February 2019 arXiv papers — page 112

Qiuyu Zhu, Ruixin Zhang

Classic variational autoencoders are used to learn complex data distributions, that are built on standard function approximators. Especially, VAE has shown promise on a lot of complex task. In this paper, a new autoencoder model - classification supervised autoencoder (CSAE) based on predefined evenly-distributed class centroids (PEDCC) is proposed. Our meth

Katsuhito Nakashima, Diptarama Hendrian, Ryo Yoshinaka, Ayumi Shinohara

In this paper, we propose a new indexing structure for parameterized strings which we call PLSTs, by generalizing linear-size suffix tries for ordinary strings. Two parameterized strings are said to match if there is a bijection on the symbol set that makes the two coincide. PLSTs are applicable to the parameterized pattern matching problem, which is to deci

Geoffrey Wolfer, Aryeh Kontorovich

We address the problem of estimating the mixing time $t_{\mathsf{mix}}$ of an arbitrary ergodic finite-state Markov chain from a single trajectory of length $m$. The reversible case was addressed by Hsu et al. [2019], who left the general case as an open problem. In the reversible case, the analysis is greatly facilitated by the fact that the Markov operator

Ruihuan Du, Yu Zhong, Harikesh Nair, Bo Cui

This paper describes a practical system for Multi Touch Attribution (MTA) for use by a publisher of digital ads. We developed this system for JD.com, an eCommerce company, which is also a publisher of digital ads in China. The approach has two steps. The first step ('response modeling') fits a user-level model for purchase of a product as a function

Ulugbek Yakhshiev, Hyun-Chul Kim, Makoto Oka

We investigate the static properties of the nucleon in the presence of strong magnetic fields and discuss the consequent changes of the nucleon structure, based on the Skyrme model. The results show that at large values of the magnetic field ($\sim10^{17}$ to $10^{18} \mathrm{G}$), which is supposed to appear in heavy-ion collision experiments at RHIC energi

S. Rial, I. Arregui, R. Oliver, J. Terradas

The purpose of this work is to develop a procedure to obtain the normal modes of a coronal loop from time-dependent numerical simulations with the aim of better understanding observed transverse loop oscillations. To achieve this goal, in this paper we present a new method and test its performance with a problem for which the normal modes can be computed ana

Michikazu Kobayashi

We numerically study the Berezinskii-Kosterlitz-Thouless (BKT) transition of a spin-1 spinor Bose gas under the quadratic Zeeman effect. A calculation of the mass and spin superfluid densities shows that (i) the BKT transition occurs only when vortices are classified by the integer group $\mathbb{Z}$, and $\mathbb{Z}_2$ vortices do not contribute to the BKT

Paolo Rossi, Alessandro Strumia, Riccardo Torre

An important issue in bibliometrics is the weighing of co-authorship in the production of scientific collaborations, which are becoming the standard modality of research activity in many disciplines. The problem is especially relevant in the field of high-energy physics, where collaborations reach 3000 authors, but it can no longer be ignored also in other d

Abolfazl Lavaei, Sadegh Soudjani, Majid Zamani

In this paper, we propose a compositional approach for the construction of finite abstractions (a.k.a. finite Markov decision processes (MDPs)) for networks of discrete-time stochastic control subsystems that are not necessarily stabilizable. The proposed approach leverages the interconnection topology and a notion of finite-step stochastic storage functions

Robert R. Junker, Florian Griessenberger, Wolfgang Trutschnig

Asymmetry is an inherent property of bivariate associations and therefore must not be ignored. The currently applicable dependence measures mask the potential asymmetry of the underlying dependence structure by implicitly assuming that quantity Y is equally dependent on quantity X, and vice versa, which is generally not true. We introduce the copula-based de

Yiding Chen, Xiaojin Zhu

We describe an optimal adversarial attack formulation against autoregressive time series forecast using Linear Quadratic Regulator (LQR). In this threat model, the environment evolves according to a dynamical system; an autoregressive model observes the current environment state and predicts its future values; an attacker has the ability to modify the enviro

Liao Wu, Ross Crawford, Jonathan Roberts

While Product of Exponentials (POE) formula has been gaining increasing popularity in modeling the kinematics of a serial-link robot, the Denavit-Hartenberg (D-H) notation is still the most widely used due to its intuitive and concise geometric interpretation of the robot. This paper has developed an analytical solution to automatically convert a POE model i

Martin J. Zhang, James Zou, David Tse

Monte Carlo (MC) permutation test is considered the gold standard for statistical hypothesis testing, especially when standard parametric assumptions are not clear or likely to fail. However, in modern data science settings where a large number of hypothesis tests need to be performed simultaneously, it is rarely used due to its prohibitive computational cos

Lê Thành Dũng Nguyên

Many applications of denotational semantics, such as higher-order model checking or the complexity of normalization, rely on finite semantics for monomorphic type systems. We exhibit such a finite semantics for a polymorphic purely linear language: more precisely, we show that in Girard's semantics of second-order linear logic using coherence spaces and

Keshab Chandra Bakshi, Ved Prakash Gupta

Given any finite index quadrilateral $(N, P, Q, M)$ of $II_1$-factors, the notions of interior and exterior angles between $P$ and $Q$ were introduced in \cite{BDLR2017}. We determine the possible values of these angles when the quadrilateral is irreducible and the subfactors $N \subset P$ and $N \subset Q$ are both regular in terms of the cardinalities of t

Raaz Dwivedi, Nhat Ho, Koulik Khamaru, Martin J. Wainwright

We study a class of weakly identifiable location-scale mixture models for which the maximum likelihood estimates based on $n$ i.i.d. samples are known to have lower accuracy than the classical $n^{- \frac{1}{2}}$ error. We investigate whether the Expectation-Maximization (EM) algorithm also converges slowly for these models. We provide a rigorous characteriz

Afshin Rahimi, Yuan Li, Trevor Cohn

In cross-lingual transfer, NLP models over one or more source languages are applied to a low-resource target language. While most prior work has used a single source model or a few carefully selected models, here we consider a `massive' setting with many such models. This setting raises the problem of poor transfer, particularly from distant languages. W

Guangmo Tong, Ruiqi Wang

Influence maximization is a prototypical problem enabling applications in various domains, and it has been extensively studied in the past decade. The classic influence maximization problem explores the strategies for deploying seed users before the start of the diffusion process such that the total influence can be maximized. In its adaptive version, seed n

Jiho Hong, Mikyoung Lim

We consider a boundary value problem for the conductivity equation in a bounded domain containing an inclusion which is nearly touching to the domain's boundary. We assume that the domain and the inclusion are disks with conductivity jump on the boundary of the inclusion. By using the layer potential technique and adopting the bipolar coordinates, we der

Steven Jens Jorgensen, James Holley, Frank Mathis, Joshua S. Mehling

The problem of finding thermally minimizing configurations of a humanoid robot to recover its actuators from unsafe thermal states is addressed. A first-order, data-driven, effort-based, thermal model of the robot's actuators is devised, which is used to predict future thermal states. Given this predictive capability, a map between configurations and fut

Evert Klaseboer, Florian D. E. Charlet, Boo-Cheong Khoo, Qiang Sun

The problem of the fictitious frequency spectrum resulting from numerical implementations of the boundary element method for the exterior Helmholtz problem is revisited. When the ordinary 3D free space Green's function is replaced by a modified Green's function, it is shown that these fictitious frequencies do not necessarily have to correspond to th

Yukiyoshi Nakkajima

In this article we prove a fundamental inequality between Artin-Mazur heights and Yobuko heights of certain proper log smooth schemes of Cartier type over a fine log scheme whose underlying scheme is the spectrum of a perfect field $κ$ of characteristic $p>0$. We also prove that the cohomologies of Witt-sheaves of them are finitely generated ${\cal W}(κ)$-mo

Yash Chandak, Georgios Theocharous, James Kostas, Scott Jordan

Most model-free reinforcement learning methods leverage state representations (embeddings) for generalization, but either ignore structure in the space of actions or assume the structure is provided a priori. We show how a policy can be decomposed into a component that acts in a low-dimensional space of action representations and a component that transforms

Yukiyoshi Nakkajima

In this book we construct fundamental filtered complexes in filtered derived categories. These are generalizations of a filtered complex constructed by Mokrane. As an application of these constructions, we develop theory of limits of weight filtrations and limits of slope filtrations on the infinitesimal cohomologies of proper smooth schemes over the fractio

Ursula Laa, Dianne Cook

Projection pursuit is used to find interesting low-dimensional projections of high-dimensional data by optimizing an index over all possible projections. Most indexes have been developed to detect departure from known distributions, such as normality, or to find separations between known groups. Here, we are interested in finding projections revealing potent

Ryan Spring, Anastasios Kyrillidis, Vijai Mohan, Anshumali Shrivastava

Many popular first-order optimization methods (e.g., Momentum, AdaGrad, Adam) accelerate the convergence rate of deep learning models. However, these algorithms require auxiliary parameters, which cost additional memory proportional to the number of parameters in the model. The problem is becoming more severe as deep learning models continue to grow larger i

George Stamatescu, Federica Gerace, Carlo Lucibello, Ian Fuss

The training of stochastic neural network models with binary ($\pm1$) weights and activations via continuous surrogate networks is investigated. We derive new surrogates using a novel derivation based on writing the stochastic neural network as a Markov chain. This derivation also encompasses existing variants of the surrogates presented in the literature. F

Dominique Beaini, Sofiane Achiche, Fabrice Nonez, Olivier Brochu Dufour

In computer vision, the gradient and Laplacian of an image are used in different applications, such as edge detection, feature extraction, and seamless image cloning. Computing the gradient of an image is straightforward since numerical derivatives are available in most computer vision toolboxes. However, the reverse problem is more difficult, since computin

Kentaro Yoshioka, Edward Lee, Simon Wong, Mark Horowitz

Real-time CNN-based object detection models for applications like surveillance can achieve high accuracy but are computationally expensive. Recent works have shown 10 to 100x reduction in computation cost for inference by using domain-specific networks. However, prior works have focused on inference only. If the domain model requires frequent retraining, tra

Dimitris M. Christodoulou, Demosthenes Kazanas

Recent ALMA/DSHARP observations reveal that the disk of RU Lup shows presently only four dark gaps at large radii in which planets are presumably already orbiting. This gap arrangement is quite similar to the current structure of the outer planets in our solar system. Although there may be more undetected planets forming in the inner disk of RU Lup, the info

Jie Wang, Tamal Batabyal, Mingxing Zhang, Ji Zhang

Bacterial biofilm segmentation poses significant challenges due to lack of apparent structure, poor imaging resolution, limited contrast between conterminous cells and high density of cells that overlap. Although there exist bacterial segmentation algorithms in the existing art, they fail to delineate cells in dense biofilms, especially in 3D imaging scenari

Mengmi Zhang, Claire Tseng, Karla Montejo, Joseph Kwon

Context reasoning is critical in a wide variety of applications where current inputs need to be interpreted in the light of previous experience and knowledge. Both spatial and temporal contextual information play a critical role in the domain of visual recognition. Here we investigate spatial constraints (what image features provide contextual information an

Nirupam Bidikar, Kotoju Rajitha, P. Usha Supriya

Solar energy is one of the most economical and clean sustainable energy sources on the planet. However, the solar energy throughput is highly unpredictable due to its dependency on a plethora of conditions including weather, seasons, and other ecological/environmental conditions. Thus, the solar energy prediction is an inevitable necessity to optimize solar

Andrea Arfé, Brian Alexander, Lorenzo Trippa

Most statistical tests for treatment effects used in randomized clinical trials with survival outcomes are based on the proportional hazards assumption, which often fails in practice. Data from early exploratory studies may provide evidence of non-proportional hazards which can guide the choice of alternative tests in the design of practice-changing confirma

Dinghan Shen, Asli Celikyilmaz, Yizhe Zhang, Liqun Chen

Variational autoencoders (VAEs) have received much attention recently as an end-to-end architecture for text generation with latent variables. In this paper, we investigate several multi-level structures to learn a VAE model to generate long, and coherent text. In particular, we use a hierarchy of stochastic layers between the encoder and decoder networks to

Meixia Lin, Defeng Sun, Kim-Chuan Toh, Yancheng Yuan

The exclusive lasso (also known as elitist lasso) regularization has become popular recently due to its superior performance on group sparsity. Compared to the group lasso regularization which enforces the competition on variables among different groups, the exclusive lasso regularization also enforces the competition within each group. In this paper, we pro

Hangjin Liu, Cynthia Rush, Dror Baron

A common goal in many research areas is to reconstruct an unknown signal x from noisy linear measurements. Approximate message passing (AMP) is a class of low-complexity algorithms for efficiently solving such high-dimensional regression tasks. Often, it is the case that side information (SI) is available during reconstruction. For this reason a novel algori

Chuan Lu, Xueyu Zhu

In this paper, we present a new nonintrusive reduced basis method when a cheap low-fidelity model and expensive high-fidelity model are available. The method relies on proper orthogonal decomposition (POD) to generate the high-fidelity reduced basis and a shallow multilayer perceptron to learn the high-fidelity reduced coefficients. In contrast to other meth

Cristián Martínez, Nicolás Parra, Nicolás Valdés, Jorge Zanelli

We present a complete study of the geodesics around naked singularities in AdS$_3$, the three-dimensional anti-de Sitter spacetime. These stationary spacetimes, characterized by two conserved charges --mass and angular momentum--, are obtained through identifications along spacelike Killing vectors with a fixed point. They are interpreted as massive spinning

Slavko Bogdanov, Wynn C. G. Ho, Teruaki Enoto, Sebastien Guillot

We present new timing and spectral analyses of PSR J1412+7922 (Calvera) and PSR J1849-0001, which are only seen as pulsars in X-rays, based on observations conducted with the Neutron Star Interior Composition Explorer (NICER). We obtain updated and substantially improved pulse ephemerides compared to previous X-ray studies, as well as spectra that can be wel

Daniele Rosso, Alistair Savage

To each symmetric algebra we associate a family of algebras that we call quantum affine wreath algebras. These can be viewed both as symmetric algebra deformations of affine Hecke algebras of type $A$ and as quantum deformations of affine wreath algebras. We study the structure theory of these new algebras and their natural cyclotomic quotients.

Jiaqi Hu, Zhaorong Wang, Seonghoon Kim, Hui Deng

Microcavity exciton polariton systems can have a wide range of macroscopic quantum effects that may be turned into better photonic technologies. Polariton Bose-Einstein condensation and photon lasing have been widely accepted in the limits of low and high carrier densities, but identification of the expected Bardeen-Cooper-Schrieffer (BCS) state at intermedi

Julian Juhi-Lian Ting

Mechanisms to use nanoparticles to separate sunlight into photovoltaic useful range and thermally useful range to increase the efficiency of solar cells and to dissipate heat radiatively are discussed based upon lessons we learnt from photosynthesis. We show that the dual-band maxima in the absorption spectrum of bacterial light harvestors not only are due t

Julien M. Hendrickx, Alex Olshevsky, Venkatesh Saligrama

We consider the problem of learning the qualities of a collection of items by performing noisy comparisons among them. Following the standard paradigm, we assume there is a fixed "comparison graph" and every neighboring pair of items in this graph is compared $k$ times according to the Bradley-Terry-Luce model (where the probability than an item wins

Mojtaba Haghighatlari, Johannes Hachmann

In this review, we highlight recent developments in the application of machine learning for molecular modeling and simulation. After giving a brief overview of the foundations, components, and workflow of a typical supervised learning approach for chemical problems, we showcase areas and state-of-the-art examples of their deployment. In this context, we disc

Stefano Sarao Mannelli, Florent Krzakala, Pierfrancesco Urbani, Lenka Zdeborová

In this work we analyse quantitatively the interplay between the loss landscape and performance of descent algorithms in a prototypical inference problem, the spiked matrix-tensor model. We study a loss function that is the negative log-likelihood of the model. We analyse the number of local minima at a fixed distance from the signal/spike with the Kac-Rice

Donya G. Dobakhshari, Vijay Gupta

We design an optimal contract between a demand response aggregator (DRA) and a customer for incentive-based demand response. We consider a setting in which the customer is asked to reduce her consumption by the DRA and she is compensated for this reduction. However, since the DRA must supply the customer with as much power as she desires, a strategic custome

Amelia Carolina Sparavigna

It is told that the Roman Colonia Marciana Ulpia Traiana Thamugadi, that is Timgad in Algeria, had been oriented to the sunrise on the day of Trajan's birthday, that is 18 September 100 AD. Here we use software CalSKY to investigate the sunrise azimuth and compare it to the direction of the decumanus of the Roman town.

Arjen van der Meer, Cornelius Steinbrink, Kai Heussen, Daniel Morales Bondy

The complex and often safety-critical nature of cyber-physical energy systems makes validation a key challenge in facilitating the energy transition, especially when it comes to the testing on system level. Reliable and reproducible validation experiments can be guided by the concept of design of experiments, which is, however, so far not fully adopted by re

T. E. Raptis

The present work attempts both a review of previous methods for transferring digital and symbolic computations in an analog or optical substrate and also to offer certain alternatives not yet fully explored. The essential difference from previous cases lies in the merging of general signal processing and computational theory with some emphasis on the foundat

Thomio Watanabe, Denis Wolf

The instance segmentation problem intends to precisely detect and delineate objects in images. Most of the current solutions rely on deep convolutional neural networks but despite this fact proposed solutions are very diverse. Some solutions approach the problem as a network problem, where they use several networks or specialize a single network to solve sev

Manuel Lagunas, Elena Garces, Diego Gutierrez

Selecting an optimal set of icons is a crucial step in the pipeline of visual design to structure and navigate through content. However, designing the icons sets is usually a difficult task for which expert knowledge is required. In this work, to ease the process of icon set selection to the users, we propose a similarity metric which captures the properties

Cristina Kadar, Rudolf Maculan, Stefan Feuerriegel

Crime events are known to reveal spatio-temporal patterns, which can be used for predictive modeling and subsequent decision support. While the focus has hitherto been placed on areas with high population density, we address the challenging undertaking of predicting crime hotspots in regions with low population densities and highly unequally-distributed crim

Paweł Lewulis, Andrzej Dragan

Instantaneous derivation of the Thomas precession.

Sourav Roy Chowdhury, Debabrata Deb, Saibal Ray, Farook Rahaman

We investigate a simplified model for the strange stars in the framework of Finslerian spacetime geometry, composed of charged fluid. It is considered that the fluid consisting of three flavor quarks including a small amount of non-interacting electrons to maintain the chemical equilibrium and assumed that the fluid is compressible by nature. To obtain the s

I. Brewis, J. A. McLaughlin

The aim of this study is to use image-processing techniques developed in the field of astrophysics as inspiration for a novel approach to the three-dimensional (3D) imaging of periprocedural medical data, with the intention of providing improved visualisation of patient-specific heart structure and thereby allowing for an improved quality of procedural plann

Rakshit Agrawal, Luca de Alfaro, David Helmbold

Adversarial attacks add perturbations to the input features with the intent of changing the classification produced by a machine learning system. Small perturbations can yield adversarial examples which are misclassified despite being virtually indistinguishable from the unperturbed input. Classifiers trained with standard neural network techniques are highl

D. Canarutto, E. Minguzzi

The Lorentzian distance formula, conjectured several years ago by Parfionov and Zapatrin, has been recently proved by the second author. In this work we focus on the derivation of an equivalent expression in terms of the geometry of 2-spinors by using a partly original approach due to the first author. Our calculations clearly show the independence of the al

Logan Francis, Doug Johnstone, Michael M. Dunham, Todd R. Hunter

Variability of pre-main-sequence stars observed at optical wavelengths has been attributed tofluctuations in the mass accretion rate from the circumstellar disk onto the forming star.Detailed models of accretion disks suggest that young deeply embedded protostars should also exhibit variations in their accretion rates, and that these changes can be tracked i

Constantia Alexandrou, Krzysztof Cichy, Martha Constantinou, Kyriakos Hadjiyiannakou

We present a detailed study of the helicity-dependent and helicity-independent collinear parton distribution functions (PDFs) of the nucleon, using the quasi-PDF approach. The lattice QCD computation is performed employing twisted mass fermions with a physical value of the light quark mass. We give a systematic and in-depth account of the salient features en

Iman Elghandour, Ahmet Kara, Dan Olteanu, Stijn Vansummeren

Many applications from various disciplines are now required to analyze fast evolving big data in real time. Various approaches for incremental processing of queries have been proposed over the years. Traditional approaches rely on updating the results of a query when updates are streamed rather than re-computing these queries, and therefore, higher execution

Churna Bhandari, S. Satpathy

Iridates are of considerable current interest because of the strong spin-orbit coupling that leads to a variety of new phenomena. Using density-functional studies, we predict the formation of a spin-orbital entangled two-dimensional electron gas (2DEG) in the $δ$-doped iridate La$_δ$Sr$_2$IrO$_4$, where a single SrO layer is replaced by a LaO layer. The extr

Haibin Di, Motaz Alfarraj, Ghassan AlRegib

The idea of curvature analysis has been widely used in subsurface structure interpretation from three-dimensional (3D) seismic data (e.g., fault/fracture detection and geomorphology delineation) by measuring the lateral changes in the geometry of seismic events. However, such geometric curvature utilizes only the kinematic information (two-way traveltime) of

Motaz Alfarraj, Haibin Di, Ghassan AlRegib

In this abstract, we propose a multiscale fusion technique to enhance seismic geometric attributes, such as dip and curvature, which are very sensitive to noise present in seismic data. For a give seismic section, first, we construct a Gaussian pyramid that allows us to generate the seismic attribute at different resolutions (scales). Then, all attributes at

Atta Norouzian, Bogdan Mazoure, Dermot Connolly, Daniel Willett

Voice controlled virtual assistants (VAs) are now available in smartphones, cars, and standalone devices in homes. In most cases, the user needs to first "wake-up" the VA by saying a particular word/phrase every time he or she wants the VA to do something. Eliminating the need for saying the wake-up word for every interaction could improve the user e

H. M. Bui, Alexandra Florea, Jonathan P. Keating, Edva Roditty-Gershon

We calculate the first and second moments of L-functions in the family of quadratic twists of a fixed elliptic curve E over F_q[x], asymptotically in the limit as the degree of the twists tends to infinity. We also compute moments involving derivatives of L-functions over quadratic twists, enabling us to deduce lower bounds on the correlations between the an

Zekun Xu, Deovrat Kakde, Arin Chaudhuri

In recent years, there have been many practical applications of anomaly detection such as in predictive maintenance, detection of credit fraud, network intrusion, and system failure. The goal of anomaly detection is to identify in the test data anomalous behaviors that are either rare or unseen in the training data. This is a common goal in predictive mainte

Laurens Weitkamp, Elise van der Pol, Zeynep Akata

Due to the capability of deep learning to perform well in high dimensional problems, deep reinforcement learning agents perform well in challenging tasks such as Atari 2600 games. However, clearly explaining why a certain action is taken by the agent can be as important as the decision itself. Deep reinforcement learning models, as other deep learning models

Julian O. Freeman, Sean Peterson, Cong Cao, Yujie Wang

We conduct experiments and simulations to study the disordered packing of rods in small containers. Experiments study cylindrical rods with aspect ratio ranging from 4 to 32; simulations use of spherocylinders with similar aspect ratios. In all cases, rods pack randomly in cylindrical containers whose smallest dimension is larger than the rod length. Packing

Emil M. Prodanov

For the radial motion of massive particles with large angular momenta in Schwarzschild geometry and that of massive charged particles with large angular momenta or energy in a particular range in Reissner--Nordström geometry, there exist classically forbidden regions on the outside of the respective event horizons which scatter certain infalling geodesics or

Tárik S. Salem, Karan Kathuria, Heri Ramampiaro, Helge Langseth

Keeping the electricity production in balance with the actual demand is becoming a difficult and expensive task in spite of an involvement of experienced human operators. This is due to the increasing complexity of the electric power grid system with the intermittent renewable production as one of the contributors. A beforehand information about an occurring

Timothy J. Kiely, Nathaniel D. Bastian

Successfully predicting gentrification could have many social and commercial applications; however, real estate sales are difficult to predict because they belong to a chaotic system comprised of intrinsic and extrinsic characteristics, perceived value, and market speculation. Using New York City real estate as our subject, we combine modern techniques of da

J. Bonetti, A. D. Sánchez, S. M. Hernandez, D. F. Grosz

We put forth an approach to obtain a quantum master equation for the propagation of light in nonlinear fiber optics by relying on simple quantum pictures of the processes (linear and nonlinear) occurring along propagation in an optical fiber. This equation is shown to be in excellent agreement with the classical Generalized Nonlinear Schrodinger Equation and

Antonio Di Bartolomeo, Aniello Pelella, Xiaowei Liu, Feng Miao

A few-layer palladium diselenide (PdSe2) field effect transistor is studied under external stimuli such as electrical and optical fields, electron irradiation and gas pressure. We observe ambipolar conduction and hysteresis in the transfer curves of the PdSe2 material unprotected and as-exfoliated. We tune the ambipolar conduction and its hysteretic behavior

Howard Jacobowitz

In 1998 the physicist Andre Trautman conjectured that a three-dimensional CR manifold is locally realizable if and only if its canonical bundle admits a closed nowhere zero section. We review the relevant definitions, give the physical context, and outline an earlier result which established a weak version of the Conjecture.

Rossen I. Ivanov, Emil M. Prodanov

The cosmological dynamics of a quintessence model based on real gas with general equation of state is presented within the framework of a three-dimensional dynamical system describing the time evolution of the number density, the Hubble parameter, and the temperature. Two global first integrals are found and examples for gas with virial expansion and van der

Anton Trushechkin

Foerster and modified Redfield theories play one of the central roles in the description of excitation energy transfer in molecular systems. However, in the present state, these theories describe only the dynamics of populations of local electronic excitations or delocalized exciton eigenstates, respectively, i.e., the diagonal elements of the density matrix

Bojun Zhou, Elizabeth Bernhardt, Ankita Bhuyan, Zoya Ghorbanishiadeh

After a brief introduction to the field of light-responsive materials, this paper provides a general theory for modeling the photomechanical response of a material, applies it to the two best-known mechanisms of photothermal heating and photo-isomerization, and then describes an experimental apparatus for quantitative measurements of the stress response. Sev

Haifa F. Alhasson, Shuaa S. Alharbi, Boguslaw Obara

The detection of vascular structures from noisy images is a fundamental process for extracting meaningful information in many applications. Most well-known vascular enhancing techniques often rely on Hessian-based filters. This paper investigates the feasibility and deficiencies of detecting curve-like structures using a Hessian matrix. The main contribution

David Rauch, Patrick Rein, Stefan Ramson, Jens Lincke

When working on a program, developers traditionally have to simulate the behavior of the abstract code in their heads until they can execute the application. Live programming aims to support the development and comprehension of programs by providing more immediate feedback on program behavior, but the divide between code and behavior often remains. The goal

Weixin Zhang, Bruno Oliveira

Context: Embedded Domain-Specific Languages (EDSLs) are a common and widely used approach to DSLs in various languages, including Haskell and Scala. There are two main implementation techniques for EDSLs: shallow embeddings and deep embeddings. Inquiry: Shallow embeddings are quite simple, but they have been criticized in the past for being quite limited in

Hrshikesh Arora, Marco Servetto, Bruno C. D. S. Oliveira

Context: Trait composition has inspired new research in the area of code reuse for object oriented (OO) languages. One of the main advantages of this kind of composition is that it makes possible to separate subtyping from subclassing; which is good for code-reuse, design and reasoning. However, handling of state within traits is difficult, verbose or ineleg

Andi Bejleri, Elton Domnori, Malte Viering, Patrick Eugster

Multiparty session types (MST) are a well-established type theory that describes the interactive structure of a fixed number of components from a global point of view and type-checks the components through projection of the global type onto the participants of the session. They guarantee communicationsafety for a language of multiparty sessions (LMS), i.e.,

Rodin Aarssen, Jurgen Vinju, Tijs van der Storm

Context: Meta programming consists for a large part of matching, analyzing, and transforming syntax trees. Many meta programming systems process abstract syntax trees, but this requires intimate knowledge of the structure of the data type describing the abstract syntax. As a result, meta programming is error-prone, and meta programs are not resilient to evol

Chin-Yun Yu, Li Su

We propose the multi-layered cepstrum (MLC) method to estimate multiple fundamental frequencies (MF0) of a signal under challenging contamination such as high-pass filter noise. Taking the operation of cepstrum (i.e., Fourier transform, filtering, and nonlinear activation) recursively, MLC is shown as an efficient method to enhance MF0 saliency in a step-by-

Guang-Lei Wang, Hong-Ya Xu, Ying-Cheng Lai

The phenomenon of chiral-symmetry protected thermalization gap in Hermitian photonic systems is counterintuitive as it implies that the photon coherence can be continuously improved by disorders towards an asymptotic limit. We show that the phenomenon disappears in time-independent, non-Hermitian photonic systems even when the chiral symmetry is well preserv

Le-Zhi Wang, Zhi-Dan Zhao, Jun-Jie Jiang, Bing-Hui Guo

We analyze five big data sets from a variety of online social networking (OSN) systems and find that the growth dynamics of meme popularity exhibit characteristically different behaviors. For example, there is linear growth associated with online recommendation and sharing platforms, a plateaued (or an ``S''-shape) type of growth behavior in a web se

Zhiyun Lu, Chao-Kai Chiang, Fei Sha

We study a budgeted hyper-parameter tuning problem, where we optimize the tuning result under a hard resource constraint. We propose to solve it as a sequential decision making problem, such that we can use the partial training progress of configurations to dynamically allocate the remaining budget. Our algorithm combines a Bayesian belief model which estima

C. L. Van Eck, M. Haverkorn, M. I. R. Alves, R. Beck

Faraday tomography allows us to map diffuse polarized synchrotron emission from our Galaxy and use it to interpret the magnetic field in the interstellar medium (ISM). We have applied Faraday tomography to 60 observations from the LOFAR Two-meter Sky Survey (LOTSS) and produced a Faraday depth cube mosaic covering 568 square degrees at high Galactic latitude

Nodoka Yamanaka

The electric dipole moment (EDM) is a very sensitive probe of CP violation beyond the standard model, and it as been measured in many systems such as atoms, neutrons, etc. The EDM of composite systems may be sensitive to several elementary level CP violating processes, but the theoretical evaluations of the CP violation at different physical (atomic, nuclear

Amir Saeidi, Jurriaan Hage, Ravi Khadka, Slinger Jansen

Program comprehension concerns the ability of an individual to make an understanding of an existing software system to extend or transform it. Software systems comprise of data that are noisy and missing, which makes program understanding even more difficult. A software system consists of various views including the module dependency graph, execution logs, e

S. Tucker Taft

ParaSail is a language specifically designed to simplify the construction of programs that make full, safe use of parallel hardware even while manipulating potentially irregular data structures. As parallel hardware has proliferated, there has been an urgent need for languages that ease the writing of correct parallel programs. ParaSail achieves these goals

Florian Myter, Christophe Scholliers, Wolfgang De Meuter

Context: The term reactivity is popular in two areas of research: programming languages and distributed systems. On one hand, reactive programming is a paradigm which provides programmers with the means to declaratively write event-driven applications. On the other hand, reactive distributed systems handle client requests in a timely fashion regardless of lo

Ratheesh K. P., Sunil Jacob John

Soft set theory can deal uncertainties in nature by parametrization process. In this paper, we explore the objects and morphisms of category of soft sets, Sset(U) in detail. Also, gives characterizations of monomorphisms and epimorphisms in Sset(U).

Gabrielle Allen, Igor Andreoni, Etienne Bachelet, G. Bruce Berriman

This report provides an overview of recent work that harnesses the Big Data Revolution and Large Scale Computing to address grand computational challenges in Multi-Messenger Astrophysics, with a particular emphasis on real-time discovery campaigns. Acknowledging the transdisciplinary nature of Multi-Messenger Astrophysics, this document has been prepared by

R. Holman, Benoit J. Richard

Entangled inflationary quantum states offer an interesting alternative to the standard Bunch-Davies vacuum. While they can be viewed from the point of view of a theory of effective initial states, it has been unclear exactly how such states might be generated. Using a model involving two scalar fields $Φ,Σ$ we show that if {\em both} fields have non-zero tim

R. M. Ludlam, J. M. Miller, D. Barret, E. M. Cackett

Atoll sources are accreting neutron star (NS) low-mass X-ray binaries. We present a spectral analysis of four persistent atoll sources (GX 3+1, 4U 1702$-$429, 4U 0614+091, and 4U 1746$-$371) observed for $\sim20$ ks each with NuSTAR to determine the extent of the inner accretion disk. These sources range from an apparent luminosity of $0.006-0.11$ of the Edd

Gabriela Barenboim, Peter B. Denton, Stephen J. Parke, Christoph A. Ternes

In this paper we review different expansions for neutrino oscillation probabilities in matter in the context of long-baseline neutrino experiments. We examine the accuracy and computational efficiency of different exact and approximate expressions. We find that many of the expressions used in the literature are not precise enough for the next generation of l

Paolo Caldiroli, Gabriele Cora

We study the motion of a charged particle under the action of a magnetic field with cylindrical symmetry. In particular we consider magnetic fields with constant direction and with magnitude depending on the distance $r$ from the symmetry axis of the form $1 + Ar^{-γ}$ as $r\to\infty$, with $A\ne 0$ and $γ> 1$. With perturbative-variational techniques, we ca

I. F. Mirabel

Theoretical models and observations suggest that primordial Stellar Black Holes (Pop-III-BHs) were prolifically formed in HMXBs, which are powerful relativistic jet sources of synchrotron radiation called Microquasars (MQs). Large populations of BH-HMXB-MQs at cosmic dawn produce a smooth synchrotron cosmic radio background (CRB) that could account for the e