November 2022 arXiv papers — page 45

Hailong Shu

Short-term wind speed prediction is essential for economical wind power utilization. The real-world wind speed data is typically intermittent and fluctuating, presenting great challenges to existing shallow models. In this paper, we present a novel deep hybrid model for multi-step wind speed prediction, namely LR-FFT-RP-MLP/LSTM (Linear Fast Fourier Transfor

Jason Kaye, Sophie Beck, Alex Barnett, Lorenzo Van Muñoz

We present efficient methods for Brillouin zone integration with a non-zero but possibly very small broadening factor $\eta$, focusing on cases in which downfolded Hamiltonians can be evaluated efficiently using Wannier interpolation. We describe robust, high-order accurate algorithms automating convergence to a user-specified error tolerance $\varepsilon$,

Jin Lan, Hanxu Ai

The interplay between spin wave and magnetic texture represents the information exchange between the fast and slow dynamical parts of magnetic systems. Here we formulate a set of magnonic torques acting on background magnetic texture, by extracting time-invariant information from the fast precessing spin waves. Under the frame of magnonic torques, we use the

Zhong-Ying Fan

In this work, we develop a universal picture from topology in the thermodynamic parameters space to describe first order phase transitions of black holes. By employing an off-shell internal energy, we find two types of topological defects. The first is normal, describing black holes which move with a nonzero velocity and acceleration, in a central force fiel

Haoqing Luo, sheng jin

The prevailing reinforcement-learning-based traffic signal control methods are typically staging-optimizable or duration-optimizable, depending on the action spaces. In this paper, we propose a novel control architecture, TBO, which is based on hybrid proximal policy optimization. To the best of our knowledge, TBO is the first RL-based algorithm to implement

Weijie Yuan, Shuangyang Li, Zhiqiang Wei, Yuanhao Cui

In the 6G era, space-air-Ground integrated networks (SAGIN) are anticipated to deliver global coverage, necessitating support for a diverse array of emerging applications in high-mobility, hostile environments. Under such conditions, conventional orthogonal frequency division multiplexing (OFDM) modulation, widely employed in cellular and Wi-Fi communication

Yassine Hamoudi, Qipeng Liu, Makrand Sinha

Collision-resistant hashing, a fundamental primitive in modern cryptography, ensures that there is no efficient way to find distinct inputs that produce the same hash value. This property underpins the security of various cryptographic applications, making it crucial to understand its complexity. The complexity of this problem is well-understood in the class

Sara Pollock, Leo G. Rebholz

This work introduces, analyzes and demonstrates an efficient and theoretically sound filtering strategy to ensure the condition of the least-squares problem solved at each iteration of Anderson acceleration. The filtering strategy consists of two steps: the first controls the length disparity between columns of the least-squares matrix, and the second enforc

Olga B. Sapir, Mikhail V. Volkov

We show that the 42-element monoid of all partial order preserving and extensive injections on the 4-element chain is not contained in any variety generated by a finitely based finite $\mathcal{R}$-trivial semigroup. This provides unified proofs for several known facts and leads to a bunch of new results on the Finite Basis Problem for finite $\mathcal{R}$-

P. Jovanović, V. Borka Jovanović, D. Borka, A. F. Zakharov

In this paper we investigate a Yukawa gravity modification of the Newtonian gravitational potential in a weak field approximation. For that purpose we derived the corresponding equations of motion and used them to perform two-body simulations of the stellar orbits. In 2020 the GRAVITY Collaboration detected the orbital precession of the S2 star around the su

Soumya Jahagirdar, Shankar Gangisetty, Anand Mishra

We present a novel problem of text-based visual question generation or TextVQG in short. Given the recent growing interest of the document image analysis community in combining text understanding with conversational artificial intelligence, e.g., text-based visual question answering, TextVQG becomes an important task. TextVQG aims to generate a natural langu

Xiaoyuan Liu, George W. A. Constable, Jonathan W. Pitchford

Complex system stability can be studied via linear stability analysis using Random Matrix Theory (RMT) or via feasibility (requiring positive equilibrium abundances). Both approaches highlight the importance of interaction structure. Here we show, analytically and numerically, how RMT and feasibility approaches can be complementary. In generalised Lotka-Volt

A. Baillod, J. Loizu, Z. Qu, H. P. Arbez

While it is important to design stellarators with high magneto-hydrodynamic (MHD) stability $\beta$-limit, it is also crucial to ensure that good magnetic surfaces exist in a large range of $\beta$ values. As $\beta$ increases, pressure-driven currents perturb the vacuum magnetic field and often lead to the emergence of magnetic field line chaos, which can w

Zihao Wang, Junli Wang, Changjun Jiang

The task of visual dialog requires a multimodal chatbot to answer sequential questions from humans about image content. Prior work performs the standard likelihood training for answer generation on the positive instances (involving correct answers). However, the likelihood objective often leads to frequent and dull outputs and fails to exploit the useful kno

Ashish Narang, Subhendra Mohanty, Soumya Jana

The rate of energy loss and orbital period decay of quasi-stable compact binary systems are derived in $f(R)$ theory of gravity using the method of a single vertex graviton emission process from a classical source. After linearising the $f(R)$ action written in an equivalent scalar-tensor format in the Einstein frame, we identify the appropriate interaction

Simon Bertrand, Nadia Tawbi, Josée Desharnais

Insider threats are a growing concern for organizations due to the amount of damage that their members can inflict by combining their privileged access and domain knowledge. Nonetheless, the detection of such threats is challenging, precisely because of the ability of the authorized personnel to easily conduct malicious actions and because of the immense siz

L. S. Ferreira, L. N. Jorge, C. J. DaSilva, A. A. Caparica

Boltzmann's microcanonical entropy is the link between statistical physics and thermodynamics, forasmuch as the behavior of any thermodynamic quantity is directly related to the number of microscopic configurations. Accordingly, in this work, we investigate the behavior of the logarithm of the density of states of the three-state Potts model with an external

Sercan Çıkıntoğlu, K. Yavuz Ekşi

Some observations and numerical simulations of disc-magnetosphere interaction show that accretion can proceed in the propeller regime. When the Alfv\'en radius is beyond the corotation radius, matter climbs up to the high latitudes where the Alfv\'en surface is inside the equilibrium surface and can accrete. We calculate the fraction of the mass flux in the

Syed Muhammad Anwar, Abhijeet Parida, Sara Atito, Muhammad Awais

Chest X-rays (CXRs) are a widely used imaging modality for the diagnosis and prognosis of lung disease. The image analysis tasks vary. Examples include pathology detection and lung segmentation. There is a large body of work where machine learning algorithms are developed for specific tasks. A significant recent example is Coronavirus disease (covid-19) dete

Mengyao Chen, Lun Guo, Qi Li

This paper deals with a coupled Hartree system with Hardy-Littlewood-Sobolev critical exponent \begin{equation*} \begin{cases} -\Delta u+(V_1(x)+\lambda_1)u=\mu_1(|x|^{-4}*u^{2})u+\beta (|x|^{-4}*v^{2})u, \ \ &x\in R^N, -\Delta v+(V_2(x)+\lambda_2)v=\mu_2(|x|^{-4}*v^{2})v+\beta (|x|^{-4}*u^{2})v, \ \ &x\in R^N, \end{cases} \end{equation*} where $N\geq 5$, $\

Huiyue Yi, Wuxiong Zhang, Hui Xu

The subspace-based techniques are widely utilized in various scientific fields, and they need accurate estimation of the signal subspace dimension. The classic RMT estimator for model order estimation based on random matrix theory assumes that the noise is white Gaussian, and performs poorly in the presence of colored noise with unknown covariance matrix. In

Minghao Xu, Yuanfan Guo, Yi Xu, Jian Tang

Modeling spatial relationship in the data remains critical across many different tasks, such as image classification, semantic segmentation and protein structure understanding. Previous works often use a unified solution like relative positional encoding. However, there exists different kinds of spatial relations, including short-range, medium-range and long

Samira Boddin, Felix Rörentrop, Dorothee Knees, Jörn Mosler

The modeling of cracks has been an intensely researched topic for decades - both from the mechanical as well as from the mathematics point of view. As far as the modeling of sharp cracks/interfaces is concerned, the resulting free boundary problem is numerically very challenging. For this reason, diffuse approximations in the sense of phase-field theories ha

Amit Pawbake, Thomas Pelini, Nathan P. Wilson, Kseniia Mosina

Magnetic excitations in layered magnetic materials that can be thinned down the two-dimensional (2D) monolayer limit are of high interest from a fundamental point of view and for applications perspectives. Raman scattering has played a crucial role in exploring the properties of magnetic layered materials and, even-though it is essentially a probe of lattice

Prateek Chawla, Adithi Ajith, C. M. Chandrashekar

The utilization of quantum entanglement as a cryptographic resource has superseded conventional approaches to secure communication. Security and fidelity of intranetwork communication between quantum devices is the backbone of a quantum network. This work presents an algorithm that generates entanglement between any two directly connected nodes of a quantum

Gaurang Parkar, Dibyendu Bala, Olaf Kaczmarek, Rasmus Larsen

We present results on the in-medium interactions of static quark anti-quark pairs using realistic 2+1 HISQ flavor lattice QCD. Focus is put on the extraction of spectral information from Wilson line correlators in Coulomb gauge using four complementary methods. Our results indicate that on HISQ lattices, the position of the dominant spectral peak associated

Dana Bartošová, Mirna Džamonja, Rehana Patel, Lynn Scow

We develop a transfer principle of structural Ramsey theory from finite structures to ultraproducts. We show that under certain mild conditions, when a class of finite structures has finite small Ramsey degrees, under the (Generalized) Continuum Hypothesis the ultraproduct has finite big Ramsey degrees for internal colorings. The necessity of restricting to

Sihao Liu, Augustine N Mavor-Parker, Caswell Barry

We aimed to explore the capability of deep learning to approximate the function instantiated by biological neural circuits-the functional connectome. Using deep neural networks, we performed supervised learning with firing rate observations drawn from synthetically constructed neural circuits, as well as from an empirically supported Boundary Vector Cell-Pla

Michael Freund, Rene Dorsch, Andreas Harth

We apply the Web of Things (WoT) communication pattern, i.e., the semantic description of metadata and interaction affordances, to Internet of Things (IoT) devices that rely on non-IP-based protocols, using Bluetooth Low Energy (LE) as an example. The reference implementation of the WoT Scripting API, node-wot, currently supports only IP-based application la

Nianhui Guo, Joseph Bethge, Christoph Meinel, Haojin Yang

Binary neural networks are the extreme case of network quantization, which has long been thought of as a potential edge machine learning solution. However, the significant accuracy gap to the full-precision counterparts restricts their creative potential for mobile applications. In this work, we revisit the potential of binary neural networks and focus on a

Bikram Ghosh, Saugata Mitra

In this present work, we have studied the traversable wormhole geometries in $f(R,T)$ gravity theory, where $R$ denotes the Ricci scalar and $T$ is the trace of the energy-momentum tensor. Firstly, two new shape functions are obtained for some assumed generating function. Also, some new generating functions are obtained in wormhole geometry for some well kno

Arsh Verma, Makarand Tapaswi

Chest radiograph (or Chest X-Ray, CXR) is a popular medical imaging modality that is used by radiologists across the world to diagnose heart or lung conditions. Over the last decade, Convolutional Neural Networks (CNN), have seen success in identifying pathologies in CXR images. Typically, these CNNs are pretrained on the standard ImageNet classification tas

Niclas Schroeter, Francisco Cruz, Stefan Wermter

With the increasing presence of robotic systems and human-robot environments in today's society, understanding the reasoning behind actions taken by a robot is becoming more important. To increase this understanding, users are provided with explanations as to why a specific action was taken. Among other effects, these explanations improve the trust of users

Gonzalo Contreras-Aso, Regino Criado, Miguel Romance

The significance of the PageRank algorithm in shaping the modern Internet cannot be overstated, and its Complex Network theory foundations continue to be a subject of research. In this article we carry out a systematic study of the structural and parametric controllability of PageRank's outcomes, translating a spectral Graph Theory problem into a geometric o

Y. Zhou, D. Hu, X. Wang, M. Shao

The cooling storage ring (CSR) external-target experiment (CEE) is a spectrometer running at the Heavy Ion Research Facility (HIRFL) at Lanzhou. The CEE is the first large-scale nuclear physics experimental device by China to operate in the fixed-target mode with an energy of 1 GeV. The purpose of the CEE is to study the properties of dense nuclear matter. C

Rami Ayoush, Michał Wojciechowski

In this article we provide lower bounds for the lower Hausdorff dimension of finite measures assuming certain restrictions on their quaternionic spherical harmonics expansion. This estimate is an analog of a result previously obtained by the authors for the complex spheres.

Nakul Sharma, Abhirama S. Penamakuri, Anand Mishra

In this paper, we study the problem of identifying logos of business brands in natural scenes in an open-set one-shot setting. This problem setup is significantly more challenging than traditionally-studied 'closed-set' and 'large-scale training samples per category' logo recognition settings. We propose a novel multi-view textual-visual encoding framework t

Yaroslav Shopa, Maciej Kolwas, Izabela Kamińska, Gennadiy Derkachov

We investigated the possibility of using charged luminescent nanoparticles as nanoprobes for studying the evolution scenarios of surface and internal structure of slowly evaporating free (light-absorbing) microdroplets of suspension. Three concentrations (1, 10 and 50 mg/ml) of luminescent nanoparticles were used. Single microdroplets were kept in a linear e

Małgorzata Curyło, Timothy T. Pennucci, Matthew Bailes, N. D. Ramesh Bhat

In 2018 an ultra-wide-bandwidth low-frequency (UWL) receiver was installed on the 64-m Parkes Radio Telescope enabling observations with an instantaneous frequency coverage from 704 to 4032 MHz. Here, we present the analysis of a three-year data set of 35 millisecond pulsars observed with the UWL by the Parkes Pulsar Timing Array (PPTA), using wideband timin

Kevin Batz, Benjamin Lucien Kaminski, Joost-Pieter Katoen, Christoph Matheja

We develop a weakest-precondition-style calculus \`a la Dijkstra for reasoning about amortized expected runtimes of randomized algorithms with access to dynamic memory - the $\textsf{aert}$ calculus. Our calculus is truly quantitative, i.e. instead of Boolean valued predicates, it manipulates real-valued functions. En route to the $\textsf{aert}$ calculus, w

Yufeng Wei, Yajun Zhu, Kun Xu

Hypersonic flow around a vehicle in near space flight is associated with multiscale non-equilibrium physics at a large variation of local Knudsen number from the leading edge highly compressible flow to the trailing edge particle free transport. To accurately capture the solution in all flow regimes from the continuum Navier-Stokes solution to the rarefied g

Tolga-Can Çallar, Sven Böttger

Applications of force control and motion planning often rely on an inverse dynamics model to represent the high-dimensional dynamic behavior of robots during motion. The widespread occurrence of low-velocity, small-scale, locally isotropic motion (LIMO) typically complicates the identification of appropriate models due to the exaggeration of dynamic effects

Yu Gao, Yu Jia, Yugen Lin, Jia-Yue Zhang

We show that loop-induced processes involving new physics particles can readily satisfy Landau Equation and trigger triangular singularities at high energy colliders, leading to fully visible Standard Model final states. Four-particle vertices in new physics allow triangular singularity diagrams to evade large virtuality suppression. In addition, a $t$-chann

Ehud Meir

In a previous paper by the author a universal ring of invariants for algebraic structures of a given type was constructed. This ring is a polynomial algebra that is generated by certain trace diagrams. It was shown that this ring admits the structure of a rational positive self adjoint Hopf algebra (abbreviated rational PSH-algebra), and was conjectured that

Ali Poursina, Ali Pourjamal, Ali Bozorg

Since Kolmogorov's theory, turbulence has been studied using various methods, many of which could be now be understood in a probabilistic framework. Herein, a comprehensive review of the advances made on stochastic theory of turbulence since Kolmogorov has been provided. It has been suggested that stochastic theory would be able to provide a natural foundati

J. Pascal Gollin, Attila Joó

A fundamental result in linear algebra states that if a homogenous linear equation system has only the trivial solution, then there are at most as many variables as equations. We prove the following generalisation of this phenomenon. If a possibly infinite homogenous linear equation system with finitely many variables in each equation has only the trivial so

Igor Buzhin, Veronica Antonova, Yury Mironov, Vladislav Gnezdilov

An approach to using the concept of Software-Defined Networking and Network Functions Virtualization (SDN/NFV) for the implementation of an information security monitoring and management system in 5G and 6G networks is proposed. SDN switches based on the OpenFlow protocol are offered as network sensors. In order to reduce the time for finding a subset of the

Pierre Palud, Pierre-Antoine Thouvenin, Pierre Chainais, Emeric Bron

This paper focuses on a challenging class of inverse problems that is often encountered in applications. The forward model is a complex non-linear black-box, potentially non-injective, whose outputs cover multiple decades in amplitude. Observations are supposed to be simultaneously damaged by additive and multiplicative noises and censorship. As needed in ma

María A. Bravo, Sudhanshu Mittal, Simon Ging, Thomas Brox

Vision-language modeling has enabled open-vocabulary tasks where predictions can be queried using any text prompt in a zero-shot manner. Existing open-vocabulary tasks focus on object classes, whereas research on object attributes is limited due to the lack of a reliable attribute-focused evaluation benchmark. This paper introduces the Open-Vocabulary Attrib

Valerio Flavio Gili, Dupish Dupish, Andres Vega, Massimo Gandola

Quantum Ghost Imaging (QGI) is an intriguing imaging protocol that exploits photon-pair correlations stemming from spontaneous parametric down-conversion (SPDC). QGI retrieves images from two-path joint measurements, where single-path detection does not allow to reconstruct the target image. This technique, has been so far limited in terms of acquisition spe

Daniel Arnström, Daniel Axehill

When Model Predictive Control (MPC) is used in real-time to control linear systems, quadratic programs (QPs) need to be solved within a limited time frame. Recently, several parametric methods have been proposed that certify the number of computations active-set QP solvers require to solve these QPs. These certification methods, hence, ascertain that the opt

Jun Xu, Fanglin Chen

Control invariant set is critical for guaranteeing safe control and the problem of computing control invariant set for linear discrete-time system is revisited in this paper by using a data-driven approach. Specifically, sample points on convergent trajectories of linear MPC are recorded, of which the convex hull formulates a control invariant set for the li

Takahito Iida, Atle Jensen

A theoretical model to explain the scattering process of wave attenuation in a marginal ice zone is developed. Many field observations offer wave energy decay in the form of exponential function with distance, and this is justified through the complex wave number for the dissipation process. On the other hand, such a mechanism is not explicitly proven for th

Barun Maity, Tirthankar Roy Choudhury

One of the most promising probes to constrain the reionization history of the universe is the power spectrum of neutral hydrogen 21 cm emission fluctuations. The corresponding analyses require computationally efficient modelling of reionization, usually achieved through semi-numerical simulations. We investigate the capability of one such semi-numerical code

Elisabeth Gaar, Markus Sinnl

The discrete $\alpha$-neighbor $p$-center problem (d-$\alpha$-$p$CP) is an emerging variant of the classical $p$-center problem which recently got attention in literature. In this problem, we are given a discrete set of points and we need to locate $p$ facilities on these points in such a way that the maximum distance between each point where no facility is

C. Bujard, E. Neufeld, M. Douglas, J. Wiart

Resource-efficient and robust validation of systems designed to measure a multi-dimensional parameter space is an unsolved problem as it would require millions of test permutations for comprehensive validation coverage. In the paper, an efficient and comprehensive validation approach based on a Gaussian Process (GP) model of the test system has been develope

Volker Burkert, Eberhard Klempt, Ulrike Thoma

This is a contribution to the review "50 Years of Quantum Chromdynamics" edited by F. Gross and E. Klempt [arXiv:2212.11107], to be published in EPJC. The contribution reviews the new baryon resonances derived from photoproduction experiments. Implications of the new results for the interpretation of baryons are discussed.

Yehui Tang, Kai Han, Jianyuan Guo, Chang Xu

Light-weight convolutional neural networks (CNNs) are specially designed for applications on mobile devices with faster inference speed. The convolutional operation can only capture local information in a window region, which prevents performance from being further improved. Introducing self-attention into convolution can capture global information well, but

Erez Shalom, Ayelet Goldstein, Roni Wais, Maya Slivanova

Background The use of a clinical decision support system for assessing the quality of care, based on computerized clinical guidelines (GLs), is likely to improve care, reduce costs, save time, and enhance the staff's capabilities. Objectives Implement and evaluate a system for assessment of the quality of the care, in the domain of management of pressure ulc

Saba Arife Bozpolat

Calculating the expected spin configuration of the chain consisting of Ferrum atoms interacting with each other through exchange interaction is fundamentally a configuration optimization problem. Quantum Approximate Optimization Algorithm is a suitable candidate to configure such systems on a quantum device. In this work we have considered Ferrum chains of t

Daniel Arnström, Daniel Axehill

We propose a mixed-integer quadratic programming (QP) solver that is suitable for use in embedded applications, for example, hybrid model predictive control (MPC). The solver is based on the branch-and-bound method, and uses a recently proposed dual active-set solver for solving the resulting QP relaxations. Moreover, we tailor the search of the branch-and-b

Eberhard Klempt

This is a contribution to the review "50 Years of Quantum Chromdynamics" edited by F. Gross and E. Klempt [arXiv:2212.11107], to be published in EPJC. The contribution remembers the early searches and explains how to find a glueball, based on its properties. The results of a coupled-channel analysis are presented that provides evidence for the scalar gluebal

William Seymour, Jose Such

Voice assistants offer a convenient and hands-free way of accessing computing in the home, but a key problem with speech as an interaction modality is how to scaffold accurate mental models of voice assistants, a task complicated by privacy and security concerns. We present the results of a survey of voice assistant users (n=1314) measuring trust, security,

Tamas Bisztray, Nils Gruschka, Thirimachos Bourlai, Lothar Fritsch

Technological advancements allow biometric applications to be more omnipresent than in any other time before. This paper argues that in the current EU data protection regulation, classification applications using biometric data receive less protection compared to biometric recognition. We analyse preconditions in the regulatory language and explore how this

M. Pelekhata, K. Murawski, S. Poedts

Context. It is known that Alfv\'en and magnetoacoustic waves both contribute to the heating of the solar chromosphere and drive plasma outflows. In both cases, the thermalization of the wave energy occurs due to ion-neutral collisions, but the obtained rates of plasma heating cannot explain the observational data. The same is true for the magnitudes of the o

Eberhard Klempt, Sebastian Neubert

This is a contribution to the review "50 Years of Quantum Chromdynamics" edited by F. Gross and E. Klempt [arXiv:2212.11107], to be published in EPJC. The contribution reviews the properties of baryons with one heavy flavor: the lifetimes of ground states and the spectrum of excited states. The importance of symmetries to understand the excitation spectrum i

Arash Nikoubashman, Miho Yanagisawa

The formation of (bio)molecular condensates via liquid-liquid phase separation in cells has received increasing attention, as these coacervates play important functional and regulatory roles within biological systems. However, the majority of studies focused on the behavior of pure systems in bulk solutions, thus neglecting confinement effects and the interp

Aleksandr O. Smirnov, Aleksandra A. Caplieva

In our work a hierarchy of integrable vector nonlinear differential equations depending on the functional parameter $r$ is constructed using a monodromy matrix. The first equation of this hierarchy for $r=\alpha(\mathbf{p}^t\mathbf{q})$ is vector analogue of the Kundu-Eckhaus equation. When $\alpha=0$, the equations of this hierarchy turn into equations of t

Longqing Ye

In this paper, we propose a simple and general approach to augment regular convolution operator by injecting extra group-wise transformation during training and recover it during inference. Extra transformation is carefully selected to ensure it can be merged with regular convolution in each group and will not change the topological structure of regular conv

Michael Paulsen, Julian Lindner, Bastian Klemke, Jörn Beyer

A novel setup for the measurement of magnetic fields external to certain antiferromagnets and generally weakly remanent magnetic materials is presented. The setup features a highly sensitive Super Conducting Quantum Interference Device (SQUID) magnetometer with a magnetic field resolution of approx. 10 fT, non-electric thermalization of the sample space for

Ningning Li, Qunwei Li, Xichen Ding, Shaohu Chen

Deep Candidate Generation plays an important role in large-scale recommender systems. It takes user history behaviors as inputs and learns user and item latent embeddings for candidate generation. In the literature, conventional methods suffer from two problems. First, a user has multiple embeddings to reflect various interests, and such number is fixed. How

Zheng Gong, Wojciech Frys, Renzo Tiranti, Carmine Ventre

In financial terms, an implied volatility surface can be described by its term structure, its skewness and its overall volatility level. We use a PCA variational auto-encoder model to perfectly represent these descriptors into a latent space of three dimensions. Our new encoding brings significant benefits for synthetic surface generation, in that (i) scenar

Meng Hua, Qingqing Wu, Wen Chen, Abbas Jamalipour

This paper studies a communication-centric integrated sensing and communication (ISAC) system, where a multi-antenna base station (BS) simultaneously performs downlink communication and target detection. A novel target detection and information transmission protocol is proposed, where the BS executes the channel estimation and beamforming successively and me

Shahar Zuri, Arthur Shapiro, Leeor Kronik, Efrat Lifshitz

Two-dimensional (2D) halide perovskites offer a unique platform to investigate the ground-state of materials possessing significant anharmonicity. In contrast to three-dimensional perovskites, their 2D counterparts offer substantially fewer degrees of freedom, resulting in multiple well-defined crystal structures. In this work, we thoroughly investigate the

Robin C. Verstraten, Tim Ludwig, Rembert A. Duine, Cristiane Morais Smith

The dynamics of a magnetic moment or spin are of high interest to applications in technology. Dissipation in these systems is therefore of importance for improvement of efficiency of devices, such as the ones proposed in spintronics. A large spin in a magnetic field is widely assumed to be described by the Landau-Lifshitz-Gilbert (LLG) equation, which includ

Fabio Iemmi

We describe the latest results obtained by the CMS Collaboration on top quark spin and polarization properties. The top quark spin asymmetry is measured both targeting single-top quark production in the $t$-channel and single-top quark production in association with a Z boson. Additionally, all the independent coefficients of the spin-dependent part of the t

Matthew Johnson, Barnaby Martin, Jelle J. Oostveen, Sukanya Pandey

For any particular class of graphs, algorithms for computational problems restricted to the class often rely on structural properties that depend on the specific problem at hand. This begs the question if a large set of such results can be explained by some common problem conditions. We propose such conditions for $HH$-subgraph-free graphs. For a set of grap

Haim Sawdayee, Amir Vaxman, Amit H. Bermano

Reconstructing 3D shapes from planar cross-sections is a challenge inspired by downstream applications like medical imaging and geographic informatics. The input is an in/out indicator function fully defined on a sparse collection of planes in space, and the output is an interpolation of the indicator function to the entire volume. Previous works addressing

Cheng Ge, Han Zhang, Jiaoyang Li, Sven Koenig

The Multi-Objective Multi-Agent Path Finding (MO-MAPF) problem is the problem of finding the Pareto-optimal frontier of collision-free paths for a team of agents while minimizing multiple cost metrics. Examples of such cost metrics include arrival times, travel distances, and energy consumption.In this paper, we focus on the Multi-Objective Conflict-Based Se

Giacomo Beccari, Mario Cadelano, Emanuele Dalessandro

The dynamical evolution of globular clusters is theoretically described by a series of well known events typical of N-body systems. Still, the identification of observational signatures able to empirically describe the stage of dynamical evolution of a stellar system of the density typical of a globular cluster, represents a challenge. In this paper we study

Haoxin Li, Yuan Liu, Hanwang Zhang, Boyang Li

In video action recognition, shortcut static features can interfere with the learning of motion features, resulting in poor out-of-distribution (OOD) generalization. The video background is clearly a source of static bias, but the video foreground, such as the clothing of the actor, can also provide static bias. In this paper, we empirically verify the exist

Chen-How Huang, Miguel A. Cazalilla

At temperatures well below the Fermi temperature $T_F$, the coupling of magnetic fluctuations to particle-hole excitations in a two-component Fermi gas makes the transition to itinerant ferromagnetism a first order phase transition. This effect is not described by the paradigm of Landau's theory of phase transitions, which assumes the free energy is an analy

Chaoran Cui, Yumo Yao, Chunyun Zhang, Hebo Ma

Knowledge tracing aims to trace students' evolving knowledge states by predicting their future performance on concept-related exercises. Recently, some graph-based models have been developed to incorporate the relationships between exercises to improve knowledge tracing, but only a single type of relationship information is generally explored. In this paper,

Chung-En Tsai, Hao-Chung Cheng, Yen-Huan Li

In maximum-likelihood quantum state tomography, both the sample size and dimension grow exponentially with the number of qubits. It is therefore desirable to develop a stochastic first-order method, just like stochastic gradient descent for modern machine learning, to compute the maximum-likelihood estimate. To this end, we propose an algorithm called stocha

Chao Hu, Liqiang Zhu, Weibin Qiu, Weijie Wu

Recently, the vision transformer (ViT) has made breakthroughs in image recognition. Its self-attention mechanism (MSA) can extract discriminative labeling information of different pixel blocks to improve image classification accuracy. However, the classification marks in their deep layers tend to ignore local features between layers. In addition, the embeddi

Xiaobao Wu, Anh Tuan Luu, Xinshuai Dong

To overcome the data sparsity issue in short text topic modeling, existing methods commonly rely on data augmentation or the data characteristic of short texts to introduce more word co-occurrence information. However, most of them do not make full use of the augmented data or the data characteristic: they insufficiently learn the relations among samples in

Nazareno Bruschi, Giuseppe Tagliavini, Angelo Garofalo, Francesco Conti

The demand for computation resources and energy efficiency of Convolutional Neural Networks (CNN) applications requires a new paradigm to overcome the "Memory Wall". Analog In-Memory Computing (AIMC) is a promising paradigm since it performs matrix-vector multiplications, the critical kernel of many ML applications, in-place in the analog domain within memor

Antoine Bret, Ramesh Narayan

Shockwaves in plasma are usually dealt with using Magnetohydrodynamics (MHD). Yet, MHD entails the assumption of a short mean free path, which is not fulfilled in a collisionless plasma. Recently, for pair plasmas, we devised a model allowing to account for kinetic effects within an MHD-like formalism. Its relies on an estimate of the anisotropy generated wh

Yue Liu, Jun Xia, Sihang Zhou, Xihong Yang

Graph clustering, which aims to divide nodes in the graph into several distinct clusters, is a fundamental yet challenging task. Benefiting from the powerful representation capability of deep learning, deep graph clustering methods have achieved great success in recent years. However, the corresponding survey paper is relatively scarce, and it is imminent to

Ayushi Chaudhuri, Arijit Nandi, Buddhadeb Pradhan

Android malware attacks are increasing daily at a tremendous volume, making Android users more vulnerable to cyber-attacks. Researchers have developed many machine learning (ML)/ deep learning (DL) techniques to detect and mitigate android malware attacks. However, due to technological advancement, there is a rise in android mobile devices. Furthermore, the

Jinu Pahk, Jungseok Shim, MinHyeok Baek, Yongseob Lim

Autonomous vehicle simulation has the advantage of testing algorithms in various environment variables and scenarios without wasting time and resources, however, there is a visual gap with the real-world. In this paper, we trained DCLGAN to realistically convert the image of the CARLA simulator and evaluated the effect of the Sim2Real conversion focusing on

Yuqi Chen, Hanyuan Zhang, Weiwei Sun, Baihua Zheng

GPS trajectories are the essential foundations for many trajectory-based applications, such as travel time estimation, traffic prediction and trajectory similarity measurement. Most applications require a large amount of high sample rate trajectories to achieve a good performance. However, many real-life trajectories are collected with low sample rate due to

Ashesh, Alexander Krull, Moises Di Sante, Francesco Silvio Pasqualini

We present {\mu}Split, a dedicated approach for trained image decomposition in the context of fluorescence microscopy images. We find that best results using regular deep architectures are achieved when large image patches are used during training, making memory consumption the limiting factor to further improving performance. We therefore introduce lateral

Mateusz P. Mrozowski, Jonathan D. Pritchard, J. Jeffers

We present a high efficiency source of picosecond pulses derived from a dual cavity optical frequency comb generator. This approach overcomes the limitations of single cavity comb generators that are restricted to efficiencies of a few percent. We achieve picosecond pulses with GHz repetition rates offering over a hundred times higher output efficiency than

Juan Esteban Suarez Cardona, Michael Hecht

We present a novel class of approximations for variational losses, being applicable for the training of physics-informed neural nets (PINNs). The loss formulation reflects classic Sobolev space theory for partial differential equations and their weak formulations. The loss computation rests on an extension of Gauss-Legendre cubatures, we term Sobolev cubatur

Muhammad Jehanzeb Mirza, Pol Jané Soneira, Wei Lin, Mateusz Kozinski

Test-Time-Training (TTT) is an approach to cope with out-of-distribution (OOD) data by adapting a trained model to distribution shifts occurring at test-time. We propose to perform this adaptation via Activation Matching (ActMAD): We analyze activations of the model and align activation statistics of the OOD test data to those of the training data. In contra

Tom Britton, Dongni Zhang

We analyze a Markovian SIR epidemic model where individuals either recover naturally or are diagnosed, leading to isolation and potential contact tracing. Our focus is on digital contact tracing via a tracing app, considering both its standalone use and combination with manual tracing. We prove that as the population size $n$ grows large, the epidemic proces

Dimitris Fotakis, Alkis Kalavasis, Christos Tzamos

In this work, we study how to efficiently obtain perfect samples from a discrete distribution $\mathcal{D}$ given access only to pairwise comparisons of elements of its support. Specifically, we assume access to samples $(x, S)$, where $S$ is drawn from a distribution over sets $\mathcal{Q}$ (indicating the elements being compared), and $x$ is drawn from the

Tinatin Baratashvili, Christine Verbeke, Nicolas Wijsen, Stefaan Poedts

Coronal Mass Ejections (CMEs) are one of the main drivers of disturbances in the interplanetary space. Strong CMEs, when directed towards the Earth, cause geo-magnetic storms upon interacting with the magnetic field of the Earthand can cause significant damage to our planet and affect everyday life. As such, efficient space weather prediction tools are neces

Peter Boyle, Matteo Di Carlo, Felix Erben, Vera Gülpers

The decreasing uncertainties in theoretical predictions and experimental measurements of several hadronic observables related to weak processes, which in many cases are now smaller than $\mathrm{O}(1\%)$, require theoretical calculations to include subleading corrections that were neglected so far. Precise determinations of leptonic and semi-leptonic decay r