June 2020 arXiv papers — page 34

Alejandro Ibarra, Patrick Strobl, Takashi Toma

We calculate the two-loop beta functions of the right-handed neutrino mass matrix in the Standard Model extended with right-handed neutrinos. We show that two-loop quantum effects induced by the heavier right-handed neutrinos can induce sizable contributions (sometimes dominant) to the physical masses of the lighter right-handed neutrinos. These effects can

Jiajia Zhou, Masao Doi

We study the dynamics of capillary rising in corners. Using Onsager principle, we derive a partial differential equation that describes the time evolution of meniscus profile. We obtain both numerical solutions and self-similar solutions to this partial differential equation. Our results show that the advance of the meniscus front follows a time-scaling of $

Hideo Bannai, Takuya Mieno, Yuto Nakashima

We revisit the so-called "Three Squares Lemma" by Crochemore and Rytter [Algorithmica 1995] and, using arguments based on Lyndon words, derive a more general variant which considers three overlapping squares which do not necessarily share a common prefix. We also give an improved upper bound of $n\log_2 n$ on the maximum number of (occurrences of) pr

Christian Kassel

We show that the Steinberg group $\text{St}(C_2,{\mathbb Z})$ associated with the Lie type $C_2$ and with integer coefficients can be realized as a quotient of the braid group $B_6$ by one relation. As an application we give a new braid-like presentation of the symplectic modular group $\text{Sp}_4({\mathbb Z})$.

Sergio Albeverio, Toshinao Kagawa, Yumi Yahagi, Minoru W. Yoshida

General theorems on the closability and quasi-regularity of non-local Markovian symmetric forms on probability spaces $(S, {\cal B}(S), μ)$, with $S$ Fr{é}chet spaces such that $S \subset {\mathbb R}^{\mathbb N}$, ${\cal B}(S)$ is the Borel $σ$-field of $S$, and $μ$ is a Borel probability measure on $S$, are introduced. Firstly, a family of non-local Markovi

Florian Wenzel, Jasper Snoek, Dustin Tran, Rodolphe Jenatton

Ensembles over neural network weights trained from different random initialization, known as deep ensembles, achieve state-of-the-art accuracy and calibration. The recently introduced batch ensembles provide a drop-in replacement that is more parameter efficient. In this paper, we design ensembles not only over weights, but over hyperparameters to improve th

Michał J. Tyszkiewicz, Pascal Fua, Eduard Trulls

Local feature frameworks are difficult to learn in an end-to-end fashion, due to the discreteness inherent to the selection and matching of sparse keypoints. We introduce DISK (DIScrete Keypoints), a novel method that overcomes these obstacles by leveraging principles from Reinforcement Learning (RL), optimizing end-to-end for a high number of correct featur

Shoutao Guo, Li Liang

In this paper, we first introduce stable functors with respect to a preenveloping/precovering subcategory and investigate some of their properties. Using that we then introduce and study a relative complete cohomology theory in abelian categories. Some properties of the cohomology including vanishing are given. As applications, we give some characterizations

Ren Yang, Fabian Mentzer, Luc Van Gool, Radu Timofte

The past few years have witnessed increasing interests in applying deep learning to video compression. However, the existing approaches compress a video frame with only a few number of reference frames, which limits their ability to fully exploit the temporal correlation among video frames. To overcome this shortcoming, this paper proposes a Recurrent Learne

Pierre Fromholz, Philippe Lecheminant

The nature of symmetry-protected topological phases of Heisenberg spin chains in totally symmetric representations of rank N of the SU(N) group is investigated through a Majorana fermion study starting from an integrable point. The latter approach generalizes the one pioneered by Tsvelik [A. M. Tsvelik, Phys. Rev. B 42, 10 499 (1990)] to describe the low-ene

Oliver Gregory, Andreas Langer

For a smooth and proper scheme over an artinian local ring with ordinary reduction over the perfect residue field we prove - under some general assumptions - that the relative de Rham-Witt spectral sequence degenerates and the relative crystalline cohomology, equipped with its display structure arising from the Nygaard complexes, has a Hodge-Witt decompositi

Yaqing Chen, Hans-Georg Müller

Local Fréchet regression is a nonparametric regression method for metric space valued responses and Euclidean predictors, which can be utilized to obtain estimates of smooth trajectories taking values in general metric spaces from noisy metric space valued random objects. We derive uniform rates of convergence, which so far have eluded theoretical analysis o

Stefan Heinrich, Yuan Yao, Tobias Hinz, Zhiyuan Liu

Human infants are able to acquire natural language seemingly easily at an early age. Their language learning seems to occur simultaneously with learning other cognitive functions as well as with playful interactions with the environment and caregivers. From a neuroscientific perspective, natural language is embodied, grounded in most, if not all, sensory and

Yang Zhang, Moyun Liu, Jingwu He, Fei Pan

This paper proposes an affinity fusion graph framework to effectively connect different graphs with highly discriminating power and nonlinearity for natural image segmentation. The proposed framework combines adjacency-graphs and kernel spectral clustering based graphs (KSC-graphs) according to a new definition named affinity nodes of multi-scale superpixels

Mitsumasa Nakajima, Kenji Tanaka, Toshikazu Hashimoto

We show a new family of neural networks based on the Schrödinger equation (SE-NET). In this analogy, the trainable weights of the neural networks correspond to the physical quantities of the Schrödinger equation. These physical quantities can be trained using the complex-valued adjoint method. Since the propagation of the SE-NET can be described by the evolu

Dan Ciubotaru

We define an involution on the space of elliptic unipotent Langlands parameters of a reductive $p$-adic group $G$ and verify that when $G$ is split adjoint exceptional, the composition of this involution with the hyperspecial parahoric restriction map agrees with Lusztig's nonabelian Fourier transform for unipotent representations of the finite reductive

Eduardo Abi Jaber, Enzo Miller, Huyên Pham

This paper concerns portfolio selection with multiple assets under rough covariance matrix. We investigate the continuous-time Markowitz mean-variance problem for a multivariate class of affine and quadratic Volterra models. In this incomplete non-Markovian and non-semimartingale market framework with unbounded random coefficients, the optimal portfolio stra

A. N. Ponomaryov, L. Zviagina, J. Wosnitza, P. Lampen-Kelley

We present comprehensive electron spin resonance (ESR) studies of in-plane oriented single crystals of $α$-RuCl$_3$, a quasi-two-dimensional material with honeycomb structure, focusing on its high-field spin dynamics. The measurements were performed in magnetic fields up to 16 T, applied along the [110] and [100] directions. Several ESR modes were detected.

STAR Collaboration, J. Adam, L. Adamczyk, J. R. Adams

Flow harmonics ($\textit{v}_{n}$) of the Fourier expansion for the azimuthal distributions of hadrons are commonly employed to quantify the azimuthal anisotropy of particle production relative to the collision symmetry planes. While lower order Fourier coefficients ($\textit{v}_{2}$ and $\textit{v}_{3}$) are more directly related to the corresponding eccentr

B. Sharmila, S. Lakshmibala, V. Balakrishnan

Extensive theoretical and experimental investigations on multipartite systems close to an avoided energy-level crossing reveal interesting features such as the extremisation of entanglement. Conventionally, the estimation of entanglement directly from experimental observation involves either one of two approaches: Uncertainty-relation-based estimation that c

Mijin Yoon, Wonki Lee, M. James Jee, Kyle Finner

A521 has been a subject of extensive panchromatic studies from X-ray to radio. The cluster possesses a number of remarkable features including a bright radio relic with a steep spectrum, more than three distinct galaxy groups forming a filament, and two disturbed X-ray peaks at odds with the distant position and tilted orientation of the radio relic. These s

Alain Rakotomamonjy, Rémi Flamary, Gilles Gasso, Joseph Salmon

Owing to their statistical properties, non-convex sparse regularizers have attracted much interest for estimating a sparse linear model from high dimensional data. Given that the solution is sparse, for accelerating convergence, a working set strategy addresses the optimization problem through an iterative algorithm by incre-menting the number of variables t

Thomas Dupic, Meriem Bensouda Koraichi, Anastasia Minervina, Mikhail Pogorelyy

Immune repertoires provide a unique fingerprint reflecting the immune history of individuals, with potential applications in precision medicine. However, the question of how personal that information is and how it can be used to identify individuals has not been explored. Here, we show that individuals can be uniquely identified from repertoires of just a fe

Oliver Kästle, Ying Hu, Alexander Carmele

We investigate the dynamical interplay between topological state of matter and a non-Markovian dissipation, which gives rise to a new and crucial time scale into the system dynamics due to its quantum memory. We specifically study a one-dimensional polaronic topological superconductor with phonon-dressed $p$-wave pairing, when a fast temperature increase in

Xinhan Di, Pengqian Yu, Hong Zhu, Lei Cai

In this paper, we propose an adversarial model for producing furniture layout for interior scene synthesis when the interior room is rotated. The proposed model combines a conditional adversarial network, a rotation module, a mode module, and a rotation discriminator module. As compared with the prior work on scene synthesis, our proposed three modules enhan

Bongjune Kim, Ko-Tang Chen, Shih-Si Hsiao, Sheng-Yang Wang

We proposed utilizing a medium with a high optical depth (OD) and a Rydberg state of low principal quantum number, $n$, to create a weakly-interacting many-body system of Rydberg polaritons, based on the effect of electromagnetically induced transparency (EIT). We experimentally verified the mean field approach to weakly-interacting Rydberg polaritons, and o

William L. Matthewson, Ruth Durrer

We derive and test an approximation for the angular power spectrum of galaxy number counts in the flat sky limit. The standard density and redshift space distortion (RSD) terms in the resulting approximation are distinct to the Limber approximation, providing an accurate result for multipoles as low as $\ell\simeq10$, where the corresponding Limber approxima

Monica Pragliola, Daniela Calvetti, Erkki Somersalo

A common task in inverse problems and imaging is finding a solution that is sparse, in the sense that most of its components vanish. In the framework of compressed sensing, general results guaranteeing exact recovery have been proven. In practice, sparse solutions are often computed combining $\ell_1$-penalized least squares optimization with an appropriate

Lionel Darondeau, Erwan Rousseau

In this work, we investigate the positivity of logarithmic and orbifold cotangent bundles along hyperplane arrangements in projective spaces. We show that a very interesting example given by Noguchi (as early as in 1986) can be pushed further to a very great extent. Key ingredients of our approach are the use of Fermat covers and the production of explicit g

Ambalika Biswas, Anirban Kundu, Poulami Mondal

Without any mechanism to protect its mass, the self-energy of the Higgs boson diverges quadratically, leading to the hierarchy or fine-tuning problem. One bottom-up solution is to postulate some yet-to-be-discovered symmetry which forces the sum of the quadratic divergences to be zero, or almost negligible; this is known as the Veltman condition. Even if one

Vinay Uday Prabhu, Abeba Birhane

In this paper we investigate problematic practices and consequences of large scale vision datasets. We examine broad issues such as the question of consent and justice as well as specific concerns such as the inclusion of verifiably pornographic images in datasets. Taking the ImageNet-ILSVRC-2012 dataset as an example, we perform a cross-sectional model-base

Roi Livni, Shay Moran

PAC-Bayes is a useful framework for deriving generalization bounds which was introduced by McAllester ('98). This framework has the flexibility of deriving distribution- and algorithm-dependent bounds, which are often tighter than VC-related uniform convergence bounds. In this manuscript we present a limitation for the PAC-Bayes framework. We demonstrate

Sankarsan Tarai, Fakhereh Md. Esmaeili, B. Mishra, S. K. Tripathy

In this paper, we have derived the field equations in an extended theory of gravity in an anisotropic space time background and in the presence of magnetic field. The physical and geometrical parameters of the models are determined with respect to the Hubble parameter using some algebraic approaches. A time varying scale factor has been introduced to analyze

Ziwei Guan, Tengyu Xu, Yingbin Liang

Generative adversarial imitation learning (GAIL) is a popular inverse reinforcement learning approach for jointly optimizing policy and reward from expert trajectories. A primary question about GAIL is whether applying a certain policy gradient algorithm to GAIL attains a global minimizer (i.e., yields the expert policy), for which existing understanding is

Kanghong Shi, Igor G. Vladimirov, Ian R. Petersen

This paper provides a control protocol for the robust output feedback consensus of networked heterogeneous nonlinear negative-imaginary (NI) systems. Heterogeneous nonlinear output strictly negative-imaginary (OSNI) controllers are applied in positive feedback according to the network topology to achieve output feedback consensus. The main contribution of th

Yingxue Zhou, Xiangyi Chen, Mingyi Hong, Zhiwei Steven Wu

We study differentially private (DP) algorithms for stochastic non-convex optimization. In this problem, the goal is to minimize the population loss over a $p$-dimensional space given $n$ i.i.d. samples drawn from a distribution. We improve upon the population gradient bound of ${\sqrt{p}}/{\sqrt{n}}$ from prior work and obtain a sharper rate of $\sqrt[4]{p}

Kazuki Aoki, Takahisa Inui, Hayato Miyazaki, Haruya Mizutani

We consider the cubic nonlinear Schrödinger equation on the star graph with the Kirchhoff boundary condition. We prove modified scattering for the final state problem and the initial value problem. Moreover, we also consider the failure of scattering for the Schrödinger equation with power-type long-range nonlinearities. These results are extension of the re

Haozhe Zhang, Yehua Li

We consider spatially dependent functional data collected under a geostatistics setting, where locations are sampled from a spatial point process. The functional response is the sum of a spatially dependent functional effect and a spatially independent functional nugget effect. Observations on each function are made on discrete time points and contaminated w

J. S. Bobowski

We describe a simple method to experimentally determine the frequency dependencies of the per-unit-length resistance and conductance of transmission lines. The experiment is intended as a supplement to the classic measurement of the transient response of a transmission line to a voltage step or pulse. In the transient experiment, an ideal (lossless) model of

Dharma Teja Vooturi, Girish Varma, Kishore Kothapalli

Sparse neural networks are shown to give accurate predictions competitive to denser versions, while also minimizing the number of arithmetic operations performed. However current hardware like GPU's can only exploit structured sparsity patterns for better efficiency. Hence the run time of a sparse neural network may not correspond to the arithmetic opera

Shuai Zheng, Haibin Lin, Sheng Zha, Mu Li

BERT has recently attracted a lot of attention in natural language understanding (NLU) and achieved state-of-the-art results in various NLU tasks. However, its success requires large deep neural networks and huge amount of data, which result in long training time and impede development progress. Using stochastic gradient methods with large mini-batch has bee

Min-xin Huang, Yuji Sugimoto, Xin Wang

We study a class of quantum integrable systems derived from dimer graphs and also described by local toric Calabi-Yau geometries with higher genus mirror curves, generalizing some previous works on genus one mirror curves. We compute the spectra of the quantum systems both by standard perturbation method and by Bohr-Sommerfeld method with quantum periods as

Masato Fujita

We consider locally o-minimal structures possessing tame topological properties shared by models of DCTC and uniformly locally o-minimal expansions of the second kind of densely linearly ordered abelian groups. We derive basic properties of dimension of a set definable in the structures including the addition property, which is the dimension equality for def

Susana Frómeta, Ricardo Misturini, Adriana Neumann

We study the asymptotic behaviour of the symmetric zero-range process in the finite lattice $\{1,\ldots, N-1\}$ with slow boundary, in which particles are created at site $1$ or annihilated at site $N\!-\!1$ with a rate proportional to $N^{-θ}$, for $θ\geq 1$. We present the invariant measure for this model and obtain the hydrostatic limit. In order to under

Jia-Yong Wu

On gradient shrinking Ricci solitons, we observe that the study of Schrödinger heat kernel seems to be more natural than the classical heat kernel. In this paper we derive sharp Gaussian upper bounds for the Schrödinger heat kernel on complete gradient shrinking Ricci solitons. As applications, we prove sharp upper bounds for the Green's function of the

Sergio Verduzco-Flores, William Dorrell, Erik DeSchutter

In this paper we explore a neural control architecture that is both biologically plausible, and capable of fully autonomous learning. It consists of feedback controllers that learn to achieve a desired state by selecting the errors that should drive them. This selection happens through a family of differential Hebbian learning rules that, through interaction

G. Li, R. K. Jaiman, B. C. Khoo

In this study, we present a global Fourier mode decomposition framework for unsteady fluid-structure interaction. We apply the framework to isolate and extract the aeroelastic modes arising from a coupled three-dimensional fluid-membrane system. The proposed framework is employed to decompose the physical variables in the fluid and structural domains into fr

Shengding Hu, Zheng Xiong, Meng Qu, Xingdi Yuan

Graph neural networks (GNNs) have been attracting increasing popularity due to their simplicity and effectiveness in a variety of fields. However, a large number of labeled data is generally required to train these networks, which could be very expensive to obtain in some domains. In this paper, we study active learning for GNNs, i.e., how to efficiently lab

Xinyue Huo, Lingxi Xie, Jianzhong He, Zijie Yang

In medical image analysis, semi-supervised learning is an effective method to extract knowledge from a small amount of labeled data and a large amount of unlabeled data. This paper focuses on a popular pipeline known as self learning, and points out a weakness named lazy learning that refers to the difficulty for a model to learn from the pseudo labels gener

Wei Luo, Hengmin Zhang, Jun Li, Xiu-Shen Wei

We aim to provide a computationally cheap yet effective approach for fine-grained image classification (FGIC) in this letter. Unlike previous methods that rely on complex part localization modules, our approach learns fine-grained features by enhancing the semantics of sub-features of a global feature. Specifically, we first achieve the sub-feature semantic

Yuta Shikuri

Gaussian process regression can flexibly represent the posterior distribution of an interest parameter given sufficient information on the likelihood. However, in some cases, we have little knowledge regarding the probability model. For example, when investing in a financial instrument, the probability model of cash flow is generally unknown. In this paper,

Gail E. Potter, Nicole Bohme Carnegie, Jonathan D. Sugimoto, Aldiouma Diallo

This study estimates the overall effect of two influenza vaccination programs consecutively administered in a cluster-randomized trial in western Senegal over the course of two influenza seasons from 2009-2011. We apply cutting-edge methodology combining social contact data with infection data to reduce bias in estimation arising from contamination between c

Shuai Zhao, Pei Zeng, Wen-Fei Cao, Xin-Yu Xu

Quantum cryptographic conferencing (QCC) holds promise for distributing information-theoretic secure keys among multiple users over long distance. Limited by the fragility of Greenberger-Horne-Zeilinger (GHZ) state, QCC networks based on directly distributing GHZ states at long distance still face big challenge. Another two potential approaches are measureme

Yi-Wei Liu, Hao Chen

Change-point analysis is thriving in this big data era to address problems arising in many fields where massive data sequences are collected to study complicated phenomena over time. It plays an important role in processing these data by segmenting a long sequence into homogeneous parts for follow-up studies. The task requires the method to be able to proces

William Craig, Anna Pun

Partitions, the partition function $p(n)$, and the hook lengths of their Ferrers-Young diagrams are important objects in combinatorics, number theory and representation theory. For positive integers $n$ and $t$, we study $p_t^e(n)$ (resp. $p_t^o(n)$), the number of partitions of $n$ with an even (resp. odd) number of $t$-hooks. We study the limiting behavior

Hans Kuzmany, Lei Shi, Miles Martinati, Sofie Cambré

Graphene nanoribbons with sub-nanometer widths are extremely interesting for nanoscale electronics and devices as they combine the unusual transport properties of graphene with the opening of a band gap due to quantum confinement in the lateral dimension. Strong research efforts are presently paid to grow such nanoribbons. Here we show the synthesis of 6- an

Jinyang Li, Gregório R. M. da Silva, Wayne C. Huang, Mohamed Fouda

A point source interferometer (PSI) is a device where atoms are split and recombined by applying a temporal sequence of Raman pulses during the expansion of a cloud of cold atoms behaving approximately as a point source. The PSI can work as a sensitive multi-axes gyroscope that can automatically filter out the signal from accelerations. The phase shift arisi

Sanguo Zhu, Youming Zhou

Let $μ$ be an Ahlfors-David probability measure on $\mathbb{R}^q$ with support $K$. For every $n\geq 1$, let $C_n(μ)$ denote the collection of all the $n$-optimal sets for $μ$ with respect to the geometric mean error. We prove that, there exist constant $d_1,d_2>0$, such that for each $n\geq 1$, every $α_n\in C_n(μ)$ and an arbitrary Voronoi partition $\{P_a

Minshuo Chen, Yu Bai, Jason D. Lee, Tuo Zhao

Deep neural networks can empirically perform efficient hierarchical learning, in which the layers learn useful representations of the data. However, how they make use of the intermediate representations are not explained by recent theories that relate them to "shallow learners" such as kernels. In this work, we demonstrate that intermediate neural re

Hideyoshi Arakida

Assuming a static and spherically symmetric spacetime, we propose a novel concept of the total deflection angle of a light ray. The concept is defined by the difference between the sum of internal angles of two triangles; one of the triangles lies on curved spacetime distorted by a gravitating body and the other on its background. The triangle required to de

Mauro R. C. da Silva, Rafael C. S. Schouery

In the MAXSPACE problem, given a set of ads A, one wants to place a subset A' of A into K slots B_1, ..., B_K of size L. Each ad A_i in A has size s_i and frequency w_i. A schedule is feasible if the total size of ads in any slot is at most L, and each ad A_i in A' appears in exactly w_i slots. The goal is to find a feasible schedule that maximizes t

Pantelis R. Vlachas, Georgios Arampatzis, Caroline Uhler, Petros Koumoutsakos

Predictive simulations of complex systems are essential for applications ranging from weather forecasting to drug design. The veracity of these predictions hinges on their capacity to capture the effective system dynamics. Massively parallel simulations predict the system dynamics by resolving all spatiotemporal scales, often at a cost that prevents experime

Mauro R. C. da Silva, Lehilton L. C. Pedrosa, Rafael C. S. Schouery

$\newcommand{\cala}{\mathcal{A}}$ In MAXSPACE, given a set of ads $\cala$, one wants to schedule a subset ${\cala'\subseteq\cala}$ into $K$ slots ${B_1, \dots, B_K}$ of size $L$. Each ad ${A_i \in \cala}$ has a size $s_i$ and a frequency $w_i$. A schedule is feasible if the total size of ads in any slot is at most $L$, and each ad ${A_i \in \cala'}$

Wei Yan, Qiaoqiao Zhang, Jinqiao Duan, Meihua Yang

In this paper, we consider the pointwise convergence problem of free Ostrovsky equation with rough data and random data. Firstly, we show the almost everywhere pointwise convergence of free Ostrovsky equation in $H^{s}(\mathbb{R})$ with $s\geq \frac{1}{4}$ with rough data. Secondly, we present counterexamples showing that the maximal function estimate relate

A. Singh, H. Y. Huang, Christopher Lane, J. H. Li

The superconductivity of cuprates, which has been a mystery ever since its discovery decades ago, is created through doping electrons or holes into a Mott insulator. There, however, exists an inherent electron-hole asymmetry in cuprates. The layered crystal structures of cuprates enable collective charge excitations fundamentally different from those of thre

Dong Li, Song Feng, Wei Su, Yu Huang

Very long-periodic pulsations during preflare phases (preflare-VLPs) have been detected in the full-disk solar soft X-Ray (SXR) flux (see Tan et al. 2016). They may be regarded as precursors to solar flares and may help us better understand the trigger mechanism of solar flares. In this letter, we report a preflare-VLP event before the onset of an M1.1 circu

Heng-Tong Ding, Christian Schmidt, Akio Tomiya, Xiao-Dan Wang

We investigate the chiral phase structure of three flavor QCD in a background $U(1)$ magnetic field using the standard staggered action and the Wilson plaquette gauge action. We perform simulations on lattices with a temporal extent of $N_τ=4$ and four spatial extents of $N_σ= 8,16, 20$ and 24. We choose a smaller-than-physical quark mass in lattice spacing

Jayanth Regatti, Hao Chen, Abhishek Gupta

We propose two novel stochastic gradient descent algorithms, ByGARS and ByGARS++, for distributed machine learning in the presence of any number of Byzantine adversaries. In these algorithms, reputation scores of workers are computed using an auxiliary dataset at the server. This reputation score is then used for aggregating the gradients for stochastic grad

Aditya Aiyer, Charles Meneveau

A population balance model coupled with large eddy simulations (LES) is adapted and applied to study the evolution of oil droplets in a turbulent jet including the effects of droplet breakup. A key unknown in simulating secondary breakup in turbulent multiphase jets is the inflow size distribution generated within the primary breakup zone near the nozzle exi

Oleksiy V. Penkov, Igor A. Kopylets, Valeriy V. Kondratenko, Mahdi Khadem

The synthesis and structural analysis of Mo/B periodical multilayer X-ray mirrors (PMMs) for beyond extreme ultraviolet (BEUV) optics was performed. The PMMs were deposited by a combination of pulsed DC and radio frequency (RF) magnetron sputtering. The structure was analyzed by high-resolution transmission electron microscopy, X-ray photoelectron spectrosco

Jung-Chao Ban, Chih-Hung Chang, Wen-Guei Hu, Yu-Liang Wu

We study the topological entropy of hom tree-shifts and show that, although the topological entropy is not a conjugacy invariant for tree-shifts in general, it remains invariant for hom tree higher block shifts. In doi:10.1016/j.tcs.2018.05.034 and doi:10.3934/dcds.2020186, Petersen and Salama demonstrated the existence of topological entropy for tree-shifts

Jinhang Zuo, Xutong Liu, Carlee Joe-Wong, John C. S. Lui

Online influence maximization has attracted much attention as a way to maximize influence spread through a social network while learning the values of unknown network parameters. Most previous works focus on single-item diffusion. In this paper, we introduce a new Online Competitive Influence Maximization (OCIM) problem, where two competing items (e.g., prod

Behrooz Ghorbani, Song Mei, Theodor Misiakiewicz, Andrea Montanari

For a certain scaling of the initialization of stochastic gradient descent (SGD), wide neural networks (NN) have been shown to be well approximated by reproducing kernel Hilbert space (RKHS) methods. Recent empirical work showed that, for some classification tasks, RKHS methods can replace NNs without a large loss in performance. On the other hand, two-layer

Holger F. Hofmann

It is difficult to evaluate the precision of quantum measurements because it is not possible to conduct a second reference measurement on the same physical system to compare the measurement outcome with a more accurate value of the measured quantity. Here, I show that a direct evaluation of measurement uncertainties is possible when the measurement outcomes

Xinshi Chen, Yufei Zhang, Christoph Reisinger, Le Song

Recently, there has been a surge of interest in combining deep learning models with reasoning in order to handle more sophisticated learning tasks. In many cases, a reasoning task can be solved by an iterative algorithm. This algorithm is often unrolled, and used as a specialized layer in the deep architecture, which can be trained end-to-end with other neur

Anton Dochtermann, Eli Meyers, Raghav Samavedan, Alex Yi

Motivated by the notion of chip-firing on the dual graph of a planar graph, we consider `integral flow chip-firing' on an arbitrary graph $G$. The chip-firing rule is governed by ${\mathcal L}^*(G)$, the dual Laplacian of $G$ determined by choosing a basis for the lattice of integral flows on $G$. We show that any graph admits such a basis so that ${\mat

Mojtaba Mahdavi, Muhammad Umar Farooq, Liang Liu, Ove Edfors

Spatially coupled serially concatenated codes (SC-SCCs) are a class of spatially coupled turbo-like codes, which have a close-to-capacity performance and low error floor. In this paper we investigate the impact of coupling memory, block length, decoding window size, and number of iterations on the performance, complexity, and latency of SC-SCCs. Several desi

Argyris Kalogeratos, Gaspard Abel, Stefano Sarao Mannelli

Motivated by scenarios of epidemic competition, as well as how social contagions spread at the level of individuals, this work considers the competition between two conflicting node states that spread over a social graph according to a generic diffusion process. For this setting, we introduce the Generalized Largest Reduction in Infectious Edges (gLRIE), whi

Dominik Müller, Iñaki Soto Rey, Frank Kramer

The coronavirus disease 2019 (COVID-19) affects billions of lives around the world and has a significant impact on public healthcare. Due to rising skepticism towards the sensitivity of RT-PCR as screening method, medical imaging like computed tomography offers great potential as alternative. For this reason, automated image segmentation is highly desired as

Taeseung Choi

We have revisited the Dirac theory in 1+1 and 2+1 dimensions by using the covariant representation of the parity-extended Poincaré group in their native dimensions. The parity operator plays a crucial role in deriving wave equations in both theories. We studied two position operators, a canonical one and a covariant one that becomes the particle position ope

Xiao Huang, Zhenlong Li, Yuqin Jiang, Xiaoming Li

The outbreak of COVID-19 highlights the need for a more harmonized, less privacy-concerning, easily accessible approach to monitoring the human mobility that has been proved to be associated with the viral transmission. In this study, we analyzed 587 million tweets worldwide to see how global collaborative efforts in reducing human mobility are reflected fro

Natalie Kiesler

This research paper focusses on the competences expected from computer science novices in the domain of basic programming and how they can be classified. By means of a qualitative content analysis of current learning objectives at German universities and the perspective of university teachers, basic programming competencies are identified. Since the competen

Roy Burson

This work provides a complete characterization of congruent numbers in terms of Pythagorean triples. Specifically, we show that every congruent number can be written as $$\frac{nm\left(m-n\right)\left(m+n\right)}{σ^2}$$ were as $$σ\vert ρ\biggl(\left(m-n\right)\left(m+n\right)\biggr),\indent \text{or} \indent σ\vert ρ( nm )$$ were $ρ(α)$ denotes the non-squa

Victor Volfson

We will study the asymptotic behavior of summation functions of a natural argument, including the asymptotic behavior of summation functions of a prime argument in the paper. A general formula is obtained for determining the asymptotic behavior of the sums of functions of a prime argument based on the asymptotic law of primes. We will show, that under certai

Mark Frenkel, Irina Legchenkova, Edward Bormashenko

Voronoi mosaics inspired by the seed points placed on the Archimedes Spirals are reported. Voronoi entropy was calculated for these patterns. Equidistant and non-equidistant patterns are treated. Voronoi mosaics built from cells of equal size which are of primary importance for decorative arts are reported. The pronounced prevalence of hexagons is inherent f

Yuanhang Yu, Dong Wen, Ying Zhang, Xiaoyang Wang

Matrix Factorization (MF) has been widely applied in machine learning and data mining. A large number of algorithms have been studied to factorize matrices. Among them, stochastic gradient descent (SGD) is a commonly used method. Heterogeneous systems with multi-core CPUs and GPUs have become more and more promising recently due to the prevalence of GPUs in

Yanxia Zhang, Jinqiao Duan, Yanfei Jin, Yang Li

Motivated by the existing difficulties in establishing mathematical models and in observing the system state time series for some complex systems, especially for those driven by non-Gaussian Levy motion, we devise a method for extracting non-Gaussian governing laws with observations only on mean exit time. It is feasible to observe mean exit time for certain

Fathi Hassine

In this paper, we study the stability problem of a star-shaped network of elastic strings with a local Kelvin-Voigt damping. Under the assumption that the damping coefficients have some singularities near the transmission point, we prove that the semigroup corresponding to the system is polynomially stable and the decay rates depends on the speed of the dege

Marat Akhmet

The abstract hyperbolic sets are introduced. Continuous and differentiable mappings as well as rate of convergence and transversal manifolds are not under discussion, and the symbolic dynamics paradigm is realized in a new way. Our suggestions are for more neat comprehension of chaos in the domain. The novelties can serve for revisited models as well as moti

Aly Sabri Abdalla, Vuk Marojevic

Unmanned aerial vehicles (UAVs) are emerging in commercial spaces and will support many applications and services, such as smart agriculture, dynamic network deployment, and network coverage extension, surveillance and security. The unmanned aircraft system (UAS) traffic management (UTM) provides a framework for safe UAV operation integrating UAV controllers

Sang Il Lee

The high sensitivity of optimized portfolios to estimation errors has prevented their practical application. To mitigate this sensitivity, we propose a new portfolio model called a Deeply Equal-Weighted Subset Portfolio (DEWSP). DEWSP is a subset of top-N ranked assets in an asset universe, the members of which are selected based on the predicted returns fro

J. P. Brennan, L. Ghezzi, J. Hong, L. Hutson

The aim of this survey is to discuss invariants of Cohen-Macaulay local rings that admit a canonical module. Attached to each such ring R with a canonical ideal C, there are integers--the type of R, the reduction number of C--that provide valuable metrics to express the deviation of R from being a Gorenstein ring. We enlarge this list with other integers--th

Wang Chi Cheung, David Simchi-Levi, Ruihao Zhu

We consider un-discounted reinforcement learning (RL) in Markov decision processes (MDPs) under drifting non-stationarity, i.e., both the reward and state transition distributions are allowed to evolve over time, as long as their respective total variations, quantified by suitable metrics, do not exceed certain variation budgets. We first develop the Sliding

Changfu Li, Vahid R. Disfani, Hamed Valizadeh Haghi, Jan Kleissl

Photovoltaic (PV) smart inverters can improve the voltage profile of distribution networks. A multi-objective optimization framework for coordination of reactive power injection of smart inverters and tap operations of on-load tap changers (OLTCs) for multi-phase unbalanced distribution systems is proposed. The optimization objective is to minimize voltage d

Dong Hui Kim

In this paper, we investigate the application of deep convolutional GANs on car image generation. We improve upon the commonly used DCGAN architecture by implementing Wasserstein loss to decrease mode collapse and introducing dropout at the end of the discrimiantor to introduce stochasticity. Furthermore, we introduce convolutional layers at the end of the g

Wenjuan Li, Huiju Wang

The goal of this article is to establish $L^{p} \rightarrow L^{q}$ estimates for maximal functions associated with nonisotropic dilations $δ_t(x)=(t^{a_1}x_1,t^{a_2}x_2,t^{a_3}x_3)$ of hypersurfaces $(x_{1}, x_{2},Φ(x_1,x_2))$ in $\mathbb{R}^3$, where the Gaussian curvatures of the hypersurfaces are allowed to vanish. When $2 α_{2} = α_{3}$, this problem is

Bar Ezra, Shimshon Kallush, Ronnie Kosloff

An ab initio simulation of strong-field photodissociation of diatomic molecules was developed, inspired by recent dissociation experiments of F2-. The transition between electronic states was modeled, including the laser pulse and transition dipole, and the angle between them. The initial conditions of the system were set to be thermal and to include differe

Jogendra Nath Kundu, Siddharth Seth, Rahul M, Mugalodi Rakesh

Estimation of 3D human pose from monocular image has gained considerable attention, as a key step to several human-centric applications. However, generalizability of human pose estimation models developed using supervision on large-scale in-studio datasets remains questionable, as these models often perform unsatisfactorily on unseen in-the-wild environments

Valentin Deaconu

We introduce certain $C^*$-algebras and $k$-graphs associated to $k$ finite dimensional unitary representations $ρ_1,...,ρ_k$ of a compact group $G$. We define a higher rank Doplicher-Roberts algebra $\mathcal{O}_{ρ_1,...,ρ_k}$, constructed from intertwiners of tensor powers of these representations. Under certain conditions, we show that this $C^*$-algebra

Rafael Pizarro Solar, Michal Pleskowicz

Block-matching and 3D filtering (BM3D) is an image denoising algorithm that works in two similar steps. Both of these steps need to perform grouping by block-matching. We implement the block-matching in an FPGA, leveraging its ability to perform parallel computations. Our goal is to enable other researchers to use our solution in the future for real-time vid