April 2019 arXiv papers — page 48

Kentaro Heya, Ken M Nakanishi, Kosuke Mitarai, Zhiguang Yan

Quantum simulation is one of the key applications of quantum computing, which accelerates research and development in the fields such as chemistry and material science. The recent development of noisy intermediate-scale quantum (NISQ) devices urges the exploration of applications without the necessity of quantum error correction. In this paper, we propose an

Mark P. Hertzberg, Masaki Yamada

We analyze vacuum tunneling in quantum field theory in a general formalism by using the Wigner representation. In the standard instanton formalism, one usually approximates the initial false vacuum state by an eigenstate of the field operator, imposes Dirichlet boundary conditions on the initial field value, and evolves in imaginary time. This approach does

Mason Ng, Nikole M. Nielsen, Glenn G. Kacprzak, Stephanie K. Pointon

We probe the high-ionization circumgalactic medium by examining absorber kinematics, absorber-galaxy kinematics, and average absorption profiles of 31 OVI absorbers from the &#34;Multiphase Galaxy Halos&#34; Survey as a function of halo mass, redshift, inclination, and azimuthal angle. The galaxies are isolated at $0.12<z_{\rm gal}<0.66$ and are probed by a

Jonathan Sterling, Carlo Angiuli, Daniel Gratzer

We contribute XTT, a cubical reconstruction of Observational Type Theory which extends Martin-L\"of's intensional type theory with a dependent equality type that enjoys function extensionality and a judgmental version of the unicity of identity types principle (UIP): any two elements of the same equality type are judgmentally equal. Moreover, we conjecture t

Lingxu Meng

We give a simple proof of a result on the $\partial\bar{\partial}$-lemma property under a blow-up transformation by Deligne--Griffiths--Morgan--Sullivan's criterion. Here, we use an explicit blow-up formula for Dolbeault cohomology given in our previous work, which can be induced by a morphism expressed on the level of spaces of forms and currents. At last,

Wei Gan, Ran-ran Feng, Hong-Fei Wang

In a recent letter (PRL,121,246101,2018), Sun et al. reported that combined MD simulation and sum frequency generation vibrational spectroscopy (SFG-VS) measurements led to conclusions of a broad and exponentially decaying orientational distribution, and the presence of the free O-H group pointing down to the bulk at the air/water interface. In this comment,

Vishnu Raj, Sheetal Kalyani

Recent research in the design of end to end communication system using deep learning has produced models which can outperform traditional communication schemes. Most of these architectures leveraged autoencoders to design the encoder at the transmitter and decoder at the receiver and train them jointly by modeling transmit symbols as latent codes from the en

Ying Wang, Xiao Xu, Tao Jin, Xiang Li

Representation learning (RL) plays an important role in extracting proper representations from complex medical data for various analyzing tasks, such as patient grouping, clinical endpoint prediction and medication recommendation. Medical data can be divided into two typical categories, outpatient and inpatient, that have different data characteristics. Howe

Hal Finkel, David Poliakoff, David F. Richards

The C++ programming language is not only a keystone of the high-performance-computing ecosystem but has proven to be a successful base for portable parallel-programming frameworks. As is well known, C++ programmers use templates to specialize algorithms, thus allowing the compiler to generate highly-efficient code for specific parameters, data structures, an

Shawn Shan, Emily Wenger, Bolun Wang, Bo Li

Deep neural networks (DNN) are known to be vulnerable to adversarial attacks. Numerous efforts either try to patch weaknesses in trained models, or try to make it difficult or costly to compute adversarial examples that exploit them. In our work, we explore a new "honeypot" approach to protect DNN models. We intentionally inject trapdoors, honeypot weaknesse

Neda Zarayeneh, Ananth Kalyanaraman

Community detection is a discovery tool used by network scientists to analyze the structure of real-world networks. It seeks to identify natural divisions that may exist in the input networks that partition the vertices into coherent modules (or communities). While this problem space is rich with efficient algorithms and software, most of this literature cat

Haifeng Dong, Jingling Chen, Jimin Li, Chen Liu

The diffusion crosstalk free distance is an important parameter for spin images in atomic vapor cells and is also regarded as a limit on the spatial resolution. However, by modulating the pumping light both spatially and temporally using a digital micromirror device, a spin image of a vapor cell has been obtained with a distinguishable stripe width of 13.7~$

Ignacio Esponda, Demian Pouzo, Yuichi Yamamoto

We consider an agent who represents uncertainty about the environment via a possibly misspecified model. Each period, the agent takes an action, observes a consequence, and uses Bayes&#39; rule to update her belief about the environment. This framework has become increasingly popular in economics to study behavior driven by incorrect or biased beliefs. Curre

Panos Stinis, Huan Lei, Jing Li, Hui Wan

Stochastic parameterizations are used in numerical weather prediction and climate modeling to help capture the uncertainty in the simulations and improve their statistical properties. Convergence issues can arise when time integration methods originally developed for deterministic differential equations are applied naively to stochastic problems. (Hodyss et

Jeong Han Kim, Minho Kim, Kyoungchul Kong, Konstantin T. Matchev

We examine the discovery potential for double Higgs production at the high luminosity LHC in the final state with two $b$-tagged jets, two leptons and missing transverse momentum. Although this dilepton final state has been considered a difficult channel due to the large backgrounds, we argue that it is possible to obtain sizable signal significance, by adop

Sinead A. Williamson, Michael Minyi Zhang, Paul Damien

In many applications, observed data are influenced by some combination of latent causes. For example, suppose sensors are placed inside a building to record responses such as temperature, humidity, power consumption and noise levels. These random, observed responses are typically affected by many unobserved, latent factors (or features) within the building s

Hongyu Yu, Magdalena Laurien, Zhenpeng Hu, Oleg Rubel

Spectral ordering between dark and bright excitons in transition metal dichalcogenides is of increasing interest for optoelectronic applications. However, little is known about dark exciton energies and their binding energies. We report the exciton landscape including momentum-forbidden dark excitons of MoS$_{2}$ monolayer using single shot GW-Bethe Salpeter

Vatsal Sharan, Aaron Sidford, Gregory Valiant

We consider the problem of performing linear regression over a stream of $d$-dimensional examples, and show that any algorithm that uses a subquadratic amount of memory exhibits a slower rate of convergence than can be achieved without memory constraints. Specifically, consider a sequence of labeled examples $(a_1,b_1), (a_2,b_2)\ldots,$ with $a_i$ drawn ind

Micah Brame, Scott Kaschner

For maps of one complex variable, $f$, given as the sum of a degree $n$ power map and a degree $d$ polynomial, we provide necessary and sufficient conditions that the geometric limit as $n$ approaches infinity of the set of points that remain bounded under iteration by $f$ is the closed unit disk or the unit circle. We also provide a general description, for

Soichiro Fujii

Universal algebra uniformly captures various algebraic structures, by expressing them as equational theories or abstract clones. The ubiquity of algebraic structures in mathematics and related fields has given rise to several variants of universal algebra, such as theories of symmetric operads, non-symmetric operads, generalised operads, PROPs, PROs, and mon

Utkarsh Sharma, Terumi Umematsu, Masanori Tsujikawa, Yoshifumi Onishi

We propose a novel heart rate (HR) estimation method from facial videos that dynamically adapts the HR pulse extraction algorithm to separately deal with noise from &#39;rigid&#39; head motion and &#39;non-rigid&#39; facial expression. We first identify the noise type, based on which, we apply specific noise removal steps. Experiments performed on popular da

M. Garau, M. J. Nieves, I. S. Jones

We consider both analytical and numerical studies of a steady-state fracture process inside a discrete mass-beam structure, composed of periodically placed masses connected by Euler-Bernoulli beams. A fault inside the structure is assumed to propagate with a constant speed and this occurs as a result of the action of a remote sinusoidal, mechanical load. The

Wei Yan, Yiting shao, Shan Liu, Thomas H Li

Point cloud is a fundamental 3D representation which is widely used in real world applications such as autonomous driving. As a newly-developed media format which is characterized by complexity and irregularity, point cloud creates a need for compression algorithms which are more flexible than existing codecs. Recently, autoencoders(AEs) have shown their eff

Tien Huu Do, Xiao Luo, Duc Minh Nguyen, Nikos Deligiannis

Rumours have existed for a long time and have been known for serious consequences. The rapid growth of social media platforms has multiplied the negative impact of rumours; it thus becomes important to early detect them. Many methods have been introduced to detect rumours using the content or the social context of news. However, most existing methods ignore

Freek Boutkan, Jorn Ranzijn, David Rau, Eelco van der Wel

The Pointer-Generator architecture has shown to be a big improvement for abstractive summarization seq2seq models. However, the summaries produced by this model are largely extractive as over 30% of the generated sentences are copied from the source text. This work proposes a multihead attention mechanism, pointer dropout, and two new loss functions to promo

Isaac Mativo, Yelena Yesha, Michael Grasso, Tim Oates

The use of artificial intelligence in clinical care to improve decision support systems is increasing. This is not surprising since, by its very nature, the practice of medicine consists of making decisions based on observations from different systems both inside and outside the human body. In this paper, we combine three general systems (ICU, diabetes, and

Robert Singer

This work presents a fully elaborated ontology, defined via the Ontology Web Language (OWL), of the Business Process Model and Notation (BPMN) standard to define business process models, and we demonstrate that any BPMN model can be serialized as OWL file. Based on ontological analysis and a corresponding definition of a modeling notation as ontology we show

Fayeem Aziz, Stephan K. Chalup, James Juniper

Big Data in IoT is a large and fast-developing area where many different methods and techniques can play a role. Due to rapid progress in Machine Learning and new hardware developments, a dynamic turnaround of methods and technologies can be observed. This overview therefore tries to be broad and highlevel without claiming to be comprehensive. Its approach t

Onkar Krishna, Kiyoharu Aizawa, Go Irie

The existing computational visual attention systems have focused on the objective to basically simulate and understand the concept of visual attention system in adults. Consequently, the impact of observer&#39;s age in scene viewing behavior has rarely been considered. This study quantitatively analyzed the age-related differences in gaze landings during sce

Antoine Viscardi, Casey Juanxi Li, Thomas Hollis

As advances in signature recognition have reached a new plateau of performance at around 2% error rate, it is interesting to investigate alternative approaches. The approach detailed in this paper looks at using Variational Auto-Encoders (VAEs) to learn a latent space representation of genuine signatures. This is then used to pass unlabelled signatures such

Tauseef Ali, Ahmed B. Altamimi

Crowd analysis and management is a challenging problem to ensure public safety and security. For this purpose, many techniques have been proposed to cope with various problems. However, the generalization capabilities of these techniques is limited due to ignoring the fact that the density of crowd changes from low to extreme high depending on the scene unde

Apostol Vassilev

How to model and encode the semantics of human-written text and select the type of neural network to process it are not settled issues in sentiment analysis. Accuracy and transferability are critical issues in machine learning in general. These properties are closely related to the loss estimates for the trained model. I present a computationally-efficient a

Cory Cornelius, Shang-Tse Chen, Jason Martin, Duen Horng Chau

In this talk we describe our content-preserving attack on object detectors, ShapeShifter, and demonstrate how to evaluate this threat in realistic scenarios. We describe how we use CARLA, a realistic urban driving simulator, to create these scenarios, and how we use ShapeShifter to generate content-preserving attacks against those scenarios.

Oscar Brauer, Miguel Socolovsky

We argue that a great circle in the 7-sphere plays the role of an Einstein-Rosen bridge in Einstein-Podolsky-Rosen-Bohm decays.

Bruno Franchi, Martin Heida, Silvia Lorenzani

In this note, we apply the theory of stochastic homogenization to find the asymptotic behavior of the solution of a set of Smoluchowski&#39;s coagulation-diffusion equations with non-homogeneous Neumann boundary conditions. This system is meant to model the aggregation and diffusion of $β$-amyloid peptide (A$β$) in the cerebral tissue, a process associated w

Muhammad Usman Sadiq, Diana Svaldi, Trey Shenk, Evan Breedlove

Several studies have used structural correlation networks, derived from anatomical covariance of brain regions, to analyze neurologic changes associated with multiple sclerosis, schizophrenia and breast cancer [1][2]. Graph-theoretical analyses of human brain structural networks have consistently shown the characteristic of small-worldness that reflects a ne

Minjung Kim, Glenn J. Martyna, Sohrab Ismail-Beigi

The GW method is a many-body electronic structure technique capable of generating accurate quasiparticle properties for realistic systems spanning physics, chemistry, and materials science. Despite its power, GW is not routinely applied to large complex assemblies due to its large computational overhead and quartic scaling with particle number. Here, the GW

Adam Santoro, Felix Hill, David Barrett, David Raposo

Brette contends that the neural coding metaphor is an invalid basis for theories of what the brain does. Here, we argue that it is an insufficient guide for building an artificial intelligence that learns to accomplish short- and long-term goals in a complex, changing environment.

Paul A. Walker

We present a local interpolation method in four dimensions utilising cubic splines. An extension of the three-dimensional tricubic method, the interpolated function has C$^1$ continuity and its partial derivatives are analytically accessible. The specific example of application of this work to a time-varying three-dimensional magnetic field is given, but thi

Dary Ruiz-Rodriguez, Joel H. Kastner, Ruobing Dong, David A. Principe

We analyze the highest-resolution millimeter continuum and near-infrared (NIR) scattered-light images presented to date of the circumbinary disk orbiting V4046 Sgr, a ~20 Myr old actively accreting, close binary T Tauri star system located a mere 72.4 pc from Earth. We observed the disk with the Atacama Large Millimeter/submillimeter Array (ALMA) at 870-micr

Brian Gaudet, Richard Linares, Roberto Furfaro

This paper proposes a novel adaptive guidance system developed using reinforcement meta-learning with a recurrent policy and value function approximator. The use of recurrent network layers allows the deployed policy to adapt real time to environmental forces acting on the agent. We compare the performance of the DR/DV guidance law, an RL agent with a non-re

Ryan M. Lau, Marianne Heida, Dominic J. Walton, Mansi M. Kasliwal

We present a mid-infrared (IR) sample study of nearby ultraluminous X-ray sources (ULXs) using multi-epoch observations with the Infrared Array Camera (IRAC) on the Spitzer Space Telescope. Spitzer/IRAC observations taken after 2014 were obtained as part of the Spitzer Infrared Intensive Transients Survey (SPIRITS). Our sample includes 96 ULXs located within

Artur Andrzejak, Oliver Wenz, Diego Costa

Data analysis is at the core of scientific studies, a prominent task that researchers and practitioners typically undertake by programming their own set of automated scripts. While there is no shortage of tools and languages available for designing data analysis pipelines, users spend substantial effort in learning the specifics of such languages/tools and o

Alexander O. Ivanov, Alexey A. Tuzhilin

We have constructed a realization of rectilinear geodesic (in the sense of~\cite{Memoli2018}), lying in Gromov-Hausdorff space, as a shortest geodesic w.r.t. the Hausdorff distance in an ambient metric space.

Tianlin Liu, Lyle Ungar, João Sedoc

Distributed representations of sentences have become ubiquitous in natural language processing tasks. In this paper, we consider a continual learning scenario for sentence representations: Given a sequence of corpora, we aim to optimize the sentence encoder with respect to the new corpus while maintaining its accuracy on the old corpora. To address this prob

J. L. M. da Silva, F. V. Mendes, R. V. Ramos

The present work brings two applications of the Lambert-Tsallis Wq function in radial basis function networks (RBFN). Initially, a RBFN is used to discriminate between entangled and disentangled bipartite of qubit states. The kernel used is based on the Lambert-Tsallis Wq function for q = 2 and the quantum relative disentropy is used as distance measure betw

Laura Bozzelli, Alberto Molinari, Angelo Montanari, Adriano Peron

Planning is one of the most studied problems in computer science. In this paper, we consider the timeline-based approach, where the domain is modeled by a set of independent, but interacting, components, identified by a set of state variables, whose behavior over time (timelines) is governed by a set of temporal constraints (synchronization rules). Timeline-

M. Človečko, E. Gažo, P. Skyba

We provide experimental details of the first experiment made in zero temperature limit ($\sim$ 600\,$μ$K) studying the magnonic black/white hole horizon analogue using absolutely pure physical system based on the spin superfluidity in superfluid $^3$He-B. We show that spin precession waves propagating on the background of the spin super-currents in a channel

Thomas Müller-Gronbach, Larisa Yaroslavtseva

In this paper we study strong approximation of the solution of a scalar stochastic differential equation (SDE) at the final time in the case when the drift coefficient may have discontinuities in space. Recently it has been shown in [Müller-Gronbach, T., and Yaroslavtseva, L., On the performance of the Euler-Maruyama scheme for SDEs with discontinuous drift

Yiannis Contoyiannis, Myron Kampitakis

The problem of the phase transition of a Z(3) spin system is a complex issue. A numerical simulation in the framework of the mean field theory using the Metropolis algorithm reveals: (a) the existence of second order phase transition with a degenerated critical point which could be considered as a state of resonance (b) hysteresis phenomena which are accompa

Yi Tian Xu, Yaqiao Li, David Meger

Inspired by ideas in cognitive science, we propose a novel and general approach to solve human motion understanding via pattern completion on a learned latent representation space. Our model outperforms current state-of-the-art methods in human motion prediction across a number of tasks, with no customization. To construct a latent representation for time-se

Shahram Ghorbani, John H. L. Hansen

Recognition of accented speech is a long-standing challenge for automatic speech recognition (ASR) systems, given the increasing worldwide population of bi-lingual speakers with English as their second language. If we consider foreign-accented speech as an interpolation of the native language (L1) and English (L2), using a model that can simultaneously addre

Chu Wang, Lei Tang, Yang Lu, Shujun Bian

ProductNet is a collection of high-quality product datasets for better product understanding. Motivated by ImageNet, ProductNet aims at supporting product representation learning by curating product datasets of high quality with properly chosen taxonomy. In this paper, the two goals of building high-quality product datasets and learning product representatio

Liqian Ma, Qianru Sun, Bernt Schiele, Luc Van Gool

Image-to-image (I2I) translation is a pixel-level mapping that requires a large number of paired training data and often suffers from the problems of high diversity and strong category bias in image scenes. In order to tackle these problems, we propose a novel BiLevel (BiL) learning paradigm that alternates the learning of two models, respectively at an inst

Thomas P Fay, David E Manolopoulos

Magnetic field effects on radical pair reactions arise due to the interplay of coherent electron spin dynamics and spin relaxation effects, a rigorous treatment of which requires the solution of the Liouville-von Neumann equation. However, it is often found that simple incoherent kinetic models of the radical pair singlet-triplet intersystem crossing provide

Alex Kearney, Patrick M. Pilarski

Within Reinforcement Learning, there is a growing collection of research which aims to express all of an agent&#39;s knowledge of the world through predictions about sensation, behaviour, and time. This work can be seen not only as a collection of architectural proposals, but also as the beginnings of a theory of machine knowledge in reinforcement learning.

Alex Kearney, Oliver Oxton

Within Reinforcement Learning, there is a fledgling approach to conceptualizing the environment in terms of predictions. Central to this predictive approach is the assertion that it is possible to construct ontologies in terms of predictions about sensation, behaviour, and time---to categorize the world into entities which express all aspects of the world us

Saeed Arabi, Arya Haghighat, Anuj Sharma

This paper aims at providing researchers and engineering professionals with a practical and comprehensive deep learning based solution to detect construction equipment from the very first step of its development to the last one which is deployment. This paper focuses on the last step of deployment. The first phase of solution development, involved data prepa

Giovanni Campagna, Silei Xu, Mehrad Moradshahi, Richard Socher

To understand diverse natural language commands, virtual assistants today are trained with numerous labor-intensive, manually annotated sentences. This paper presents a methodology and the Genie toolkit that can handle new compound commands with significantly less manual effort. We advocate formalizing the capability of virtual assistants with a Virtual Assi

Deyi Liu, Quoc Tran-Dinh

We develop a new inexact interior-point Lagrangian decomposition method to solve a wide range class of constrained composite convex optimization problems. Our method relies on four techniques: Lagrangian dual decomposition, self-concordant barrier smoothing, path-following, and proximal-Newton technique. It also allows one to approximately compute the soluti

Andrew Rouditchenko, Hang Zhao, Chuang Gan, Josh McDermott

Segmenting objects in images and separating sound sources in audio are challenging tasks, in part because traditional approaches require large amounts of labeled data. In this paper we develop a neural network model for visual object segmentation and sound source separation that learns from natural videos through self-supervision. The model is an extension o

Casey Johnson, Roman M. Taranets, Natalia Vasylyeva, Marina Chugunova

The hypothalamic pituitary adrenal (HPA) axis responds to physical and mental challenge to maintain homeostasis in part by controlling the body&#39;s cortisol level. Dysregulation of the HPA axis is implicated in numerous stress-related diseases. For a structured model of the HPA axis that includes the glucocorticoid receptor but does not take into account t

Yanhui Zhang, Stefano Sanvito

The mechanical properties of heterophase interfaces are critically important for the behaviour of graphene-reinforced composites. In this work, the structure, adhesion, cleavage and sliding of heterophase interfaces, formed between a ZrB$_2$ matrix and graphene nanosheets, are systematically investigated by density functional theory, and compared to availabl

Alexandra Z. Greenbaum, Anthony Cheetham, Anand Sivaramakrishnan, Fredrik T. Rantakyrö

The Gemini Planet Imager (GPI) contains a 10-hole non-redundant mask (NRM), enabling interferometric resolution in complement to its coronagraphic capabilities. The NRM operates both in spectroscopic (integral field spectrograph, henceforth IFS) and polarimetric configurations. NRM observations were taken between 2013 and 2016 to characterize its performance

M. Antonello, A. Belov, G. Bonomi, R. S. Brusa

We investigate experimentally the possibility of enhancing the production of $2^3S$ positronium atoms by driving the $1^3S$-$3^3P$ and $3^3P$-$2^3S$ transitions, overcoming the natural branching ratio limitation of spontaneous decay from $3^3P$ to $2^3S$. The decay of $3^3P$ positronium atoms towards the $2^3S$ level has been effciently stimulated by a 1312.

Daniel Grin, Mustafa A. Amin, Vera Gluscevic, Renée Hlǒzek

The axion is a hypothetical, well-motivated dark-matter particle whose existence would explain the lack of charge-parity violation in the strong interaction. In addition to this original motivation, an `axiverse&#39; of ultra-light axions (ULAs) with masses $10^{-33}\,{\rm eV}\lesssim m_{\rm a}\lesssim 10^{-10}\,{\rm eV}$ also emerges from string theory. Dep

Qing Liu, Gong Tang, Joseph P. Costantino, Chung-Chou H. Chang

An individualized risk prediction model that dynamically updates the probability of a clinical event from a specific cause is valuable for physicians to be able to optimize personalized treatment strategies in real-time by incorporating all available information collected over the follow-up. However, this is more complex and challenging when competing risks

Jacob P. Covey, Alp Sipahigil, Mark Saffman

Interfacing superconducting qubits with optical photons requires noise-free microwave-to-optical transducers, a technology currently not realized at the single-photon level. We propose to use four-wave-mixing in an ensemble of cold ytterbium (Yb) atoms prepared in the metastable &#39;clock&#39; state. The parametric process uses two high-lying Rydberg states

M. Nazif Faqiry, Lawryn Edmonds, Hongyu Wu

The largescale penetration of variable renewable energy (VRE) and their generation uncertainties poses a major challenge for the distribution system operator (DSO) to efficiently determine the day-ahead real and reactive power distribution locational marginal prices (DLMPs) and their underlying components. In this paper, we propose a DLMP-based transactive d

Austin D. Sendek, Gowoon Cheon, Mauro Pasta, Evan J. Reed

We compile data and machine learned models of solid Li-ion electrolyte performance to assess the state of materials discovery efforts and build new insights for future efforts. Candidate electrolyte materials must satisfy several requirements, chief among them fast ionic conductivity and robust electrochemical stability. Considering these two requirements, w

Lilian Weng

This paper explains the math behind a generative adversarial network (GAN) model and why it is hard to be trained. Wasserstein GAN is intended to improve GANs&#39; training by adopting a smooth metric for measuring the distance between two probability distributions.

Sajjad Abdoli, Patrick Cardinal, Alessandro Lameiras Koerich

In this paper, we present an end-to-end approach for environmental sound classification based on a 1D Convolution Neural Network (CNN) that learns a representation directly from the audio signal. Several convolutional layers are used to capture the signal&#39;s fine time structure and learn diverse filters that are relevant to the classification task. The pr

Parag Mhashilkar, Mine Altunay, Eileen Berman, David Dagenhart

HEPCloud is rapidly becoming the primary system for provisioning compute resources for all Fermilab-affiliated experiments. In order to reliably meet the peak demands of the next generation of High Energy Physics experiments, Fermilab must plan to elastically expand its computational capabilities to cover the forecasted need. Commercial cloud and allocation-

Alaa H. El-Qadeem, Sameerah K. Al-ghazal

In this paper, we define the subclasses $R_{μ,p}^δ(α;A,B)\ $ and $ P_{μ,p}^δ(α;A,B)\ $ of analytic functions in the open unit disc of complex plain. Then the neighborhood properties, integral means inequalities and some results concerning the partial sums of the functions belonging to these two subclasses are discussed.

E. I. Mason, S. K. Antiochos, N. M. Viall

Coronal rain is the well-known phenomenon in which hot plasma high in the Sun&#39;s corona undergoes rapid cooling (from > 10^6 K to < 10^4 K), condenses, and falls to the surface. Coronal rain appears frequently in active region coronal loops and is very common in post-flare loops. This Letter presents discovery observations, which show that coronal rain is

Felipe Codevilla, Eder Santana, Antonio M. López, Adrien Gaidon

Driving requires reacting to a wide variety of complex environment conditions and agent behaviors. Explicitly modeling each possible scenario is unrealistic. In contrast, imitation learning can, in theory, leverage data from large fleets of human-driven cars. Behavior cloning in particular has been successfully used to learn simple visuomotor policies end-to

Lucie Tvrznikova

The nature of dark matter (DM) remains a mystery since it has so far eluded detection in the laboratory. To that end, the Large Underground Xenon (LUX) experiment was built to directly observe the interaction of DM with xenon target nuclei. LUX acquired data from April 2013 to May 2016 at SURF in South Dakota, which led to publications of many world-leading

Nathaniel B. Price, Mathieu Balesdent, Sébastien Defoort, Rodolphe Le Riche

At the initial design stage engineers often rely on low-fidelity models that have high epistemic uncertainty. Traditional safety-margin-based deterministic design resorts to testing (e.g. prototype experiment, evaluation of high-fidelity simulation, etc.) to reduce epistemic uncertainty and achieve targeted levels of safety. Testing is used to calibrate mode

Joost Kruis, Gunter Maris, Maarten Marsman, Dylan Molenaar

Even though classic theories and models of discrete choice pose man as a rational being, it has been shown extensively that people persistently violate rationality in their actual choices. Recent models of decision-making take these violations often (partially) into account, however, a unified framework has not been established. Here we propose such a framew

Eric Copenhaver, Kayleigh Cassella, Robert Berghaus, Holger Müller

Atom interferometers typically use the total populations the interferometer&#39;s output ports as the signal, but finer spatial structure can contain useful information. We pattern a matter-wave phase profile onto an atomic sample. An interferometer translates the phase into a measurable pattern in the atomic density that we use perform the first direct prec

Ahmed Khalifa, Michael Cerny Green, Gabriella Barros, Julian Togelius

The procedural generation of levels and content in video games is a challenging AI problem. Often such generation relies on an intelligent way of evaluating the content being generated so that constraints are satisfied and/or objectives maximized. In this work, we address the problem of creating levels that are not only playable but also revolve around speci

Amit Chhetri, Mohamed Mansour, Wontak Kim, Guangdong Pan

In this work, we describe limitations of the free-field propagation model for designing broadband beamformers for microphone arrays on a rigid surface. Towards this goal, we describe a general framework for quantifying the microphone array performance in a general wave-field by directly solving the acoustic wave equation. The model utilizes Finite-Element-Me

Marwen Zorgui, Mohammed E. Fouda, Zhiying Wang, Ahmed M. Eltawil

Resistive memories are considered a promising memory technology enabling high storage densities with in-memory computing capabilities. However, the readout reliability of resistive memories is impaired due to the inevitable existence of wire resistance, resulting in the sneak path problem. Motivated by this problem, we study polar coding over channels with d

Hang Zhu, Zhihao Bai, Jialin Li, Ellis Michael

Distributed storage employs replication to mask failures and improve availability. However, these systems typically exhibit a hard tradeoff between consistency and performance. Ensuring consistency introduces coordination overhead, and as a result the system throughput does not scale with the number of replicas. We present Harmonia, a replicated storage arch

Xingjian Du, Xuan Shi, Risheng Huang

Region based object detectors achieve the state-of-the-art performance, but few consider to model the relation of proposals. In this paper, we explore the idea of modeling the relationships among the proposals for object detection from the graph learning perspective. Specifically, we present relational proposal graph network (RepGN) which is defined on objec

Jinhong Wang, Xiong Xiao, Reid Pinkham, Liang Guan

The Trigger Data Serializer (TDS) is a custom ASIC designed for the upgrade of the innermost station of the endcap ATLAS Muon Spectrometer. It is a mixed-signal chip with two working modes that can handle up to 128 detector channels. A total of 6,000 TDS ASICs have been produced for detector operation. This paper discusses a custom automatic test platform we

Bohdan Kulchytskyy, Lauren E. Hayward Sierens, Roger G. Melko

The entanglement entropy of a quantum critical system can provide new universal numbers that depend on the geometry of the entangling bipartition. We calculate a universal number called $κ$, which arises when a quantum critical system is embedded on a two-dimensional torus and bipartitioned into two cylinders. In the limit when one of the cylinders is a thin

Sungjin Im, Benjamin Moseley

MapReduce (and its open source implementation Hadoop) has become the de facto platform for processing large data sets. MapReduce offers a streamlined computational framework by interleaving sequential and parallel computation while hiding underlying system issues from the programmer. Due to the popularity of MapReduce, there have been attempts in the theoret

Krista Lynne Smith

The photometric precision, monitoring baselines, and rapid, even sampling rates required by modern satellites designed for detecting the signal of transiting exoplanets are ideally suited to a large number of applications in high-energy astrophysics. I will exemplify this by discussing the results for active galactic nuclei from Kepler and summarizing other

Xiaochuang Han, Eunsol Choi, Chenhao Tan

Understanding the dynamics of international politics is important yet challenging for civilians. In this work, we explore unsupervised neural models to infer relations between nations from news articles. We extend existing models by incorporating shallow linguistics information and propose a new automatic evaluation metric that aligns relationship dynamics w

William J. Gray, P. A. Keiter, H. Lefevre, C. R. Patterson

Photoionization fronts play a dominant role in many astrophysical environments, but remain difficult to achieve in a laboratory experiment. Recent papers have suggested that experiments using a nitrogen medium held at ten atmospheres of pressure that is irradiated by a source with a radiation temperature of T$_{\rm R}\sim$ 100 eV can produce viable photoioni

Atsushi Tanimoto, Yoshihiro Ueda, Hirokazu Odaka, Toshihiro Kawaguchi

We construct an X-ray spectral model from the clumpy torus in an active galactic nucleus (AGN), designated as &#34;XCLUMPY&#34;, utilizing the Monte Carlo simulation for Astrophysics and Cosmology framework (MONACO: Odaka et al. 2011, 2016). The adopted geometry of the torus is the same as that in Nenkova et al. (2008), who assume a power law distribution of

M. McDonald, B. R. McNamara, G. M. Voit, M. Bayliss

We present new, deep observations of the Phoenix cluster from the Chandra X-ray Observatory, the Hubble Space Telescope, and the Karl Jansky Very Large Array. These data provide an order of magnitude improvement in depth and/or angular resolution at X-ray, optical, and radio wavelengths, yielding an unprecedented view of the core of the Phoenix cluster. We f

P. Bartczak, G. Dudziński

In this work we present SAGE (Shaping Asteroid models using Genetic Evolution) asteroid modelling algorithm based solely on photometric lightcurve data. It produces non-convex shapes, rotation axes orientati and rotational periods of asteroids. The main concept behind a genetic evolution algorithm is to produce random populations of shapes and spin orientati

Anh Nguyen, Jason Yosinski, Jeff Clune

A neuroscience method to understanding the brain is to find and study the preferred stimuli that highly activate an individual cell or groups of cells. Recent advances in machine learning enable a family of methods to synthesize preferred stimuli that cause a neuron in an artificial or biological brain to fire strongly. Those methods are known as Activation

Anupiya Nugaliyadde, Kok Wai Wong, Ferdous Sohel, Hong Xie

Recurrent Neural Networks (RNN), Long Short-Term Memory Networks (LSTM), and Memory Networks which contain memory are popularly used to learn patterns in sequential data. Sequential data has long sequences that hold relationships. RNN can handle long sequences but suffers from the vanishing and exploding gradient problems. While LSTM and other memory network

Utkan Onur Candogan, Venkat Chandrasekaran

The edit distance between two graphs is a widely used measure of similarity that evaluates the smallest number of vertex and edge deletions/insertions required to transform one graph to another. It is NP-hard to compute in general, and a large number of heuristics have been proposed for approximating this quantity. With few exceptions, these methods generall

Ali Yazdizadeh, Zachary Patterson, Bilal Farooq

We develop ensemble Convolutional Neural Networks (CNNs) to classify the transportation mode of trip data collected as part of a large-scale smartphone travel survey in Montreal, Canada. Our proposed ensemble library is composed of a series of CNN models with different hyper-parameter values and CNN architectures. In our final model, we combine the output of

Kevis-Kokitsi Maninis, Ilija Radosavovic, Iasonas Kokkinos

In this work we address task interference in universal networks by considering that a network is trained on multiple tasks, but performs one task at a time, an approach we refer to as &#34;single-tasking multiple tasks&#34;. The network thus modifies its behaviour through task-dependent feature adaptation, or task attention. This gives the network the abilit

AJ Piergiovanni, Michael S. Ryoo

Injuries are a major cost in sports. Teams spend millions of dollars every year on players who are hurt and unable to play, resulting in lost games, decreased fan interest and additional wages for replacement players. Modern convolutional neural networks have been successfully applied to many video recognition tasks. In this paper, we introduce the problem o