November 2018 arXiv papers — page 125

Alexander V. Osipov

For a Hausdorff space $X$ we denote be $2^X$ the family of all closed subsets of $X$. In this paper we continue to research relationships between closure -type properties of hyperspaces over a space $X$ and covering properties of $X$. We investigate selectors for sequence of subsets of the space $2^{X}$ with the $Z^{+}$-topology and the upper Fell topology.

Alexander H. Liu, Hung-yi Lee, Lin-shan Lee

In this paper we proposed a novel Adversarial Training (AT) approach for end-to-end speech recognition using a Criticizing Language Model (CLM). In this way the CLM and the automatic speech recognition (ASR) model can challenge and learn from each other iteratively to improve the performance. Since the CLM only takes the text as input, huge quantities of unp

Constança Providência, Morgan Fortin, Helena Pais, Aziz Rabhi

In the present study we analyse the effect of the density dependence of the symmetry energy on the hyperonic content of neutron stars within a relativistic mean field description of stellar matter. For the $Λ$-hyperon, we consider parametrizations calibrated to $Λ$-hypernuclei. For the $Σ$ and $Ξ$-hyperons uncertainties that reflect the present lack of exper

Tamara Mchedlidze, Marcel Radermacher, Ignaz Rutter, Nina Zimbel

Let $G=(V, E)$ be a planar graph and let $\mathcal{C}$ be a partition of $V$. We refer to the graphs induced by the vertex sets in $\mathcal{C}$ as Clusters. Let $D_{\mathcal C}$ be an arrangement of disks with a bijection between the disks and the clusters. Akitaya et al. give an algorithm to test whether $(G, \mathcal{C})$ can be embedded onto $D_{\mathcal

J. R. Caldwell, R. A. Watson, C. Thies, J. D. Knowles

Deep Optimisation (DO) combines evolutionary search with Deep Neural Networks (DNNs) in a novel way - not for optimising a learning algorithm, but for finding a solution to an optimisation problem. Deep learning has been successfully applied to classification, regression, decision and generative tasks and in this paper we extend its application to solving op

Sofie Pødenphant, Kasper Kristensen, Per B. Brockhoff

Multiplicative mixed models can be applied in a wide range of scientific disciplines, since they are relevant in every situation where an interaction between a fixed effect and a random effect is present. Until now, no R package has been published, which can fit this type of models. The lack of user-friendly open source tools to fit these models, is the main

Avetik Karagulyan

Sampling from various kinds of distributions is an issue of paramount importance in statistics since it is often the key ingredient for constructing estimators, test procedures or confidence intervals. In many situations, the exact sampling from a given distribution is impossible or computationally expensive and, therefore, one needs to resort to approximate

S. Vempati, J. -C. Deinert, L. Gierster, L. Bogner

The energy level alignment at organic/inorganic (o/i) semiconductor interfaces is crucial for any light-emitting or -harvesting functionality. Essential is the access to both occupied and unoccupied electronic states directly at the interface, which is often deeply buried underneath thick organic films and challenging to characterize. We use several compleme

Qinghai Zhong

Let $R$ be a Mori domain with complete integral closure $\widehat R$, nonzero conductor $\mathfrak f = (R \ :\ \widehat R)$, and suppose that both $v$-class groups $\mathcal C_v (R)$ and $\mathcal C_v (\widehat R)$ are finite. If $R/\mathfrak f$ is finite, then the elasticity of $R$ is either rational or infinite. If $R/\mathfrak f$ is artinian, then unions

Bohayra Mortazavi, Masoud Shahrokhi, Meysam Makaremi, Gianaurelio Cuniberti

In the latest experimental advances, Ni-, Co- and Cu-HAB MOFs were synthesized in 2D forms, with high electrical conductivities and capacitances as well. Motivated by these experimental advances, we employed first-principles simulations to explore the mechanical, thermal stability and electronic properties of single-layer Ag-, Co-, Cr-, Cu-, Fe-, Mn-, Ni-, P

Hiroyuki Nakaoka

Among finite dimensional algebras over a field $K$, the class of gentle algebras is known to be closed by derived equivalences. Although a classification up to derived equivalences is usually a difficult problem, Avella-Alaminos and Geiss have introduced derived invariants for gentle algebras $A$, which can be calculated combinatorially from their bound quiv

Nurdagul Anbar, Seher Tutdere

There are two types of Belyi's Theorem for curves defined over finite fields of characteristic p, namely the Wild and the Tame p-Belyi Theorems. In this paper, we discuss them in the language of function fields. We provide a self-contained proof for the Wild p-Belyi Theorem for any prime p and the Tame 2-Belyi Theorem.

B. Xu, L. X. Zhao, P. Marsik, E. Sheveleva

We present an infrared spectroscopy study of ZrTe$_5$, which confirms a recent theoretical proposal that this material exhibits a temperature-driven topological quantum phase transition from a weak to a strong topological insulating state with an intermediate Dirac semimetal state around $T_p \simeq$ 138K. Our study details the temperature evolution of the e

Amrita Bhattacharya, Ramis Kh. Karimov

The purpose of this Comment is to point out that there are conceptual and mathematical flaws in the analysis in "Light deflection by Damour-Solodukhin wormholes and Gauss-Bonnet theorem, Phys. Rev. D 98, 044033 (2018) by Ali Övgün".

Muhammad Qasim, Samed Özkan

In this paper, we characterize the local T0 and T1 separation axioms for quantale-valued gauge space, show how these concepts are related to each other and apply them to L-approach space and L-approach system. Furthermore, we give the characterization of a closed point and D-connectedness in quantale-valued gauge space. Finally, we compare all these concepts

Antonio Hernández-Gómez, Emna Sahnoun, Emmanuel Caux, Laurent Wiesenfeld

Isocyanic acid (HNCO), the most stable of the simplest molecules containing the four main elements essential for organic chemistry, has been observed in several astrophysical environments such as molecular clouds, star-forming regions, external galaxies and comets. In this work, we model HNCO spectral line profiles toward the low-mass solar type protostar IR

Igor E. Shparlinski, Jose Felipe Voloch

We obtain new bounds of exponential sums modulo a prime $p$ with binomials $ax^k + bx^n$. In particular, for $k=1$, we improve the bound of Karatsuba (1967) from $O(n^{1/4} p^{3/4})$ to $O\left(p^{3/4} + n^{1/3}p^{2/3}\right)$ for any $n$, and then use it to improve the bound of Akulinichev (1965) from $O(p^{5/6})$ to $O(p^{4/5})$ for $n | (p-1)$. The result

Yaonan Jin, Pinyan Lu, Qi Qi, Zhihao Gavin Tang

We consider two canonical Bayesian mechanism design settings. In the single-item setting, we prove tight approximation ratio for anonymous pricing: compared with Myerson Auction, it extracts at least $\frac{1}{2.62}$-fraction of revenue; there is a matching lower-bound example. In the unit-demand single-buyer setting, we prove tight approximation ratio betwe

Katherine Blundell, Robert Laing, Steven Lee, Anita Richards

We present a comparison of Doppler-shifted H-alpha line emission observed by the Global Jet Watch from freshly-launched jet ejecta at the nucleus of the Galactic microquasar SS433 with subsequent ALMA imaging at mm-wavelengths of the same jet ejecta. There is a remarkable similarity between the transversely-resolved synchrotron emission and the prediction of

LiangLiang Yu, Wing-Huen Ip, Tilman Spohn

A long-term sublimation model to explain how Phaethon could provide the Geminid stream is proposed. We find that it would take $\sim6$ Myr or more for Phaethon to lose all of its internal ice (if ever there was) in its present orbit. Thus, if the asteroid moved from the region of a 5:2 or 8:3 mean motion resonance with Jupiter to its present orbit less than

Matthew M. Lin, Chun-Yueh Chiang

Nonlinear matrix equations play a crucial role in science and engineering problems. However, solutions of nonlinear matrix equations cannot, in general, be given analytically. One standard way of solving nonlinear matrix equations is to apply the fixed-point iteration with usually only the linear convergence rate. To advance the existing methods, we exploit

Chandan Kalita, Gautam Barua, Priya Sehgal

With the availability of hybrid DRAM-NVRAM memory on the memory bus of CPUs, a number of file systems on NVRAM have been designed and implemented. In this paper we present the design and implementation of a file system on NVRAM called DurableFS, which provides atomicity and durability of file operations to applications. Due to the byte level random accessibi

Uri Abraham

The aim of this article is to employ the Lazy Set algorithm as an example for a mathematical framework for proving the linearizability of distributed systems. The proof in this approach is divided into two stages of lower and higher abstraction level. At the higher level a list of "axioms" is formulated and a proof is given that any model theoretic s

Jialin Song, Yuxin Chen, Yisong Yue

How can we efficiently gather information to optimize an unknown function, when presented with multiple, mutually dependent information sources with different costs? For example, when optimizing a robotic system, intelligently trading off computer simulations and real robot testings can lead to significant savings. Existing methods, such as multi-fidelity GP

Kartik Ahuja, Mihaela van der Schaar

A clinician desires to use a risk-stratification method that achieves confident risk-stratification - the risk estimates of the different patients reflect the true risks with a high probability. This allows him/her to use these risks to make accurate predictions about prognosis and decisions about screening, treatments for the current patient. We develop Ris

Nojoon Myoung, Jung-Wan Ryu, Hee Chul Park, Seung Joo Lee

We report a dual resonance feature in ballistic conductance through a quantum Hall graphene nanoribbon with a magnetic quantum dot. Such a magnetic quantum dot localizes Dirac fermions exhibiting anisotropic eigenenergy spectra with broken time-reversal symmetry. Interplay between the localized states and quantum Hall edge states is found to be two-fold, sho

Shuo Yang

With the expeditious advancement of information technologies, health-related data presented unprecedented potentials for medical and health discoveries but at the same time significant challenges for machine learning techniques both in terms of size and complexity. Those challenges include: the structured data with various storage formats and value types cau

Branko Malesevic, Tatjana Lutovac, Bojan Banjac

In this paper we propose a method for proving some exponential inequalities based on power series expansion and analysis of derivations of the corresponding functions. Our approach provides a simple proof and generates a new class of appropriate inequalities, as well as allows direct establishment of the dependence between (the exponent of) some functions th

Jingyi Li, Michelle X. Zhou, Huahai Yang, Gloria Mark

We present an intelligent virtual interviewer that engages with a user in a text-based conversation and automatically infers the user's psychological traits, such as personality. We investigate how the personality of a virtual interviewer influences a user's behavior from two perspectives: the user's willingness to confide in, and listen to, a vi

Kookjin Lee, Bedřich Sousedík

We study two inexact methods for solutions of random eigenvalue problems in the context of spectral stochastic finite elements. In particular, given a parameter-dependent, symmetric matrix operator, the methods solve for eigenvalues and eigenvectors represented using polynomial chaos expansions. Both methods are based on the stochastic Galerkin formulation o

Jean-Philippe Martin, Eunjin, Jung

Blockchain systems benefit from lessons in prior art such as fault tolerance, distributed systems, peer-to-peer systems, and game theory. In this paper we argue that blockchain algorithms should tolerate both rational (self-interested) users and Byzantine (malicious) ones, rather than assuming all non-Byzantine users are altruistic and follow the protocols b

Xiaoyu Wang, Cailian Chen, Yang Min, Jianping He

Traffic prediction is a fundamental and vital task in Intelligence Transportation System (ITS), but it is very challenging to get high accuracy while containing low computational complexity due to the spatiotemporal characteristics of traffic flow, especially under the metropolitan circumstances. In this work, a new topological framework, called Linkage Netw

Xuan Zhang, Gaurav Kumar, Huda Khayrallah, Kenton Murray

Machine translation systems based on deep neural networks are expensive to train. Curriculum learning aims to address this issue by choosing the order in which samples are presented during training to help train better models faster. We adopt a probabilistic view of curriculum learning, which lets us flexibly evaluate the impact of curricula design, and perf

Abhijit Chakraborty, Narayan Banerjee, Anandamohan Ghosh

A comparative study of thawing and tracking models of dark energy is carried out with the help of a dynamical systems analysis. It is found that both of them have stable solutions which are consistent with the requirement of a dark energy. So none of them is actually favored from the consideration of stability. The trackers have the interesting possibility t

Chang-Yin Zhao, Ming-Jiang Zhang

Three-body and n-body problems in celestial mechanics are age-old and challenging puzzles. In recent years, several breakthroughs are made in finding periodic orbits for three-body problem. And Bohua Sun proposed a conjecture on Kepler's third law of three-body and n-body problems by using the dimensional analysis method and the mass product symmetry of

Yiling Yuan, Tao Yang, Yuedong Xu, Bo Hu

The device-to-device (D2D) communication theoretically provides both the cellular traffic offloading and convenient content delivery directly among proximity users. However, in practice, no matter in underlay or overlay mode, the employment of D2D may impair the performance of the cellular links. Therefore, it is important to design a spectrum sharing scheme

Phil Attard

Quantum statistical mechanics is formulated as an integral over classical phase space. Some details of the commutation function for averages are discussed, as is the factorization of the symmetrization function used for the grand potential and for the multi-particle density. Three binary choices (eight routes) for the average energy are shown to be mutually

Cheng Tan, Wing Shing Wong

In this paper, we propose a learning approach to analyze dynamic systems with asymmetric information structure. Instead of adopting a game theoretic setting, we investigate an online quadratic optimization problem driven by system noises with unknown statistics. Due to information asymmetry, it is infeasible to use classic Kalman filter nor optimal control s

Xiang Li, Haiyang Xue, Wei Chen, Yang Liu

Although neural machine translation (NMT) has achieved impressive progress recently, it is usually trained on the clean parallel data set and hence cannot work well when the input sentence is the production of the automatic speech recognition (ASR) system due to the enormous errors in the source. To solve this problem, we propose a simple but effective metho

Rabeya Basu, Manish Kumar Singh

In this paper we deduce a graded version of Quillen--Suslin's Local-Global Principle for the traditional classical groups, viz. general linear, symplectic and orthogonal groups and establish its equivalence of the normality property of the respective elementary subgroups. This generalizes previous result of Basu--Rao--Khanna. Then, as an application, we

Jinqiao Duan, Hui Wang

Noisy fluctuations are ubiquitous in complex systems. They play a crucial or delicate role in the dynamical evolution of gene regulation, signal transduction, biochemical reactions, among other systems. Therefore, it is essential to consider the effects of noise on dynamical systems. It has been a challenging topic to have better understanding of the impact

Victor Flambaum, Gaven Martin, Boris Pavlov

This paper discusses resonance effects to advance a classical earthquake model, namely the celebrated M8 global test algorithm. This algorithmgives high confidence levels for prediction of Time Intervals of Increased Probability (TIP) of an earthquake. It is based on observation that almost 80\% of earthquakes occur due to the stress accumulated from previou

Lacey Johnson, Kevin Knudson

In the study of smooth functions on manifolds, min-max theory provides a mechanism for identifying critical values of a function. In this paper we introduce a discretized version of this theory associated to a discrete Morse function on a (regular) cell complex. As applications we prove a discrete version of the Mountain Pass Lemma and give an alternate proo

He Zhao, Lan Du, Wray Buntine, Mingyuan Zhou

Recently, considerable research effort has been devoted to developing deep architectures for topic models to learn topic structures. Although several deep models have been proposed to learn better topic proportions of documents, how to leverage the benefits of deep structures for learning word distributions of topics has not yet been rigorously studied. Here

Ahmad Borzou

We show that if dark matter in a typical galaxy is a degenerate Fermi gas, particles should have a mass of $\sim$ 1eV for the galaxy to be stable. While this is the mass range of the active neutrinos, they are not a dark matter candidate in SM-GR-$Λ$-CDM. To show that the bounds on active neutrino dark matter are model dependent, we explore the predictions o

Xiao Chen, Ming Jiang, Qiang Zhou

An expression of the filtered Eulerian drag force is proposed based on the second order Taylor polynomial approximation of the microscopic Eulerian drag coefficient. Theoretical computations of the expression are performed at low Reynolds numbers based on an available microscopic drag model. It shows that four sub-grid quantities, i.e., the gas drift velocit

Tomáš Babej, Christopher Ing, Mark Fingerhuth

Lattice models have been used extensively over the past thirty years to examine the principles of protein folding and design. These models can be used to determine the conformation of the lowest energy fold out of a large number of possible conformations. However, due to the size of the conformational space, new algorithms are required for folding longer pro

Pai Liu, William R. Heinson, Christopher M. Sorensen, Rajan K. Chakrabarty

A comprehensive theory encompassing the kinetics of sol-to-gel transition is yet to be formulated due to break-down of the mean-field Smoluchowski Equation. Using high temporal-resolution Monte Carlo simulation of irreversible aggregation systems, we show that this transition has three distinct regimes with kinetic exponent z ranging between 1 and 2, corresp

Marek Cygan, Guy Kortsarz, Bundit Laekhanukit

This paper concerns proving almost tight (super-polynomial) running times, for achieving desired approximation ratios for various problems. To illustrate, the question we study, let us consider the Set-Cover problem with n elements and m sets. Now we specify our goal to approximate Set-Cover to a factor of (1-d)ln n, for a given parameter 0<d<1. What is the

Shigeru Yamagami

In connection with parametric rescaling of free dynamics of CCR, we introduce a flow on the set of covariance forms and investigate its thermodynamic behavior at low temperature with the conclusion that every free state approaches to a selected Fock state as a limit.

Jason Li, Ravi Gadde, Boris Ginsburg, Vitaly Lavrukhin

Building an accurate automatic speech recognition (ASR) system requires a large dataset that contains many hours of labeled speech samples produced by a diverse set of speakers. The lack of such open free datasets is one of the main issues preventing advancements in ASR research. To address this problem, we propose to augment a natural speech dataset with sy

Luís Borges, Bruno Martins, Pável Calado

Fake news are nowadays an issue of pressing concern, given their recent rise as a potential threat to high-quality journalism and well-informed public discourse. The Fake News Challenge (FNC-1) was organized in 2017 to encourage the development of machine learning-based classification systems for stance detection (i.e., for identifying whether a particular n

Xueliang Wang, Guosheng Shi, Shanshan Liang, Jian Liu

We experimentally demonstrate the formation of salt aggregations with unexpectedly high concentration inside multi-walled carbon nanotubes (CNTs) soaked only in dilute salt solutions and even in solutions containing only traces of salts. This finding suggests the blocking of fluid across CNTs by the salt aggregations when CNTs are soaked in a dilute salt sol

Yanwen Chen, Tianmeng Wang, Zhipeng Li, Huanbin Li

Organic-inorganic perovskite as a promising candidate for solar energy harvesting has attracted immense interest for its low-cost preparation and extremely high quantum efficiency. However, the fundamental understanding of the photophysics in perovskite remains elusive. In this work, we have revealed two distinct states in MAPbI3 thin films at low temperatur

Chol-Jun Yu

Perovskite solar cells (PSCs) are attracting great attention as the most promising candidate for the next generation solar cells. This is due to their low cost and high power conversion efficiency in spite of their relatively short period of development. Key components of PSCs are a variety of halide perovskites with ABX3 stoichiometry used as a photoabsorbe

Nickolaos Koroniotis, Nour Moustafa, Elena Sitnikova, Benjamin Turnbull

The proliferation of IoT systems, has seen them targeted by malicious third parties. To address this, realistic protection and investigation countermeasures need to be developed. Such countermeasures include network intrusion detection and network forensic systems. For that purpose, a well-structured and representative dataset is paramount for training and v

Chong Gao, Taolue Chen, Zhilin Wu

We identify difference-bound set constraints (DBS), an analogy of difference-bound arithmetic constraints for sets. DBS can express not only set constraints but also arithmetic constraints over set elements. We integrate DBS into separation logic with linearly compositional inductive predicates, obtaining a logic thereof where set data constraints of linear

Ga Wu, Maksims Volkovs, Chee Loong Soon, Scott Sanner

Previous highly scalable one-class collaborative filtering methods such as Projected Linear Recommendation (PLRec) have advocated using fast randomized SVD to embed items into a latent space, followed by linear regression methods to learn personalized recommendation models per user. Unfortunately, naive SVD embedding methods often exhibit a popularity bias t

Ruiyi Zhang, Changyou Chen, Zhe Gan, Wenlin Wang

Sequence generation with reinforcement learning (RL) has received significant attention recently. However, a challenge with such methods is the sparse-reward problem in the RL training process, in which a scalar guiding signal is often only available after an entire sequence has been generated. This type of sparse reward tends to ignore the global structural

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 can be significantly reduced by computerizing medical best practice guidelines. To facilitate the development of computerized medical best practice guidelines, statecharts are often used as a modeling tool because

Yanran Li, Wenjie Li

We propose a chatbot, namely Mocha to make good use of relevant entities when generating responses. Augmented with meta-path information, Mocha is able to mention proper entities following the conversation flow.

Yuanpeng Li, Yi Yang, Jianyu Wang, Wei Xu

Acquiring a large vocabulary is an important aspect of human intelligence. Onecommon approach for human to populating vocabulary is to learn words duringreading or listening, and then use them in writing or speaking. This ability totransfer from input to output is natural for human, but it is difficult for machines.Human spontaneously performs this knowledge

Chuong Le, Huy Xuan Pham, Hung Manh La

There is a lot of waste in an industrial environment that could cause harmful effects to both the products and the workers resulting in product defects, itchy eyes or chronic obstructive pulmonary disease, etc. While automative cleaning robots could be used, the environment is often too big for one robot to clean alone in addition to the fact that it does no

S. R. Yang, Y. T. Fanchiang, C. C. Chen, C. C. Tseng

Transport signatures of exchange gap opening because of magnetic proximity effect (MPE) are reported for bilayer structures of Bi2Se3 thin films on yttrium iron garnet (YIG) and thulium iron garnet (TmIG) of perpendicular magnetic anisotropy (PMA). Pronounced negative magnetoresistance (MR) was detected, and attributed to an emergent weak localization (WL) e

Vamsi K. Amalladinne, Krishna R. Narayanan, Jean-Francois Chamberland, Dongning Guo

The neighbor discovery paradigm finds wide application in Internet of Things networks, where the number of active devices is orders of magnitude smaller than the total device population. Designing low-complexity schemes for asynchronous neighbor discovery has recently gained significant attention from the research community. Concurrently, a divide-and-conque

Shashank Jaiswal, Alina A. Alexeenko, Jingwei Hu

The Boltzmann equation, an integro-differential equation for the molecular distribution function in the physical and velocity phase space, governs the fluid flow behavior at a wide range of physical conditions, including compressible, turbulent, as well as flows involving further physics such as non-equilibrium internal energy exchange and chemical reactions

Idoia Cortes Garcia, Sebastian Schöps, Herbert De Gersem, Sascha Baumanns

Starting from space-discretisation of Maxwell&#39;s equations, various classical formulations are proposed for the simulation of electromagnetic fields. They differ in the phenomena considered as well as in the variables chosen for discretisation. This contribution presents a literature survey of the most common approximations and formulations with a focus o

Davide Torlo, Francesco Ballarin, Gianluigi Rozza

In this work, we propose viable and efficient strategies for stabilized parametrized advection dominated problems, with random inputs. In particular, we investigate the combination of wRB (weighted reduced basis) method for stochastic parametrized problems with stabilized reduced basis method, which is the integration of classical stabilization methods (SUPG

Martina Hofmanová, Marvin Knöller, Katharina Schratz

We consider the nonlinear Schrödinger equation with dispersion modulated by a (formal) derivative of a time-dependent function with fractional Sobolev regularity of class $W^{α,2}$ for some $α\in (0,1)$. Due to the loss of smoothness in the problem classical numerical methods face severe order reduction. In this work, we develop and analyze a new randomized

Francesco Ballarin, Alessandro D&#39;Amario, Simona Perotto, Gianluigi Rozza

Efficient shape morphing techniques play a crucial role in the approximation of partial differential equations defined in parametrized domains, such as for fluid-structure interaction or shape optimization problems. In this paper, we focus on Inverse Distance Weighting (IDW) interpolation techniques, where a reference domain is morphed into a deformed one vi

Yong-Liang Zhao, Pei-Yong Zhu, Xian-Ming Gu, Xi-Le Zhao

An implicit finite difference scheme based on the $L2$-$1_σ$ formula is presented for a class of one-dimensional time fractional reaction-diffusion equations with variable coefficients and time drift term. The unconditional stability and convergence of this scheme are proved rigorously by the discrete energy method, and the optimal convergence order in the $

Matt Wala, Andreas Klöckner

We derive a representation formula for harmonic polynomials and Laurent polynomials in terms of densities of the double-layer potential on bounded piecewise smooth and simply connected domains. From this result, we obtain a method for the numerical computation of conformal maps that applies to both exterior and interior regions. We present analysis and numer

Marius Hofert, Avinash Prasad, Mu Zhu

Generative moment matching networks (GMMNs) are introduced for generating quasi-random samples from multivariate models with any underlying copula in order to compute estimates under variance reduction. So far, quasi-random sampling for multivariate distributions required a careful design, exploiting specific properties (such as conditional distributions) of

Sheng Shen, Yaliang Li, Nan Du, Xian Wu

Question answering (QA) has achieved promising progress recently. However, answering a question in real-world scenarios like the medical domain is still challenging, due to the requirement of external knowledge and the insufficient quantity of high-quality training data. In the light of these challenges, we study the task of generating medical QA pairs in th

Jeffrey S. Meyer, Christian Millichap, Rolland Trapp

This paper examines number theoretic and topological properties of fully augmented pretzel link complements. In particular, we determine exactly when these link complements are arithmetic and exactly which are commensurable with one another. We show these link complements realize infinitely many CM-fields as invariant trace fields, which we explicitly comput

Christopher Monahan

First principles calculations of the Bjorken-$x$ dependence of hadron structure have been a long-standing challenge for lattice QCD. This year marks a significant milestone: the first determinations of parton distribution functions, which capture the longitudinal momentum structure of fast-moving hadrons, at physical pion masses. Moreover, there has been sig

Saket Choudhary

For two hermitian matrices $A$ and $B$, Golden-Thompson inequality \cite{golden1965lower, thompson1965inequality} states that $$ \mathrm{tr}\left[ \exp{(A+B)} \right] \leq \mathrm{tr}\left[ \exp{(A)}\exp{(B)} \right]. $$ We elaborate here the proof from Sutter et al. (2017).

Raouf Dridi, Hedayat Alghassi, Sridhar Tayur

We import the tools of Morse theory to study quantum adiabatic evolution, the core mechanism in adiabatic quantum computations (AQC). AQC is computationally equivalent to the (pre-eminent paradigm) of the Gate model but less error-prone, so it is ideally suitable to practically tackle a large number of important applications. AQC remains, however, poorly und

Eric Steinbring

Viewing two astronomical sources at large enough distance and angular separation can assure, by light-travel-time arguments, the acausality of their emitted photons. Using such photons to set apparatus parameters in a laboratory-based quantum-mechanical experiment could ensure those switch settings are independent and fair, allowing a loophole-free test of B

Sean Welleck, Jason Weston, Arthur Szlam, Kyunghyun Cho

Consistency is a long standing issue faced by dialogue models. In this paper, we frame the consistency of dialogue agents as natural language inference (NLI) and create a new natural language inference dataset called Dialogue NLI. We propose a method which demonstrates that a model trained on Dialogue NLI can be used to improve the consistency of a dialogue

Kenichi Nagasawa

I develop a new identification strategy for treatment effects when noisy measurements of unobserved confounding factors are available. I use proxy variables to construct a random variable conditional on which treatment variables become exogenous. The key idea is that, under appropriate conditions, there exists a one-to-one mapping between the distribution of

Bao-Dong Sun, Yu-Bing Dong

The $ρ$ meson polarized generalized parton distribution functions, its structure functions $g_1$ and $g_2$ and its axial form factors ${\tilde G}_{1,2}$ are studied based on a light-front quark model for the first time. Comparing our obtained moments of $g_1$ to lattice QCD calculation, we find that our results are reasonably consistent to the Lattice predic

E. Yaksic, M. V. Simkin, V. P. Roychowdhury

We study the distribution of 2,837 inter-murder intervals (cooling off periods) for 1,012 American serial killers. The distribution is smooth, following a power law in the region of 10-10,000 days. The power law cuts off where inter-murder intervals become comparable with the length of human life. Otherwise there is no other characteristic scale in the distr

Ji Zhang, Kevin Shih, Andrew Tao, Bryan Catanzaro

This article describes the model we built that achieved 1st place in the OpenImage Visual Relationship Detection Challenge on Kaggle. Three key factors contribute the most to our success: 1) language bias is a powerful baseline for this task. We build the empirical distribution $P(predicate|subject,object)$ in the training set and directly use that in testin

Xin Yang, Yuezun Li, Siwei Lyu

In this paper, we propose a new method to expose AI-generated fake face images or videos (commonly known as the Deep Fakes). Our method is based on the observations that Deep Fakes are created by splicing synthesized face region into the original image, and in doing so, introducing errors that can be revealed when 3D head poses are estimated from the face im

Angelo Bella, Santi Spadaro

A space $X$ is said to be &#34;cellular-Lindelöf&#34; if for every cellular family $\mathcal{U}$ there is a Lindelöf subspace $L$ of $X$ which meets every element of $\mathcal{U}$. Cellular-Lindelöf spaces generalize both Lindelöf spaces and spaces with the countable chain condition. Solving questions of Xuan and Song, we prove that every cellular-Lindelöf m

Marina Danilova, Anastasiya Kulakova, Boris Polyak

We focus on the solutions of second-order stable linear difference equations and demonstrate that their behavior can be non-monotone and exhibit peak effects depending on initial conditions. The results are applied to the analysis of the accelerated unconstrained optimization method -- the Heavy Ball method. We explain non-standard behavior of the method dis

Yuezun Li, Siwei Lyu

In this work, we describe a new deep learning based method that can effectively distinguish AI-generated fake videos (referred to as {\em DeepFake} videos hereafter) from real videos. Our method is based on the observations that current DeepFake algorithm can only generate images of limited resolutions, which need to be further warped to match the original f

Matthias Rottmann, Pascal Colling, Thomas-Paul Hack, Robin Chan

We present a method that &#34;meta&#34; classifies whether seg-ments predicted by a semantic segmentation neural networkintersect with the ground truth. For this purpose, we employ measures of dispersion for predicted pixel-wise class probability distributions, like classification entropy, that yield heat maps of the input scene&#39;s size. We aggregate thes

Leandro Beraldo e Silva, Walter de Siqueira Pedra, Monica Valluri, Laerte Sodré

We investigate the old problem of the fast relaxation of collisionless $N$-body systems which are collapsing or perturbed, emphasizing the importance of (non-collisional) discreteness effects. We integrate orbit ensembles in fixed external potentials, estimating the entropy of the ensemble to analyze the time evolution of the distribution function. We show t

Wesley Tansey, Victor Veitch, Haoran Zhang, Raul Rabadan

We propose the holdout randomization test (HRT), an approach to feature selection using black box predictive models. The HRT is a specialized version of the conditional randomization test (CRT; Candes et al., 2018) that uses data splitting for feasible computation. The HRT works with any predictive model and produces a valid $p$-value for each feature. To ma

Mohammadreza Rezvan, Saeedeh Shekarpour, Faisal Alshargi, Krishnaprasad Thirunarayan

Disclaimer: This paper is concerned with violent online harassment. To describe the subject at an adequate level of realism, examples of our collected tweets involve violent, threatening, vulgar and hateful speech language in the context of racial, sexual, political, appearance and intellectual harassment. The presence of a significant amount of harassment i

Guangmo Tong, Ruiqi Wang, Xiang Li, Weili Wu

Guiding users to actively expanding their online social circles is one of the primary strategies for enhancing user participation and growing online social networks. In this paper, we study the active friending problem which aims at providing users with the strategy for methodically sending invitations to successfully build a friendship with target users. We

Tyler J. VanderWeele, Yige Li

Simple sensitivity analysis results are given for differential measurement error of either the exposure or the outcome. In the case of differential measurement error of the outcome it is shown that the true effect of the exposure on the outcome on the risk ratio scale must be at least as large as the observed association between the exposure and the mis-meas

Uddipan Banik, Frank C. van den Bosch, Michael Tremmel, Anupreeta More

Strong lensing of active galactic nuclei in the radio can result in razor-thin arcs, with a thickness of less than a milli-arcsecond, if observed at the resolution achievable with very long baseline interferometry (VLBI). Such razor-thin arcs provide a unique window on the coarseness of the matter distribution between source and observer. In this paper, we i

Ankur Chowdhary, Adel Alshamrani, Dijiang Huang, Myong Kang

Software Defined Networking (SDN) has emerged as a revolutionary paradigm to manage cloud infrastructure. SDN lacks scalable trust setup and verification mechanism between Data Plane-Control Plane elements, Control Plane elements, and Control Plane-Application Plane. Trust management schemes like Public Key Infrastructure (PKI) used currently in SDN are slow

Shirshendu Chatterjee, David Sivakoff, Matthew Wascher

We study a generalization of the classical contact process (SIS epidemic model) in a directed graph $G$. Our model is a continuous-time interacting particle system in which at every time, each vertex is either healthy or infected, and each oriented edge is either active or inactive. Infected vertices become healthy at rate $1$, and pass the infection along e

Chirag Agarwal, Anh Nguyen, Dan Schonfeld

Deep neural networks (DNNs) have achieved state-of-the-art results in various pattern recognition tasks. However, they perform poorly on out-of-distribution adversarial examples i.e. inputs that are specifically crafted by an adversary to cause DNNs to misbehave, questioning the security and reliability of applications. In this paper, we encourage DNN classi

David Cruz-Uribe, Oscar Mauricio Guzman

We extend the theory of weighted norm inequalities on variable Lebesgue spaces to the case of bilinear operators. We introduce a bilinear version of the variable $\A_\pp$ condition, and show that it is necessary and sufficient for the bilinear maximal operator to satisfy a weighted norm inequality. Our work generalizes the linear results of the first author,

Michael Benedicks, Marco Martens, Liviana Palmisano

Newhouse laminations occur in unfoldings of rank-one homoclinic tangencies. Namely, in these unfoldings, there exist codimension $2$ laminations of maps with infinitely many sinks which move simultaneously along the leaves. As consequence, in the space of real polynomial maps, there are examples of: Hénon maps, in any dimension, with infinitely many sinks, q