October 2023 arXiv papers — page 70

Fabio Podestà, Fangyang Zheng

In this article, we investigate the class of Hermitian manifolds whose Bismut connection has parallel torsion ({\rm BTP} for brevity). In particular, we focus on the case where the manifold is (locally) homogeneous with respect to a group of holomorphic isometries and we fully characterize the compact Chern flat {\rm BTP} manifolds. Moreover we show that cer

Pierre Colombo, Marine Picot, Nathan Noiry, Guillaume Staerman

The landscape of available textual adversarial attacks keeps growing, posing severe threats and raising concerns regarding the deep NLP system's integrity. However, the crucial problem of defending against malicious attacks has only drawn the attention of the NLP community. The latter is nonetheless instrumental in developing robust and trustworthy systems.

Louis Betzer, Vorapong Suppakitpaisarn, Quentin Hillebrand

In this paper, we give an algorithm to publish the number of paths and Katz centrality under the local differential privacy (LDP), providing a thorough theoretical analysis. Although various works have already introduced subgraph counting algorithms under LDP, they have primarily concentrated on subgraphs of up to five nodes. The challenge in extending this

Sanjana Das

We consider the local properties problem for difference sets: we define $g(n, k, \ell)$ to be the minimum value of $\lvert A - A\rvert$ over all $n$-element sets $A \subseteq \mathbb{R}$ with the `local property' that $\lvert A' - A'\rvert \geq \ell$ for all $k$-element subsets $A' \subseteq A$. We view $k$ and $\ell$ as fixed, and study the asymptotic behav

Pierre Colombo, Victor Pellegrain, Malik Boudiaf, Victor Storchan

Proprietary and closed APIs are becoming increasingly common to process natural language, and are impacting the practical applications of natural language processing, including few-shot classification. Few-shot classification involves training a model to perform a new classification task with a handful of labeled data. This paper presents three contributions

Fei Li, Hao Nie, Yu Zhao, Zhihe Zhao

Magnetic skyrmions offer promising prospects for constructing future energy-efficient and high-density information technology, leading to extensive explorations of new skyrmionic materials recently. The topological Hall effect has been widely adopted as a distinctive marker of skyrmion emergence. Alternately, here we propose a novel signature of skyrmion sta

Mohammad Hossein Khojasteh, Najmeh Torabian, Ali Farjami, Saeid Hosseini

Link prediction is an important task in addressing the incompleteness problem of knowledge graphs (KG). Previous link prediction models suffer from issues related to either performance or explanatory capability. Furthermore, models that are capable of generating explanations, often struggle with erroneous paths or reasoning leading to the correct answer. To

Yaoyiran Li, Anna Korhonen, Ivan Vulić

Bilingual Lexicon Induction (BLI) is a core task in multilingual NLP that still, to a large extent, relies on calculating cross-lingual word representations. Inspired by the global paradigm shift in NLP towards Large Language Models (LLMs), we examine the potential of the latest generation of LLMs for the development of bilingual lexicons. We ask the followi

Ian Smith, Janosch Ortmann, Farnoosh Abbas-Aghababazadeh, Petr Smirnov

Cosine similarity is an established similarity metric for computing associations on vectors, and it is commonly used to identify related samples from biological perturbational data. The distribution of cosine similarity changes with the covariance of the data, and this in turn affects the statistical power to identify related signals. The relationship betwee

Luping Xiang, Ke Xu, Jie Hu, Kun Yang

In this paper, we propose a green beamforming design for the integrated sensing and communication (ISAC) system, using beam-matching error to assess radar performance. The beam-matching error metric, which considers the mean square error between the desired and designed beam patterns, provides a more practical evaluation approach. To tackle the non-convex ch

Sébastien Huot, Abbas Edalat

We study $n$-agent Bayesian Games with $m$-dimensional vector types and linear payoffs, also called Linear Multidimensional Bayesian Games. This class of games is equivalent with $n$-agent, $m$-game Uniform Multigames. We distinguish between games that have a discrete type space and those with a continuous type space. More specifically, we are interested in

Ethungshan Shitiri, Ho-Shin Cho

Concentration shift keying (CSK) is a widely studied modulation technique for molecular communication-based nanonetworks, which is a key enabler for the Internet of Bio-NanoThings (IoBNT). Existing CSK methods, while offering optimal error performance, suffer from increased operational complexity that scales poorly as the number of transmitters, $K$, grows.

Pierre Colombo, Nathan Noiry, Guillaume Staerman, Pablo Piantanida

One of the pursued objectives of deep learning is to provide tools that learn abstract representations of reality from the observation of multiple contextual situations. More precisely, one wishes to extract disentangled representations which are (i) low dimensional and (ii) whose components are independent and correspond to concepts capturing the essence of

Asid Ur Rehman, Vassilis Glenis, Elizabeth Lewis, Chris Kilsby

This study aims to find a cost-effective Blue-Green Infrastructure placement scheme by developing an improved approach called the Cost Optimization Framework for Implementing blue-Green infrastructure (CONFIGURE). The optimisation framework integrates a detailed hydrodynamic flood simulation model with a multi-objective optimisation algorithm (Non-dominated

Yingchao Liu, Jinlong Ren, Decheng Kong, Guangcun Shan

Edge effect is one of the detrimental factors preventing superlubricity in laminar solid lubricants. Separating the friction contribution from the edge atom and inner atom is of paramount importance for rational design of ultralow friction across scales in van der Waals heterostructures. To decouple these contributions and provide the underlying microscopic

Tom Kocmi, Christian Federmann

This paper introduces GEMBA-MQM, a GPT-based evaluation metric designed to detect translation quality errors, specifically for the quality estimation setting without the need for human reference translations. Based on the power of large language models (LLM), GEMBA-MQM employs a fixed three-shot prompting technique, querying the GPT-4 model to mark error qua

Antonio Lanteri, Carla Novelli

Let $Y$ be a smooth projective variety of dimension $n \geq 2$ endowed with a finite morphism $\phi:Y \to \mathbb P^n$ of degree $3$, and suppose that $Y$, polarized by some ample line bundle, is a scroll over a smooth variety $X$ of dimension $m$. Then $n \leq 3$ and either $m=1$ or $2$. When $m=1$, a complete description of the few varieties $Y$ satisfying

Mukhtar Lawan Adam, Ibrahim Buba Garba, Sulaiman Muhammad Gana, Bala Ismail Adamu

TlTaSe2 is a non-centrosymmetric quasi-2D crystal semi-metal hosting nodal-line topological features protected by mirror-reflection symmetry. Here, we investigated the superconducting properties of TlTaSe2 using the first-principles anisotropic Migdal-Eliashberg theory. The Fermi surface hosts well gapped multiband features contributed by the Ta 5d and Tl 6p

Vibhor Agarwal, Yu Chen, Nishanth Sastry

Hate speech has become pervasive in today's digital age. Although there has been considerable research to detect hate speech or generate counter speech to combat hateful views, these approaches still cannot completely eliminate the potential harmful societal consequences of hate speech -- hate speech, even when detected, can often not be taken down or is oft

Luping Xiang, Ke Xu, Jie Hu, Christos Masouros

This paper proposes a novel non-orthogonal multiple access (NOMA)-assisted orthogonal time-frequency space (OTFS)-integrated sensing and communication (ISAC) network, which uses unmanned aerial vehicles (UAVs) as air base stations to support multiple users. By employing ISAC, the UAV extracts position and velocity information from the user's echo signals, an

Takatoshi Hirano, Ryuya Namba

The Bernstein operator is known as a typical example of positive linear operators which uniformly approximates continuous functions on $[0, 1]$. In the present paper, we introduce a multidimensional extension of the Bernstein operator which is associated with a transition probability of a certain discrete Markov chain. In particular, we show that the iterate

D. Bockelee-Morvan, E. Lellouch, O. Poch, E. Quirico

JWST NIRSpec IFU (2.9-5.3 mu) and MIRI MRS (4.9-28.5 mu) observations were performed on both the leading and trailing hemispheres of Ganymede with a spectral resolution of ~2700. Reflectance spectra show signatures of water ice, CO2 and H2O2. An absorption feature at 5.9 mu is revealed and is tentatively assigned to sulfuric acid hydrates. The CO2 4.26-mu ba

Peichun Li, Hanwen Zhang, Yuan Wu, Liping Qian

Distributed Artificial Intelligence (AI) model training over mobile edge networks encounters significant challenges due to the data and resource heterogeneity of edge devices. The former hampers the convergence rate of the global model, while the latter diminishes the devices' resource utilization efficiency. In this paper, we propose a generative AI-empower

Krassimira Vlachkova

It is known that B\'{e}zier curves and surfaces may have multiple representations by different control polygons. The polygons may have different number of control points and may even be disjoint. Up to our knowledge, Pekerman et al. (2005) were the first to address the problem of testing two parametric polynomial curves for coincidence. Their approach is bas

Dimitra Eleftheriou, Thomas Piper, Mario Thevis, Tereza Neocleous

Biomarker analysis of athletes' urinary steroid profiles is crucial for the success of anti-doping efforts. Current statistical analysis methods generate personalised limits for each athlete based on univariate modelling of longitudinal biomarker values from the urinary steroid profile. However, simultaneous modelling of multiple biomarkers has the potential

Yu. A. Lashko, V. S. Vasilevsky, V. I. Zhaba

We present a many-channel microscopic model that extends the three-cluster model previously formulated in \cite{2009NPA...V37}. This extended model incorporates multiple three-cluster configurations, which are subsequently reduced to a comprehensive set of binary channels. These channels dictate the dynamics of various nuclear processes and the resonance str

J. F. H. Buist, B. Sanderse, S. Dubinkina, C. W. Oosterlee

In this paper we present a complete framework for the energy-stable simulation of stratified incompressible flow in channels, using the one-dimensional two-fluid model. Building on earlier energy-conserving work on the basic two-fluid model, our new framework includes diffusion, friction, and surface tension. We show that surface tension can be added in an e

Francesco Alesiani, Shujian Yu, Mathias Niepert

Empirical risk minimization can lead to poor generalization behavior on unseen environments if the learned model does not capture invariant feature representations. Invariant risk minimization (IRM) is a recent proposal for discovering environment-invariant representations. IRM was introduced by Arjovsky et al. (2019) and extended by Ahuja et al. (2020). IRM

Chetan Arora, John Grundy, Mohamed Abdelrazek

Requirements Engineering (RE) is a critical phase in software development including the elicitation, analysis, specification, and validation of software requirements. Despite the importance of RE, it remains a challenging process due to the complexities of communication, uncertainty in the early stages and inadequate automation support. In recent years, larg

Hao Ran, Yang Bai

Bayes additive regression trees(BART) is a nonparametric regression model which has gained wide-spread popularity in recent years due to its flexibility and high accuracy of estimation. Soft BART,one variation of BART,improves both practically and heoretically on existing Bayesian sum-of-trees models. One bottleneck for Soft BART is its slow speed in the lon

Yassine El Kheir, Ahmed Ali, Shammur Absar Chowdhury

Pronunciation assessment and its application in computer-aided pronunciation training (CAPT) have seen impressive progress in recent years. With the rapid growth in language processing and deep learning over the past few years, there is a need for an updated review. In this paper, we review methods employed in pronunciation assessment for both phonemic and p

Fadoua Balabdaoui, Alexander Henzi, Lukas Looser

The distributional single index model is a semiparametric regression model in which the conditional distribution functions $P(Y \leq y | X = x) = F_0(\theta_0(x), y)$ of a real-valued outcome variable $Y$ depend on $d$-dimensional covariates $X$ through a univariate, parametric index function $\theta_0(x)$, and increase stochastically as $\theta_0(x)$ increa

F. Flandoli, S. Galatolo, P. Giulietti, S. Vaienti

We investigate the distribution and multiple occurrences of extreme events stochastic processes constructed by sampling the solution of a Stochastic Differential Equation on $\mathbb{R}^n$. We do so by studying the action of an annealead transfer operators on ad-hoc spaces of probability densities. The spectral properties of such operators are obtained by em

Aekta Aggarwal, Veerappa Gowda G. D., Sudarshan Kumar K

A well-balanced second-order finite volume scheme is proposed and analyzed for a 2 X 2 system of non-linear partial differential equations which describes the dynamics of growing sandpiles created by a vertical source on a flat, bounded rectangular table in multiple dimensions. To derive a second-order scheme, we combine a MUSCL type spatial reconstruction w

D. Naito, N. Yamamoto, T. Takahashi, A. Motomura

In 2023, we are replacing the LLRF system for the KEK-PF 2.5 GeV ring. The new system is composed of digital boards such as eRTM, AMC, and {\mu}RTM, based on the MTCA.4 standard. In our system, we adopted the non-IQ direct sampling method for RF detection. We set the sampling frequency at 8/13 (307.75 MHz) of the RF frequency, where the denominator (13) is t

Yue Chao, Lei Huang, Xuejun Ma

With the availability of extraordinarily huge data sets, solving the problems of distributed statistical methodology and computing for such data sets has become increasingly crucial in the big data area. In this paper, we focus on the distributed sparse penalized linear log-contrast model in massive compositional data. In particular, two distributed optimiza

F. Minotti, G. Modanese

The extended Aharonov-Bohm electrodynamics has a simple formal structure and allows to couple the e.m. field also to currents which are not locally conserved, like those resulting from certain non-local effective quantum models of condensed matter. As it often happens in physics and mathematics when one tries to extend the validity of some equations or opera

Sixiu Liu, Claire Birnie, Andrey Bakulin, Ali Dawood

In recent years, self-supervised procedures have advanced the field of seismic noise attenuation, due to not requiring a massive amount of clean labeled data in the training stage, an unobtainable requirement for seismic data. However, current self-supervised methods usually suppress simple noise types, such as random and trace-wise noise, instead of the com

Zenin Easa Panthakkalakath, Juraj Kardoš, Olaf Schenk

The boundary control problem is a non-convex optimization and control problem in many scientific domains, including fluid mechanics, structural engineering, and heat transfer optimization. The aim is to find the optimal values for the domain boundaries such that the enclosed domain adhering to the governing equations attains the desired state values. Traditi

Chao Wang, Caixing Wang, Xin He, Xingdong Feng

In recent years, transfer learning has garnered significant attention. Its ability to leverage knowledge from related studies to improve generalization performance in a target study has made it highly appealing. This paper focuses on investigating the transfer learning problem within the context of nonparametric regression over a reproducing kernel Hilbert s

Azlaan Ranjha, Laiba Jabbar, Osaid Ahmed

This study describes the creation of a wireless, transportable Holter monitor to improve the accuracy of cardiac disease diagnosis. The main goal of this study is to develop a low-cost cardiac screening system suited explicitly for underprivileged areas, addressing the rising rates of cardiovascular death. The suggested system includes a wireless Electrocard

Natalia P. Bondarenko

In this paper, we derive sharp asymptotics for the spectral data (eigenvalues and weight numbers) of the fourth-order linear differential equation with a distribution coefficient and three types of separated boundary conditions. Our methods rely on the recent results concerning regularization and asymptotic analysis for higher-order differential operators wi

Areti Kotsi, Evangelos Mitsakis

This paper aims to provide insights related to the impact assessment and evaluation results from the use of CITS services in the Greek pilot of the CRoads Greece project, i.e., Attica Tollway and Egnatia Odos Tollway. The impact assessment and evaluation of the CITS services includes aspects related to user acceptance, real world pilot logs collected from th

XueGuang Zhang

In this Letter, we report a potential candidate of recoiling supermassive black hole (rSMBH) in SDSS J1619 based on similar velocity shifts and line widths of the blue-shifted broad components in H$\alpha$ and [O~{\sc iii}] doublet. The measured line width ratio between blue-shifted broad H$\alpha$ and broad [O~{\sc iii}] line is 1.06, if compared with commo

Abhishek Grewal, Christopher C. Leon, Klaus Kuhnke, Klaus Kern

We report on scanning tunneling microscopy (STM) topographs of individual metal phthalocyanines (MPc) on a thin salt (NaCl) film on a gold substrate, at tunneling energies within the molecule's electronic transport gap. Theoretical models of increasing complexity are discussed. The calculations for MPcs adsorbed on a thin NaCl layer on Au(111) demonstrate th

Osama Bsher, Ameer Sabri

Morality is a fundamental aspect of human behavior and ethics, influencing how we interact with each other and the world around us. When faced with a moral dilemma, a person's ability to make clear moral judgments can be clouded. Due to many factors such as personal biases, emotions and situational factors people can find it difficult to decide their best co

Young-Suk Lee, Md Arafat Sultan, Yousef El-Kurdi, Tahira Naseem Asim Munawar

Using in-context learning (ICL) for data generation, techniques such as Self-Instruct (Wang et al., 2023) or the follow-up Alpaca (Taori et al., 2023) can train strong conversational agents with only a small amount of human supervision. One limitation of these approaches is that they resort to very large language models (around 175B parameters) that are also

Zesheng Zhu, Lifeng Wang, Xin Wang, Bo Tan

Cell-free network architecture plays a promising role in the terahertz (THz) networks since it provides better link reliability and uniformly good services for all the users compared to the co-located massive MIMO counterpart, and the spatial-spectral THz link has the advantages of lower initial access latency and fast beam operations. To this end, this work

Amit Moryossef, Zifan Jiang, Mathias Müller, Sarah Ebling

Sign language segmentation is a crucial task in sign language processing systems. It enables downstream tasks such as sign recognition, transcription, and machine translation. In this work, we consider two kinds of segmentation: segmentation into individual signs and segmentation into phrases, larger units comprising several signs. We propose a novel approac

Xuemin Chi, Jun Zeng, Jihao Huang, Zhitao Liu

We present a fast planning architecture called Hamilton-Jacobi-based bidirectional A* (HJBA*) to solve general tight parking scenarios. The algorithm is a two-layer composed of a high-level HJ-based reachability analysis and a lower-level bidirectional A* search algorithm. In high-level reachability analysis, a backward reachable tube (BRT) concerning vehicl

Chuang Zhao, Hongke Zhao, Hengshu Zhu, Zhenya Huang

Unsupervised domain adaptation aims to transfer rich knowledge from the annotated source domain to the unlabeled target domain with the same label space. One prevalent solution is the bi-discriminator domain adversarial network, which strives to identify target domain samples outside the support of the source domain distribution and enforces their classifica

Jesse Leo Kass

This is a historical article on J. C. Corbin, a nineteenth century mathematician and the founding president of the Historically Black University of Arkansas at Pine Bluff. This version omits the figures that appeared in the published edition. It also includes a lengthier bibliography and a new section ("Added after publication") which addresses some historic

Antonio J Bárcenas-Luque, Wilfried Coenen, Cándido Gutiérrez-Montes, Carlos Martínez-Bazán

We investigate the linear Floquet stability of two fluid layers undergoing oscillations in the direction parallel to the flexible wall that separates them. This canonical configuration is inspired by the cerebrospinal fluid flow in the spinal canal of subjects with hydro-/syringomyelia.The analysis focuses on the marginal conditions for the onset of instabil

Filip P. Adamus, Ashley Stanton-Yonge, Thomas M. Mitchell, David Healy

3D printing technology offers the possibility of producing synthetic samples with accurately defined microstructures. As indicated by effective medium theory (EMT), the shapes, orientations, and sizes of voids significantly affect the overall elastic response of a solid body. By performing uniaxial compression tests on twenty types of 3D-printed samples cont

Yuyan Shi, Yichi Zhang, Shasha Wang

Semi-supervised learning has greatly advanced medical image segmentation since it effectively alleviates the need of acquiring abundant annotations from experts and utilizes unlabeled data which is much easier to acquire. Among existing perturbed consistency learning methods, mean-teacher model serves as a standard baseline for semi-supervised medical image

Eduard-Matei Constantinescu, Mohammed Elhajj, Luca Mariot

This paper presents the Smooth Number Message Authentication Code (SNMAC) for the context of lightweight IoT devices. The proposal is based on the use of smooth numbers in the field of cryptography, and investigates how one can use them to improve the security and performance of various algorithms or security constructs. The literature findings suggest that

Wen Teng, Jiulin Jin

In this paper, we first introduce the notion of a weighted $\mathcal{O}$-operator on Hom-Lie triple systems with respect to an action on another Hom-Lie triple system. Next, we construct a cohomology of weighted $\mathcal{O}$-operator on Hom-Lie triple systems, we use the first cohomology group to classify linear deformations and we investigate the obstructi

Vasiliy Seibert

Several approaches have been presented, which aim to extract models from natural language specifications. These approaches have inherent weaknesses for they assume an initial problem understanding that is perfect, and they leave no room for feedback. Motivated by real-world collaboration settings between requirements engineers and customers, this paper propo

Peter Burgholzer, Johannes Bauer-Marschallinger, Mike Hettich, Markus Haltmeier

In this tutorial, we aim to directly recreate some of our "aha" moments when exploring the impact of heat diffusion on the spatial resolution limit of photothermal imaging. Our objective is also to communicate how this physical limit can nevertheless be overcome and include some concrete technological applications. Describing diffusion as a random walk, one

Li Wang, Xinyu Zhang, Fachuan Zhao, Chuze Wu

Non-maximum suppression (NMS) is an essential post-processing module used in many 3D object detection frameworks to remove overlapping candidate bounding boxes. However, an overreliance on classification scores and difficulties in determining appropriate thresholds can affect the resulting accuracy directly. To address these issues, we introduce fuzzy learni

Ayberk Aydin, Alptekin Temizel

Deep neural networks are known to be vulnerable to adversarial perturbations. The amount of these perturbations are generally quantified using $L_p$ metrics, such as $L_0$, $L_2$ and $L_\infty$. However, even when the measured perturbations are small, they tend to be noticeable by human observers since $L_p$ distance metrics are not representative of human p

Sara Mollaeivaneghi, Julia Barbosa, Florian Steinke

Renewable distributed energy resources (DERs) have the potential to provide multi-location electricity consumers (MLECs) with electricity at prices lower than those offered by the grid using behind-the-meter advantages. This study examines the pricing power of such DER owners in a local environment with few competitors and how it depends on the MLEC's abilit

Paolo Di Lorenzo, Mattia Merluzzi, Francesco Binucci, Claudio Battiloro

Internet of Things (IoT) applications combine sensing, wireless communication, intelligence, and actuation, enabling the interaction among heterogeneous devices that collect and process considerable amounts of data. However, the effectiveness of IoT applications needs to face the limitation of available resources, including spectrum, energy, computing, learn

Xi'an Li, Jinran Wu, Yujia Huang, Zhe Ding

To address the sensitivity of parameters and limited precision for physics-informed extreme learning machines (PIELM) with common activation functions, such as sigmoid, tangent, and Gaussian, in solving high-order partial differential equations (PDEs) relevant to scientific computation and engineering applications, this work develops a Fourier-induced PIELM

Daniel Jampolski, Luciano Rezzolla

Black holes are normally and naturally associated to the end-point of gravitational collapse. Yet, alternatives have been proposed and a particularly interesting one is that of gravitational condensate stars, or gravastars. We here revisit the gravastar model and increase the degree of speculation by considering new solutions that are inspired by the origina

Xin Yang, Wenbo Zhao, Liang Ma, Wencheng Lu

Metal borides have been considered as potential high-temperature superconductors since the discovery of record-holding 39 K superconductivity in bulk MgB2. In this work, we identified a superconducting yet thermodynamically stable F43m Sr2B5 at 40 GPa with a unique covalent sp3-hybridized boron framework through extensive first-principles structure searches.

Yiwen Ding, Krishna Manoorkar, Mattia Panettiere, Apostolos Tzimoulis

In this paper, we generalize modal $\mu$-calculus to the non-distributive (lattice-based) modal $\mu$-calculus and formalize some scenarios regarding categorization using it. We also provide a game semantics for the developed logic. The proof of adequacy of this game semantics proceeds by generalizing the unfolding games on the power-set algebras to the arbi

Peter Burgholzer, Günther Mayr, Gregor Thummerer, Markus Haltmeier

In this tutorial, we aim to directly recreate some of our "aha" moments when exploring the impact of heat diffusion on the spatial resolution limit of photothermal imaging. Our objective is also to communicate how this physical limit can nevertheless be overcome and include some concrete technological applications. Describing diffusion as a random walk, one

Batya Kenig

The graphical structure of Probabilistic Graphical Models (PGMs) represents the conditional independence (CI) relations that hold in the modeled distribution. Every separator in the graph represents a conditional independence relation in the distribution, making them the vehicle through which new conditional independencies are inferred and verified. The noti

W. Zhao, J. Wu

In this paper, the main aim is to consider the mapping properties of the maximal or nonlinear commutator for the fractional maximal operator with the symbols belong to the Lipschitz spaces on variable Lebesgue spaces in the context of stratified Lie group, with the help of which some new characterizations to the Lipschitz spaces and nonnegative Lipschitz fun

Shuo Yuan, Yaohua Sun, Mugen Peng

Software-defined satellite-terrestrial integrated networks (SDSTNs) are seen as a promising paradigm for achieving high resource flexibility and global communication coverage. However, low latency service provisioning is still challenging due to the fast variation of network topology and limited onboard resource at low earth orbit satellites. To address this

Xiaoyu Liu, Yiming Liu, Guangming Pan, Lingyue Zhang

This paper is to study a signal-plus-noise model in high dimensional settings when the dimension and the sample size are comparable. Specifically, we assume that the noise has a general covariance matrix that allows for heteroskedasticity, and that the deterministic signal has the same magnitude as the noise and can have a rank that tends to infinity. We dev

Thomas Claudet, Davide Martire, Damiana Losa, Francesco Sanfedino

Optimizing high-level mission planning constraints is traditionally solved in exponential time and requires to split the problem into several ones, making the connections between them a convoluted task. This paper aims at generalizing recent works on the convexification of Signal Temporal Logic (STL) constraints converting them into linear approximations. Gr

Laura Boca de Giuli, Alessio La Bella, Riccardo Scattolini

This paper addresses the data-based modelling and optimal control of District Heating Systems (DHSs). Physical models of such large-scale networked systems are governed by complex nonlinear equations that require a large amount of parameters, leading to potential computational issues in optimizing their operation. A novel methodology is hence proposed, explo

J. W. Zhao, B. -H. Sun, I. Tanihata, S. Terashima

We present the charge-changing cross sections (CCCS) of $^{11-15}$C, $^{13-17}$N, and $^{15,17-18}$O at around 300 MeV/nucleon on a carbon target, which extends to $p$-shell isotopes with $N < Z$ for the first time. The Glauber model, which considers only the proton distribution of projectile nuclei, underestimates the cross sections by more than 10\%. We sh

Chao Wang, Alessandro Finamore, Pietro Michiardi, Massimo Gallo

Data Augmentation (DA)-augmenting training data with synthetic samples-is wildly adopted in Computer Vision (CV) to improve models performance. Conversely, DA has not been yet popularized in networking use cases, including Traffic Classification (TC). In this work, we present a preliminary study of 14 hand-crafted DAs applied on the MIRAGE19 dataset. Our res

Nengqun Li, Yuming Liu

We give a unified generalization of Dugas' construction on stable auto-equivalences of Morita type from local symmetric algebras to arbitrary symmetric algebras. For group algebras $kP$ of $p$-groups in characteristic $p$, we recover all the stable auto-equivalences corresponding to endo-trivial modules over $kP$ except that $P$ is generalized quaternion of

Wencai Yan, Wanming Hao, Gangcan Sun, Chongwen Huang

In this paper, we consider the simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS)-assisted THz communications with three-side beam split. Except for the beam split at the base station (BS), we analyze the double-side beam split at the STAR-RIS for the first time. To relieve the double-side beam split effect, we propose a

Hongjiang Lei, Jiacheng Jiang, Imran Shafique Ansari, Gaofeng Pan

Unmanned aerial vehicles (UAVs) can provide wireless access to terrestrial users, regardless of geographical constraints, and will be an important part of future communication systems. In this paper, a multi-user downlink dual-UAVs enabled covert communication system was investigated, in which a UAV transmits secure information to ground users in the presenc

Hongjiang Lei, Jiacheng Jiang, Haosi Yang, Ki-Hong Park

Unmanned aerial vehicles (UAVs) can provide wireless access services to terrestrial users without geographical limitations and will become an essential part of the future communication system. However, the openness of wireless channels and the mobility of UAVs make the security of UAV-based communication systems particularly challenging. This work investigat

Abdelrahman Ramzy

In this paper, we show that if the numbers in the range $[1,2^n]$ satisfy Collatz conjecture, then almost all integers in the range $[2^n+1,2^{n+1}]$ will satisfy the conjecture as $n \to \infty$. The previous statement is equivalent to claiming that almost all integers in $[2^n+1,2^{n+1}]$ will iterate to a number less than $2^n$. This actually has been pro

Feiyu Zhao, Ruxi Liang, Zepei Yang, Huanyuan Shan

We calculate the reflection of diffuse galactic emission by meteor trails and investigate its potential relationship to Meteor Radio Afterglow (MRA). The formula to calculate the reflection of diffuse galactic emission is derived from a simplified case, assuming that the signals are mirrored by the cylindrical over-dense ionization trail of meteors. The over

Weichao Yao, Mohsen Khalili, Yicheng Wang, Martin Hoffmann

We report on a GHz fundamental repetition rate Kerr-lens mode-locked Ho:CALGO laser emitting at 2.1 um. The laser employs a ring-cavity to increase the fundamental repetition rate to 1.179 GHz and can be made to oscillate in both directions stably with nearly identical performance: for counterclockwise oscillation, it generates 93-fs pulses at 1.68 W of aver

Florian Pausinger

We study the expected $\mathcal{L}_2$-discrepancy of stratified samples generated from special equi-volume partitions of the unit square. The partitions are defined via parallel lines that are all orthogonal to the diagonal of the square. It is shown that the expected discrepancy of stratified samples derived from these partitions is a factor 2 smaller than

Kang Liu, Mengzhao Li, Junhua Zhang, Weiyi Sun

AC-coupled Low Gain Avalanche Detector (AC-LGAD) is a new precise detector technology developed in recent years. Based on the standard Low Gain Avalanche Detector (LGAD) technology, AC-LGAD sensors can provide excellent timing performance and spatial resolution. This paper presents the design and performance of several prototype AC-LGAD strip sensors for the

Yongjing Hao, Pengpeng Zhao, Junhua Fang, Jianfeng Qu

Sequential Recommendation (SR) has received increasing attention due to its ability to capture user dynamic preferences. Recently, Contrastive Learning (CL) provides an effective approach for sequential recommendation by learning invariance from different views of an input. However, most existing data or model augmentation methods may destroy semantic sequen

Chumki Nayak, Suvadip Masanta, Sukanya Ghosh, Shubhadip Moulick

Monolayer transition metal dichalcogenides (TMDCs) constitute the core group of materials in the emerging semiconductor technology of valleytronics. While the coupled spin-valley physics of pristine TMDC materials and their heterstructures has been extensively investigated, less attention was given to TMDC alloys, which could be useful in optoelectronic appl

Jianing Zhu, Geng Yu, Jiangchao Yao, Tongliang Liu

Out-of-distribution (OOD) detection is important for deploying reliable machine learning models on real-world applications. Recent advances in outlier exposure have shown promising results on OOD detection via fine-tuning model with informatively sampled auxiliary outliers. However, previous methods assume that the collected outliers can be sufficiently larg

Yuping Wang, Jier Chen

In autonomous driving, deep learning enabled motion prediction is a popular topic. A critical gap in traditional motion prediction methodologies lies in ensuring equivariance under Euclidean geometric transformations and maintaining invariant interaction relationships. This research introduces a groundbreaking solution by employing EqMotion, a theoretically

Jiayi Xie, Shang Liu, Gao Cong, Zhenzhong Chen

In this work, we propose a Unified framework of Sequential Search and Recommendation (UnifiedSSR) for joint learning of user behavior history in both search and recommendation scenarios. Specifically, we consider user-interacted products in the recommendation scenario, user-interacted products and user-issued queries in the search scenario as three distinct

Xuehai Huang, Chao Zhang, Yaqian Zhou, Yangxing Zhu

New low-order $H(\textrm{div})$-conforming finite elements for symmetric tensors are constructed in arbitrary dimension. The space of shape functions is defined by enriching the symmetric quadratic polynomial space with the $(d+1)$-order normal-normal face bubble space. The reduced counterpart has only $d(d+1)^2$ degrees of freedom. Basis functions are expli

Pengfei Li, Zhibo Zhang, Ameena S. Al-Sumaiti, Naoufel Werghi

Metaverse is trending to create a digital circumstance that can transfer the real world to an online platform supported by large quantities of real-time interactions. Pre-trained Artificial Intelligence (AI) models are demonstrating their increasing capability in aiding the metaverse to achieve an excellent response with negligible delay, and nowadays, many

Sina Salimian, Nicklas Anttu

Metal halide perovskite nanowires (PrvNWs) have recently emerged as an interesting path for nanostructured solar cells. Here, we model the absorption of light in PrvNW arrays for varying diameter and length of the PrvNWs and period for the array by solving the Maxwell equations. For long enough bare PrvNW arrays, we find that the optimum diameter is fixed to

Xinyang Lua, M. C. van Beurdenb, Qingbiao Wua

We utilize the domain integral equation formulation to simulate two-dimensional transverse electric scattering in a homogeneous medium and a summation of modulated Gaussian functions to approximate the dual Gabor window. Then we apply Ewald Green function transformation to separate the integrals related to x and z in the integral equation, which produce Gaus

Koushik Mandal, Chandrashekar Radhakrishnan, M. V. Satyanarayana

The effects of squeezed photons and thermal photons on the entanglement dynamics of atom-atom, atom-field and field-field subsystems are studied for the double Jaynes-Cummings model. For this purpose, squeezed coherent states and Glauber-Lachs states of radiation are chosen as field states. For the atomic states, we choose one of the Bell state as pure state

Gangcan Sun, Wencai Yan, Wanming Hao, Chongwen Huang

In this paper, we investigate the reconfigurable intelligent surface (RIS)-aided terahertz (THz) communication system with the sparse radio frequency chains antenna structure at the base station (BS). To overcome the beam split of the BS, different from the conventional single-layer true-time-delay (TTD) scheme, we propose a double-layer TTD scheme that can

William Yik, Maike Sonnewald, Mariana C. A. Clare, Redouane Lguensat

Complex ocean systems such as the Antarctic Circumpolar Current play key roles in the climate, and current models predict shifts in their strength and area under climate change. However, the physical processes underlying these changes are not well understood, in part due to the difficulty of characterizing and tracking changes in ocean physics in complex mod

Karina Vida, Judith Simon, Anne Lauscher

With language technology increasingly affecting individuals' lives, many recent works have investigated the ethical aspects of NLP. Among other topics, researchers focused on the notion of morality, investigating, for example, which moral judgements language models make. However, there has been little to no discussion of the terminology and the theories unde

Zidan Wang, Takeru Oba, Takuma Yoneda, Rui Shen

Learning from demonstrations (LfD) has successfully trained robots to exhibit remarkable generalization capabilities. However, many powerful imitation techniques do not prioritize the feasibility of the robot behaviors they generate. In this work, we explore the feasibility of plans produced by LfD. As in prior work, we employ a temporal diffusion model with

Lihang Liu, Shanzhuo Zhang, Donglong He, Xianbin Ye

Protein-ligand structure prediction is an essential task in drug discovery, predicting the binding interactions between small molecules (ligands) and target proteins (receptors). Recent advances have incorporated deep learning techniques to improve the accuracy of protein-ligand structure prediction. Nevertheless, the experimental validation of docking confo