October 2023 arXiv papers — page 111

Dichucheng Li, Yinghao Ma, Weixing Wei, Qiuqiang Kong

Instrument playing techniques (IPTs) constitute a pivotal component of musical expression. However, the development of automatic IPT detection methods suffers from limited labeled data and inherent class imbalance issues. In this paper, we propose to apply a self-supervised learning model pre-trained on large-scale unlabeled music data and finetune it on IPT

Julien Pourcel, Cédric Colas, Gaia Molinaro, Pierre-Yves Oudeyer

The ability to invent novel and interesting problems is a remarkable feature of human intelligence that drives innovation, art, and science. We propose a method that aims to automate this process by harnessing the power of state-of-the-art generative models to produce a diversity of challenging yet solvable problems, here in the context of Python programming

Arpan Dasgupta, Pawan Kumar

A large number of computational and scientific methods commonly require decomposing a sparse matrix into triangular factors as LU decomposition. A common problem faced during this decomposition is that even though the given matrix may be very sparse, the decomposition may lead to a denser triangular factors due to fill-in. A significant fill-in may lead to p

Yanfang Wang, Shibei Xue, Hongbin Song, Min Jiang

In this paper, we propose a non-Markovian quantum channel approach to mitigating the degradation of the average fidelity in continuous-variable quantum teleportation. The non-Markovian quantum channel is modeled by an augmented system, where ancillary systems are introduced to represent the internal modes of non-Markovian environments. With a proper non-Mark

Roberto Ragazzoni

After 28 years from the conception of the pyramid WFS several new kind of devices able to convert wavefront shape into some sort of different illumination on a detector have been conceived. While, suspending momentarily any kind of modesty, I claim credit for being among the few that contributed to show at the time that there could be much more than just a l

Pei Li, Song Li, Péter Udvarhelyi, Bing Huang

Point defects may introduce defect levels into the fundamental band gap of the host semiconductors that alter the electrical properties of the material. As a consequence, the in-gap defect levels and states automatically lower the threshold energy of optical excitation associated with the optical gap of the host semiconductor. It is, therefore, a common assu

Hong Zhang, Quoc-Nam Nguyen, Prasanta Bhattacharya, Wei Gao

This study enhances stance detection on social media by incorporating deeper psychological attributes, specifically individuals' moral foundations. These theoretically-derived dimensions aim to provide a comprehensive profile of an individual's moral concerns which, in recent work, has been linked to behaviour in a range of domains, including society, politi

Prasha Srivastava, Pawan Kumar, Zia Abbas

Generative AI has seen remarkable growth over the past few years, with diffusion models being state-of-the-art for image generation. This study investigates the use of diffusion models in generating artificial data generation for electronic circuits for enhancing the accuracy of subsequent machine learning models in tasks such as performance assessment, desi

Xiaoming Li, Hubert Normandin-Taillon, Chun Wang, Xiao Huang

In the realm of Mobility-on-Demand (MoD) systems, the forecasting of rider demand is a cornerstone for operational decision-making and system optimization. Traditional forecasting methodologies primarily yield point estimates, thereby neglecting the inherent uncertainty within demand projections. Moreover, MoD demand levels are profoundly influenced by both

Zihan Wang, Ziqi Zhao, Zhumin Chen, Pengjie Ren

Few-shot named entity recognition (NER) has shown remarkable progress in identifying entities in low-resource domains. However, few-shot NER methods still struggle with out-of-domain (OOD) examples due to their reliance on manual labeling for the target domain. To address this limitation, recent studies enable generalization to an unseen target domain with o

Monica Motta, Franco Rampazzo

This article makes no claim to originality, other than, perhaps, the simple statement here called the {\it Abstract Maximum Principle}. Actually, the whole contents are strongly based on some H. Sussmann's and coauthors' papers, in which, in a much more general context, the set-separation approach is regarded as foundational for necessary conditions for mini

Johannes O. Royset, Miguel A. Lejeune

For parameterized mixed-binary optimization problems, we construct local decision rules that prescribe near-optimal courses of action across a set of parameter values. The decision rules stem from solving risk-adaptive training problems over classes of continuous, possibly nonlinear mappings. In asymptotic and nonasymptotic analysis, we establish that the de

Jiuyang Zhou, Tengfei Niu, Hong Zhu, Xingping Wang

This paper explores the modeling method of polyphonic music sequence. Due to the great potential of Transformer models in music generation, controllable music generation is receiving more attention. In the task of polyphonic music, current controllable generation research focuses on controlling the generation of chords, but lacks precise adjustment for the c

Ruofan Chen, Xiansong Xu, Chu Guo

Tensor-network-based methods are promising candidates to solve quantum impurity problems. They are free of sampling noises and the sign problem compared to state-of-the-art continuous-time quantum Monte Carlo methods. Recent progress made in tensor-network-based impurity solvers is to use the Feynman-Vernon influence functional to integrate out the bath anal

Hyuga Ito

We will introduce a cyclic derivative for fully (stably) matricial functions and study its basic properties. In particular, we will show the Poincar\'{e} lemma for stably matricial functions of certain classes. We will also position Voiculescu's framework of fully matricial functions in the context of nc functions due to Kaliuzhnyi-Verbovetskyi and Vinnikov

Zhongxiang Wei, Ping Wang, Qingjiang Shi, Xu Zhu

In last decades, dynamic resource programming in partial resource domains has been extensively investigated for single time slot optimizations. However, with the emerging real-time media applications in fifth-generation communications, their new quality of service requirements are often measured in temporal dimension. This requires multistage optimization fo

Ananay Vikram Gupta, Aaditya Prakash Kattekola, Ansh Vikram Gupta, Dacharla Venkata Abhiram

The exponential growth of Advanced Air Mobility (AAM) services demands assurances of safety in the airspace. This research a Traffic Control Framework (TCF) for developing digital flight rules for Uncrewed Aircraft System (UAS) flying in designated air corridors. The proposed TCF helps model, deploy, and test UAS control, agents, regardless of their hardware

Keisuke Nakashi, Masashi Kimura, Hayato Motohashi, Kazufumi Takahashi

We study odd-parity perturbations about static and spherically symmetric black hole solutions with a linearly time-dependent scalar field in higher-order scalar-tensor theories. In particular, we consider stealth Schwarzschild and stealth Schwarzschild-de Sitter solutions, where the deviation from the general relativity case is controlled by a single paramet

Huilin Zhou, Huijie Tang, Mingjie Li, Hao Zhang

The AI model has surpassed human players in the game of Go, and it is widely believed that the AI model has encoded new knowledge about the Go game beyond human players. In this way, explaining the knowledge encoded by the AI model and using it to teach human players represent a promising-yet-challenging issue in explainable AI. To this end, mathematical sup

Hongxiang Zhao

Ando established an algebraic criterion for when a complex orientation for a Morava E-theory is an $H_\infty$-map. The criterion relates such an orientation to a specific property of the formal group associated to the E-theory, namely, a norm coherence condition on its coordinate. On the other hand, Coleman constructed a norm operator for interpolating divis

Yu-Shan Ren, Guang-Juan Wang, Zhi Yang, Jia-Jun Wu

We investigate the doubly bottom state $T^-_{bb}$ composed of two bottom mesons with $J^P=1^+$. The potentials are obtained using the one-boson exchange model. With the heavy quark flavor symmetry, all the parameters in the model are determined by fitting the experimental data of doubly charmed state $T_{cc}^{+}$ from our previous work. Our analysis indicate

Selen Gecgel Cetin, Gunes Karabulut Kurt, Angeles Vazquez-Castro

There is no doubt that the Moon has become the center of interest for commercial and international actors. Over the past decade, the number of planned long-term missions has increased dramatically. This makes the establishment of cislunar space networks (CSNs) crucial to orchestrate uninterrupted communications between the Moon and Earth. However, there are

Anthony Kenyon, David Elizondo, Lipika Deka

Typical event datasets such as those used in network intrusion detection comprise hundreds of thousands, sometimes millions, of discrete packet events. These datasets tend to be high dimensional, stateful, and time-series in nature, holding complex local and temporal feature associations. Packet data can be abstracted into lower dimensional summary data, suc

Simin Li, Ruixiao Xu, Jingqiao Xiu, Yuwei Zheng

In multi-agent reinforcement learning (MARL), ensuring robustness against unpredictable or worst-case actions by allies is crucial for real-world deployment. Existing robust MARL methods either approximate or enumerate all possible threat scenarios against worst-case adversaries, leading to computational intensity and reduced robustness. In contrast, human l

Shwai He, Run-Ze Fan, Liang Ding, Li Shen

Scaling the size of language models usually leads to remarkable advancements in NLP tasks. But it often comes with a price of growing computational cost. Although a sparse Mixture of Experts (MoE) can reduce the cost by activating a small subset of parameters (e.g., one expert) for each input, its computation escalates significantly if increasing the number

Zian Jia, Yun Xiong, Yuhong Nan, Yao Zhang

Advance Persistent Threats (APTs), adopted by most delicate attackers, are becoming increasing common and pose great threat to various enterprises and institutions. Data provenance analysis on provenance graphs has emerged as a common approach in APT detection. However, previous works have exhibited several shortcomings: (1) requiring attack-containing data

Jonas Blessing, Lianzi Jiang, Michael Kupper, Gechun Liang

We provide explicit convergence rates for Chernoff-type approximations of convex monotone semigroups which have the form $S(t)f=\lim_{n\to\infty}I(\frac{t}{n})^n f$ for bounded continuous functions $f$. Under suitable conditions on the one-step operators $I(t)$ regarding the time regularity and consistency of the approximation scheme, we obtain $\|S(t)f-I(\f

Anas El Balali

In this paper, we investigate the optical behaviors of a quantum Schwarzschild black hole with a spacetime solution including a parameter $\lambda$ that encodes its discretization. Concretly, we derive the effective potential of such solution. In particular, we study the circular orbits around the quantum black hole. Indeed, we find that the effective potent

Wangyu Wu, Tianhong Dai, Xiaowei Huang, Fei Ma

Weakly Supervised Semantic Segmentation (WSSS) using only image-level labels has gained significant attention due to cost-effectiveness. Recently, Vision Transformer (ViT) based methods without class activation map (CAM) have shown greater capability in generating reliable pseudo labels than previous methods using CAM. However, the current ViT-based methods

Tianyuan Zou, Zixuan Gu, Yu He, Hideaki Takahashi

Vertical Federated Learning (VFL) has emerged as a collaborative training paradigm that allows participants with different features of the same group of users to accomplish cooperative training without exposing their raw data or model parameters. VFL has gained significant attention for its research potential and real-world applications in recent years, but

Yanjun Ji, Xi Chen, Ilia Polian, Yue Ban

Quantum algorithms implemented on near-term devices require qubit mapping due to noise and limited qubit connectivity. In this paper we propose a strategy called algorithm-oriented qubit mapping (AOQMAP) that aims to bridge the gap between exact and scalable mapping methods by utilizing the inherent structure of algorithms. While exact methods provide optima

Nyanga Honda Masasila, Rigobert Charles Ngeleja, Odeli John Kigodi

This study presents a deterministic model to examine how information affects the spread of Typhoid Fever. The models properties, including its stability and basic reproduction number, are analyzed. Simulations show that information can influence behavior in ways that may increase disease transmission. Nota bly, the rise in Typhoid cases is linked to poor adh

Haoran Sun, Bangchao Huang, Zishang Zhang, Ronghan Xu

This paper studies the design, control, and learning of a novel robotic limb that produces overconstrained locomotion by employing the Bennett linkage for motion generation, capable of parametric reconfiguration between a reptile- and mammal-inspired morphology within a single quadruped. In contrast to the prevailing focus on planar linkages, this research d

Ataklti Kahsu, Solomon Teferra

This thesis proposes and describes a research attempt at designing and developing a speaker independent spontaneous automatic speech recognition system for Tigrigna The acoustic model of the Speech Recognition System is developed using Carnegie Mellon University Automatic Speech Recognition development tool (Sphinx) while the SRIM tool is used for the develo

Manh Hung Nguyen, Sebastian Tschiatschek, Adish Singla

Student modeling is central to many educational technologies as it enables predicting future learning outcomes and designing targeted instructional strategies. However, open-ended learning domains pose challenges for accurately modeling students due to the diverse behaviors and a large space of possible misconceptions. To approach these challenges, we explor

Mingfu Xue, Leo Yu Zhang, Yushu Zhang, Weiqiang Liu

The intellectual property protection of deep learning (DL) models has attracted increasing serious concerns. Many works on intellectual property protection for Deep Neural Networks (DNN) models have been proposed. The vast majority of existing work uses DNN watermarking to verify the ownership of the model after piracy occurs, which is referred to as passive

Hans van Haren

The extent of anthropogenic influence on the Earths climate warrants studies of the ocean as a major player. The ocean circulation is important for transporting properties like heat, carbon and nutrients. A supposed major conduit is the Atlantic Meridional Overturning Circulation (AMOC). Schematically, it transports heat from the equator to the poles near th

Yiming Lei, Zilong Li, Yangyang Li, Junping Zhang

Interpreting the decisions of deep learning models has been actively studied since the explosion of deep neural networks. One of the most convincing interpretation approaches is salience-based visual interpretation, such as Grad-CAM, where the generation of attention maps depends merely on categorical labels. Although existing interpretation methods can prov

Weixuan Wang, Barry Haddow, Alexandra Birch, Wei Peng

Large language models (LLMs) have been treated as knowledge bases due to their strong performance in knowledge probing tasks. LLMs are typically evaluated using accuracy, yet this metric does not capture the vulnerability of LLMs to hallucination-inducing factors like prompt and context variability. How do we evaluate the capabilities of LLMs to consistently

Ravil Mussabayev, Rustam Mussabayev

This paper presents a comparative analysis of different optimization techniques for the K-means algorithm in the context of big data. K-means is a widely used clustering algorithm, but it can suffer from scalability issues when dealing with large datasets. The paper explores different approaches to overcome these issues, including parallelization, approximat

Mario Beraha, Stefano Favaro, Vinayak Rao

Estimating the probability density of a population while preserving the privacy of individuals in that population is an important and challenging problem that has received considerable attention in recent years. While the previous literature focused on frequentist approaches, in this paper, we propose a Bayesian nonparametric mixture model under differential

Junjie Gao, Qiujie Dong, Ruian Wang, Shuangmin Chen

In the domain of point cloud registration, the coarse-to-fine feature matching paradigm has received substantial attention owing to its impressive performance. This paradigm involves a two-step process: first, the extraction of multi-level features, and subsequently, the propagation of correspondences from coarse to fine levels. Nonetheless, this paradigm ex

Dheeraj Kumar Singh, Yunkyu Bang

We examine charge correlations and instabilities in the pseudogap phase of high-$T_c$ cuprates modeled by $d$-density wave ordering. The latter has a gap symmetry similar to the one observed in the $d$-wave superconductor. We use $t$-$J$ model to describe the charge correlations in the presence of electron-phonon interaction. Our finding suggest that the cha

Faizan Habib Vance, Arne Scholtissek, Philip de Goey, Jeroen van Oijen

Combustion of hydrogen can help in reducing carbon-based emissions but it also poses unique challenges related to the high flame speed and Lewis number effects of the hydrogen flame. When operated with conventional burners, a hydrogen flame can flashback at higher volumetric flow rates than a methane flame due to the difference in stabilization mechanisms of

Zhengtian Qiu, Guiyun Chen, Jianjun Liu

Let $ H $ be a subgroup of a finite group $ G $. We say that $ H $ satisfies $ \mathscr L $-$ \Pi $-property in $ G $ if $ | G / K : N _{G / K} (HK/K)| $ is a $ \pi (HK/K) $-number for all maximal $ G $-invariant subgroup $ K $ of $ H^{G} $. In this paper, we give a characterization of finite $p$-supersoluble groups under assumption that some subgroups of pr

Abhinav Chakraborty, Krishnendu Mukhopadhyaya

In this paper, the parking problem of a swarm of mobile robots has been studied. The robots are deployed at the nodes of an infinite grid, which has a subset of prefixed nodes marked as parking nodes. Each parking node p_i has a capacity of k_i which is given as input and equals the maximum number of robots a parking node can accommodate. As a solution to th

Salman Beigi, Hami Mehrabi

The quantum central limit theorem for bosonic quantum systems states that the sequence of states $\rho^{\boxplus n}$ obtained from the $n$-fold convolution of a centered quantum state $\rho$ converges to a quantum Gaussian state $\rho_G$ that has the same first and second moments as $\rho$. In this paper, we contribute to the problem of finding the optimal r

Daniel Braak, Linh Thi Hoai Nguyen, Cid Reyes-Bustos, Masato Wakayama

The asymmetric quantum Rabi model (AQRM) is a fundamental model in quantum optics describing the interaction of light and matter. Besides its immediate physical interest, the AQRM possesses an intriguing mathematical structure which is far from being completely understood. In this paper, we focus on the distribution of the level spacing, the difference betwe

Michael Fu, Chakkrit Tantithamthavorn, Van Nguyen, Trung Le

Large language models (LLMs) like ChatGPT (i.e., gpt-3.5-turbo and gpt-4) exhibited remarkable advancement in a range of software engineering tasks associated with source code such as code review and code generation. In this paper, we undertake a comprehensive study by instructing ChatGPT for four prevalent vulnerability tasks: function and line-level vulner

P. Colcombet, N. Dinu-Jaeger, C. Inguimbert, T. Nuns

This study investigates the effects of space environmental radiation on the performance of InGaAs Quadrant Photodiodes (QPDs) and assesses their suitability for the Laser Interferometer Space Antenna (LISA) mission. QPDs of 1.0, 1.5 and 2.0 mm have been irradiated with 20 and 60 MeV protons, 0.5 and 1 MeV electrons, and Co$^{60}$ gamma. An exposure correspon

MohammadJavad Vaez, Alireza Hosseini, Kamal Jamshidi

Our paper introduces a novel method for calculating the inverse $\mathcal{Z}$-transform of rational functions. Unlike some existing approaches that rely on partial fraction expansion and involve dividing by $z$, our method allows for the direct computation of the inverse $\mathcal{Z}$-transform without such division. Furthermore, our method expands the ratio

M. H. Kim, O. Adriani, E. Berti, L. Bonechi

Transverse single-spin asymmetries $A_{\textrm{N}}$ of forward neutrons at pseudorapidities larger than 6 had only been studied in the transverse momentum range of $p_{\textrm{T}} < 0.4$ GeV/$c$. The RHICf Collaboration has extended the previous measurements up to 1.0 GeV/$c$ in polarized $p+p$ collisions at $\sqrt{s}~=~510$GeV, using an electromagnetic calo

Renyang Liu, Jun Zhao, Xing Chu, Yu Liang

With the rapid development of GPU (Graphics Processing Unit) technologies and neural networks, we can explore more appropriate data structures and algorithms. Recent progress shows that neural networks can partly replace traditional data structures. In this paper, we proposed a novel DNN (Deep Neural Network)-based learned locality-sensitive hashing, called

Arsen Khvedelidze, Astghik Torosyan

We consider the nonclassicality distance indicator of a state in finite-dimensional quantum systems which is evaluating a state nonclassicality by its remoteness from the set of "classical states". The latter are identified with those states whose Wigner function is non-negative. The corresponding Wigner function's positivity polytope in the simplex of qudit

Luis A. Balona

TESS observations of pulsating hot main sequence stars paint a very different picture from what is currently accepted. There are large numbers of delta Scuti (DSCT) stars hotter than the theoretical hot edge of the instability strip, continuing to what appear to be DSCT stars of mid-B type (historically known as Maia variables). The frequencies of maximum am

Ahmad Rammal, Kaja Gruntkowska, Nikita Fedin, Eduard Gorbunov

Byzantine robustness is an essential feature of algorithms for certain distributed optimization problems, typically encountered in collaborative/federated learning. These problems are usually huge-scale, implying that communication compression is also imperative for their resolution. These factors have spurred recent algorithmic and theoretical developments

Bojan Žunkovič, Pedro Ribeiro

We study the equilibrium properties of the spin-$1/2$ XY chain with an infinite-range transverse interaction. At zero temperature, competition between the XY- and the $z$-ordered phases induced by the infinite-range interactions gives rise to a first-order transition upon increasing the transverse coupling. We show that the two gapless points of the XY model

Kwangseob Ahn

This paper investigates the "Exploitation Business" model, which capitalizes on information asymmetry to exploit vulnerable populations. It focuses on businesses targeting non-experts or fraudsters who capitalize on information asymmetry to sell their products or services to desperate individuals. This phenomenon, also described as "profit-making activities

Nikita Shulga

Famous Zaremba's conjecture (1971) states that for each positive integer $q\geq2$, there exists positive integer $1\leq a <q$, coprime to $q$, such that if you expand a fraction $a/q$ into a continued fraction $a/q=[a_1,\ldots,a_n]$, all of the coefficients $a_i$'s are bounded by some absolute constant $\mathfrak k$, independent of $q$. Zaremba conjectured t

Renyang Liu, Wei Zhou, Jinhong Zhang, Xiaoyuan Liu

Recently, Graph Neural Networks (GNNs), including Homogeneous Graph Neural Networks (HomoGNNs) and Heterogeneous Graph Neural Networks (HeteGNNs), have made remarkable progress in many physical scenarios, especially in communication applications. Despite achieving great success, the privacy issue of such models has also received considerable attention. Previ

Xin-Xiang Ju, Bo-Hao Liu, Wen-Bin Pan, Ya-Wen Sun

We investigate the bipartite and multipartite quantum entanglement structure in gravity and the dual holographic field theory based on the generalized Rindler wedge formalism. We deduce a separation theorem, which asserts that for subregions satisfying a certain geometric condition, the bipartite/multipartite squashed entanglement or the conditional entangle

Gang Liu, Jiaze Gao, Yufen Han, Yuhao Mu

In this paper, we investigate the momentum coupling between early dark energy (EDE) and cold dark matter to alleviate cosmological tensions. EDE has exhibited promising efficacy in addressing the Hubble tension, but it exacerbates the large-scale structure tension. We consider the interaction between EDE and cold dark matter, introducing a pure momentum exch

Stefan-Dan Ciocirlan, Dumitrel Loghin

The introduction of posit reopened the debate about the utility of IEEE754 in specific domains. In this context, we propose a high-level language (Scala) library that aims to reduce the effort of designing and testing new number representation systems (NRSs). The library's efficiency is tested with three new NRSs derived from Morris Tapered Floating-Point by

Ghazal Jamirad, Abbas Montazeri, Ali Rajabpour

The relatively low thermal conductivity of biodegradable polylactic acid (PLA) has limited its applications in various fields. To address this issue, the incorporation of nanofillers, such as boron nitride nanosheets (BNNSs), has emerged as an effective method to enhance PLA's thermal properties. However, the thermal conduction of polymer-based nanocomposite

Renyang Liu, Jinhong Zhang, Haoran Li, Jin Zhang

Extensive studies have demonstrated that deep neural networks (DNNs) are vulnerable to adversarial attacks. Despite the significant progress in the attack success rate that has been made recently, the adversarial noise generated by most of the existing attack methods is still too conspicuous to the human eyes and proved to be easily detected by defense mecha

R. Molina, E. Oset

We investigate the $D^*K^*$ and $D^*_s\rho$ interaction in coupled channels within the hidden gauge formalism. A structure is developed around their thresholds, short of producing a bound state, which leads to a peak in the $D_s^+ \pi^-$ mass distribution in the $B^0 \to \bar{D}^0 D_s^+ \pi^-$ decay compatible with the experimental data. We conclude that the

George Martvel, Ilan Shimshoni, Anna Zamansky

The field of animal affective computing is rapidly emerging, and analysis of facial expressions is a crucial aspect. One of the most significant challenges that researchers in the field currently face is the scarcity of high-quality, comprehensive datasets that allow the development of models for facial expressions analysis. One of the possible approaches is

Renyang Liu, Jinhong Zhang, Kwok-Yan Lam, Jun Zhao

Previous studies have revealed that artificial intelligence (AI) systems are vulnerable to adversarial attacks. Among them, model extraction attacks fool the target model by generating adversarial examples on a substitute model. The core of such an attack is training a substitute model as similar to the target model as possible, where the simulation process

Shaokang Wu, Yijin Wang, Yanlong Huang

Over the past few years, there have been numerous works towards advancing the generalization capability of robots, among which learning from demonstrations (LfD) has drawn much attention by virtue of its user-friendly and data-efficient nature. While many LfD solutions have been reported, a key question has not been properly addressed: how can we evaluate th

Alex Chow, Sung Kei Li, Tom Broadhurst, Jeremy Lim

The first science image released by the JWST reveals numerous galaxies in the distant background of the galaxy cluster SMACS J0723.3-7327. Some have claimed redshifts of up to $z \simeq 20$, challenging standard cosmological models for structure formation. Here, we present a lens model for SMACS J0723.3-7327 anchored on five spectroscopically-confirmed syste

Ziru Niu, Hai Dong, A. Kai Qin, Tao Gu

Federated Learning (FL) achieves great popularity in the Internet of Things (IoT) as a powerful interface to offer intelligent services to customers while maintaining data privacy. Under the orchestration of a server, edge devices (also called clients in FL) collaboratively train a global deep-learning model without sharing any local data. Nevertheless, the

Kaiying Hou

For $n\geq 3$ and $r\geq n$, we show that there are rank-$r$ vector bundles on $\mathbb{P}^n$ with arbitrary homological dimension. We apply the Bernstein-Gel'fand-Gel'fand correspondence to translate the vector bundle question into a problem on modules over the exterior algebra. Then, we use linear algebra to construct the desired modules.

Xiaobo Zhu, Yan Wu, Qinhu Zhang, Zhanheng Chen

Modelling temporal networks for dynamic link prediction of new nodes has many real-world applications, such as providing relevant item recommendations to new customers in recommender systems and suggesting appropriate posts to new users on social platforms. Unlike old nodes, new nodes have few historical links, which poses a challenge for the dynamic link pr

Verena Biener, Snehanjali Kalamkar, John J Dudley, Jinghui Hu

Recent commercial off-the-shelf virtual and augmented reality devices have been promoted as tools for knowledge work and research findings show how this kind of work can benefit from the affordances of extended reality (XR). One major advantage that XR can provide is the enlarged display space that can be used to display virtual screens which is a feature al

Marc Schmitt

The last decades have been characterized by unprecedented technological advances, many of them powered by modern technologies such as Artificial Intelligence (AI) and Machine Learning (ML). The world has become more digitally connected than ever, but we face major challenges. One of the most significant is cybercrime, which has emerged as a global threat to

Norihiro Oyama, Takeshi Kawasaki, Kang Kim, Hideyuki Mizuno

In a sheared steady state, glasses reach a nonequilibrium criticality called yielding. In this letter, we report that the qualitative nature of this nonequilibrium critical phenomenon depends on the details of the system and that responses and fluctuations are governed by different critical correlation lengths in specific situations. This scale separation of

A. Chilingarian, D. Pokhsraryan, F. Zagumenov, M. Zazyan

We analyzed the structure of the Thunderstorm Ground Enhancement using a particle detector network on Aragats. We performed a statistical analysis of the particle flux enhancement time series on a nanosecond time scale using the largest TGE event on record, which occurred on May 23, 2023. Our findings confirm that the TGE combines multiple Extensive Cloud Sh

A. Chilingarian

Various particle accelerators operate in the space plasmas, filling the Galaxy with high energy particles, primary cosmic rays. Reaching the atmosphere of the earth, these particles originate extensive air showers consisting of millions of elementary particles, secondary cosmic rays, covering several large areas on the ground. During thunderstorms, strong el

Roger Lee

In a universal framework that expresses any market system in terms of state transition rules, we prove that every DeFi market system has an invariant function and is thus by definition a CFMM; indeed, all automated market makers (AMMs) are CFMMs. Invariants connect directly to arbitrage and to completeness, according to two fundamental equivalences. First, a

Xiangnan Chen, Wen Zhang, Zhen Yao, Mingyang Chen

Knowledge graph embedding (KGE) aims to map entities and relations of a knowledge graph (KG) into a low-dimensional and dense vector space via contrasting the positive and negative triples. In the training process of KGEs, negative sampling is essential to find high-quality negative triples since KGs only contain positive triples. Most existing negative samp

Naofumi Hama

Data analysis that uses the output of topological data analysis as input for machine learning algorithms has been the subject of extensive research. This approach offers a means of capturing the global structure of data. Persistent homology (PH), a common methodology within the field of TDA, has found wide-ranging applications in machine learning. One of the

Yan Xiang, Danni Chang, Jieli Cheng

Different social backgrounds and planning policies give rise to diverse urban morphologies. These morphologies influence urban microclimate factors and contribute to the formation of unique local microclimates, particularly in terms of outdoor temperature. In recent times, the heat island effect has gained increasing significance during the summer season. Th

Yan Xiang, Danni Chang, Xuan Feng

With the development of the information age, cities provide a large amount of data that can be analyzed and utilized to facilitate the decision-making process. Urban big data and analytics are particularly valuable in the analysis of business location decisions, providing insight and supporting informed choices. By examining data relating to commercial locat

A. Y. Yang, S. A. Dzib, J. S. Urquhart, A. Brunthaler

As part of the GLOSTAR (GLObal view of STAR formation in the Milky Way) survey, we present the high-resolution continuum source catalog for the regions (l = 2-28, 36-40, 56-60, &|b|<1.0), observed with the Karl G. Jansky Very Large Array (VLA) in its B-configuration. The continuum images are optimized to detect compact sources on angular scales up to 4", and

Hongyu Fu, Xin Yu, Lincheng Li, Li Zhang

Existing volumetric neural rendering techniques, such as Neural Radiance Fields (NeRF), face limitations in synthesizing high-quality novel views when the camera poses of input images are imperfect. To address this issue, we propose a novel 3D reconstruction framework that enables simultaneous optimization of camera poses, dubbed CBARF (Cascaded Bundle-Adjus

Tong Wu, Yong Wang, Sining Wei

In this paper, we give the proof of the general Kastler-Kalau-Walze type theorem and the Dabrowski-Sitarz-Zalecki type theorem on odd dimensional compact manifolds with boundary.

Hongjun Wu, Di Wang

The worst-case resource usage of a program can provide useful information for many software-engineering tasks, such as performance optimization and algorithmic-complexity-vulnerability discovery. This paper presents a generic, adaptive, and sound fuzzing framework, called DSE-SMC, for estimating worst-case resource usage. DSE-SMC is generic because it is bla

Yu-Chien Tang, Wei-Yao Wang, An-Zi Yen, Wen-Chih Peng

The dialogue systems in customer services have been developed with neural models to provide users with precise answers and round-the-clock support in task-oriented conversations by detecting customer intents based on their utterances. Existing intent detection approaches have highly relied on adaptively pre-training language models with large-scale datasets,

Luca Deck, Jakob Schoeffer, Maria De-Arteaga, Niklas Kühl

In this critical survey, we analyze typical claims on the relationship between explainable AI (XAI) and fairness to disentangle the multidimensional relationship between these two concepts. Based on a systematic literature review and a subsequent qualitative content analysis, we identify seven archetypal claims from 175 scientific articles on the alleged fai

Chi Shu, Simone Colombo, Zeyang Li, Albert Adiyatullin

The strong coupling of atoms to optical cavities can improve optical lattice clocks as the cavity enables metrologically useful collective atomic entanglement and high-fidelity measurement. To this end, it is necessary to cool the ensemble to suppress motional broadening, and advantageous to maximize and homogenize the atom-cavity coupling. We demonstrate re

Li Zhou, Wenyu Chen, Dingyi Zeng, Malu Zhang

In the field of natural language understanding, the intersection of neural models and graph meaning representations (GMRs) remains a compelling area of research. Despite the growing interest, a critical gap persists in understanding the exact influence of GMRs, particularly concerning relation extraction tasks. Addressing this, we introduce DAGNN-plus, a sim

Dung Le

We study the regularity of weak solutions and the global existence of classical to cross-diffusion systems of $m$ equations on $N$-dimensional domains ($m,N\ge2$).

Jaydip Sen, Arup Dasgupta, Subhasis Dasgupta, Sayantani Roychoudhury

This chapter presents a calendar rebalancing approach to portfolios of stocks in the Indian stock market. Ten important sectors of the Indian economy are first selected. For each of these sectors, the top ten stocks are identified based on their free-float market capitalization values. Using the ten stocks in each sector, a sector-specific portfolio is desig

Zahra Mobini, Hien Quoc Ngo, Michail Matthaiou, Lajos Hanzo

Wireless surveillance, in which untrusted communications links are proactively monitored by legitimate agencies, has started to garner a lot of interest for enhancing the national security. In this paper, we propose a new cell-free massive multiple-input multiple-output (CF-mMIMO) wireless surveillance system, where a large number of distributed multi-antenn

Krishnarjun Krishnamoorthy

Suppose $K$ is a number field and $a_K(m)$ is the number of integral ideals of norm equal to $m$ in $K$, then for any integer $l$, we asymptotically evaluate the sum \[ \sum_{m\leqslant T} a_K^l(m) \] as $T\to\infty$. We also consider the moments of the corresponding Dedekind zeta function. We prove lower bounds of expected order of magnitude and slightly im

Jiwan Chung, Youngjae Yu

Multimodal language generation, which leverages the synergy of language and vision, is a rapidly expanding field. However, existing vision-language models face challenges in tasks that require complex linguistic understanding. To address this issue, we introduce Visual-Language models as Importance Sampling weights (VLIS), a novel framework that combines the

Marc Schmitt, Ivan Flechais

The advancement of Artificial Intelligence (AI) and Machine Learning (ML) has profound implications for both the utility and security of our digital interactions. This paper investigates the transformative role of Generative AI in Social Engineering (SE) attacks. We conduct a systematic review of social engineering and AI capabilities and use a theory of soc

Haoxian Chen, Henry Lam

Bayesian Optimization is a popular approach for optimizing expensive black-box functions. Its key idea is to use a surrogate model to approximate the objective and, importantly, quantify the associated uncertainty that allows a sequential search of query points that balance exploitation-exploration. Gaussian process (GP) has been a primary candidate for the

A. D. Alhaidari

Within the recently proposed structure-inclusive algebraic formulation of quantum field theory, we show that a massless particle can acquire mass by special nonlinear coupling to a universal massless scalar field; establishing an alternative to the Higgs mechanism in the standard model of particle physics. We end with a conjecture concerning dark energy and

Sayan Mahapatra, Debtanu Datta, Shubham Soni, Adrijit Goswami

Most legal text in the Indian judiciary is written in complex English due to historical reasons. However, only a small fraction of the Indian population is comfortable in reading English. Hence legal text needs to be made available in various Indian languages, possibly by translating the available legal text from English. Though there has been a lot of resea