January 2022 arXiv papers — page 39

Yarden As, Ilnura Usmanova, Sebastian Curi, Andreas Krause

Improving sample-efficiency and safety are crucial challenges when deploying reinforcement learning in high-stakes real world applications. We propose LAMBDA, a novel model-based approach for policy optimization in safety critical tasks modeled via constrained Markov decision processes. Our approach utilizes Bayesian world models, and harnesses the resulting

Minheng Xiao

This paper investigates the application of Time Series K-means (TS-K-means) within the context of causal inference and mechanism clustering of financial time series data. Traditional clustering approaches like K-means often rely on static distance metrics, such as Euclidean distance, which inadequately capture the temporal dependencies intrinsic to financial

Yuanhong Wang, Haowen Su, Min Jiang, Ying Huan

Searches for the axion and axionlike particles may hold the key to unlocking some of the deepest puzzles about our universe, such as dark matter and dark energy. Here we use the recently demonstrated spin-based amplifier to constrain such hypothetical particles within the well-motivated ``axion window'' (1 $\mu$eV-1 meV) through searching for an exotic spin-

V. P. Mineev

The measurements (Phys.Rev.B {\bf 100}, 220504(R) (2019)) do not detect noticeable thermal conductivity in superconducting UTe$_2$ in the T=0 limit. At the same time the same crystals exhibit a large residual density of states comparable with its normal state value. The improvement of samples quality leads to the augmentation of critical temperature of trans

Mingzhe Chen, Xi Xiao, Bin Zhang, Xinyu Liu

Recent studies show that depression can be partially reflected from human facial attributes. Since facial attributes have various data structure and carry different information, existing approaches fail to specifically consider the optimal way to extract depression-related features from each of them, as well as investigates the best fusion strategy. In this

Thomas Y. Chen

Natural disasters ravage the world's cities, valleys, and shores on a regular basis. Deploying precise and efficient computational mechanisms for assessing infrastructure damage is essential to channel resources and minimize the loss of life. Using a dataset that includes labeled pre- and post- disaster satellite imagery, we take a machine learning-based rem

Nan Wang, Hongning Wang, Maryam Karimzadehgan, Branislav Kveton

Most real-world optimization problems have multiple objectives. A system designer needs to find a policy that trades off these objectives to reach a desired operating point. This problem has been studied extensively in the setting of known objective functions. We consider a more practical but challenging setting of unknown objective functions. In industry, t

Eric Wolf, Ravi Kopparapu, Jacob Haqq-Misra, Thomas J. Fauchez

The TRAPPIST-1 Habitable Atmosphere Intercomparison (THAI) project was initiated to compare 3D climate models that are commonly used for predicting theoretical climates of habitable zone extrasolar planets. One of the core models studied as part of THAI is ExoCAM, an independently curated exoplanet branch of the National Center for Atmospheric Research (NCAR

Gavin P Lamb, Lorenzo Nativi, Stephan Rosswog, D. Alexander Kann

Using the resultant profiles from 3D hydrodynamic simulations of relativistic jets interacting with neutron star merger wind ejecta, we show how the inhomogeneity of energy and velocity {across the jet surface profile} can alter the observed afterglow lightcurve. We find that the peak afterglow flux depends sensitively on the observer's line-of-sight, not on

J. F. Gloy, A. Siemens, P. Schmelcher

We explore a model system consisting of a particle confined to move along a toroidal helix while being exposed to a static potential as well as a driving force due to a harmonically oscillating electric field. It is shown that in the limit of a vanishing helix radius the governing equations of motion coincide with those of the well-known Kapitza pendulum - a

Valentino Vito

An old result of M\"uller and R\"odl states that a countable graph $G$ has a subgraph whose vertices all have infinite degree if and only if for any vertex labeling of $G$ by positive integers, an infinite increasing path can be found. They asked whether an analogous equivalence holds for edge labelings, which Reiterman answered in the affirmative. Recently,

Ziv Ran

On a general hypersurface of degree $d\leq n$ in $\mathbb P^n$ or $\mathbb P^n$ itself, we prove the existence of curves of any genus and high enough degree depending on the genus passing through the expected number $t$ of general points or incident to a general collection of subvarieties of suitable codimensions. In some cases we also show that the family o

Asher Trockman, J. Zico Kolter

Although convolutional networks have been the dominant architecture for vision tasks for many years, recent experiments have shown that Transformer-based models, most notably the Vision Transformer (ViT), may exceed their performance in some settings. However, due to the quadratic runtime of the self-attention layers in Transformers, ViTs require the use of

Albert F. Rigosi, Dinesh Patel, Martina Marzano, Mattias Kruskopf

We have demonstrated the millimeter-scale fabrication of monolayer epitaxial graphene $p-n$ junction devices using simple ultraviolet photolithography, thereby significantly reducing device processing time compared to that of electron beam lithography typically used for obtaining sharp junctions. This work presents measurements yielding nonconventional, frac

Marcell T. Kurbucz, Péter Pósfay, Antal Jakovác

The goals of this paper are twofold: (1) to present a new method that is able to find linear laws governing the time evolution of Markov chains and (2) to apply this method for anomaly detection in Bitcoin prices. To accomplish these goals, first, the linear laws of Markov chains are derived by using the time embedding of their (categorical) autocorrelation

Adrià Delhom

In this PhD thesis we deal with several theoretical and phenomenological apsects of metric-affine theories of gravity. Concretely, we first give a broad introduction to the necessary tools to understand the framework and elaborate on some subtleties of the minimal coupling prescription between geometry and matter in presence of torsion and nonmetricity. Then

Gaétan Facchinetti, Martin Stref, Julien Lavalle

The cold dark matter (CDM) scenario predicts that galactic halos should host a huge amount of subhalos possibly lighter than planets, depending on the nature of dark matter. Predicting their abundance and distribution has important implications for dark matter searches and searches for subhalos themselves, as they could provide a decisive test of the CDM par

Lama Alqazlan, Rob Procter, Michael Castelle

The introduction of online marketplace platforms has led to the advent of new forms of flexible, on-demand (or 'gig') work. Yet, most prior research concerning the experience of gig workers examines delivery or crowdsourcing platforms, while the experience of the large numbers of workers who undertake educational labour in the form of tutoring gigs remains u

Yao Shu, Zhongxiang Dai, Zhaoxuan Wu, Bryan Kian Hsiang Low

Neural architecture search (NAS) has gained immense popularity owing to its ability to automate neural architecture design. A number of training-free metrics are recently proposed to realize NAS without training, hence making NAS more scalable. Despite their competitive empirical performances, a unified theoretical understanding of these training-free metric

Abdelkrim Abdelli

We explore in this paper a novel approach that builds an overapproximation of the state space of preemptive real time systems. Our graph construction extends the expression of a class to the time distance system that encodes the quantitative properties of past fired subsequences. This makes it possible to restore relevant time information that is used to tig

Tushar Mitra, Md. Kamrul Hassan

In this article, we extend the idea of stochastic dyadic Cantor set to weighted planar stochastic lattice that leads to a stochastic porous lattice. The process starts with an initiator which we choose to be a square of unit area for convenience. We then define a generator that divides the initiator or one of the blocks, picked preferentially with respect to

Alessandro Zito, Tommaso Rigon, David B. Dunson

In ecology it has become common to apply DNA barcoding to biological samples leading to datasets containing a large number of nucleotide sequences. The focus is then on inferring the taxonomic placement of each of these sequences by leveraging on existing databases containing reference sequences having known taxa. This is highly challenging because i) sequen

Alexander Dicke, Albrecht Seelmann

In this note, an alternative approach to establish observability for semigroups based on their smoothing properties is presented. The results discussed here are closely related to those recently obtained in [arXiv:2112.01788], but the current proof allows to get rid of several technical assumptions by following the standard complex analytic approach establis

Hyeju Kim, David M. Ambrose

We study dispersive models of fluid flow in viscoelastic vessels, derived in the study of blood flow. The unknowns in the models are the velocity of the fluid in the axial direction and the displacement of the vessel wall from rest. We prove that one such model has a well-posed initial value problem, while we argue that a related model instead has an ill-pos

Daria Gusenkova, Francesco Valenti, Martin Spiecker, Simon Günzler

We demonstrate flux-bias locking and operation of a gradiometric fluxonium artificial atom using two symmetric granular aluminum (grAl) loops to implement the superinductor. The gradiometric fluxonium shows two orders of magnitude suppression of sensitivity to homogeneous magnetic fields, which can be an asset for hybrid quantum systems requiring strong magn

Guy Garty, Andrew D. Harken, David J. Brenner

Standard dosimetry protocols exist for highly penetrating photon and particle beams used in the clinic and in research. However, these protocols cannot be directly applied to shallow penetration MeV-range ion beams. The Radiological Research Accelerator Facility has been using such beams for almost 50 years to irradiate cell monolayers, using self-developed

Davide Evangelista, Elena Morotti, Elena Loli Piccolomini

This paper proposes a new two-step procedure for sparse-view tomographic image reconstruction. It is called RISING, since it combines an early-stopped Rapid Iterative Solver with a subsequent Iteration Network-based Gaining step. So far, regularized iterative methods have widely been used for X-ray computed tomography image reconstruction from low-sampled da

Bianca Giaccone, Paolo Berrutti, Martina Martinello, Sam Posen

Plasma cleaning is a technique that can be applied in superconducting radio-frequency (SRF) cavities in situ in cryomodules in order to decrease their level of field emission. We developed the technique for the Linac Coherent Light Source II (LCLS-II) cavities and we present in this paper the full development and application of plasma processing to the LCLS-

V. O. Kozlov, N. S. Kuznetsov, D. S. Smirnov, I. I. Ryzhov

It is known that linear birefringence of the medium essentially hinders measuring the Faraday effect. For this reason, optically anisotropic materials have never been considered as objects of the Faraday-rotation-based spin noise spectroscopy (SNS). We show, both theoretically and experimentally, that strong optical anisotropy that may badly suppress the reg

Siddhartha Datta, Nigel Shadbolt

Attack vectors that compromise machine learning pipelines in the physical world have been demonstrated in recent research, from perturbations to architectural components. Building on this work, we illustrate the self-obfuscation attack: attackers target a pre-processing model in the system, and poison the training set of generative models to obfuscate a spec

A. Lister, M. Stancari

Ionization electron diffusion in Liquid Argon Time Projection Chambers (LArTPCs) has typically been considered at the detector design stage, but little attention has been given to its effects on calibration and particle identification. We use a GEANT4-based simulation to study how diffusion impacts these techniques, and give consideration to how this effect

Aoyu Wu, Dazhen Deng, Furui Cheng, Yingcai Wu

The last decade has witnessed many visual analytics (VA) systems that make successful applications to wide-ranging domains like urban analytics and explainable AI. However, their research rigor and contributions have been extensively challenged within the visualization community. We come in defence of VA systems by contributing two interview studies for gath

Antonio Giuseppe Grimaldi, Erica Ipocoana

We study the higher fractional differentiability properties of the gradient of the solutions to variational obstacle problems of the form \begin{gather*} \min \biggl\{ \int_{\Omega} F(x,w,Dw) d x \ : \ w \in \mathcal{K}_{\psi}(\Omega) \biggr\}, \end{gather*} with $F$ double phase functional of the form \begin{equation*} F(x,w,z)=b(x,w)(|z|^p+a(x)|z|^q), \end

Victor S. Monakhov

Supersolubility of a finite group $G=\langle A,B\rangle$ with the nilpotent derived subgroup $G^\prime$ is established under the condition that the subgroups $A$ and $B$ are both subnormal and supersoluble.

Martin Bromberger, Irina Dragoste, Rasha Faqeh, Christof Fetzer

In a previous paper, we have shown that clause sets belonging to the Horn Bernays-Sch\"onfinkel fragment over simple linear real arithmetic (HBS(SLR)) can be translated into HBS clause sets over a finite set of first-order constants. The translation preserves validity and satisfiability and it is still applicable if we extend our input with positive universa

V. A. Kovtunenko, L. Karpenko-Jereb

This paper is devoted to study the electrochemical behavior of Pt catalyst in a polymer electrolyte fuel cell at various operating conditions and at different electric potential difference (also known as voltage) cycling applied in accelerated stress tests. The degradation of platinum is considered with respect to the Pt ion dissolution and the Pt oxide cove

Suyeon Son, Minjin Kim, Luis C. Ho

We utilize mid-infrared multi-epoch data from Wide-field Infrared Survey Explorer (WISE) over a $\sim10$-year period in W1 (3.4$\mu$m) and W2 (4.6$\mu$m) bands to investigate the structure of dusty torus in low-redshift ($0.15<z\le 0.4$) active galactic nuclei (AGN). We calculate a Spearman correlation coefficient ($r_{12}$) between W1 magnitude and W1$-$W2

Yueyao Wang, Li Xu, Yili Hong, Rong Pan

Performance variability management is an active research area in high-performance computing (HPC). We focus on input/output (I/O) variability. To study the performance variability, computer scientists often use grid-based designs (GBDs) to collect I/O variability data, and use mathematical approximation methods to build a prediction model. Mathematical appro

Haichao Zhang, Wei Xu, Haonan Yu

Standard model-free reinforcement learning algorithms optimize a policy that generates the action to be taken in the current time step in order to maximize expected future return. While flexible, it faces difficulties arising from the inefficient exploration due to its single step nature. In this work, we present Generative Planning method (GPM), which can g

Cheuk-Yin Wong

The X17 particle, the E38 particle, and the anomalous soft photons are anomalous particles because they do not appear to belong to any known Standard Model families. We propose a QED meson description of the anomalous particles as composite systems of a light quark and a light antiquark bound and confined by the compact QED interaction, by combining Polyakov

B. Eslam Panah, K. Jafarzade, A. Rincón

Recently, it was shown that the power-Maxwell (PM) theory could remove the singularity of the electric field \cite{PM2}. Motivated by a great interest in three-dimensional black holes and a surge of success in studying massive gravity from both the cosmological and astrophysical points of view, we investigate three-dimensional black hole solutions in de Rham

Baha Sakar, Sibylle Sievers, Alexander Fernandez Scarioni, Felipe Garcia-Sanchez

Magnetic force microscopy is a widespread technique for imaging magnetic structures with a resolution of some 10 nanometers. MFM can be calibrated to obtain quantitative spatially resolved magnetization data in units of A/m by determining the calibrated point spread function of the instrument, its instrument calibration function (ICF), from a measurement of

David Ruiz

In this paper we prove symmetry of compactly supported steady solutions of the 2D Euler equations. Assuming that $\Omega = \{x \in \mathbb{R}^2:\ u(x) \neq 0\}$ is an annular domain, we prove that the streamlines of the flow are circular. We are also able to remove the topological condition on $\Omega$ if we impose regularity and nondegeneracy assumptions on

Martina Favero, Gianpaolo Scalia Tomba, Tom Britton

We present a stochastic epidemic model to study the effect of various preventive measures, such as uniform reduction of contacts and transmission, vaccination, isolation, screening and contact tracing, on a disease outbreak in a homogeneously mixing community. The model is based on an infectivity process, which we define through stochastic contact and infect

Shangbin Wu, Xu Yan, Xiaoliang Fan, Shirui Pan

Learning the embeddings for urban regions from human mobility data can reveal the functionality of regions, and then enables the correlated but distinct tasks such as crime prediction. Human mobility data contains rich but abundant information, which yields to the comprehensive region embeddings for cross domain tasks. In this paper, we propose multi-graph f

Una Pale, Tomas Teijeiro, David Atienza

Wearable and unobtrusive monitoring and prediction of epileptic seizures has the potential to significantly increase the life quality of patients, but is still an unreached goal due to challenges of real-time detection and wearable devices design. Hyperdimensional (HD) computing has evolved in recent years as a new promising machine learning approach, especi

Alaa Abouhalaka, Sehmus Findik

A proper ideal $P$ of a commutative ring with identity is an almost prime ideal if $ab \in P{\setminus}P^2$ implies $a \in P$ or $b \in P$. In this paper we define almost prime ideals of a noncommutative ring, and provide some equivalent definitions. We also examine some cases such that all right ideals of a noncommutative ring are almost prime right ideals.

Victor Bailey

Motivated by recent work in Dynamical Sampling, we prove a necessary and sufficient condition for a frame in a separable and infinite-dimensional Hilbert space to admit the form $\{T^{n} \varphi \}_{n \geq 0}$ with $T \in B(H)$. Also, a characterization of all the vectors $\varphi$ for which $\{T^{n} \varphi \}_{n \geq 0}$ is a frame for some $T \in B(H)$ is

Berenike Masing, Niels Lindner, Ralf Borndörfer

We consider the line planning problem in public transport in the Parametric City, an idealized model that captures typical scenarios by a (small) number of parameters. The Parametric City is rotation symmetric, but optimal line plans are not always symmetric. This raises the question to quantify the symmetry gap between the best symmetric and the overall bes

Ella M. King, Zizhao Wang, David A. Weitz, Frans Spaepen

Despite significant advances in particle imaging technologies over the past two decades, few advances have been made in particle tracking, i.e. linking individual particle positions across time series data. The state-of-the-art tracking algorithm is highly effective for systems in which the particles behave mostly independently. However, these algorithms bec

Ayse Unsal, Melek Onen

This work studies anomaly detection under differential privacy (DP) with Gaussian perturbation using both statistical and information-theoretic tools. In our setting, the adversary aims to modify the content of a statistical dataset by inserting additional data without being detected by using the DP guarantee to her own benefit. To this end, we characterize

Bilge Celik, Prabhant Singh, Joaquin Vanschoren

Automated Machine Learning (AutoML) has been used successfully in settings where the learning task is assumed to be static. In many real-world scenarios, however, the data distribution will evolve over time, and it is yet to be shown whether AutoML techniques can effectively design online pipelines in dynamic environments. This study aims to automate pipelin

Hugo Jacob, Marcin Pilipczuk

Twin-width is a newly introduced graph width parameter that aims at generalizing a wide range of "nicely structured" graph classes. In this work, we focus on obtaining good bounds on twin-width $\text{tww}(G)$ for graphs $G$ from a number of classic graph classes. We prove the following: - $\text{tww}(G) \leq 3\cdot 2^{\text{tw}(G)-1}$, where $\text{tw}(G)$

Amr Zeedan, Tamer Khattab

Small satellite communications recently entered a period of massive interest driven by the uprising space applications. CubeSats are particularly attractive due to their low development costs which makes them very promising in playing a central role in the global wireless communication sector with numerous applications. Moreover, constellations of CubeSats i

Jan Horak, Friederike Ihssen, Joannis Papavassiliou, Jan M. Pawlowski

Lattice simulations along with studies in continuum QCD indicate that non-perturbative quantum fluctuations lead to an infrared regularisation of the gluon propagator in covariant gauges in the form of an effective mass-like behaviour. In the present work we propose an analytic understanding of this phenomenon in terms of gluon condensation through a dynamic

Haoyu Dong, Zhoujun Cheng, Xinyi He, Mengyu Zhou

Since a vast number of tables can be easily collected from web pages, spreadsheets, PDFs, and various other document types, a flurry of table pre-training frameworks have been proposed following the success of text and images, and they have achieved new state-of-the-arts on various tasks such as table question answering, table type recognition, column relati

Joseph Sykes, Ryan Barnett

Local topological markers are effective tools for determining the topological properties of both homogeneous and inhomogeneous systems. The Chern marker is an established topological marker that has previously been shown to effectively reveal the topological properties of 2D systems. In an earlier work, the present authors have developed a marker that can be

Anna Hasenfratz, Christopher J. Monahan, Matthew D. Rizik, Andrea Shindler

The gradient flow exponentially suppresses ultraviolet field fluctuations and removes ultraviolet divergences (up to a multiplicative fermionic wavefunction renormalization). It can be used to describe real-space Wilsonian renormalization group transformations and determine the corresponding beta function. We propose a new nonperturbative renormalization sch

D Sirghi, F Sirghi, F Sgaramella, M Bazzi

The SIDDHARTINO experiment at the DA{\Phi}NE Collider of INFN-LNF, the pilot run for the SIDDHARTA-2 experiment which aims to perform the measurement of kaonic deuterium transitions to the fundamental level, has successfully been concluded. The paper reports the main results of this run, including the optimization of various components of the apparatus, amon

Aref Jeribi, Najib Kaddachi, Zahra Laouar

In this paper, a general hybrid fixed point theorem for the contractive mappings in generalized Banach spaces is proved via measure of weak non-compactness and it is further applied to fractional integral equations for proving the existence results for the solutions under mixed Lipschitz and weakly sequentially continuous conditions. Finally, an example is g

Lucius Bushnaq, Isabel Campos, Marco Catillo, Alessandro Cotellucci

We present the results of testing a new technique for stochastic noise reduction in the calculation of propagators by implementing it in OpenQ*D for two ensembles with O(a) improved Wilson fermion action, with periodic boundary conditions and pion masses of 437 MeV and 331 MeV, for the connected vector and pseudoscalar correlators. We find that the technique

Francesco Bacchiocchi, Matteo Castiglioni, Alberto Marchesi, Giulia Romano

We study signaling in Bayesian ad auctions, in which bidders' valuations depend on a random, unknown state of nature. The auction mechanism has complete knowledge of the actual state of nature, and it can send signals to bidders so as to disclose information about the state and increase revenue. For instance, a state may collectively encode some features of

Angelot Behajaina, Roghayeh Maleki, Andriaherimanana Sarobidy Razafimahatratra

Given a finite transitive group $G\leq \operatorname{Sym}(\Omega)$, a subset $\mathcal{F}$ of $G$ is \emph{intersecting} if any two elements of $\mathcal{F}$ agree on some element of $\Omega$. The \emph{intersection density} of $G$, denoted by $\rho(G)$, is the maximum of the rational number $|\mathcal{F}|\left(\frac{|G|}{|\Omega|}\right)^{-1}$ when $\mathca

Varghese Babu, Srikanth Sastry

We study numerically the critical behavior and marginal stability of the shear jamming transition for frictionless soft spheres, observed to occur over a finite range of densities, associated with isotropic jamming for densities above the minimum jamming (J-point) density. Several quantities are shown to scale near the shear jamming point in the same way as

Binjie Zhang, Yixiao Ge, Yantao Shen, Yu Li

The task of hot-refresh model upgrades of image retrieval systems plays an essential role in the industry but has never been investigated in academia before. Conventional cold-refresh model upgrades can only deploy new models after the gallery is overall backfilled, taking weeks or even months for massive data. In contrast, hot-refresh model upgrades deploy

Alessio Stefani, Ivan D. Rukhlenko, Antoine F. J. Runge, Maryanne C. J. Large

Wearable devices are becoming increasingly important, addressing needs in both the fitness and the medical markets. In this paper, we describe a novel sensing platform based on a hollow-core polyurethane optical fiber, operating through capillary guidance, that acts as a conformal sensor of pressure or deformation. The novelty is achieved by combining a simp

Francesc Font-Clos, Marco Zanchi, Stefan Hiemer, Silvia Bonfanti

Being able to predict the failure of materials based on structural information is a fundamental issue with enormous practical and industrial relevance for the monitoring of devices and components. Thanks to recent advances in deep learning, accurate failure predictions are becoming possible even for strongly disordered solids, but the sheer number of paramet

Raphael Morsomme, Jason Xu

Stochastic epidemic models provide an interpretable probabilistic description of the spread of a disease through a population. Yet, fitting these models to partially observed data is a notoriously difficult task due to intractability of the likelihood for many classical models. To remedy this issue, this article introduces a novel data-augmented MCMC algorit

Jeffrey Galkowski, Euan A. Spence

In $d$ dimensions, accurately approximating an arbitrary function oscillating with frequency $\lesssim k$ requires $\sim k^d$ degrees of freedom. A numerical method for solving the Helmholtz equation (with wavenumber $k$ and in $d$ dimensions) suffers from the pollution effect if, as $k\to\infty$, the total number of degrees of freedom needed to maintain acc

Romain A. Meyer, Roberto Decarli, Fabian Walter, Qiong Li

We quantify galaxy overdensities around three high-redshift quasars with known [CII] 158um companions: PJ231-20 (z=6.59), PJ308-21 (z=6.24) and J0305-3150 (z=6.61). Recent SCUBA2 imaging revealed the presence of 17 submillimeter galaxies (SMG) with sky separations $0.7'< \theta < 2.4'$ from these three quasars. We present ALMA Band 6 follow-up observations o

Andrei Agrachev, Stefano Baranzini, Ivan Beschastnyi

In this paper we prove Morse index theorems for a big class of constrained variational problems on graphs. Such theorems are useful in various physical and geometric applications. Our formulas compute the difference of Morse indices of two Hessians related to two different graphs or two different sets of boundary conditions. Several applications such as the

Oliver Cooley, Julian Zalla

Motivated by the bootstrap percolation process for graphs, we define a new, high-order generalisation to $k$-uniform hypergraphs, in which we infect $j$-sets of vertices for some integer $1\le j \le k-1$. We investigate the smallest possible size of an initially infected set which ultimately percolates and determine the exact size in almost all cases of $k$

Hao-Chiang Shao, Hsing-Lei Ping, Kuo-shiuan Chen, Weng-Tai Su

Learning-based pre-simulation (i.e., layout-to-fabrication) models have been proposed to predict the fabrication-induced shape deformation from an IC layout to its fabricated circuit. Such models are usually driven by pairwise learning, involving a training set of layout patterns and their reference shape images after fabrication. However, it is expensive an

Liqiang Zhang, Kai Guo, Yu Liu

Kalman filter-based Inertial Navigation System (INS) is a reliable and efficient method to estimate the position of a pedestrian indoors. Classical INS-based methodology which is called IEZ (INS-EKF-ZUPT) makes use of an Extended Kalman Filter (EKF), a Zero velocity UPdaTing (ZUPT) to calculate the position and attitude of a person. However, heading error wh

Ratchaphat Nakarachinda, Chakrit Pongkitivanichkul, Daris Samart, Lunchakorn Tannukij

The anti-de Sitter (AdS) black hole plays an important role in the holographic principle. In this study, the upper bound in energy corresponding to the mass of the Schwarzschild black hole is modified to be that of the AdS black hole. Via the correspondence between the ultraviolet (UV) and infrared (IR) cutoffs, the constant term in the energy density of the

Nicholas Christoffersen, Dorin Ervin Dutkay, Gabriel Picioroaga, Eric Weber

A row co-isometry is a family $(V_i)_{i=0}^{N-1}$ of operators on a Hilbert space, subject to the relation $$\sum_{i=0}^{N-1}V_iV_i^*=I.$$ As shown in \cite{BJK00}, row co-isometries appear as compressions of representations of Cuntz algebras. In this paper we will present some general constructions of Parseval frames for Hilbert spaces, obtained by iteratin

Hermano Frid, Yachun Li, Daniel Marroquin, João F. C. Nariyoshi

We establish the well-posedness of an initial-boundary value problem of mixed type for a stochastic nonlinear parabolic-hyperbolic equation on a space domain $\cO=\cO'\X\cO''$ where a Neumann boundary condition is imposed on $\po\cO'\X\cO"$, the hyperbolic boundary, and a Dirichlet condition is imposed on $\cO'\X\po\cO"$, the parabolic boundary. Among other

Suraka Bhattacharjee, Urbashi Satpathi, Supurna Sinha

We analyse the long time tails of a charged quantum Brownian particle in a harmonic potential in the presence of a magnetic field using the Quantum Langevin Equation as a starting point. We analyse the long time tails in the position autocorrelation function, position-velocity correlation function and velocity autocorrelation function. We study these correla

Ulrich Haisch, Gabriël Koole

A Higgs portal in the form of the operator $|H|^2$ provides a minimal and theoretically motivated link between the Standard Model (SM) and new physics. While Higgs portals can be constrained well by exotic Higgs decays if the beyond-the-SM states are light, testing scenarios where these particles are kinematically inaccessible is known to be challenging. We

BESIII Collaboration, M. Ablikim, M. N. Achasov, P. Adlarson

Using a sample of $(10.09~\pm~0.04)\times10^{9} ~J/\psi$ events collected with the BESIII detector, a partial wave analysis of $J/\psi\to\gamma\eta^{\prime}\eta^{\prime}$ is performed. The masses and widths of the observed resonances and their branching fractions are reported. The main contribution is from $J/\psi\rightarrow\gamma f_0(2020)$ with $f_0(2020)\

Anssi Kanervisto, Ville Hautamäki, Tomi Kinnunen, Junichi Yamagishi

As automatic speaker verification (ASV) systems are vulnerable to spoofing attacks, they are typically used in conjunction with spoofing countermeasure (CM) systems to improve security. For example, the CM can first determine whether the input is human speech, then the ASV can determine whether this speech matches the speaker's identity. The performance of s

Xiangkun Hu, Hang Yan, Qipeng Guo, Xipeng Qiu

Knowledge and expertise in the real-world can be disjointedly owned. To solve a complex question, collaboration among experts is often called for. In this paper, we propose CollabQA, a novel QA task in which several expert agents coordinated by a moderator work together to answer questions that cannot be answered with any single agent alone. We make a synthe

Marthe Fogstad Dynge, Ugur Halden, Gro Klæboe, Umit Cali

Support schemes like the Feed-in-Tariff (FiT) have for many years been an important driver for the deployment of distributed energy resources, and the transition from consumerism to prosumerism. This democratization and decarbonization of the energy system has led to both challenges and opportunities for the system operators, paving the way for emerging conc

Geoff K. Nicholls, Jeong Eun Lee, Chieh-Hsi Wu, Chris U. Carmona

Model-based Bayesian evidence combination leads to models with multiple parameteric modules. In this setting the effects of model misspecification in one of the modules may in some cases be ameliorated by cutting the flow of information from the misspecified module. Semi-Modular Inference (SMI) is a framework allowing partial cuts which modulate but do not c

Mónica Clapp, Andrzej Szulkin

We establish the existence of positive normalized (in the $L^2$ sense) solutions to non-variational weakly coupled elliptic systems of $\ell$ equations. We consider couplings of both cooperative and competitive type. We show the problem can be formulated as an operator equation on the product of $\ell$ $L^2$-spheres and apply a degree-theoretical argument on

N. Yadav, Rony Keppens, B. Popescu Braileanu

We present a new MHD wave decomposition method that overcomes the limitations of existing wave identification methods. Our method allows to investigate the energy fluxes in different MHD modes at different locations of the solar atmosphere as waves generated by vortex flows travel through the solar atmosphere and pass near the magnetic null. We simulate wave

Shao-Jun Zhang

Previous studies showed that, in the presence of a simple and well-motivated self-interaction scalar potential, asymptotically flat and spherical black holes can carry minimally coupled and charged scalar cloud/hair in Einstein-Maxwell gravity. We extend these studies to Einstein-Born-Infeld gravity to consider the effect of nonlinearity of the electromagnet

Marta Kwiatkowska, Gethin Norman, David Parker, Gabriel Santos

Game-theoretic techniques and equilibria analysis facilitate the design and verification of competitive systems. While algorithmic complexity of equilibria computation has been extensively studied, practical implementation and application of game-theoretic methods is more recent. Tools such as PRISM-games support automated verification and synthesis of zero-

Valerio Paolicelli, Antonio Tavera, Carlo Masone, Gabriele Berton

In this paper we address the task of visual place recognition (VPR), where the goal is to retrieve the correct GPS coordinates of a given query image against a huge geotagged gallery. While recent works have shown that building descriptors incorporating semantic and appearance information is beneficial, current state-of-the-art methods opt for a top down def

Loris Nanni, Michelangelo Paci, Sheryl Brahnam, Alessandra Lumini

A problem with Convolutional Neural Networks (CNNs) is that they require large datasets to obtain adequate robustness; on small datasets, they are prone to overfitting. Many methods have been proposed to overcome this shortcoming with CNNs. In cases where additional samples cannot easily be collected, a common approach is to generate more data points from ex

Yassir Bendou, Yuqing Hu, Raphael Lafargue, Giulia Lioi

Few-shot learning aims at leveraging knowledge learned by one or more deep learning models, in order to obtain good classification performance on new problems, where only a few labeled samples per class are available. Recent years have seen a fair number of works in the field, introducing methods with numerous ingredients. A frequent problem, though, is the

Wei Ye, Zexi Huang, Yunqi Hong, Ambuj Singh

Graph Convolutional Networks (GCN) is a pioneering model for graph-based semi-supervised learning. However, GCN does not perform well on sparsely-labeled graphs. Its two-layer version cannot effectively propagate the label information to the whole graph structure (i.e., the under-smoothing problem) while its deep version over-smoothens and is hard to train (

Lucio Galeati, Dejun Luo

In this work we consider solutions to stochastic partial differential equations with transport noise, which are known to converge, in a suitable scaling limit, to solution of the corresponding deterministic PDE with an additional viscosity term. Large deviations and Gaussian fluctuations underlying such scaling limit are investigated in two cases of interest

Wei Yuan, Hongzhi Yin, Tieke He, Tong Chen

Question Generation (QG), as a challenging Natural Language Processing task, aims at generating questions based on given answers and context. Existing QG methods mainly focus on building or training models for specific QG datasets. These works are subject to two major limitations: (1) They are dedicated to specific QG formats (e.g., answer-extraction or mult

Tobias Beran, Felix Rott

We introduce an analogue to the amalgamation of metric spaces into the setting of Lorentzian pre-length spaces. This provides a very general process of constructing new spaces out of old ones. The main application in this work is an analogue of the gluing theorem of Reshetnyak for CAT($k$) spaces, which roughly states that gluing is compatible with upper cur

Enrique Iglesias, Samaneh Jozashoori, Maria-Esther Vidal

RDF knowledge graphs (KG) are powerful data structures to represent factual statements created from heterogeneous data sources. KG creation is laborious and demands data management techniques to be executed efficiently. This paper tackles the problem of the automatic generation of KG creation processes declaratively specified; it proposes techniques for plan

Leo Segre, Or Hirschorn, Dvir Ginzburg, Dan Raviv

Image translation across domains for unpaired datasets has gained interest and great improvement lately. In medical imaging, there are multiple imaging modalities, with very different characteristics. Our goal is to use cross-modality adaptation between CT and MRI whole cardiac scans for semantic segmentation. We present a segmentation network using synthesi

Tina Raissi, Eugen Beck, Ralf Schlüter, Hermann Ney

In this work, we show that a factored hybrid hidden Markov model (FH-HMM) which is defined without any phonetic state-tying outperforms a state-of-the-art hybrid HMM. The factored hybrid HMM provides a link to transducer models in the way it models phonetic (label) context while preserving the strict separation of acoustic and language model of the hybrid HM

Eliezer Oliveira, Chenxi Li, Xiang Zhang, Anand Puthirath

The surface functionalization of diamond has been extensively studied through a variety of techniques, such as oxidation. Several oxygen groups have been correspondingly detected on the oxidized diamond, such as COC (ester), CO (ketonic), and COH (hydroxyl). However, the composition and relative concentration of these groups on diamond surfaces can be affect

Bo Li, Qiulin Wang, Jiquan Pei, Yu Yang

The semantically disentangled latent subspace in GAN provides rich interpretable controls in image generation. This paper includes two contributions on semantic latent subspace analysis in the scenario of face generation using StyleGAN2. First, we propose a novel approach to disentangle latent subspace semantics by exploiting existing face analysis models, e