March 2020 arXiv papers — page 113

Rebecca L. Brouillette, Élie Besserer-Offroy, Christine E. Mona, Magali Chartier

Pepducins are cell-penetrating, membrane-tethered lipopeptides designed to target the intracellular region of a G protein-coupled receptor (GPCR) in order to allosterically modulate the receptor's signaling output. In this proof-of-concept study, we explored the pain-relief potential of a pepducin series derived from the first intracellular loop of neuro

Lluís Alemany-Puig, Ramon Ferrer-i-Cancho

The crossing number of a graph $G$, $\mathrm{cr}(G)$, is the minimum number of edge crossings arising when drawing a graph on a certain surface. Determining $\mathrm{cr}(G)$ is a problem of great importance in Graph Theory. Its maximum variant, i.e. the maximum crossing number, $\mathrm{max-cr}(G)$, is receiving growing attention. Instead of an optimization

Pierre Fraigniaud, Ami Paz

Modeling distributed computing in a way enabling the use of formal methods is a challenge that has been approached from different angles, among which two techniques emerged at the turn of the century: protocol complexes, and directed algebraic topology. In both cases, the considered computational model generally assumes communication via shared objects, typi

Guodong Pang, Etienne Pardoux

We study non-Markovian stochastic epidemic models (SIS, SIR, SIRS, and SEIR), in which the infectious (and latent/exposing, immune) periods have a general distribution. We provide a representation of the evolution dynamics using the time epochs of infection (and latency/exposure, immunity). Taking the limit as the size of the population tends to infinity, we

Christopher Hahn, Frederik Schmitt, Jens U. Kreber, Markus N. Rabe

We study two fundamental questions in neuro-symbolic computing: can deep learning tackle challenging problems in logics end-to-end, and can neural networks learn the semantics of logics. In this work we focus on linear-time temporal logic (LTL), as it is widely used in verification. We train a Transformer on the problem to directly predict a solution, i.e. a

M. Redies, F. R. Lux, J. -P. Hanke, P. M. Buhl

Skyrmion lattices as a novel type of chiral spin states are attracting increasing attention, owing to their peculiar properties stemming from real-space topological properties. At the same time, the properties of magnetic Weyl semimetals with complex $k$-space topology are moving into the focus of research in spintronics. We consider the Hall transport prope

Vasyl Kovalchuk, Barbara Gołubowska, Ewa Eliza Rożko

In the present paper we have discussed the mechanics of incompressible test bodies moving in Riemannian spaces with non-trivial curvature tensors. For Hamilton's equations of motion the solutions have been obtained in the parametrical form and the special case of the purely gyroscopic motion on the sphere has been discussed. For the geodetic case when th

Theodore Papamarkou, Hayley Guy, Bryce Kroencke, Jordan Miller

Nondestructive evaluation methods play an important role in ensuring component integrity and safety in many industries. Operator fatigue can play a critical role in the reliability of such methods. This is important for inspecting high value assets or assets with a high consequence of failure, such as aerospace and nuclear components. Recent advances in conv

Shusuke Otabe, Fabio Tonini, Lei Zhang

In the present paper, we study a purely inseparable counterpart of Abhyankar's conjecture for the affine line in positive characteristic, and prove its validity for all the finite local non-abelian simple group schemes in characteristic $p>5$. The crucial point is how to deal with finite local group schemes which cannot be realized as the Frobenius kerne

Yasushi Ieno

A research for a general expression of the triplets of integer sided triangles with a $120^{\circ}$ angle, in parallel with the case of a $60^{\circ}$ angle.

Mutian He, Yangqiu Song, Kun Xu, Dong Yu

Commonsense knowledge graphs (CKGs) like Atomic and ASER are substantially different from conventional KGs as they consist of much larger number of nodes formed by loosely-structured text, which, though, enables them to handle highly diverse queries in natural language related to commonsense, leads to unique challenges for automatic KG construction methods.

Wenhua Wang, Yuqun Zhang, Zhengran Zeng, Guandong Xu

Code summarization and code search have been widely adopted in sofwaredevelopmentandmaintenance. However, fewstudieshave explored the efcacy of unifying them. In this paper, we propose TranS^3 , a transformer-based framework to integrate code summarization with code search. Specifcally, for code summarization,TranS^3 enables an actor-critic network, where in

Kunihiro Noda, Takashi Kobayashi, Kiyoshi Agusa

Comprehending the behavior of an object-oriented system solely from its source code is troublesome, owing to its dynamism. To aid comprehension, visualizing program behavior through reverse-engineered sequence diagrams from execution traces is a promising approach. However, because of the massiveness of traces, recovered diagrams tend to become very large, c

Qurat-ul-Ain, Muhammad Ismail

Metastatic Melanoma, the fifth most common cancer in the western countries and the most common malignancy diagnosed in United States present itself as the most lethal treatment resistant cancer worldwide. In addition to the reactive oxygen species(ROS), mutations in the genes encoding receptors and non-receptor tyrosine/serene/threonine protein kinases are k

Raphael Steck, Arthur Ulmer

We prove that the ladder with $3$~rungs and the house graph have the edge-Erd\H{o}s-P\'{o}sa property, while ladders with $14$~rungs or more have not. Additionally, we prove that the latter bound is optimal in the sense that the only known counterexample graph does not permit a better result.

Paul Tod

We consider four-dimensional, Riemannian, Ricci-flat metrics for which one or other of the self-dual or anti-self-dual Weyl tensors is type-D. Such metrics always have a valence-2 Killing spinor, and therefore a Hermitian structure and at least one Killing vector. We rederive the results of Przanowski and collaborators, that these metrics can all be given in

Connah Kendrick, David Gillespie, Moi Hoon Yap

We propose a new type of General Adversarial Network (GAN) to resolve a common issue with Deep Learning. We develop a novel architecture that can be applied to existing latent vector based GAN structures that allows them to generate on-the-fly images of any size. Existing GAN for image generation requires uniform images of matching dimensions. However, publi

J. Baglio, F. Campanario, S. Glaus, M. M. Muhlleitner

Higgs-pair production via gluon fusion is the dominant production mechanism of Higgs-boson pairs at hadron colliders. In this work, we present details of our numerical determination of the full next-to-leading-order (NLO) QCD corrections to the leading top-quark loops. Since gluon fusion is a loop-induced process at leading order, the NLO calculation require

Sarper Aydin, Ceyhun Eksin

We consider a team of mobile autonomous robots with the aim to cover a given set of targets. Each robot aims to select a target to cover and physically reach it by the final time in coordination with other robots given the locations of targets. Robots are unaware of which targets other robots intend to cover. Each robot can control its mobility and who to se

Seong Hyeon Park, Gyubok Lee, Manoj Bhat, Jimin Seo

Multi-agent trajectory forecasting in autonomous driving requires an agent to accurately anticipate the behaviors of the surrounding vehicles and pedestrians, for safe and reliable decision-making. Due to partial observability in these dynamical scenes, directly obtaining the posterior distribution over future agent trajectories remains a challenging problem

Yuanhang Zhang, Shuang Yang, Jingyun Xiao, Shiguang Shan

Recent advances in deep learning have heightened interest among researchers in the field of visual speech recognition (VSR). Currently, most existing methods equate VSR with automatic lip reading, which attempts to recognise speech by analysing lip motion. However, human experience and psychological studies suggest that we do not always fix our gaze at each

M. Holtkemper, G. F. Quinteiro, D. E. Reiter, T. Kuhn

Several important proposals to use semiconductor quantum dots in quantum information technology rely on the control of the dark exciton ground states, such as dark exciton based qubits with a $μ$s life time. In this paper, we present an efficient way to occupy the dark exciton ground state by a single short laser pulse. The scheme is based on an optical exci

Houquan Zhou, Yu Zhang, Zhenghua Li, Min Zhang

In the pre deep learning era, part-of-speech tags have been considered as indispensable ingredients for feature engineering in dependency parsing. But quite a few works focus on joint tagging and parsing models to avoid error propagation. In contrast, recent studies suggest that POS tagging becomes much less important or even useless for neural parsing, espe

Bhargav Nagaraja Bhatt, Carlo A. Furia

Resource leaks -- a program does not release resources it previously acquired -- are a common kind of bug in Android applications. Even with the help of existing techniques to automatically detect leaks, writing a leak-free program remains tricky. One of the reasons is Android's event-driven programming model, which complicates the understanding of an applic

Napat Karnchanachari, Miguel I. Valls, David Hoeller, Marco Hutter

Model Predictive Control (MPC) is a powerful control technique that handles constraints, takes the system's dynamics into account, and optimizes for a given cost function. In practice, however, it often requires an expert to craft and tune this cost function and find trade-offs between different state penalties to satisfy simple high level objectives. In

Jaehong Yoon, Wonyong Jeong, Giwoong Lee, Eunho Yang

There has been a surge of interest in continual learning and federated learning, both of which are important in deep neural networks in real-world scenarios. Yet little research has been done regarding the scenario where each client learns on a sequence of tasks from a private local data stream. This problem of federated continual learning poses new challeng

Michael C. Knaus

This paper reviews, applies and extends recently proposed methods based on Double Machine Learning (DML) with a focus on program evaluation under unconfoundedness. DML based methods leverage flexible prediction models to adjust for confounding variables in the estimation of (i) standard average effects, (ii) different forms of heterogeneous effects, and (iii

Theodoros T. Koutserimpas, Romain Fleury

In this paper, we study the interactions of electromagnetic waves with a non-dispersive dynamic medium that is temporally dependent. Electromagnetic fields under material time-modulation conserve their momentum but not their energy. We assume a time-variation of the permittivity, permeability and conductivity and derive the appropriate time-domain solutions

Jouni Helske, Santtu Tikka, Juha Karvanen

We consider the problem of estimating causal effects of interventions from observational data when well-known back-door and front-door adjustments are not applicable. We show that when an identifiable causal effect is subject to an implicit functional constraint that is not deducible from conditional independence relations, the estimator of the causal effect

Elad Amrani, Rami Ben-Ari, Daniel Rotman, Alex Bronstein

One of the key factors of enabling machine learning models to comprehend and solve real-world tasks is to leverage multimodal data. Unfortunately, annotation of multimodal data is challenging and expensive. Recently, self-supervised multimodal methods that combine vision and language were proposed to learn multimodal representations without annotation. Howev

Aya Mostafa Ahmed, Aydin Sezgin, Eduard A. Jorswieck

The impact of spatial correlation on mutual information (MI) is analyzed for MIMO radar. Unlike the work done in literature for statistical MIMO radar, we consider the spatial correlation of the target matrix elements to study the correlated MIMO radar performance. There is a trade-off between coherent processing gain in correlated MIMO radar and spatial div

ALICE Collaboration

The study of (anti-)deuteron production in pp collisions has proven to be a powerful tool to investigate the formation mechanism of loosely bound states in high energy hadronic collisions. In this paper the production of (anti-)deuterons is studied as a function of the charged particle multiplicity in inelastic pp collisions at $\sqrt{s}=13$ TeV using the AL

H. Song, N. Mitsuo, S. Uchida, D. Suehiro

Deep neural networks (DNNs) suffer from noisy-labeled data because of the risk of overfitting. To avoid the risk, in this paper, we propose a novel DNN training method with sample selection based on adaptive k-set selection, which selects k (< n) clean sample candidates from the whole n noisy training samples at each epoch. It has a strong advantage of guara

Alexander Haluszczynski, Jonas Aumeier, Joschka Herteux, Christoph Räth

Reservoir computing is a very promising approach for the prediction of complex nonlinear dynamical systems. Besides capturing the exact short-term trajectories of nonlinear systems, it has also proved to reproduce its characteristic long-term properties very accurately. However, predictions do not always work equivalently well. It has been shown that both sh

Tomoya Naito, Xavier Roca-Maza, Gianluca Colò, Haozhao Liang

The electromagnetic effects of the finite size of the nucleon are implemented self-consistently on top of the Skyrme Hartree-Fock calculation, where the electric form factors of both protons and neutrons are considered. Furthermore, the vacuum polarization and the electromagnetic spin-orbit interaction are taken into account. The self-consistent finite-size

Jens Hesse

We investigate the geometry of the special fiber of the integral model of a Shimura variety with parahoric level at a given prime place. To be more precise, we deal with the EKOR stratification which interpolates between the Ekedahl-Oort and Kottwitz-Rapoport stratifications. In the Siegel case we give a geometric description by suitably generalizing the the

D. Cattiaux, X. Zhou, S. Kumar, I. Golokolenov

We explore the nonlinear dynamics of a cavity optomechanical system. Our realization consisting of a drumhead nano-electro-mechanical resonator (NEMS) coupled to a microwave cavity, allows for a nearly ideal platform to study the nonlinearities arising purely due to radiation-pressure physics. Experiments are performed under a strong microwave Stokes pumping

Jens Hesse

We investigate the geometry of the special fiber of the integral model of a Shimura variety with parahoric level at a given prime place. To be more precise, we deal with the definition of central leaves in this situation, their local closedness, and the relationship between the folations for varying parahoric level. This is connected to the verification of a

Tao Zhang, Quanyan Zhu

We study the design of decision-making mechanism for resource allocations over a multi-agent system in a dynamic environment. Agents&#39; privately observed preference over resources evolves over time and the population is dynamic due to the adoption of stopping rules. The proposed model designs the rules of encounter for agents participating in the dynamic

Gourab Bhattacharya, Maxim Kontsevich

We introduce a framework in noncommutative geometry consisting of a $*$-algebra $\mathcal A$, a bimodule $Ω^1$ endowed with a derivation $\mathcal A\to Ω^1$ and with a Hermitian structure $Ω^1\otimes \barΩ^1\to \mathcal A$ (a &#34;noncommutative Kähler form&#34;), and a cyclic 1-cochain $\mathcal A\to \mathbb C$ whose coboundary is determined by the previous

Patrick Bahr, Christian Uldal Graulund, Rasmus Møgelberg

When designing languages for functional reactive programming (FRP) the main challenge is to provide the user with a simple, flexible interface for writing programs on a high level of abstraction while ensuring that all programs can be implemented efficiently in a low-level language. To meet this challenge, a new family of modal FRP languages has been propose

Victor Villena-Martinez, Sergiu Oprea, Marcelo Saval-Calvo, Jorge Azorin-Lopez

Registration is the process that computes the transformation that aligns sets of data. Commonly, a registration process can be divided into four main steps: target selection, feature extraction, feature matching, and transform computation for the alignment. The accuracy of the result depends on multiple factors, the most significant are the quantity of input

Tom Carroll, Xavier Massaneda, Joaquim Ortega-Cerdà

We improve some recent results of Sagiv and Steinerberger that quantify the following uncertainty principle: for a function $f$ with mean zero, either the size of the zero set of the function or the cost of transporting the mass of the positive part of $f$ to its negative part must be big. We also provide a sharp upper estimate of the transport cost of the p

Florian Meinert, Christian Hölzl, Mehmet Ali Nebioglu, Alessandro D&#39;Arnese

Long-lived circular Rydberg atoms are picking up increasing interest for boosting coherence times in Rydberg-based quantum simulation. We elaborate a novel approach to stabilize circular Rydberg states against spontaneous and blackbody-induced decay using a suppression capacitor made from indium tin oxide (ITO) thin films, which combine reflection of microwa

Eric Guizzo, Alberto Novello

This paper describes a preliminary approach to algorithmically reproduce the archetypical structure adopted by humans to classify sounds. In particular, we propose an approach to predict the human perceived chaos/order level in a sound and synthesize new timbres that present the desired amount of this feature. We adopted a Neural Network based method, in ord

Johan L. Freiherr von Forstner, Jingnan Guo, Robert F. Wimmer-Schweingruber, Mateja Dumbović

Forbush decreases (FDs), which are short-term drops in the flux of galactic cosmic rays, are caused by the shielding from strong and/or turbulent magnetic structures in the solar wind, especially interplanetary coronal mass ejections (ICMEs) and their associated shocks, as well as corotating interaction regions. Such events can be observed at Earth, for exam

Shrestha Ghosh, Simon Razniewski, Gerhard Weikum

Knowledge Bases (KBs) contain a wealth of structured information about entities and predicates. This paper focuses on set-valued predicates, i.e., the relationship between an entity and a set of entities. In KBs, this information is often represented in two formats: (i) via counting predicates such as numberOfChildren and staffSize, that store aggregated int

Amos Uderzo

The subject of the present paper are stability properties of the solution set to set-valued inclusions. The latter are problems emerging in robust optimization and mathematical economics, which can not be cast in traditional generalized equations. The analysis here reported focuses on several quantitative forms of semicontinuity for set-valued mappings, wide

Meiling Fang, Naser Damer, Florian Kirchbuchner, Arjan Kuijper

With the widespread use of biometric systems, the demographic bias problem raises more attention. Although many studies addressed bias issues in biometric verification, there are no works that analyze the bias in presentation attack detection (PAD) decisions. Hence, we investigate and analyze the demographic bias in iris PAD algorithms in this paper. To enab

Christian Tutschku, Jan Böttcher, René Meyer, E. M. Hankiewicz

The Dirac mass of a two-dimensional QAH insulator is directly related to the parity anomaly of planar quantum electrodynamics, as shown initially in Phys. Rev. Lett. 52, 18 (1984). In this work, we connect the additional momentum-dependent Newtonian mass term of a QAH insulator to the parity anomaly. By calculating the effective action, we reveal that the Ne

Dmitry K. Efimov, Jakub S. Prauzner-Bechcicki, Jakub Zakrzewski

For a model atom with the $p^3$ valence shell we construct consistent three- and two-active electrons models enabling their direct comparison. Within these models, we study the influence of the third active electron on the double ionization yield in strong femtosecond laser fields. We reveal proportionality between double ionization signals obtained with bot

Helmut Abels, Andreas Marquardt

We consider the sharp interface limit of a coupled Stokes/Cahn\textendash Hilliard system in a two dimensional, bounded and smooth domain, i.e., we consider the limiting behavior of solutions when a parameter $ε>0$ corresponding to the thickness of the diffuse interface tends to zero. We show that for sufficiently short times the solutions to the Stokes/Cahn

András Szegleti, Ferenc Márkus

In this paper we present a method with which it is possible to describe a dissipative system in Lagrangian formalism, without the trouble of finding the proper way to model the environment. The concept of the presented method is to create a function that generates the measurable physical quantity, similarly to electrodynamics, where the scalar potential and

Manuel Formela, Niclas Hamann, Klaus Floettmann, Gudrid Moortgat-Pick

In the baseline design of the International Linear Collider (ILC) an undulator-based source is foreseen for the positron source in order to match the physics requirements. The recently chosen first energy stage with sqrt(s)=250 GeV requires high luminosity and imposes an effort for all positron source designs at high-energy colliders. In this paper we perfor

Joseph Ortiz, Mark Pupilli, Stefan Leutenegger, Andrew J. Davison

Graph processors such as Graphcore&#39;s Intelligence Processing Unit (IPU) are part of the major new wave of novel computer architecture for AI, and have a general design with massively parallel computation, distributed on-chip memory and very high inter-core communication bandwidth which allows breakthrough performance for message passing algorithms on arb

Kshitij Tiwari, Ville Kyrki, Allen Cheung, Naohide Yamamoto

With the advent of consumer-grade products for presenting an immersive virtual environment (VE), there is a growing interest in utilizing VEs for testing human navigation behavior. However, preparing a VE still requires a high level of technical expertise in computer graphics and virtual reality, posing a significant hurdle to embracing the emerging technolo

Igor Tominec, Elisabeth Larsson, Alfa Heryudono

Localized collocation methods based on radial basis functions (RBFs) for elliptic problems appear to be non-robust in the presence of Neumann boundary conditions. In this paper we overcome this issue by formulating the RBF-generated finite difference method in a discrete least-squares setting instead. This allows us to prove high-order convergence under node

Sebastian J. Müller, Franziska Weigl, Carina Bezold, Ana Sancho

In the emerging field of 3D bioprinting, cell damage due to large deformations is considered a main cause for cell death and loss of functionality inside the printed construct. Those deformations, in turn, strongly depend on the mechano-elastic response of the cell to the hydrodynamic stresses experienced during printing. In this work, we present a numerical

Oliver G. Ernst, Alois Pichler, Björn Sprungk

When propagating uncertainty in the data of differential equations, the probability laws describing the uncertainty are typically themselves subject to uncertainty. We present a sensitivity analysis of uncertainty propagation for differential equations with random inputs to perturbations of the input measures. We focus on the elliptic diffusion equation with

Harald Garcke, Robert Nürnberg

The form and evolution of multi-phase biomembranes is of fundamental importance in order to understand living systems. In order to describe these membranes, we consider a mathematical model based on a Canham--Helfrich--Evans two-phase elastic energy, which will lead to fourth order geometric evolution problems involving highly nonlinear boundary conditions.

Onofrio Mazzarisi, Federico Corberi, Leticia F. Cugliandolo, Marco Picco

We study the metastable equilibrium properties of the Potts model with heat-bath transition rates using a novel expansion. The method is especially powerful for large number of state spin variables and it is notably accurate in a rather wide range of temperatures around the phase transition.

Radha Kopparti, Tillman Weyde

Learning abstract and systematic relations has been an open issue in neural network learning for over 30 years. It has been shown recently that neural networks do not learn relations based on identity and are unable to generalize well to unseen data. The Relation Based Pattern (RBP) approach has been proposed as a solution for this problem. In this work, we

Johannes Gasteiger, Janek Groß, Stephan Günnemann

Graph neural networks have recently achieved great successes in predicting quantum mechanical properties of molecules. These models represent a molecule as a graph using only the distance between atoms (nodes). They do not, however, consider the spatial direction from one atom to another, despite directional information playing a central role in empirical po

Tomas R. Rodriguez, J. Luis Egido

The potential sub-shell closures N = 32 and N = 34 are analyzed at high spins in the titanium isotopes within the generalized beyond mean field theory considering triaxial deformations and the angular frequency as generator coordinates together with the particle number and the angular momentum conservation. A good description of bulk properties, high angular

Philippe Grangier

In standard quantum mechanics (QM), a state vector $| ψ\rangle$ may belong to infinitely many different orthogonal bases, as soon as the dimension $N$ of the Hilbert space is at least three. On the other hand, a complete physical observable $A$ (with no degeneracy left) is associated with a $N$-dimensional orthogonal basis of eigenvectors. In an idealized ca

Simon Caron-Huot, Dmitry Chicherin, Johannes Henn, Yang Zhang

In previous work, the two-loop five-point amplitudes in $\mathcal{N}=4$ super Yang-Mills theory and $\mathcal{N}=8$ supergravity were computed at symbol level. In this paper, we compute the full functional form. The amplitudes are assembled and simplified using the analytic expressions of the two-loop pentagon integrals in the physical scattering region. We

J. J. Hagenaars, F. Paredes-Vallés, S. M. Bohté, G. C. H. E. de Croon

Flying insects are capable of vision-based navigation in cluttered environments, reliably avoiding obstacles through fast and agile maneuvers, while being very efficient in the processing of visual stimuli. Meanwhile, autonomous micro air vehicles still lag far behind their biological counterparts, displaying inferior performance at a much higher energy cons

A. Biborski, M. Zegrodnik, J. Spalek

The three-band \emph{d-p} model is investigated by means of Variational Monte-Carlo (VMC) method with the BCS-like wave-function supplemented by the Gutzwiller and Jastrow correlators. The VMC optimization leads to $d$-$wave$ superconducting state with a characteristic dome-like shape of the order parameter for hole doping $δ\lesssim 0.4$, in a good agreemen

James T. Kindt, Jack W. Szostak, Anna Wang

The desire to create cell-like models for fundamental science and applications has spurred extensive effort towards creating giant unilamellar vesicles (GUVs). However, a route to selectively self-assemble GUVs in bulk has remained elusive. In bulk solution, membrane-forming molecules such as phospholipids, single-tailed surfactants, and block copolymers typ

Sondre Tesdal Galtung, Xavier Raynaud

We define a kinetic and a potential energy such that the principle of stationary action from Lagrangian mechanics yields a Camassa--Holm system (2CH) as the governing equations. After discretizing these energies, we use the same variational principle to derive a semi-discrete system of equations as an approximation of the 2CH system. The discretizaton is onl

Wei. Lin, Junyu. Gao, Qi. Wang, Xuelong. Li

Recently, lots of deep networks are proposed to improve the quality of predicted super-resolution (SR) images, due to its widespread use in several image-based fields. However, with these networks being constructed deeper and deeper, they also cost much longer time for training, which may guide the learners to local optimization. To tackle this problem, this

Paloma T. Lima, Erik Jan van Leeuwen, Marieke van der Wegen

Given a vertex-colored graph, we say a path is a rainbow vertex path if all its internal vertices have distinct colors. The graph is rainbow vertex-connected if there is a rainbow vertex path between every pair of its vertices. In the Rainbow Vertex Coloring (RVC) problem we want to decide whether the vertices of a given graph can be colored with at most $k$

Aitor García-Pablos, Naiara Perez, Montse Cuadros

Massive digital data processing provides a wide range of opportunities and benefits, but at the cost of endangering personal data privacy. Anonymisation consists in removing or replacing sensitive information from data, enabling its exploitation for different purposes while preserving the privacy of individuals. Over the years, a lot of automatic anonymisati

Xinrong Guan, Qingqing Wu, Rui Zhang

In cognitive radio (CR) communication systems, achieving high secondary user (SU) rate in the presence of strong cross-link interference with the primary user (PU) is challenging. In this letter, we exploit the emerging intelligent reflecting surface (IRS) technology to tackle this problem. Specifically, we investigate an IRS-assisted CR communication system

Wei Song, Xuezixiang Li, Sadia Afroz, Deepali Garg

Modern commercial antivirus systems increasingly rely on machine learning to keep up with the rampant inflation of new malware. However, it is well-known that machine learning models are vulnerable to adversarial examples (AEs). Previous works have shown that ML malware classifiers are fragile to the white-box adversarial attacks. However, ML models used in

D. Guberman, R. Paoletti

In the last ten years silicon photomultipliers (SiPMs) have gained terrain in experiments and applications in which photomultiplier tubes have been the dominant photosensors during decades. Imaging Atmospheric Cherenkov Telescopes (IACTs) for very high energy (VHE, E$>$50 GeV) gamma-ray astronomy are experiencing the same process. Until now FACT was the only

Ofir Tal-Friedman, Arnab Pal, Amandeep Sekhon, Shlomi Reuveni

Stochastic resetting is prevalent in natural and man-made systems giving rise to a long series of non-equilibrium phenomena. Diffusion with stochastic resetting serves as a paradigmatic model to study these phenomena, but the lack of a well-controlled platform by which this process can be studied experimentally has been a major impediment to research in the

Sergii Morozov, Stefano Vezzoli, Ali Hossain Khan, Iwan Moreels

The efficient excitation of quantum sources such as quantum dots or single molecules requires high NA optics which is often a challenge in cryogenics, or in ultrafast optics. Here we propose a 3.2 um wide parabolic mirror, with a 0.8 um focal length, fabricated by direct laser writing on CdSe/CdS colloidal quantum dots, capable of focusing the excitation lig

Xiaofen Gao, Zhiqing Yin, Junyong Zhang, Jiqiang Zheng

We study the $L^1\to L^\infty$-decay estimates for dispersive equations in the Aharonov-Bohm magnetic fields, and further prove Strichartz estimates for the Klein-Gordon equation with critical electromagnetic potentials. The novel ingredients are the construction of Schwartz kernels of the spectral measure and heat propagator for the Schrödinger operator in

Diana Savin, Vincenzo Acciaro

Let $p$ be an odd prime, let $K=\mathbb{Q}(ε)$ where $ε$ is a primitive cubic root of unity, and let $L$ be the Kummer field $\mathbb{Q}\left(ε, \sqrt[3]α\right)$. In this paper we obtain a characterization of the splitting behavior of the symbol algebras $\left( \frac{α,p}{K,ε}\right)$ and $\left( \frac{α,p^{h_{p}}}{K,ε}\right)$, where $h_{p}$ is the order

Ying Dai

Aesthetic assessment is subjective, and the distribution of the aesthetic levels is imbalanced. In order to realize the auto-assessment of photo aesthetics, we focus on using repetitive self-revised learning (RSRL) to train the CNN-based aesthetics classification network by imbalanced data set. As RSRL, the network is trained repetitively by dropping out the

Simone Rossi, Markus Heinonen, Edwin V. Bonilla, Zheyang Shen

Variational inference techniques based on inducing variables provide an elegant framework for scalable posterior estimation in Gaussian process (GP) models. Besides enabling scalability, one of their main advantages over sparse approximations using direct marginal likelihood maximization is that they provide a robust alternative for point estimation of the i

Yuya Sasaki, George Fletcher, Makoto Onizuka

Conjunctive path queries (CPQ) are one of the most frequently used queries for complex graph analysis. However, current graph indexes are not tailored to fully support the power of query languages to express CPQs. Consequently, current methods do not take advantage of significant pruning opportunities during \cpq{} evaluation, resulting in poor query process

Rachid Guennouni Hassani, Alexis Gilles, Emmanuel Lassalle, Arthur Dénouveaux

We extend the AROW regression algorithm developed by Vaits and Crammer in [VC11] to handle synchronous mini-batch updates and apply it to stock return prediction. By design, the model should be more robust to noise and adapt better to non-stationarity compared to a simple rolling regression. We empirically show that the new model outperforms more classical a

Amine Asselah, Bruno Schapira

We prove that in any finite set of $\mathbb Z^d$ with $d\ge 3$, there is a subset whose capacity and volume are both of the same order as the capacity of the initial set. As an application we obtain estimates on the probability of {\it covering uniformly} a finite set, and characterize some {\it folding} events, under optimal hypotheses. For instance, knowin

Edwige Simonet, Balz Winteler, Jana Frangi, Magdalena Suter

Non-specific chronic low back pain (NSCLBP) is a major health problem, affecting about one fifth of the population worldwide. To avoid further pain or injury, patients with NSCLBP seem to adopt a stiffer movement pattern during everyday living activities. However, it remains unknown how NSCLBP affects the lumbar lordosis angle (LLA) during repetitive activit

Minh-Tien Nguyen, Viet-Anh Phan, Le Thai Linh, Nguyen Hong Son

This paper presents a practical approach to fine-grained information extraction. Through plenty of experiences of authors in practically applying information extraction to business process automation, there can be found a couple of fundamental technical challenges: (i) the availability of labeled data is usually limited and (ii) highly detailed classificatio

Tetsuya Zenko, Tetsuya Nagata, Mikio Kurita, Masaru Kino

A new method for measuring the global magnetic field structure of the Galactic plane is presented. We have determined the near-infrared polarization of field stars around 52 Cepheids found in recent surveys toward the Galactic plane. The Cepheids are located at the galactic longitudes $-10^{\circ}\leq \, l\, \leq +10.5^{\circ}$ and latitudes $-0.22^{\circ}\l

Dimitrios Fraggedakis, Martin Z. Bazant

The morphology of interfaces is known to play fundamental role on the efficiency of energy-related applications, such light harvesting or ion intercalation. Altering the morphology on demand, however, is a very difficult task. Here, we show ways the morphology of interfaces can be tuned by driven electron transfer reactions. By using non-equilibrium thermody

Yuedong Chen, Guoxian Song, Zhiwen Shao, Jianfei Cai

Automatic facial action unit (AU) recognition has attracted great attention but still remains a challenging task, as subtle changes of local facial muscles are difficult to thoroughly capture. Most existing AU recognition approaches leverage geometry information in a straightforward 2D or 3D manner, which either ignore 3D manifold information or suffer from

Vitalik Buterin, Diego Hernandez, Thor Kamphefner, Khiem Pham

We present &#34;Gasper,&#34; a proof-of-stake-based consensus protocol, which is an idealized version of the proposed Ethereum 2.0 beacon chain. The protocol combines Casper FFG, a finality tool, with LMD GHOST, a fork-choice rule. We prove safety, plausible liveness, and probabilistic liveness under different sets of assumptions.

Phillip Helms, Garnet Kin-Lic Chan

We demonstrate the power of 2D tensor networks for obtaining large deviation functions of dynamical observables in a classical nonequilibrium setting. Using these methods, we analyze the previously unstudied dynamical phase behavior of the fully 2D asymmetric simple exclusion process with biases in both the x and y directions. We identify a dynamical phase t

Fei Li, Xiumei Li

The weight distribution and weight hierarchy of linear codes are two important research topics in coding theory. In this paper, by choosing proper defining sets from inhomogeneous quadratic functions over $\mathbb{F}_{q}^{2},$ we construct a family of $3$-weight $p$-ary linear codes and determine their weight distributions and weight hierarchies. Most of the

James David Nixon

The goal of this paper is to formalize the notion of The Compositional Integral in The Complex Plane. We prove a convergence theorem guaranteeing its existence. We prove an analogue of Cauchy&#39;s Integral Theorem--and suggest an approach at recovering Cauchy&#39;s Integral Formula. With this we derive a modified form of Cauchy&#39;s Residue Theorem. Then,

Bingyao Huang, Haibin Ling

Image-based relighting, projector compensation and depth/normal reconstruction are three important tasks of projector-camera systems (ProCams) and spatial augmented reality (SAR). Although they share a similar pipeline of finding projector-camera image mappings, in tradition, they are addressed independently, sometimes with different prerequisites, devices a

Osvaldo Angtuncio Hernández

The degree sequence $(n_{i,j}(k), 1\leq i,j\leq d, k\geq 0)$ of a multitype forest with $d$ types encodes the number of individuals of type $i$ with $k$ children of type $j$. In this paper, we introduce a simple algorithm to sample a multitype forest uniformly from the set of all forests with a given degree sequence (MFGDS). This generalizes the single-type

Naoki Kitazawa

Constructing Morse functions and their higher dimensional versions or fold maps is fundamental, important and challenging in investigating the topologies and the differentiable structures of differentiable manifolds via Morse functions, fold maps and more general generic maps. It is one of important and interesting branches of the singularity theory of diffe

Masaaki Fujii, Akihiko Takahashi

In this work, we study an equilibrium-based continuous asset pricing problem which seeks to form a price process endogenously by requiring it to balance the flow of sales-and-purchase orders in the exchange market, where a large number of agents are interacting through the market price. Adopting a mean field game (MFG) approach, we find a special form of for

Jiang Zeng, Ming Lu, Haiwen Liu, Hua Jiang

Though several theoretical models have been proposed to design electronic flat-bands, the definite experimental realization in two-dimensional atomic crystal is still lacking. Here we propose a novel and realistic flat-band model based on threefold degenerate $p$-orbitals in two-dimensional ionic materials. Our theoretical analysis and first-principles calcu

Shihao Zhao, Xingjun Ma, Xiang Zheng, James Bailey

Deep neural networks (DNNs) are vulnerable to backdoor attacks which can hide backdoor triggers in DNNs by poisoning training data. A backdoored model behaves normally on clean test images, yet consistently predicts a particular target class for any test examples that contain the trigger pattern. As such, backdoor attacks are hard to detect, and have raised