November 2018 arXiv papers — page 51

Ricson Cheng, Arpit Agarwal, Katerina Fragkiadaki

We consider artificial agents that learn to jointly control their gripperand camera in order to reinforcement learn manipulation policies in the presenceof occlusions from distractor objects. Distractors often occlude the object of in-terest and cause it to disappear from the field of view. We propose hand/eye con-trollers that learn to move the camera to ke

Mark Bradshaw, Ping Koy Lam, Syed M. Assad

Quantum discord is a measure of non-classical correlations, which are excess correlations inherent in quantum states that cannot be accessed by classical measurements. For multipartite states, the classically accessible correlations can be defined by the mutual information of the multipartite measurement outcomes. In general the quantum discord of an arbitra

Feiyang Chen, Ziqian Luo

Audio Sentiment Analysis is a popular research area which extends the conventional text-based sentiment analysis to depend on the effectiveness of acoustic features extracted from speech. However, current progress on audio sentiment analysis mainly focuses on extracting homogeneous acoustic features or doesn't fuse heterogeneous features effectively. In this

Anh-Dzung Doan, Yasir Latif, Tat-Jun Chin, Yu Liu

A major focus of current research on place recognition is visual localization for autonomous driving. In this scenario, as cameras will be operating continuously, it is realistic to expect videos as an input to visual localization algorithms, as opposed to the single-image querying approach used in other visual localization works. In this paper, we show that

Bryan R. Goldsmith, Jacob Florian, Jin-Xun Liu, Philipp Gruene

We predict the structures of neutral gas-phase gold clusters ($Au_n$, $n$ = 5$-$13) at finite temperatures based on free-energy calculations obtained by replica-exchange ab initio molecular dynamics. The structures of neutral $Au_5$$-$$Au_{13}$ clusters are assigned at 100 K based on a comparison of experimental far-infrared multiple photon dissociation spec

Yuko Matsushita, Satoko Takahashi, Masahiro N. Machida, Kohji Tomisaka

Both high- and low-velocity outflows are occasionally observed around a protostar by molecular line emission. The high-velocity component is called `Extremely High-Velocity (EHV) flow,' while the low-velocity component is simply referred as `(molecular) outflow.' This study reports a newly found EHV flow and outflow around MMS $5$ in the Orion Molecular Clou

Andrea Collevecchio, Cong Bang Huynh, Daniel Kious

The branching-ruin number of a tree, which describes its asymptotic growth and geometry, can be seen as a polynomial version of the branching number. This quantity was defined by Collevecchio, Kious and Sidoravicius (2018) in order to understand the phase transitions of the once-reinforced random walk (ORRW) on trees. Strikingly, this number was proved to be

Xiaomeng Dong, EN Barnett, Sudarshan K. Dhall

Calculating the log-determinant of a matrix is useful for statistical computations used in machine learning, such as generative learning which uses the log-determinant of the covariance matrix to calculate the log-likelihood of model mixtures. The log-determinant calculation becomes challenging as the number of variables becomes large. Therefore, finding a p

Dae Hoon Park, Chiu Man Ho, Yi Chang, Huaqing Zhang

Regularization plays an important role in generalization of deep neural networks, which are often prone to overfitting with their numerous parameters. L1 and L2 regularizers are common regularization tools in machine learning with their simplicity and effectiveness. However, we observe that imposing strong L1 or L2 regularization with stochastic gradient des

Michael S. Harre

Entropy measures in their various incarnations play an important role in the study of stochastic time series providing important insights into both the correlative and the causative structure of the stochastic relationships between the individual components of a system. Recent applications of entropic techniques and their linear progenitors such as Pearson c

Chuxu Zhang, Dongjin Song, Yuncong Chen, Xinyang Feng

Nowadays, multivariate time series data are increasingly collected in various real world systems, e.g., power plants, wearable devices, etc. Anomaly detection and diagnosis in multivariate time series refer to identifying abnormal status in certain time steps and pinpointing the root causes. Building such a system, however, is challenging since it not only r

Katsuyoshi Ohara, Shinichi Tajima

Grothendieck local residue is considered in the context of symbolic computation. Based on the theory of holonomic D-modules, an effective method is proposed for computing Grothendieck local residues. The key is the notion of Noether operator associated to a local cohomology class. The resulting algorithm and an implementation are described with illustrations

Neha R. Hippalgaonkar, Alexa D. Sider, Felix G. Hamza-Lup, Anand P. Santhanam

Simulation and modeling represent promising tools for several application domains from engineering to forensic science and medicine. Advances in 3D imaging technology convey paradigms such as augmented reality (AR) and mixed reality inside promising simulation tools for the training industry. Motivated by the requirement for superimposing anatomically correc

Jianyi Zhang, Yang Zhao, Changyou Chen

Stochastic particle-optimization sampling (SPOS) is a recently-developed scalable Bayesian sampling framework that unifies stochastic gradient MCMC (SG-MCMC) and Stein variational gradient descent (SVGD) algorithms based on Wasserstein gradient flows. With a rigorous non-asymptotic convergence theory developed recently, SPOS avoids the particle-collapsing pi

Prithviraj Dhar, Rajat Vikram Singh, Kuan-Chuan Peng, Ziyan Wu

Incremental learning (IL) is an important task aimed at increasing the capability of a trained model, in terms of the number of classes recognizable by the model. The key problem in this task is the requirement of storing data (e.g. images) associated with existing classes, while teaching the classifier to learn new classes. However, this is impractical as i

Lawrence Jack Barrott

The construction introduced by Gross, Hacking and Keel allows one to construct a formal mirror family to a pair $(S,D)$ where $S$ is a smooth rational projective surface and $D$ a certain type of Weil divisor supporting an ample or anti-ample class. In that paper they proved two convergence results. Firstly that if the intersection matrix of $D$ is not negat

G. Tovmassian, J. F. Gonzalez, M. -S. Hernandez, D. Gonzalez-Buitrago

We obtained high-resolution spectra and multicolor photometry of V1082 Sgr to study the donor star in this 20.8 hr orbital period binary, which is assumed to be a detached system. We measured the rotational velocity (v sin i = 26.5+/-2.0 km/s), which, coupled with the constraints on the white dwarf mass from the X-ray spectroscopy, leads to the conclusion th

Oyvind Tafjord, Peter Clark, Matt Gardner, Wen-tau Yih

Many natural language questions require recognizing and reasoning with qualitative relationships (e.g., in science, economics, and medicine), but are challenging to answer with corpus-based methods. Qualitative modeling provides tools that support such reasoning, but the semantic parsing task of mapping questions into those models has formidable challenges.

Le Bin Ho, Yasushi Kondo

We analyze simultaneous quantum estimations of multiple parameters with postselection measurements in terms of a tradeoff relation. The system, or a sensor, is characterized by a set of parameters, interacts with a measurement apparatus (MA), and then is postselected onto a set of orthonormal final states. Measurements of the MA yield an estimation of the pa

Guofei Pang, Lu Lu, George Em Karniadakis

Physics-informed neural networks (PINNs) are effective in solving integer-order partial differential equations (PDEs) based on scattered and noisy data. PINNs employ standard feedforward neural networks (NNs) with the PDEs explicitly encoded into the NN using automatic differentiation, while the sum of the mean-squared PDE-residuals and the mean-squared erro

John Thickstun, Zaid Harchaoui, Dean P. Foster, Sham M. Kakade

This paper introduces a novel recurrent model for music composition that is tailored to the structure of polyphonic music. We propose an efficient new conditional probabilistic factorization of musical scores, viewing a score as a collection of concurrent, coupled sequences: i.e. voices. To model the conditional distributions, we borrow ideas from both convo

Kristian Hauser A. Villegas, Fedor V. Kusmartsev, Y. Luo, Ivan G. Savenko

We propose a new type of optical transistor for a broadband amplification of THz radiation. It is made of a graphene--superconductor hybrid, where electrons and Cooper pairs couple by Coulomb forces. The transistor operates via the propagation of surface plasmons in both layers, and the origin of amplification is the quantum capacitance of graphene. It leads

Zhenning Cai, Jianfeng Lu, Siyao Yang

We investigate in this work a recently proposed diagrammatic quantum Monte Carlo method --- the inchworm Monte Carlo method --- for open quantum systems. We establish its validity rigorously based on resummation of Dyson series. Moreover, we introduce an integro-differential equation formulation for open quantum systems, which illuminates the mathematical st

Rezaul Karim, Md Amirul Islam, Neil D. B. Bruce

In this paper, we present an approach for Recurrent Iterative Gating called RIGNet. The core elements of RIGNet involve recurrent connections that control the flow of information in neural networks in a top-down manner, and different variants on the core structure are considered. The iterative nature of this mechanism allows for gating to spread in both spat

Yongqiang Tang

An efficient monotone data augmentation (MDA) algorithm is proposed for missing data imputation for incomplete multivariate nonnormal data that may contain variables of different types, and are modeled by a sequence of regression models including the linear, binary logistic, multinomial logistic, proportional odds, Poisson, negative binomial, skew-normal, sk

Paula Jofré, Ulrike Heiter, Caroline Soubiran

There has been an incredibly large investment in obtaining high-resolution stellar spectra for determining chemical abundances of stars. This information is crucial to answer fundamental questions in Astronomy by constraining the formation and evolution scenarios of the Milky Way as well as the stars and planets residing in it. We have just entered a new era

Xiangan Liu, Keyang Xu, Pengtao Xie, Eric Xing

Extractive summarization is very useful for physicians to better manage and digest Electronic Health Records (EHRs). However, the training of a supervised model requires disease-specific medical background and is thus very expensive. We studied how to utilize the intrinsic correlation between multiple EHRs to generate pseudo-labels and train a supervised mod

Fangda Gu, Armin Askari, Laurent El Ghaoui

Despite the recent successes of deep neural networks, the corresponding training problem remains highly non-convex and difficult to optimize. Classes of models have been proposed that introduce greater structure to the objective function at the cost of lifting the dimension of the problem. However, these lifted methods sometimes perform poorly compared to tr

Raymond Brummelhuis, Zhongmin Luo

Absence-of-Arbitrage (AoA) is the basic assumption underpinning derivatives pricing theory. As part of the OTC derivatives market, the CDS market not only provides a vehicle for participants to hedge and speculate on the default risks of corporate and sovereign entities, it also reveals important market-implied default-risk information concerning the counter

Dandan Ma, Keye Zhang, Jing Qian

Motivated by experimental advances [e.g. A. Ga{\"e}tan {\it et.al.} Nat. Phys. 5 115 (2009)] that the collective excitation of two Rydberg atoms was observed, we provide an elaborate theoretical study for the dynamical behavior of two-atom Rabi oscillations. In the large-intermediate-detuning case, the two-photon Rabi oscillation is found to be significantly

Hiroyuki Minamoto, Kota Yamaura

Happel constructed a fully faithful functor $\mathcal{H} :\mathsf{D}^{\mathrm{b}}(\text{mod} \ \Lambda) \to \underline{\text{mod}}^{\Bbb{Z}} \ \text{T}(\Lambda)$ for a finite dimensional algebra $\Lambda$. He also showed that this functor $\mathcal{H}$ gives an equivalence precisely when $\text{gldim } \Lambda < \infty$. Thus if $\mathcal{H}$ gives an equiva

Kahkashan Afrin, Parikshit Verma, Sanjay S. Srivatsa, Satish T. S. Bukkapatnam

Recent introduction of wearable single-lead ECG devices of diverse configurations has caught the intrigue of the medical community. While these devices provide a highly affordable support tool for the caregivers for continuous monitoring and to detect acute conditions, such as arrhythmia, their utility for cardiac diagnostics remains limited. This is because

Yusuf Buyukdag, Tony Gherghetta, Andrew S. Miller

We use the idea of partial compositeness in a minimal supersymmetric model to relate the fermion and sfermion masses. By assuming that the Higgs and third-generation matter is (mostly) elementary, while the first- and second-generation matter is (mostly) composite, the Yukawa coupling hierarchy can be explained by a linear mixing between elementary states an

Andrey Filippov, Oleg Dzhimiev

Throughout the evolution of the neural networks more specialized cells were added to the set of basic building blocks. These cells aim to improve training convergence, increase the overall performance, and reduce the number of required labels, all while preserving the expressive power of the universal network. Inspired by the partitioning of the human visual

Łukasz Patryk Michalak

We prove that, up to homeomorphism, any graph subject to natural necessary conditions on orientation and the cycle rank can be realized as the Reeb graph of a Morse function on a given closed manifold $M$. Along the way, we show that the Reeb number $\mathcal{R}(M)$, i.e. the maximum cycle rank among all Reeb graphs of functions on $M$, is equal to the coran

Richard Evan Schwartz, Serge Tabachnikov

A Steiner chain of length k consists of k circles, tangent to two given non-intersecting circles (the parent circles) and tangent to each other in a cyclic pattern. The Steiner porism states that once a chain of k circles exists, there exists a 1-parameter family of such chains with the same parent circles that can be constructed starting with any initial ci

Pramit Saha, Debasish Ray Mohapatra, Praneeth SV, Sidney Fels

We present an interface involving four degrees-of-freedom (DOF) mechanical control of a two dimensional, mid-sagittal tongue through a biomechanical toolkit called ArtiSynth and a sound synthesis engine called JASS towards articulatory sound synthesis. As a demonstration of the project, the user will learn to produce a range of JASS vocal sounds, by varying

BESIII Collaboration, M. Ablikim, M. N. Achasov, S. Ahmed

We study the hadronic decays of $\Lambda_{c}^{+}$ to the final states $\Sigma^{+}\eta$ and $\Sigma^+\eta^\prime$, using an $e^{+}e^{-}$ annihilation data sample of 567 pb$^{-1}$ taken at a center-of-mass energy of 4.6 GeV with the BESIII detector at the BEPCII collider. We find evidence for the decays $\Lambda_{c}^{+}\rightarrow\Sigma^{+}\eta$ and $\Sigma^+\

Antonio Longo, Chiara Ravazzi, Fabrizio Dabbene, Giuseppe Calafiore

Motivated by the increasing interest of the control community towards social sciences and the study of opinion formation and belief systems, in this paper we address the problem of exploiting voting data for inferring the underlying affinity of individuals to competing ideology groups. In particular, we mine key voting records of the Italian Senate during th

Austin McDaniel, Alex Mahalov

We study the simultaneous paraxial and white-noise limit of the Helmholtz equation in randomly layered media where the refractive index fluctuations are in the direction of propagation. We consider the regime in which the wavelength is of the same order as the correlation length of the random fluctuations of the refractive index. We show that this simultaneo

Jose-Javier Martinez

The approach to solving linear systems with structured matrices by means of the bidiagonal factorization of the inverse of the coefficient matrix is first considered, the starting point being the classical Bjorck-Pereyra algorithms for Vandermonde systems, published in 1970 and carefully analyzed by Higham in 1987. The work of Higham showed the crucial role

Maximilian Behr, Peter Benner, Jan Heiland

The differential Sylvester equation and its symmetric version, the differential Lyapunov equation, appear in different fields of applied mathematics like control theory, system theory, and model order reduction. The few available straight-forward numerical approaches if applied to large-scale systems come with prohibitively large storage requirements. This s

Gianluigi Rozza, Haris Malik, Nicola Demo, Marco Tezzele

Reduced order modeling has gained considerable attention in recent decades owing to the advantages offered in reduced computational times and multiple solutions for parametric problems. The focus of this manuscript is the application of model order reduction techniques in various engineering and scientific applications including but not limited to mechanical

Pierre Etoré, Clémentine Prieur, Dang Khoi Pham, Long Li

Many mathematical models involve input parameters, which are not precisely known. Global sensitivity analysis aims to identify the parameters whose uncertainty has the largest impact on the variability of a quantity of interest. One of the statistical tools used to quantify the influence of each input variable on the quantity of interest are the Sobol&#39; s

Chunhui Guo, Zhicheng Fu, Zhenyu Zhang, Shangping Ren

Improving effectiveness and safety of patient care is an ultimate objective for medical cyber-physical systems. A recent study shows that the patients&#39; death rate can be reduced by computerizing medical guidelines. Most existing medical guideline models are validated and/or verified based on the assumption that all necessary medical resources needed for

Chunhui Guo, Zhicheng Fu, Zhenyu Zhang, Shangping Ren

Improving patient care safety is an ultimate objective for medical cyber-physical systems. A recent study shows that the patients&#39; death rate is significantly reduced by computerizing medical best practice guidelines. Recent data also show that some morbidity and mortality in emergency care are directly caused by delayed or interrupted treatment due to l

Jincheng Ren, Hong-lin Liao, Jiwei Zhang, Zhimin Zhang

Due to the intrinsically initial singularity of solution and the discrete convolution form in numerical Caputo derivatives, the traditional $H^1$-norm analysis (corresponding to the case for a classical diffusion equation) to the time approximations of a fractional subdiffusion problem always leads to suboptimal error estimates (a loss of time accuracy). To

Chunhui Guo, Hao Wu, Xiayu Hua, Shangping Ren

Reliability, longevity, availability, and deadline guarantees are the four most important metrics to measure the QoS of long-running safety-critical real-time applications. Software aging is one of the major factors that impact the safety of long-running real-time applications as the degraded performance and increased failure rate caused by software aging ca

Xiaobing Feng, Yukun Li, Yi Zhang

This paper develops and analyzes a fully discrete finite element method for a class of semilinear stochastic partial differential equations (SPDEs) with multiplicative noise. The nonlinearity in the diffusion term of the SPDEs is assumed to be globally Lipschitz and the nonlinearity in the drift term is only assumed to satisfy a one-side Lipschitz condition.

Pantita Palittapongarnpim, Barry C. Sanders

As all physical adaptive quantum-enhanced metrology schemes operate under noisy conditions with only partially understood noise characteristics, so a practical control policy must be robust even for unknown noise. We aim to devise a test to evaluate the robustness of AQEM policies and assess the resource used by the policies. The robustness test is performed

Enes Makalic, Daniel F. Schmidt

In this note, we develop a novel algorithm for generating random numbers from a distribution with a probability density function proportional to $\sin^k(x)$, $x \in (0,π)$ and $k \geq 1$. Our algorithm is highly efficient and is based on rejection sampling where the envelope distribution is an appropriately chosen beta distribution. An example application il

Philipp W. Schroeder, Volker John, Philip L. Lederer, Christoph Lehrenfeld

Two-dimensional Kelvin-Helmholtz instability problems are popular examples for assessing discretizations for incompressible flows at high Reynolds number. Unfortunately, the results in the literature differ considerably. This paper presents computational studies of a Kelvin-Helmholtz instability problem with high order divergence-free finite element methods.

Andreas Varga

The Fault Detection and Isolation Tools (FDITOOLS) is a collection of MATLAB functions for the analysis and solution of fault detection and model detection problems. The implemented functions are based on the computational procedures described in the Chapters 5, 6 and 7 of the book: &#34;A. Varga, Solving Fault Diagnosis Problems - Linear Synthesis Technique

Victoria Kostina, Babak Hassibi

Consider a control problem with a communication channel connecting the observer of a linear stochastic system to the controller. The goal of the controller is to minimize a quadratic cost function in the state variables and control signal, known as the linear quadratic regulator (LQR). We study the fundamental tradeoff between the communication rate $r$ bits

Guanya Shi, Xichen Shi, Michael O'Connell, Rose Yu

Precise near-ground trajectory control is difficult for multi-rotor drones, due to the complex aerodynamic effects caused by interactions between multi-rotor airflow and the environment. Conventional control methods often fail to properly account for these complex effects and fall short in accomplishing smooth landing. In this paper, we present a novel deep-

Mark Tygert, Jure Zbontar

We convert the information-rich measurements of parallel and phased-array MRI into noisier data that a corresponding single-coil scanner could have taken. Specifically, we replace the responses from multiple receivers with a linear combination that emulates the response from only a single, aggregate receiver, replete with the low signal-to-noise ratio and ph

Mohammad W. Alomari

In this work, a pre-Gr\"{u}ss inequality for positive Hilbert space operators is proved. So that, some numerical radius inequalities are proved. On the other hand, based on a non-commutative Binomial formula, a non-commutative upper bound for the numerical radius of the summand of two bounded linear Hilbert space operators is proved. A commutative version is

Kristoffer Varholm, Erik Wahlén, Samuel Walsh

This paper investigates the stability of traveling wave solutions to the free boundary Euler equations with a submerged point vortex. We prove that sufficiently small-amplitude waves with small enough vortex strength are conditionally orbitally stable. In the process of obtaining this result, we develop a quite general stability/instability theory for bound

D. M. Bowman, C. Aerts, C. Johnston, M. G. Pedersen

Context. Main sequence stars with a convective core are predicted to stochastically excite Internal Gravity Waves (IGWs), which effectively transport angular momentum throughout the stellar interior and explain the observed near-uniform interior rotation rates of intermediate-mass stars. However, there are few detections of IGWs, and fewer still made using p

José A. Garrido Torres, Paul C. Jennings, Martin H. Hansen, Jacob R. Boes

We present the incorporation of a surrogate Gaussian Process Regression (GPR) atomistic model to greatly accelerate the rate of convergence of classical Nudged Elastic Band (NEB) calculations. In our surrogate model approach, the cost of converging the elastic band no longer scales with the number of moving images on the path. This provides a far more effici

Nisha Chandramoorthy, Pablo Fernandez, Chaitanya Talnikar, Qiqi Wang

In chaotic systems, such as turbulent flows, the solutions to tangent and adjoint equations exhibit an unbounded growth in their norms. This behavior renders the instantaneous tangent and adjoint solutions unusable for sensitivity analysis. The Lea-Allen-Haine ensemble sensitivity (ES) estimates provide a way of computing meaningful sensitivities in chaotic

David A Strubbe, Jeffrey C Grossman

Solar thermal fuels (STFs) are an unconventional paradigm for solar energy conversion and storage which is attracting renewed attention. In this concept, a material absorbs sunlight and stores the energy chemically via an induced structural change, which can later be reversed to release the energy as heat. An example is the azobenzene molecule which has a ci

George Berry

We consider the question of embedding nodes with similar local neighborhoods together in embedding space, commonly referred to as "role embeddings." We propose RAE, an unsupervised framework that learns role embeddings. It combines a within-node loss function and a graph neural network (GNN) architecture to place nodes with similar local neighborhoods close

Steve M. Young, Mohan Sarovar, François Léonard

Photodetection plays a key role in basic science and technology, with exquisite performance having been achieved down to the single photon level. Further improvements in photodetectors would open new possibilities across a broad range of scientific disciplines, and enable new types of applications. However, it is still unclear what is possible in terms of ul

Earl Campbell

The dynamics of a quantum system can be simulated using a quantum computer by breaking down the unitary into a quantum circuit of one and two qubit gates. The most established methods are the Trotter-Suzuki decompositions, for which rigorous bounds on the circuit size depend on the number of terms $L$ in the system Hamiltonian and the size of the largest ter

Francesco Della Porta

Energy functionals describing phase transitions in crystalline solids are often non-quasiconvex and minimizers might therefore not exist. On the other hand, there might be infinitely many gradient Young measures, modelling microstructures, generated by minimizing sequences, and it is an open problem how to select the physical ones. In this work we consider t

Shuhui Jiang, Zhaowen Wang, Aaron Hertzmann, Hailin Jin

This paper introduces the problem of automatic font pairing. Font pairing is an important design task that is difficult for novices. Given a font selection for one part of a document (e.g., header), our goal is to recommend a font to be used in another part (e.g., body) such that the two fonts used together look visually pleasing. There are three main challe

D. N. Hoang, T. W. Shimwell, R. J. van Weeren, H. J. A. Röttgering

Collisions of galaxy clusters generate shocks and turbulence in the intra-cluster medium (ICM). The presence of relativistic particles and magnetic fields is inferred through the detection of extended synchrotron radio sources such as haloes and relics and implies that merger shocks and turbulence are capable of (re-)accelerating particles to relativistic en

D. N. Hoang, T. W. Shimwell, R. J. van Weeren, G. Brunetti

We examine the possible acceleration mechanisms of the relativistic particles responsible for the extended radio emission in Abell 520. We used new LOFAR 145 MHz, archival GMRT 323 MHz and VLA 1.5 GHz data to study the morphological and spectral properties of extended cluster emission. The observational properties are discussed in the framework of particle a

Jonathan A. Campbell, Inna Zakharevich

We introduce a new perspective on the $K$-theory of exact categories via the notion of a CGW-category. CGW-categories are a generalization of exact categories that admit a Qullen $Q$-construction, but which also include examples such as finite sets and varieties. By analyzing Quillen's proofs of d\'evissage and localization we define ACGW-categories, an anal

Dogancan Temel, Ghassan AlRegib

Objective metrics model image quality by quantifying image degradations or estimating perceived image quality. However, image quality metrics do not model what makes an image more appealing or beautiful. In order to quantify the aesthetics of an image, we need to take it one step further and model the perception of aesthetics. In this paper, we examine compu

Denis Gudovskiy, Alec Hodgkinson, Takuya Yamaguchi, Yasunori Ishii

Explaining predictions of deep neural networks (DNNs) is an important and nontrivial task. In this paper, we propose a practical approach to interpret decisions made by a DNN object detector that has fidelity comparable to state-of-the-art methods and sufficient computational efficiency to process large datasets. Our method relies on recent theory and approx

Hongyang Zhang, Susu Xu, Jiantao Jiao, Pengtao Xie

We study the problem of alleviating the instability issue in the GAN training procedure via new architecture design. The discrepancy between the minimax and maximin objective values could serve as a proxy for the difficulties that the alternating gradient descent encounters in the optimization of GANs. In this work, we give new results on the benefits of mul

Alexander Peysakhovich

Inferring a person's goal from their behavior is an important problem in applications of AI (e.g. automated assistants, recommender systems). The workhorse model for this task is the rational actor model - this amounts to assuming that people have stable reward functions, discount the future exponentially, and construct optimal plans. Under the rational acto

Istvan Fehervari, Srikar Appalaraju

Logo recognition is the task of identifying and classifying logos. Logo recognition is a challenging problem as there is no clear definition of a logo and there are huge variations of logos, brands and re-training to cover every variation is impractical. In this paper, we formulate logo recognition as a few-shot object detection problem. The two main compone

Daniel Gillick, Alessandro Presta, Gaurav Singh Tomar

Most text-based information retrieval (IR) systems index objects by words or phrases. These discrete systems have been augmented by models that use embeddings to measure similarity in continuous space. But continuous-space models are typically used just to re-rank the top candidates. We consider the problem of end-to-end continuous retrieval, where standard

Francesca Vidotto

A recent understanding on how quantum effects may affect black-hole evolution opens new scenarios for dark matter, in connection with the presence of black holes in the very early universe. Quantum fluctuations of the geometry allow for black holes to decay into white holes via a tunnelling. This process yields to an explosion and possibly to a long remnant

Walter A. Simmons

In spite of decades of research, much remains to be discovered about folding: the detailed structure of the initial (unfolded) state, vestigial folding instructions remaining only in the unfolded state, the interaction of the molecule with the solvent, instantaneous power at each point within the molecule during folding, the fact that the process is stable i

David C. Johnston

A helix has a wavevector along the z axis with the magnetic moments ferromagnetically-aligned within xy planes with a turn angle kd between the moments in adjacent planes in transverse field Hx = 0. The magnetic structure and x-axis average magnetization per spin of this system in a classical XY anisotropy field HA is studied versus kd, HA, and large Hx at z

Yoav Afik, Fabio Maltoni, Kentarou Mawatari, Priscilla Pani

Searches for dark matter (DM) produced using collider data probe wide regions of the allowed parameter space of many models and have become competitive with more traditional searches. The interpretations of the results can be efficiently performed in simplified models, which feature only a mediator and a DM candidate together with the interactions among them

Peyman Nasehpour

In this paper, we investigate the algebraic properties of the expectation semirings which are semiring version of the concept of trivial extension in ring theory. We discuss ideals, primes, maximals and primary ideals of these semirings. We also discuss the distinguished elements such as the units, idempotents, and zero-divisors of the expectations semirings

Mohammad A. Alhejji, Graeme Smith

All correlation measures, classical and quantum, must be monotonic under local operations. In this paper, we characterize monotonic formulas that are linear combinations of the von Neumann entropies associated with the quantum state of a physical system that has n parts. We show that these formulas form a polyhedral convex cone, which we call the monotonicit

Steve Kommrusch, Louis-Noël Pouchet

One of the challenges of using machine learning techniques with medical data is the frequent dearth of source image data on which to train. A representative example is automated lung cancer diagnosis, where nodule images need to be classified as suspicious or benign. In this work we propose an automatic synthetic lung nodule image generator. Our 3D shape gen

Jacob Shapiro

Studying deterministic operators, we define an appropriate topology on the space of mobility-gapped insulators such that topological invariants are continuous maps into discrete spaces, we prove that this is indeed the case for the integer quantum Hall effect, and lastly we show why our "insulator" condition makes sense from the point of view of the localiza

Arash Bazdar, Andrei Teleman

Let $g$ be locally homogeneous (LH) Riemannian metric on a differentiable compact manifold $M$, and $K$ be a compact Lie group endowed with an $\mathrm {ad}$-invariant inner product on its Lie algebra $\mathfrak{k}$. A connection $A$ on a principal $K$-bundle $p:P\to M$ on $M$ is locally homogeneous if for any two points $x_1$, $x_2\in M$ there exists an iso

Nathan Brunetti, Christine D. Wilson

We present 1.3 and 3.2 mm continuum maps of three star forming regions in the Large Magellanic Cloud (LMC) observed with the Atacama Large Millimeter/Submillimeter Array (ALMA). The data were taken as part of two projects retrieved from the ALMA public archive plus one project observed specifically for this work. We develop a technique to combine maps at the

Pengkai Zhu, Hanxiao Wang, Venkatesh Saligrama

We propose a novel Generalized Zero-Shot learning (GZSL) method that is agnostic to both unseen images and unseen semantic vectors during training. Prior works in this context propose to map high-dimensional visual features to the semantic domain, we believe contributes to the semantic gap. To bridge the gap, we propose a novel low-dimensional embedding of v

Vyacheslav Futorny, Dimitar Grantcharov, Luis Enrique Ramirez, Pablo Zadunaisky

We introduce the notion of essential support of a simple Gelfand-Tsetlin $\mathfrak{gl}_n$-module as an important tool towards understanding the character formula of such module. This support detects the weights in the module having maximal possible Gelfand-Tsetlin multiplicities. Using combinatorial tools we describe the essential supports of the simple soc

CMS Collaboration

A search for long-lived particles decaying into jets is presented. Data were collected with the CMS detector at the LHC from proton-proton collisions at a center-of-mass energy of 13 TeV in 2016, corresponding to an integrated luminosity of 35.9 fb$^{-1}$. The search examines the distinctive topology of displaced tracks and secondary vertices. The selected e

Muhammad Salman Bashir

Detector arrays are commonly used for free-space optical communications in deep space. Such detector arrays---by virtue of their size---help in the collection of the optical signal even when there is some misalignment between the transmitter and receiver systems. In this paper, we argue that for the common Gaussian beam profile, a detector array receiver is

Jesse Geneson

Jungi\'{c} et al (2003) defined $T_{k}$ as the minimal number $t \in \mathbb{N}$ such that there is a rainbow arithmetic progression of length $k$ in every equinumerous $t$-coloring of $[t n]$ for every $n \in \mathbb{N}$. They proved that for every $k \geq 3$, $\lfloor \frac{k^2}{4} \rfloor < T_{k} \leq \frac{k(k-1)^2}{2}$ and conjectured that $T_{k} = \The

Daniel E. Maison, Leonid V. Skripnikov, Dmitry A. Glazov

Highly accurate measurements of the $g$-factor of boronlike Ar are currently implemented within the ARTEMIS experiment at GSI (Darmstadt, Germany) and within the ALPHATRAP experiment at the MPIK (Heidelberg, Germany). A comparison with the corresponding theoretical predictions will allow one to test the modern methods of bound-state QED. However, at least th

Gláucia Murta, Suzanne B. van Dam, Jérémy Ribeiro, Ronald Hanson

In the implementation of device-independent quantum key distribution we are interested in maximizing the key rate, i.e. the number of key bits that can be obtained per signal, for a fixed security parameter. In the finite size regime, we furthermore also care about the minimum number of signals required before key can be obtained at all. Here, we perform a f

Edward Crane, Balazs Rath, Dominic Yeo

We study the distribution of ages in the mean field forest fire model introduced by R\'ath and T\'oth. This model is an evolving random graph whose dynamics combine Erd\H{o}s-R\'enyi edge-addition with a Poisson rain of lightning strikes. All edges in a connected component are deleted when any of its vertices is struck by lightning. We consider the asymptoti

Marcelo D. Mora, Thomas H. Puzia, Julio Chanamé

The LMC hosts a rich variety of star clusters seen in close projected proximity. Ages have been derived for few of them showing differences up to few million years, hinting at being binary star clusters. However, final confirmation needs to be done through spectroscopic analysis. Here we focus on the LMC cluster pair NGC2006-SL538 and aim to determine whethe

Adam Kortylewski, Bernhard Egger, Andreas Morel-Forster, Andreas Schneider

It is well known that deep learning approaches to face recognition and facial landmark detection suffer from biases in modern training datasets. In this work, we propose to use synthetic face images to reduce the negative effects of dataset biases on these tasks. Using a 3D morphable face model, we generate large amounts of synthetic face images with full co

IceCube Collaboration, M. G. Aartsen, M. Ackermann, J. Adams

The IceCube Collaboration has observed a high-energy astrophysical neutrino flux and recently found evidence for neutrino emission from the blazar TXS 0506+056. These results open a new window into the high-energy universe. However, the source or sources of most of the observed flux of astrophysical neutrinos remains uncertain. Here, a search for steady poin

A. Widom, J. Swain, Y. N. Srivastava, M. Blasone

An earlier forward and backward in time formalism developed by us to discuss non-relativistic electron diffraction is generalized to the relativistic case and here applied to photons. We show how naturally the zero-point energy emerges in the Planck black-body spectrum once symmetric in time motion - inherent in the Maxwell equations - is invoked for photons

R. P. Lepping, C. -C. Wu, D. B. Berdichevsky, C. Kay

A scheme was developed by Lepping, Berdichevsky, and Wu (Solar Phys., doi.10. 1007/ s11207-016-1040-9, 2017) [called the LBW article here] to approximate the average magnetic field magnitude (B-) profile of a "typical" magnetic cloud (MC) at/near 1AU. It was based on actual Wind MC data, taken over 21 years, that was used to modify a time shifted Bessel func

Tarique Siddiqui, Zesheng Wang, Paul Luh, Karrie Karahalios

Identifying trendline visualizations with desired patterns is a common and fundamental data exploration task. Existing visual analytics tools offer limited flexibility and expressiveness for such tasks, especially when the pattern of interest is under-specified and approximate, and do not scale well when the pattern searching needs are ad-hoc, as is often th