June 2019 arXiv papers — page 48

Rongfang Wang, Jia-Wei Chen, Yule Wang, Licheng Jiao

The log-ratio (LR) operator has been widely employed to generate the difference image for synthetic aperture radar (SAR) image change detection. However, the difference image generated by this pixel-wise operator can be subject to SAR images speckle and unavoidable registration errors between bitemporal SAR images. In this letter, we proposed a spatial metri

Yanir A. Rubinstein

We introduce an asymptotic notion of positivity in algebraic geometry that turns out to be related to some high-dimensional convex sets. The dimension of the convex sets grows with the number of birational operations. In the case of complex surfaces we explain how to associate a linear program to certain sequences of blow-ups and how to reduce verifying the

Tomonari Mizoguchi, Hiromu Araki, Yasuhiro Hatsugai

Higher-order topological insulators have attracted considerable interests as a novel topological phase of matter, where topologically non-trivial nature of bulk protects boundary states whose co-dimension is larger than one. It has been revealed that the alternating pattern of hopping amplitudes in two-dimensional lattices provides a promising route to reali

Haonan Qiu, Chaowei Xiao, Lei Yang, Xinchen Yan

Deep neural networks (DNNs) have achieved great success in various applications due to their strong expressive power. However, recent studies have shown that DNNs are vulnerable to adversarial examples which are manipulated instances targeting to mislead DNNs to make incorrect predictions. Currently, most such adversarial examples try to guarantee "subtle pe

Alexander Moll

In this paper we describe the spectrum of the quantum periodic Benjamin-Ono equation in terms of the multi-phase solutions of the underlying classical system (the periodic multi-solitons). To do so, we show that the semi-classical quantization of this system given by Abanov-Wiegmann is exact and equivalent to the geometric quantization by Nazarov-Sklyanin. F

Zhe Ji, Lin Fu, Xiangyu Hu, Nikolaus Adams

In this paper, we propose a consistent parallel unstructured mesh generator based on a multi-phase SPH method. A set of physics-motivated modeling equations are developed to achieve the targets of domain decomposition, communication volume optimization and high-quality unstructured mesh generation simultaneously. A unified density field is defined as the tar

Igor Kondrashuk

We propose an algorithm to find a solution to an integro-differential equation of the DGLAP type for all the orders in the running coupling $\alpha$ with splitting functions given at a fixed order in $\alpha.$ Complex analysis is significantly used in the construction of the algorithm, we found a simpler way to calculate the involved integrals over contours

Heetae Kim, Mi Jin Lee, Sang Hoon Lee, Seung-Woo Son

In electric power systems delivering alternating current, it is essential to maintain its synchrony of the phase with the rated frequency. The synchronization stability that quantifies how well the power-grid system recovers its synchrony against perturbation depends on various factors. As an intrinsic factor that we can design and control, the transmission

Supanut Chaidee, Kokichi Sugihara

Given a set of radii measured from a fixed point, the existence of a convex configuration with respect to the set of distinct radii in the two-dimensional case is proved when radii are distinct or repeated at most four points. However, we proved that there always exists a convex configuration in the three-dimensional case. In the application, we can imply th

Andrei Gaponenko

Unfolding is a well-established tool in particle physics. However, a naive application of the standard regularization techniques to unfold the momentum spectrum of protons ejected in the process of negative muon nuclear capture led to a result exhibiting unphysical artifacts. A finite data sample limited the range in which unfolding can be performed, thus in

Dustin L. Arendt, Matthew Broussard, Bala Krishnamoorthy, Nathaniel Saul

We define a new filtration called the Steinhaus filtration built from a single cover based on a generalized Steinhaus distance, a generalization of Jaccard distance. The homology persistence module of a Steinhaus filtration with infinitely many cover elements may not be $q$-tame, even when the covers are in a totally bounded space. While this may pose a chal

Ruiqi Gao, Tianle Cai, Haochuan Li, Liwei Wang

Neural networks are vulnerable to adversarial examples, i.e. inputs that are imperceptibly perturbed from natural data and yet incorrectly classified by the network. Adversarial training, a heuristic form of robust optimization that alternates between minimization and maximization steps, has proven to be among the most successful methods to train networks to

Yu Nakayama

We study conformal properties of local terms such as contact terms and semi-local terms in correlation functions of a conformal field theory. Not all of them are universal observables but they do appear in physically important correlation functions such as (anomalous) Ward-Takahashi identities or Schwinger-Dynson equations. We develop some tools such as embe

Adam Bowditch, Yuki Tokushige

We prove that the speed of a $\lambda$-biased random walk on a supercritical Galton-Watson tree is differentiable for $\lambda$ such that the walk is ballistic and obeys a central limit theorem, and give an expression of the derivative using a certain $2$-dimensional Gaussian random variable. The proof heavily uses the renewal structure of Galton-Watson tree

Zhuo Chen, Jiyuan Zhang, Ruizhou Ding, Diana Marculescu

In recent years, Convolutional Neural Networks (CNNs) have shown superior capability in visual learning tasks. While accuracy-wise CNNs provide unprecedented performance, they are also known to be computationally intensive and energy demanding for modern computer systems. In this paper, we propose Virtual Pooling (ViP), a model-level approach to improve spee

A. Viitanen, V. Allevato, A. Finoguenov, A. Bongiorno

We study the spatial clustering through the projected two-point correlation function of $632$ $(1130)$ XMM-COSMOS Active Galactic Nuclei (AGNs) with known spectroscopic (spectroscopic or photometric) redshifts in the range $z = [0.1 - 2.5]$ in order to measure the AGN bias and estimate the typical mass of the hosting dark matter (DM) halo as a function of AG

Gabriel A. Oio, Luis R. Vega, Eduardo O. Schmidt, Diego Ferreiro

In order to study the slope and strength of the non-stellar continuum, we analyzed a sample of nearby Narrow Line Seyfert 1 (NLS1). Also, we re-examined the location of NLS1 galaxies on the M $-$ $σ$ relation using the stellar velocity dispersion and the [OIII]$λ$5007 emission line as surrogate of the former. We studied spectra of a sample of 131 NLS1 galaxi

Brian M. de Silva, David M. Higdon, Steven L. Brunton, J. Nathan Kutz

Machine learning (ML) and artificial intelligence (AI) algorithms are now being used to automate the discovery of physics principles and governing equations from measurement data alone. However, positing a universal physical law from data is challenging without simultaneously proposing an accompanying discrepancy model to account for the inevitable mismatch

Trajan Hammonds, Casimir Kothari, Noah Luntzlara, Steven J. Miller

Let $\tau(n)$ be Ramanujan's tau function, defined by the discriminant modular form \[ \Delta(z) = q\prod_{j=1}^{\infty}(1-q^{j})^{24}\ =\ \sum_{n=1}^{\infty}\tau(n) q^n \,,q=e^{2\pi i z} \] (this is the unique holomorphic normalized cuspidal newform of weight 12 and level 1). Lehmer's conjecture asserts that $\tau(n)\neq 0$ for all $n\geq 1$; since $\tau(n)

Han Zhao, Jianfeng Chi, Yuan Tian, Geoffrey J. Gordon

Crowdsourced data used in machine learning services might carry sensitive information about attributes that users do not want to share. Various methods have been proposed to minimize the potential information leakage of sensitive attributes while maximizing the task accuracy. However, little is known about the theory behind these methods. In light of this ga

Chen Wang, Hui Ma, Gang Chen, Sven Hartmann

Comprehensive quality-aware automated semantic web service composition is an NP-hard problem, where service composition workflows are unknown, and comprehensive quality, i.e., Quality of services (QoS) and Quality of semantic matchmaking (QoSM) are simultaneously optimized. The objective of this problem is to find a solution with optimized or near-optimized

Shihao Yan, Xiangyun Zhou, Jinsong Hu, Stephen V. Hanly

Low probability of detection (LPD) communication has recently emerged as a new transmission technology to address privacy and security in wireless networks. Recent studies have established the fundamental limits of LPD communication in terms of the amount of information bits that can be conveyed from a transmitter to a receiver subject to a constraint on a w

Jinlong Zhang, Dinghui Wang, Minji Shi, Tongshuai Zhu

The dynamical axion field is a new state of quantum matter where the magnetoelectric response couples strongly to its low-energy magnetic fluctuations. It is fundamentally different from an axion insulator with a static quantized magnetoelectric response. The dynamical axion field exhibits many exotic phenomena such as axionic polariton and axion instability

Matthias Minderer, Chen Sun, Ruben Villegas, Forrester Cole

Extracting and predicting object structure and dynamics from videos without supervision is a major challenge in machine learning. To address this challenge, we adopt a keypoint-based image representation and learn a stochastic dynamics model of the keypoints. Future frames are reconstructed from the keypoints and a reference frame. By modeling dynamics in th

Suraj Tripathi, Abhiram Ramesh, Abhay Kumar, Chirag Singh

This paper proposes a Convolutional Neural Network (CNN) inspired by Multitask Learning (MTL) and based on speech features trained under the joint supervision of softmax loss and center loss, a powerful metric learning strategy, for the recognition of emotion in speech. Speech features such as Spectrograms and Mel-frequency Cepstral Coefficient s (MFCCs) hel

Jane Chandlee, Remi Eyraud, Jeffrey Heinz, Adam Jardine

This paper examines the characterization and learning of grammars defined with enriched representational models. Model-theoretic approaches to formal language theory traditionally assume that each position in a string belongs to exactly one unary relation. We consider unconventional string models where positions can have multiple, shared properties, which ar

Jingcao Wu

Given a fibration $f$ between two projective manifolds $X$ and $Y$, we discuss the nefness of the direct images $f_{\ast}(K_{X/Y}\otimes L)$, where $(L,h)$ is a pseudo-effective line bundle with mild singularity.

Michael Khanevsky

Consider a symplectic surface $\Sigma$ with two properly embedded Hamiltonian isotopic curves $L$ and $L'$. Suppose $g \in Ham (\Sigma)$ is a Hamiltonian diffeomorphism which sends $L$ to $L'$. Which dynamical properties of $g$ can be detected by the pair $(L, L')$? We discuss two cases where one can deduce that $g$ is `chaotic': non-autonomous or even of po

Lucy Lu Wang, Gabriel Stanovsky, Luca Weihs, Oren Etzioni

A large-scale, up-to-date analysis of Computer Science literature (11.8M papers through 2019) reveals that, if trends from the last 50 years continue, parity between the number of male and female authors will not be reached in this century. In contrast, parity is projected to be reached within two to three decades or may have already been reached in other fi

Xinyu Wang, Jingxian Huang, Kewei Tu

Semantic dependency parsing aims to identify semantic relationships between words in a sentence that form a graph. In this paper, we propose a second-order semantic dependency parser, which takes into consideration not only individual dependency edges but also interactions between pairs of edges. We show that second-order parsing can be approximated using me

Qing Yang, Wei Wen, Zuoguan Wang, Hai Li

With the rapid scaling up of deep neural networks (DNNs), extensive research studies on network model compression such as weight pruning have been performed for improving deployment efficiency. This work aims to advance the compression beyond the weights to neuron activations. We propose the joint regularization technique which simultaneously regulates the d

Can Shao, Takami Tohyama, Hong-Gang Luo, Hantao Lu

We investigate the short-time evolution of the half filled one-dimensional extended Hubbard model in the strong-coupling regime, driven by a transient laser pump. Combining twisted boundary conditions with the time-dependent Lanczos technique, we obtain snapshots of the single-particle spectral function with high momentum resolution. The analysis of the osci

Yuqi Gu, Gongjun Xu

Hierarchical Latent Attribute Models (HLAMs) are a family of discrete latent variable models that are attracting increasing attention in educational, psychological, and behavioral sciences. The key ingredients of an HLAM include a binary structural matrix and a directed acyclic graph specifying hierarchical constraints on the configurations of latent attribu

Xuechen Li, Denny Wu, Lester Mackey, Murat A. Erdogdu

Sampling with Markov chain Monte Carlo methods often amounts to discretizing some continuous-time dynamics with numerical integration. In this paper, we establish the convergence rate of sampling algorithms obtained by discretizing smooth It\^o diffusions exhibiting fast Wasserstein-$2$ contraction, based on local deviation properties of the integration sche

Jelena Diakonikolas, Alejandro Carderera, Sebastian Pokutta

Conditional gradients constitute a class of projection-free first-order algorithms for smooth convex optimization. As such, they are frequently used in solving smooth convex optimization problems over polytopes, for which the computational cost of orthogonal projections would be prohibitive. However, they do not enjoy the optimal convergence rates achieved b

Samya Bagchi, Tat-Jun Chin

Star trackers are state-of-the-art attitude estimation devices which function by recognising and tracking star patterns. Most commercial star trackers use conventional optical sensors. A recent alternative is to use event sensors, which could enable more energy efficient and faster star trackers. However, this demands new algorithms that can efficiently cope

Cam Linke, Nadia M. Ady, Martha White, Thomas Degris

Learning about many things can provide numerous benefits to a reinforcement learning system. For example, learning many auxiliary value functions, in addition to optimizing the environmental reward, appears to improve both exploration and representation learning. The question we tackle in this paper is how to sculpt the stream of experience---how to adapt th

Jacob Bernstein, Lu Wang

We study a notion of relative entropy motivated by self-expanders of mean curvature flow. In particular, we obtain the existence of this quantity for arbitrary hypersurfaces trapped between two disjoint self-expanders asymptotic to the same cone. This allows us to begin to develop the variational theory for the relative entropy functional for the associated

Dan Wan, Hao Zhan

People hope automated driving technology should be always in a stable and controllable state, accurately, which can be divided into controllable planning, responsibility, and information. Otherwise, it would bring about the problems of tram dilemma, responsibility attribution, information leakage, and security. This article discusses these three types of iss

Ulrich Aïvodji, François Bidet, Sébastien Gambs, Rosin Claude Ngueveu

The widespread use of automated decision processes in many areas of our society raises serious ethical issues concerning the fairness of the process and the possible resulting discriminations. In this work, we propose a novel approach called GANsan whose objective is to prevent the possibility of any discrimination i.e., direct and indirect) based on a sensi

Yalong Cao, Martijn Kool, Sergej Monavari

Recently, Nekrasov discovered a new "genus" for Hilbert schemes of points on $\mathbb{C}^4$. We conjecture a DT/PT correspondence for Nekrasov genera for toric Calabi-Yau 4-folds. We verify our conjecture in several cases using a vertex formalism. Taking a certain limit of the equivariant parameters, we recover the cohomological DT/PT correspondence for tori

Vladimir A. Dzuba, Andrei Derevianko

Black-body radiation (BBR) shifts of $^3\!P_0-^1\!S_0$ clock transition in divalent atoms Cd and Zn are evaluated using accurate relativistic many-body techniques of atomic structure. Static polarizabilities of the clock levels and relevant electric-dipole matrix elements are computed. We also present a comparative overview of the BBR shifts in optical clock

Yanhong Wang, Reuven Gordon

We use metal nanostructures (nanoplasmonics) excited with dual frequency lasers to generate and detect high frequency (> 10 GHz) sound wave resonances in water. The difference frequency between the two lasers causes beating in the intensity, which results in a drop in the transmission through the nanostructure when an acoustic resonance is excited. By observ

Bita Sadeghinasr, Armin Akhavan, Qi Wang

The rise of location positioning technologies has generated enormous volumes of digital footprints. Translating this big data into understandable trip patterns plays a crucial role in estimating infrastructure demands. Previous studies were unable to correctly represent commuting patterns on smaller urban scales due to insufficient spatial accuracy. In this

Hanqing Liu, Na Ruan, Joseph K. Liu

The consensus protocol named proof of work (PoW) is widely applied by cryptocurrencies like Bitcoin. Although security of a PoW cryptocurrency is always the top priority, it is threatened by mining attacks like selfish mining. Researchers have proposed many mining attack models with one attacker, and optimized the attacker's strategy. During these mining

Mohamed Khalifa, Farah Magrabi, Blanca Gallego

Background: Clinical predictive tools quantify contributions of relevant patient characteristics to derive likelihood of diseases or predict clinical outcomes. When selecting a predictive tool, for implementation at clinical practice or for recommendation in clinical guidelines, clinicians are challenged with an overwhelming and ever growing number of tools,

Shuai Li, Xinjun Liu, Sanjoy Kumar Nandi, Shimul Kanti Nath

Current-controlled negative differential resistance has significant potential as a fundamental building block in brain-inspired neuromorphic computing. However, achieving desired negative differential resistance characteristics, which is crucial for practical implementation, remains challenging due to little consensus on the underlying mechanism and unclear

Xiaolin Wang, Feixiang Xiang, David Cortie, Zengji Yue

Disorder-induced magnetoresistance has been reported in a range of solid metals and semiconductors, however, the underlying physical mechanism is still under debate because it is difficult to experimentally control. Liquid metals, due to lack of long-range order, offers an ideal model system where many forms of disorder can be deactivated by freezing the liq

Anders Simonsen, Juan Diego Sanchez, Sampo Antero Saarinen, Jan Henrik Ardenkjær-Larsen

Magnetic resonance (MR) imaging relies on conventional electronics that is increasingly challenged by the push for stronger magnetic fields and higher channel count. These problems can be avoided by utilizing optical technologies. As a replacement for the standard low-noise preamplifier, we have implemented a new transduction principle that upconverts an MR

Alfonso Maiellaro, Francesco Romeo, Carmine Antonio Perroni, Vittorio Cataudella

In this work, the general problem of the characterization of the topological phase of an open quantum system is addressed. In particular, we study the topological properties of Kitaev chains and ladders under the perturbing effect of a current flux injected into the system using an external normal lead and derived from it via a superconducting electrode. Aft

Shane D. Sims, Vanessa Putnam, Cristina Conati

Encouraged by the success of deep learning in a variety of domains, we investigate the suitability and effectiveness of Recurrent Neural Networks (RNNs) in a domain where deep learning has not yet been used; namely detecting confusion from eye-tracking data. Through experiments with a dataset of user interactions with ValueChart (an interactive visualization

Paul A. Hwang

A conceptual model relating the whitecap coverage to the bubble plume buoyancy is developed following the observation that the entrained bubble plume buoyancy constitutes a large portion of the breaking wave energy dissipation. The formulation leads to estimations of an effective or equivalent-buoyancy depth of bubble entrainment as well as the volume and su

Paul A. Hwang, Ivan B. Savelyev, Steve L. Means, Magdalena D. Anguelova

Simultaneous measurements of sea spray aerosol (SSA), wind, wave, underwater acoustic noise, and microwave brightness temperature are obtained in the open ocean. These data are analyzed to clarify the ocean surface processes important to SSA production. Parameters are formulated to represent surface processes with characteristic length scales over a broad ra

Paul A. Hwang

The directional distribution of ocean surface roughness is examined using the Ku, C and L band microwave radar backscattering. The parameters characterizing the upwind-downwind and upwind-crosswind variations show nonmonotonic dependence on wind speed based on the analysis of Ku, C and L band geophysical model functions (GMFs). A similarity relationship is d

M. Selvarathi

In this paper implication-based intuitionistic fuzzy finite state machine otherwise called as Implication-based intuitionistic fuzzy semiautomaton (IB-IFSA) over a finite group is defined and investigated intensively. The abstraction of implication-based intuitionistic fuzzy kernel and implication-based intuitionistic fuzzy subsemiautomaton of an IB-IFSA ove

Absos Ali Shaikh, Pinaki Ranjan Ghosh

The present paper deals with some characterizations of rectifying and osculating curves on a smooth surface with respect to the reference frame $\{\vec{T},\ \vec{N},\ \vec{T}\times\vec{N}\}$. We have computed the components of position vectors of rectifying and osculating curves along $\vec{T},\ \vec{N},\ \vec{T}\times\vec{N}$ and then investigated their inv

Jérôme Bastien

We consider the set E of curves with positive algebraic curvature, whose extremities and tangents in their extremities are given. For each of the curves of E, we define the minimum of the radius of curvature. We first prove that there exists a curve of E which maximizes this minimum. Numerically, we observe then that this curve is equal to the unique curve o

Dmitrii N. Gabyshev, Alexander A. Fedorets, Nurken E. Aktaev, Otto Klemm

The condensational growth of spherical water microdroplets is studied in a laboratory setup and with a mathematical model. In the experiment, droplet clusters are kept in a freely levitated state within an upward-oriented flow of water vapor. In the presence of an electrostatic field of 1.5 * 10^5 V / m, droplet growth is accelerated by factors 1.5 to 2.0 as

Zhuogang Peng, Ryan G. McClarren, Martin Frank

Low-rank approximation is a technique to approximate a tensor or a matrix with a reduced rank to reduce the memory required and computational cost for simulation. Its broad applications include dimension reduction, signal processing, compression, and regression. In this work, a dynamical low-rank approximation method is developed for the time-dependent radia

Pinaki Kumar, Evangelos Korkolis, Roberto Benzi, Dmitry Denisov

Physical systems characterized by stick-slip dynamics often display avalanches. Regardless of the diversity of their microscopic structure, these systems are governed by a power-law distribution of avalanche size and duration. Here we focus on the interevent times between avalanches and show that, unlike their distributions of size and duration, the intereve

Paul A. Hwang

Recent research shows that the surface wave energy dissipation, which is the vertical energy flux across the air-sea interface, can be calculated as the product of air density, reference wind speed cubed and an energy transfer coefficient determined by the dimensionless parameters made of wind speed, significant wave height and dominant wave period. In a sim

Kunal Garg, Dimitra Panagou

This paper studies finite-time stability of a class of hybrid systems. We present sufficient conditions in terms of multiple generalized Lyapunov functions for the origin of the hybrid system to be finite-time stable. More specifically, we show that even if the value of the generalized Lyapunov functions increase between consecutive switches, finite-time sta

Tiago M. L. de Veras, Arthur F. Ramos, Ruy J. G. B. de Queiroz, Anjolina G. de Oliveira

In this work, we use a labelled deduction system based on the concept of computational paths (sequence of rewrites) as equalities between two terms of the same type. We also define a term rewriting system that is used to make computations between these computational paths, establishing equalities between equalities. We use a labelled deduction system based o

Manuel Ladra, Sherzod N. Murodov

The paper is devoted to study new classes of chains of evolution algebras and their time-depending dynamics. Moreover, we construct some Rote-Baxter operators of such algebras.

James Usevitch, Dimitra Panagou

Several algorithms in prior literature have been proposed which guarantee consensus of normally behaving agents in a network that may contain adversarially behaving agents. These algorithms guarantee that the consensus value lies within the convex hull of initial normal agents' states, with the exact consensus value possibly being unknown. In leader-foll

Alan Lukežič, Luka Čehovin Zajc, Tomáš Vojíř, Jiří Matas

A long-term visual object tracking performance evaluation methodology and a benchmark are proposed. Performance measures are designed by following a long-term tracking definition to maximize the analysis probing strength. The new measures outperform existing ones in interpretation potential and in better distinguishing between different tracking behaviors. W

Wei Song, Yan Wang, Dongmei Huang, Antonio Liotta

Underwater images often have severe quality degradation and distortion due to light absorption and scattering in the water medium. A hazed image formation model is widely used to restore the image quality. It depends on two optical parameters: the background light and the transmission map. Underwater images can also be enhanced by color and contrast correcti

Christoph Alt, Marc Hübner, Leonhard Hennig

Distantly supervised relation extraction is widely used to extract relational facts from text, but suffers from noisy labels. Current relation extraction methods try to alleviate the noise by multi-instance learning and by providing supporting linguistic and contextual information to more efficiently guide the relation classification. While achieving state-o

A. Bononi, J. -C. Antona, A. Carbo Méseguer, P. Serena

We provide a new analytical model that fully justifies the recently disclosed Generalized Droop Formula of the nonlinear signal-to-noise (SNR) ratio in very-long submarine links with power-mode amplifiers, and show its relation with the Gaussian-Noise model SNR.

K. Castillo, I. Zaballa

The purpose of this note is twofold: firstly to improve the known results on variation of extreme eigenvalues of birth and death matrices and random walk matrices; and secondly to progress towards the solution of a thirty years old open problem concerning the variation of eigenvalues of these matrices. Keywords: Birth and death matrices, random walk matrices

Victor L. Chernyak

Considered is the ${\cal N}=1$ SQCD-like theory with $SU(N_c)$ colors and $0< N_F<2N_c$ flavors of equal mass $0< m_Q\llΛ_Q$ quarks. Besides, it includes $N^2_F$ additional colorless but flavored fields $Φ_{i}^{j}$, with the large mass parameter $μ_Φ\ggΛ_Q$. The mass spectra of this $Φ$-theory are first directly calculated at $0<N_F<N_c$ where the quarks are

Antonino Morassi, Edi Rosset, Sergio Vessella

The main result of this paper is a doubling inequality at the boundary for solutions to the Kirchhoff-Love isotropic plate&#39;s equation satisfying homogeneous Dirichlet conditions. This result, like the three sphere inequality with optimal exponent at the boundary proved in Alessandrini, Rosset, Vessella, Arch. Ration. Mech. Anal. (2019), implies the Stron

L. Herrera, A. Di Prisco, J. Carot

A recently introduced concept of complexity for relativistic fluids is extended to the vacuum solutions represented by the Bondi metric. A complexity hierarchy is established, ranging from the Minkowski spacetime (the simplest one) to gravitationally radiating systems (the more complex). Particularly interesting is the possibility to differentiate between na

Joseph Bethge, Haojin Yang, Marvin Bornstein, Christoph Meinel

Binary Neural Networks (BNNs) show promising progress in reducing computational and memory costs but suffer from substantial accuracy degradation compared to their real-valued counterparts on large-scale datasets, e.g., ImageNet. Previous work mainly focused on reducing quantization errors of weights and activations, whereby a series of approximation methods

Xiaoxu Xu, Bo Zhang, Haiwen Zhang

This paper is concerned with uniqueness in inverse electromagnetic scattering with phaseless far-field pattern at a fixed frequency. In our previous work [{\em SIAM J. Appl. Math.} {\bf 78} (2018), 3024-3039], by adding a known reference ball into the acoustic scattering system, it was proved that the impenetrable obstacle and the index of refraction of an i

E. J. Kolmes, I. E. Ochs, M. E. Mlodik, J. -M. Rax

In a rotating magnetized plasma cylinder with shear, cross-field current can arise from inertial mechanisms and from the cross-field viscosity. Considering these mechanisms, it is possible to calculate the irreducible radial current draw in a cylindrical geometry as a function of the rotation frequency. The resulting expressions raise novel possibilities for

Daochen Zha, Kwei-Herng Lai, Kaixiong Zhou, Xia Hu

Experience replay enables reinforcement learning agents to memorize and reuse past experiences, just as humans replay memories for the situation at hand. Contemporary off-policy algorithms either replay past experiences uniformly or utilize a rule-based replay strategy, which may be sub-optimal. In this work, we consider learning a replay policy to optimize

Gheorghe Craciun, Abhishek Deshpande

An important dynamical property of biological interaction networks is persistence, which intuitively means that &#34;no species goes extinct&#34;. It has been conjectured that dynamical system models of weakly reversible networks (i.e., networks for which each reaction is part of a cycle) are persistent. The property of persistence is also related to the wel

Haseeb Shah, Johannes Villmow, Adrian Ulges, Ulrich Schwanecke

We present a novel extension to embedding-based knowledge graph completion models which enables them to perform open-world link prediction, i.e. to predict facts for entities unseen in training based on their textual description. Our model combines a regular link prediction model learned from a knowledge graph with word embeddings learned from a textual corp

Claudio Zito, Maxime Adjigble, Brice D. Denoun, Lorenzo Jamone

Remote manipulation is emerging as one of the key robotics tasks needed in extreme environments. Several researchers have investigated how to add AI components into shared controllers to improve their reliability. Nonetheless, the impact of novel research approaches in real-world applications can have a very slow in-take. We propose a set of benchmarks and m

Inom Mirzaev, Anthony Schulte, Michael Conover, Sam Shah

Word embedding spaces are powerful tools for capturing latent semantic relationships between terms in corpora, and have become widely popular for building state-of-the-art natural language processing algorithms. However, studies have shown that societal biases present in text corpora may be incorporated into the word embedding spaces learned from them. Thus,

Hans J. Witt, F. Atrio-Barandela

Extended source effects can be seen in gravitational lensing events when sources cross critical lines. Those events probe the stellar intensity profile and could be used to measure limb darkening coefficients to test stellar model predictions. A data base of accurately measured stellar profiles will be needed to correctly subtract the stellar flux in planeta

Marie Ernst, Gesine Reinert, Yvik Swan

In this paper we provide a probabilistic representation of Lagrange&#39;s identity which we use to obtain Papathanasiou-type variance expansions of arbitrary order. Our expansions lead to generalized sequences of weights which depend on an arbitrarily chosen sequence of (non-decreasing) test functions. The expansions hold for arbitrary univariate target dist

Maizura Mokhtar, Valentin Robu, David Flynn, Ciaran Higgins

The energy landscape for the Low-Voltage (LV) networks are beginning to change; changes resulted from the increase penetration of renewables and/or the predicted increase of electric vehicles charging at home. The previously passive `fit-and-forget&#39; approach to LV network management will be inefficient to ensure its effective operations. A more adaptive

Marie Ernst, Gesine Reinert, Yvik Swan

We propose probabilistic representations for inverse Stein operators (i.e. solutions to Stein equations) under general conditions; in particular we deduce new simple expressions for the Stein kernel. These representations allow to deduce uniform and non-uniform Stein factors (i.e. bounds on solutions to Stein equations) and lead to new covariance identities

Mohammed Laroui, Akrem Sellami, Boubakr Nour, Hassine Moungla

Vehicular Ad Hoc Network has attracted both research and industrial community due to its benefits in facilitating human life and enhancing the security and comfort. However, various issues have been faced in such networks such as information security, routing reliability, dynamic high mobility of vehicles, that influence the stability of communication. To ov

René Haberland

The processing of XML documents often includes creation and validation. These two operations are typically performed in two different nodes within a computer network that do not correlate with each other. The process of creation is also called instantiation of a template and can be described by filling a template with data from external repositories. Initial

Sudipta Hensh, Zdeněk Stuchlík

Using the gravitational decoupling by the minimal geometric deformation approach, we build an anisotropic version of the well-known Tolman VII solution, determining an exact and physically acceptable interior two-fluid solution that can represent behavior of compact objects. Comparison of the effective density and density of the perfect fluid is demonstrated

Caleb Camrud, Evan Camrud, Lee Przybylski, Eric S. Weber

The Kaczmarz algorithm is an iterative method to reconstruct an unknown vector $f$ from inner products $\langle f , φ_{n} \rangle $. We consider the problem of how additive noise affects the reconstruction under the assumption that $\{ φ_{n} \}$ form a stationary sequence. Unlike other reconstruction methods, such as frame reconstructions, the Kaczmarz recon

René Haberland, Igor L. Bratchikov

Transforming XML documents with conventional XML languages, like XSL-T, is disadvantageous because there is too lax abstraction on the target language and it is rather difficult to recognize rule-oriented transformations. Prolog as a programming language of declarative paradigm is especially good for implementation of analysis of formal languages. Prolog see

Paul Vos, Don Holbert

Frequentist inference typically is described in terms of hypothetical repeated sampling but there are advantages to an interpretation that uses a single random sample. Contemporary examples are given that indicate probabilities for random phenomena are interpreted as classical probabilities, and this interpretation is applied to statistical inference using u

Daisuke Yagi, Yining Chen, Andrew L. Johnson, Hiroshi Morita

We develop a new approach to estimate a production function based on the economic axioms of the Regular Ultra Passum law and convex non-homothetic input isoquants. Central to the development of our estimator is stating the axioms as shape constraints and using shape constrained nonparametric regression methods. We implement this approach using data from the

Michael S. Floater, Francesco Patrizi

Mean value interpolation is a method for fitting a smooth function to piecewise-linear data prescribed on the boundary of a polygon of arbitrary shape, and has applications in computer graphics and curve and surface modelling. The method generalizes to transfinite interpolation, i.e., to any continuous data on the boundary but a mathematical proof that inter

Joseph Hollowed

This document describes the LANTERN (Lightcone generAtion via sNapshoT intERpolatioN) code module, our current approach for generating lightcones from HACC [5] simulation products. In sec. 1, we derive the condition which defines all spacetime events that can be seen by an observer at any given time (we parameterize the surface of the observer&#39;s past lig

Zaili Peng, Timur Biktagirov, Franklin H. Cho, Uwe Gerstmann

Nanodiamond (ND) hosting nitrogen-vacancy (NV) centers is a promising platform for quantum sensing applications. Sensitivity of the applications using NV centers in NDs is often limited due to presence of paramagnetic impurity contents near the ND surface. Here, we investigate near-surface paramagnetic impurities in NDs. Using high-frequency (HF) electron pa

C. E. Ekuma, S. Najmaei, M. Dubey

Atomically transparent vertically aligned ZnO-based van der Waals material have been developed by surface passivation and encapsulation with atomic layers of MgO using materials by design; the physical properties investigated. The passivation and encapsulation led to a remarkable improvement in optical and electronic properties. The valence-band offset $ΔE_v

Damian M. Lyons, Saba B. Zahra, Thomas M. Marshall

Large software systems often comprise programs written in different programming languages. In the case when cross-language interoperability is accomplished with a Foreign Function Interface (FFI), for example pybind11, Boost.Python, Emscripten, PyV8, or JNI, among many others, common software engineering tools, such as call-graph analysis, are obstructed by

Giuseppe Longo, Erzsébet Merényi, Peter Tino

Astronomical observations already produce vast amounts of data through a new generation of telescopes that cannot be analyzed manually. Next-generation telescopes such as the Large Synoptic Survey Telescope and the Square Kilometer Array are planned to become operational in this decade and the next, and will increase the data volume by many orders of magnitu

Alex Dugas

We give examples of finite-dimensional algebras $A$ for which the silting objects in $K^b(\mbox{proj-}A)$ are not connected by any sequence of (possibly reducible) silting mutations. The argument is based on the fact that silting mutation preserves invariance under twisting by a fixed algebra automorphism, combined with the existence of spherical modules tha

Holger Dette, Viatcheslav B. Melas, Petr Shpilev

In a seminal paper \cite{studden1968} characterized $c$-optimal designs in regression models, where the regression functions form a Chebyshev system. He used these results to determine the optimal design for estimating the individual coefficients in a polynomial regression model on the interval $[-1,1]$ explicitly. In this note we identify the optimal design