July 2022 arXiv papers — page 2

Zeki Hayran, Jacob B. Khurgin, Francesco Monticone

Photonic time-varying systems have attracted significant attention owing to their rich physics and potential opportunities for new and enhanced functionalities. In this context, the duality of space and time in wave physics has been particularly fruitful to uncover interesting physical effects in the temporal domain, such as reflection/refraction at temporal

Luciano Margara

We study the following problem. Given a multiset $M$ of non-negative integers, decide whether there exist and, in the positive case, compute two non-trivial multisets whose Minkowski sum is equal to $M$. The Minkowski sum of two multisets A and B is a multiset containing all possible sums of any element of A and any element of B. This problem was proved to b

Linde S. Hesse, Ana I. L. Namburete

Convolutional neural networks (CNNs) have shown exceptional performance for a range of medical imaging tasks. However, conventional CNNs are not able to explain their reasoning process, therefore limiting their adoption in clinical practice. In this work, we propose an inherently interpretable CNN for regression using similarity-based comparisons (INSightR-N

Long Li, Jiansheng Yang, Bo Zhang, Haiwen Zhang

In this paper, we establish new results for the uniform far-field asymptotics of the two-layered Green function (together with its derivatives) in 2D in the frequency domain. To the best of our knowledge, our results are the sharpest yet obtained. The steepest descent method plays an important role in the proofs of our results. Further, as an application of

Yirun Wang, Gongpu Wang, Ruisi He, Bo Ai

The critical distinction between the emerging intelligent transparent surface (ITS) and intelligent reflection surface (IRS) is that the incident signals can penetrate the ITS instead of being reflected, which enables the ITS to combat the severe signal penetration loss for high-speed railway (HSR) wireless communications. This paper thus investigates the ch

Chun-Kai Kevin Chien

Consider a fractional operator $P^s$, $0<s<1$, for connection Laplacian $P:=\nabla^*\nabla+A$ on a smooth Hermitian vector bundle over a closed, connected Riemannian manifold of dimension $n\geq 2$. We show that local knowledge of the metric, Hermitian bundle, connection, potential, and source-to-solution map associated with $P^s$ determines these structures

Zihao Yin, Ping Gong, Chunyu Wang, Yizhou Yu

As an important upstream task for many medical applications, supervised landmark localization still requires non-negligible annotation costs to achieve desirable performance. Besides, due to cumbersome collection procedures, the limited size of medical landmark datasets impacts the effectiveness of large-scale self-supervised pre-training methods. To address

G. G. Matos, T. Koide, T. Kodama

In the stochastic formulation of viscous hydrodynamics, the velocity of a fluid element fluctuates satisfying a similar relation to the quantum-mechanical uncertainty relation. Using a non-relativistic toy model, we show that the presence of such a velocity fluctuation increases the local anisotropy of the momentum distributions of produced hadrons, and thus

Yash Sanghvi, Abhiram Gnanasambandam, Zhiyuan Mao, Stanley H. Chan

Blind deconvolution is a challenging problem, but in low-light it is even more difficult. Existing algorithms, both classical and deep-learning based, are not designed for this condition. When the photon shot noise is strong, conventional deconvolution methods fail because (1) the image does not have enough signal-to-noise ratio to perform the blur estimatio

Yun-Fei Niu, Shuo Zhang, Chen Ding, Wan-Su Bao

Variational quantum algorithms (VQAs) have emerged as a promising near-term technique to explore practical quantum advantage on noisy intermediate-scale quantum (NISQ) devices. However, the inefficient parameter training process due to the incompatibility with backpropagation and the cost of a large number of measurements, posing a great challenge to the lar

Yabo Chen, Yuchen Liu, Dongsheng Jiang, Xiaopeng Zhang

With the development of generative-based self-supervised learning (SSL) approaches like BeiT and MAE, how to learn good representations by masking random patches of the input image and reconstructing the missing information has grown in concern. However, BeiT and PeCo need a "pre-pretraining" stage to produce discrete codebooks for masked patches representin

Charles-Edouard Bréhier

We study a family of numerical schemes applied to a class of multiscale systems of stochastic differential equations. When the time scale separation parameter vanishes, a well-known homogenization or Wong--Zakai diffusion approximation result states that the slow component of the considered system converges to the solution of a stochastic differential equati

Charles-Edouard Bréhier

We study a family of numerical schemes applied to a class of multiscale systems of stochastic differential equations. When the time scale separation parameter vanishes, a well-known Smoluchowski--Kramers diffusion approximation result states that the slow component of the considered system converges to the solution of a standard It\^o stochastic differential

Yijun Yang, Ruiyuan Gao, Qiang Xu

This paper proposes a novel out-of-distribution (OOD) detection framework named MoodCat for image classifiers. MoodCat masks a random portion of the input image and uses a generative model to synthesize the masked image to a new image conditioned on the classification result. It then calculates the semantic difference between the original image and the synth

Guojun Huang, Jiaxing Zhao, Pengfei Zhuang

We calculate color screening mass in a thermalized and magnetized QCD matter in the frame of loop resummation theory without restriction to the magnetic field strength. Our full calculation covers the often used approximations for weak magnetic field at high temperature and strong magnetic field at low temperature. We find that while the magnetic field creat

Romain Ducasse, Samuel Nordmann

We consider a multi-species reaction-diffusion system that arises in epidemiology to describe the spread of several strains, or variants, of a disease in a population. Our model is a natural spatial, multi-species, extension of the classical SIR model of Kermack and McKendrick. First, we study the long-time behavior of the solutions and show that there is a

Siladittya Manna, Rakesh Dey, Souvik Chakraborty

Applications on Medical Image Analysis suffer from acute shortage of large volume of data properly annotated by medical experts. Supervised Learning algorithms require a large volumes of balanced data to learn robust representations. Often supervised learning algorithms require various techniques to deal with imbalanced data. Self-supervised learning algorit

Christoph Schiller

A compact summary of present fundamental physics is given and evaluated. Its 9 lines describe all observations exactly and contain both general relativity and the standard model of particle physics. Their precise agreement with experiments, in combination with their extreme simplicity and their internal consistency, suggest that there are no experimental eff

Christoph Treude

Recommender systems are a valuable tool for software engineers. For example, they can provide developers with a ranked list of files likely to contain a bug, or multiple auto-complete suggestions for a given method stub. However, the way these recommender systems interact with developers is often rudimentary -- a long list of recommendations only ranked by t

Ping Tang, Gerrit E. W. Bauer

We address the sliding thermodynamics of van der Waals-bonded bilayers by the continuum elasticity theory. We attribute the robustness of the ferroelectricity recently observed in h-BN and WTe2 bilayers to large monolayer in-plane stiffness. We compute the electric susceptibility and specific heat in the mean-field self-consistent phonon approximation. We co

Andre Carlon, Luis Espath, Raul Tempone

Using quasi-Newton methods in stochastic optimization is not a trivial task given the difficulty of extracting curvature information from the noisy gradients. Moreover, pre-conditioning noisy gradient observations tend to amplify the noise. We propose a Bayesian approach to obtain a Hessian matrix approximation for stochastic optimization that minimizes the

Bnaya Gross, Ivan Bonamassa, Shlomo Havlin

We study the geometrical features of the order parameter's fluctuations near the critical point of mixed-order phase transitions in randomly interdependent spatial networks. In contrast to continuous transitions, where the structure of the order parameter at criticality is fractal, in mixed-order transitions the structure of the order parameter is known to b

Yinpeng Chen, Zhiyu Pan, Min Shi, Hao Lu

Generative adversarial networks (GANs) have been trained to be professional artists able to create stunning artworks such as face generation and image style transfer. In this paper, we focus on a realistic business scenario: automated generation of customizable icons given desired mobile applications and theme styles. We first introduce a theme-application i

Xudong Xie, Ling Fu, Zhifei Zhang, Zhaowen Wang

Artistic text recognition is an extremely challenging task with a wide range of applications. However, current scene text recognition methods mainly focus on irregular text while have not explored artistic text specifically. The challenges of artistic text recognition include the various appearance with special-designed fonts and effects, the complex connect

Jun Yin, Amilcare Porporato

While it is well known that water availability controls vegetation growth and soil microbial activity, how aridity affects ecosystem carbon patterns is not completely understood. Towards a more quantitative assessment of terrestrial carbon stocks, here we uncover a remarkable self-similar behavior of the global carbon stock. Using international survey and re

Luca Dieci, Fabio V. Difonzo

We prove that Mean Value mapping between convex quadrilaterals is injective, affirmatively proving a conjecture stated in M. S. Floater and J. Kosinka, On the injectivity of Wachspress and mean value mappings between convex polygons, Adv. in Comp. Math. 32 (2010), 163-174.

Bingyan Jiang, Jiaji Zhao, Lujunyu Wang, Ran Bi

The Onsager-Casimir reciprocal relations are a fundamental symmetry of nonequilibrium statistical systems. Here we study an unusual chirality-dependent Hall effect in a tilted Weyl semimetal Co$_3$Sn$_2$S$_2$ with broken time reversal symmetry. It is confirmed that the reciprocal relations are satisfied. Since two Berry curvature effects, an anomalous veloci

Ryo Ohkawa, Yutaka Yoshida

We investigate vortex partition functions defined from integrals over the handsaw quiver varieties of type $A_{1}$ via wall-crossing phenomena. We consider vortex partition functions defined by two types of cohomology classes, and get functional equations for each of them. We also give explicit formula for these partition functions. This gives proofs to form

Bingyan Jiang, Jiaji Zhao, Jiangyuan Qian, Shen Zhang

Tilting the Weyl cone breaks the Lorentz invariance and enriches the Weyl physics. Here, we report the observation of a magnetic-field-antisymmetric Seebeck effect in a tilted Weyl semimetal, Co$_3$Sn$_2$S$_2$. Moreover, it is found that the Seebeck effect and the Nernst effect are antisymmetric in both the in-plane magnetic field and the magnetization. We a

Injy Hamed, Alia El Bolock, Cornelia Herbert, Slim Abdennadher

Code-switching (CS) is a common linguistic phenomenon exhibited by multilingual individuals, where they tend to alternate between languages within one single conversation. CS is a complex phenomenon that not only encompasses linguistic challenges, but also contains a great deal of complexity in terms of its dynamic behaviour across speakers. Given that the f

Gábor Hegedüs, Lajos Rónyai

Let $q,n \geq 1$ be integers, $[q]=\{1,\ldots, q\}$, and $\mathbb F$ be a field with $|\mathbb F|\geq q$. The set of increasing sequences $$ I(n,q)=\{(f_1,f_2, \dots, f_n) \in [q]^n:~ f_1\leq f_2\leq\cdots \leq f_n \} $$ can be mapped via an injective map $i: [q]\rightarrow \mathbb F $ into a subset $J(n,q)$ of the affine space ${\mathbb F}^n$. We describe r

Jia-Jun Ma, Congling Qiu, Jialiang Zou

We study the Hecke algebra modules arising from theta correspondence between certain Harish-Chandra series for type I dual pairs over finite fields. For the product of the pair of Hecke algebras under consideration, we show that there is a generic Hecke algebra module whose specializations at prime powers give the Hecke algebra modules and whose specializati

Baochi Fu, Longgang Pang, Huichao Song, Yi Yin

In this proceeding, we present our recent prediction on the local net Lambda polarization to search for the baryonic spin Hall effect (SHE) at RHIC BES energies. The baryonic SHE is induced by the gradients of baryon chemical potential, which leads to local polarization separation between baryons and anti-baryons. Based on hydrodynamic simulations with spin

Shaunak V. Deo, Anna Medvedovsky

We use deformation theory to study the big Hecke algebra acting on mod-2 modular forms of prime level $N$ and all weights, especially its local component at the trivial representation. For $N = 3, 5$, we prove that the maximal reduced quotient of this big Hecke algebra is isomorphic to the maximal reduced quotient of the corresponding universal deformation r

Jiutao Yue, Haofeng Li, Pengxu Wei, Guanbin Li

Recently deep neural networks (DNNs) have achieved significant success in real-world image super-resolution (SR). However, adversarial image samples with quasi-imperceptible noises could threaten deep learning SR models. In this paper, we propose a robust deep learning framework for real-world SR that randomly erases potential adversarial noises in the frequ

Khursid Alam, Koushik Dutta

Moduli potential loses its minima due to external energy sources of inflaton energy density or radiation produced at the end of inflation. But, the non-existence of minima does not necessarily mean destabilization of moduli. In fact, the destabilization of moduli is always dependent on the initial field values of the fields. In this work, we study carefully

Fabio Caleffi

Taking inspiration from the state-of-the art knowledge of the Bose-Hubbard (BH) model and recent methodological developments in its fermionic counterpart, this work deals with the study of the collective dynamics of a lattice Bose gas beyond the mean-field picture through a quantum description of its elementary excitations. The Hamiltonian quantization, perf

Yin Cai, Fu-Lin Zhang

Galperin introduced an interesting method to learn the digits of $\pi $ by counting the collisions of two billiard balls and a hard wall. This paper studies two quantum versions of the Galperin billiards. It is shown that the digits of $\pi $ can be observed in the phase shifts of the quantum models.

Beatriz Elizaga Navascués, Alejandro García-Quismondo, Guillermo A. Mena Marugán

We study the Hamiltonian formulation of the Ashtekar-Olmedo-Singh model for the description of the interior geometry of non-rotating, uncharged black holes. This model incorporates loop quantum effects through the introduction of two regularization parameters. We consider an extended phase space formalism proposed by the creators of the model that includes s

J. A. Aguilar-Saavedra

We put forward a method to tune the polarisation state of decaying heavy particles (top quarks and $W/Z$ bosons) in a pre-existing Monte Carlo sample. With this technique, dubbed as `custom angle replacement', the decay angular distributions are modified in such a way that the desired polarisation state is reproduced, while all the production kinematics are

George Davie

We are interested in the computability between left c.e. reals $\alpha$ and their initial segments. We show that the quantity $C(C(\alpha_n)|\alpha_n)$ plays a crucial role in this and in their completeness. We look in particular at Chaitin's theorem and its relativisation due to Frank Stephan.

Zhengdao Yuan, Qinghua Guo, Yonina C. Eldar, Yonghui Li

We consider matrix factorization (MF) with certain constraints, which finds wide applications in various areas. Leveraging variational inference (VI) and unitary approximate message passing (UAMP), we develop a Bayesian approach to MF with an efficient message passing implementation, called UAMPMF. With proper priors imposed on the factor matrices, UAMPMF ca

Benjamin Aslan

This article explores special Lagrangian submanifolds in $\mathbb{CP}^3$, viewed as a nearly K\"ahler manifold, from two different perspectives. Intrinsically, using a moving frame set-up, and extrinsically, using $\mathrm{SU}(2)$ moment-type maps. We describe new homogeneous examples, from both perspectives, and classify totally geodesic special Lagrangian

Vinícius Oliveira, C. A. de S. Pires

In 3-3-1 models anomaly cancellation requires that one of the three families of quarks transforms as triplet by $\text{SU}(3)_\text{L}$ with the other two transforming necessarily as anti-triplet. This is an important feature of the model because with it we explain family replication. Thus it is mandatory to discriminate which of the families will transform

Hanne Kekkonen

Building blocks and tiles are an excellent way of learning about geometry and mathematics in general. There are several versions of tiles that are either snapped together or connected with magnets that can be used to introduce topics like volume, tessellations, and Platonic solids. However, since these tiles are made of hard plastic, they are not very suitab

Xipeng Hu, Lingping Zhong

The general Sombor index of $G$ is defined as $SO_{\alpha}(G)= \sum_{uv\in G}\left(d^2_{G}(u)+d^2_{G}(v)\right)^{\alpha}$. For $0<\alpha<1$, we have the upper bound of $SO_{\alpha}(G)$ on unicyclic graphs with a fixed diameter, and the extremal graph is also characterized.

Colin Klaus, Matthew Wascher, Wasiur R. KhudaBukhsh, Grzegorz A. Rempala

The Dynamical Survival Analysis (DSA) is a framework for modeling epidemics based on mean field dynamics applied to individual (agent) level history of infection and recovery. Recently, DSA has been shown to be an effective tool in analyzing complex non-Markovian epidemic processes that are otherwise difficult to handle using standard methods. One of the adv

Ryan Barouki, Giacomo Marocco, Subir Sarkar

We revisit the search for heavy neutral leptons with the Big European Bubble Chamber in the 1982 proton beam dump experiment at CERN, focussing on those heavier than the kaon and mixing only with the tau neutrino, as these are far less constrained than their counterparts with smaller mass or other mixings. Recasting the previous search in terms of this model

Dominic Breit

We prove partial regularity of suitable weak solutions to the Navier--Stokes equations at the boundary in irregular domains. In particular, we provide a criterion which yields continuity of the velocity field in a boundary point and obtain solutions which are continuous in a.a. boundary boundary point (their existence is a consequence of a new maximal regula

Neha Karanjkar, Subodh M. Joshi

The use of Digital Twins is set to transform the manufacturing sector by aiding monitoring and real-time decision making. For several applications in this sector, the system to be modeled consists of a mix of discrete-event and continuous processes interacting with each other. Building simulation-based Digital Twins of such systems necessitates an open, flex

Yasaman Hosseini, Mojtaba Mohammadi Najafabadi

Axionlike particles (ALPs) emerge from spontaneously broken global symmetries in high energy extensions of the Standard Model (SM). This causes ALPs to be among the objectives of future experiments which intend to search for new physics beyond the SM. We discuss the reach of future pp collider FCC-hh in probing the ALP model parameters through top quark pair

Dario Bambusi, Roberto Feola, Riccardo Montalto

In this paper we prove a result of almost global existence for some abstract nonlinear PDEs on flat tori and apply it to some concrete equations, namely a nonlinear Schr\"odinger equation with a convolution potential, a beam equation and a quantum hydrodinamical equation. We also apply it to the stability of plane waves in NLS. The main point is that the abs

Runqing Xu, Jie An, Bohua Zhan

Active automata learning in the framework of Angluin's $L^*$ algorithm has been applied to learning many kinds of automata models. In applications to timed models such as timed automata, the main challenge is to determine guards on the clock value in transitions as well as which transitions reset the clock. In this paper, we introduce a new algorithm for act

Mahmoud Mansour, Mohamed Aboshady

This study aims to predict failure times for some units in some lifetime experiments. In some practical situations, the experimenter may not be able to register the failure times of all units during the experiment. Recently, this situation can be described by a new type of censored data called multiply-hybrid censored data. In this paper, the linear failure

Carlo Maccaferri, Jakub Vošmera

We consider deformations of D-brane systems induced by a change in the closed string background in the framework of bosonic open-closed string field theory, where it is possible to unambiguously tame infrared divergences originating from both open and closed string degenerations. A closed string classical solution induces a tadpole for the open strings which

Eduardo Guendelman, Fabian Wagner

In this work we investigate the relation between curved momentum space and momentum-dependent gauge fields. While the former is a classic idea that has been shown to be tied to minimal-length models, the latter constitutes a relatively recent development in quantum gravity phenomenology. In particular, the gauge principle in momentum space amounts to a modif

Guangyao Zhai, Yu Zheng, Ziwei Xu, Xin Kong

In this paper, we introduce DA$^2$, the first large-scale dual-arm dexterity-aware dataset for the generation of optimal bimanual grasping pairs for arbitrary large objects. The dataset contains about 9M pairs of parallel-jaw grasps, generated from more than 6000 objects and each labeled with various grasp dexterity measures. In addition, we propose an end-t

Ioannis Chiotis, Aris L. Moustakas

Cell-Free (CF) Massive MIMO (mMIMO) is a technology which can potentially augment not only the deployment of 5G, but also the deployment of beyond 5G (B5G) wireless networks. However, the cost for rolling out such systems may be significant. Radio stripes form a promising solution which offers the potential of scalability at a reduced price. This paper inves

Semen Budennyy, Vladimir Lazarev, Nikita Zakharenko, Alexey Korovin

The size and complexity of deep neural networks continue to grow exponentially, significantly increasing energy consumption for training and inference by these models. We introduce an open-source package eco2AI to help data scientists and researchers to track energy consumption and equivalent CO2 emissions of their models in a straightforward way. In eco2AI

Leesa Fleury, Ilaria Caiazzo, Jeremy Heyl

The axion-nucleon coupling enables the production of axions through the decay of excited ${}^{57}\textrm{Fe}$ isotopes, and axions produced in the Sun through this process are often a target of helioscope searches. We show for the first time that hot, highly magnetic white dwarfs such as ZTF J1901+1458 are a viable target to search for the X-ray signature of

Bin Liu, Jiwen Wang, Ruirui Wang, Yaxu Wang

The decision-making of TBM operating parameters has an important guiding significance for TBM safe and efficient construction, and it has been one of the research hotpots in the field of TBM tunneling. For this purpose, this paper introduces rock-breaking rules into machine learning method, and a rock-machine mapping dual-driven by physical-rule and data-min

Yanbo Cao, Derui Gao, Jingyu Xu

Due to their low latency, high capacity, and seamless worldwide coverage, low Earth orbit (LEO) satellites are essential to the equal access network. Stochastic geometry (SG) is an appropriate method for such a large and irregular system. The SG model can effectively assess and estimate the performance of the network as well as handle the growing network sca

Rüdiger Göbl, Christoph Hennersperger, Nassir Navab

In ultrasound imaging the appearance of homogeneous regions of tissue is subject to speckle, which for certain applications can make the detection of tissue irregularities difficult. To cope with this, it is common practice to apply speckle reduction filters to the images. Most conventional filtering techniques are fairly hand-crafted and often need to be fi

Luca Tosi, Paolo Rocca, Nicola Anselmi, Andrea Massa

A method for the analysis of the power pattern of phased array antennas (PAs) based on the quantum Fourier transform (QFT) is proposed. The computation of the power pattern given the set of complex excitations of the PA elements is addressed within the quantum computing (QC) framework by means of a customized procedure that exploits the quantum mechanics pri

Pratima Upretee, Bishesh Khanal

Supervised deep learning methods for semantic medical image segmentation are getting increasingly popular in the past few years.However, in resource constrained settings, getting large number of annotated images is very difficult as it mostly requires experts, is expensive and time-consuming.Semi-supervised segmentation can be an attractive solution where a

Damai Dai, Wenbin Jiang, Qingxiu Dong, Yajuan Lyu

The ability of pretrained Transformers to remember factual knowledge is essential but still limited for existing models. Inspired by existing work that regards Feed-Forward Networks (FFNs) in Transformers as key-value memories, we design a Neural Knowledge Bank (NKB) and a knowledge injection strategy to introduce extra factual knowledge for pretrained Trans

Changqing Xu, Yijian Pei, Zili Wu, Yi Liu

Spiking neural network (SNN) is a brain-inspired model which has more spatio-temporal information processing capacity and computational energy efficiency. However, with the increasing depth of SNNs, the memory problem caused by the weights of SNNs has gradually attracted attention. Inspired by Artificial Neural Networks (ANNs) quantization technology, binari

Ozioma Collins Oguine, Kanyifeechukwu Jane Oguine

Advancements in Telemedicine as an approach to healthcare delivery have heralded a new dawn in modern Medicine. Its fast-paced development in our contemporary society is credence to the advances in Artificial Intelligence and Information Technology. This paper carries out a descriptive study to broadly explore AI's implementations in healthcare delivery with

Rakshit P. Vyas, Mihir J. Joshi

Consequences of new quantum spin perspective in quantum gravity are far-reaching. Results of this novel perspective in loop quantum gravity, i.e., the modification of the equation of geometrical operators such as the area and the volume operator are known. Using newly proposed formula from this perspective, the magnitude of fundamental constants such as the

Heikki Mäntysaari, Björn Schenke, Chun Shen, Wenbin Zhao

We determine the likelihood distribution for the model parameters describing the event-by-event fluctuating proton geometry at small $x$ by performing a Bayesian analysis within the Color Glass Condensate framework. The exclusive $\mathrm{J}/\psi$ production data from HERA is found to constrain the model parameters well, and we demonstrate that complementary

C. J. Riseley, E. Bonnassieux, T. Vernstrom, T. J. Galvin

Abell 3266 is a massive and complex merging galaxy cluster that exhibits significant substructure. We present new, highly sensitive radio continuum observations of Abell 3266 performed with the Australian Square Kilometre Array Pathfinder (0.8$-$1.1 GHz) and the Australia Telescope Compact Array (1.1$-$3.1 GHz). These deep observations provide new insights i

Taehyeong Kim, Hyeonseop Song, Byoung-Tak Zhang

Human infants learn the names of objects and develop their own conceptual systems without explicit supervision. In this study, we propose methods for learning aligned vision-language conceptual systems inspired by infants' word learning mechanisms. The proposed model learns the associations of visual objects and words online and gradually constructs cross-mo

Haochen Liu, Zhiyu Huang, Chen Lv

Forecasting the future states of surrounding traffic participants is a crucial capability for autonomous vehicles. The recently proposed occupancy flow field prediction introduces a scalable and effective representation to jointly predict surrounding agents' future motions in a scene. However, the challenging part is to model the underlying social interactio

A. Khodam-Mohammadi

Among many models which can describe the bouncing cosmology, A matter bounce scenario that is deformed by a running vacuum model of dark energy (RVM-DE) has been interested. In this research, I show that a class of RVM-CDM (cold dark matter) model can also describe a cyclical cosmology in which the universe undergoes cycles of expansion to the contraction ph

Prashanti Jami, Biswarup Ash, Amit Ghosal

We comprehend the role of imperfections in materials consisting of interacting particles, arising from different origins on their universal features. Specifically, we report the static and dynamic responses in a cluster of Coulomb interacting particles in two dimensions. Confined systems with pinned impurities are studied, and results are compared with those

Jonathan Fhima, Jan Van Eijgen, Ingeborg Stalmans, Yevgeniy Men

Introduction: Blood vessels can be non-invasively visualized from a digital fundus image (DFI). Several studies have shown an association between cardiovascular risk and vascular features obtained from DFI. Recent advances in computer vision and image segmentation enable automatising DFI blood vessel segmentation. There is a need for a resource that can auto

Yixian Zhu, Ketan Savla

We study route choice in a repeated routing game where an uncertain state of nature determines link latency functions, and agents receive private route recommendation. The state is sampled in an i.i.d. manner in every round from a publicly known distribution, and the recommendations are generated by a randomization policy whose mapping from the state is know

Xiaofan Zhou, Jian-Song Pan, Suotang Jia

Exploring topological phases in interacting systems is a challenging task. We investigate many-body topological physics of interacting fermions in an extended Su-Schrieffer-Heeger (SSH) model, which extends the two sublattices of SSH model into four sublattices and thus is dubbed SSH4 model, based on the density-matrix renormalization-group numerical method.

Akshay Kumar, Varun Jindal

Salas and Garcia introduced the concept of an extended locally convex space in [D. Salas and S. Tapia-Garcia. Extended seminorms and extended topological vector spaces. Topology and its Applications, 2016] which extends the idea of an extended normed space (introduced by Beer in G. Beer. Norms with infinite values. Journal of Convex Analysis, 2015). This art

Gabriel Chen, Rick Wanner

During today's digital age, emails have become a crucial part of communications for both personal and enterprise usage. However, email transmission protocols were not designed with security in mind, and this has always been a challenge while trying to make email transmission more secure. On top of the basic layer of SMTP, POP3, and IMAP protocols to send and

P. Bantay

The mathematics underlying the connection between deconstruction lattices and locality diagrams of conformal models is developed from scratch, with special emphasis on classification issues. In particular, the notions of equilocality classes, deflation map, essential vertices and stem graphs are introduced in order to characterize those graphs that may arise

Jan Ondras, Di Ni, Xi Deng, Zeqi Gu

Robotic manipulation of deformable objects gains great attention due to its wide applications including medical surgery, home assistance, and automatic food preparation. The ability to deform soft objects remains a great challenge for robots due to difficulties in defining the problem mathematically. In this paper, we address the problem of shaping a piece o

Tarun Kumar, Himanshu Sharad Bhatt

Existing metrics used to evaluate table structure recognition algorithms have shortcomings with regard to capturing text and empty cells alignment. In this paper, we build on prior work and propose a new metric - TEDS based IOU similarity (TEDS (IOU)) for table structure recognition which uses bounding boxes instead of text while simultaneously being robust

David Ellerman

The purpose of this paper is to show that the mathematics of quantum mechanics (QM) is the mathematics of set partitions (which specify indefiniteness and definiteness) linearized to vector spaces, particularly in Hilbert spaces. That is, the math of QM is the Hilbert space version of the math to describe objective indefiniteness that at the set level is the

Chenwei Zhao, Miao Ye, Xingsi Xue, Jianhui Lv

Traditional multicast routing methods have some problems in constructing a multicast tree, such as limited access to network state information, poor adaptability to dynamic and complex changes in the network, and inflexible data forwarding. To address these defects, the optimal multicast routing problem in software-defined networking (SDN) is tailored as a m

Long Yuan, Yuqian Zhao, Boqiang Li, Yiru Song

We show that short-range resonating-valence-bond correlations and long-range order can coexist in the ground state (GS) of a frustrated spin system. Our study comprises a comprehensive investigation of the quantum magnetism on the structurally disorder-free single crystal of Cu$_2$(OH)$_3$NO$_3$, which realizes the $s$ = 1/2 Heisenberg model on a spatially a

Philippe Feautrier, Jean-Luc Gach, Dan Owton, Matthew Hicks

There have been no significant breakthroughs in infrared imagery since the hybridization of III-V or II-VI narrow-bandgap semiconductors on complementary metal-oxide semiconductor (CMOS) read-out integrated circuits (ROICs). The development of third-generation, linear-mode avalanche photodiode arrays (LmAPDs) using mercury cadmium telluride (MCT) has resulte

Yiran Wang, Zhiyu Pan, Xingyi Li, Zhiguo Cao

Temporal consistency is the key challenge of video depth estimation. Previous works are based on additional optical flow or camera poses, which is time-consuming. By contrast, we derive consistency with less information. Since videos inherently exist with heavy temporal redundancy, a missing frame could be recovered from neighboring ones. Inspired by this, w

Fanyuan Meng, Jiadong Zhu, Yuheng Yao, Enrico Maria Fenoaltea

The arise of disagreement is an emergent phenomenon that can be observed within a growing social group and, beyond a certain threshold, can lead to group fragmentation. To better understand how disagreement emerges, we introduce an analytically tractable model of group formation where individuals have multidimensional binary opinions and the group grows thro

Sungyoon Cho

In this paper, we prove that there are certain relations among representation densities and provide an efficient way to compute representation densities by using these relations. As an application, we compute some arithmetic intersection numbers of special cycles on unitary Shimura varieties and propose a conjecture on these.

Philippe Feautrier, Jean-Luc Gach

We present here the latest results obtained with the C-RED One camera developed by First Light Imaging for fast ultra-low noise infrared applications. This camera uses the Leonardo Saphira e-APD 320x256 infrared sensor in an autonomous cryogenic environment with a low vibration pulse tube and with embedded readout electronics system. Some recent improvements

Yu Li, Chun-Yen Chen, Dian Yu, Sam Davidson

This paper reports on progress towards building an online language learning tool to provide learners with conversational experience by using dialog systems as conversation practice partners. Our system can adapt to users' language proficiency on the fly. We also provide automatic grammar error feedback to help users learn from their mistakes. According to ou

Thaís Bardini Idalino, Lucia Moura, Ricardo Felipe Custódio, Daniel Panario

We consider the problem of detecting and locating modifications in signed data to ensure partial data integrity. We assume that the data is divided into $n$ blocks (not necessarily of the same size) and that a threshold $d$ is given for the maximum amount of modified blocks that the scheme can support. We propose efficient algorithms for signature and verifi

Katsuya T. Abe

In this work, we investigate the effect of Population~III~(Pop.~III) stars in ultracompact minihalos~(UCMHs) on the cosmic ionization history using the Planck observation data. Although high-redshift astrophysics is not understood yet, UCMHs could host the Pop.~III stars like the halos formed in the standard structure formation scenario. Such Pop.~III stars

Muhammad Hassan, Haifei Guan, Aikaterini Melliou, Yuqi Wang

Neural networks have been rapidly expanding in recent years, with novel strategies and applications. However, challenges such as interpretability, explainability, robustness, safety, trust, and sensibility remain unsolved in neural network technologies, despite the fact that they will unavoidably be addressed for critical applications. Attempts have been mad

Thais Bardini Idalino, Lucia Moura, Carlisle Adams

This paper considers malleable digital signatures, for situations where data is modified after it is signed. They can be used in applications where either the data can be modified (collaborative work), or the data must be modified (redactable and content extraction signatures) or we need to know which parts of the data have been modified (data forensics). A

Qinghui Zhang, Weidong Li, Qian Su, Xuejie Zhang

Terminal devices (TDs) connect to networks through access points (APs) integrated into the edge server. This provides a prerequisite for TDs to upload tasks to cloud data centers or offload them to edge servers for execution. In this process, signal coverage, data transmission, and task execution consume energy, and the energy consumption of signal coverage

Yukang Jiang, Xueqin Wang, Zhixi Xiong, Haisheng Yang

The paper proposes a time-varying parameter global vector autoregressive (TVP-GVAR) framework for predicting and analysing developed region economic variables. We want to provide an easily accessible approach for the economy application settings, where a variety of machine learning models can be incorporated for out-of-sample prediction. The LASSO-type techn

Thais Bardini Idalino, Lucia Moura

Aggregate signatures are used to create one short proof of authenticity and integrity from a set of digital signatures. However, one invalid signature in the set invalidates the entire aggregate, giving no information on which signatures are valid. Hartung et al. (2016) propose a fault-tolerant aggregate signature scheme based on combinatorial group testing.

Jingtai Shi, Pikai Zhang, Yong Xu, Wanxing Ren

Methane/coal dust hybrid explosion is one of the common hazards in process and mining industries. In this study, methane detonation propagation in dilute coal char particle suspensions is studied based on Eulerian-Lagrangian method. The effects of char combustion on methane detonation dynamics are focused on. The results show that propagation of the methane