April 2019 arXiv papers — page 45

Christina Psaroudaki, Pavel Aseev, Daniel Loss

Within a microscopic theory, we study the quantum Brownian motion of a skyrmion in a magnetic insulator coupled to a bath of magnon-like quantum excitations. The intrinsic skyrmion-bath coupling gives rise to damping terms for the skyrmion center-of-mass, which remain finite down to zero temperature due to the quantum nature of the magnon bath. We show that

Matteo Griseri, Bensu Tunca, Thomas Lapauw, Shuigen Huang

The present work describes a synthesis route for bulk Ta4AlC3 MAX phase ceramics with high phase purity. Pressure assisted densification was achieved by both hot pressing and spark plasma sintering of tantalum hydride, aluminium and carbon powder mixtures in the 1200-1650°C range. The phases present and microstructures were characterized as a function of the

Philippe Lacaille

As artificial intelligence systems spread to more diverse and larger tasks in many domains, the machine learning algorithms, and in particular the deep learning models and the databases required to train them are getting bigger themselves. Some algorithms do allow for some scaling of large computations by leveraging data parallelism. However, they often requ

N. Chaturvedi, Randall D. Kamien

While twist-bend nematic phases have been extensively studied, the experimental observation of two dimensional, oscillating splay-bend phases is recent. We consider two theoretical models that have been used to explain the formation of twist-bend phases -- flexoelectricity and bond orientational order -- as mechanisms to induce splay-bend phases. Flexoelectr

Aydin Ayanzadeh, Shokoufeh Yazdanian, Ehsan Shahamatnia

A new class of affine scaling matrices for the interior point Newton-type methods is considered to solve the nonlinear systems with simple bounds. We review the essential properties of a scaling matrix and consider several well-known scaling matrices proposed in the literature. We define a new scaling matrix that is the convex combination of these matrices.

Scott D. Baalrud, Jerome Daligault

A new closure of the BBGKY hierarchy is developed, which results in a convergent kinetic equation that provides a rigorous extension of plasma kinetic theory into the regime of strong Coulomb coupling. The approach is based on a single expansion parameter which enforces that the exact equilibrium limit is maintained at all orders. Because the expansion param

Karina Cho, Sam Nelson

We incorporate quandle cocycle information into the quandle coloring quivers we defined in arXiv:1807.10465 to define weighted directed graph-valued invariants of oriented links we call \textit{quandle cocycle quivers}. This construction turns the quandle cocycle invariant into a small category, yielding a categorification of the quandle cocycleinvariant. Fr

Bernhard Hidding, Simon Hooker, Steven Jamison, Bruno Muratori

The acceleration gradients generated in a laser- or beam-driven plasma wakefield accelerator are typically three orders of magnitude greater than those produced by a conventional accelerator, and hence plasma accelerators can open a route to a new generation of very compact machines. In addition, plasma-based accelerators can generate beams with unique prope

Riccardo Pisoni, Tim Davatz, Kenji Watanabe, Takashi Taniguchi

In Bernal stacked bilayer graphene interlayer coupling significantly affects the electronic bandstructure compared to monolayer graphene. Here we present magnetotransport experiments on high-quality $n$-doped bilayer MoS$_{2}$. By measuring the evolution of the Landau levels as a function of electron density and applied magnetic field we are able to investig

Shichao Li, Kwang-Ting Cheng

Deep neural decision forest (NDF) achieved remarkable performance on various vision tasks via combining decision tree and deep representation learning. In this work, we first trace the decision-making process of this model and visualize saliency maps to understand which portion of the input influence it more for both classification and regression problems. W

Sujeet Kumar Choudhary, Aparna Baskaran, Prerna Sharma

Phototaxis is one of the most fundamental stimulus-response behaviors in biology wherein motile micro-organisms sense light gradients to swim towards the light source. Apart from single cell survival and growth, it plays a major role at the global scale of aquatic ecosystem and bio-reactors. We study photoaxis of single celled algae Chalmydomonas reinhardtii

Silas Alben, Alex A. Gorodetsky, Donghak Kim, Robert D. Deegan

Recent applications (e.g. active gels and self-assembly of elastic sheets) motivate the need to efficiently simulate the dynamics of thin elastic sheets. We present semi-implicit time stepping algorithms to improve the time step constraints that arise in explicit methods while avoiding much of the complexity of fully-implicit approaches. For a triangular lat

Véronique Aguié-Beghin, Zouleika Abdallah, Véronique Aguié, Roger Douillard

The effervescence and the stability of the ring of fine bubbles crowning the surface of a champagne glass, the "collar", constitute one of the hallmarks of Champagne. Defects in the stability of this collar are not well understood and account for a significant proportion of bottle return. This study aims to better understand the surface properties of

Sonia Keys, Peter Vereš, Matthew J. Payne, Matthew J. Holman

We describe the digest2 software package, a fast, short-arc orbit classifier for small Solar System bodies. The digest2 algorithm has been serving the community for more than 13 years. The code provides a score, D2, which represents a pseudo-probability that a tracklet belongs to a given Solar System orbit type. digest2 is primarily used as a classifier for

Guillaume Ducourthial, Jean-sébastien Affagard, Margaux Schmeltz, Xavier Solinas

The mechanical properties of biological tissues are strongly correlated to the specific distribution of their collagen fibers. Monitoring the dynamic reorganization of the collagen network during mechanical stretching is however a technical challenge because it requires mapping orientation of collagen fibers in a thick and deforming sample. In this work, a f

Alexandru Ionescu, Hao Jia

We prove a definitive theorem on the asymptotic stability of point vortex solutions to the full Euler equation in 2 dimensions. More precisely, we show that a small, Gevrey smooth, and compactly supported perturbation of a point vortex leads to a global solution of the Euler equation in 2D, which converges weakly as $t\to\infty$ to a radial profile with resp

Mohamed Attia, Mohammed Hossny, Saeid Nahavandi, Anousha Yazdabadi

In this presented work, we propose a realistic hair simulator using image blending for dermoscopic images. This hair simulator can be used for benchmarking and validation of the hair removal methods and in data augmentation for improving computer aided diagnostic tools. We adopted one of the popular implementation of image blending to superimpose realistic h

H. Gfrerer, J. V. Outrata

In the paper, a Newton-type method for the solution of generalized equations (GEs) is derived, where the linearization concerns both the single-valued and the multi-valued part of the considered GE. The method is based on the new notion of semismoothness${}^*$ which, together with a suitable regularity condition, ensure the local superlinear convergence. An

Tembo Nakamoto, Odile Rouhban, Yuichi Ikeda

Currently all countries including developing countries are expected to utilize their own tax revenues and carry out their own development for solving poverty in their countries. However, developing countries cannot earn tax revenues like developed countries partly because they do not have effective countermeasures against international tax avoidance. Our ana

Jérémy Anger, Carlo de Franchis, Gabriele Facciolo

New micro-satellite constellations enable unprecedented systematic monitoring applications thanks to their wide coverage and short revisit capabilities. However, the large volumes of images that they produce have uneven qualities, creating the need for automatic quality assessment methods. In this work, we quantify the sharpness of images from the PlanetScop

Tim Ng, Pascal Ochem, Narad Rampersad, Jeffrey Shallit

We start by considering binary words containing the minimum possible numbers of squares and antisquares (where an antisquare is a word of the form $x \overline{x}$), and we completely classify which possibilities can occur. We consider avoiding $x p(x)$, where $p$ is any permutation of the underlying alphabet, and $x t(x)$, where $t$ is any transformation of

Gianluca Ruffato, Michele Massari, Filippo Romanato

We present a method to efficiently multiply or divide the orbital angular momentum (OAM) of light beams using a sequence of two optical elements. The key-element is represented by an optical transformation mapping the azimuthal phase gradient of the input OAM beam onto a circular sector. By combining multiple circular-sector transformations into a single opt

Jun-jie Zhang, Ren-hong Fang, Qun Wang, Xin-Nian Wang

We propose a microscopic description for the polarization from the first principle through the spin-orbit coupling in particle collisions. The model is different from previous ones based on local equilibrium assumptions for the spin degree of freedom. It is based on scatterings of particles as wave packets, an effective method to deal with particle scatterin

C. Quintero Noda, H. Iijima, Y. Katsukawa, T. Shimizu

We investigate the diagnostic potential of the spectral lines at 850 nm for understanding the magnetism of the lower atmosphere. For that purpose, we use a newly developed 3D simulation of a chromospheric jet to check the sensitivity of the spectral lines to this phenomenon as well as our ability to infer the atmospheric information through spectropolarimetr

Xiao Jin, Baoyun Peng, Yichao Wu, Yu Liu

Distillation-based learning boosts the performance of the miniaturized neural network based on the hypothesis that the representation of a teacher model can be used as structured and relatively weak supervision, and thus would be easily learned by a miniaturized model. However, we find that the representation of a converged heavy model is still a strong cons

Francisco J. Aragón Artacho, Rubén Campoy, Matthew K. Tam

The Douglas-Rachford method, a projection algorithm designed to solve continuous optimization problems, forms the basis of a useful heuristic for solving combinatorial optimization problems. In order to successfully use the method, it is necessary to formulate the problem at hand as a feasibility problem with constraint sets having efficiently computable nea

Dennis Ludl, Thomas Gulde, Cristóbal Curio

Recognizing human actions is a core challenge for autonomous systems as they directly share the same space with humans. Systems must be able to recognize and assess human actions in real-time. In order to train corresponding data-driven algorithms, a significant amount of annotated training data is required. We demonstrated a pipeline to detect humans, estim

Fabien Trousselet, Aurélien Archereau, Anne Boutin, François-Xavier Coudert

We study the energetic stability and structural features of bimetallic metal-organic frameworks. Such heterometallic MOFs, which can result from partial substitutions between two types of cations, can have specific physical or chemical properties used for example in catalysis or gas adsorption. We work here to provide through computational chemistry a micros

Fabio Henrique Kiyoiti dos Santos Tanaka, Claus Aranha

In this paper we propose the use of Generative Adversarial Networks (GAN) to generate artificial training data for machine learning tasks. The generation of artificial training data can be extremely useful in situations such as imbalanced data sets, performing a role similar to SMOTE or ADASYN. It is also useful when the data contains sensitive information,

Olivier Carton, Elisa Orduna

We consider input-deterministic finite state transducers with infinite inputs and infinite outputs, and we consider the property of Borel normality on infinite words. When these transducers are given by a strongly connected set of states, and when the input is a Borel normal sequence, the output is an infinite word such that every word has a frequency given

L. Q. Yin, S. S. Zhang, Z. Cao, B. Y. Bi

Large High Altitude Air Shower Observatory(LHAASO) is a composite cosmic ray observatory consisting of three detector arrays: kilometer square array (KM2A) which includes the electromagnetic detector array and muon detector array, water Cherenkov detector array (WCDA) and wide field of view Cherenkov telescope array (WFCTA). One of the main scientific object

Jae-Woo Lee

The double red clumps (DRCs) are now dominantly believed to be the strong observational line of evidence of the so-called X-shaped Galactic bar structures. Recently, Lee et al. reported a subtle mean \ds\ difference between the DRCs and suggested a dichotomic picture that can be seen in globular clusters: the faint red clump is the first generation, while th

Z. Ammari, A. Ratsimanetrimanana

The Kubo-Martin-Schwinger condition is a widely studied fundamental property in quantum statistical mechanics which characterises the thermal equilibrium states of quantum systems. In the seventies, G. Gallavotti and E. Verboven, proposed an analogue to the KMS condition for classical mechanical systems and highlighted its relationship with the Kirkwood-Salz

Marat Akhmet, Mehmet Onur Fen, Astrit Tola

This paper reveals a novel numerical method, the sequential test, which approves chaos through sequences of numbers observations. The method alights alongside the Lyapunov exponent and bifurcation diagram test. Explicitly elucidation of the method application for both continuous and discrete systemswas given affiliated with the corresponding algorithms. The

Venelin Kozhuharov

The phenomenology in the neutrino sector requires physics beyond the Standard Model. One possibility is the existence of new massive leptonic states which could be probed at the high intensity machines. The present results on heavy neutral leptons from the study of kaon decays in flight with the NA48/2 and NA62 experiments are presented and the future prospe

Fabien Casenave, Nissrine Akkari

The industrial application motivating this work is the fatigue computation of aircraft engines' high-pressure turbine blades. The material model involves nonlinear elastoviscoplastic behavior laws, for which the parameters depend on the temperature. For this application, the temperature loading is not accurately known and can reach values relatively clos

Soufian Jebbara, Philipp Cimiano

Aspect-based sentiment analysis involves the recognition of so called opinion target expressions (OTEs). To automatically extract OTEs, supervised learning algorithms are usually employed which are trained on manually annotated corpora. The creation of these corpora is labor-intensive and sufficiently large datasets are therefore usually only available for a

Yunze Man, Yangsibo Huang, Junyi Feng, Xi Li

Segmentation of pancreas is important for medical image analysis, yet it faces great challenges of class imbalance, background distractions and non-rigid geometrical features. To address these difficulties, we introduce a Deep Q Network(DQN) driven approach with deformable U-Net to accurately segment the pancreas by explicitly interacting with contextual inf

Pengpeng Liu, Michael Lyu, Irwin King, Jia Xu

We present a self-supervised learning approach for optical flow. Our method distills reliable flow estimations from non-occluded pixels, and uses these predictions as ground truth to learn optical flow for hallucinated occlusions. We further design a simple CNN to utilize temporal information from multiple frames for better flow estimation. These two princip

Arturo Cruz-Maya, Fernando Garcia, Amit Kumar Pandey

Over the last years, social robots have been deployed in public environments making evident the need of human-aware navigation capabilities. In this regard, the robotics community have made efforts to include proxemics or social conventions within the navigation approaches. Nevertheless, few works have tackled the problem of labelling humans as an interactiv

Di Hu, Dong Wang, Xuelong Li, Feiping Nie

Visual-to-auditory sensory substitution devices can assist the blind in sensing the visual environment by translating the visual information into a sound pattern. To improve the translation quality, the task performances of the blind are usually employed to evaluate different encoding schemes. In contrast to the toilsome human-based assessment, we argue that

F. Cangemi, T. Beuchert, T. Siegert, V. Grinberg

We utilize the joint capabilities of IBIS and SPI to perform a state-resolved 20--2000\,keV analysis of the microquasar Cygnus X-1. In both LHS and HSS, the spectral analysis reveals the presence of a high-energy tail above 400\,keV in addition to a standard, Compton-like continuum in the 20--400\,keV range. We study the polarisation properties of the hard X

Erwan Vaineau, Alexandre Barachant, Anton Andreev, Pedro C. Rodrigues

We describe the experimental procedures for a dataset that we have made publicly available at https://doi.org/10.5281/zenodo.1494163 in mat and csv formats. This dataset contains electroencephalographic (EEG) recordings of 24 subjects doing a visual P300 Brain-Computer Interface experiment on PC. The visual P300 is an event-related potential elicited by visu

Chol-Hui Yun

In this paper, we introduce a construction of hidden variable recurrent fractal interpolation functions (HVRFIF) with four function contractivity factors. In the fractal interpolation theory, it is very important to ensure flexibility and diversity of the construction of interpolation function. Recurrent iterated function system (RIFS) produce fractal sets w

Youngjae Min, Hye Won Chung

Guiding the design of neural networks is of great importance to save enormous resources consumed on empirical decisions of architectural parameters. This paper constructs shallow sigmoid-type neural networks that achieve 100% accuracy in classification for datasets following a linear separability condition. The separability condition in this work is more rel

Maria José Finatto, Oto Vale, Eric Laporte

We report an experiment to check the identification of a set of words in popular written Portuguese with two versions of a computational dictionary of Brazilian Portuguese, DELAF PB 2004 and DELAF PB 2015. This dictionary is freely available for use in linguistic analyses of Brazilian Portuguese and other researches, which justifies critical study. The vocab

Wenjia Wang, Junxuan Chen, Jie Zhao, Ying Chi

The identification of pulmonary lobes is of great importance in disease diagnosis and treatment. A few lung diseases have regional disorders at lobar level. Thus, an accurate segmentation of pulmonary lobes is necessary. In this work, we propose an automated segmentation of pulmonary lobes using coordination-guided deep neural networks from chest CT images.

Yupeng Yang

The particles emitted from PBHs through the Hawking radiation have interactions with the particles present in the Universe. Due to the interactions, the evolution of the intergalactic medium (IGM) is changed and the changes have imprints on the anisotropies of the cosmic microwave background (CMB). In this paper, we focus on the PBHs with the lifetime in the

Junghwan Lee, Yong-Hyuk Kim

An evolutionary search space can be smoothly transformed via a suitable change of basis; however, it can be difficult to determine an appropriate basis. In this paper, a method is proposed to select an optimum basis can be used to simplify an evolutionary search space in a binary encoding scheme. The basis search method is based on a genetic algorithm and th

Yaolan Tian, Tuomas A. Puurtinen, Zhuoran Geng, Ilari J. Maasilta

Periodic hole array phononic crystals (PnC) can strongly modify the phonon dispersion relations, and have been shown to influence thermal conductance coherently, especially at low temperatures where scattering is suppressed. One very important parameter influencing this effect is the period of the structure. Here, we measured the sub-Kelvin thermal conductan

Can Koc, Cem Koc, Brian Su, Carlos Casarez

Much current study of legged locomotion has rightly focused on foot traction forces, including on granular media. Future legged millirobots will need to go through terrain, such as brush or other vegetation, where the body contact forces significantly affect locomotion. In this work, a (previously developed) low-cost 6-axis force/torque sensing shell is used

Garima Bajwa, Mohamed Fazeen, Ram Dantu

Detecting driver distraction is a significant concern for future intelligent transportation systems. We present a new approach for identifying distracted driving behavior by evaluating a stimulus and response interaction with the brain signals in two ways. First, measuring the driver response through EEG by creating various types of distraction stimuli such

Xianzhi Du, Mostafa El-Khamy, Jungwon Lee

In this paper, a new deep learning architecture for stereo disparity estimation is proposed. The proposed atrous multiscale network (AMNet) adopts an efficient feature extractor with depthwise-separable convolutions and an extended cost volume that deploys novel stereo matching costs on the deep features. A stacked atrous multiscale network is proposed to ag

Ashutosh Mahesh Pednekar

This paper proposes the use of an optimization algorithm, namely PSO to decide the initial centroids in K-means, to eventually get better accuracy. The vectorized notation of the optimal centroids can be thought of as entities in an optimization space, where the accuracy of K-means over a random subset of the data could act as a fitness measure. The resultan

Souvik Bandyopadhyay, Utso Bhattacharya, Amit Dutta

Considering a BDI symmetric one-dimensional SSH model, we explore the fate of the bulk topological invariant, namely, the winding number under a generic time dependent perturbation; the effective Hamiltonian, that generates the temporal evolution of the initial (ground) state of the completely symmetric initial Hamiltonian, may have the same or different sym

Ricardo Pio Monti, Kun Zhang, Aapo Hyvarinen

We consider the problem of inferring causal relationships between two or more passively observed variables. While the problem of such causal discovery has been extensively studied especially in the bivariate setting, the majority of current methods assume a linear causal relationship, and the few methods which consider non-linear dependencies usually make th

Qi Wang, Junyu Gao, Xuelong Li

Semantic segmentation, a pixel-level vision task, is developed rapidly by using convolutional neural networks (CNNs). Training CNNs requires a large amount of labeled data, but manually annotating data is difficult. For emancipating manpower, in recent years, some synthetic datasets are released. However, they are still different from real scenes, which caus

N. Meunier, A. -M. Lagrange

Magnetic activity and surface flows at different scales pertub radial velocity measurements. This affects the detectability of low-mass exoplanets. In these flows, the effect of supergranulation is not as well characterized as the other flows, and we wish to estimate its effect on the detection of Earth-like planets in the habitable zone of Sun-like stars. W

Junyao Chen, Tony Sit, Hoi Ying Wong

Value-at-risk (VaR) has been playing the role of a standard risk measure since its introduction. In practice, the delta-normal approach is usually adopted to approximate the VaR of portfolios with option positions. Its effectiveness, however, substantially diminishes when the portfolios concerned involve a high dimension of derivative positions with nonlinea

Bernie Wang, Virginia Wu, Bichen Wu, Kurt Keutzer

LiDAR (Light Detection And Ranging) is an essential and widely adopted sensor for autonomous vehicles, particularly for those vehicles operating at higher levels (L4-L5) of autonomy. Recent work has demonstrated the promise of deep-learning approaches for LiDAR-based detection. However, deep-learning algorithms are extremely data hungry, requiring large amou

Yingtian Zou, Jiashi Feng

Meta learning is a promising solution to few-shot learning problems. However, existing meta learning methods are restricted to the scenarios where training and application tasks share the same out-put structure. To obtain a meta model applicable to the tasks with new structures, it is required to collect new training data and repeat the time-consuming meta t

Jun-Ho Choi, Jong-Seok Lee

Classification using multimodal data arises in many machine learning applications. It is crucial not only to model cross-modal relationship effectively but also to ensure robustness against loss of part of data or modalities. In this paper, we propose a novel deep learning-based multimodal fusion architecture for classification tasks, which guarantees compat

Sarthak Anand, Debanjan Mahata, Kartik Aggarwal, Laiba Mehnaz

In this paper we present our approach and the system description for Sub Task A of SemEval 2019 Task 9: Suggestion Mining from Online Reviews and Forums. Given a sentence, the task asks to predict whether the sentence consists of a suggestion or not. Our model is based on Universal Language Model Fine-tuning for Text Classification. We apply various pre-proc

Md Zahangir Alom, Theus Aspiras, Tarek M. Taha, Vijayan K. Asari

Deep Learning (DL) approaches have been providing state-of-the-art performance in different modalities in the field of medical imagining including Digital Pathology Image Analysis (DPIA). Out of many different DL approaches, Deep Convolutional Neural Network (DCNN) technique provides superior performance for classification, segmentation, and detection tasks.

Haimin Zhang, Debanjan Mahata, Simra Shahid, Laiba Mehnaz

In this paper we present our approach and the system description for Sub-task A and Sub Task B of SemEval 2019 Task 6: Identifying and Categorizing Offensive Language in Social Media. Sub-task A involves identifying if a given tweet is offensive or not, and Sub Task B involves detecting if an offensive tweet is targeted towards someone (group or an individua

M. I. Qureshi, Showkat Ahmad

In this paper, we obtain analytical solutions of some definite integrals of Srinivasa Ramanujan [Mess. Math., XLIV, 75-86, 1915] in terms of Meijer's $G$-function by using Laplace transforms of $ \sin(βx^{2}),\cos(βx^{2}), x\sin(βx^{2})$ and $x\cos(βx^{2})$. Further, we obtain some infinite summation formulas connected with Meijer's G-function and nu

Riccardo Adami, Reika Fukuizumi, Justin Holmer

We consider the 1D nonlinear Schrödinger equation with focusing point nonlinearity. "Point" means that the pure-power nonlinearity has an inhomogeneous potential and the potential is the delta function supported at the origin. This equation is used to model a Kerr-type medium with a narrow strip in the optic fibre. There are several mathematical stud

Ya Wang, Wentao Ji, Zihua Chai, Yuhang Guo

We experimentally demonstrate a dynamical classification approach for investigation of topological quantum phases using a solid-state spin system through nitrogen-vacancy (NV) center in diamond. Similar to the bulkboundary correspondence in real space at equilibrium, we observe a dynamical bulk-surface correspondence in the momentum space from a dynamical qu

Su Houng Lee

After decades-long attempts to measure the mass shift and understand the origin of hadron mass, it became clear that one has to analyze hadrons with small vacuum width. Also, to identify the effect of chiral symmetry breaking, one has to start by looking at chiral partners. In this talk, I will review why such consideration inevitably led us to consider $K^*

Honglin Chen, Hao Li, Alexander Song, Matt Haberland

Body-worn cameras are now commonly used for logging daily life, sports, and law enforcement activities, creating a large volume of archived footage. This paper studies the problem of classifying frames of footage according to the activity of the camera-wearer with an emphasis on application to real-world police body-worn video. Real-world datasets pose a dif

Jingwei Liu

Microbial clades modeling is a challenging problem in biology based on microarray genome sequences, especially in new species gene isolates discovery and category. Marker family genome sequences play important roles in describing specific microbial clades within species, a framework of support vector machine (SVM) based microbial species classification with

Masahiro Yano, Alexei Zhidkov, James Koga, Tomonao Hosokai

Propagation of short and ultra-intense laser pulses in a semi-infinite space of overdense hydrogen plasma is analyzed via fully-relativistic, real geometry particle-in-cell (PIC) simulations including radiation friction. The relativistic transparency and hole-boring regimes are found to be sensitive to the transverse plasma field, backward light reflection,

V. H. Ramírez-Siordia, G. Bruzual, B. Cervantes Sodi, T. Bitsakis

We present a Bayesian method to determine simultaneously the age, metallicity, distance modulus, and interstellar reddening by dust of any resolved stellar population, by comparing the observed and synthetic color magnitude diagrams on a star by star basis, with no need to bin the data into a carefully selected magnitude grid. We test the method with mock st

Norihiro Oyama, Takeshi Kawasaki, Hideyuki Mizuno, Atsushi Ikeda

Recent experiments have revealed that cytoplasms become glassy when their metabolism is suppressed, while they maintain fluidity in a living state. The mechanism of this active fluidization is not clear, especially for bacterial cytoplasms, since they lack traditional motor proteins, which can cause directed motions. We introduce a model of bacterial cytopla

Xuelei Sui, Jianfeng Wang, Wenhui Duan

Electrides are special ionic solids with excess cavity-trapped electrons serving as anions. Despite the extensive studies on electrides, the interplay between electrides and magnetism is not well understood due to the lack of stable magnetic electrides, particularly the lack of inorganic magnetic electrides. Here, based on the mechanism of Stoner-type magnet

Michael Weber, Michael Fürst, J. Marius Zöllner

Current state-of-the-art object detection algorithms still suffer the problem of imbalanced distribution of training data over object classes and background. Recent work introduced a new loss function called focal loss to mitigate this problem, but at the cost of an additional hyperparameter. Manually tuning this hyperparameter for each training task is high

Siqi Yi, Stewart Worrall, Eduardo Nebot

Centimeter level globally accurate and consistent maps for autonomous vehicles navigation has long been achieved by on board real-time kinematic(RTK)-GPS in open areas. However when dealing with urban environments, GPS will experience multipath and blockage in urban canyon, under bridges, inside tunnels and in underground environments. In this paper we prese

Guiquan Wang, Kee Onn Fong, Filippo Coletti, Jesse Capecelatro

This study is concerned with the statistics of vertical turbulent channel flow laden with inertial particles for two different volume concentrations ($Φ_{V} = 3 \times 10^{-6}$ and $Φ_{V} = 5 \times 10^{-5}$) at a Stokes number of $St^{+} = 58.6$ based on viscous units. Two independent direct numerical simulation models utilizing the point-particle approach

Linqi Guo, Chen Liang, Alessandro Zocca, Steven H. Low

Cascading failures in power systems exhibit non-local propagation patterns which make the analysis and mitigation of failures difficult. In this work, we propose a distributed control framework inspired by the recently proposed concepts of unified controller and network tree-partition that offers strong guarantees in both the mitigation and localization of c

Matthias Maier, Marios Mattheakis, Efthimios Kaxiras, Mitchell Luskin

By using an asymptotic analysis and numerical simulations, we derive and investigate a system of homogenized Maxwell's equations for conducting material sheets that are periodically arranged and embedded in a heterogeneous and anisotropic dielectric host. This structure is motivated by the need to design plasmonic crystals that enable the propagation of

Ankush Desai, Shromona Ghosh, Sanjit A. Seshia, Natarajan Shankar

The recent drive towards achieving greater autonomy and intelligence in robotics has led to high levels of complexity. Autonomous robots increasingly depend on third party off-the-shelf components and complex machine-learning techniques. This trend makes it challenging to provide strong design-time certification of correct operation. To address these challen

Hossein K. Mousavi, Christoforos Somarakis, Qiyu Sun, Nader Motee

Spectral decomposition of dynamical systems is a popular methodology to investigate the fundamental qualitative and quantitative properties of these systems and their solutions. In this chapter, we consider a class of nonlinear cooperative protocols, which consist of multiple agents that are coupled together via an undirected state-dependent graph. We develo

Erhan Bayraktar, Jingjie Zhang, Zhou Zhou

Inspired by Strotz's consistent planning strategy, we formulate the infinite horizon mean-variance stopping problem as a subgame perfect Nash equilibrium in order to determine time consistent strategies with no regret. Equilibria among stopping times or randomized stopping times may not exist. This motivates us to consider the notion of liquidation strat

Nand Sharma, Prathamesh Verlekar, Rehab Ashary, Sui Zhiquan

Feature selection has evolved to be an important step in several machine learning paradigms. In domains like bio-informatics and text classification which involve data of high dimensions, feature selection can help in drastically reducing the feature space. In cases where it is difficult or infeasible to obtain sufficient number of training examples, feature

Yu Zhang, Haowei Deng, Quanxi Li, Haoze Song

Quantum computing technology has reached a second renaissance in the last decade. However, in the NISQ era pointed out by John Preskill in 2018, quantum noise and decoherence, which affect the accuracy and execution effect of quantum programs, cannot be ignored and corrected by the near future NISQ computers. In order to let users more easily write quantum p

Pavel Guerzhoy, Michael H. Mertens, Larry Rolen

Congruences of Fourier coefficients of modular forms have long been an object of central study. By comparison, the arithmetic of other expansions of modular forms, in particular Taylor expansions around points in the upper-half plane, has been much less studied. Recently, Romik made a conjecture about the periodicity of coefficients around $\tau=i$ of the cl

K. E. Mesick, W. C. Feldman, E. R. Mullin, L. C. Stonehill

The Los Alamos National Laboratory designed and built Mars Odyssey Neutron Spectrometer (MONS) has been in excellent health operating from February 2002 to the present. MONS measures the neutron leakage albedo from galactic cosmic ray bombardment of Mars. These signals can indicate the presence of near-surface water deposits on Mars, and can also be used to

Vladimir Eiderman, Michael Larsen

We prove that for every at most countable family $\{f_k(x)\}$ of real functions on $[0,1)$ there is a single-valued real function $F(x)$, $x\in[0,1)$, such that the Hausdorff dimension of the graph $Γ$ of $F(x)$ equals 2, and for every $C\in\mathbb R$ and every $k$, the intersection of $Γ$ with the graph of the function $f_k(x)+C$ consists of at most one poi

Jianqing Fan, Yongyi Guo, Bai Jiang

High-dimensional linear regression has been intensively studied in the community of statistics in the last two decades. For the convenience of theoretical analyses, classical methods usually assume independent observations and sub-Gaussian-tailed errors. However, neither of them hold in many real high-dimensional time-series data. Recently [Sun, Zhou, Fan, 2

María J. Martín, Alejandro Mas, Dragan Vukotić

We obtain a necessary and sufficient condition for a weighted composition operator to be co-isometric on a general weighted Hardy space of analytic functions in the unit disk whose reproducing kernel has the usual natural form. This turns out to be equivalent to the property of being unitary. The result reveals a dichotomy identifying a specific family of we

Julien Korinman

A marked surface is a compact oriented surface equipped with some pairwise disjoint arcs embedded in its boundary. In this paper, we extend the notion of character varieties to marked surfaces, in such a way that they have a nice behaviour for the operation of gluing two boundary arcs together. These stated character varieties are affine Poisson varieties wh

Ferran Alet, Adarsh K. Jeewajee, Maria Bauza, Alberto Rodriguez

We explore the use of graph neural networks (GNNs) to model spatial processes in which there is no a priori graphical structure. Similar to finite element analysis, we assign nodes of a GNN to spatial locations and use a computational process defined on the graph to model the relationship between an initial function defined over a space and a resulting funct

Chinmoy Samanta

We provide a new approach to solve one dimension Fokker-Planck equation in the Laplace domain for the case where a particle is evolving in a potential energy curve in the presence of general delocalized sink. We also calculate rate constants in the presence of non-localized sink on different potential energy curves. In the previous method, we need to solve m

Grégoire Sergeant-Perthuis

The decomposition into interaction subspaces is a hierarchical decomposition of the spaces of cylindrical functions of a finite product space, also called factor spaces. It is an important construction in graphical models and a standard way to prove the Hammersley-Clifford theorem that relates Markov fields to Gibbs fields and plays a central role in Kellere

Darina Dvinskikh, Alexander Gasnikov

We introduce primal and dual stochastic gradient oracle methods for decentralized convex optimization problems. Both for primal and dual oracles, the proposed methods are optimal in terms of the number of communication steps. However, for all classes of the objective, the optimality in terms of the number of oracle calls per node takes place only up to a log

Maria-Florina Balcan, Travis Dick, Wesley Pegden

The goal of data-driven algorithm design is to obtain high-performing algorithms for specific application domains using machine learning and data. Across many fields in AI, science, and engineering, practitioners will often fix a family of parameterized algorithms and then optimize those parameters to obtain good performance on example instances from the app

Monjurul Meem, Sourangsu Banerji, Apratim Majumder, Fernando Guevara Vasquez

We experimentally demonstrate imaging in the longwave-infrared (LWIR) spectral band (8um to 12um) using a single polymer flat lens based upon multi-level diffractive optics. The device thickness is only 10{\mu}m, and chromatic aberrations are corrected over the entire LWIR band with one surface. Due to the drastic reduction in device thickness, we are able t

Sergey Grigorian

Given a $7$-dimensional compact Riemannian manifold $\left( M,g\right) $ that admits $G_{2}$-structure, all the $G_{2}$-structures that are compatible with the metric $g$ are parametrized by unit sections of an octonion bundle over $M$. We define a natural energy functional on unit octonion sections and consider its associated heat flow. The critical points

M. Nashaat, I. V. Bobkova, A. M. Bobkov, Yu. M. Shukrinov

Strong dependence of the Josephson energy on the magnetization orientation in Josephson junctions with ferromagnetic interlayers and spin-orbit coupling opens a way to control magnetization by Josephson current or Josephson phase. Here we investigate the perspectives of magnetization control in superconductor/ferromagnet/superconductor (S/F/S) Josephson junc

Nico Engel, Stefan Hoermann, Markus Horn, Vasileios Belagiannis

We address the problem of vehicle self-localization from multi-modal sensor information and a reference map. The map is generated off-line by extracting landmarks from the vehicle's field of view, while the measurements are collected similarly on the fly. Our goal is to determine the autonomous vehicle's pose from the landmark measurements and map la