May 2022 arXiv papers — page 124

Zhiming Pan, Xiao-Tian Zhang

We study the fermion-boson coupled system in $d=3-\epsilon$ space dimensions near the quantum phase transition; infinite many boson modes located on a sphere become critical simultaneously, which is dubbed "critical boson sphere" (CBS). The fermions on the Fermi surface can be scattered to nearby points located on a boson ring in the low-energy limit. The nu

R. Vilela Mendes

The distributional support of the sample paths of L\'evy processes is an important issue for the construction of sparse statistical models, theories of integration in infinite dimensions and the existence of generalized solutions of stochastic partial differential equations driven by L\'evy white noise. Here one considers a family K_\alpha (0<\alpha<2) of L\

Eduardo Casas, Alberto Domínguez Corella, Nicolai Jork

This paper is dedicated to the stability analysis of the optimal solutions of a control problem associated with a semilinear elliptic equation. The linear differential operator of the equation is neither monotone nor coercive due to the presence of a convection term. The control appears only linearly, or even it can not appear in an explicit form in the obje

Erhan Karakoca, Güneş Karabulut Kurt, Ali Görçin

Due to the unique channel characteristics of Terahertz (THz), comprehensive propagation channel modeling is essential to understand the spectrum and develop reliable communication systems in these bands. In this work, we propose the utilization of the hierarchical Dirichlet process Gamma mixture model (DPGMM) to characterize THz channels statistically in the

Yuanxin Zhuang, Lingjuan Lyu, Chuan Shi, Carl Yang

Graph neural networks (GNNs) have been widely used in modeling graph structured data, owing to its impressive performance in a wide range of practical applications. Recently, knowledge distillation (KD) for GNNs has enabled remarkable progress in graph model compression and knowledge transfer. However, most of the existing KD methods require a large volume o

Jiaxiu Li, Ning Wu

We generalize the Heisenberg star consisting of a spin-1/2 central spin and a homogeneously coupled spin bath modeled by the XXX ring [Richter J and Voigt A 1994 \emph{J. Phys. A: Math. Gen.} \textbf{27} 1139-1149] to the case of arbitrary central-spin size $S<N/2$, where $N$ is the number of bath spins. We describe how to block-diagonalize the model based o

Debasish Bhattacharjee, Tapasvini Roy, Santanu Acharjee, Tarini Kumar Dutta

Dynamic exploration for a predator-prey bio-system of two species with ratio-dependent functional response is carried out, where the capability to predate in both the stages of the predator, the juvenile and the matured, is taken into account. But, only the matured predators are inferred to be efficient in killing the prey without any negative repercussions.

Yunqing Zhao, Henghui Ding, Houjing Huang, Ngai-Man Cheung

Modern GANs excel at generating high quality and diverse images. However, when transferring the pretrained GANs on small target data (e.g., 10-shot), the generator tends to replicate the training samples. Several methods have been proposed to address this few-shot image generation task, but there is a lack of effort to analyze them under a unified framework.

A. Sajid, Kristian S. Thygesen

The spin coherence times of 69 triplet defect centers in 45 different 2D host materials are calculated using the cluster correlation expansion (CCE) method with parameters of the spin Hamiltonian obtained from density functional theory (DFT). Several of the triplets are found to exhibit extraordinarily large spin coherence times making them interesting for q

Chunlin Liu, Leiye Xu

In this paper, we introduce the directional Pinsker algebra, and construct a skew product to study it. As applications, we show that 1. if a $\mathbb{Z}^2$-system with positive directional measure-theoretic entropy then it is multivariant directional mean Li-Yorke chaotic along the corresponding direction; 2. for any ergodic measure on a $\mathbb{Z}^2$-syste

Saeed Rastgoo, Saurya Das

We review, as well as provide some new results regarding the study of the structure of spacetime and the singularity in the interior of the Schwarzschild black hole in both loop quantum gravity and generalized uncertainty principle approaches, using congruences and their associated expansion scalar and the Raychaudhuri equation. We reaffirm previous results

Christos A. Athanasiadis, Katerina Kalampogia-Evangelinou

The coefficients of the chain polynomial of a finite poset enumerate chains in the poset by their number of elements. The chain polynomials of the partition lattices and their standard type $B$ analogues are shown to have only real roots. The real-rootedness of the chain polynomial is conjectured for all geometric lattices and is shown to be preserved by the

Cong Tuan Huynh, Chang-Mo Ryu, Chulmin Kim

Density filamentation has been observed in many beam-plasma simulations and experiments. Because current filamentation is a pure transverse mode, charge density filamentation cannot be produced directly by the current filamentation process. To explain this phenomenon, several mechanisms are proposed such as the coupling of the Weibel instability to the two-s

Tomoharu Suda

The first-return map, or the Poincar\'e map, is a fundamental concept in the theory of flows. However, it can generally be defined only partially, and additional conditions are required to define it globally. Since this partiality reflects the dynamics, the flow can be described by considering the domain and behavior of such maps. In this study, we define th

Soumya Chakrabarti, Koushik Dutta, Jackson Levi Said

We discuss a way in which the geometric scalar field in a Brans-Dicke theory can evade local astronomical tests and act as a driver of the late-time cosmic acceleration. This requires a self-interaction of the Brans-Dicke scalar as well as an interaction with ordinary matter. The scalar field in this construct acquires a density-dependent effective mass much

Terrence W. K. Mak, Minas Chatzos, Mathieu Tanneau, Pascal Van Hentenryck

One potential future for the next generation of smart grids is the use of decentralized optimization algorithms and secured communications for coordinating renewable generation (e.g., wind/solar), dispatchable devices (e.g., coal/gas/nuclear generations), demand response, battery & storage facilities, and topology optimization. The Alternating Direction Meth

Tewodros Eyob, Kenate Nemera, Lemi Demeyu

This study is carried out using first principles density functional theory calculations within gpaw code. Atomic size effect is analyzed and investigated by doping either Li, Cs or both on Barium doped BiFeO$_3$ (BFO) which belongs to monoclinic $P2_1/m$ space group. The calculated results reveal that Cs doped BFO had significantly improved photocurrent dens

Jielian Lin, Hongbin Lin, Zhichen Zhang, Yiwen Xu

To date, Versatile Video Coding (VVC) has a more magnificent overall performance than High Efficiency Video Coding (HEVC). The Quadtree with Nested Multi-Type Tree (QTMT) coding block structure can substantially enhance video coding quality in VVC. However, the coding gain also leads to a greater coding complexity. Therefore, this letter proposes a Fast Deci

Chaoqi Liang, Yu Zhang, Xinyuan Li, Jinyu Zhang

On the Internet, fake news exists in various domain (e.g., education, health). Since news in different domains has different features, researchers have be-gun to use single domain label for fake news detection recently. This emerg-ing field is called multi-domain fake news detection (MFND). Existing works show that using single domain label can improve the a

Hao Hou, Jun Xu, Yingkun Hou, Xiaotao Hu

Real-world face super-resolution (SR) is a highly ill-posed image restoration task. The fully-cycled Cycle-GAN architecture is widely employed to achieve promising performance on face SR, but prone to produce artifacts upon challenging cases in real-world scenarios, since joint participation in the same degradation branch will impact final performance due to

Jiajun Shen

With the rapid development of big data technologies, how to dig out useful information from massive data becomes an essential problem. However, using machine learning algorithms to analyze large data may be time-consuming and inefficient on the traditional single machine. To solve these problems, this paper has made some research on the parallelization of se

Yang Wang, Zhen Gao, Dezhi Zheng, Sheng Chen

It is anticipated that 6G wireless networks will accelerate the convergence of the physical and cyber worlds and enable a paradigm-shift in the way we deploy and exploit communication networks. Machine learning, in particular deep learning (DL), is expected to be one of the key technological enablers of 6G by offering a new paradigm for the design and optimi

Sutanu Ghosh, Mohamed-Slim Alouini

This paper investigates the system outage performance of a useful architecture of two-way hybrid visible light communication/radio frequency (VLC/RF) communication using overlay mode of cooperative cognitive radio network (CCRN). The demand of high data rate application can be fulfilled using VLC link and communication over a wide area of coverage with high

Xu-Chang Zheng, Xing-Gang Wu, Xi-Jie Zhan, Hua Zhou

It has been found that at a high luminosity $e^+ e^-$ collider, sizable $\eta_c+c\bar{c}X$ and $\eta_b+b\bar{b}X$ events can be produced when it works around the $Z$ peak. In this paper, we calculate the decay widths of $Z \to \eta_c+c+\bar{c}+X$ and $Z \to \eta_b+b+\bar{b}+X$ up to next-to-leading order (NLO) accuracy. We find that the NLO corrections are s

Shanqing Cai, Subhashini Venugopalan, Katrin Tomanek, Ajit Narayanan

Motivated by the need for accelerating text entry in augmentative and alternative communication (AAC) for people with severe motor impairments, we propose a paradigm in which phrases are abbreviated aggressively as primarily word-initial letters. Our approach is to expand the abbreviations into full-phrase options by leveraging conversation context with the

Yunlong Liang, Fandong Meng, Jinan Xu, Yufeng Chen

Neural Chat Translation (NCT) aims to translate conversational text into different languages. Existing methods mainly focus on modeling the bilingual dialogue characteristics (e.g., coherence) to improve chat translation via multi-task learning on small-scale chat translation data. Although the NCT models have achieved impressive success, it is still far fro

Mousumi Mandal, Dipak Kumar Pradhan

Let $D$ be a weighted oriented graph and $I(D)$ be its edge ideal. We provide one method to find all the minimal generators of $ I_{\subseteq C} $, where $ C $ is a maximal strong vertex cover of $D$ and $ I_{\subseteq C} $ is the intersections of irreducible ideals associated to the strong vertex covers contained in $C$. If $ D^{\prime} $ is an induced digr

Weiyi Pan

Electronic correlation can strongly influence the electronic properties of two-dimensional (2D) materials with open d- or f-orbitals. Herein, by taking single-layer (SL) H-FeBr$_2$ as a representative of the SL H-FeX$_2$ (X=Cl, Br, I) family, we investigated the electronic correlation effects in the magnetic anisotropy and electronic topology of such a syste

Fattoum Harrathi, Sami Mabrouk, Othmen Ncib, Sergei Silvestrov

The main feature of Hom-algebras is that the identities defining the structures are twisted by linear maps. The purpose of this paper is to introduce and study a Hom-type generalization of pre-Malcev algebras, called Hom-pre-Malcev algebras. We also introduce the notion of Kupershmidt operators of Hom-Malcev and Hom-pre-Malcev algebras and show the connectio

Rui Meng, Xianjin Yang

This article proposes an efficient numerical method for solving nonlinear partial differential equations (PDEs) based on sparse Gaussian processes (SGPs). Gaussian processes (GPs) have been extensively studied for solving PDEs by formulating the problem of finding a reproducing kernel Hilbert space (RKHS) to approximate a PDE solution. The approximated solut

An Xie, Shaodong Zhou, Kelei Xi, Li Ding

Photonic lattices have been widely used for simulating quantum physics, owing to the similar evolutions of paraxial waves and quantum particles. However, nonparaxial wave propagations in photonic lattices break the paradigm of the quantum-optical analogy. Here, we reveal that nonparaxiality exerts stretched and compressed forces on the energy spectrum in the

Ze You, Haisen Zhang

The multistage stochastic variational inequality is reformulated into a variational inequality with separable structure through introducing a new variable. The prediction-correction ADMM which was originally proposed in [B.-S. He, L.-Z. Liao and M.-J. Qian, J. Comput. Math., 24 (2006), 693--710] for solving deterministic variational inequalities in finite di

Wei Chen, Cheng Zhong, Jiajie Peng, Zhongyu Wei

Diagnosis-oriented dialogue system queries the patient's health condition and makes predictions about possible diseases through continuous interaction with the patient. A few studies use reinforcement learning (RL) to learn the optimal policy from the joint action space of symptoms and diseases. However, existing RL (or Non-RL) methods cannot achieve suffici

Marcos Salvai

Let M be an oriented three-dimensional Riemannian manifold. We define a notion of vorticity of local sections of the bundle SO(M) --> M of all its positively oriented orthonormal tangent frames. When M is a space form, we relate the concept to a suitable invariant split pseudo-Riemannian metric on Iso_o (M) \cong SO(M): A local section has positive vorticity

Shuhao Shi, Jian Chen, Kai Qiao, Shuai Yang

The Graph Convolutional Networks (GCNs) have achieved excellent results in node classification tasks, but the model's performance at low label rates is still unsatisfactory. Previous studies in Semi-Supervised Learning (SSL) for graph have focused on using network predictions to generate soft pseudo-labels or instructing message propagation, which inevitably

Aviv Adler, Jennifer Tang, Yury Polyanskiy

A number of engineering and scientific problems require representing and manipulating probability distributions over large alphabets, which we may think of as long vectors of reals summing to $1$. In some cases it is required to represent such a vector with only $b$ bits per entry. A natural choice is to partition the interval $[0,1]$ into $2^b$ uniform bins

Jin Tang, Yaodong Wu, Lingyao Kong, Weiwei Wang

We report differential phase contrast scanning transmission electron microscopy (TEM) of nanoscale magnetic objects in Kagome ferromagnet Fe$_3$Sn$_2$ nanostructures. This technique can directly detect the deflection angle of a focused electron beam, thus allowing clear identification of the real magnetic structures of two magnetic objects including three-ri

Anastasios Kyrillidis, Moshe Y. Vardi, Zhiwei Zhang

Boolean MaxSAT, as well as generalized formulations such as Min-MaxSAT and Max-hybrid-SAT, are fundamental optimization problems in Boolean reasoning. Existing methods for MaxSAT have been successful in solving benchmarks in CNF format. They lack, however, the ability to handle 1) (non-CNF) hybrid constraints, such as XORs and 2) generalized MaxSAT problems

Gongbo Sun, Zijie Zheng, Ming Zhang

Rubbing restorations are significant for preserving world cultural history. In this paper, we propose the RubbingGAN model for restoring incomplete rubbing characters. Specifically, we collect characters from the Zhang Menglong Bei and build up the first rubbing restoration dataset. We design the first generative adversarial network for rubbing restoration.

Omatharv Bharat Vaidya, Rithvik Terence DSouza, Snehanshu Saha, Soma Dhavala

We introduce the Hamiltonian Monte Carlo Particle Swarm Optimizer (HMC-PSO), an optimization algorithm that reaps the benefits of both Exponentially Averaged Momentum PSO and HMC sampling. The coupling of the position and velocity of each particle with Hamiltonian dynamics in the simulation allows for extensive freedom for exploration and exploitation of the

A. Ali Heydari, Oscar A. Davalos, Katrina K. Hoyer, Suzanne S. Sindi

Single-cell RNA sequencing (scRNAseq) is rapidly advancing our understanding of cellular composition within complex tissues and organisms. A major limitation in most scRNAseq analysis pipelines is the reliance on manual annotations to determine cell identities, which are time consuming, subjective, and require expertise. Given the surge in cell sequencing, s

Yuanzhe Geng, Erwu Liu, Rui Wang, Pengcheng Sun

In this paper, we study the outage minimization problem in a decode-and-forward cooperative network with relay uncertainty. To reduce the outage probability and improve the quality of service, existing researches usually rely on the assumption of both exact instantaneous channel state information (CSI) and environmental uncertainty. However, it is difficult

Di Huang, Rui Zhang, Xing Hu, Xishan Zhang

Aiming to find a program satisfying the user intent given input-output examples, program synthesis has attracted increasing interest in the area of machine learning. Despite the promising performance of existing methods, most of their success comes from the privileged information of well-designed input-output examples. However, providing such input-output ex

Omobayode Fagbohungbe, Lijun Qian

The interest in analog computation has grown tremendously in recent years due to its fast computation speed and excellent energy efficiency, which is very important for edge and IoT devices in the sub-watt power envelope for deep learning inferencing. However, significant performance degradation suffered by deep learning models due to the inherent noise pres

Vishal Kuber, Divakar Yadav, Arun Kr Yadav

Designing robust and accurate prediction models has been a viable research area since a long time. While proponents of a well-functioning market predictors believe that it is difficult to accurately predict market prices but many scholars disagree. Robust and accurate prediction systems will not only be helpful to the businesses but also to the individuals i

Zeinab Zoghi, Gursel Serpen

Machine Learning-based supervised approaches require highly customized and fine-tuned methodologies to deliver outstanding performance. This paper presents a dataset-driven design and performance evaluation of a machine learning classifier for the network intrusion dataset UNSW-NB15. Analysis of the dataset suggests that it suffers from class representation

Martin H. Krieger

In statistical mechanics one packages the possible energies of a system into a partition function. In number theory, and elsewhere in mathematics, one packages the spectrum of a phenomenon, say the prime numbers, into a $ζ$-function or more generally into an L-function. These packaging functions have symmetries and properties not at all apparent from the ene

Ning Wang

In the real world, long sequence time-series forecasting (LSTF) is needed in many cases, such as power consumption prediction and air quality prediction.Multi-dimensional long time series model has more strict requirements on the model, which not only needs to effectively capture the accurate long-term dependence between input and output, but also needs to c

Terrence Chan, Carla Arus Gomez, Anish Kothikar, Pedro Baiz

Land Carbon verification has long been a challenge in the carbon credit market. Carbon verification methods currently available are expensive, and may generate low-quality credit. Scalable and accurate remote sensing techniques enable new approaches to monitor changes in Above Ground Biomass (AGB) and Soil Organic Carbon (SOC). The majority of state-of-the-a

Nafiul Rashid, Trier Mortlock, Mohammad Abdullah Al Faruque

Detecting human stress levels and emotional states with physiological body-worn sensors is a complex task, but one with many health-related benefits. Robustness to sensor measurement noise and energy efficiency of low-power devices remain key challenges in stress detection. We propose SELFCARE, a fully wrist-based method for stress detection that employs con

Fei Wang, Zhewei Xu, Pedro Szekely, Muhao Chen

Controlled table-to-text generation seeks to generate natural language descriptions for highlighted subparts of a table. Previous SOTA systems still employ a sequence-to-sequence generation method, which merely captures the table as a linear structure and is brittle when table layouts change. We seek to go beyond this paradigm by (1) effectively expressing t

Evgenya Pergament, Pulkit Tandon, Kedar Tatwawadi, Oren Rippel

Human perception is at the core of lossy video compression and yet, it is challenging to collect data that is sufficiently dense to drive compression. In perceptual quality assessment, human feedback is typically collected as a single scalar quality score indicating preference of one distorted video over another. In reality, some videos may be better in some

Walterio W. Mayol-Cuevas

This position paper argues on the utility of rebellion and disobedience (RaD) in human-robot interaction (HRI). In general, we see two main opportunities in the use of controlled and well designed rebellion and disobedience: i) illuminate insight into the effectiveness of the collaboration (or lack of) and ii) prevent mistakes and correct user actions when i

Sha Wang, Ruyu Song, Yixin Zhang, Yanbo Zhang

Given two graphs $G_1, G_2$, the connected size Ramsey number ${\hat{r}}_c(G_1,G_2)$ is defined to be the minimum number of edges of a connected graph $G$, such that for any red-blue edge colouring of $G$, there is either a red copy of $G_1$ or a blue copy of $G_2$. Concentrating on ${\hat{r}}_c(nK_2,G_2)$ where $nK_2$ is a matching, we generalise and improv

Xing Yang

For decades, the difficulty of tackling a strong coupling model with a perturbative approach remained regardless of numerous inquiries. In the current work, a typical mean field theory procedure transforms a strong coupling Hamiltonian into a weak one, which can be solved within a perturbative approach. The Hamiltonian of 2-dimensional free electron gas with

Matthew Olszta, Kevin Fiedler

With the increasing diversity in material systems, ever-expanding number of analysis techniques, and the large capital costs of next generation instruments the ability to quickly and efficiently collect data in the electron microscope has become paramount to successful data analysis. Therefore, this research proposes a methodology of nanocartography that com

Aryaman Arora, Nitin Venkateswaran, Nathan Schneider

We present a completed, publicly available corpus of annotated semantic relations of adpositions and case markers in Hindi. We used the multilingual SNACS annotation scheme, which has been applied to a variety of typologically diverse languages. Building on past work examining linguistic problems in SNACS annotation, we use language models to attempt automat

Lea Kats, Malka Gorfine

This work presents a new model and estimation procedure for the illness-death survival data where the hazard functions follow accelerated failure time (AFT) models. A shared frailty variate induces positive dependence among failure times of a subject for handling the unobserved dependency between the non-terminal and the terminal failure times given the obse

Enyu Cai, Zhankun Luo, Sriram Baireddy, Jiaqi Guo

The number of panicles (or heads) of Sorghum plants is an important phenotypic trait for plant development and grain yield estimation. The use of Unmanned Aerial Vehicles (UAVs) enables the capability of collecting and analyzing Sorghum images on a large scale. Deep learning can provide methods for estimating phenotypic traits from UAV images but requires a

F. A. Benimetskiy, A. Yulin, A. O. Mikhin, V. Kravtsov

Recently reported large values of exciton-polariton nonlinearity of transition metal dichalcogenide (TMD) monolayers coupled to optically resonant structures approach the values characteristic for GaAs-based systems in the regime of strong light-matter coupling. Contrary to the latter, TMD-based polaritonic devices remain operational at ambient conditions an

Daniele Reda, Hung Yu Ling, Michiel van de Panne

Brachiation is the primary form of locomotion for gibbons and siamangs, in which these primates swing from tree limb to tree limb using only their arms. It is challenging to control because of the limited control authority, the required advance planning, and the precision of the required grasps. We present a novel approach to this problem using reinforcement

Jun Gao, DianYu Liu, Keping Xie

We study the dependence of the transverse mass distribution of the charged lepton and the missing energies on the parton distributions (PDFs) adapted to the $W$ boson mass measurements at the CDF and ATLAS experiments. We compare the shape variations of the distribution induced by different PDFs and find that spread of predictions from different PDF sets can

Krish Muralidhar

Recent analysis by researchers at the U.S. Census Bureau claims that by reconstructing the tabular data released from the 2010 Census, it is possible to reconstruct the original data and, using an accurate external data file with identity, reidentify 179 million respondents (approximately 58% of the population). This study shows that there are a practically

P. Zasche, Z. Henzl, M. Masek

We present the catalogue of the TESS targets showing multiple eclipses. It means that in all of these stars we detected two sets of eclipses, for which their two distinctive periods can be derived. These multiple stellar systems can be either doubly eclipsing quadruples, or triple-star coplanar systems showing besides the inner eclipses also the eclipses on

Yonatan Yehezkeally, Daniella Bar-Lev, Sagi Marcovich, Eitan Yaakobi

This paper introduces a new family of reconstruction codes which is motivated by applications in DNA data storage and sequencing. In such applications, DNA strands are sequenced by reading some subset of their substrings. While previous works considered two extreme cases in which \emph{all} substrings of some fixed length are read or substrings are read with

Kashif Mehmood, David Palma, Katina Kralevska

Intent-based networking (IBN) provides a promising approach for managing networks and orchestrating services in beyond 5G (B5G) deployments using modern service-based architectures. Public safety (PS) services form the basis of keeping society functional, owing to the responsiveness and availability throughout the network. The provisioning of these services

Yaowei Long, Thatchaphol Saranurak

We revisit the vertex-failure connectivity oracle problem. This is one of the most basic graph data structure problems under vertex updates, yet its complexity is still not well-understood. We essentially settle the complexity of this problem by showing a new data structure whose space, preprocessing time, update time, and query time are simultaneously optim

Anwita Bhowmik, Rupam Barman

For a prime $p\equiv 3\pmod{4}$ and a positive integer $t$, let $q=p^{2t}$. Let $g$ be a primitive element of the finite field $\mathbb{F}_q$. The Peisert graph $P^\ast(q)$ is defined as the graph with vertex set $\mathbb{F}_q$ where $ab$ is an edge if and only if $a-b\in\langle g^4\rangle \cup g\langle g^4\rangle$. We provide a formula, in terms of finite f

Antik Sihi, Sudhir K. Pandey

Recent studies on different topological materials in condensed matter physics have provided the evidence of topological nature for bosonic particle like phonons by performing various theoretical calculations and experimental observations. Here, the topological behaviours of phonons of SnS, SnSe and SnTe materials in rock-salt structure are investigated using

Louai Al-Awami

LoRaWAN is a promising IoT access technology that is growing in popularity. This study addresses the issue of duplicate packets forwarding by LoRaWAN gateways and proposes a novel forwarding scheme to eliminate forwarding duplicate packets by utilizing inter-flow network coding. The proposed scheme is distributed and requires no coordination between gateways

Xueyuan Duan, Yu Fu, Kun Wang

To address the problem that traditional network traffic anomaly detection algorithms do not suffi-ciently mine potential features in long time domain, an anomaly detection method based on mul-ti-scale residual features of network traffic is proposed. The original traffic is divided into subse-quences of different time spans using sliding windows, and each su

Yida Wang, David Joseph Tan, Nassir Navab, Federico Tombari

We propose a novel convolutional operator for the task of point cloud completion. One striking characteristic of our approach is that, conversely to related work it does not require any max-pooling or voxelization operation. Instead, the proposed operator used to learn the point cloud embedding in the encoder extracts permutation-invariant features from the

Declan McIntosh, Tunai Porto Marques, Alexandra Branzan Albu

Chest radiographs are used for the diagnosis of multiple critical illnesses (e.g., Pneumonia, heart failure, lung cancer), for this reason, systems for the automatic or semi-automatic analysis of these data are of particular interest. An efficient analysis of large amounts of chest radiographs can aid physicians and radiologists, ultimately allowing for bett

Youcheng Sun, Muhammad Usman, Divya Gopinath, Corina S. Păsăreanu

Neural networks are successfully used in a variety of applications, many of them having safety and security concerns. As a result researchers have proposed formal verification techniques for verifying neural network properties. While previous efforts have mainly focused on checking local robustness in neural networks, we instead study another neural network

Bin Zhu, Chong-Wah Ngo, Jingjing Chen, Wing-Kwong Chan

Cross-modal recipe retrieval has attracted research attention in recent years, thanks to the availability of large-scale paired data for training. Nevertheless, obtaining adequate recipe-image pairs covering the majority of cuisines for supervised learning is difficult if not impossible. By transferring knowledge learnt from a data-rich cuisine to a data-sca

Boris Ryabko

In 2002, Russell and Wang proposed a definition of entropically security that was developed within the framework of secret key cryptography. An entropically-secure system is unconditionally secure, that is, unbreakable, regardless of the enemy&#39;s computing power. In 2004, Dodis and Smith developed the results of Russell and Wang and, in particular, stated

Mohammad Babar, Hasnain Hafiz, Zeeshan Ahmad, Bernardo Barbiellini

V$_{2}$O$_{5}$ in its $ω$ phase (Li$_{3}$V$_{2}$O$_{5}$) with excess lithium is a potential alternative to the graphite anode for lithium-ion batteries at low temperature and fast charging conditions due to its safer voltage (0.6 V vs Li$^{+}$/Li(s)) and high lithium transport rate. In-operando cationic disorder, as observed in most ordered materials, can pr

Yongqiang Wang, H. Vincent Poor

Decentralized stochastic optimization is the basic building block of modern collaborative machine learning, distributed estimation and control, and large-scale sensing. Since involved data usually contain sensitive information like user locations, healthcare records and financial transactions, privacy protection has become an increasingly pressing need in th

Florian Plötzky, Wolf-Tilo Balke

Our lives are ruled by events of varying importance ranging from simple everyday occurrences to incidents of societal dimension. And a lot of effort is taken to exchange information and discuss about such events: generally speaking, stringent narratives are formed to reduce complexity. But when considering complex events like the current conflict between Rus

Enore Guadagnini, Federico Rottoli, Frank Thuillier

We consider topological gauge theories in three dimensions which are defined by metric independent lagrangians. It has been claimed that the functional integration necessarily depends nontrivially on the gauge-fixing metric. We demonstrate that the partition function and the mean values of the gauge invariant observables do not really depend on the gauge-fix

Valentina Egorova, Halina Grushevskaya, Nina Krylova, Igor Lipnevich

We offer new high-performance label-free electrochemical impedimetric DNA sensors of non-faradaic type. The DNA sensors based on a platform of crystalline carbon nanotube (CNT) arrays are fabricated by the Langmuir--Blodgett (LB) deposition technique. The CNT arrays are suspended on a nanoporous anodic alumina (aluminium oxide, AOA) support. Single-stranded

Aviad Aberdam, Roy Ganz, Shai Mazor, Ron Litman

Until recently, the number of public real-world text images was insufficient for training scene text recognizers. Therefore, most modern training methods rely on synthetic data and operate in a fully supervised manner. Nevertheless, the amount of public real-world text images has increased significantly lately, including a great deal of unlabeled data. Lever

Wenxuan Fang, Kai Zhang, Yoli Shavit, Wensen Feng

Image retrieval methods for place recognition learn global image descriptors that are used for fetching geo-tagged images at inference time. Recent works have suggested employing weak and self-supervision for mining hard positives and hard negatives in order to improve localization accuracy and robustness to visibility changes (e.g. in illumination or view p

Kengo Okura

We investigate the shellability of the polyhedral join $\mathcal{Z}^*_M (K, L)$ of simplicial complexes $K, M$ and a subcomplex $L \subset K$. We give sufficient conditions and necessary conditions on $(K, L)$ for $\mathcal{Z}^*_M (K, L)$ being shellable. In particular, we show that for some pairs $(K, L)$, $\mathcal{Z}^*_M (K, L)$ becomes shellable regardle

Andrzej Szepietowski

We present a few algorithms and methods to count fixes of permutations acting on monotone Boolean functions. Some of these methods was used by Pawelski \cite{P} to compute the number of inequivalent monotone Boolean functions with 8 variables.

Chenghao Zhu, Jiajun Zhang

Fast Radio Bursts (hereafter FRBs) can be used in cosmology by studying the Dispersion Measure (hereafter DM) as a function of redshift. The large scale structure of matter distribution is regarded as a major error budget for such application. Using optical galaxy and dispersion measure mocks built from N-body simulations, we have shown that the galaxy numbe

Igor V. Minin, Song Zhou, Oleg V. Minin

Recently, we showed that dielectric mesoscale spheres support super-resonance effect, i.e. high-order Mie resonance modes with giant field enhancement. The presence of the surrounding medium leads to a significant influence in the intensity of the electric and magnetic fields in the particle. In this paper, we show that this effect can be used for highly pre

Vedant Singh, Surgan Jandial, Ayush Chopra, Siddharth Ramesh

Conditional image generation has paved the way for several breakthroughs in image editing, generating stock photos and 3-D object generation. This continues to be a significant area of interest with the rise of new state-of-the-art methods that are based on diffusion models. However, diffusion models provide very little control over the generated image, whic

Xiying Fan, Tianzhi Xia, Huahui Qiu, Qicheng Zhang

Inspired by the newly emergent valleytronics, great interest has been attracted to the topological valley transport in classical metacrystals. The presence of nontrivial domain-wall states is interpreted with a concept of valley Chern number, which is well defined only in the limit of small bandgap. Here, we propose a new visual angle to track the intricate

John E. Laird

This paper is the recommended initial reading for a functional overview of Soar, version 9.6. It includes an abstract overview of the architectural structure of Soar including its processing, memories, learning modules, their interfaces, and the representations of knowledge used by those modules. From there it describes the processing supported by those modu

Jiawei Xu, Wenxuan Fu, Haoyu Bu, Zhi Wang

Malware continues to evolve rapidly, and more than 450,000 new samples are captured every day, which makes manual malware analysis impractical. However, existing deep learning detection models need manual feature engineering or require high computational overhead for long training processes, which might be laborious to select feature space and difficult to r

Michael Harris

The first part of this article is a review of the properties expected of any local Langlands correspondence that aims to be considered &#34;canonical,&#34; and of known results that establish some or all of these properties for specific groups. In the absence of compatibility with a global correspondence it is not known in general that this list of desirable

Alain Bourmaud, Lola Pinsard, Elouan Guillou, Emmanuel De Luycker

The creation and ultrastructure of kink-bands in flax fibres are key issues for developing more and more performing biobased composite materials. Nevertheless, despite many hypotheses and structural characterization, the exact origin of kink-bands and the moment they appear remain unexplained. Here, by using cutting-edge techniques such as microtomography, a

Mathew Thomas Arun, Debajyoti Choudhury

Considering a six-dimensional geometry orbifolded on $S^1/Z_2\times S^1/Z_2$ with quarks and leptons localised on orthogonal branes, we show that the construction admits observable $n-\bar{n}$ oscillation while naturally suppressing the proton decay rates. Consistent with other low-energy observables, the model also accommodates baryogenesis at $\mathcal{O}$

Russell Jay Hendel

This paper further generalizes a recent result of Shannon and Ollerton who resurrected an old identity due to Ledin. This paper generalizes the Ledin-Shannon-Ollerton result to all the metallic sequences. The results give closed formulas for the sum of products of powers of the first $n$ integers with the first $n$ members of the metallic sequence. Three key

Ali Salah Hameed, Adil Al-Azzawi

Today, finger vein identification is gaining popularity as a potential biometric identification framework solution. Machine learning-based unsupervised, supervised, and deep learning algorithms have had a significant influence on finger vein detection and recognition at the moment. Deep learning, on the other hand, necessitates a large number of training dat

Hanxuan Cai, Huimin Zhang, Duancheng Zhao, Jingxing Wu

Deep learning is an important method for molecular design and exhibits considerable ability to predict molecular properties, including physicochemical, bioactive, and ADME/T (absorption, distribution, metabolism, excretion, and toxicity) properties. In this study, we advanced a novel deep learning architecture, termed FP-GNN, which combined and simultaneousl

Bohdan Novosyadlyj, Yuriy Kulinich, Bohdan Melekh, Valerii Shulga

We study the formation and destruction of the first molecules at the epochs of the Dark Ages and Cosmic Dawn to evaluate the luminosity of the protogalaxy clumps (halos) in the molecular lines. The cosmological recombination is described using the model of an effective three-level atom, while the chemistry of the molecules is examined using the relevant basi

Sergey N. Galyamin, Andrey V. Tyukhtin

Cherenkov radiation which generates by a charge moving in presence of a dielectric prism from the base to the top is analyzed. Unlike our previous works, here we consider the case when the charge trajectory is not parallel to the prism face. We apply a new version of the aperture method which uses the expansion in terms of plane waves in the prism material a

Sihan Wang, Jingran Xu, Yong Zeng

Symbiotic radio (SR) communication is a promising technology to achieve spectrum- and energy-efficient wireless communication, by enabling passive backscatter devices (BDs) reuse not only the spectrum, but also the power of active primary transmitters (PTs). In this paper, we aim to characterize the energy-efficiency (EE) region of multiple-input single-outp