August 2022 arXiv papers — page 86

Moritz von Zahn, Oliver Hinz, Stefan Feuerriegel

Machine learning can provide predictions with disparate outcomes, in which subgroups of the population (e.g., defined by age, gender, or other sensitive attributes) are systematically disadvantaged. In order to comply with upcoming legislation, practitioners need to locate such disparate outcomes. However, previous literature typically detects disparities th

Gulshan Sharma, Pankaj Pandey, Ramanathan Subramanian, Krishna. P. Miyapuram

We examine user and song identification from neural (EEG) signals. Owing to perceptual subjectivity in human-media interaction, music identification from brain signals is a challenging task. We demonstrate that subjective differences in music perception aid user identification, but hinder song identification. In an attempt to address intrinsic complexities i

S. M. A. K. Rajin, M. Murshed, M. Paul, S. W. Teng

To exploit high temporal correlations in video frames of the same scene, the current frame is predicted from the already-encoded reference frames using block-based motion estimation and compensation techniques. While this approach can efficiently exploit the translation motion of the moving objects, it is susceptible to other types of affine motion and objec

Xingyu Xie, Pan Zhou, Huan Li, Zhouchen Lin

In deep learning, different kinds of deep networks typically need different optimizers, which have to be chosen after multiple trials, making the training process inefficient. To relieve this issue and consistently improve the model training speed across deep networks, we propose the ADAptive Nesterov momentum algorithm, Adan for short. Adan first reformulat

Yulan Zhang, Anna C. Gilbert, Stefan Steinerberger

Modern methods in dimensionality reduction are dominated by nonlinear attraction-repulsion force-based methods (this includes t-SNE, UMAP, ForceAtlas2, LargeVis, and many more). The purpose of this paper is to demonstrate that all such methods, by design, come with an additional feature that is being automatically computed along the way, namely the vector fi

Luca De Benedictis, Vania Licio, Anna Pinna

An integrated and widespread road system, like the one built during the Roman Empire in Italy, plays an important role today in facilitating the construction of new infrastructure. This paper investigates the historical path of Roman roads as main determinant of both motorways and railways in the country. The empirical analysis shows how the modern Italian t

Jingliang Li, Zhengda Lu, Yiqun Wang, Ying Wang

In recent years, supervised or unsupervised learning-based MVS methods achieved excellent performance compared with traditional methods. However, these methods only use the probability volume computed by cost volume regularization to predict reference depths and this manner cannot mine enough information from the probability volume. Furthermore, the unsuperv

Keiji Nakatsugawa, Tatsuhiko N. Ikeda, Takeshi Toshima, Satoshi Tanda

New quantum phenomena are continuously being discovered in 2D systems. In particular, the charge density wave (CDW) has the aspect of a quantum crystal with a macroscopic wave function (order parameter), so unlike quantum liquids (superconductivity, quantum Hall liquids $^3$He, $^4$He), new ground states such as supersolid and Moir\'e solids can be expected.

Joseph Mathews, Scott C. Schmidler

We prove finite sample complexities for sequential Monte Carlo (SMC) algorithms which require only local mixing times of the associated Markov kernels. Our bounds are particularly useful when the target distribution is multimodal and global mixing of the Markov kernel is slow; in such cases our approach establishes the benefits of SMC over the corresponding

Yongqiang Mao, Zonghao Guo, Xiaonan Lu, Zhiqiang Yuan

Few-shot segmentation of point cloud remains a challenging task, as there is no effective way to convert local point cloud information to global representation, which hinders the generalization ability of point features. In this study, we propose a bidirectional feature globalization (BFG) approach, which leverages the similarity measurement between point fe

Zhichao Shao, Xiaojun Yuan, Wei Zhang, Marco Di Renzo

In this work, we investigate a reconfigurable intelligent surface (RIS) aided integrated sensing and communication scenario, where a base station (BS) communicates with multiple devices in a full-duplex mode, and senses the positions of these devices simultaneously. An RIS is assumed to be mounted on each device to enhance the reflected echoes. Meanwhile, th

Mark Gibbons

Pressure-induced, spontaneous diamagnetism associated with critical behaviour is determined experimentally in a polar dielectric fluid containing nanoscale, clathrate hydrate cage structures. As with Type II superconductivity, Abrikosov vortices come to penetrate the external diamagnetic field such that it reduces to zero for particular values of the magnet

Sharun Kuhar, Jae Ho Lee, Jung-Hee Seo, Pankaj J Pasricha

The peristaltic motion of the stomach walls combines with the secretion of enzymes to initiate the process that breaks down food. Computational modelling of this phenomenon can help reveal the details that would be hard to capture via in-vivo or in-vitro means. In this study, the digestion of a liquid meal containing protein is simulated in a human-stomach m

Aihua Zhang, Sun Choi

We have developed efficient techniques to solve the first-time problems of Brownian motion. Based on a time-scale separation of recrossings, we show that Eyring's transmission coefficient ($\kappa$) equals to the one ($\kappa_\mathrm{V}$) corresponding to an absorbing boundary consistent with the transition state theory, which is greater than the one ($\kapp

Weiming Sun, Zimao Zhang

Fourier series multiscale method, a concise and efficient analytical approach for multiscale computation, will be developed out of this series of papers. In the fourth paper, the application of the Fourier series multiscale method to the one- and two-dimensional convection-diffusion-reaction equations is implemented, where the Fourier series multiscale solut

Brian Belgodere, Vijil Chenthamarakshan, Payel Das, Pierre Dognin

With the prospect of automating a number of chemical tasks with high fidelity, chemical language processing models are emerging at a rapid speed. Here, we present a cloud-based real-time platform that allows users to virtually screen molecules of interest. For this purpose, molecular embeddings inferred from a recently proposed large chemical language model,

Reggie C. Pantig, Leonardo Mastrototaro, Gaetano Lambiase, Ali Övgün

Motivated by recent work on the Modified Maxwell (ModMax) black holes [Phys.Lett.B 10.1016/j.physletb.2020.136011], which are invariant in duality rotations and conformal transformations founded in [ Phys.Rev.D 10.1103/PhysRevD.102.121703], we probe its effects on the shadow cast, weak field gravitational lensing, and neutrino propagation in its vicinity. Us

Shreya Khisa, Mohamad Elhattab, Chadi Assi, Sanaa Sharafeddine

This paper presents a downlink reconfigurable intelligent surface (RIS)-assisted half-duplex (HD) cooperative rate splitting multiple access (C-RSMA) networks. The proposed system model is built up considering one base station (BS), one RIS, and two users. With the goal of minimizing the network energy consumption, a joint framework to optimize the precoding

Melika Ayoughi, Pascal Mettes, Paul Groth

This paper introduces the task of visual named entity discovery in videos without the need for task-specific supervision or task-specific external knowledge sources. Assigning specific names to entities (e.g. faces, scenes, or objects) in video frames is a long-standing challenge. Commonly, this problem is addressed as a supervised learning objective by manu

Ruida Zhang, Yan Di, Fabian Manhardt, Federico Tombari

Category-level pose estimation is a challenging problem due to intra-class shape variations. Recent methods deform pre-computed shape priors to map the observed point cloud into the normalized object coordinate space and then retrieve the pose via post-processing, i.e., Umeyama's Algorithm. The shortcomings of this two-stage strategy lie in two aspects: 1) T

Yiheng Lu, Ziyu Guan, Yaming Yang, Maoguo Gong

Structured pruning techniques have achieved great compression performance on convolutional neural networks for image classification task. However, the majority of existing methods are weight-oriented, and their pruning results may be unsatisfactory when the original model is trained poorly. That is, a fully-trained model is required to provide useful weight

K. K. Rao, K. Vaidya, M. Agarwal, A. Panthi

Ultraviolet (UV) wavelength observations have made a significant contribution to our understanding of hot stellar populations of star clusters. Multi-wavelength spectral energy distributions (SEDs) of stars, including ultraviolet observations, have proven to be an excellent tool for discovering unresolved hot companions in exotic stars such as blue straggler

Jiazhi Li, Tingting Zhou, Yunnong Chen, Yanfang Chang

While some work attempt to generate front-end code intelligently from UI screenshots, it may be more convenient to utilize UI design drafts in Sketch which is a popular UI design software, because we can access multimodal UI information directly such as layers type, position, size, and visual images. However, fragmented layers could degrade the code quality

Tim Kokkeler, Yukio Tanaka, Alexander A. Golubov

An SNN-junction in which the superconducting potential is a mixture between s-wave and p-wave potentials is investigated using the Usadel equation equipped with Tanaka-Nazarov boundary conditions. The article provides several ways to distinguish between s + chiral and s + helical p-wave superconductors and a way to determine whether a superconductor has a mi

Soumya Sengupta

The solutions of the \textit{diffuse reflection finite atmosphere problem} are very useful in the astrophysical context. Chandrasekhar was the first to solve this problem analytically, by considering atmospheric scattering. These results have wide applications in the modeling of planetary atmospheres. However, they cannot be used to model an atmosphere with

Kanchanok Kannee, Supatsara Wattanakriengkrai, Ruksit Rojpaisarnkit, Raula Gaikovina Kula

An increase in diverse technology stacks and third-party library usage has led developers to inevitably switch technologies. To assist these developers, maintainers have started to release their libraries to multiple technologies, i.e., a cross-ecosystem library. Our goal is to explore the extent to which these cross-ecosystem libraries are intertwined betwe

Guilong Gui, Zhifei Zhang

We investigate in this paper the global stability of the compressible viscous surface waves in the absence of surface tension effect with a steady-state violating Rayleigh-Taylor instability and the reference domain being the horizontal infinite layer. The fluid dynamics are governed by the 3-D gravity-driven isentropic compressible Navier-Stokes equations.

Dan-Yang Liu, Chengfeng Cai, Xue-Min Jiang, Zhao-Huan Yu

We propose an ultraviolet completion model for pseudo-Nambu-Goldstone dark matter with a hidden $\mathrm{U}(1)$ gauge symmetry. Compared to previous studies, this setup is simpler, introducing less interactions. Dark matter scattering off nucleons is highly suppressed by the ultraviolet scale and direct detection constraints can be easily evaded. The kinetic

Stanisław J. Purgał, David M. Cerna, Cezary Kaliszyk

Synthesizing large logic programs through symbolic Inductive Logic Programming (ILP) typically requires intermediate definitions. However, cluttering the hypothesis space with intensional predicates typically degrades performance. In contrast, gradient descent provides an efficient way to find solutions within such high-dimensional spaces. Neuro-symbolic ILP

Xin Wang, Heng Chang, Beini Xie, Tian Bian

Graph neural networks (GNNs) have achieved tremendous success in the task of graph classification and its diverse downstream real-world applications. Despite the huge success in learning graph representations, current GNN models have demonstrated their vulnerability to potentially existent adversarial examples on graph-structured data. Existing approaches ar

Junhang Li, Yezhenyang Tang, Chen Wang

Let $p$ be an odd prime. In this paper, by using the well-known Karlsson-Minton summation formula, we mainly prove two supercongruences as variants of a supercongruence of Deines-Fuselier-Long-Swisher-Tu, which confirm some recent conjectures of V.J.W. Guo.

Mohamed A. Zaitri, Cristiana J. Silva, Delfim F. M. Torres

We analyze mathematical models for COVID-19 with discrete time delays and vaccination. Sufficient conditions for the local stability of the endemic and disease-free equilibrium points are proved for any positive time delay. The stability results are illustrated through numerical simulations performed in MATLAB.

Vincent Jeanselme, Maria De-Arteaga, Zhe Zhang, Jessica Barrett

Machine learning risks reinforcing biases present in data and, as we argue in this work, in what is absent from data. In healthcare, societal and decision biases shape patterns in missing data, yet the algorithmic fairness implications of group-specific missingness are poorly understood. The way we address missingness in healthcare can have detrimental impac

Jan-Niclas Siemß, Florian Fitzek, Christian Schubert, Ernst M. Rasel

Large-momentum-transfer~(LMT) atom interferometers using elastic Bragg scattering on light waves are among the most precise quantum sensors to date. To advance their accuracy from the mrad to the $\mu$rad regime, it is necessary to understand the rich phenomenology of the Bragg interferometer, which differs significantly from that of a standard two-mode inte

Xiaoliang Lei, Hao Mei, Bin Shi, Hua Wei

Modeling how network-level traffic flow changes in the urban environment is useful for decision-making in transportation, public safety and urban planning. The traffic flow system can be viewed as a dynamic process that transits between states (e.g., traffic volumes on each road segment) over time. In the real-world traffic system with traffic operation acti

John K. Aceti, Jeremy Brazas

When non-trivial local structures are present in a topological space $X$, a common approach to characterizing the isomorphism type of the $n$-th homotopy group $\pi_n(X,x_0)$ is to consider the image of $\pi_n(X,x_0)$ in the $n$-th \v{C}ech homotopy group $\check{\pi}_n(X,x_0)$ under the canonical homomorphism $\Psi_{n}:\pi_n(X,x_0)\to \check{\pi}_n(X,x_0)$.

Neetu Raj Singh Chundawat, Arindam Mandal, Trisha Sarkar

The phenomena of neutrino spin flavour precession in the presence of an extraneous magnetic field is a repercussion of neutrino magnetic moment which is consociated with the physics beyond the standard model of electroweak interactions. Ultra high energy neutrinos are spawned from a number of sources in the universe including the highly energetic astrophysic

Zhaoshan Liu, Qiujie Lv, Ziduo Yang, Yifan Li

The transformer is primarily used in the field of natural language processing. Recently, it has been adopted and shows promise in the computer vision (CV) field. Medical image analysis (MIA), as a critical branch of CV, also greatly benefits from this state-of-the-art technique. In this review, we first recap the core component of the transformer, the attent

Elif Deniz, Adnan Rashid, Osman Hasan, Sofiène Tahar

Partial Differential Equations (PDEs) are widely used for modeling the physical phenomena and analyzing the dynamical behavior of many engineering and physical systems. The heat equation is one of the most well-known PDEs that captures the temperature distribution and diffusion of heat within a body. Due to the wider utility of these equations in various saf

Salih Kibaroğlu

In this study, we consider a cosmological model for the Maxwell gravity which is constructed by gauging the semi-simple extended Poincar\'e algebra. Inspired by the Einstein-Yang-Mills theory, we describe the Maxwell gauge field in terms of two additional time-dependent scalar fields. Within the context of a homogeneous and isotropic Friedmann-Lema\^itre-Rob

Keyur Patel, Joao Faccin, Abdelwahab Hamou-Lhadj, Ingrid Nunes

Logging plays a crucial role in software engineering because it is key to perform various tasks including debugging, performance analysis, and detection of anomalies. Despite the importance of log data, the practice of logging still suffers from the lack of common guidelines and best practices. Recent studies investigated logging in C/C++ and Java open-sourc

Caiyu Jiao, Changpin Li, Hexiang Wang, Zhongqiang Zhang

We develop walk-on-sphere for fractional Poisson equations with Dirichilet boundary conditions in high dimensions. The walk-on-sphere method is based on probabilistic represen tation of the fractional Poisson equation. We propose effcient quadrature rules to evaluate integral representation in the ball and apply rejection sampling method to drawing from the

Mridula Mundalia, S. Sivaprasad Kumar

In this paper, sharp bounds are established for the second Hankel determinant of logarithmic coefficients for normalised analytic functions satisfying certain differential inequality.

Yuanyang Hu, Chengxia Lei

This paper is devoted to investigate the extinction and propagation properties of solutions to the graph Laplacian parabolic problems with Kpp type or Allen-Cahn type forcing terms on graphs. To this end, we establish the (strong) maximum principle and the upper and lower solutions method for parabolic and elliptic problems on graphs. The stability of equili

Shigemichi Matsuzaki, Hiroaki Masuzawa, Jun Miura

This paper describes a method of online refinement of a scene recognition model for robot navigation considering traversable plants, flexible plant parts which a robot can push aside while moving. In scene recognition systems that consider traversable plants growing out to the paths, misclassification may lead the robot to getting stuck due to the traversabl

V. Uma

We describe the $G$-equivariant Grothendieck ring of a regular compactification $X$ of an adjoint symmetric space $G/H$ of minimal rank. This extends the results of Brion and Joshua for the equivariant Chow ring of wonderful symmetric varieties of minimal rank and generalizes the results by the author on the regular compactification of an adjoint semisimple

R. Razera, B. Barbuy, T. C. Moura, H. Ernandes

The central part of the Galaxy host a multitude of stellar populations, including the spheroidal bulge stars, stars moved to the bulge through secular evolution of the bar, inner halo, inner thick disk, inner thin disk, as well as debris from past accretion events. We identified a sample of 58 candidate stars belonging to the stellar population of the sphero

Torben L. Purz, Blake T. Hipsley, Eric W. Martin, Ronald Ulbricht

We demonstrate rapid imaging based on four-wave mixing (FWM) by assessing the quality of advanced materials through measurement of their nonlinear response, exciton dephasing, and exciton lifetimes. We use a WSe$_2$ monolayer grown by chemical vapor deposition as a canonical example to demonstrate these capabilities. By comparison, we show that extracting ma

Jaymark A. Yambao, John Paul P. Miranda, Earl Lawrence B. Pelayo

The focus of the study was to develop a mobile application utilizing marker-less augmented reality for specific made-to-order products to support furniture and fixtures businesses. The study implemented mixed-methodology to properly identify the various stakeholders' considerations in developing the application. Interviews with key informants were conducted

Nicolas Kruchten, Jon Mease, Dominik Moritz

The Vega grammar has been broadly adopted by a growing ecosystem of browser-based visualization tools. However, the reference Vega renderer does not scale well to large datasets (e.g., millions of rows or hundreds of megabytes) because it requires the entire dataset to be loaded into browser memory. We introduce VegaFusion, which brings automatic server-side

Carla Groenland, Tom Johnston, Jamie Radcliffe, Alex Scott

We investigate the following generalisation of permutation networks. We say a sequence $T=(T_1,\dots,T_\ell)$ of transpositions in $S_n$ forms a $t$-reachability network if, for every choice of $t$ distinct points $x_1, \dots, x_t\in \{1,\dots,n\}$, there is a subsequence of $T$ whose composition maps $j$ to $x_j$ for every $1\leq j\leq t$. When $t=n$, any p

Carla Groenland, Tom Johnston, Jamie Radcliffe, Alex Scott

A lazy transposition $(a,b,p)$ is the random permutation that equals the identity with probability $1-p$ and the transposition $(a,b)\in S_n$ with probability $p$. How long must a sequence of independent lazy transpositions be if their composition is uniformly distributed? It is known that there are sequences of length $\binom{n}2$, but are there shorter seq

Stefano Longari, Alessandro Nichelini, Carlo Alberto Pozzoli, Michele Carminati

Over the years, the increasingly complex and interconnected vehicles raised the need for effective and efficient Intrusion Detection Systems against on-board networks. In light of the stringent domain requirements and the heterogeneity of information transmitted on Controller Area Network, multiple approaches have been proposed, which work at different abstr

Surabhi Gupta, Sudhir Kumar Gupta

Information theory turns out to be an interesting tool for studying the consequences of Higgs observations to various new physics candidate theories by means of the information measure as the entropy of Higgs-Boson through its various detection modes at the Large Hadron Collider. The present article investigates the parameter space of a supersymmetric scenar

Maria Axenovich, József Balogh, Felix Christian Clemen, Lea Weber

Erd\H{o}s, F\"uredi, Rothschild and S\'os initiated a study of classes of graphs that forbid every induced subgraph on a given number $m$ of vertices and number $f$ of edges. Extending their notation to $r$-graphs, we write $(n,e) \to_r (m,f)$ if every $r$-graph $G$ on $n$ vertices with $e$ edges has an induced subgraph on $m$ vertices and $f$ edges. The \em

Andrea Maino, Matteo Alberi, Emiliano Anceschi, Enrico Chiarelli

Soil texture is key information in agriculture for improving soil knowledge and crop performance, so the accurate mapping of this crucial feature is imperative for rationally planning cultivations and for targeting interventions. We studied the relationship between radioelements and soil texture in the Mezzano Lowland (Italy), a 189 $km^2$ agricultural plain

Robert Raussendorf

We describe a joint cohomological framework for measurement-based quantum computation (MBQC) and the corresponding contextuality proofs. The central object in this framework is an element in the second cohomology group of the chain complex describing a given MBQC. It contains the function computed, up to gauge equivalence, and at the same time is a contextua

Mani A

Up-directed rough sets are introduced and studied by the present author in earlier papers. This is extended by her in two different granular directions in this research, with a surprising algebraic semantics. The granules are based on ideas of generalized closure under up-directedness that may be read as a form of weak consequence. This yields approximation

Ibrahim Yildirim, Asil Koc, Ertugrul Basar, Tho Le-Ngoc

This paper proposes a reconfigurable intelligent surface (RIS)-aided and angular-based hybrid beamforming (AB-HBF) technique for the millimeter wave (mmWave) massive multiple-input multiple-output (MIMO) systems. The proposed RIS-AB-HBF architecture consists of three stages: (i) RF beamformer, (ii) baseband (BB) precoder/combiner, and (iii) RIS phase shift d

S. Iimura, M. Rosenbusch, A. Takamine, Y. Tsunoda

The atomic masses of $^{55}$Sc, $^{56,58}$Ti, and $^{56-59}$V have been determined using the high-precision multi-reflection time-of-flight technique. The radioisotopes have been produced at RIKEN's RIBF facility and delivered to the novel designed gas cell and multi-reflection system (ZD MRTOF), which has been recently commissioned downstream of the ZeroDeg

Pio Calderon, Rohit Ram, Marian-Andrei Rizoiu

Online extremism has severe societal consequences, including normalizing hate speech, user radicalization, and increased social divisions. Various mitigation strategies have been explored to address these consequences. One such strategy uses positive interventions: controlled signals that add attention to the opinion ecosystem to boost certain opinions. To e

Andi Han, Bamdev Mishra, Pratik Jawanpuria, Junbin Gao

In this paper, we propose a simple acceleration scheme for Riemannian gradient methods by extrapolating iterates on manifolds. We show when the iterates are generated from Riemannian gradient descent method, the accelerated scheme achieves the optimal convergence rate asymptotically and is computationally more favorable than the recently proposed Riemannian

Dmitry A. Garanin, Eugene M. Chudnovsky

Abstract We report Monte Carlo studies of lattices of up to $10^{5}$ skyrmions treated as particles with negative core energy and repulsive interaction obtained from a microscopic spin model. Temperature dependence of translational and orientational correlations has been investigated for different experimental protocols and initial conditions. Cooling the sk

Johann Christian Stumpenhusen

We generalise the sum-of-divisors-function $\sigma$ and evenness to the rings of integers of certain algebraic number fields. In particular, we present necessary and sufficient conditions for even Eisenstein integers to be (norm-)perfect based on the work of McDaniel [McD] on Gaussian integers. Furthermore, some results concerning odd norm-perfect Eisenstein

Emadeldeen Eldele, Mohamed Ragab, Zhenghua Chen, Min Wu

Learning time-series representations when only unlabeled data or few labeled samples are available can be a challenging task. Recently, contrastive self-supervised learning has shown great improvement in extracting useful representations from unlabeled data via contrasting different augmented views of data. In this work, we propose a novel Time-Series repres

Zhenshan Tan, Cheng Chen, Keyu Wen, Yuzhuo Qin

Co-saliency detection (CoSOD) aims at discovering the repetitive salient objects from multiple images. Two primary challenges are group semantics extraction and noise object suppression. In this paper, we present a unified Two-stage grOup semantics PropagatIon and Contrastive learning NETwork (TopicNet) for CoSOD. TopicNet can be decomposed into two substruc

R. S. Watson, S. A. Simmons, K. V. Kheruntsyan

Generalized hydrodynamics (GHD) is a recent theoretical approach that is becoming a go-to tool for characterizing out-of-equilibrium phenomena in integrable and near-integrable quantum many-body systems. Here, we benchmark its performance against an array of alternative theoretical methods, for an interacting one-dimensional Bose gas described by the Lieb-Li

Stefanos Ioannou, Hana Chockler, Alexander Hammers, Andrew P. King

Convolutional neural networks (CNNs) are increasingly being used to automate the segmentation of brain structures in magnetic resonance (MR) images for research studies. In other applications, CNN models have been shown to exhibit bias against certain demographic groups when they are under-represented in the training sets. In this work, we investigate whethe

Itzik Malkiel, Dvir Ginzburg, Oren Barkan, Avi Caciularu

Recently, there has been growing interest in the ability of Transformer-based models to produce meaningful embeddings of text with several applications, such as text similarity. Despite significant progress in the field, the explanations for similarity predictions remain challenging, especially in unsupervised settings. In this work, we present an unsupervis

Huan Huang

This paper shows that the endograph metric and the $\Gamma$-convergence are compatible on a large class of fuzzy set in $\mathbb{R}^m$.

Valerii Kachin, Teemu Ojanen, Jose L. Lado, Timo Hyart

In two-dimensional superconductors, Yu-Shiba-Rusinov bound states, induced by the magnetic impurities, extend over long distances giving rise to a long-range hopping model supporting a large number of topological phases with distinct Chern numbers. Here, we study how the electron-electron interactions affect on a mean-field level the selection of the realize

Itzik Malkiel, Dvir Ginzburg, Oren Barkan, Avi Caciularu

We present MetricBERT, a BERT-based model that learns to embed text under a well-defined similarity metric while simultaneously adhering to the ``traditional'' masked-language task. We focus on downstream tasks of learning similarities for recommendations where we show that MetricBERT outperforms state-of-the-art alternatives, sometimes by a substantial marg

Mohammadreza Ebrahimi Khuzani, Behrouz Mirza, Mahnaz Tavakoli Kachi

In this paper, we study thermodynamic geometry for pure Lovelock black holes. The thermodynamics scalar curvature contains information about the interaction of microstates that might be repulsive or attractive. We obtain critical exponents and critical amplitudes of scalar and extrinsic curvatures for small and large black holes for various dimensions. We de

Michael Ruderman, Johann Reger, Benjamin Calmbach, Leonid Fridman

Continuous higher order sliding mode (CHOSM) controllers represent an efficient tool for disturbance rejection. For the systems with relative degree r, CHOSM approaches provide theoretically exact compensation of the matched Lipschitz perturbation, ensuring the finite-time convergence to the (r+1)-th sliding-mode set, by using only information on the sliding

Kaiguang Yang, Yeping Wang, Qianhao Luo, Xin Liu

Occupational pneumoconiosis (OP) staging is a vital task concerning the lung healthy of a subject. The staging result of a patient is depended on the staging standard and his chest X-ray. It is essentially an image classification task. However, the distribution of OP data is commonly imbalanced, which largely reduces the effect of classification models which

Abhijit Let, Arunoday Sarkar, Chitrak Sarkar, Buddhadeb Ghosh

We derive a single field slow-roll inflaton potential in three intersecting $D7$ branes configuration under type IIB/F-theory compactification. Among three resulting K\"{a}hler moduli corresponding to three orthogonal directions, two are stabilized via perturbative corrections in K\"{a}hler potential arising from large volume scenario ($\alpha'^3$) and four

M. G Hariprasad, P. Bandyopadhyay, V. S. Nikolaev, D. A. Kolotinskii

A complex (dusty) plasma system is well known as a paradigmatic model for studying the kinetics of solid-liquid phase transitions in inactive condensed matter. At the same time, under certain conditions a complex plasma system can also display characteristics of an active medium with the micron-sized particles converting energy of the ambient environment int

Sehyun Hwang, Sohyun Lee, Sungyeon Kim, Jungseul Ok

We consider the problem of active domain adaptation (ADA) to unlabeled target data, of which subset is actively selected and labeled given a budget constraint. Inspired by recent analysis on a critical issue from label distribution mismatch between source and target in domain adaptation, we devise a method that addresses the issue for the first time in ADA.

Yuanyi Liu, Jia Chen, Di Wu

Extracting the latent information in high-dimensional and incomplete matrices is an important and challenging issue. The Latent Factor Analysis (LFA) model can well handle the high-dimensional matrices analysis. Recently, Particle Swarm Optimization (PSO)-incorporated LFA models have been proposed to tune the hyper-parameters adaptively with high efficiency.

Sanoli Gun, Olivier Ramaré, Jyothsnaa Sivaraman

Let $\mathbf{K}$ be a number field and $\mathfrak{q}$ an integral ideal in $\mathcal{O}_{\mathbf{K}}$. A result of Tatuzawa from 1973, computes the asymptotic (with an error term) for the number of ideals with norm at most $x$ in a class of the narrow ray class group of $\mathbf{K}$ modulo $\mathfrak{q}$. This result bounds the error term with a constant who

Yangyang Xu Zeyang Zhou, Shengfeng He

We resolve the ill-posed alpha matting problem from a completely different perspective. Given an input portrait image, instead of estimating the corresponding alpha matte, we focus on the other end, to subtly enhance this input so that the alpha matte can be easily estimated by any existing matting models. This is accomplished by exploring the latent space o

Wen-Qiang Liu, Hai-Rui Wei

Universal quantum gates lie at the heart of designing quantum computer. We construct two compact quantum circuits to implement post-selected controlled-phase-flip (CPF) gate and Toffoli gate with linear optics assisted by one and two single photons, respectively. The current existing maximum success probability of 1/4 for linear optical CPF gate is achieved

Dragos Oprea

We study tautological vector bundles over the Hilbert scheme of points on surfaces. For each K-trivial surface, we write down a simple criterion ensuring that the tautological bundles are big and nef, and illustrate it by examples. In the K3 case, we extend recent constructions and results of Bini, Boissi\`ere and Flamini from the Hilbert scheme of 2 and 3 p

Zhaomin Wu, Qinbin Li, Bingsheng He

As societal concerns on data privacy recently increase, we have witnessed data silos among multiple parties in various applications. Federated learning emerges as a new learning paradigm that enables multiple parties to collaboratively train a machine learning model without sharing their raw data. Vertical federated learning, where each party owns different

Jie Ran, Rui Lin, Jason Chun Lok Li, Jiajun Zhou

A novel deep neural network (DNN) architecture is proposed wherein the filtering and linear transform are realized solely with product quantization (PQ). This results in a natural implementation via content addressable memory (CAM), which transcends regular DNN layer operations and requires only simple table lookup. Two schemes are developed for the end-to-e

Karolina Stefańska, Pierre Béjot, Karol Tarnowski, Bertrand Kibler

We provide a complete analysis, from theory to experiment, of the spontaneous emergence of a discretized conical wave of X-type (i.e., a localized 2D+1 space-time wavepacket) when an intense ultrashort pulse nonlinearly propagates in a multimode fiber. In particular, we reveal that this spatiotemporal phenomenon corresponds to broadband intermodal dispersive

Yinong Zhao, Cristian Huepe, Pawel Romanczuk

We study a model of self-propelled particles interacting with their $k$ nearest neighbors through polar alignment. By exploring its phase space as a function of two nondimensional parameters (alignment strength $g$ and Peclet number $\mathrm{Pe}$), we identify two distinct order-disorder transitions. One is continuous, occurs at a low critical $g$ value inde

Yufeng Lu

We show some new local smoothing estimates of the fractional Schr\"odinger equations with initial data in $\alpha$-modulation spaces via decoupling inequalities. Furthermore, our necessary conditions show that the local smoothing estimates are sharp in some cases. As applications, the local smoothing estimates could show some new local well-posedness on modu

Tadeusz Kulczycki, Oleksii Kulyk, Michał Ryznar

We study SDE $$ d X_t = b(X_t) \, dt + A(X_{t-}) \, d Z_t, \quad X_{0} = x \in \mathbb{R}^d, \quad t \geq 0 $$ where $Z=(Z^1, \dots, Z^d)^T$, with $Z^i, i=1,\dots, d$ being independent one-dimensional symmetric jump L\'evy processes, not necessarily identically distributed. In particular, we cover the case when each $Z^i$ is one-dimensional symmetric $\alpha

V Mora-Afonso, Pino Caballero-Gil

This work includes a review of two cases study of mobile applications that use Identity-Based Cryptography (IBC) to protect communications. It also describes a proposal of a new mobile application that combines the use of IBC for Wi-Fi or Bluetooth communication between smartphones, with the promising Near Field Communication (NFC) technology for secure auth

Jezabel Molina-Gil, Pino Caballero-Gil, Cándido Caballero-Gil, Amparo Fúster-Sabater

The fourth generation of cell phones, marketed as 4G/LTE (Long-Term Evolution) is being quickly adopted worldwide. Given the mobile and wireless nature of the involved communications, security is crucial. This paper includes both a theoretical study and a practical analysis of the SNOW 3G generator, included in such a standard for protecting confidentiality

Tong Wang, Yuan Yao, Feng Xu, Miao Xu

Backdoor attacks have been shown to be a serious security threat against deep learning models, and detecting whether a given model has been backdoored becomes a crucial task. Existing defenses are mainly built upon the observation that the backdoor trigger is usually of small size or affects the activation of only a few neurons. However, the above observatio

Wolfram Bauer, Robert Fulsche

The resolvent algebra $\mathcal{R}(X, \sigma)$ associated to a symplectic space $(X, \sigma)$ was introduced by D. Buchholz and H. Grundling as a convenient model of the canonical commutation relation (CCR) in quantum mechanics. We first study a representation of $\mathcal{R}(\mathbb{C}^n, \sigma)$ with the standard symplectic form $\sigma$ inside the full T

Hiroshi Yamakawa, Yutaka Matsuo

Human-level AI will have significant impacts on human society. However, estimates for the realization time are debatable. To arrive at human-level AI, artificial general intelligence (AGI), as opposed to AI systems that are specialized for a specific task, was set as a technically meaningful long-term goal. But now, propelled by advances in deep learning, th

Jingxi Li, Bijie Bai, Yi Luo, Aydogan Ozcan

We report deep learning-based design of a massively parallel broadband diffractive neural network for all-optically performing a large group of arbitrarily-selected, complex-valued linear transformations between an input and output field-of-view, each with N_i and N_o pixels, respectively. This broadband diffractive processor is composed of N_w wavelength ch

Musavvir Ali, Ehtesham Akhter

A class of real functions, which is the generalization of a family of convex functions, is introduced; in this connection, we have defined $X$-convex, strictly $X$-convex, quasi-$X$-convex, strictly quasi-$X$-convex, and semi-strictly quasi-$X$-convex functions. Moreover, in this paper, we give a detailed study of the fundamental properties of these function

Jiahui Zhang, Yinhua Xia, Yan Xu

This paper develops the structure-preserving finite volume weighted essentially non-oscillatory (WENO) hybrid schemes for the shallow water equations under the arbitrary Lagrangian-Eulerian (ALE) framework, dubbed as ALE-WENO schemes. The WENO hybrid reconstruction is adopted on moving meshes, which distinguishes the smooth, non-smooth, and transition stenci

Jin Won Kim, Prashant G. Mehta

This paper is concerned with the development and use of duality theory for a nonlinear filtering model with white noise observations. The main contribution of this paper is to introduce a stochastic optimal control problem as a dual to the nonlinear filtering problem. The mathematical statement of the dual relationship between the two problems is given in th

Jin Won Kim, Prashant G. Mehta

This paper is concerned with the development and use of duality theory for a hidden Markov model (HMM) with white noise observations. The main contribution of this work is to introduce a backward stochastic differential equation (BSDE) as a dual control system. A key outcome is that stochastic observability (resp. detectability) of the HMM is expressed in du

Naser Ahmadiniaz, James P. Edwards, Cristhiam Lopez-Arcos, Misha A. Lopez-Lopez

The worldline formalism shares with string theory the property that it allows one to write down master integrals that effectively combine the contributions of many Feynman diagrams. While at the one-loop level these diagrams differ only by the position of the external legs along a fixed line or loop, at multiloop they generally involve different topologies.