October 2020 arXiv papers — page 121

Farzin Negahbani, Rasool Sabzi, Bita Pakniyat Jahromi, Fateme Movahedi

The nuclear protein Ki-67 and Tumor infiltrating lymphocytes (TILs) have been introduced as prognostic factors in predicting tumor progression and its treatment response. The value of the Ki-67 index and TILs in approach to heterogeneous tumors such as Breast cancer (BC), known as the most common cancer in women worldwide, has been highlighted in the literat

Farshid Asadi, Alaa A. Olleak, Jingang Yi, Yuebin Guo

Selective laser melting (SLM) is one of emerging processes for effective metal additive manufacturing. Due to complex heat exchange and material phase changes, it is challenging to accurately model the SLM dynamics and design robust control of SLM process. In this paper, we first present a data-driven Gaussian process based dynamic model for SLM process and

Henning Bahl, Nick Murphy, Heidi Rzehak

Recently, the Higgs boson masses in the Minimal Supersymmetric Standard Model (MSSM) and their mixing have been calculated using the complex Two-Higgs-DoubletModel (cTHDM) as an effective field theory (EFT) of the MSSM. Here, we discuss the implementation of this calculation, which we improve in several aspects, into the hybrid framework of FeynHiggs by comb

Sibel Sahin

In this work, the density in $H(b)$ spaces of finitely connected planar domains and the boundedness of composition operators on these function spaces are studied. Density of the algebra $\mathcal{A}(D)$ is considered for both in the cases where the defining function $b$ is an extreme and non-extreme point of the unit ball of $H^\infty(D)$. In the last part b

Ralph Foorthuis

This study explores the concept of high-density anomalies. As opposed to the traditional concept of anomalies as isolated occurrences, high-density anomalies are deviant cases positioned in the most normal regions of the data space. Such anomalies are relevant for various practical use cases, such as misbehavior detection and data quality analysis. Effective

G. G. L. Nashed, Shin'ichi Nojiri

In this article, we seek exact charged spherically symmetric black holes (BHs) with considering $f(\mathcal{R})$ gravitational theory. These BHs are characterized by convolution and error functions. Those two functions depend on a constant of integration which is responsible to make such a solution deviate from the Einstein general relativity (GR). The error

Jamie J Alnasir

The current COVID-19 global pandemic caused by the SARS-CoV-2 betacoronavirus has resulted in over a million deaths and is having a grave socio-economic impact, hence there is an urgency to find solutions to key research challenges. Much of this COVID-19 research depends on distributed computing. In this article, I review distributed architectures -- various

Pan Zhao, Yanbing Mao, Chuyuan Tao, Naira Hovakimyan

This paper presents adaptive robust quadratic program (QP) based control using control Lyapunov and barrier functions for nonlinear systems subject to time-varying and state-dependent uncertainties. An adaptive estimation law is proposed to estimate the pointwise value of the uncertainties with pre-computable estimation error bounds. The estimated uncertaint

Ellie Kitanidis, Martin White

Cross-correlations between the lensing of the cosmic microwave background (CMB) and other tracers of large-scale structure provide a unique way to reconstruct the growth of dark matter, break degeneracies between cosmology and galaxy physics, and test theories of modified gravity. We detect a cross-correlation between DESI-like luminous red galaxies (LRGs) s

Ryan Plumadore, Mohammed M. Al Ezzi, Shaffique Adam, Adina Luican-Mayer

Vertical stacking of atomically thin materials offers a large platform for realizing novel properties enabled by proximity effects and moiré patterns. Here we focus on mechanically assembled heterostructures of graphene and ReS$_2$, a van der Waals layered semiconductor. Using scanning tunneling microscopy and spectroscopy (STM/STS) we image the sharp edge b

A. D. N. James, E. I. Harris-Lee, A. Hampel, M. Aichhorn

Many-body theories such as dynamical mean field theory (DMFT) have enabled the description of the electron exchange-correlation interactions that are missing in current density functional theory (DFT) calculations. However, there has been relatively little focus on the wavefunctions from these theories. We present the methodology of the newly developed Elk-T

Jesus Arturo Jimenez Gonzalez

In the context of signed line graphs, this article introduces a modified inflation technique to study strong Gram congruence of non-negative (integral quadratic) unit forms, and uses it to show that weak and strong Gram congruence coincide among positive unit forms of Dynkin type A. The concept of inverse of a quiver is also introduced, and is used to obtain

Shaifali Parashar, Adrien Bartoli, Daniel Pizarro

Non-Rigid Structure-from-Motion (NRSfM) reconstructs a deformable 3D object from the correspondences established between monocular 2D images. Current NRSfM methods lack statistical robustness, which is the ability to cope with correspondence errors.This prevents one to use automatically established correspondences, which are prone to errors, thereby strongly

Andrea Ferrario, Michele Loi

Counterfactual explanations are a prominent example of post-hoc interpretability methods in the explainable Artificial Intelligence research domain. They provide individuals with alternative scenarios and a set of recommendations to achieve a sought-after machine learning model outcome. Recently, the literature has identified desiderata of counterfactual exp

Saar Cohen, Noa Agmon

This work concentrates on different aspects of the \textit{consensus problem}, when applying it to a swarm of flocking agents. We examine the possible influence an external agent, referred to as {\em influencing agent} has on the flock. We prove that even a single influencing agent with a \textit{Face Desired Orientation behaviour} that is injected into the

Xiao Ling, J. Paul Brooks

This work develops a sparse and outlier-insensitive method to fit a one-dimensional subspace that can be used as a replacement for eigenvector methods such as principal component analysis (PCA). The method is insensitive to outlier observations by formulating procedures as optimization problems that seek the best-fit line according to the $\ell^1$ norm. It i

Jovita Lukasik, David Friede, Arber Zela, Frank Hutter

Neural architecture search (NAS) has recently been addressed from various directions, including discrete, sampling-based methods and efficient differentiable approaches. While the former are notoriously expensive, the latter suffer from imposing strong constraints on the search space. Architecture optimization from a learned embedding space for example throu

O. I. Hryhorchak

On the base of a 1D Shrödinger equation the non-linear first-order differential equation (Ricatti type) for a quantum wave impedance function was derived. The advantages of this approach were discussed and demonstrated for a case of a single rectangular barrier. Both the scattering and the bound states problem were reformulated in terms of a quantum wave imp

Stefan Kallweit, Vasily Sotnikov, Marius Wiesemann

We present next-to-next-to-leading-order (NNLO) QCD corrections to the production of three isolated photons in hadronic collisions at the fully differential level. We employ qT subtraction within MATRIX and an efficient implementation of analytic two-loop amplitudes in the leading-colour approximation to achieve the first on-the-fly calculation for this proc

Christos Psarras, Lars Karlsson, Rasmus Bro, Paolo Bientinesi

Tensor decompositions, such as CANDECOMP/PARAFAC (CP), are widely used in a variety of applications, such as chemometrics, signal processing, and machine learning. A broadly used method for computing such decompositions relies on the Alternating Least Squares (ALS) algorithm. When the number of components is small, regardless of its implementation, ALS exhib

Darina Dvinskikh, Daniil Tiapkin

In this paper, we focus on computational aspects of the Wasserstein barycenter problem. We propose two algorithms to compute Wasserstein barycenters of $m$ discrete measures of size $n$ with accuracy $\e$. The first algorithm, based on mirror prox with a specific norm, meets the complexity of celebrated accelerated iterative Bregman projections (IBP), namely

Paulo R. C. Mendes, Eduardo S. Vieira, Álan L. V. Guedes, Antonio J. G. Busson

Discovering and accessing specific content within educational video bases is a challenging task, mainly because of the abundance of video content and its diversity. Recommender systems are often used to enhance the ability to find and select content. But, recommendation mechanisms, especially those based on textual information, exhibit some limitations, such

Brian DuSell, David Chiang

We present a differentiable stack data structure that simultaneously and tractably encodes an exponential number of stack configurations, based on Lang's algorithm for simulating nondeterministic pushdown automata. We call the combination of this data structure with a recurrent neural network (RNN) controller a Nondeterministic Stack RNN. We compare our

David Durel, Michael Urban

Due to the large neutron-neutron scattering length, dilute neutron matter resembles the unitary Fermi gas, which lies half-way in the crossover from the BCS phase of weakly coupled Cooper pairs to the Bose-Einstein condensate of dimers. We discuss crossover effects in analogy with the T-matrix theory used in the physics of ultracold atoms, which we generaliz

Kevin Lin, Sumant Guha, Joe Spaniac, Andy Zheng

While many students now interact with web apps across a variety of smart devices, the vast majority of our Nifty Assignments still present traditional user interfaces such as console input/output and desktop GUI. In this tutorial session, participants will learn to build simple web apps for programming assignments that execute student-written code to dynamic

Antonio J G Busson, Paulo R C Mendes, Daniel de S Moraes, Álvaro M da Veiga

Recent works have successfully applied some types of Convolutional Neural Networks (CNNs) to reduce the noticeable distortion resulting from the lossy JPEG/MPEG compression technique. Most of them are built upon the processing made on the spatial domain. In this work, we propose a MPEG video decoder that is purely based on the frequency-to-frequency domain:

Oren Nuriel, Sagie Benaim, Lior Wolf

Recent work has shown that convolutional neural network classifiers overly rely on texture at the expense of shape cues. We make a similar but different distinction between shape and local image cues, on the one hand, and global image statistics, on the other. Our method, called Permuted Adaptive Instance Normalization (pAdaIN), reduces the representation of

Javier Cerrillo, Santiago Oviedo Casado, Javier Prior

Conventional control strategies for NV centers in quantum sensing are based on a two-level model of their triplet ground state. However, this approach fails in regimes of weak bias magnetic fields or strong microwave pulses, as we demonstrate. To overcome this limitation, we propose a novel control sequence that exploits all three levels by addressing a hidd

Toby Pereira

This paper uses anthropic reasoning to argue for the Non-Arbitrary Existence Hypothesis (NAEH). Nick Bostrom's Self-Sampling Assumption (SSA) combined with NAEH is compared against SSA without such an assumption and also SSA with the Self-Indication Assumption (SIA). When considered in the light of various thought experiments, including the Incubator Gedanke

Hugo Frezat, Guillaume Balarac, Julien Le Sommer, Ronan Fablet

In this paper we present a new strategy to model the subgrid-scale scalar flux in a three-dimensional turbulent incompressible flow using physics-informed neural networks (NNs). When trained from direct numerical simulation (DNS) data, state-of-the-art neural networks, such as convolutional neural networks, may not preserve well known physical priors, which

Vincent Neiger, Clément Pernet

This paper describes an algorithm which computes the characteristic polynomial of a matrix over a field within the same asymptotic complexity, up to constant factors, as the multiplication of two square matrices. Previously, this was only achieved by resorting to genericity assumptions or randomization techniques, while the best known complexity bound with a

Shrimai Prabhumoye, Brendon Boldt, Ruslan Salakhutdinov, Alan W Black

Recent work in natural language processing (NLP) has focused on ethical challenges such as understanding and mitigating bias in data and algorithms; identifying objectionable content like hate speech, stereotypes and offensive language; and building frameworks for better system design and data handling practices. However, there has been little discussion abo

Takashi Horiuchi, Hidekazu Hanayama, Masatoshi Ohishi

We present the $g'$-, $R_{\rm c}$-, and $I_{\rm c}$-band magnitudes and associated colors of the Starlink's STARLINK-1113 (one of the standard Starlink satellites) and 1130 (Darksat) with a darkening treatment to its surface. By the 105 cm Murikabushi telescope/$\it{MITSuME}$, simultaneous multicolor observations for the above satellites were conduct

Thomas Dresselhaus, Callum B. A. Bungey, Peter J. Knowles, Frederick R. Manby

We derive an electron-vibration model Hamiltonian in a quantum chemical framework, and explore the extent to which such a Hamiltonian can capture key effects of nonadiabatic dynamics. The model Hamiltonian is a simple two-body operator, and we make preliminary steps at applying standard quantum chemical methods to evaluating its properties, including mean-fi

Lorenzo Tomaselli, Coty Jen, Ann B. Lee

Prescribed burns are currently the most effective method of reducing the risk of widespread wildfires, but a largely missing component in forest management is knowing which fuels one can safely burn to minimize exposure to toxic smoke. Here we show how machine learning, such as spectral clustering and manifold learning, can provide interpretable representati

Bo Li, Yezhen Wang, Shanghang Zhang, Dongsheng Li

The success of supervised learning hinges on the assumption that the training and test data come from the same underlying distribution, which is often not valid in practice due to potential distribution shift. In light of this, most existing methods for unsupervised domain adaptation focus on achieving domain-invariant representations and small source domain

Tailia Malloy, Chris R. Sims, Tim Klinger, Miao Liu

Biological agents learn and act intelligently in spite of a highly limited capacity to process and store information. Many real-world problems involve continuous control, which represents a difficult task for artificial intelligence agents. In this paper we explore the potential learning advantages a natural constraint on information flow might confer onto a

Klaus Heeger, Danny Hermelin, George B. Mertzios, Hendrik Molter

We introduce a natural but seemingly yet unstudied generalization of the problem of scheduling jobs on a single machine so as to minimize the number of tardy jobs. Our generalization lies in simultaneously considering several instances of the problem at once. In particular, we have $n$ clients over a period of $m$ days, where each client has a single job wit

Zhen Wu, Chengcan Ying, Fei Zhao, Zhifang Fan

Aspect-oriented Fine-grained Opinion Extraction (AFOE) aims at extracting aspect terms and opinion terms from review in the form of opinion pairs or additionally extracting sentiment polarity of aspect term to form opinion triplet. Because of containing several opinion factors, the complete AFOE task is usually divided into multiple subtasks and achieved in

Christopher Hahne, Amar Aggoun, Vladan Velisavljevic, Susanne Fiebig

In this paper, we demonstrate light field triangulation to determine depth distances and baselines in a plenoptic camera. Advances in micro lenses and image sensors have enabled plenoptic cameras to capture a scene from different viewpoints with sufficient spatial resolution. While object distances can be inferred from disparities in a stereo viewpoint pair

Ludovica Pannitto, Aurélie Herbelot

Recurrent Neural Networks (RNNs) have been shown to capture various aspects of syntax from raw linguistic input. In most previous experiments, however, learning happens over unrealistic corpora, which do not reflect the type and amount of data a child would be exposed to. This paper remedies this state of affairs by training a Long Short-Term Memory network

Zemer Kosloff, Terry Soo

We give elementary constructions of factors of nonsingular Bernoulli shifts. In particular, we show that all nonsingular Bernoulli shifts on a finite number of symbols which satisfy the Doeblin condition have a factor that is equivalent to an independent and identically distributed system. We also prove that there are type-III:1 Bernoulli shifts of every pos

Philipp Klaus Krause, Nicolas Lesser

We have implemented support for Padauk microcontrollers, tiny 8-Bit devices with 60 B to 256 B of RAM, in the Small Device C Compiler (SDCC), showing that the use of (mostly) standard C to program such minimal devices is feasible. We report on our experience and on the difficulties in supporting the hardware multithreading present on some of these devices. T

Haiyang S. Wang, Thierry Morel, Sascha P. Quanz, Stephen J. Mojzsis

Long-lived radioactive nuclides, such as $^{40}$K, $^{232}$Th, $^{235}$U and $^{238}$U, contribute to persistent heat production in the mantle of terrestrial-type planets. As refractory elements, the concentrations of Th and U in a terrestrial exoplanet are implicitly reflected in the photospheric abundances in the stellar host. However, a robust determinati

Alejandro Mendoza-Coto, Rómulo Cenci, Guido Pupillo, Rogelio Díaz-Méndez

We present a sufficient criterion for the emergence of cluster phases in an ensemble of interacting classical particles with repulsive two-body interactions. Through a zero-temperature analysis in the low density region we determine the relevant characteristics of the interaction potential that make the energy of a two-particle cluster-crystal become smaller

William Borrelli

In this paper we show the existence of infinitely many symmetric solutions for a cubic Dirac equation in two dimensions, which appears as effective model in systems related to honeycomb structures. Such equation is critical for the Sobolev embedding and solutions are found by variational methods. Moreover, we prove also prove smoothness and exponential decay

Michael Kretschmer, Avishai Dekel, Jonathan Freundlich, Sharon Lapiner

We provide prescriptions to evaluate the dynamical mass ($M_{\rm dyn}$) of galaxies from kinematic measurements of stars or gas using analytic considerations and the VELA suite of cosmological zoom-in simulations at $z=1-5$. We find that Jeans or hydrostatic equilibrium is approximately valid for galaxies of stellar masses above $M_\star \!\sim\! 10^{9.5}M_\

Ruben A. Hidalgo, Henry F. Hughes, Maximiliano Leyton-Alvarez

Let $d \geq 1$, $k \geq 2$ and $n\geq d+1$ be integers. A $d$-dimensional smooth complex algebraic variety $M$ is called a generalized Fermat variety of type $(d;k,n)$ if there is a Galois holomorphic branched covering $\pi:M \to {\mathbb P}^{d}$, with deck group $H\cong {\mathbb Z}_{k}^{n}$, whose branch divisor consists of $n+1$ hyperplanes in general posi

Valentina Zantedeschi, Matt J. Kusner, Vlad Niculae

We address the problem of learning binary decision trees that partition data for some downstream task. We propose to learn discrete parameters (i.e., for tree traversals and node pruning) and continuous parameters (i.e., for tree split functions and prediction functions) simultaneously using argmin differentiation. We do so by sparsely relaxing a mixed-integ

Omar Shaikh, Jiaao Chen, Jon Saad-Falcon, Duen Horng Chau

Interpreting how persuasive language influences audiences has implications across many domains like advertising, argumentation, and propaganda. Persuasion relies on more than a message's content. Arranging the order of the message itself (i.e., ordering specific rhetorical strategies) also plays an important role. To examine how strategy orderings contri

M. N. Ellingham, Linyuan Lu, Zhiyu Wang

In this paper, we study the maximum spectral radius of outerplanar $3$-uniform hypergraphs. Given a hypergraph $\mathcal{H}$, the shadow of $\mathcal{H}$ is a graph $G$ with $V(G)= V(\mathcal{H})$ and $E(G) = \{uv: uv \in h \textrm{ for some } h\in E(\mathcal{H})\}$. A graph is \textit{outerplanar} if it can be embedded in the plane such that all its vertice

Anna Laura Suarez

The theory of finitary biframes as order-theoretical duals of bitopological spaces is explored. The category of finitary biframes is a coreflective subcategory of that of biframes. Some of the advantages of adopting finitary biframes as a pointfree notion of bispaces are studied. In particular, it is shown that for every finitary biframe there is a biframe w

Pierre Auclair, Patrick Peter, Christophe Ringeval, Daniele Steer

The existence of a scaling network of current-carrying cosmic strings in our Universe is expected to continuously create loops endowed with a conserved current during the cosmological expansion. These loops radiate gravitational waves and may stabilise into centrifugally supported configurations. We show that this process generates an irreducible population

Kristina Asimi, Libor Barto

The Promise Constraint Satisfaction Problem (PCSP) is a generalization of the Constraint Satisfaction Problem (CSP) that includes approximation variants of satisfiability and graph coloring problems. Barto [LICS '19] has shown that a specific PCSP, the problem to find a valid Not-All-Equal solution to a 1-in-3-SAT instance, is not finitely tractable in that

Olguta Buse, Jun Li

We prove the stability of $Symp(X,ω)\cap Diff_0(X)$ for a one-point blow-up of irrational ruled surfaces and study their topological colimit. Non-trivial generators of $π_0[Symp(X,ω)\cap Diff_0(X)]$ that differ from Lagrangian Dehn twists are detected.

Sean Ingimarson

We introduce a new regularization model for incompressible fluid flow, which is a regularization of the EMAC formulation of the Navier-Stokes equations (NSE) that we call EMAC-Reg. The EMAC (energy, momentum, and angular momentum conserving) formulation has proved to be a useful formulation because it conserves energy, momentum and angular momentum even when

Florian Seck, Tetyana Galatyuk, Ayon Mukherjee, Ralf Rapp

The search for a first-order phase transition in strongly interacting matter is one of the major objectives in the exploration of the phase diagram of Quantum Chromodynamics (QCD). In the present work we investigate dilepton radiation from the hot and dense fireballs created in Au-Au collisions at projectile energies of 1-2 $A$GeV for potential signatures of

Gonzalo Camacho, Peter Schmitteckert, Sam T. Carr

We present results of the impurity local density of states of the interacting resonant level model at zero temperature. We concentrate on low-energy properties and predominantly use the numerical renormalisation group technique. As interaction is increased, we find that the resonance peak at zero energy disappears, while two new peaks at finite energy emerge

Anine E. Bolko, Kim Christensen, Mikko S. Pakkanen, Bezirgen Veliyev

We develop a GMM approach for estimation of log-normal stochastic volatility models driven by a fractional Brownian motion with unrestricted Hurst exponent. We show that a parameter estimator based on the integrated variance is consistent and, under stronger conditions, asymptotically normally distributed. We inspect the behavior of our procedure when integr

Alexander Katzmann, Oliver Taubmann, Stephen Ahmad, Alexander Mühlberg

Clinical decision support using deep neural networks has become a topic of steadily growing interest. While recent work has repeatedly demonstrated that deep learning offers major advantages for medical image classification over traditional methods, clinicians are often hesitant to adopt the technology because its underlying decision-making process is consid

Giovanni Russo

This paper is concerned with the problem of designing, from data, agents that are able to craft their behavior from a number of contributors in order to fulfill some agent-specific task. This is not necessarily known to the contributors. After formalizing this crowdsourcing process as a control problem, we present a result to synthesize behaviors from the in

Zhi Wang, Chunlin Chen, Daoyi Dong

Evolution strategies (ES), as a family of black-box optimization algorithms, recently emerge as a scalable alternative to reinforcement learning (RL) approaches such as Q-learning or policy gradient, and are much faster when many central processing units (CPUs) are available due to better parallelization. In this paper, we propose a systematic incremental le

Pan Zhao, Steven Snyder, Naira Hovakimyana, Chengyu Cao

In controlling systems with large operating envelopes, it is often necessary to adjust the desired dynamics according to operating conditions. This paper presents a robust adaptive control architecture for linear parameter-varying (LPV) systems that allows for the desired dynamics to be systematically scheduled, while being able to handle a broad class of un

Alex Trevithick, Bo Yang

We present a simple yet powerful neural network that implicitly represents and renders 3D objects and scenes only from 2D observations. The network models 3D geometries as a general radiance field, which takes a set of 2D images with camera poses and intrinsics as input, constructs an internal representation for each point of the 3D space, and then renders t

Joshua Robinson, Ching-Yao Chuang, Suvrit Sra, Stefanie Jegelka

How can you sample good negative examples for contrastive learning? We argue that, as with metric learning, contrastive learning of representations benefits from hard negative samples (i.e., points that are difficult to distinguish from an anchor point). The key challenge toward using hard negatives is that contrastive methods must remain unsupervised, makin

Georg Frenck, Jens Reinhold

We construct smooth bundles with base and fiber products of two spheres whose total spaces have non-vanishing $\hat{A}$-genus. We then use these bundles to locate non-trivial rational homotopy groups of spaces of Riemannian metrics with lower curvature bounds for all Spin-manifolds of dimension six or at least ten which admit such a metric and are a connecte

Patryk Lipka-Bartosik, Andrés Ducuara, Tom Purves, Paul Skrzypczyk

Although entanglement is necessary for observing nonlocality in a Bell experiment, there are entangled states which can never be used to demonstrate nonlocal correlations. In a seminal paper [PRL 108, 200401 (2012)] F. Buscemi extended the standard Bell experiment by allowing Alice and Bob to be asked quantum, instead of classical, questions. This gives rise

Hamidreza Bakhshzad Mahmoodi, Jarkko Kaleva, Seyed Pooya Shariatpanahi, Antti Tolli

A device-to-device (D2D) aided multi-antenna coded caching scheme is proposed to improve the average delivery rate and reduce the downlink (DL) beamforming complexity.} Novel beamforming and resource allocation schemes are proposed where local data exchange among nearby users is exploited. The transmission is split into two phases: local D2D content exchange

David Higgins

Medical Artificial Intelligence (AI) involves the application of machine learning algorithms to biomedical datasets in order to improve medical practices. Products incorporating medical AI require certification before deployment in most jurisdictions. To date, clear pathways for regulating medical AI are still under development. Below the level of formal pat

Ilya Bogdanov

We study the most elementary model of electron motion introduced by R.Feynman in 1965. It is a game, in which a checker moves on a checkerboard by simple rules, and we count the turnings. The model is also known as one-dimensional quantum walk. In his publication, R.Feynman introduces a discrete version of path integral and poses the problem of computing the

M. K. Volkov, A. B. Arbuzov, A. A. Pivovarov

The processes $τ^- \to π^- π^0 ν_τ$ and $e^{+}e^{-} \to π^{+} π^{-}$ are considered within the chiral Nambu--Jona-Lasinio model with taking into account the pion final state interactions beyond the leading $1/N_c$ approximation. The contribution of the loop correction caused by a $ρ$ meson exchange between the pions, which gives the main contribution in the

Kevin Schultz, Gregory Quiroz, Paraj Titum, B. D. Clader

Temporal noise correlations are ubiquitous in quantum systems, yet often neglected in the analysis of quantum circuits due to the complexity required to accurately characterize and model them. Autoregressive moving average (ARMA) models are a well-known technique from time series analysis that model time correlations in data. By identifying the space of comp

Hardik Bohra, David Dudal, Ali Hajilou, Subhash Mahapatra

We refine an earlier introduced 5-dimensional gravity solution capable of holographically capturing several qualitative aspects of (lattice) QCD in a strong magnetic background such as the anisotropic behaviour of the string tension, inverse catalysis at the level of the deconfinement transition or sensitivity of the entanglement entropy to the latter. Here,

Luiz M. Faria, Carlos Pérez-Arancibia, Marc Bonnet

This paper presents a general high-order kernel regularization technique applicable to all four integral operators of Calderón calculus associated with linear elliptic PDEs in two and three spatial dimensions. Like previous density interpolation methods, the proposed technique relies on interpolating the density function around the kernel singularity in term

Yue Xiu, Jun Zhao, Zhongpei Zhang

In this letter, we investigate the secrecy rate of an reconfigurable intelligent surface (RIS)-aided millimeter-wave (mmWave) system with hardware limitations. Compared to the RIS-aided systems in most existing works, we consider the case of the RIS-aided mmWave system with low-resolution digital-to-analog converters (LDACs). We formulate a secrecy rate maxi

Masato Shirasaki, Naonori S. Sugiyama, Ryuichi Takahashi, Francisco-Shu Kitaura

Galaxy bispectrum is a promising probe of inflationary physics in the early universe as a measure of primordial non-Gaussianity (PNG), whereas its signal-to-noise ratio is significantly affected by the mode coupling due to non-linear gravitational growth. In this paper, we examine the standard reconstruction method of linear cosmic mass density fields from n

XiaoHuang Huang

In this paper, we study the unicity of entire functions concerning their $q-$shifts and $k-$th derivatives and prove: Let $f(z)$ be a transcendental entire function of zero-order, and $g(z)$ define as in (1.1). Let $a(z), b(z)$ be two distinct small functions of $f(z)$. If $f(z)$ and $g(z)$ share $a(z), b(z)$ IM, then $f(z)\equiv g(z)$.

Christophe M. F. Hugon, Vladimir Kulikovskiy

Using the model in which the vacuum is filled with virtual fermion pairs we propose an effective description of photon propagation compatible with the wave-particle duality and the quantum field theory. In this model the origin of the vacuum permittivity and permeability appear naturally in the statistical description of the gas of the virtual pairs. Assumin

Yassine Himeur, Khalida Ghanem, Abdullah Alsalemi, Faycal Bensaali

Enormous amounts of data are being produced everyday by sub-meters and smart sensors installed in residential buildings. If leveraged properly, that data could assist end-users, energy producers and utility companies in detecting anomalous power consumption and understanding the causes of each anomaly. Therefore, anomaly detection could stop a minor problem

Anna Bonnet, Claire Lacour, Franck Picard, Vincent Rivoirard

We focus on the estimation of the intensity of a Poisson process in the presence of a uniform noise. We propose a kernel-based procedure fully calibrated in theory and practice. We show that our adaptive estimator is optimal from the oracle and minimax points of view, and provide new lower bounds when the intensity belongs to a Sobolev ball. By developing th

Giovanni Morrone, Daniel Michelsanti, Zheng-Hua Tan, Jesper Jensen

In this paper, we present a deep-learning-based framework for audio-visual speech inpainting, i.e., the task of restoring the missing parts of an acoustic speech signal from reliable audio context and uncorrupted visual information. Recent work focuses solely on audio-only methods and generally aims at inpainting music signals, which show highly different st

Tomasz Komorowski, Stefano Olla

We investigate how a thermal boundary, modelled by a Langevin dynamics, affect the macroscopic evolution of the energy at different space-time scales.

Julien Garaud, Jin Dai, Antti J. Niemi

Vortices in a Bose-Einstein condensate are modelled as spontaneously symmetry breaking minimum energy solutions of the time dependent Gross-Pitaevskii equation, using the method of constrained optimization. In a non-rotating axially symmetric trap, the core of a single vortex precesses around the trap center and, at the same time, the phase of its wave funct

Han Zhang

Let $G$ be a Lie group and $Γ$ be a lattice in $G$. We introduce the notion of locally unipotent invariant measures on $G/Γ$. We then prove that under some conditions, the limit measure supported on the image of polynomial trajectories on $G/Γ$ is locally unipotent invariant, thus give a partial answer to an equidistribution problem for higher dimensional po

Pavel Shvartsman

Let ${\mathfrak M}=({\mathcal M},ρ)$ be a metric space and let $X$ be a Banach space. Let $F$ be a set-valued mapping from ${\mathcal M}$ into the family ${\mathcal K}_m(X)$ of all compact convex subsets of $X$ of dimension at most $m$. The main result in our recent joint paper with Charles Fefferman (which is referred to as a "Finiteness Principle for L

Ryan Henderson, Djork-Arné Clevert, Floriane Montanari

Due to the nature of deep learning approaches, it is inherently difficult to understand which aspects of a molecular graph drive the predictions of the network. As a mitigation strategy, we constrain certain weights in a multi-task graph convolutional neural network according to the Gini index to maximize the "inequality" of the learned representatio

Andrea Nava, Marco Rossi, Domenico Giuliano

Using the Lindblad equation approach, we derive the range of the parameters of an interacting one-dimensional electronic chain connected to two reservoirs in the large bias limit in which an optimal working point (corresponding to a change in the monotonicity of the stationary current as a function of the applied bias) emerges in the nonequilibrium stationar

Ralf Kundel, Amr Rizk, Jeremias Blendin, Boris Koldehofe

Fixed buffer sizing in computer networks, especially the Internet, is a compromise between latency and bandwidth. A decision in favor of high bandwidth, implying larger buffers, subordinates the latency as a consequence of constantly filled buffers. This phenomenon is called Bufferbloat. Active Queue Management (AQM) algorithms such as CoDel or PIE, designed

Philipp Klaus Krause

We present implementations of constant-time algorithms for connectivity tests and related problems. Some are implementations of slightly improved variants of previously known algorithms; for other problems we present new algorithms that have substantially better runtime than previously known algorithms (estimates of the distance to and tolerant testers for c

Zhizheng Zhang, Cuiling Lan, Wenjun Zeng, Zhibo Chen

Few-shot image classification learns to recognize new categories from limited labelled data. Metric learning based approaches have been widely investigated, where a query sample is classified by finding the nearest prototype from the support set based on their feature similarities. A neural network has different uncertainties on its calculated similarities o

Varun Ojha, Giuseppe Nicosia

We propose an algorithm and a new method to tackle the classification problems. We propose a multi-output neural tree (MONT) algorithm, which is an evolutionary learning algorithm trained by the non-dominated sorting genetic algorithm (NSGA)-III. Since evolutionary learning is stochastic, a hypothesis found in the form of MONT is unique for each run of evolu

Karel Devriendt

This article discusses a geometric perspective on the well-known fact in graph theory that the effective resistance is a metric on the nodes of a graph. The classical proofs of this fact make use of ideas from electrical circuits or random walks; here we describe an alternative approach which combines geometric (using simplices) and algebraic (using the Schu

Bastian Harrach, Tim Jahn, Roland Potthast

We deal with the solution of a generic linear inverse problem in the Hilbert space setting. The exact right hand side is unknown and only accessible through discretised measurements corrupted by white noise with unknown arbitrary distribution. The measuring process can be repeated, which allows to reduce and estimate the measurement error through averaging.

Ryota Hanaoka, Norio Konno

The quantum walk is a counterpart of the random walk. The 2-state quantum walk in one dimension can be determined by a measure on the unit circle in the complex plane. As for the singular continuous measure, results on the corresponding quantum walk are limited. In this situation, we focus on a quantum walk, called the Riesz walk, given by the Riesz measure

Mohammad Hasan Yeganegi, Majid Khadiv, Andrea Del Prete, S. Ali A. Moosavian

Model predictive control (MPC) has shown great success for controlling complex systems such as legged robots. However, when closing the loop, the performance and feasibility of the finite horizon optimal control problem (OCP) solved at each control cycle is not guaranteed anymore. This is due to model discrepancies, the effect of low-level controllers, uncer

Teresa Gomez-Diaz, Tomas Recio

It is widely recognised nowadays that there is no single, accepted, unified definition of Open Science, which motivates our proposal of an Open Science definition as a political and legal framework where research outputs are shared and disseminated in order to be rendered visible, accessible, reusable is developed, standing over the concepts enhanced by the

Yu Wan, Baosong Yang, Derek F. Wong, Yikai Zhou

Recent studies have proven that the training of neural machine translation (NMT) can be facilitated by mimicking the learning process of humans. Nevertheless, achievements of such kind of curriculum learning rely on the quality of artificial schedule drawn up with the handcrafted features, e.g. sentence length or word rarity. We ameliorate this procedure wit

Muqian Wen

This paper adds birefringence consideration to the conventional classical Raman scattering theory commonly found in literature to address birefringence and interference effects in Raman spectroscopy. It is hoped that the new results can be useful for ab initio calculations as well as experimental studies.

Rezvan Jalali, Ahmad Shirzad

We study the constraint structure of Fierz -Pauli action in both flat and curved space in the framework of Hamiltonian formalism. We observe an abrupt change in the constraint algebra and the characteristics of the constraints when the mass term is turned off. As is well-known, for de Sitter background with a special tuning of the mass, we will have a gauge

S. M. Koksbang

The redshift and redshift-distance relation in different Einstein-Straus models are considered. Specifically, the mean of these observables along 1000 light rays in different specific models are compared with predictions based on the Dyer-Roeder approximation and relations based on spatial averaging. It is shown that in certain limits, including those studie