October 2020 arXiv papers — page 75

Erik Tamsen, Daniel Balzani

In this paper we present a fully-coupled, two-scale homogenization method for dynamic loading in the spirit of FE$^2$ methods. The framework considers the balance of linear momentum including inertia at the microscale to capture possible dynamic effects arising from micro heterogeneities. A finite-strain formulation is adapted to account for geometrical nonl

Guangzhi Tang, Neelesh Kumar, Raymond Yoo, Konstantinos P. Michmizos

The energy-efficient control of mobile robots is crucial as the complexity of their real-world applications increasingly involves high-dimensional observation and action spaces, which cannot be offset by limited on-board resources. An emerging non-Von Neumann model of intelligence, where spiking neural networks (SNNs) are run on neuromorphic processors, is r

Honglin Li, Yifei Fan, Frieder Ganz, Anthony Yezzi

The robustness of neural networks is challenged by adversarial examples that contain almost imperceptible perturbations to inputs, which mislead a classifier to incorrect outputs in high confidence. Limited by the extreme difficulty in examining a high-dimensional image space thoroughly, research on explaining and justifying the causes of adversarial example

José A. Adell

Associated to each complex-valued random variable satisfying appropriate integrability conditions, we introduce a different generalization of the Stirling numbers of the second kind. Various equivalent definitions are provided. Attention, however, is focused on applications. Indeed, such numbers describe the moments of sums of i.i.d. random variables, determ

J. M. de Souza Sant'Ana, A. Hoeller, R. D. Souza, H. Alves

This paper considers the use of successive interference cancellation (SIC) to decode superposed signals in Long Range (LoRa) networks. We build over a known stochastic geometry model for LoRa networks and include the effect of recovering colliding packets through SIC. We derive closed-form expressions for the successful decoding of packets using SIC taking p

Nils Strodthoff, Claas Strodthoff, Tobias Becher, Norbert Weiler

Electrical impedance tomography (EIT) is a noninvasive imaging modality that allows a continuous assessment of changes in regional bioimpedance of different organs. One of its most common biomedical applications is monitoring regional ventilation distribution in critically ill patients treated in intensive care units. In this work, we put forward a proof-of-

Dusan Sarenac, Andrew E. Silva, Connor Kapahi, Ben Thompson

We tested the ability of human observers to discriminate distinct profiles of spatially dependant geometric phases when directly viewing stationary structured light beams. Participants viewed polarization coupled orbital angular momentum (OAM) states, or ``spin-orbit'' states, in which the OAM was induced through Pancharatnam-Berry phases. The coupli

Abbaraju Praveen, Haoguang Yang, Hyukjun Jang, Richard M Voyles

Problems associated with physical interactions using aerial mobile manipulators (AMM) are being independently addressed with respect to mobility and manipulability. Multirotor unmanned aerial vehicles (UAV) are a common choice for mobility while on-board manipulators are increasingly be used for manipulability. However, the dynamic coordination between the U

Mustapha Hamad, Michèle Wigger, Mireille Sarkiss

We investigate the testing-against-independence problem \mw{over a cooperative MAC} with two sensors and a single detector under an average rate constraint on the sensors-detector links. For this setup, we design a variable-length coding scheme that maximizes the achievable type-II error exponent when the type-I error probability is limited to $ε$. Similarly

Andrea Bianchi

We give an upper bound on the topological complexity of varieties $\mathcal{V}$ obtained as complements in $\mathbb{C}^m$ of the zero locus of a polynomial. As an application, we determine the topological complexity of unordered configuration spaces of the plane.

Sirag Erkol, Dario Mazzilli, Filippo Radicchi

We consider the optimization problem of seeding a spreading process on a temporal network so that the expected size of the resulting outbreak is maximized. We frame the problem for a spreading process following the rules of the susceptible-infected-recovered model with temporal scale equal to the one characterizing the evolution of the network topology. We p

Guido Mazzuca, Tamara Grava, Thomas Kriecherbauer, Ken D. T. -R. McLaughlin

We consider a system of harmonic oscillators with short range interactions and we study their correlation functions when the initial data is sampled with respect to the Gibbs measure. Such correlation functions display rapid oscillations that travel through the chain. We show that the correlation functions always have two fastest peaks which move in opposite

R. L. Wilmington, H. Ardekani, A. Rustagi, A. Bataller

2D transition metal dichalcogenides (TMDCs) exhibit an electron-hole liquid phase transition at unusually high temperatures. Because these materials are atomically thin, optical excitation leads to material expansion. As a result, during the EHL phase transition the electronic band structure evolves due to both material thermal expansion and renormalization

David Wan, Chris Kedzie, Faisal Ladhak, Marine Carpuat

Users of machine translation (MT) may want to ensure the use of specific lexical terminologies. While there exist techniques for incorporating terminology constraints during inference for MT, current APE approaches cannot ensure that they will appear in the final translation. In this paper, we present both autoregressive and non-autoregressive models for lex

A. M. E. Saad, F. Kocak

In this paper, we investigate the effect on energy resolution from changing the angle and the position of incidence photon for a 5 $\times$ 5 crystal matrix built with (25 $\times$ 25 $\times$ 200) mm$^{3}$ LYSO scintillating crystals. Those crystals have been proposed for the electromagnetic calorimeter of the Turkish Accelerator Center-Particle Factory (TA

Abbaraju Praveen, Xin Ma, Harikrishnan Manoj, Vishnunandan LN. Venkatesh

Inspection for structural properties (surface stiffness and coefficient of restitution) is crucial for understanding and performing aerial manipulations in unknown environments, with little to no prior knowledge on their state. Inspection-on-the-fly is the uncanny ability of humans to infer states during manipulation, reducing the necessity to perform inspec

S. Friedrich, G. B. Kim, C. Bray, R. Cantor

Sterile neutrinos are natural extensions to the standard model of particle physics and provide a possible portal to the dark sector. We report a new search for the existence of sub-MeV sterile neutrinos using the decay-momentum reconstruction technique in the decay of $^7$Be. The experiment measures the total energy of the $^7$Li daughter atom from the elect

Carl J. Nelson, Stephen Bonner

Connected networks are a fundamental structure of neurobiology. Understanding these networks will help us elucidate the neural mechanisms of computation. Mathematically speaking these networks are `graphs' - structures containing objects that are connected. In neuroscience, the objects could be regions of the brain, e.g. fMRI data, or be individual neuro

Jeroen Offerijns, Suzan Verberne, Tessa Verhoef

For the field of education, being able to generate semantically correct and educationally relevant multiple choice questions (MCQs) could have a large impact. While question generation itself is an active research topic, generating distractors (the incorrect multiple choice options) receives much less attention. A missed opportunity, since there is still a l

S. Danial Forghani, Ibrahim Gullu, S. Habib Mazharimousavi

A spherical planetary nebula is described as a geometric model. The nebula itself is considered as a thin-shell which visualized as a boundary of two spacetimes. The inner and outer curvature tensors of the thin-shell are found in order to get an expression of the energy-momentum tensor on the thin-shell. The energy density and pressure expressions are deriv

Sarvesh Patil, Chava Y P D Phani Rajanish, Naveen Margankunte

Particle Size Analysis (PSA) is an important process carried out in a number of industries, which can significantly influence the properties of the final product. A ubiquitous instrument for this purpose is the Optical Microscope (OM). However, OMs are often prone to drawbacks like low resolution, small focal depth, and edge features being masked due to diff

Behrouz Rostami, D. M. Anisuzzaman, Chuanbo Wang, Sandeep Gopalakrishnan

Acute and chronic wounds are a challenge to healthcare systems around the world and affect many people's lives annually. Wound classification is a key step in wound diagnosis that would help clinicians to identify an optimal treatment procedure. Hence, having a high-performance classifier assists the specialists in the field to classify the wounds with l

Srimanta Mitra, Aquil Ahmad, Shamik Chakrabarti, Sajib Biswas

Experimental and computational studies were performed on Co2FeGe Heusler alloy. It was found that the alloy has very high experimental magnetic moment of 6.1 muB/f.u., curie temperature of 1073K and very high spin-wave stiffness constant of 10.4 nm2-meV, which indicates that the magnetic moment is very high and do not vary with change in temperature in the r

Marlene Böhmer, Andreas Schmidt, Pablo Gil Pereira, Thorsten Herfet

In order to create cooperating swarms of Unmanned Autonomous Vehicles (UAVs) that also interact with various other systems and devices, open and free communication systems are mandatory. This paper presents an implementation of such a communication system to incorporate the Crazyflie nano-drone as a UAV platform. The protocol stack leverages the open Predict

Nadav Rotem, Lee Howes, David Goldblatt

This paper presents Warrior1, a tool that detects performance anti-patterns in C++ libraries. Many programs are slowed down by many small inefficiencies. Large-scale C++ applications are large, complex, and developed by large groups of engineers over a long period of time, which makes the task of identifying inefficiencies difficult. Warrior1 was designed to

Bo Yang

To endow machines with the ability to perceive the real-world in a three dimensional representation as we do as humans is a fundamental and long-standing topic in Artificial Intelligence. Given different types of visual inputs such as images or point clouds acquired by 2D/3D sensors, one important goal is to understand the geometric structure and semantics o

Bibekananda Sitha, Jajati Keshari Sahoo, Ratikanta Behera

The notion of weighted $(b,c)$-inverse of an element in rings were introduced, very recently [Comm. Algebra, 48 (4) (2020): 1423-1438]. In this paper, we further elaborate on this theory by establishing a few characterizations of this inverse and their relationships with other $(v, w)$-weighted $(b,c)$-inverses. We introduce some necessary and sufficient con

Eduard Eiben, Fedor V. Fomin, Petr A. Golovach, William Lochet

We consider a generalization of the fundamental $k$-means clustering for data with incomplete or corrupted entries. When data objects are represented by points in $\mathbb{R}^d$, a data point is said to be incomplete when some of its entries are missing or unspecified. An incomplete data point with at most $Δ$ unspecified entries corresponds to an axis-paral

Joyce Byun, Andrea Oddo, Cristiano Porciani, Emiliano Sefusatti

Higher-order clustering statistics, like the galaxy bispectrum, can add complementary cosmological information to what is accessible with two-point statistics, like the power spectrum. While the standard way of measuring the bispectrum involves estimating a bispectrum value in a large number of Fourier triangle bins, the compressed modal bispectrum approxima

Xiaohan Guo, Sebastian Kurtek, Karthik Bharath

Spatial, amplitude and phase variations in spatial functional data are confounded. Conclusions from the popular functional trace variogram, which quantifies spatial variation, can be misleading when analysing misaligned functional data with phase variation. To remedy this, we describe a framework that extends amplitude-phase separation methods in functional

Sandhya Tripathi, N Hemachandra

We use Generative Adversarial Networks (GANs) to design a class conditional label noise (CCN) robust scheme for binary classification. It first generates a set of correctly labelled data points from noisy labelled data and 0.1% or 1% clean labels such that the generated and true (clean) labelled data distributions are close; generated labelled data is used t

Di Wang, Xiangyu Guo, Shi Li, Jinhui Xu

In this paper, we study the problem of estimating latent variable models with arbitrarily corrupted samples in high dimensional space ({\em i.e.,} $d\gg n$) where the underlying parameter is assumed to be sparse. Specifically, we propose a method called Trimmed (Gradient) Expectation Maximization which adds a trimming gradients step and a hard thresholding s

Marina Sokolova, Victoria Bobicev

We propose the Echo-Chamber Effect assessment of an online forum. Sentiments perceived by the forum readers are at the core of the analysis; a complete message is the unit of the study. We build 14 models and apply those to represent discussions gathered from an online medical forum. We use four multi-class sentiment classification applications and two Machi

A. V. Volotka, A. Surzhykov, S. Fritzsche

The process of the elastic scattering of photons on atoms, known as the Rayleigh scattering, is investigated. Expressing the scattering observables in terms of the electric and magnetic complex scattering amplitudes, we work over the scenarios for their independent benchmarking by experiments. In particular, the combination of the cross section and polarizat

Erwin Quiring, Lukas Pirch, Michael Reimsbach, Daniel Arp

Machine learning-based systems for malware detection operate in a hostile environment. Consequently, adversaries will also target the learning system and use evasion attacks to bypass the detection of malware. In this paper, we outline our learning-based system PEberus that got the first place in the defender challenge of the Microsoft Evasion Competition, r

Warren Siegel, Di Wang

We write down a $GL(D+1)$ (D for the dimension of string theory) manifest fundamental brane worldvolume current algebra description of M theory, which consists of a pair of vector field $X^m$ and dual 2-form field $X_{mn}$, compositing together to parametrize the spacetime, with a selfduality condition for sectioning. The worldvolume of the brane itself is a

Ci-Siang Lin, Yuan-Chia Cheng, Yu-Chiang Frank Wang

Aiming at recognizing images of the same person across distinct camera views, person re-identification (re-ID) has been among active research topics in computer vision. Most existing re-ID works require collection of a large amount of labeled image data from the scenes of interest. When the data to be recognized are different from the source-domain training

Milind G. Padalkar, Carlos Beltrán-González, Matteo Bustreo, Alessio Del Bue

This paper presents a novel setup for automatic visual inspection of cracks in ceramic tile as well as studies the effect of various classifiers and height-varying illumination conditions for this task. The intuition behind this setup is that cracks can be better visualized under specific lighting conditions than others. Our setup, which is designed for fiel

Qiao Jin, Hu Cheng, Zhiwen Wang, Qinghua Zhang

Strain engineering provides the ability to control the ground states and associated phase transition in the epitaxial films. However, the systematic study of intrinsic characters and their strain dependency in transition-metal nitrides remains challenging due to the difficulty in fabricating the stoichiometric and high-quality films. Here we report the obser

Fabian Duerr, Hugo Thienpont

For more than 150 years, scientists have advanced aberration theory to describe, analyse, and eliminate imperfections that disturb the imaging quality of optical components and systems. Simultaneously, they have developed optical design methods for and manufacturing techniques of imaging systems with ever-increasing complexity and performance up to the point

Sergey Tarima, Zhanna Zenkova

The newsvendor problem is a popular inventory management problem in supply chain management and logistics. Solutions to the newsvendor problem determine optimal inventory levels. This model is typically fully determined by a purchase and sale prices and a distribution of random market demand. From a statistical point of view, this problem is often considered

Hao Wang, Xun Guo, Jianming Xue

Potentials that could accurately describe the irradiation damage processes are highly desired to figure out the atomic-level response of various newly-discovered materials under irradiation environments. In this work, we introduce a deep-learning interatomic potential for monolayer MoS2 by combining all-electron calculations, an active-learning sampling meth

Hamza Cherkaoui, Jeremias Sulam, Thomas Moreau

Total Variation (TV) is a popular regularization strategy that promotes piece-wise constant signals by constraining the $\ell_1$-norm of the first order derivative of the estimated signal. The resulting optimization problem is usually solved using iterative algorithms such as proximal gradient descent, primal-dual algorithms or ADMM. However, such methods ca

M. Mirazita, H. Avakian, A. Courtoy, S. Pisano

A first measurement of the longitudinal beam spin asymmetry ALU in the semi-inclusive electroproduction of pairs of charged pions is reported. ALU is a higher-twist observable and offers the cleanest access to the nucleon twist-3 parton distribution function e(x). Data have been collected in the Hall-B at Jefferson Lab by impinging a 5.498 GeV electron beam

Haider Al-Tahan, Yalda Mohsenzadeh

Learning rich visual representations using contrastive self-supervised learning has been extremely successful. However, it is still a major question whether we could use a similar approach to learn superior auditory representations. In this paper, we expand on prior work (SimCLR) to learn better auditory representations. We (1) introduce various data augment

Jochen Heitger, Fabian Joswig

We present the results of a non-perturbative determination of the improvement coefficient $c_\mathrm{V}$ and the renormalisation factor $Z_\mathrm{V}$, which define the renormalised vector current in three-flavour $\mathrm{O}(a)$ improved lattice QCD with Wilson quarks and tree-level Symanzik-improved gauge action. In case of the improvement coefficient, we

Rémi Dupuy, Michal Haubner, Bernard Henrist, Jean-Hugues Fillion

Electron-stimulated desorption (ESD) of cryosorbed molecules on surfaces is a process of relevance to fields as varied as vacuum dynamics in accelerators and astrochemistry. While desorption from such molecular systems induced by keV electrons and fast ions has been extensively studied, the sub-keV electron regime is comparatively little known. We measured a

Yongchao Wang, Jing Bai

In this paper, we develop an efficient decoder via the proximal alternating direction method of multipliers (proximal-ADMM) technique for nonbinary linear block codes in the Galois field. Its main contents are as follows: first, exploiting the decomposition technique based on the three-variables check equation, we formulate the maximum likelihood (ML) decodi

J. Gospodaric, V. Dziom, A. Shuvaev, A. A. Dobretsova

From the analysis of the cyclotron resonance, we experimentally obtain the band structure of the three-dimensional topological insulator based on a HgTe thin film. Top gating was used to shift the Fermi level in the film, allowing us to detect separate resonance modes corresponding to the surface states at two opposite film interfaces, the bulk conduction ba

Gongjin Lan, Maarten van Hooft, Matteo De Carlo, Jakub M. Tomczak

The challenge of robotic reproduction -- making of new robots by recombining two existing ones -- has been recently cracked and physically evolving robot systems have come within reach. Here we address the next big hurdle: producing an adequate brain for a newborn robot. In particular, we address the task of targeted locomotion which is arguably a fundamenta

Michael Baddeley, Adnan Aijaz, Usman Raza, Aleksandar Stanoev

Targeting dependable communications for industrial Internet of Things applications, IETF 6TiSCH provides mechanisms for efficient scheduling, routing, and forwarding of IPv6 traffic across low-power mesh networks. Yet, despite an overwhelming body of literature covering both centralized and distributed scheduling schemes for 6TiSCH, an effective control solu

Étienne André, Aleksander Kryukov

We consider a notion of non-interference for timed automata (TAs) that allows to quantify the frequency of an attack; that is, we infer values of the minimal time between two consecutive actions of the attacker, so that (s)he disturbs the set of reachable locations. We also synthesize valuations for the timing constants of the TA (seen as parameters) guarant

Biswajit Basu, Calin Iulian Martin

The classical irrotational capillary-gravity water wave problem described by the Euler equations with a nonlinear free surface boundary condition over a flat bed is considered. A modified flow force has been defined and a new formulation of capillary-gravity waves in the framework of the modified flow force function has been developed. Using bifurcation theo

Hao Wang, Jia Zhang, Yingce Xia, Jiang Bian

Semantic code search, which aims to retrieve code snippets relevant to a given natural language query, has attracted many research efforts with the purpose of accelerating software development. The huge amount of online publicly available code repositories has prompted the employment of deep learning techniques to build state-of-the-art code search models. P

Bowen Li, Taeuk Kim, Reinald Kim Amplayo, Frank Keller

Transformer-based pre-trained language models (PLMs) have dramatically improved the state of the art in NLP across many tasks. This has led to substantial interest in analyzing the syntactic knowledge PLMs learn. Previous approaches to this question have been limited, mostly using test suites or probes. Here, we propose a novel fully unsupervised parsing app

A. Biancalani, A. Bottino, A. Di Siena, Ö. Gürcan

The nonlinear dynamics of beta-induced Alfvén Eigenmodes (BAE) driven by energetic particles (EP) in the presence of ion-temperature-gradient (ITG) turbulence is investigated, by means of selfconsistent global gyrokinetic simulations and analytical theory. A tokamak magnetic equilibrium with large aspect ratio and reversed shear is considered. A previous stu

Shi-Mei Ma, Jun Ma, Jean Yeh, Yeong-Nan Yeh

In this paper, we characterize a duality relation between Eulerian recurrences and Eulerian recurrence systems, which generalizes and unifies Hermite-Biehler decompositions of several enumerative polynomials, including flag descent polynomials for hyperoctahedral group, flag ascent-plateau polynomials for Stirling permutations, up-down run polynomials for sy

David Goltzsche, Tim Siebels, Lennard Golsch, Rüdiger Kapitza

Web applications are on the rise and rapidly evolve into more and more mature replacements for their native counterparts. This disruptive trend is mainly driven by the attainment of platform-independence and instant deployability. On top of this, web browsers offer the opportunity for seamless browser-to-browser communication for distributed interaction. In

Clement Cosco, Inbar Seroussi, Ofer Zeitouni

We study the directed polymer model for general graphs (beyond $\mathbb Z^d$) and random walks. We provide sufficient conditions for the existence or non-existence of a weak disorder phase, of an $L^2$ region, and of very strong disorder, in terms of properties of the graph and of the random walk. We study in some detail (biased) random walk on various trees

Semyon Yakubovich

Discrete analogs of the index transforms with squares of Bessel functions of the first and second kind $J_ν(z),\ Y_ν(z)$ are introduced and investigated. The corresponding inversion theorems for suitable classes of functions and sequences are established.

Linhang Cai, Zhulin An, Chuanguang Yang, Yongjun Xu

Network pruning is widely used to compress Deep Neural Networks (DNNs). The Soft Filter Pruning (SFP) method zeroizes the pruned filters during training while updating them in the next training epoch. Thus the trained information of the pruned filters is completely dropped. To utilize the trained pruned filters, we proposed a SofteR Filter Pruning (SRFP) met

Estefanía Garijo del Río, Sami Kaappa, José A. Garrido Torres, Thomas Bligaard

Local optimization of adsorption systems inherently involves different scales: within the substrate, within the molecule, and between molecule and substrate. In this work, we show how the explicit modeling of the different character of the bonds in these systems improves the performance of machine learning methods for optimization. We introduce an anisotropi

Adrian Hauswirth, Lukas Ortmann, Saverio Bolognani, Florian Dörfler

In this paper, we study the stability and convergence of continuous-time Lagrangian saddle flows to solutions of a convex constrained optimization problem. Convergence of these flows is well-known when the underlying saddle function is either strictly convex in the primal or strictly concave in the dual variables. In this paper, we show convergence under non

Lassi Meronen, Christabella Irwanto, Arno Solin

We introduce a new family of non-linear neural network activation functions that mimic the properties induced by the widely-used Matérn family of kernels in Gaussian process (GP) models. This class spans a range of locally stationary models of various degrees of mean-square differentiability. We show an explicit link to the corresponding GP models in the cas

Jingjing Huo, Christian Herold, Yingbo Gao, Leonard Dahlmann

Context-aware neural machine translation (NMT) is a promising direction to improve the translation quality by making use of the additional context, e.g., document-level translation, or having meta-information. Although there exist various architectures and analyses, the effectiveness of different context-aware NMT models is not well explored yet. This paper

Martin Kološ, Arman Tursunov, Zdeněk Stuchlík

We demonstrate an extraordinary effect of energy gain by a single radiating charged particle inside the ergosphere of a Kerr black hole in presence of magnetic field. We solve numerically the covariant form of the Lorentz-Dirac equation reduced from the DeWitt-Brehme equation and analyze energy evolution of the radiating charged particle inside the ergospher

H. A. J. Middleton-Spencer, N. G. Parker, L. Galantucci, C. F. Barenghi

We present a method to detect the presence and depth of dark solitons within repulsive one-dimensional harmonically trapped Bose-Einstein condensates. For a system with one soliton, we prove that the shift of the density in Fourier space directly maps onto the depth of the soliton. For multi-soliton systems, combining our spectral method with established ima

Charalambos Chrysostomou, Loizos Koutsantonis, Christos Lemesios, Costas N. Papanicolas

In this paper, we explore a novel method for tomographic image reconstruction in the field of SPECT imaging. Deep Learning methodologies and more specifically deep convolutional neural networks (CNN) are employed in the new reconstruction method, which is referred to as "CNN Reconstruction - CNNR". For training of the CNNR Projection data from softwa

Alessandro Giuseppi, Antonio Pietrabissa

This paper proposes a lexicographic Deep Reinforcement Learning (DeepRL)-based approach to chance-constrained Markov Decision Processes, in which the controller seeks to ensure that the probability of satisfying the constraint is above a given threshold. Standard DeepRL approaches require i) the constraints to be included as additional weighted terms in the

Lanfranco Zanzi, Vincenzo Sciancalepore, Andres Garcia-Saavedra, Xavier Costa-Perez

Mass events represent one of the most challenging scenarios for mobile networks because, although their date and time are usually known in advance, the actual demand for resources is difficult to predict due to its dependency on many different factors. Based on data provided by a major European carrier during mass events in a football stadium comprising up t

Bowen Wang, Liangzhi Li, Yuta Nakashima, Ryo Kawasaki

Semantic video segmentation is a key challenge for various applications. This paper presents a new model named Noisy-LSTM, which is trainable in an end-to-end manner, with convolutional LSTMs (ConvLSTMs) to leverage the temporal coherency in video frames. We also present a simple yet effective training strategy, which replaces a frame in video sequence with

Emilio Calvanese Strinati, Sergio Barbarossa, Taesang Choi, Antonio Pietrabissa

6G will exploit satellite, aerial and terrestrial platforms jointly to improve radio access capability and to unlock the support of on-demand edge cloud services in the three dimensional space (3D) by incorporating Mobile Edge Computing (MEC) functionalities on aerial platforms and low orbit satellites. This will extend the MEC support to devices and network

Eyal Shnarch, Leshem Choshen, Guy Moshkowich, Noam Slonim

Approaching new data can be quite deterrent; you do not know how your categories of interest are realized in it, commonly, there is no labeled data at hand, and the performance of domain adaptation methods is unsatisfactory. Aiming to assist domain experts in their first steps into a new task over a new corpus, we present an unsupervised approach to reveal c

Zhanwei Xu, Yukun Cao, Cheng Jin, Guozhu Shao

Segmentation of infected areas in chest CT volumes is of great significance for further diagnosis and treatment of COVID-19 patients. Due to the complex shapes and varied appearances of lesions, a large number of voxel-level labeled samples are generally required to train a lesion segmentation network, which is a main bottleneck for developing deep learning

Joe Townsend, Theodoros Kasioumis, Hiroya Inakoshi

Our main contribution is to show that the behaviour of kernels across multiple layers of a convolutional neural network can be approximated using a logic program. The extracted logic programs yield accuracies that correlate with those of the original model, though with some information loss in particular as approximations of multiple layers are chained toget

Matteo Becchetti, Roberto Bonciani, Vittorio Del Duca, Valentin Hirschi

We present for the first time the exact NLO QCD corrections to the light-quark part of the mixed QCD-EW contributions to Higgs production via gluon fusion at LHC13, with exact EW-boson mass dependence. The relevant two-loop real-emission matrix element is computed using a dynamic one-dimensional series expansion strategy whose stability and speed allows for

Cunsheng Ding, Chunming Tang, Vladimir D. Tonchev

The projective general linear group $\mathrm{PGL}_2(\mathrm{GF}(2^m))$ acts as a $3$-transitive permutation group on the set of points of the projective line. The first objective of this paper is to prove that all linear codes over $\mathrm{GF}(2^h)$ that are invariant under $\mathrm{PGL}_2(\mathrm{GF}(2^m))$ are trivial codes: the repetition code, the whole

Craig R. Stark, Declan A. Diver

Understanding the source of sub-stellar polarimetric observations in the optical and near-infrared is key to characterizing sub-stellar objects and developing potential diagnostics for determining properties of their atmospheres. Differential scattering from a population of aligned, non-spherical dust grains is a potential source of polarization that could b

Jacob Bradd, Nigel Higson

We give an exposition of Novodvorskii's theorem in Banach algebra K-theory, asserting that the Gelfand transform for a commutative Banach algebra induces an isomorphism in topological K-theory.

James Jin Kang, Mahdi Dibaei, Gang Luo, Wencheng Yang

Privacy protection in electronic healthcare applications is an important consideration due to the sensitive nature of personal health data. Internet of Health Things (IoHT) networks have privacy requirements within a healthcare setting. However, these networks have unique challenges and security requirements (integrity, authentication, privacy and availabili

Ishita Doshi, Dhritiman Das, Ashish Bhutani, Rajeev Kumar

Nearest neighbor search (NNS) has a wide range of applications in information retrieval, computer vision, machine learning, databases, and other areas. Existing state-of-the-art algorithm for nearest neighbor search, Hierarchical Navigable Small World Networks(HNSW), is unable to scale to large datasets of 100M records in high dimensions. In this paper, we p

Nasir Hayat, Munawar Hayat, Shafin Rahman, Salman Khan

The existing zero-shot detection approaches project visual features to the semantic domain for seen objects, hoping to map unseen objects to their corresponding semantics during inference. However, since the unseen objects are never visualized during training, the detection model is skewed towards seen content, thereby labeling unseen as background or a seen

Ajay Deep Kachhvah, Xiangfeng Dai, Stefano Boccaletti, Sarika Jalan

Adaptation plays a pivotal role in the evolution of natural and artificial complex systems, and in the determination of their functionality. Here, we investigate the impact of adaptive inter-layer processes on intra-layer synchronization in multiplex networks. The considered adaptation mechanism is governed by a Hebbian learning rule, i.e., the link weight b

Christos Tselios, Stavros Nousias, Dimitris Bitzas, Dimitrios Amaxilatis

As road transportation has been identified as a major contributor of environmental pollution, motivating individuals to adopt a more eco-friendly driving style could have a substantial ecological as well as financial benefit. With gamification being an effective tool towards guiding targeted behavioural changes, the development of realistic frameworks delive

Dimitrios Amaxilatis, Christos Tselios, Orestis Akrivopoulos, Ioannis Chatzigiannakis

Recent technological improvements in vehicle manufacturing may greatly improve safety however, the individuals' driving behaviour still remains a factor of paramount importance with aggressiveness, lack of focus and carelessness being the main cause of the majority of traffic incidents. The imminent deployment of 5G networking infrastructure, paired with

Blanche Buet, Martin Rumpf

This paper investigates a discretization scheme for mean curvature motion on point cloud varifolds with particular emphasis on singular evolutions. To define the varifold a local covariance analysis is applied to compute an approximate tangent plane for the points in the cloud. The core ingredient of the mean curvature motion model is the regularization of t

Faical Ndairou, Ivan Area, Delfim F. M. Torres

We propose a mathematical model for the spread of Japanese encephalitis, with emphasis on environmental effects on the aquatic phase of mosquitoes. The model is shown to be biologically well-posed and to have a biologically and ecologically meaningful disease free equilibrium point. Local stability is analyzed in terms of the basic reproduction number and nu

Huijuan Zhou, Yong Chen

The extended nonlinear Schrödinger (ENLS) equation with third-order term and fourth-order term which describes the wave propagation in the optical fibers is more accurate than the NLS equation. A study of high-order soliton matrix is presented for an ENLS equation in the framework of the Riemann-Hilbert problem (RHP). Through a standard dressing procedure an

Paz Albares, Pilar García Estévez, Juan Domingo Lejarreta

We present a generalized study and characterization of the integrability properties of the derivative non-linear Schrödinger equation in 1+1 dimensions. A Lax pair is derived for this equation by means of a Miura transformation and the singular manifold method. This procedure, together with the Darboux transformations, allow us to construct a wide class of r

Kotaro Matsuoka, Ryotaro Banno, Naoki Matsumoto, Takashi Sato

We present Virtual Secure Platform (VSP), the first comprehensive platform that implements a multi-opcode general-purpose sequential processor over Fully Homomorphic Encryption (FHE) for Secure Multi-Party Computation (SMPC). VSP protects both the data and functions on which the data are evaluated from the adversary in a secure computation offloading situati

Juan J. Gómez Rodríguez, José Lamarca, Javier Morlana, Juan D. Tardós

Conventional SLAM techniques strongly rely on scene rigidity to solve data association, ignoring dynamic parts of the scene. In this work we present Semi-Direct DefSLAM (SD-DefSLAM), a novel monocular deformable SLAM method able to map highly deforming environments, built on top of DefSLAM. To robustly solve data association in challenging deforming scenes,

Anwesha Chattopadhyay

We report the emergence or broadening of exotic magnetic metallic phases upon explicit breaking of $SU(2)$ symmetry by introduction of mass imbalance in a variant of Hubbard model, known as the ionic Hubbard model in the presence of frustration at half-filling on a square lattice. The ionic Hubbard model has in addition to hopping($\sim t$) and onsite coulom

Jonas Kirchhoff

In the present work we investigate topological properties of the set of controllable differential-algebraic systems of the form $\tfrac{\text{d}}{\text{d}t}Ex = Ax+Bu$ with real matrices $E,A\in\mathbb{R}^{\ell\times n}$ and $B\in\mathbb{R}^{\ell\times m}$. We consider the five controllability concepts free initializability (controllability at infinity), imp

Dušan Variš, Ondřej Bojar

This work presents our ongoing research of unsupervised pretraining in neural machine translation (NMT). In our method, we initialize the weights of the encoder and decoder with two language models that are trained with monolingual data and then fine-tune the model on parallel data using Elastic Weight Consolidation (EWC) to avoid forgetting of the original

Sungwon Lyu, Bokyung Son, Kichang Yang, Jaekyoung Bae

The complete sharing of parameters for multilingual translation (1-1) has been the mainstream approach in current research. However, degraded performance due to the capacity bottleneck and low maintainability hinders its extensive adoption in industries. In this study, we revisit the multilingual neural machine translation model that only share modules among

N. Dagoneau, S. Schanne, J. Rodriguez, J. -L. Atteia

The SVOM mission under development will carry various instruments, and in particular the coded-mask telescope ECLAIRs, with a large field of view of about 2 sr, operating in the 4--150 keV energy band, whose goal is to detect high energy transients such as gamma-ray bursts. The trigger software onboard ECLAIRs will search for new hard X-ray sources appearing

C. Alard

Numerical simulations demonstrate a link between dynamically cold initial solutions and an evolution towards self-similarity. However the nature of this link is not fully understood. In this work the link between cold initial conditions and self-similarity near equilibrium is established. The evolution towards self-similarity is analyzed using an analytical

Rahul Basu, Dipanjan Mitra, George I. Melikidze

We have revisited the problem of off-pulse emission in pulsars, where detailed search for the presence of low level radio emission outside the pulse window is carried out. The presence of off-pulse emission was earlier reported in two long period pulsars, PSR B0525+21 and B2046-16 at frequencies below 1 GHz using the Giant Meterwave Radio Telescope (GMRT). H

Calin Iulian Martin, Ronald Quirchmayr

We use spherical coordinates to devise a new exact solution to the governing equations of geophysical fluid dynamics for an inviscid and incompressible fluid with a general density distribution and subjected to forcing terms. The latter are of paramount importance for the modeling of realistic flows-that is, flows that are observed in some averaged sense in

Guang-Juan Wang, Lu Meng, Li-Ye Xiao, Makoto Oka

We systematically study the mass spectrum and strong decays of the S-wave $\bar c\bar s q q$ states in the compact tetraquark scenario with the quark model. The key ingredients of the model are the Coulomb, the linear confinement, and the hyperfine interactions. The hyperfine potential leads to the mixing between different color configurations, as well as th