi-Ju Yen, De-Yan Lu, Sing-Yuan Yeh, Jian-Jiun Ding

This study focuses on the analysis of signals containing multiple components with crossover instantaneous frequencies (IF). This problem was initially solved with the chirplet transform (CT). Also, it can be sharpened by adding the synchrosqueezing step, which is called the synchrosqueezed chirplet transform (SCT). However, we found that the SCT goes wrong w

Vladimir Bobkov, Sergey Kolonitskii

Let $u$ be either a second eigenfunction of the fractional $p$-Laplacian or a least energy nodal solution of the equation $(-\Delta)^s_p \, u = f(u)$ with superhomogeneous and subcritical nonlinearity $f$, in a bounded open set $\Omega$ and under the nonlocal zero Dirichlet conditions. Assuming only that $\Omega$ is Steiner symmetric, we show that the suppor

Nobuaki Yagita

We give many examples of non stable rationalities for projective approximations of classifying spaces. Here we use the new invariant by Benoit-Ottem, and also use the classical unramified cohomology..

Xiaoxiang Chai, Yimin Chen

We prove a new Minkowski type formula for capillary hypersurfaces supported on totally geodesic hyperplanes in hyperbolic space. It leads to a volume-preserving flow starting from a star-shaped initial hypersurface. We prove the long-time existence of the flow and its uniform convergence to a $\theta$-totally umbilical cap. Additionally, we establish that a

Wen-Hsuan Li, Yu-Chih Huang

In this paper, the heterogeneous distributed quickest change detection (HetDQCD) with 1-bit non-anonymous feedback is studied. The concept of syndromes is introduced and the family of syndrome-based fusion rules is proposed, which encompasses all deterministic fusion rules as special cases. Through the Hasse diagram of syndromes, upper and lower bounds on th

Junqin Huang, Zhongjie Hu, Zihao Jing, Mengya Gao

In this report, we introduce Piccolo2, an embedding model that surpasses other models in the comprehensive evaluation over 6 tasks on CMTEB benchmark, setting a new state-of-the-art. Piccolo2 primarily leverages an efficient multi-task hybrid loss training approach, effectively harnessing textual data and labels from diverse downstream tasks. In addition, Pi

Jaekeol Choi

Relevance evaluation of a query and a passage is essential in Information Retrieval (IR). Recently, numerous studies have been conducted on tasks related to relevance judgment using Large Language Models (LLMs) such as GPT-4, demonstrating significant improvements. However, the efficacy of LLMs is considerably influenced by the design of the prompt. The purp

Elham Mohammadrezaei, Denis Gracanin

Smart Built Environment is an eco-system of `connected' and `smart' Internet of Things (IoT) devices that are embedded in a built environment. Smart lighting is an important category of smart IoT devices that has recently attracted research interest, particularly for residential areas. In this paper, we present an extended reality based smart lighting design

Shenglin He, Xiaoyang Qu, Jiguang Wan, Guokuan Li

Recognizing human actions from point cloud sequence has attracted tremendous attention from both academia and industry due to its wide applications. However, most previous studies on point cloud action recognition typically require complex networks to extract intra-frame spatial features and inter-frame temporal features, resulting in an excessive number of

Elham Mohammadrezaei, Shiva Ghasemi, Poorvesh Dongre, Denis Gracanin

This systematic literature review paper explores the use of extended reality {(XR)} technology for smart built environments and particularly for smart lighting systems design. Smart lighting is a novel concept that has emerged over a decade now and is being used and tested in commercial and industrial built environments. We used PRISMA methodology to review

Jieming Zhu, Chuhan Wu, Rui Zhang, Zhenhua Dong

Personalized recommendation stands as a ubiquitous channel for users to explore information or items aligned with their interests. Nevertheless, prevailing recommendation models predominantly rely on unique IDs and categorical features for user-item matching. While this ID-centric approach has witnessed considerable success, it falls short in comprehensively

Xiangyu Wu, Qing-Yuan Jiang, Yang Yang, Yi-Feng Wu

The recent introduction of prompt tuning based on pre-trained vision-language models has dramatically improved the performance of multi-label image classification. However, some existing strategies that have been explored still have drawbacks, i.e., either exploiting massive labeled visual data at a high cost or using text data only for text prompt tuning an

Xiangwei Chen, Ruliang Xiaoa, Zhixia Zeng, Zhipeng Qiu

Semi-supervised anomaly detection for sensor signals is critical in ensuring system reliability in smart manufacturing. However, existing methods rely heavily on data correlation, neglecting causality and leading to potential misinterpretations due to confounding factors. Moreover, while current reinforcement learning-based methods can effectively identify k

Arnub Ghosh

The Many-worlds Interpretation (MWI) of quantum mechanics has captivated physicists and philosophers alike since its inception in the mid-20th century. This paper explores the historical roots, evolution, and implications of the MWI within the context of quantum theory. Beginning with an overview of early developments in quantum mechanics and the emergence o

Hongtao Yang, Guoxing Chen, Wei-Chyung Wang, Qing Bao

Large bias exists in shortwave cloud radiative effect (SWCRE) of general circulation models (GCMs), attributed mainly to the combined effect of cloud fraction and water contents, whose representations in models remain challenging. Here we show an effective machine-learning approach to dissect the individual bias of relevant cloud parameters determining SWCRE

Nishat Raihan, Dhiman Goswami, Antara Mahmud, Antonios Anastasopoulos

Code-mixing is a well-studied linguistic phenomenon that occurs when two or more languages are mixed in text or speech. Several studies have been conducted on building datasets and performing downstream NLP tasks on code-mixed data. Although it is not uncommon to observe code-mixing of three or more languages, most available datasets in this domain contain c

Anxianyi Xiong, Xin-zhe Zhang, Taotao Qiu

The mimetic gravity theory is one of the interesting modified gravity theories, which aims to unify the matter component of our universe within the power of gravity. The mimetic-like theory can also be responsible for primordial perturbations production, e.g., when the mimetic field is set to be like a curvaton field, and the adiabatic perturbation can thus

Xiaomeng Zhao, Ganghua Yuan

In this paper, we focus on the analysis of discrete versions of the Calderon problem with partial boundary data in dimension d >= 3. In particular, we establish logarithmic stability estimates for the discrete Calderon problem on an arbitrarily small portion of the boundary under suitable a priori bounds. For this end, we will use CGO solutions and derive a

Awais Hameed Khan, Hiruni Kegalle, Rhea D'Silva, Ned Watt

Large Language Models (LLMs) are promising analytical tools. They can augment human epistemic, cognitive and reasoning abilities, and support 'sensemaking', making sense of a complex environment or subject by analysing large volumes of data with a sensitivity to context and nuance absent in earlier text processing systems. This paper presents a pilot experim

Chi Zhang, Mingyuan Lin, Xiang Zhang, Chenxu Jiang

Super-resolution from motion-blurred images poses a significant challenge due to the combined effects of motion blur and low spatial resolution. To address this challenge, this paper introduces an Event-based Blurry Super Resolution Network (EBSR-Net), which leverages the high temporal resolution of events to mitigate motion blur and improve high-resolution

Pantea Habibi, Peyman Baghershahi, Sourav Medya, Debaleena Chattopadhyay

Graph neural networks (GNNs) are powerful graph-based machine-learning models that are popular in various domains, e.g., social media, transportation, and drug discovery. However, owing to complex data representations, GNNs do not easily allow for human-intelligible explanations of their predictions, which can decrease trust in them as well as deter any coll

Ashutosh Kumar, Sonali Agarwal, D Jude Hemanth

Human being and different species of animals having the skills to gather, transferring knowledge, processing, fine-tune and generating information throughout their lifetime. The ability of learning throughout their lifespan is referred as continuous learning which is using neurocognition mechanism. Consequently, in real world computational system of incremen

Jinkun Jiang, Qingxuan Lv, Yuezun Li, Yong Du

Source-free Unsupervised Domain Adaptation (SFDA) aims to classify target samples by only accessing a pre-trained source model and unlabelled target samples. Since no source data is available, transferring the knowledge from the source domain to the target domain is challenging. Existing methods normally exploit the pair-wise relation among target samples an

Ming-Hui Huang, Roland T. Rust

Generative AI (GenAI) has spurred the expectation of being creative, due to its ability to generate content, yet so far, its creativity has somewhat disappointed, because it is trained using existing data following human intentions to generate outputs. The purpose of this paper is to explore what is required to evolve AI from generative to creative. Based on

Wang Lin, Jingyuan Chen, Jiaxin Shi, Yichen Zhu

We tackle the common challenge of inter-concept visual confusion in compositional concept generation using text-guided diffusion models (TGDMs). It becomes even more pronounced in the generation of customized concepts, due to the scarcity of user-provided concept visual examples. By revisiting the two major stages leading to the success of TGDMs -- 1) contra

Mehmet Atçeken, Tuğba Mert

In the present paper, Tachibana operatory is applied to an invariant submanifold of a lorentzian trans-Sasakian manifold by means of through various tensors and the results obtained are discussed in terms of geometry. Finally, we give a non-trivial example in order to our results illustrate.

Takeshi Torii

Twisted arrow $\infty$-categories of $(\infty,1)$-categories were introduced by Lurie, and they have various applications in higher category theory. Abell\'{a}n Garc\'{i}a and Stern gave a generalization to twisted arrow $\infty$-categories of $(\infty,2)$-categories. In this paper we introduce another simple model for twisted arrow $\infty$-categories of $(

Pierre-Luc Asselin, Vincent Coulombe, William Guimont-Martin, William Larrivée-Hardy

This work examines the reproducibility and benchmarking of state-of-the-art real-time object detection models. As object detection models are often used in real-world contexts, such as robotics, where inference time is paramount, simply measuring models' accuracy is not enough to compare them. We thus compare a large variety of object detection models' accur

Hartej Soin, Tapas Tripura, Souvik Chakraborty

We propose a generative flow-induced neural architecture search algorithm. The proposed approach devices simple feed-forward neural networks to learn stochastic policies to generate sequences of architecture hyperparameters such that the generated states are in proportion with the reward from the terminal state. We demonstrate the efficacy of the proposed se