October 2022 arXiv papers — page 82

Claire-Marie Bergaentzlé, Philipp Andreas Gunkel, Mohammad Ansarin, Yashar Ghiassi-Farrokhfal

Developing new electricity grid tariffs in the context of household electrification raises old questions about who pays for what and to what extent. When electric vehicles (EVs) and heat pumps (HPs) are owned primarily by households with higher financial status than others, new tariff designs may clash with the economic argument for efficiency and the politi

V. O. Manturov, I. M. Nikonov

In the present paper, we address the problem how to get a map from knots in the cylinder and on the thickened torus to some (generalisation of) virtual knots called virtual-flat knots. The main construction takes a diagram on a cylinder (torus) and adds some ``invisible'' crossings which gives rise to a diagram which can be formally immersed but not embedded

Williams Rizzi, Chiara Di Francescomarino, Chiara Ghidini, Fabrizio Maria Maggi

Predictive Process Monitoring is a field of Process Mining that aims at predicting how an ongoing execution of a business process will develop in the future using past process executions recorded in event logs. The recent stream of publications in this field shows the need for tools able to support researchers and users in analyzing, comparing and selecting

Ruyong Feng, Wei Lu

We study the relation between the Galois group $G$ of a linear difference-differential system and two classes $\mathcal{C}_1$ and $\mathcal{C}_2$ of groups that are the Galois groups of the specializations of the linear difference equation and the linear differential equation in this system respectively. We show that almost all groups in $\mathcal{C}_1\cup \

Kenta Nakata

Delay-difference and delay-differential analogues of the KdV and Boussinesq (BSQ) equations are presented. Each of them has the N-soliton solution and reduces to an already known soliton equation as the delay parameter approaches 0. In addition, a delay-differential analogue of the KP equation is proposed. We discuss its N-soliton solution and the limit as t

Qiuyue Ma, Wenhui Wan, Yanfeng Ge, Yingmei Li

Janus materials possess extraordinary physical, chemical, and mechanical properties caused by symmetry breaking. Here, the mechanic properties, electronic structure, magnetic properties, and optical properties of Janus Cr$_2$TeX (X= P, As, Sb) monolayers are systematically investigated by the density functional theory. Janus Cr$_2$TeP, Cr$_2$TeAs, and Cr$_2$

Keqin Bao, Yu Wan, Dayiheng Liu, Baosong Yang

In this paper, we present our submission to the sentence-level MQM benchmark at Quality Estimation Shared Task, named UniTE (Unified Translation Evaluation). Specifically, our systems employ the framework of UniTE, which combined three types of input formats during training with a pre-trained language model. First, we apply the pseudo-labeled data examples f

Feng-Ping Li, Vemund Stenbekk Thorkildsen, Leiv-J Gelius, Jian-Hua Yue

Marine Controlled Source Electromagnetic (CSEM) is employed both in large-scale geophysical applications as well as within exploration of hydrocarbons and gas hydrates. Due to the diffusive character of the EM field only very low frequencies are used leading to inversion results with rather low resolution. In this paper, we calculate the resolution matrix as

Mingming Cao, Gonzalo Ibañez-Firnkorn, Israel P. Rivera-Ríos, Qingying Xue

In this paper, we develop a comprehensive weighted theory for a class of Banach-valued multilinear bounded oscillation operators on measure spaces, which merges multilinear Calder\'{o}n-Zygmund operators with a quantity of operators beyond the multilinear Calder\'{o}n-Zygmund theory. We prove that such multilinear operators and corresponding commutators are

Yu Wan, Keqin Bao, Dayiheng Liu, Baosong Yang

In this report, we present our submission to the WMT 2022 Metrics Shared Task. We build our system based on the core idea of UNITE (Unified Translation Evaluation), which unifies source-only, reference-only, and source-reference-combined evaluation scenarios into one single model. Specifically, during the model pre-training phase, we first apply the pseudo-l

Ziqi Su, Wendong Mao, Zhongfeng Wang, Jun Lin

Three-dimensional generative adversarial networks (3D-GAN) have attracted widespread attention in three-dimension (3D) visual tasks. 3D deconvolution (DeConv), as an important computation of 3D-GAN, significantly increases computational complexity compared with 2D DeConv. 3D DeConv has become a bottleneck for the acceleration of 3D-GAN. Previous accelerators

Saad Kriouile, Mohamad Assaad

The age of information minimization problems has been extensively studied in Real-time monitoring applications frameworks. In this paper, we consider the problem of monitoring the states of unknown remote source that evolves according to a Markovian Process. A central scheduler decides at each time slot whether to schedule the source or not in order to recei

Jin-Hui Fang, Csaba Sándor

Two sets $A,B$ of nonnegative integers are called \emph{additive complements}, if all sufficiently large integers can be expressed as the sum of two elements from $A$ and $B$. We further call $A,B$ \emph{perfect additive complements} if every nonnegative integer can be uniquely expressed as the sum of two elements from $A$ and $B$. Let $A(x)$ be the counting

Minsoo Kim, Donghoon Kim, Buse Aktas, Hongsoo Choi

Advanced flexible electronics and soft robotics require the development and implementation of flexible functional materials. Magnetoelectric (ME) oxide materials can convert magnetic input into electric output and vice versa, making them excellent candidates for advanced sensing, actuating, data storage, and communication. However, their application has been

Jongseok Lee, Ribin Balachandran, Konstantin Kondak, Andre Coelho

This article presents a novel telepresence system for advancing aerial manipulation in dynamic and unstructured environments. The proposed system not only features a haptic device, but also a virtual reality (VR) interface that provides real-time 3D displays of the robot's workspace as well as a haptic guidance to its remotely located operator. To realize th

Niklas A. Weber, Miru Lee, Florian Schönewald, Leonard Schüler

We conducted lateral force microscopy measurements on seven [LaMnO$_{3}$]$_{m}$/[SrMnO$_{3}$]$_{n}$ superlattices with varied layer thicknesses. We observe that the friction forces and the friction coefficients initially increase with increasing LaMnO3 top layer thickness, followed by saturation when the top layer thickness exceeds a few nanometers. These ob

J. M. Losada, A. Theodorsen, O. E. Garcia

A stochastic model is presented for a super-position of uncorrelated pulses with a random distribution of amplitudes, sizes, velocities and arrival times. The pulses are assumed to move radially with fixed shape and amplitudes decaying exponentially in time due to linear damping. The pulse velocities are taken to be time-independent but randomly distributed.

Camiel van Efferen, Jeison Fischer, Theo A. Costi, Achim Rosch

A many-body resonance emerges at the Fermi energy when an electron bath screens the magnetic moment of a half-filled impurity level. This Kondo effect, originally introduced to explain the abnormal resistivity behavior in bulk magnetic alloys, has been realized in many quantum systems over the past decades, such as quantum dots, quantum point contacts, nanow

Adrian Ortega, Orsolya Kálmán, Tamás Kiss

The presence of noise in quantum computers hinders their effective operation. Even though quantum error correction can theoretically remedy this problem, its practical realization is still a challenge. Testing and benchmarking noisy, intermediate-scale quantum (NISC) computers is therefore of high importance. Here, we suggest the application of the so-called

Esteban Jiménez, Claudia del P. Lagos, Aaron D. Ludlow, Emily Wisnioski

We use the EAGLE cosmological simulations to study the evolution of the vertical velocity dispersion of cold gas, $\sigma_{z}$, in central disc galaxies and its connection to stellar feedback, gravitational instabilities, cosmological gas accretion and galaxy mergers. To isolate the impact of feedback, we analyse runs that turn off stellar and (or) AGN feedb

Guo linxin, Tao yinghui, Gao Min, Yu Junliang

The task of recommending items to a group of users, a.k.a. group recommendation, is receiving increasing attention. However, the cold-start problem inherent in recommender systems is amplified in group recommendation because interaction data between groups and items are extremely scarce in practice. Most existing work exploits associations between groups and

Yu Yang, Tian Yu Liu, Baharan Mirzasoleiman

Data poisoning causes misclassification of test time target examples by injecting maliciously crafted samples in the training data. Existing defenses are often effective only against a specific type of targeted attack, significantly degrade the generalization performance, or are prohibitive for standard deep learning pipelines. In this work, we propose an ef

Chi Zhang, Wei Yin, Zhibin Wang, Gang Yu

In this paper, we address monocular depth estimation with deep neural networks. To enable training of deep monocular estimation models with various sources of datasets, state-of-the-art methods adopt image-level normalization strategies to generate affine-invariant depth representations. However, learning with image-level normalization mainly emphasizes the

Jason Joseph, Jeffrey Meier, Maggie Miller, Alexander Zupan

We adapt Seifert's algorithm for classical knots and links to the setting of tri-plane diagrams for bridge trisected surfaces in the 4-sphere. Our approach allows for the construction of a Seifert solid that is described by a Heegaard diagram. The Seifert solids produced can be assumed to have exteriors that can be built without 3-handles; in contrast, we gi

Sushil Thapa

Knowledge distillation is a popular machine learning technique that aims to transfer knowledge from a large 'teacher' network to a smaller 'student' network and improve the student's performance by training it to emulate the teacher. In recent years, there has been significant progress in novel distillation techniques that push performance frontiers across m

Luca Reali, Mark R. Gilbert, Max Boleininger, Sergei L. Dudarev

The effects of neutron irradiation on materials are often interpreted in terms of atomic recoils, initiated by neutron impacts and producing crystal lattice defects. In addition, there is a remarkable two-step process, strongly pronounced in the medium-weight and heavy elements. This process involves the generation of energetic {\gamma} photons in nonelastic

Yuji Omachi, Nen Saito, Chikara Furusawa

The genetic code refers to a rule that maps 64 codons to 20 amino acids. Nearly all organisms, with few exceptions, share the same genetic code, the standard genetic code (SGC). While it remains unclear why this universal code has arisen and been maintained during evolution, it may have been preserved under selection pressure. Theoretical studies comparing t

Stanislaw Galeski, Rafał Wawrzyńczak, Claudius Riek, Johannes Gooth

We describe application of a state-of-the art digital FPGA based Lock-In amplifier to measurements of ultrasound propagation and attenuation at fixed frequency in low temperatures and in high static magnetic fields. Our implementation significantly simplifies electronics required for high resolution measurements, allows to record the full echo train in singl

Alireza Ghaffari-Hadigheh, Lennart Sinjorgo, Renata Sotirov

We propose a random coordinate descent algorithm for optimizing a non-convex objective function subject to one linear constraint and simple bounds on the variables. Although it is common use to update only two random coordinates simultaneously in each iteration of a coordinate descent algorithm, our algorithm allows updating arbitrary number of coordinates.

Hisato Komatsu

We considered a long-range Ising model under Glauber dynamics and calculated the difference from the mean-field approximation in a finite-size system using perturbation theory. To deal with the BBGKY hierarchy, we assumed that certain types of extensive properties have a Gaussian distribution, which turned out to be equivalent to the Kirkwood superposition a

Stefan Czimek, Igor Rodnianski

In this paper we develop a new approach to the gluing problem in General Relativity, that is, the problem of matching two solutions of the Einstein equations along a spacelike or characteristic (null) hypersurface. In contrast to the previous constructions, the new perspective actively utilizes the nonlinearity of the constraint equations. As a result, we ar

D. M. -A. Meyer, E. I. Vorobyov, V. G. Elbakyan, S. Kraus

The burst mode of accretion in massive star formation is a scenario linking the initial gravitational collapse of parent pre-stellar cores to the properties of their gravitationally unstable discs and of their accretion-driven bursts. In this study, we present a series of high-resolution 3D radiation-hydrodynamics numerical simulations for young massive star

Marie Gueguen

Measuring the rate at which the universe expands at a given time -- the 'Hubble constant' -- has been a topic of controversy since the first measure of its expansion by Edwin Hubble in the 1920's. As early as the 1970's, Sandage et de Vaucouleurs have been arguing about the adequate methodology for such a measurement. Should astronomers focus only on their b

Kah Heng Lee

This study proposes a method for producing an infinite number of fractals using aperiodic substitution tilings, exemplified by the Ammann Chair tiling. Higher-order substitutions of aperiodic tilings are utilized in relation to the Sierpinski carpet concept. The similarity dimensions of the fractals generated by the Ammann Chair tiling are calculated and sho

Asen Mutafov, Evgeni Semkov, Stoyanka Peneva, Sunay Ibryamov

In this paper results from the optical photometric observations of the pre-main-sequence star V1180 Cas are reported. The star is a young variable associated with the dark cloud Lynds 1340, located at a distance of 600 pc from the Sun in the star forming region in Cassiopeia. V1180 Cas shows a large amplitude variability interpreted as a combination of accre

Xinrao Li, Tong Zhang, Shuai Wang, Guangxu Zhu

Edge intelligence autonomous driving (EIAD) offers computing resources in autonomous vehicles for training deep neural networks. However, wireless channels between the edge server and the autonomous vehicles are time-varying due to the high-mobility of vehicles. Moreover, the required number of training samples for different data modalities, e.g., images, po

Anirudh Pradhan, Archana Dixit, Dinesh Chandra Maurya

In this article, we have discussed the results of our investigation. We consider an anisotropic viscous cosmological model of (LRS) Bianchi type I spacetime universe filled with a viscous fluid under $f(Q)$ gravity. We have studied the modified $f(Q)$ gravity with quadratic form $f(Q)=\alpha Q^{2}+\beta$, where $Q$ is called as a non-metricity scalar and $\a

Lan Jiang, Hao Zhou, Yankai Lin, Peng Li

Even though the large-scale language models have achieved excellent performances, they suffer from various adversarial attacks. A large body of defense methods has been proposed. However, they are still limited due to redundant attack search spaces and the inability to defend against various types of attacks. In this work, we present a novel fine-tuning appr

F. Hamid, C. Sasmal

This study presents a detailed investigation of how the rheological behaviour of fluid could influence the electrokinetic instability (EKI) phenomenon in a microfluidic T-junction. The non-Newtonian power-law model with different values of the power-law index (n) is used to obtain fluids of different rheological behaviours. We find that as the fluid rheologi

Huijuan Zhou, Juncai Pu, Yong Chen

Data-driven forward-inverse problems for the variable coefficients Hirota (VCH) equation are discussed in this paper. The main idea is to use the improved physics-informed neural networks (IPINN) algorithm with neuron-wise locally adaptive activation function, slope recovery term and parameter regularization to recover the data-driven solitons and high-order

Chaewon Kim, Seung-Jun Moon, Gyeong-Moon Park

Recent advanced GAN inversion models aim to convey high-fidelity information from original images to generators through methods using generator tuning or high-dimensional feature learning. Despite these efforts, accurately reconstructing image-specific details remains as a challenge due to the inherent limitations both in terms of training and structural asp

Tsung-Ming Huang, Wei-Hung Liao, Wen-Wei Lin, Mei-Heng Yueh

The volumetric stretch energy has been widely applied to the computation of volume-/mass-preserving parameterizations of simply connected tetrahedral mesh models. However, this approach still lacks theoretical support. In this paper, we provide the theoretical foundation for volumetric stretch energy minimization (VSEM) to compute volume-/mass-preserving par

Eszter Saly, Dávid Ferenc, Edit Mátyus

For rovibronic states corresponding to the $B$ and $B'\ ^1\Sigma_\text{u}^+$ electronic states of the hydrogen molecule, the pre-Born--Oppenheimer (four-particle) non-relativistic energy is converged to a 1-3 parts-per-billion relative precision. The four-particle non-relativistic energy is appended with leading-order relativistic, leading- and estimated hig

Hsuan-Ting Lai, Wing-Huen Ip

Asteroids having perihelion distance $q$ $<$ 1.3 AU are classified as near-Earth objects (NEOs), which are divided into different sub-groups: Vatira-class, Atira-class, Aten-class, Apollo-class, and Amor-class. 2020 $AV_2$, the first Vatira (Orbiting totally inside Venus' orbit) was discovered by the Twilight project of the Zwicky Transient Facility (ZTF) on

Yuancheng Sun, Yimeng Chen, Weizhi Ma, Wenhao Huang

Molecular property prediction is essential for drug discovery. In recent years, deep learning methods have been introduced to this area and achieved state-of-the-art performances. However, most of existing methods ignore the intrinsic relations between molecular properties which can be utilized to improve the performances of corresponding prediction tasks. I

Rohan Kumar, Dhruv Nagpal, Vinayak Naik, Dipanjan Chakraborty

Video conferencing platforms have been appropriated during the COVID-19 pandemic for different purposes, including classroom teaching. However, the platforms are not designed for many of these objectives. When users, like educationists, select a platform, it is unclear which platform will perform better given the same network and hardware resources to meet t

Igor Barg, Niklas Kohlmann, Florian Rasch, Thomas Strunskus

Soft and flexible conductors are essential in the development of soft robots, wearable electronics, as well as electronic tissue and implants. However, conventional soft conductors are inherently characterized by a large change in conductance upon mechanical deformation or under alternating environmental conditions, e.g., humidity, drastically limiting their

Wu-yi Pan

The purpose of this note is to find the least weak type $(1,1)$ bound for the almost uncentered maximal operator on radial decreasing functions.

Boyka Aneva, Sergej Faletič, Mihály Hömöstrei, Péter Jenei

We investigated how students perceive the role of IYPT (www.iypt.org) participation in their development of soft skills. We also investigated how students teachers assess the contribution of YPT participation to students soft skills development. Third, we link self-reported soft-skill development to performance in research tasks, as assessed by international

Alexandre Belin, Robert C. Myers, Shan-Ming Ruan, Gábor Sárosi

We expand on our results in arXiv:2111.02429 to present a broad new class of gravitational observables in asymptotically Anti-de Sitter space living on general codimension-zero regions of the bulk spacetime. By taking distinct limits, these observables can reduce to well-studied holographic complexity proposals, e.g., the volume of the maximal slice and the

Wei Qiu, Xiao Ma, Bo An, Svetlana Obraztsova

Despite the recent advancement in multi-agent reinforcement learning (MARL), the MARL agents easily overfit the training environment and perform poorly in the evaluation scenarios where other agents behave differently. Obtaining generalizable policies for MARL agents is thus necessary but challenging mainly due to complex multi-agent interactions. In this wo

Sam Adriaensen, Jonathan Mannaert, Paolo Santonastaso, Ferdinando Zullo

This paper mainly focuses on cones whose basis is a maximum $h$-scattered linear set. We start by investigating the intersection sizes of such cones with the hyperplanes. Then we analyze two constructions of point sets with few intersection sizes with the hyperplanes. In particular, the second one extends the construction of translation KM-arcs in projective

Wenxiang Jiao, Zhaopeng Tu, Jiarui Li, Wenxuan Wang

This paper describes Tencent's multilingual machine translation systems for the WMT22 shared task on Large-Scale Machine Translation Evaluation for African Languages. We participated in the $\mathbf{constrained}$ translation track in which only the data and pretrained models provided by the organizer are allowed. The task is challenging due to three problems

Yidong Ouyang, Liyan Xie, Guang Cheng

Synthetic data generation has become an emerging tool to help improve the adversarial robustness in classification tasks since robust learning requires a significantly larger amount of training samples compared with standard classification tasks. Among various deep generative models, the diffusion model has been shown to produce high-quality synthetic images

T. Gortsas, D. G. Aggelis, D. Polyzos

Strain gradient elasticity and nonlocal elasticity are two enhanced elastic theories intensively used over the last fifty years to explain static and dynamic phenomena that classical elasticity fails to do. The nonlocal elastic theory has a clear differentiation from the classical case by considering stresses in a point of the continuum as an integral of all

Nuno Castro, Kirill Skovpen

A study of the top quark interactions via flavour-changing neutral current (FCNC) processes provides an intriguing connection between the heaviest elementary particle of the standard model (SM) of particle physics and the new scalar bosons that are predicted in several notable SM extensions. The production cross sections of the processes with top-scalar FCNC

Surya Teja Gavva, Karthik C. S., Sharath Punna

Over the last three decades, researchers have intensively explored various clustering tools for categorical data analysis. Despite the proposal of various clustering algorithms, the classical k-modes algorithm remains a popular choice for unsupervised learning of categorical data. Surprisingly, our first insight is that in a natural generative block model, t

Stuart Martin, Robert A. Spencer

We examine the cell modules for the category of type An webs and their natural cellular forms. We modify the bases of these modules, as described by Elias, to obtain an orthogonal basis of each cell module. Hence, we calculate the determinant of the Gram matrix with respect to such bases. These Gram determinants are given in terms of intersection forms, comp

Seung Park, Yong-Goo Shin

Generative adversarial networks (GANs), built with a generator and discriminator, significantly have advanced image generation. Typically, existing papers build their generators by stacking up multiple residual blocks since it makes ease the training of generators. However, some recent papers commented on the limitation of the residual block and proposed a n

Krzysztof Pomorski, Bożena Nerlo-Pomorska

A new, rapidly convergent Fourier over spheroid parametrization is developed to describe the shape of a fissioning nucleus: its elongation, non-axiality and left-right asymmetry and neck formation. The 4D Potential Energy Surfaces (PES) of even-even actinide nuclei are evaluated within the macro-micro model. The Langevin trajectories generated on such PESs a

Geon Choi, Jeonghun Park, Nir Shlezinger, Yonina C. Eldar

Simultaneous localization and mapping (SLAM) is a method that constructs a map of an unknown environment and localizes the position of a moving agent on the map simultaneously. Extended Kalman filter (EKF) has been widely adopted as a low complexity solution for online SLAM, which relies on a motion and measurement model of the moving agent. In practice, how

Scott Harper

It is well known that every finite simple group has a generating pair. Moreover, Guralnick and Kantor proved that every finite simple group has the stronger property, known as $\frac{3}{2}$-generation, that every nontrivial element is contained in a generating pair. Much more recently, this result has been generalised in three different directions, which for

Jianxin Wei, Ergute Bao, Xiaokui Xiao, Yin Yang

Nowadays, differential privacy (DP) has become a well-accepted standard for privacy protection, and deep neural networks (DNN) have been immensely successful in machine learning. The combination of these two techniques, i.e., deep learning with differential privacy, promises the privacy-preserving release of high-utility models trained with sensitive data su

Sullivan Marafico, Jonathan Biteau, Antonio Condorelli, Olivier Deligny

We explore two generic hypotheses for tracing the sources of ultra-high energy cosmic rays (UHECRs) in the Universe: star formation rate density or stellar mass density. For each scenario, we infer a set of constraints for the emission mechanisms in the accelerators, for their energetics and for the abundances of elements at escape from their environments. F

Hai-Liang Li, Chuangchuang Liang

In the present paper, the primitive equations, which can be used to simulate the large scale motion of ocean and atmosphere, are considered in the three-dimensional domain bounded below by a fixed solid boundary and above by a free moving boundary. The global existence and uniqueness of strong solutions are established and the long time convergence to the eq

Greg Knapp

In this paper, we study the number of integer pair solutions to the equation $|F(x,y)| = 1$ where $F(x,y) \in \mathbb{Z}[x,y]$ is an irreducible (over $\mathbb{Z}$) binary form with degree $n \geqslant 3$ and exactly three nonzero summands. In particular, we improve Emery Thomas' explicit upper bounds on the number of solutions to this equation. For instance

Yuki Nishimura

Hybrid logic is one of the extensions of modal logic. The many-dimensional product of hybrid logic is called hybrid product logic (HPL). We construct a sound and complete tableau calculus for two-dimensional HPL. Also, we made a tableau calculus for hybrid dependent product logic (HdPL), where one dimension depends on the other. In addition, we add a special

Jiajun Zhang, Boyu Chen, Zhilong Ji, Jinfeng Bai

This paper describes the approach we have taken in the challenge. We still adopted the two-stage scheme same as the last champion, that is, detection first and segmentation followed. We trained more powerful detector and segmentor separately. Besides, we also perform pseudo-label training on the test set, based on student-teacher framework and end-to-end tra

Fachrina Dewi Puspitasari, Lik-Hang Lee

In the era of virtuality, the increasingly ubiquitous technology bears the challenge of excessive user dependency, also known as user addiction. Augmented reality (AR) and virtual reality (VR) have become increasingly integrated into daily life. Although discussions about the drawbacks of these technologies are abundant, their exploration for solutions is st

Benjamin A. McNaughton, Nicola Pinto, Andrea Perali, Milorad V. Milosevic

Understanding the behaviour of vortices under nanoscale confinement in superconducting circuits is of importance for development of superconducting electronics and quantum technologies. Using numerical simulations based on the Ginzburg-Landau theory for non-homogeneous superconductivity in the presence of magnetic fields, we detail how lateral confinement or

Artem Agafonov, Brahim Erraji, Martin Takáč

In the recent paper FLECS (Agafonov et al, FLECS: A Federated Learning Second-Order Framework via Compression and Sketching), the second-order framework FLECS was proposed for the Federated Learning problem. This method utilize compression of sketched Hessians to make communication costs low. However, the main bottleneck of FLECS is gradient communication wi

Simona Diaconu

Let $G=G(n,p_n)$ be a homogeneous Erd\"os-R\'enyi graph, and $A$ its adjacency matrix with eigenvalues $\lambda_1(A) \geq \lambda_2(A) \geq ... \geq \lambda_n(A).$ Local laws have been used to show that $lambda_2(A)$ can exhibit fundamentally different behaviors: Tracy-Widom ($p_n \gg n^{-2/3}$), normal ($n^{-7/9} \ll p_n \ll~n^{-2/3}$), and a mix of both ($

Anders M. Buvarp, Daniel J. Jakubisin, William C. Headley, Jeffrey H. Reed

With the recent introduction of electromagnetic meta-surfaces and reconfigurable intelligent surfaces, a paradigm shift is currently taking place in the world of wireless communications and related industries. These new technologies have enabled the inclusion of the wireless channel as part of the optimization process. This is of great interest as we transit

Huy H. Nguyen, Trung-Nghia Le, Junichi Yamagishi, Isao Echizen

Finger vein recognition (FVR) systems have been commercially used, especially in ATMs, for customer verification. Thus, it is essential to measure their robustness against various attack methods, especially when a hand-crafted FVR system is used without any countermeasure methods. In this paper, we are the first in the literature to introduce master vein att

D. Werner, J. Lotze, E. Arrigoni

We present a solver for correlated impurity problems out of equilibrium based on a combination of the so-called auxiliary master equation approach (AMEA) and the configuration interaction expansion. Within AMEA one maps the original impurity model onto an auxiliary open quantum system with a restricted number of bath sites which can be addressed by numerical

Fabian Otto, Onur Celik, Hongyi Zhou, Hanna Ziesche

\Episode-based reinforcement learning (ERL) algorithms treat reinforcement learning (RL) as a black-box optimization problem where we learn to select a parameter vector of a controller, often represented as a movement primitive, for a given task descriptor called a context. ERL offers several distinct benefits in comparison to step-based RL. It generates smo

Miaomiao Zhang, Jouni Kainulainen

Dust extinction is one of the most reliable tracers of the gas distribution in the Milky Way. The near-infrared (NIR) Vista Variables in the Via Lactea (VVV) survey enables extinction mapping based on stellar photometry over a large area in the Galactic plane. We devise a novel extinction mapping approach, XPNICER, by bringing together VVV photometric catalo

Kijeong Yim, Tony Wong, Richard J. Rand

We observe the almost edge-on (i $\sim$ 90 degrees) galaxy NGC 4302 using ALMA (CO) and VLA (H I) to measure the gas disk thickness for investigating the volumetric star formation law (SFL). The recent star formation rate (SFR) is estimated based on a linear combination of IR 24 micron and H$\alpha$ emissions. The measured scale heights of CO and H I increas

Suchetana Sadhukhan, Poulomi Sadhukhan

This paper, for the first time, focuses on the sector-wise analysis of a stock market through multifractal analysis. We have considered Bombay Stock Exchange, India, and identified two time scales, short ($<200$ days) and long time-scale ($>200$ days) for investment. We infer that long-term investment will be more profitable. For long time scale, sectors can

Imran Khan Mirani, Chen Tianhua, Malak Abid Ali Khan, Syed Muhammad Aamir

Existing computer vision and object detection methods strongly rely on neural networks and deep learning. This active research area is used for applications such as autonomous driving, aerial photography, protection, and monitoring. Futuristic object detection methods rely on rectangular, boundary boxes drawn over an object to accurately locate its location.

Fei Zheng, Chaochao Chen, Lingjuan Lyu, Xinyi Fu

As a practical privacy-preserving learning method, split learning has drawn much attention in academia and industry. However, its security is constantly being questioned since the intermediate results are shared during training and inference. In this paper, we focus on the privacy leakage from the forward embeddings of split learning. Specifically, since the

Ishan Deo, Krishnacharya Khare

A nonlinear electronic circuit comprising of three nodes with a feedback loop is analyzed. The system has two stable states, a uniform state and a sinusoidal oscillating state, and it transitions from one to another by means of a Hopf bifurcation. The stability of this system is analyzed with nonlinear equations derived from a repressilator-like transistor c

Xin Li, Botian Shi, Yuenan Hou, Xingjiao Wu

Multi-modal 3D object detection has been an active research topic in autonomous driving. Nevertheless, it is non-trivial to explore the cross-modal feature fusion between sparse 3D points and dense 2D pixels. Recent approaches either fuse the image features with the point cloud features that are projected onto the 2D image plane or combine the sparse point c

Vsevolod F. Lev, Ilya D. Shkredov

Suppose that $A$ is a finite, nonempty subset of a cyclic group of either infinite or prime order. We show that if the difference set $A-A$ is ``not too large'', then there is a nonzero group element with at least as many as $(2+o(1))|A|^2/|A-A|$ representations as a difference of two elements of $A$; that is, the second largest number of representations is,

Peixuan Li, Wenlin Bai, Xihua Zou, Ningyuan Zhong

We here propose a novel cost-effective millimeter-level resolution photonic multiband radar system using a single MZM driven by a 1-GHz-bandwidth LFM signal. It experimentally shows an ~8.5-mm range resolution through coherence-processing-free multiband data fusion.

E. S. Pikina, E. I. Kats, A. R. Muratov, V. V. Lebedev

We present nonlinear dynamic equations for nematic and smectic $A$ liquid crystals in the presence of an alternating electric field and explain their derivation in detail. The local electric field acting in any liquid-crystalline system is expressed as a sum of external electric field and the fields originating from feedback of liquid crystal order parameter

Chandra Shekhar Murmu, Raghunath Ghara, Suman Majumdar, Kanan K. Datta

The Epoch of Reionization (EoR) remains a poorly understood cosmic era for the most part. Yet, efforts are still going on to probe and understand this epoch. We present a review of the latest developments in the techniques (especially line-intensity mapping) to study the EoR and try to highlight the contribution of the Indian community in this field. Line-em

Regina Tuganova, Anna Permyakova, Anna Kuznetsova, Karina Rakhmanova

Green technology is viewed as a means of creating a sustainable society and a catalyst for sustainable development by the global community. It is responsible for both the potential reduction of production waste and the reduction of carbon footprint and CO2 emissions. However, alongside with the growing popularity of green technologies, there is an emerging s

Narayan Mohanta, Rahul Soni, Satoshi Okamoto, Elbio Dagotto

Lattice geometry continues providing exotic topological phases in condensed matter physics. Exciting recent examples are the higher-order topological phases, manifesting via localized lower-dimensional boundary states. Moreover, flat electronic bands with a non-trivial topology arise in various lattices and can hold a finite superfluid density, bounded by th

Jie Chen, Shouzhen Chen, Mingyuan Bai, Junbin Gao

The message-passing mechanism helps Graph Neural Networks (GNNs) achieve remarkable results on various node classification tasks. Nevertheless, the recursive nodes fetching and aggregation in message-passing cause inference latency when deploying GNNs to large-scale graphs. One promising inference acceleration direction is to distill the GNNs into message-pa

P. K. Sreejith, T. S. Suraj, Hari Babu Vasili, Suresh Sreya

Realization of novel functionalities by tuning magnetic interactions in rare earth perovskite oxide thin films opens up exciting technological prospects. Strain-induced tuning of magnetic interactions in rare earth cobaltates and nickelates is of central importance due to their versatility in electronic transport properties. Here we reported the spin re-orie

Li-Juan Cheng, Anton Thalmaier, Feng-Yu Wang

Let $M$ be a complete connected Riemannian manifold with boundary $\partial M$, and let $P_t$ be the Neumann semigroup generated by $\frac{ 1}{ 2} L$ where $L=\Delta+Z$ for a $C^1$-vector field $Z$ on $M$. We establish Bismut type formulae for $LP_t f$ and ${\rm Hess}_{P_tf}$ and present estimates of these quantities under suitable curvature conditions. In c

Haofeng Liu, Heng Li, Huazhu Fu, Ruoxiu Xiao

As an economical and efficient fundus imaging modality, retinal fundus images have been widely adopted in clinical fundus examination. Unfortunately, fundus images often suffer from quality degradation caused by imaging interferences, leading to misdiagnosis. Despite impressive enhancement performances that state-of-the-art methods have achieved, challenges

Chelsea L. Miller, Peter J. Smith, Pawel A. Dmochowski

We develop an optimal version of a prior two-stage channel estimation protocol for RIS-assisted channels. The new design uses a modified DFT matrix (MDFT) for the training phases at the RIS and is shown to minimize the total channel estimation error variance. In conjunction with interpolation (estimating fewer RIS channels), the MDFT approach accelerates cha

Paul H. Frampton

In recent work we have extended the theory that dark matter is composed of primordial black hole (PBHs) to extremely high masses and made an assumption that the holographic entropy bound is saturated. Astrophysicists have recently suggested that PBHs are formed with electric charges Q, retain their charges for the age of the universe, all charges have the sa

Xiaoning Liu

As an ever-increasing demand for high dynamic range (HDR) scene shooting, multi-exposure image fusion (MEF) technology has abounded. In recent years, multi-scale exposure fusion approaches based on detail-enhancement have led the way for improvement in highlight and shadow details. Most of such methods, however, are too computationally expensive to be deploy

Yaoyao Ding, Cody Hao Yu, Bojian Zheng, Yizhi Liu

As deep learning models nowadays are widely adopted by both cloud services and edge devices, reducing the latency of deep learning model inferences becomes crucial to provide efficient model serving. However, it is challenging to develop efficient tensor programs for deep learning operators due to the high complexity of modern accelerators and the rapidly gr

Chen Cheng, Jinglai Li

Predicting the behaviors of pedestrian crowds is of critical importance for a variety of real-world problems. Data driven modeling, which aims to learn the mathematical models from observed data, is a promising tool to construct models that can make accurate predictions of such systems. In this work, we present a data-driven modeling approach based on the OD

BESIII Collaboration, M. Ablikim, M. N. Achasov, P. Adlarson

The decays $J/\psi\to\eta\Sigma^{+}\bar{\Sigma}{}^-$ and $\psi(3686)\to\eta\Sigma^{+}\bar{\Sigma}{}^-$ are observed for the first time, using $(10087 \pm 44)\times 10^{6}$ $J/\psi$ and $(448.1 \pm 2.9)\times 10^{6}$ $\psi(3686)$ events collected with the BESIII detector at the BEPCII collider. We determine the branching fractions of these two decays to be ${

Ioakeim Ampatzoglou, Irene M. Gamba, Nataša Pavlović, Maja Tasković

In this paper, we show generation and propagation of polynomial and exponential moments, as well as global well-posedness of the homogeneous binary-ternary Boltzmann equation. We also show that the co-existence of binary and ternary collisions yields better generation properties and time decay, than when only binary or ternary collisions are considered. To a