March 2020 arXiv papers — page 105

Vittorio De Falco, Emmanuele Battista

We determine for the first time in the literature the analytic form of the Rayleigh potential of the general relativistic Poynting-Robertson effect. The employed procedure is based on the use of an integrating factor and a new integration strategy where the test particle's dissipated energy represents the fundamental variable. The obtained results and th

Diego Figueira, Adwait Godbole, S. Krishna, Wim Martens

Testing containment of queries is a fundamental reasoning task in knowledge representation. We study here the containment problem for Conjunctive Regular Path Queries (CRPQs), a navigational query language extensively used in ontology and graph database querying. While it is known that containment of CRPQs is expspace-complete in general, we focus here on se

Wentao Wu

External query execution cost modeling using query execution feedback has found its way in various database applications such as admission control and query scheduling. Existing techniques in general fall into two categories, plan-level cost modeling and operator-level cost modeling. It has been shown in the literature that operator-level cost modeling can o

Pierre Laclau, Vladislav Tempez, Franck Ruffier, Enrico Natalizio

Miniature multi-rotors are promising robots for navigating subterranean networks, but maintaining a radio connection underground is challenging. In this paper, we introduce a distributed algorithm, called U-Chain (for Underground-chain), that coordinates a chain of flying robots between an exploration drone and an operator. Our algorithm only uses the measur

Fabrice Baudoin, Céline Lacaux

We define and study a fractional Gaussian field $X$ with Hurst parameter $H$ on the Sierpiński gasket $K$ equipped with its Hausdorff measure $μ$. It appears as a solution, in a weak sense, of the equation $(-Δ)^s X =W$ where $W$ is a Gaussian white noise on $L_0^2(K,μ)$, $Δ$ the Laplacian on $K$ and $s= \frac{d_h+2H}{2d_w}$, where $d_h$ is the Hausdorff dim

Kevin Duisters, Johannes Schmidt-Hieber

Frequentist coverage of $(1-α)$-highest posterior density (HPD) credible sets is studied in a signal plus noise model under a large class of noise distributions. We consider a specific class of spike-and-slab prior distributions. Different regimes are identified and we derive closed form expressions for the $(1-α)$-HPD on each of these regimes. Similar to th

Micha Berkooz, Nadav Brukner, Vladimir Narovlansky, Amir Raz

We compute the exact density of states and 2-point function of the $\mathcal{N} =2$ super-symmetric SYK model in the large $N$ double-scaled limit, by using combinatorial tools that relate the moments of the distribution to sums over oriented chord diagrams. In particular we show how SUSY is realized on the (highly degenerate) Hilbert space of chords. We fur

Ruochen Yin, Biao Yu, Huapeng Wu, Yutao Song

It is a crucial step to achieve effective semantic segmentation of lane marking during the construction of the lane level high-precision map. In recent years, many image semantic segmentation methods have been proposed. These methods mainly focus on the image from camera, due to the limitation of the sensor itself, the accurate three-dimensional spatial posi

Lukas Fischer, Andreas M. Menzel

Soft actuators allow to transform external stimuli to mechanical deformations. Because of their deformational response to external magnetic fields, magnetic gels and elastomers represent ideal candidates for such tasks. Mostly, linear magnetostrictive deformations, that is, elongations or contractions along straight axes are discussed in this context. In con

Michelle Kunimoto, Jaymie M. Matthews, Henry Ngo

We present the results of an independent search of all ~200,000 stars observed over the four year Kepler mission (Q1-Q17) for multiplanet systems, using a three-transit minimum detection criteria to search orbital periods up to hundreds of days. We incorporate both automated and manual triage, and provide estimates of the completeness and reliability of our

Naveed Naimipour, Shahin Khobahi, Mojtaba Soltanalian

The problem of phase retrieval has been intriguing researchers for decades due to its appearance in a wide range of applications. The task of a phase retrieval algorithm is typically to recover a signal from linear phase-less measurements. In this paper, we approach the problem by proposing a hybrid model-based data-driven deep architecture, referred to as t

Joseph N. Burchett, Oskar Elek, Nicolas Tejos, J. Xavier Prochaska

Modern cosmology predicts that matter in our Universe has assembled today into a vast network of filamentary structures colloquially termed the Cosmic Web. Because this matter is either electromagnetically invisible (i.e., dark) or too diffuse to image in emission, tests of this cosmic web paradigm are limited. Wide-field surveys do reveal web-like structure

Jonathan Ariel Barmak

We study the free metabelian group $M(2,n)$ of prime power exponent $n$ on two generators by means of invariants $M(2,n)'\to \mathbb{Z}_n$ that we construct from colorings of the squares in the integer grid $\mathbb{R} \times \mathbb{Z} \cup \mathbb{Z} \times \mathbb{R}$. In particular we improve bounds found by M.F. Newman for the order of $M(2,2^k)$. W

Emma M. Simmerman, Hsuan-Hao Lu, Andrew M. Weiner, Joseph M. Lukens

Frequency-bin qudits constitute a promising tool for quantum information processing, but their high dimensionality can make for tedious characterization measurements. Here we introduce and compare compressive sensing and Bayesian mean estimation for recovering the spectral correlations of entangled photon pairs. Using a conventional compressive sensing algor

Hassan Shokouhandeh, MahmoodReza Ghaharpour, Hamid Ghobadi Lamouki, Yaser Rahmani Pashakolaei

In this paper, estimation of optimal capacity and location of installation of wind, solar and fuel cell sources in distribution systems to reduce loss and improve voltage profile, by considering load changes, is carried out with Lightning Search Algorithm (LSA). Studies have been conducted on the standard IEEE 33 bus power system in two scenarios. In the fir

S. A. Antipov, D. Amorim, N. Biancacci, X. Buffat

Landau damping is an essential mechanism for ensuring collective beam stability in particle accelerators. Precise knowledge of how strong Landau damping is, is key to making accurate predictions on beam stability for state-of-the-art high energy colliders. In this paper we demonstrate an experimental procedure that would allow quantifying the strength of Lan

Qicheng Lao, Xiang Jiang, Mohammad Havaei, Yoshua Bengio

Learning in non-stationary environments is one of the biggest challenges in machine learning. Non-stationarity can be caused by either task drift, i.e., the drift in the conditional distribution of labels given the input data, or the domain drift, i.e., the drift in the marginal distribution of the input data. This paper aims to tackle this challenge in the

Sergiy Silich, Guillermo Tenorio-Tagle, Sergio Martinez-Gonzalez, Jean Turner

We discuss a theoretical model for the early evolution of massive star clusters and confront it with the ALMA, radio and infrared observations of the young stellar cluster highly obscured by the molecular cloud D1 in the nearby dwarf spheroidal galaxy NGC 5253. We show that a large turbulent pressure in the central zones of D1 cluster may cause individual wi

Kyle M. Whitcomb, Chandralekha Singh

An analysis of institutional data to understand the outcome of the many obstacles faced by students from historically disadvantaged backgrounds is important in order to work towards promoting equity and inclusion for all students. We use 10 years of institutional data at a large public research university to investigate the grades earned (both overall and in

Mujahid Sultan

Clustering is a NP-hard problem. Thus, no optimal algorithm exists, heuristics are applied to cluster the data. Heuristics can be very resource-intensive, if not applied properly. For substantially large data sets computational efficiencies can be achieved by reducing the input space if a minimal loss of information can be achieved. Clustering algorithms, in

Z. N. Khangale, P. A. Woudt, S. B. Potter, B. Warner

We present phase-resolved optical spectroscopy of the eclipsing nova-like cataclysmic variable EC21178-5417 obtained between 2002 and 2013. The average spectrum of EC21178-5417 shows broad double-peaked emission lines from HeII 4686 Å (strongest feature) and the Balmer series. The high-excitation feature, CIII/NIII at 4640-4650 Å, is also present and appears

I. Leonov, S. L. Skornyakov, S. Y. Savrasov

Motivated by the recent discovery of superconductivity in the infinite-layer (Sr,Nd)NiO$_2$ films with Sr content $x \simeq0.2$ [Li et al., Nature (London) \textbf{572}, 624 (2019)], we examine the effects of electron correlations and Sr-doping on the electronic structure, Fermi surface topology, and magnetic correlations in (Nd,Sr)NiO$_2$ using a combinatio

Kate Eckerle

This paper presents a simple framework that organizes thin-wall Coleman-De Luccia instantons based on the Euclidean geometries of their original and tunneled vacuum patches. We consider all a priori allowed vacuum pairs (de Sitter or Anti-de Sitter for either patch, Minkowski can be obtained as a limit of either), and $O(4)$-symmetric thin-wall geometries co

D. Lozano-Gómez, N. G. Kelkar

The average dwell time of an electron in a potential barrier formed by an external electric field and the potential of a helium atom is evaluated within a semi classical one-dimensional tunneling approach. The tunneling electron is considered to interact with a nuclear charge screened by the electron in the helium ion. It is found that screening leads to sma

Sudeep Kumar Ghosh, Michael Smidman, Tian Shang, James F. Annett

Superconductivity and magnetism are antagonistic states of matter. The presence of spontaneous magnetic fields inside the superconducting state is, therefore, an intriguing phenomenon prompting extensive experimental and theoretical research. In this review, we discuss recent experimental discoveries of unconventional superconductors which spontaneously brea

Anirudh Paranjothi

Vehicular Ad-hoc Networks (VANET) is a derived subclass of Mobile Ad-hoc Networks (MANET) with vehicles as mobile nodes. VANET facilitate vehicles to share safety and non-safety information through messages. Safety information includes road accidents, natural hazards, roadblocks, etc. Non-safety information includes tolling information, traveler information,

LHCb collaboration, R. Aaij, C. Abellán Beteta, T. Ackernley

A search is presented for the lepton flavour violating decay $B^+ \rightarrow K^+ μ^- τ^+$ using a sample of proton--proton collisions at centre-of-mass energies of 7, 8, and 13 TeV, collected with the LHCb detector and corresponding to a total integrated luminosity of 9 fb${}^{-1}$. The $τ$ leptons are selected inclusively, primarily via decays with a singl

O. Olendski

A comparative analysis of the Dirichlet and Neumann boundary conditions (BCs) of the one-dimensional (1D) quantum well extracts similarities and differences of the Rényi $R(α)$ as well as Tsallis $T(α)$ entropies between these two geometries. It is shown, in particular, that for either BC the dependencies of the Rényi position components on the parameter $α$

Julia Brandes, Scott T. Parsell

We establish the analytic Hasse principle for Diophantine systems consisting of one diagonal form of degree $k$ and one general form of degree $d$, where $d$ is smaller than $k$. By employing a hybrid method that combines ideas from the study of general forms with techniques adapted to the diagonal case, we are able to obtain bounds that grow exponentially i

Manoj Kumar, Federico Corberi, Eugenio Lippiello, Sanjay Puri

We study numerically the ordering kinetics in a two-dimensional Ising model with random coupling where the fraction of antiferromagnetic links $a$ can be gradually tuned. We show that, upon increasing such fraction, the behavior changes in a radical way. Small $a$ does not prevent the system from a complete ordering, but this occurs in an extremely (logarith

Tsubasa Sakai, Shuhei Kudo, Hiroto Imachi, Yuto Miyatake

In simulating physical systems, conservation of the total energy is often essential, especially when energy conversion between different forms of energy occurs frequently. Recently, a new fourth order energy-preserving integrator named MB4 was proposed based on the so-called continuous stage Runge--Kutta methods (Y.~Miyatake and J.~C.~Butcher, SIAM J.~Numer.

Valeriia Liakh, Manuel Luna, Elena Khomenko

Context. Large-amplitude oscillations (LAOs) of the solar prominences are very spectacular but poorly understood phenomena. These motions have amplitudes larger than $10 {\mathrm{\, km \,s^{-1}}}$ and can be triggered by the external perturbations, e.g., Moreton or EIT waves. Aims. Our aim is to analyze the properties of large-amplitude oscillations using re

C. D'Eugenio, E. Daddi, R. Gobat, V. Strazzullo

We have obtained spectroscopic confirmation with Hubble Space Telescope WFC3/G141 of a first sizeable sample of nine quiescent galaxies at 2.4<z<3.3. Their average near-UV/optical rest-frame spectrum is characterized by low attenuation (Av$\sim$0.6 mag) and a strong Balmer break, larger than the 4000 A break, corresponding to a fairly young age of $\sim$300

Arabinda Behera, Maowu Nie, Jiangyong Jia

In heavy-ion collisions, the harmonic flow $V_n$ of final-state particles are driven by the eccentricity vector ${\mathcal{E}}_n$ that describe the shape of the initial fireball projected in the transverse plane. It is realized recently that the structure and shape of the fireball, and consequently the ${\mathcal{E}}_n$, fluctuate in pseudorapidity $η$ in a

Axel Potier, Pierre Baudoz, Raphaël Galicher, Garima Singh

Fewer than 1% of all exoplanets detected to date have been characterized on the basis of spectroscopic observations of their atmosphere. Unlike indirect methods, high-contrast imaging offers access to atmospheric signatures by separating the light of a faint off-axis source from that of its parent star. Forthcoming space facilities, such as WFIRST/LUVOIR/Hab

Salomón Wollenstein-Betech, Arian Houshmand, Mauro Salazar, Marco Pavone

This paper studies congestion-aware route-planning policies for Autonomous Mobility-on-Demand (AMoD) systems, whereby a fleet of autonomous vehicles provides on-demand mobility under mixed traffic conditions. Specifically, we first devise a network flow model to optimize the AMoD routing and rebalancing strategies in a congestion-aware fashion by accounting

T. Danilovich, A. M. S. Richards, L. Decin, M. Van de Sande

We present and analyse SO and SO$_2$, recently observed with high angular resolution and sensitivity in a spectral line survey with ALMA, for two oxygen-rich AGB stars: the low mass-loss rate R Dor and high mass-loss rate IK Tau. We analyse 8 lines of SO detected towards both stars, 78 lines of SO$_2$detected towards R Dor and 52 lines of SO$_2$ detected tow

Boy Lankhaar, Wouter Vlemmings

Context. Magnetic fields are important to the dynamics of many astrophysical processes and can typically be studied through polarization observations. Polarimetric interferometry capabilities of modern (sub)millimeter telescope facilities have made it possible to obtain detailed velocity resolved maps of molecular line polarization. To properly analyze these

Kevin N. Hainline, Raphael E. Hviding, Marcia Rieke, Irene Shivaei

The NIRCam instrument on the upcoming James Webb Space Telescope (JWST) will offer an unprecedented view of the most distant galaxies. In preparation for future deep NIRCam extragalactic surveys, it is crucial to understand the color selection of high-redshift galaxies using the Lyman dropout technique. To that end, we have used the JAdes extraGalactic Ultra

Abderahmen Zoghbi, Sihem Kalli, Jon M Miller, Misaki Mizumoto

The lamp-post geometry is often used to model X-ray data of accreting black holes. Despite its simple assumptions, it has proven to be powerful in inferring fundamental black hole properties such as the spin. Early results of X-ray reverberations showed support for such a simple picture, though wind-reverberation models have also been shown to explain the ob

Till Sawala, Adrian Jenkins, Stuart McAlpine, Jens Jasche

We study structure formation in a set of cosmological simulations to uncover the scales in the initial density field that gave rise to the formation of present-day structures. Our simulations share a common primordial power spectrum (here Lambda-CDM), but the introduction of hierarchical variations of the phase information allows us to systematically study t

Gabriele L. Betancourt-Martinez, Renata S. Cumbee, Maurice A. Leutenegger

Charge exchange (CX) is a semi-resonant recombination process that can lead to spectral line emission in the X-ray band. It occurs in nearly any environment where hot plasma and cold gas interact: in the solar system, in comets and planetary atmospheres, and likely astrophysically, in, for example, supernova remnants and galaxy clusters. It also contributes

Dong Bai, Zhongzhou Ren

The non-localized cluster model provides a new perspective on nuclear cluster effects and has been applied successfully to study cluster structures in various bound states and quasi-bound states (i.e., long-lived resonant states). In this work, we extend the application scope of the non-localized cluster model further to resonant and scattering states. Follo

Ajay Kumar, R. S. Dhaka

We investigate the structural, magnetic and spin-state transitions, and magnetocaloric properties of Sr$_{2-x}$La$_x$CoNbO$_6$ ($x=$ 0--1) double perovskites. The structural transition from tetragonal to monoclinic phase at $x$ $\geqslant$ 0.6, and an evolution of (101)/(103) superlattice reflections and Raman active modes indicate the enhancement in the B-s

Filip Tolovski

As the share of renewable energy sources in the present electric energy mix rises, their intermittence proves to be the biggest challenge to carbon free electricity generation. To address this challenge, we propose an electricity pricing agent, which sends price signals to the customers and contributes to shifting the customer demand to periods of high renew

Li-Juan Liu, Zhi-Hui Li, Zhao-Wei Han, Dan-Li Zhi

In the $\left( {t,n} \right)$ threshold quantum secret sharing scheme, it is difficult to ensure that internal participants are honest. In this paper, a verifiable $\left( {t,n} \right)$ threshold quantum secret sharing scheme is designed combined with classical secret sharing scheme. First of all, the distributor uses the asymmetric binary polynomials to ge

Daniel Sobral-Blanco, Lucas Lombriser

Recently the global variation of the Planck mass in the General Relativistic Einstein-Hilbert action was proposed as a self-tuning mechanism of the cosmological constant preventing vacuum energy from freely gravitating. We show that this global mechanism emerges for generic local scalar-tensor theories with additional coupling of the scalar field to the fiel

Huizhuo Yuan, Xiangru Lian, Ji Liu, Yuren Zhou

In this paper, we propose a novel algorithm named STOchastic Recursive Momentum for Policy Gradient (STORM-PG), which operates a SARAH-type stochastic recursive variance-reduced policy gradient in an exponential moving average fashion. STORM-PG enjoys a provably sharp $O(1/ε^3)$ sample complexity bound for STORM-PG, matching the best-known convergence rate f

Robert Beekveldt, Michael Borinsky, Franz Herzog

We give a Hopf-algebraic formulation of the $R^*$-operation, which is a canonical way to render UV and IR divergent Euclidean Feynman diagrams finite. Our analysis uncovers a close connection to Brown&#39;s Hopf algebra of motic graphs. Using this connection we are able to provide a verbose proof of the long observed &#39;commutativity&#39; of UV and IR subt

Xinlei Chen, Haoqi Fan, Ross Girshick, Kaiming He

Contrastive unsupervised learning has recently shown encouraging progress, e.g., in Momentum Contrast (MoCo) and SimCLR. In this note, we verify the effectiveness of two of SimCLR&#39;s design improvements by implementing them in the MoCo framework. With simple modifications to MoCo---namely, using an MLP projection head and more data augmentation---we estab

Mahdi Karami, Dale Schuurmans

In this paper, we propose a deep probabilistic multi-view model that is composed of a linear multi-view layer based on probabilistic canonical correlation analysis (CCA) description in the latent space together with deep generative networks as observation models. The network is designed to decompose the variations of all views into a shared latent representa

Jing Yang, Brais Martinez, Adrian Bulat, Georgios Tzimiropoulos

This paper addresses the problem of model compression via knowledge distillation. To this end, we propose a new knowledge distillation method based on transferring feature statistics, specifically the channel-wise mean and variance, from the teacher to the student. Our method goes beyond the standard way of enforcing the mean and variance of the student to b

Behzad Ghazanfari, Fatemeh Afghah

This paper introduces a novel perspective about error in machine learning and proposes inverse feature learning (IFL) as a representation learning approach that learns a set of high-level features based on the representation of error for classification or clustering purposes. The proposed perspective about error representation is fundamentally different from

Justin Kulp

In this short note, we comment on the existence of two more fermionic unitary minimal models not included in recent work by Hsieh, Nakayama, and Tachikawa. These theories are obtained by fermionizing the $\mathbb{Z}_2$ symmetry of the m=11 and m=12 exceptional unitary minimal models. Furthermore, these should be the only missing cases.

Tiberiu Harko, Man Kwong Mak, Chun Sing Leung

We study the dynamics of vortices with arbitrary topological charges in weakly interacting Bose-Einstein condensates using the Adomian Decomposition Method to solve the nonlinear Gross-Pitaevskii equation in polar coordinates. The solutions of the vortex equation are expressed in the form of infinite power series. The power series representations are compare

Antonio Candelieri, Riccardo Perego, Ilaria Giordani, Andrea Ponti

Modelling human function learning has been the subject of in-tense research in cognitive sciences. The topic is relevant in black-box optimization where information about the objective and/or constraints is not available and must be learned through function evaluations. In this paper we focus on the relation between the behaviour of humans searching for the

Saket Dingliwal, Divyansh Pareek, Jatin Arora

Deep Neural Networks (DNNs) have emerged as a powerful mechanism and are being increasingly deployed in real-world safety-critical domains. Despite the widespread success, their complex architecture makes proving any formal guarantees about them difficult. Identifying how logical notions of high-level correctness relate to the complex low-level network archi

Jyotisman Sahoo, Rebecca Flint

We use a projective symmetry group analysis to determine all symmetric spin liquids on the stuffed honeycomb lattice Heisenberg model. This lattice interpolates between honeycomb, triangular and dice lattices, always preserving hexagonal symmetry, and it already has one spin liquid candidate, TbInO$_3$, albeit with strong spin-orbit coupling not considered h

Antonio Candelieri, Bruno Galuzzi, Ilaria Giordani, Francesco Archetti

Sequential Model-based Bayesian Optimization has been successful-ly applied to several application domains, characterized by complex search spaces, such as Automated Machine Learning and Neural Architecture Search. This paper focuses on optimal control problems, proposing a Sequential Model-based Bayesian Optimization framework to learn optimal control strat

A. Pastor Yabar, M. J. Martínez González, M. Collados

Aims. We aim to characterise the magnetism of a large fraction of the north polar region close to a maximum of activity, when the polar regions are reversing their dominant polarity. Methods. We make use of full spectropolarimetric data from the CRisp Imaging Spectro-Polarimeter installed at the Swedish Solar Telescope. The data consist of a photospheric spe

John H. J. Einmahl, Ana Ferreira, Laurens de Haan, Claudia Neves

The statistical theory of extremes is extended to observations that are non-stationary and not independent. The non-stationarity over time and space is controlled via the scedasis (tail scale) in the marginal distributions. Spatial dependence stems from multivariate extreme value theory. We establish asymptotic theory for both the weighted sequential tail em

E. Elizalde, S. D. Odintsov, V. K. Oikonomou, Tanmoy Paul

In the context of a ghost free $f(R,\mathcal{G})$ model, an extended matter bounce scenario is studied where the form of the scale factor is given by $a(t) = (a_0t^2 + 1)^n$. The ghost free character of the model is ensured by the presence of a Lagrange multiplier, as developed in \cite{Nojiri:2018ouv}. The conditions under which, in this model, the speed of

Sara Mousavi, Dylan Lee, Tatianna Griffin, Dawnie Steadman

Domain-specific image collections present potential value in various areas of science and business but are often not curated nor have any way to readily extract relevant content. To employ contemporary supervised image analysis methods on such image data, they must first be cleaned and organized, and then manually labeled for the nomenclature employed in the

Weimin Wang, Shohei Nobuhara, Ryosuke Nakamura, Ken Sakurada

This paper presents a novel semantic-based online extrinsic calibration approach, SOIC (so, I see), for Light Detection and Ranging (LiDAR) and camera sensors. Previous online calibration methods usually need prior knowledge of rough initial values for optimization. The proposed approach removes this limitation by converting the initialization problem to a P

Jung-Su Ha, Danny Driess, Marc Toussaint

Logic-Geometric Programming (LGP) is a powerful motion and manipulation planning framework, which represents hierarchical structure using logic rules that describe discrete aspects of problems, e.g., touch, grasp, hit, or push, and solves the resulting smooth trajectory optimization. The expressive power of logic allows LGP for handling complex, large-scale

Raffaele Resta

The x-ray magnetic circular dichroism (XMCD) sum rule yields an extremely useful ground-state observable, which provides a quantitative measure of spontaneous time-reversal symmetry breaking (T-breaking) in a given material. I derive here its explicit expression within band-structure theory, in the general case: trivial insulators, topological insulators, an

Antoine Boniface, Jonathan Dong, Sylvain Gigan

In biological microscopy, light scattering represents the main limitation to image at depth. Recently, a set of wavefront shaping techniques has been developed in order to manipulate coherent light in strongly disordered materials. The Transmission Matrix approach has shown its capability to inverse the effect of scattering and efficiently focus light. In pr

Tsigigenet Dessalgn, Seungmo Kim

IEEE 802.11p is one of the key technologies that enable Dedicated Short-Range Communications (DSRC) in intelligent transportation system (ITS) for safety on the road. The main challenge in vehicular communication is the large amount of data to be processed. As vehicle density and velocity increases, the data to be transmitted also increases. We proposed a pr

Brendan John, Sophie Jörg, Sanjeev Koppal, Eakta Jain

The gaze behavior of virtual avatars is critical to social presence and perceived eye contact during social interactions in Virtual Reality. Virtual Reality headsets are being designed with integrated eye tracking to enable compelling virtual social interactions. This paper shows that the near infra-red cameras used in eye tracking capture eye images that co

Lyndsay Kerr, Wilson Lamb, Matthias Langer

We investigate an infinite, linear system of ordinary differential equations that models the evolution of fragmenting clusters. We assume that each cluster is composed of identical units (monomers) and we allow mass to be lost, gained or conserved during each fragmentation event. By formulating the initial-value problem for the system as an abstract Cauchy p

Pratham Oza, Mahsa Foruhandeh, Ryan Gerdes, Thidapat Chantem

Rapid urbanization calls for smart traffic management solutions that incorporate sensors, distributed traffic controllers and V2X communication technologies to provide fine-grained traffic control to mitigate congestion. As in many other cyber-physical systems, smart traffic management systems typically lack security measures. This allows numerous opportunit

Matteo Trudu, Maura Pilia, Gregory Hellbourg, Pierpaolo Pari

In this work, we propose a new method of computing the Karhunen-Loève Transform (KLT) applied to complex voltage data for the detection and noise level reduction in astronomical signals. We compared this method with the standard KLT techniques based on the Toeplitz correlation matrix and we conducted a performance analysis for the detection and extraction of

Vincent Roger, Jérôme Farinas, Julien Pinquier

Most state-of-the-art speech systems are using Deep Neural Networks (DNNs). Those systems require a large amount of data to be learned. Hence, learning state-of-the-art frameworks on under-resourced speech languages/problems is a difficult task. Problems could be the limited amount of data for impaired speech. Furthermore, acquiring more data and/or expertis

Bingrong Huang, Yongxiao Lin, Zhiwei Wang

In this note, we give a detailed proof of an asymptotic for averages of coefficients of a class of degree three $L$-functions which can be factorized as a product of a degree one and a degree two $L$-functions. We emphasize that we can break the $1/2$-barrier in the error term, and we get an explicit exponent.

Ali Shojaie

Networks effectively capture interactions among components of complex systems, and have thus become a mainstay in many scientific disciplines. Growing evidence, especially from biology, suggest that networks undergo changes over time, and in response to external stimuli. In biology and medicine, these changes have been found to be predictive of complex disea

Payal Bal, Simon Kapitza, Natasha Cadenhead, Tom Kompas

Mapping pathways to achieving the sustainable development goals requires understanding and predicting how social, economic and political factors impact biodiversity. Trends in demography, economic growth, regional alliances and consumption behaviours can have profound effects on the environment by driving resource use and production. While these distant soci

Tarek Mealy, Filippo Capolino

We present the general conditions to realize a fourth order exceptional point of degeneracy (EPD) in two uniform (i.e., invariant along z) lossless and gainless coupled transmission lines (CTLs), namely, a degenerate band edge (DBE). Until now the DBE has been shown only in periodic structures. In contrast, the CTLs considered here are uniform and subdivided

Rania Ibrahim, David F. Gleich

Local graph clustering is an important machine learning task that aims to find a well-connected cluster near a set of seed nodes. Recent results have revealed that incorporating higher order information significantly enhances the results of graph clustering techniques. The majority of existing research in this area focuses on spectral graph theory-based tech

Miguel Cárdenas-Montes

In this report, the evolution of PM2.5 concentration at Escuelas Aguirre monitoring station during the quarters of 2019 and the significance of the differences with the concentration of the quarters of previous years is analysed. These periods include the activation of Madrid Central, which is a major initiative for reducing motor traffic and the associated

Arun Balajee Vasudevan, Dengxin Dai, Luc Van Gool

Humans can robustly recognize and localize objects by integrating visual and auditory cues. While machines are able to do the same now with images, less work has been done with sounds. This work develops an approach for dense semantic labelling of sound-making objects, purely based on binaural sounds. We propose a novel sensor setup and record a new audio-vi

Magnus Fontes, Rasmus Henningsson

Principal Moment Analysis is a method designed for dimension reduction, analysis and visualization of high dimensional multivariate data. It generalizes Principal Component Analysis and allows for significant statistical modeling flexibility, when approximating an unknown underlying probability distribution, by enabling direct analysis of general approximate

Antonio Candelieri, Ilaria Giordani, Riccardo Perego, Francesco Archetti

Bayesian Optimization has become the reference method for the global optimization of black box, expensive and possibly noisy functions. Bayesian Op-timization learns a probabilistic model about the objective function, usually a Gaussian Process, and builds, depending on its mean and variance, an acquisition function whose optimizer yields the new evaluation

F. Thomas Bruss

This article studies the stability of solutions of equilibrium equations arising in so-called resource dependent branching processes. We argue that these new models, building on the model already presented by Bruss (1984 a), refined and elaborated in Bruss and Duerinckx (2015) and now extended to allow immigration, are suitable to cope with specific properti

Neda Navidi

Reinforcement Learning (RL) in various decision-making tasks of machine learning provides effective results with an agent learning from a stand-alone reward function. However, it presents unique challenges with large amounts of environment states and action spaces, as well as in the determination of rewards. This complexity, coming from high dimensionality a

Axel Böhm, Aris Daniilidis

In this work we show that various algorithms, ubiquitous in convex optimization (e.g. proximal-gradient, alternating projections and averaged projections) generate self-contracted sequences $\{x_{k}\}_{k\in\mathbb{N}}$. As a consequence, a novel universal bound for the \emph{length} ($\sum_{k\ge 0}\Vert x_{k+1}-x_k\Vert$) can be deduced. In addition, this bo

Deniz Mostarac, Ekaterina Novak, Pedro A. Sánchez, Sofia Kantorovich

We investigate the properties of magnetic supracolliodal polymers -- magnetic filaments (MFs) -- with super-paramagnetic monomers, with and without Van der Waals (VdW) attraction between them. We employ molecular dynamics (MD) simulations to elucidate the impact of crosslinking mechanism on the structural and magnetic response of MFs to an applied external h

Jinjiang Li, Min Zhang

Let $\mathcal{P}_r$ denote an almost-prime with at most $r$ prime factors, counted according to multiplicity. In this paper, we establish a theorem of Bombieri-Vinogradov type for the Piatetski-Shapiro primes $p=[n^{1/γ}]$ with $\frac{85}{86}<γ<1$. Moreover, we use this result to prove that, for $0.9989445<γ<1$, there exist infinitely many Piatetski-Shapiro

Piji Li

We present an empirical investigation of pre-trained Transformer-based auto-regressive language models for the task of open-domain dialogue generation. Training paradigm of pre-training and fine-tuning is employed to conduct the parameter learning. Corpora of News and Wikipedia in Chinese and English are collected for the pre-training stage respectively. Dia

Thai-Son Nguyen, Sebastian Stüker, Alex Waibel

In the area of multi-domain speech recognition, research in the past focused on hybrid acoustic models to build cross-domain and domain-invariant speech recognition systems. In this paper, we empirically examine the difference in behavior between hybrid acoustic models and neural end-to-end systems when mixing acoustic training data from several domains. For

Achiel Colpaert, Evgenii Vinogradov, Sofie Pollin

In this paper, we present a fixed mmWave Multi-User Multiple-Input Multiple-Output (MIMO) system for fixed wireless access with a unique architecture. A digital MIMO system is combined with an analog multi-beam antenna array which uses a high-dimension 16x16 Butler matrix to obtain 16 orthogonal beams. A system model of this architecture is presented and use

Yuta Uchikawa, Les Cowley, Hisashi Hayakawa, David M. Willis

While graphical records of astronomical/meteorological events before telescopic observations are of particular interest, they have frequently undergone multiple copying and may have been modified from the original. Here, we analyze a graphical record of the cross-sign of 806 CE in the Anglo-Saxon Chronicle, which has been considered one of the earliest datab

Nima Mohammadi Meshky, Sara Iodice, Krystian Mikolajczyk

Person re-identification (re-ID) is a very active area of research in computer vision, due to the role it plays in video surveillance. Currently, most methods only address the task of matching between colour images. However, in poorly-lit environments CCTV cameras switch to infrared imaging, hence developing a system which can correctly perform matching betw

Francesco Catalano, Johan Nilsson

We introduce a general scheme to consistently truncate equations of motion for Green&#39;s functions. Our scheme is guaranteed to generate physical Green&#39;s functions with real excitation energies and positive spectral weights. There are free parameters in our scheme akin to mean field parameters that may be determined to get as good an approximation to t

Claudia Chaio, Victoria Guazzelli, Pamela Suarez

Let $A$ be a finite dimensional representation-finite algebra over an algebraically closed field. The aim of this work is to determine which vertices of $Q_A$ are suficient to be consider in order to compute the nilpotency index of the radical of the module category of $A$. In many cases, we give a formula to compute such index taking into account the ordina

Alejandro Barrera, Carlos Guindel, Jorge Beltrán, Fernando García

On-board 3D object detection in autonomous vehicles often relies on geometry information captured by LiDAR devices. Albeit image features are typically preferred for detection, numerous approaches take only spatial data as input. Exploiting this information in inference usually involves the use of compact representations such as the Bird&#39;s Eye View (BEV)

Pierre-François Loos, Anthony Scemama, Martial Boggio-Pasqua, Denis Jacquemin

Aiming at completing the sets of FCI-quality transition energies that we recently developed (\textit{J.~Chem.~Theory Comput.} \textbf{14} (2018) 4360--4379, \textit{ibid.}~\textbf{15} (2019) 1939--1956, and \textit{ibid.}~\textbf{16} (2020) 1711--1741), we provide, in the present contribution, ultra-accurate vertical excitation energies for a series of &#34;

V. L. Sousa Junior

In this paper, we study the convergence of an interior subgradient and proximal methods for a DC (difference of convex functions) constrained minimization problem.

Pritish Kamath, Omar Montasser, Nathan Srebro

We present and study approximate notions of dimensional and margin complexity, which correspond to the minimal dimension or norm of an embedding required to approximate, rather then exactly represent, a given hypothesis class. We show that such notions are not only sufficient for learning using linear predictors or a kernel, but unlike the exact variants, ar

Christoph Lehner, Aaron S. Meyer

There are emerging tensions for theory results of the hadronic vacuum polarization contribution to the muon anomalous magnetic moment both within recent lattice QCD calculations and between some lattice QCD calculations and R-ratio results. In this paper we work towards scrutinizing critical aspects of these calculations. We focus in particular on a precise

Xiwen Dai

To investigate the mechanism of wave trapping, acoustic embedded trapped modes associated with two-resonant-mode interference in two-dimensional duct-cavity structures are calculated by the feedback-loop closure principle, which allows us to analyse the travelling modes that construct the trapped modes. The exact two coexisting resonant modes that underpin a