November 2018 arXiv papers — page 6

Jixia Yuan, Liangyun Chen, Yan Cao

In this paper, we characterize the super-biderivations of Cartan type Lie superalgebras over the complex field $\mathbb{C}$. Furthermore, we prove that all super-biderivations of Cartan type simple Lie superalgebras are inner super-biderivations.

Juhan Bae, Guodong Zhang, Roger Grosse

Variational Bayesian neural networks combine the flexibility of deep learning with Bayesian uncertainty estimation. However, inference procedures for flexible variational posteriors are computationally expensive. A recently proposed method, noisy natural gradient, is a surprisingly simple method to fit expressive posteriors by adding weight noise to regular

Idel Waisberg, Jason Dexter, Pierre Olivier-Petrucci, Guillaume Dubus

The microquasar SS 433 is well-known for its precessing, relativistic baryonic jets. Depending on their heating mechanism, the optical jet bullets may serve as a probe of the collimated radiation coming from the inner region close to the compact object. The optical interferometer VLTI/GRAVITY has allowed to spatially resolved the optical jets in SS 433 for t

Tianqi Zhao

Videos have become ubiquitous on the Internet. And video analysis can provide lots of information for detecting and recognizing objects as well as help people understand human actions and interactions with the real world. However, facing data as huge as TB level, effective methods should be applied. Recurrent neural network (RNN) architecture has wildly been

Zlatko Drmač, Igor Mezić, Ryan Mohr

This paper proposes a new computational method for solving structured least squares problems that arise in the process of identification of coherent structures in fluid flows. It is deployed in combination with dynamic mode decomposition (DMD) which provides a non-orthogonal set of modes --- corresponding to particular temporal frequencies --- a subset of wh

Z. L. Tu, F. Y. Wang

In this paper, we study the correlation between isotropic energy and duration of gamma-ray bursts (GRBs) for the first time. The correlation is found to be $T_d \propto {E_{iso}}^{0.34\pm 0.03}$ from the {\em Swift} GRB sample. After comparing with solar flares from {\em RHESSI} and stellar superflares from {\em Kepler} satellite, we find that the correlatio

Vincent Francois-Lavet, Peter Henderson, Riashat Islam, Marc G. Bellemare

Deep reinforcement learning is the combination of reinforcement learning (RL) and deep learning. This field of research has been able to solve a wide range of complex decision-making tasks that were previously out of reach for a machine. Thus, deep RL opens up many new applications in domains such as healthcare, robotics, smart grids, finance, and many more.

Terence L. J. Harris

An improved a.e. lower bound is given for Hausdorff dimension under vertical projections in the first Heisenberg group.

Idel Waisberg, Jason Dexter, Pierre Olivier-Petrucci, Guillaume Dubus

The microquasar SS 433 is the only known steady supercritical accretor in the Galaxy. It is well-known for its relativistic baryonic jets, but the system also drives equatorial outflows. These have been routinely detected in radio images, and components associated with a circumbinary disk have also been suggested in optical emission lines. We wish to spatial

Aleksandra Malysheva, Tegg Taekyong Sung, Chae-Bong Sohn, Daniel Kudenko

Over recent years, deep reinforcement learning has shown strong successes in complex single-agent tasks, and more recently this approach has also been applied to multi-agent domains. In this paper, we propose a novel approach, called MAGnet, to multi-agent reinforcement learning (MARL) that utilizes a relevance graph representation of the environment obtaine

Dhaval Adjodah, Dan Calacci, Abhimanyu Dubey, Peter Krafft

In this empirical paper, we investigate how learning agents can be arranged in more efficient communication topologies for improved learning. This is an important problem because a common technique to improve speed and robustness of learning in deep reinforcement learning and many other machine learning algorithms is to run multiple learning agents in parall

Keuntaek Lee, Ziyi Wang, Bogdan I. Vlahov, Harleen K. Brar

This work presents a novel ensemble of Bayesian Neural Networks (BNNs) for control of safety-critical systems. Decision making for safety-critical systems is challenging due to performance requirements with significant consequences in the event of failure. In practice, failure of such systems can be avoided by introducing redundancies of control. Neural Netw

MohammadHossein Bateni, MohammadTaghi Hajiaghayi, Saeed Seddighin, Cliff Stein

The \Problem{knapsack} problem is a fundamental problem in combinatorial optimization. It has been studied extensively from theoretical as well as practical perspectives as it is one of the most well-known NP-hard problems. The goal is to pack a knapsack of size $t$ with the maximum value from a collection of $n$ items with given sizes and values. Recent evi

Kyoung-Woong Moon, Jungbum Yoon, Changsoo Kim, Chanyong Hwang

A magnetic skyrmion isusually refers to a twisted spin texture surrounded by uniformly aligned out-of-plane spinsin the background of a uniformly magnetized state. The invariance of the magnetic skyrmion conserves its topological charge under any continuous transformations of the spin textures, leads to which represents the robustness of a magnetic skyrmion

Fabian Paul, Hao Wu, Maximilian Vossel, Bert L. de Groot

A popular approach to analyze the dynamics of high-dimensional many-body systems, such as macromolecules, is to project the trajectories onto a space of slowly-varying collective variables, where subsequent analyses are made, such as clustering or estimation of free energy profiles or Markov state models (MSMs). However, existing "dynamical" dimension reduct

W. H. Brito, G. Kotliar

We investigate the electronic structure of the highly anisotropic $\beta$ phase of metallic plutonium, within the combination of density functional theory (DFT) and dynamical mean field theory (DMFT). Its crystal structure gives rise to site and orbital selective electronic correlations, with coherent Pu-5$f_{5/2}$ states and very incoherent Pu-5$f_{7/2}$ st

Shira Chapman, Dongsheng Ge, Giuseppe Policastro

We explore the two holographic complexity proposals for the case of a 2d boundary CFT with a conformal defect. We focus on a Randall-Sundrum type model of a thin AdS$_2$ brane embedded in AdS$_3$. We find that, using the "complexity=volume" proposal, the presence of the defect generates a logarithmic divergence in the complexity of the full boundary state wi

Han Huang, Boaz A. Slomka, Tomasz Tkocz, Beatrice-Helen Vritsiou

A central problem in discrete geometry, known as Hadwiger's covering problem, asks what the smallest natural number $N\left(n\right)$ is such that every convex body in ${\mathbb R}^{n}$ can be covered by a union of the interiors of at most $N\left(n\right)$ of its translates. Despite continuous efforts, the best general upper bound known for this number rema

Édouard Bonnet, Sergio Cabello, Bojan Mohar, Hebert Pérez-Rosés

We introduce the inverse Voronoi diagram problem in graphs: given a graph $G$ with positive edge-lengths and a collection $\mathbb{U}$ of subsets of vertices of $V(G)$, decide whether $\mathbb{U}$ is a Voronoi diagram in $G$ with respect to the shortest-path metric. We show that the problem is NP-hard, even for planar graphs where all the edges have unit len

Shahin Khobahi, Naveed Naimipour, Mojtaba Soltanalian, Yonina C. Eldar

Machine learning, and more specifically deep learning, have shown remarkable performance in sensing, communications, and inference. In this paper, we consider the application of the deep unfolding technique in the problem of signal reconstruction from its one-bit noisy measurements. Namely, we propose a model-based machine learning method and unfold the iter

Jun-Ho Choi, Jun-Hyuk Kim, Manri Cheon, Jong-Seok Lee

Recently, several deep learning-based image super-resolution methods have been developed by stacking massive numbers of layers. However, this leads too large model sizes and high computational complexities, thus some recursive parameter-sharing methods have been also proposed. Nevertheless, their designs do not properly utilize the potential of the recursive

Octavio Arizmendi, Mauricio Salazar, Jiun-Chau Wang

Two different aspects of parabolic iteration in the complex upper half-plane are considered here. First, from a noncommutative probability perspective, a Berry-Esseen type estimate for the convergence speed of the monotone central limit theorem is proved. Secondly, if the underlying measure in this central limit process is singular to the Lebesgue measure on

Ruslan Skuratovskii

We consider algebraic affine and projective curves of Edwards \cite{E, SkOdProj} over a finite field $\text{F}_{p^n}$. Most cryptosystems of the modern cryptography \cite{SkBlock} can be naturally transform into elliptic curves \cite{Kob}. We research Edwards algebraic curves over a finite field, which at the present time is one of the most promising support

Ali Khademi, Sergey Korotov, Jon Eivind Vatne

In this note we present a generalization of the maximum angle condition, proposed by J. L. Synge in 1957 and M. Křížek in 1992 for triangular and tetrahedral elements, respectively, for the case of higher-dimensional simplicial finite elements. Its relations to the other angle-type conditions commonly used in finite element methods are analysed.

S. J. Hamilton, A. Hänninen, A. Hauptmann, V. Kolehmainen

Objective: To develop, and demonstrate the feasibility of, a novel image reconstruction method for absolute Electrical Impedance Tomography (a-EIT) that pairs deep learning techniques with real-time robust D-bar methods. Approach: A D-bar method is paired with a trained Convolutional Neural Network (CNN) as a post-processing step. Training data is simulated

M. P. Calvo, J. M. Sanz-Serna, Beibei Zhu

We introduce and analyze a family of heterogeneous multiscale methods for the numerical integration of highly oscillatory systems of delay differential equations with constant delays. The methodology suggested provides algorithms of arbitrarily high accuracy.

Weizhu Bao, Rémi Carles, Chunmei Su, Qinglin Tang

We present and analyze two numerical methods for the logarithmic Schr{ö}dinger equation (LogSE) consisting of a regularized splitting method and a regularized conservative Crank-Nicolson finite difference method (CNFD). In order to avoid numerical blow-up and/or to suppress round-off error due to the logarithmic nonlinearity in the LogSE, a regularized logar

Bianca Gariboldi, Giacomo Gigante

We extend to the case of a $d$-dimensional compact connected oriented Riemannian manifold $\mathcal M$ the theorem of A. Bondarenko, D. Radchenko and M. Viazovska on the existence of $L$-designs consisting of $N$ nodes, for any $N\ge C_{\mathcal M} L^d$. For this, we need to prove a version of the Marcinkiewicz-Zygmund inequality for the gradient of diffusio

Jeffrey M. Hokanson, Caleb C. Magruder

Rational approximation appears in many contexts throughout science and engineering, playing a central role in linear systems theory, special function approximation, and many others. There are many existing methods for solving the rational approximation problem, from fixed point methods like the Sanathanan-Koerner iteration and Vector Fitting, to partial inte

Soheil Mohseni, Seyed Masoud Moghaddas-Tafreshi

In this paper, a novel intelligent method based on a multi-agent system (MAS) is applied to the problem of optimal sizing in a stand-alone office complex microgrid such that the electricity demand of the office building and the charging demand of the plug-in hybrid electric vehicle (PHEV) charging station are met. The proposed MAS-based architecture consists

Yang Liu, Sebastien Blandin, Samitha Samaranayake

This article considers the stochastic on-time arrival problem in transit networks where both the travel time and the waiting time for transit services are stochastic. A specific challenge of this problem is the combinatorial solution space due to the unknown ordering of transit line arrivals. We propose a network structure appropriate to the online decision-

Longhao Yuan, Jianting Cao, Qiang Wu, Qibin Zhao

The problem of incomplete data is common in signal processing and machine learning. Tensor completion algorithms aim to recover the incomplete data from its partially observed entries. In this paper, taking advantages of high compressibility and flexibility of recently proposed tensor ring (TR) decomposition, we propose a new tensor completion approach named

Monimoy Bujarbaruah, Xiaojing Zhang, H. Eric Tseng, Francesco Borrelli

This paper proposes an Adaptive Robust Model Predictive Control strategy for lateral control in lane keeping problems, where we continuously learn an unknown, but constant steering angle offset present in the steering system. Longitudinal velocity is assumed constant. The goal is to minimize the outputs, which are distance from lane center line and the stead

Longhao Yuan, Qibin Zhao, Lihua Gui, Jianting Cao

Tensor train (TT) decomposition has drawn people's attention due to its powerful representation ability and performance stability in high-order tensors. In this paper, we propose a novel approach to recover the missing entries of incomplete data represented by higher-order tensors. We attempt to find the low-rank TT decomposition of the incomplete data w

Giovanni S. Alberti, Habib Ammari, Francisco Romero, Timothée Wintz

We consider the dynamical super-resolution problem consisting in the recovery of positions and velocities of moving particles from low-frequency static measurements taken over multiple time steps. The standard approach to this issue is a two-step process: first, at each time step some static reconstruction method is applied to locate the positions of the par

Adrian Šošić, Elmar Rueckert, Jan Peters, Abdelhak M. Zoubir

Advances in the field of inverse reinforcement learning (IRL) have led to sophisticated inference frameworks that relax the original modeling assumption of observing an agent behavior that reflects only a single intention. Instead of learning a global behavioral model, recent IRL methods divide the demonstration data into parts, to account for the fact that

Michael Holst, Yuwen Li, Adam Mihalik, Ryan Szypowski

Finite Element Exterior Calculus (FEEC) was developed by Arnold, Falk, Winther and others over the last decade to exploit the observation that mixed variational problems can be posed on a Hilbert complex, and Galerkin-type mixed methods can then be obtained by solving finite-dimensional subcomplex problems. Chen, Holst, and Xu (Math. Comp. 78 (2009) 35-53) e

Rickard Brüel-Gabrielsson, Vignesh Ganapathi-Subramanian, Primoz Skraba, Leonidas J. Guibas

We present an approach to inform the reconstruction of a surface from a point scan through topological priors. The reconstruction is based on basis functions which are optimized to provide a good fit to the point scan while satisfying predefined topological constraints. We optimize the parameters of a model to obtain likelihood function over the reconstructi

Alejandro Parada-Mayorga, Daniel L. Lau, Jhony H. Giraldo, Gonzalo R. Arce

In the area of graph signal processing, a graph is a set of nodes arbitrarily connected by weighted links; a graph signal is a set of scalar values associated with each node; and sampling is the problem of selecting an optimal subset of nodes from which a graph signal can be reconstructed. This paper proposes the use of spatial dithering on the vertex domain

Yao Cui, Zhehan Yi, Jiajun Duan, Di Shi

This paper proposes a resilient-backpropagation-neural-network-(Rprop-NN) based algorithm for Photovoltaic (PV) maximum power point tracking (MPPT). A supervision mechanism is proposed to calibrate the Rprop-NN-MPPT reference and limit short-circuit current caused by incorrect prediction. Conventional MPPT algorithms (e.g., perturb and observe (P&O), hill cl

Xinyi Chen-Lin, Luca V. Delacrétaz, Sean A. Hartnoll

The recently developed effective field theory of fluctuations around thermal equilibrium is used to compute late-time correlation functions of conserved densities. Specializing to systems with a single conservation law, we find that the diffusive pole is shifted in the presence of non-linear hydrodynamic self-interactions, and that the density-density Green'

Jiajun Duan, Zhehan Yi, Di Shi, Hao Xu

In this paper, a neural-network (NN)-based online optimal control method (NN-OPT) is proposed for ultra-capacitors (UCs) energy storage system (ESS) in hybrid AC/DC microgrids involving multiple distributed generations (e.g., Photovoltaic (PV) system, battery storage, diesel generator). Conventional control strategies usually produce large disturbances to bu

Kosuke Ishigaki, Joji Nasu, Akihisa Koga, Shintaro Hoshino

We study ordered phases with broken translational symmetry in the half-filled three-orbital Hubbard model with antiferromagnetic Hund coupling by means of dynamical mean-field theory (DMFT) and continuous-time quantum Monte Carlo simulations. The stability regions of the antiferro-orbital (AFO), antiferro-magnetic (AFM), and charge density wave (CDW) states

Xin Li, D. Thirumalai

Intratumor heterogeneity (ITH), referring to coexistence of different cell subpopulations in a single tumor, has been a major puzzle in cancer research for almost half a century. The lack of understanding of the underlying mechanism of ITH hinders progress in developing effective therapies for cancers. Based on the findings in a recent quantitative experimen

Benoit Fresse

This paper is a survey on the homotopy theory of $E_n$-operads written for the new handbook of homotopy theory.

Jiaming Zeng, Adam Lesnikowski, Jose M. Alvarez

One of the main challenges of deep learning tools is their inability to capture model uncertainty. While Bayesian deep learning can be used to tackle the problem, Bayesian neural networks often require more time and computational power to train than deterministic networks. Our work explores whether fully Bayesian networks are needed to successfully capture m

Mario Graff, Sabino Miranda-Jiménez, Eric S. Tellez, Daniela Moctezuma

Sentiment analysis (SA) is a task related to understanding people's feelings in written text; the starting point would be to identify the polarity level (positive, neutral or negative) of a given text, moving on to identify emotions or whether a text is humorous or not. This task has been the subject of several research competitions in a number of languages,

Gautham Narayan, Thomas Matheson, Abhijit Saha, Tim Axelrod

We have established a network of 19 faint (16.5 mag $< V < $19 mag) northern and equatorial DA white dwarfs as spectrophotometric standards for present and future wide-field observatories. Our analysis infers SED models for the stars that are tied to the three CALSPEC primary standards. Our SED models are consistent with panchromatic Hubble Space Telescope (

Alexander Haber

Matter at intermediate baryon densities and low temperatures is notoriously hard to tackle theoretically. Whereas lattice methods cannot cover more than rather small densities, perturbative methods are only applicable at much higher densities. The regime of intermediate chemical potential at low temperatures in the QCD-phase diagram is therefore out of reach

Mike Garcia, Sumita Pennathur

The recent advent of advanced microfabrication capabilities of microfluidic devices has driven attention towards the behavior of particles in inertial flows within microchannels for applications related to the separation and concentration of bio-particles. The phenomena of inertial focusing has been demonstrated to be a robust technique in such applications,

Ovidiu Savin, Hui Yu

For the Monge-Amp\`ere equation with a right-hand side bounded away from 0 and infinity, we show that the solution, subject to the natural boundary condition arising in optimal transport, is in $W^{2,1+\varepsilon}$ up to the boundary.

Anurag Koul, Sam Greydanus, Alan Fern

Recurrent neural networks (RNNs) are an effective representation of control policies for a wide range of reinforcement and imitation learning problems. RNN policies, however, are particularly difficult to explain, understand, and analyze due to their use of continuous-valued memory vectors and observation features. In this paper, we introduce a new technique

Philippe Landry, Reed Essick

We develop a non-parametric method for inferring the universal neutron star (NS) equation of state (EOS) from gravitational wave (GW) observations. Many different possible realizations of the EOS are generated with a Gaussian process conditioned on a set of nuclear-theoretic models. These synthetic EOSs are causal and thermodynamically stable by construction

Jack E. Graver, Mark E. Watkins

We give necessary and sufficient conditions for lobe-transitivity of locally finite and locally countable graphs whose connectivity equals 1. We show further that, given any biconnected graph $\Lambda$ and a "code" assigned to each orbit of Aut($\Lambda$), there exists a unique lobe-transitive graph $\Gamma$ of connectivity 1 whose lobes are copies of $\Lamb

Bertie Ancona, Monika Henzinger, Liam Roditty, Virginia Vassilevska Williams

The diameter, radius and eccentricities are natural graph parameters. While these problems have been studied extensively, there are no known dynamic algorithms for them beyond the ones that follow from trivial recomputation after each update or from solving dynamic All-Pairs Shortest Paths (APSP), which is very computationally intensive. This is the situatio

S. V. Anishchenko, V. G. Baryshevsky, A. A. Gurinovich

The electrostatic cumulation of current density in relativistic vacuum diodes with ring-type cathodes is described theoretically and confirmed experimentally. The distinctive feature of the suggested cumulation mechanism is a very low energy spread of electrons. As a result of electrostatic cumulation, a thin relativistic electron beam with a current density

R. J. Ehlers, J. D. Mulligan

ALICE Overwatch is a project started in late 2015 to provide augmented online monitoring and data quality assurance utilizing time-stamped QA histograms produced by the ALICE High Level Trigger. The system receives the data via ZeroMQ, stores it for later review, enriches it with detector specific functionality, and visualizes it via a web application. These

Qingyuan Jiang, Naichung Conan Leung

In this paper, we prove a generalization of Orlov's projectivization formula for the derived category $D^b_{\rm coh} (\mathbb{P}(\mathscr{E}))$, where $\mathscr{E}$ does not need to be a vector bundle; Instead, $\mathscr{E}$ is a coherent sheaf which locally admits two-step resolutions. As a special case, this also gives Orlov's generalized universal hyperpl

David Dudal, Caroline Felix, Leticia Palhares, François Rondeau

In this proceeding, $SU(N)$ Yang-Mills theory is quantized in the linear covariant gauges, while taking into account the issue of Gribov copies and we construct the one-loop effective potential for a set of mass dimension 2 condensates, including the Gribov parameter, that refines the infrared region of the Gribov-Zwanziger theory, whilst respecting renormal

Amir Shakouri, Maryam Kiani, Seid H. Pourtakdoust

A novel trajectory design methodology is proposed in the current work to minimize the state uncertainty in the crucial mission of spacecraft rendezvous. The trajectory is shaped under constraints utilizing a multiple-impulse approach. State uncertainty is characterized in terms of covariance, and the impulse time as the only affective parameter in uncertaint

FASER Collaboration, Akitaka Ariga, Tomoko Ariga, Jamie Boyd

FASER,the ForwArd Search ExpeRiment,is a proposed experiment dedicated to searching for light, extremely weakly-interacting particles at the LHC. Such particles may be produced in the LHC's high-energy collisions and travel long distances through concrete and rock without interacting. They may then decay to visible particles in FASER, which is placed 480 m d

Thomas Morrill, Tim Trudgian

We consider Dirichlet $L$-functions $L(s, \chi)$ where $\chi$ is a real, non-principal character modulo $q$. Using Pintz's refinement of Page's theorem, we prove that for $q\geq 3$ the function $L(s, \chi)$ has at most one real zero $\beta$ with $1- 1.011/\log q < \beta < 1$.

Eli Sherman, Hitinder Gurm, Ulysses Balis, Scott Owens

In healthcare, patient risk stratification models are often learned using time-series data extracted from electronic health records. When extracting data for a clinical prediction task, several formulations exist, depending on how one chooses the time of prediction and the prediction horizon. In this paper, we show how the formulation can greatly impact both

Evgeny F. Talantsev, A. E. Pantoja, Wayne P. Crump, Jeffery L. Tallon

The current distribution across the thickness of a current-carrying rectangular film in the Meissner state was established long ago by the London brothers. The distribution across the width is more complicated but was later shown to be highly non-uniform, diverging at the edges. Accordingly, the standard view for type II superconductors is that vortices ente

Evgeny F. Talantsev, Nick M. Strickland, Stuart C. Wimbush, Justin Brooks

Recently, we showed that the self-field transport critical current, Ic(sf), of a superconducting wire can be defined in a more fundamental way than the conventional (and arbitrary) electric field criterion, Ec = 1 microV/cm. We defined Ic(sf) as the threshold current, Ic,B, at which the perpendicular component of the local magnetic flux density, measured at

E. I. Rashba, V. I. Sheka

Resonance phenomena in solids generally fall into two distinct classes, electric and magnetic, driven, respectively, by the $E$ and $H$ components of the electromagnetic wave incident on the solid. The canonical examples of the two types of resonances are the electron cyclotron resonance (CR) and the electron paramagnetic resonance (EPR), originating from th

Daniel Jönsson, Peter Steneteg, Erik Sundén, Rickard Englund

The complexity of today's visualization applications demands specific visualization systems tailored for the development of these applications. Frequently, such systems utilize levels of abstraction to improve the application development process, for instance by providing a data flow network editor. Unfortunately, these abstractions result in several issues,

Ulrik W. Nash

We suggest that one individual holds multiple degrees of belief about an outcome, given the evidence. We then investigate the implications of such noisy probabilities for a buyer and a seller of binary options and find the odds agreed upon to ensure zero-expectation betting, differ from those consistent with the relative frequency of outcomes. More precisely

Morgan Jones, Matthew M. Peet

We consider a general class of Dynamic Programming (DP) problems with non-separable objective functions. We show that for any problem in this class, there exists an augmented-state DP problem which satisfies the Principle of Optimality and the solutions to which yield solutions to the original problem. Furthermore, we identify a subclass of DP problems with

Bouillaguet Quentin, Bobot François, Sighireanu Mihaela, Yakobowski Boris

Cooperation between verification methods is crucial to tackle the challenging problem of software verification. The paper focuses on the verification of C programs using pointers and it formalizes a cooperation between static analyzers doing pointer analysis and a deductive verification tool based on first order logic. We propose a framework based on memory

S. Hosseinzadeh, P. Elahi, M. Behboudni, M. H. Sheikhi

The crystallization and magnetic behavior of yttrium iron garnet (YIG) prepared by metallo-organic decomposition (MOD) method are discussed. The chemistry and physics related to synthesis of iron and yttrium carboxylates based on 2-ethylhexanoic acid (2EHA) are studied, since no literature was found which elucidates synthesis of metallo-organic precursor of

Michael Maier-Gerber, Michael Riemer, Andreas H. Fink, Peter Knippertz

Tropical cyclones that evolve from a non-tropical origin may pose a special challenge for predictions, as they often emerge at the end of a multi-scale cascade of atmospheric processes. Climatological studies have shown that the 'tropical transition' (TT) pathway plays a prominent role in cyclogenesis, in particular over the North Atlantic Ocean. Here we use

Miljan Dašić, Igor Stanković, Konstantinos Gkagkas

We present a theoretical study of the influence of the molecular geometry of the cation on the response of ionic liquid (IL) to confinement and mechanical strain. The so-called {\it tailed} model includes a large spherical anion and asymmetric cation consisting of a charged head and neutral tail. Despite its simplicity, this model recovers a wide range of st

S. Hosseinzadeh, M. Behboudnia, L. Jamilpanah, M. H. Sheikhi

Nanoparticles with their specific properties newly have drawn a great deal of attention of researchers [1-3]Yttrium iron Garnet magnetic nanoparticles (YIG-NPs) are promising materials with novel applications in microwave, spintronics, magnonics, and magneto-optical devices. However, achieving stable and remarkable magnetic YIG-NPs has been remaining as a gr

A. Boulouz, H. Bounit, A. Driouich, S. Hadd

The main purpose of this paper is to treat semigroups properties, like norm continuity, compactness and differentiability for perturbed semigroups in Banach spaces. In particular, we investigate three large classes of perturbations, Miyadera-Voigt, Desch-Schappacher and Staffans-Weiss perturbations. Our approach is mainly based on feedback theory of Salamon-

Jean Serra, Jesus Angulo, B Ravi Kiran

Consider a family $Z=\{\boldsymbol{x_{i}},y_{i}$,$1\leq i\leq N\}$ of $N$ pairs of vectors $\boldsymbol{x_{i}} \in \mathbb{R}^d$ and scalars $y_{i}$ that we aim to predict for a new sample vector $\mathbf{x}_0$. Kriging models $y$ as a sum of a deterministic function $m$, a drift which depends on the point $\boldsymbol{x}$, and a random function $z$ with zer

Yingda Xia, Fengze Liu, Dong Yang, Jinzheng Cai

While making a tremendous impact in various fields, deep neural networks usually require large amounts of labeled data for training which are expensive to collect in many applications, especially in the medical domain. Unlabeled data, on the other hand, is much more abundant. Semi-supervised learning techniques, such as co-training, could provide a powerful

I. -G. Shin, Y. -H. Ryu, J. C. Yee, A. Gould

We report two microlensing events, KMT-2017-BLG-1038 and KMT-2017-BLG-1146 that are caused by planetary systems. These events were discovered by KMTNet survey observations from the $2017$ bulge season. The discovered systems consist of a planet and host star with mass ratios, $5.3_{-0.4}^{+0.2} \times 10^{-3}$ and $2.0_{-0.1}^{+0.6} \times 10^{-3}$, respecti

Evgeny F. Talantsev, Wayne P. Crump, Jeffery L. Tallon

Universal scaling behaviour in superconductors has significantly elucidated fluctuation and phase transition phenomena in these materials. However, universal behaviour for the most practical property, the critical current, was not contemplated because prevailing models invoke nucleation and migration of flux vortices. Such migration depends critically on pin

Larry McLerran, Sanjay Reddy

We consider Quarkyonic Matter to naturally explain the observed properties of neutron stars. We argue that such matter might exist at densities close to that of nuclear matter and at the onset, the pressure and the sound velocity in Quarkyonic matter increase rapidly. In the limit of large number of quark colors $N_c$, this transition is characterized by a d

Benjamin Leiva

The observed proportionality between nominal prices and average embodied energies cannot be interpreted with conventional economic theory. A model is presented that places energy transfers as the focal point of scarcity based on the idea that (1) goods are material rearrangements, and (2) humans can only rearrange matter with energy transfers. Modified consu

Joel Fotso Tachago, Hubert Nnang, Elvira Zappale

An integral representation result is obtained for the variational limit of the family functionals $\int_{\Omega}f\left(\frac{x}{\varepsilon}, Du\right)dx$, as $\varepsilon \to 0$, when the integrand $f = f (x,v)$ is a Carath\'eodory function, periodic in $x$, convex in $v$ and with nonstandard growth.

Tiehang Duan, Qi Lou, Sargur N. Srihari, Xiaohui Xie

Current state-of-the-art nonparametric Bayesian text clustering methods model documents through multinomial distribution on bags of words. Although these methods can effectively utilize the word burstiness representation of documents and achieve decent performance, they do not explore the sequential information of text and relationships among synonyms. In th

Alfonso Landeros, Timothy Stutz, Kevin L. Keys, Alexander Alekseyenko

Biological systems with intertwined feedback loops pose a challenge to mathematical modeling efforts. Moreover, rare events, such as mutation and extinction, complicate system dynamics. Stochastic simulation algorithms are useful in generating time-evolution trajectories for these systems because they can adequately capture the influence of random fluctuatio

Lei Wang, Svetlana Tlupova, Robert Krasny

The Stokeslet and stresslet kernels are commonly used in boundary element simulations and singularity methods for slow viscous flow. Evaluating the velocity induced by a collection of Stokeslets and stresslets by direct summation requires $O(N^2)$ operations, where $N$ is the system size. The present work develops a treecode algorithm for 3D Stokeslets and s

Mark Allen, Mariana Smit Vega Garcia

We study a model for combustion on a boundary. Specifically, we study certain generalized solutions of the equation \[ (-\Delta)^s u = \chi_{\{u>c\}} \] for $0<s<1$ and an arbitrary constant $c$. Our main object of study is the free boundary $\partial\{u>c\}$. We study the behavior of the free boundary and prove an upper bound for the Hausdorff dimension of

Dushyant Mehta, Kwang In Kim, Christian Theobalt

We investigate filter level sparsity that emerges in convolutional neural networks (CNNs) which employ Batch Normalization and ReLU activation, and are trained with adaptive gradient descent techniques and L2 regularization or weight decay. We conduct an extensive experimental study casting our initial findings into hypotheses and conclusions about the mecha

Che-Yu Chen, Mariam Bouhmadi-López, Pisin Chen

The possibility of testing gravity theories with the help of gravitational wave detections has become an interesting arena of recent research. In this paper, we follow this direction by investigating the quasinormal modes (QNMs) of the axial perturbations for charged black holes in the Palatini-type theories of gravity, specifically ($i$) the Palatini $f(R)$

Francisco Leiva, Nicolás Cruz, Ignacio Bugueño, Javier Ruiz-del-Solar

The goal of this paper is to propose a vision system for humanoid robotic soccer that does not use any color information. The main features of this system are: (i) real-time operation in the NAO robot, and (ii) the ability to detect the ball, the robots, their orientations, the lines and key field features robustly. Our ball detector, robot detector, and rob

Hans Christianson, Evan Stafford

We consider the wave equation $(\partial_t^2-\Delta)u=0$ on a planar triangular domain $\Omega\subset\mathbb{R}^2$ with Dirichlet boundary conditions. We use a commutator and integration by parts argument similar to that in \cite{Chr2DTriangles} by the first author to obtain an observability asymptotic for any one side of the triangle. Our result is particul

H. S. Abdel-Aziz, M. Khalifa Saad, Haytham. A. Ali

An affine factorable surface of the second kind in the three dimensional pseudo-Galilean space G13 is studied depending on the invariant theory and theory of differential equation. The first and second fundamental forms, Gaussian curvature and mean curvature of the meant surface are obtained according to the basic principles of differential geometry. Also, s

Pedro H. C. Sant'Anna, Jun B. Zhao

This article proposes doubly robust estimators for the average treatment effect on the treated (ATT) in difference-in-differences (DID) research designs. In contrast to alternative DID estimators, the proposed estimators are consistent if either (but not necessarily both) a propensity score or outcome regression working models are correctly specified. We als

Mikhail Zhitlukhin

We consider a stochastic game-theoretic model of an investment market in continuous time with short-lived assets and study strategies, called survival, which guarantee that the relative wealth of an investor who uses such a strategy remains bounded away from zero. The main results consist in obtaining a sufficient condition for a strategy to be survival and

Helmut Abels, Johannes Kampmann

We rigorously prove the convergence of weak solutions to a model for lipid raft formation in cell membranes which was recently proposed by Garcke et al. to weak (varifold) solutions of the corresponding sharp-interface problem for a suitable subsequence. In the system a Cahn-Hilliard type equation on the boundary of a domain is coupled to a diffusion equatio

Fahad Shamshad, Muhammad Awais, Muhammad Asim, Zain ul Aabidin Lodhi

Among the plethora of techniques devised to curb the prevalence of noise in medical images, deep learning based approaches have shown the most promise. However, one critical limitation of these deep learning based denoisers is the requirement of high-quality noiseless ground truth images that are difficult to obtain in many medical imaging applications such

Mingzhao Song, Kseniia Baryshnikova, Aleksandr Markvart, Pavel Belov

Metasurfaces have been investigated and its numerous exotic functionalities and the potentials to arbitrarily control of the electromagnetic fields have been extensively explored. However, only limited types of metasurface have finally entered into real products. Here, we introduce a concept of a metasurface-based smart table for wirelessly charging portable

Alban Sauret, Katarzyna Somszor, Emmanuel Villermaux, Emilie Dressaire

During the transport of colloidal suspensions in microchannels, the deposition of particles can lead to the formation of clogs, typically at constrictions. Once a clog is formed in a microchannel, advected particles form an aggregate upstream from the site of the blockage. This aggregate grows over time, which leads to a dramatic reduction of the flow rate.

Vasilii Duzhin, Nikolay Vassiliev

There exists a well-known hook-length formula for calculating the dimensions of 2D Young diagrams. Unfortunately, the analogous formula for 3D case is unknown. We introduce an approach for calculating the estimations of dimensions of three-dimensional Young diagrams also known as plane partitions. The most difficult part of this task is the calculation of co

Peijun Li, Jue Wang, Lei Zhang

This paper is concerned with analysis of electromagnetic wave scattering by an obstacle which is embedded in a two-layered lossy medium separated by an unbounded rough surface. Given a dipole point source, the direct problem is to determine the electromagnetic wave field for the given obstacle and unbounded rough surface; the inverse problem is to reconstruc

Weimin Yi, M. Vivek, W. N. Brandt, T. Wang

We report the discovery of disappearance of Mg ii, Al iii, C iv, and Si iv broad absorption lines (BALs) at the same velocity, accompanied by a new Civ BAL emerging at a higher velocity, in the quasar J0827+4252 at z = 2.038. This is the first report of BAL disappearance (i) over Mg ii, Al iii, C iv, and Si iv ions and (ii) in a weak emission-line quasar (WL