November 2018 arXiv papers — page 15

Atsufumi Honda, Kentaro Saji, Keisuke Teramoto

A mixed type surface is a connected regular surface in a Lorentzian 3-manifold with non-empty spacelike and timelike point sets. The induced metric of a mixed type surface is a signature-changing metric, and their lightlike points may be regarded as singular points of such metrics. In this paper, we investigate the behavior of Gaussian curvature at a non-deg

Bo Zhao, Lili Meng, Weidong Yin, Leonid Sigal

Despite significant recent progress on generative models, controlled generation of images depicting multiple and complex object layouts is still a difficult problem. Among the core challenges are the diversity of appearance a given object may possess and, as a result, exponential set of images consistent with a specified layout. To address these challenges,

Longlong Jing, Xiaodong Yang, Jingen Liu, Yingli Tian

The success of deep neural networks generally requires a vast amount of training data to be labeled, which is expensive and unfeasible in scale, especially for video collections. To alleviate this problem, in this paper, we propose 3DRotNet: a fully self-supervised approach to learn spatiotemporal features from unlabeled videos. A set of rotations are applie

M. T. Bell, B. Doucot, M. E. Gershenson, L. B. Ioffe

We propose a novel platform for the study of quantum phase transitions in one dimension (1D QPT). The system consists of a specially designed chain of asymmetric SQUIDs; each SQUID contains several Josephson junctions with one junction shared between the nearest-neighbor SQUIDs.We develop the theoretical description of the low energy part of the spectrum. In

Tattwamasi Amrutam

We show that for a large class of actions $Γ\curvearrowright \mathcal{A}$ of $C^*$-simple groups $Γ$ on unital $C^*$-algebras $\mathcal{A}$, including any non-faithful action of a hyperbolic group with trivial amenable radical, every intermediate $C^*$-subalgebra $\mathcal{B}$, $C_λ^*(Γ)\subseteq \mathcal{B} \subseteq \mathcal{A}\rtimes_{r}Γ$, is of the form

Anindya Goswami, Omkar Manjarekar, Anjana R

This paper presents the solution to a European option pricing problem by considering a regime-switching jump diffusion model of the underlying financial asset price dynamics. The regimes are assumed to be the results of an observed pure jump process, driving the values of interest rate and volatility coefficient. The pure jump process is assumed to be a semi

Jiaming Liang, Renato D. C. Monteiro

This paper describes and establishes the iteration-complexity of a doubly accelerated inexact proximal point (D-AIPP) method for solving the nonconvex composite minimization problem whose objective function is of the form $f+h$ where $f$ is a (possibly nonconvex) differentiable function whose gradient is Lipschitz continuous and $h$ is a closed convex functi

Andrew Hassell, Pierre Portal, Jan Rozendaal

We define a scale of Hardy spaces $\mathcal{H}^{p}_{FIO}(\mathbb{R}^{n})$, $p\in[1,\infty]$, that are invariant under suitable Fourier integral operators of order zero. This builds on work by Smith for $p=1$. We also introduce a notion of off-singularity decay for kernels on the cosphere bundle of $\mathbb{R}^{n}$, and we combine this with wave packet transf

Peng Lu, Hangyu Lin, Yanwei Fu, Shaogang Gong

Sketch has been employed as an effective communicative tool to express the abstract and intuitive meanings of object. Recognizing the free-hand sketch drawing is extremely useful in many real-world applications. While content-based sketch recognition has been studied for several decades, the instance-level Sketch-Based Image Retrieval (SBIR) tasks have attra

Xi Chen, Weidong Liu, Yichen Zhang

This paper studies distributed estimation and inference for a general statistical problem with a convex loss that could be non-differentiable. For the purpose of efficient computation, we restrict ourselves to stochastic first-order optimization, which enjoys low per-iteration complexity. To motivate the proposed method, we first investigate the theoretical

Jun Ho Son, Jing-Yuan Chen, S. Raghu

We generalize our previous lattice construction of the abelian bosonization duality in $2+1$ dimensions to the entire web of dualities as well as the $N_f=2$ self-duality, via the lattice implementation of a set of modular transformations in the theory space. The microscopic construction provides explicit operator mappings, and allows the manifestation of so

Injo Hur, Darren C. Ong

The KdV hierarchy is a family of evolutions on a Schrödinger operator that preserves its spectrum. Canonical systems are a generalization of Schrödinger operators, that nevertheless share many features with Schrödinger operators. Since this is a very natural generalization, one would expect that it would also be straightforward to build a hierarchy of isospe

Yuanhang Su, Kai Fan, Nguyen Bach, C. -C. Jay Kuo

Unsupervised neural machine translation (UNMT) has recently achieved remarkable results with only large monolingual corpora in each language. However, the uncertainty of associating target with source sentences makes UNMT theoretically an ill-posed problem. This work investigates the possibility of utilizing images for disambiguation to improve the performan

Kabir Chakravarti, Sumanta Chakraborty, Sukanta Bose, Soumitra SenGupta

We calculate the tidal Love numbers of black holes and neutron stars in the presence of higher dimensions. The perturbation equations around an arbitrary static and spherically symmetric metric for the even parity modes are presented in the context of an effective four-dimensional theory on the brane. This subsequently leads to the sought expression for the

Campo Elías González Pineda, Sandra Milena García

In this article we calculate the length of the golden spiral, and we study the golden rectangles. We calculate some measures of interest. We also show that the only rectangles that can be subdivided or that generate sub rectangles indefinitely are the golden rectangles. We emphasize that when subdividing a rectangle into sub rectangles, the Fibonacci sequenc

Ryan Turner, Jane Hung, Eric Frank, Yunus Saatci

We introduce the Metropolis-Hastings generative adversarial network (MH-GAN), which combines aspects of Markov chain Monte Carlo and GANs. The MH-GAN draws samples from the distribution implicitly defined by a GAN's discriminator-generator pair, as opposed to standard GANs which draw samples from the distribution defined only by the generator. It uses th

Drew Linsley, Junkyung Kim, David Berson, Thomas Serre

Recent successes in deep learning have started to impact neuroscience. Of particular significance are claims that current segmentation algorithms achieve "super-human" accuracy in an area known as connectomics. However, as we will show, these algorithms do not effectively generalize beyond the particular source and brain tissues used for training --

Alex G. C. de Sá, Cristiano G. Pimenta, Gisele L. Pappa, Alex A. Freitas

This supplementary material aims to describe the proposed multi-label classification (MLC) search spaces based on the MEKA and WEKA softwares. First, we overview 26 MLC algorithms and meta-algorithms in MEKA, presenting their main characteristics, such as hyper-parameters, dependencies and constraints. Second, we review 28 single-label classification (SLC) a

Xiongfei Wang, Bo Li, Yuanning Gao, Xinchou Lou

We perform a helicity amplitude analysis for the processes of $e^+e^-$$\rightarrow$$J/ψ,ψ(2S)\rightarrowΞ(1530)\barΞ(1530)\rightarrowππΞ\barΞ\rightarrowΛ\bar\Lambda4π\rightarrow p\bar{p}6π$. The joint angular distribution for these processes are obtained, which allows a proper estimation of the detection efficiency using helicity amplitude information. In ad

M. Ablikim, M. N. Achasov, S. Ahmed, M. Albrecht

We report a study of the decay $D^0 \rightarrow \bar{K}^0π^-e^+ν_{e}$ based on a sample of $2.93~\mathrm{fb}^{-1}$ $e^+e^-$ annihilation data collected at the center-of-mass energy of 3.773~GeV with the BESIII detector at the BEPCII collider. The total branching fraction is determined to be $\mathcal{B}(D^0\rightarrow \bar{K}^0π^-e^+ν_{e})=(1.434\pm0.029({\r

Yufang Cui, Anders Lindquist

This paper provides a new method to solve analytic interpolation problems with rationality and derivative constraints, occurring in many applications to system and control. It is based on the covariance extension equation previously proposed by Byrnes and Lindquist in a different context. A complete solution for the scalar problem is provided, and a homotopy

J Ignacio Deza, Hisham Ihshaish

We introduce a software generator for a class of colored (self-correlated) and non-Gaussian noise, whose statistics and spectrum depend on two parameters, $q$ and $τ$. Inspired by Tsallis' nonextensive formulation of statistical physics, the so-called $q$-distribution is a handy source of self-correlated noise for a large range of applications. The $q$-n

Dong-Hui Li, Hong-Bo Guan, Xiao-Zhou Wang

We are concerned with the tensor equation with an M-tensor or Z-tensor, which we call the M- tensor equation or Z-tensor equation respectively. We derive a necessary and sufficient condition for a Z (or M)-tensor equation to have nonnegative solutions. We then develop a monotone iterative method to find a nonnegative solution to an M-tensor equation. The met

Yunsheng Sun, Congjian Ni, Yingda Ge, Hong Qian

In pancreatic $β$-cells, calcium oscillation signal is the core part of glucose-stimulated insulin secretion. Intracellular calcium concentration oscillates in response to the intake of glucose, which triggers the exocytosis of insulin secretory granules. ATP plays a crucial part in this process. ATP increases as the result of glucose intake, then ATP binds

Jiyoung Jung, Sangryun Lee, Byungki Ryu, Seunghwa Ryu

We obtained the analytical expression for the effective thermoelectric properties and dimensionless figure of merit of a composite with interfacial electrical and thermal resistances using a micromechanics-based homogenisation. For the first time, we derived the Eshelby tensor for a spherical inclusion as a function of the interfacial resistances and obtaine

K. L. Yang, J. M. Zhang

We point out that by the ``smoothness means fast decay'' principle in Fourier analysis, it is possible to infer the smoothness (or nonsmoothness) of the autocorrelation function from a mere glimpse of the initial state. Specifically, for a generic system with smooth eigenstates, the smoother an initial state is, the faster its decomposition coefficie

Yanchao Zhang, Jincan Chen

A theoretical proposal that Coulomb-coupled quantum dots can be used as quantum probes to determine the temperature of a sample (i.e., an electronic reservoir) is proposed. Through the regulation of the positive or negative voltage bias in the thermometer, we are able to judge whether the temperature of the sample is higher or lower than that of the referenc

Sheng-cai Zhu, Lei Wang, Jing-yu Qu, Jun-jie Wang

Electrides, with their excess electrons distributed in crystal cavities playing the role of anions, exhibit a variety of unique electronic and magnetic properties. In this work, we employ the first-principles crystal structure prediction to identify a new prototype of A$_3$B electride in which both interlayer spacings and intralayer vacancies provide channel

Amit Sharma

In this paper we construct a symmetric monoidal closed model category of coherently commutative monoidal categories. The main aim of this paper is to establish a Quillen equivalence between a model category of coherently commutative monoidal categories and a natural model category of Permutative (or strict symmetric monoidal) categories, $\mathbf{Perm}$, whi

Vishnu Vardhan Chetlur, Harpreet S. Dhillon, Carl P. Dettmann

While the Euclidean distance characteristics of the Poisson line Cox process (PLCP) have been investigated in the literature, the analytical characterization of the path distances is still an open problem. In this paper, we solve this problem for the stationary Manhattan Poisson line Cox process (MPLCP), which is a variant of the PLCP. Specifically, we deriv

Yuefeng Yin, Michael S. Fuhrer, Nikhil V. Medhekar

Topological materials host robust surface states, which could form the basis for future electronic devices. As such states have spins that are locked to the momentum, they are of particular interest for spintronic applications. Understanding spin textures of the surface states of topologically nontrivial materials, and being able to manipulate their polariza

Sen Wang, Daoyuan Jia, Xinshuo Weng

Reinforcement learning has steadily improved and outperform human in lots of traditional games since the resurgence of deep neural network. However, these success is not easy to be copied to autonomous driving because the state spaces in real world are extreme complex and action spaces are continuous and fine control is required. Moreover, the autonomous dri

Kai Fukami, Koji Fukagata, Kunihiko Taira

We use machine learning to perform super-resolution analysis of grossly under-resolved turbulent flow field data to reconstruct the high-resolution flow field. Two machine-learning models are developed; namely the convolutional neural network (CNN) and the hybrid Downsampled Skip-Connection Multi-Scale (DSC/MS) models. These machine-learning models are appli

Xinshuo Weng, Wentao Han

Across a majority of modern learning-based tracking systems, expensive annotations are needed to achieve state-of-the-art performance. In contrast, the Lucas-Kanade (LK) algorithm works well without any annotation. However, LK has a strong assumption of photometric (brightness) consistency on image intensity and is easy to drift because of large motion, occl

Xuan Thinh Duong, Ji Li, Dongyong Yang

Let $p\in(1,\infty)$, $ρ\in (2, \infty)$ and $W$ be a matrix $A_p$ weight. In this article, we introduce a version of variation $\mathcal{V}_ρ({\mathcal T_n}_{\,,\,\ast})$ for matrix Calderón--Zygmund operators with modulus of continuity satisfying the Dini condition. We then obtain the $L^p(W)$-boundedness of $\mathcal{V}_ρ({\mathcal T_n}_{\,,\,\ast})$ with

Shangxuan Wu, Xinshuo Weng

Most existing methods for object segmentation in computer vision are formulated as a labeling task. This, in general, could be transferred to a pixel-wise label assignment task, which is quite similar to the structure of hidden Markov random field. In terms of Markov random field, each pixel can be regarded as a state and has a transition probability to its

Dipayan Banerjee, Karen Smilowitz

We consider the school bus scheduling problem (SBSP) which simultaneously determines school bell times and route schedules. Often, the goal of the SBSP is to minimize the number of buses required by a school district. We extend a time-indexed integer programming model to incorporate additional considerations related to equity and efficiency. We seek to equit

Yu-Chen Cheng, Wenning Wang, Zhiyue Lu, Hong Qian

The theory of probability shows that, as the fraction $X_n/Y\to 0$, the conditional probability for $X_n$, given $X_n+Y \in h_δ:=[h, h+δ]$, has a limit law $f_{X_n}(x)e^{-ψ_n(h_δ)x}$, where $ψ_n(h_δ) $ equals to $[\partial \ln P(Y \in y_δ)/\partial y]_{y=h}$ plus an additional term, contributed from the correlation between $X_n$ and bath $Y$. By applying thi

Yu Shi, Xinwei He, Naijing Zhang, Carl Yang

Heterogeneous information networks (HINs) with rich semantics are ubiquitous in real-world applications. For a given HIN, many reasonable clustering results with distinct semantic meaning can simultaneously exist. User-guided clustering is hence of great practical value for HINs where users provide labels to a small portion of nodes. To cater to a broad spec

Ryusuke Hamazaki, Kohei Kawabata, Masahito Ueda

Many-body localization is shown to suppress imaginary parts of complex eigenenergies for general non-Hermitian Hamiltonians having time-reversal symmetry. We demonstrate that a real-complex transition, which we conjecture occurs upon many-body localization, profoundly affects the dynamical stability of non-Hermitian interacting systems with asymmetric hoppin

Hongyu Xu, Xutao Lv, Xiaoyu Wang, Zhou Ren

In this paper, we propose a novel object detection algorithm named "Deep Regionlets" by integrating deep neural networks and a conventional detection schema for accurate generic object detection. Motivated by the effectiveness of regionlets for modeling object deformations and multiple aspect ratios, we incorporate regionlets into an end-to-end train

Gouranga C Nayak

Recently we have reported the correct formulation of the lattice QCD method at the zero temperature to study the hadron formation from the quarks and gluons by incorporating the non-zero boundary surface term in QCD which arises due to the confinement of quarks and gluons inside the finite size hadron. In this paper we extend this to the finite temperature Q

Leonardo Enzo Brito da Silva, Islam Elnabarawy, Donald C. Wunsch

This paper presents a novel adaptive resonance theory (ART)-based modular architecture for unsupervised learning, namely the distributed dual vigilance fuzzy ART (DDVFA). DDVFA consists of a global ART system whose nodes are local fuzzy ART modules. It is equipped with the distinctive features of distributed higher-order activation and match functions, using

M. F. Francis

Colossal Ion Conductivity (CIC) is the phenomenon in which large changes in oxygen conductivity are observed when a solid state oxygen electrolyte is placed in a heterolayer super lattice. Several hypotheses have been posed concerning both structural changes of the ion conducting material and of the ion conducting mechanism. The posed structural hypotheses a

Dáibhid Ó Maoiléidigh, AJ Hudspeth

Determining the conditions under which an active system best detects sinusoidal signals is important for numerous fields. It is known that a quiescent, deterministic system possessing a supercritical Hopf bifurcation is more sensitive to sinusoidal stimuli the closer it operates to the bifurcation. To understand signal detection in many natural settings, how

Jose A. Perea

Time series are ubiquitous in our data rich world. In what follows I will describe how ideas from dynamical systems and topological data analysis can be combined to gain insights from time-varying data. We will see several applications to the live sciences and engineering, as well as some of the theoretical underpinnings.

Yunrong Zhu

In this paper, we develop the auxiliary space preconditioners for solving the linear system arising from the virtual element methods discretization on polytopal meshes for the second order elliptic equations. The preconditioners are constructed based on an auxiliary simplicial mesh. The condition numbers of the preconditioned systems are uniformly bounded, i

Saïd Abdeddaïm, Sylvestre Vimard, Lina Fatima Soualmia

Eliciting semantic similarity between concepts in the biomedical domain remains a challenging task. Recent approaches founded on embedding vectors have gained in popularity as they risen to efficiently capture semantic relationships The underlying idea is that two words that have close meaning gather similar contexts. In this study, we propose a new neural n

Jan Kremer, Lasse Borgholt, Lars Maaløe

End-to-end automatic speech recognition (ASR) commonly transcribes audio signals into sequences of characters while its performance is evaluated by measuring the word-error rate (WER). This suggests that predicting sequences of words directly may be helpful instead. However, training with word-level supervision can be more difficult due to the sparsity of ex

Yingxue Zhang, Michael Rabbat

Graph convolutional neural networks (Graph-CNNs) extend traditional CNNs to handle data that is supported on a graph. Major challenges when working with data on graphs are that the support set (the vertices of the graph) do not typically have a natural ordering, and in general, the topology of the graph is not regular (i.e., vertices do not all have the same

Tatsuki Koga, Naoki Nonaka, Jun Sakuma, Jun Seita

Deep learning has significant potential for medical imaging. However, since the incident rate of each disease varies widely, the frequency of classes in a medical image dataset is imbalanced, leading to poor accuracy for such infrequent classes. One possible solution is data augmentation of infrequent classes using synthesized images created by Generative Ad

Avishay Gal-Yam

Over a decade ago, a group of supernova explosions with peak luminosities far exceeding (often by >100) those of normal events, has been identified. These superluminous supernovae (SLSNe) have been a focus of intensive study. I review the accumulated observations and discuss the implications for the physics of these extreme explosions. SLSNe can be classifie

Shazia Akbar, Anne L. Martel

To alleviate the burden of gathering detailed expert annotations when training deep neural networks, we propose a weakly supervised learning approach to recognize metastases in microscopic images of breast lymph nodes. We describe an alternative training loss which clusters weakly labeled bags in latent space to inform relevance of patch-instances during tra

Rajesh P. N. Rao

The Indus script remains one of the last major undeciphered scripts of the ancient world. We focus here on Indus inscriptions on a group of miniature tablets discovered by Meadow and Kenoyer in Harappa in 1997. By drawing parallels with proto-Elamite and proto-Cuneiform inscriptions, we explore how these miniature tablets may have been used to record rations

Kazuto Fukuchi, Jun Sakuma

This paper addresses a problem of estimating an additive functional given $n$ i.i.d. samples drawn from a discrete distribution $P=(p_1,...,p_k)$ with alphabet size $k$. The additive functional is defined as $θ(P;ϕ)=\sum_{i=1}^kϕ(p_i)$ for a function $ϕ$, which covers the most of the entropy-like criteria. The minimax optimal risk of this problem has been al

Cristina Turcu, Cornel Turcu, Vasile Gaitan

This paper aims to propose a more efficient distributed monitoring and control approach for water utility in order to reduce the current water loss. This approach will help utilities operators improve water management systems, especially by exploiting the emerging technologies. The Internet of Things could prove to be one of the most important methods for de

Pei Pei Chen, Anna Guitart, Ana Fernández del Río, África Periáñez

Nowadays, video game developers record every virtual action performed by their players. As each player can remain in the game for years, this results in an exceptionally rich dataset that can be used to understand and predict player behavior. In particular, this information may serve to identify the most valuable players and foresee the amount of money they

Albert Kotvytskiy, Volodymir Shablenko, Evgenij Bronza

In this paper, we study fixed points of N-point gravitational lenses. We use complex form of lens mapping to study fixed points. Complex form has an advantage over coordinate one because we can describe N-point gravitational lens by system of two equation in coordinate form and we can describe it by one equation in complex form. We can easily transform the e

Kelley M. Hess, Nicholas M. Luber, Ximena Fernández, Hansung B. Gim

We present a study of 16 HI-detected galaxies found in 178 hours of observations from Epoch 1 of the COSMOS HI Large Extragalactic Survey (CHILES). We focus on two redshift ranges between 0.108 <= z <= 0.127 and 0.162 <= z <= 0.183 which are among the worst affected by radio frequency interference (RFI). While this represents only 10% of the total frequency

Oleg O. Khamisov, Janusz W. Bialek, Anatoly Dymarsky

Introduction of renewable generation leads to significant reduction of inertia in power system, which deteriorates the quality of frequency control. This paper suggests a new control scheme utilizing controllable load to deal with low inertia systems. Optimization problem is formulated to minimize the systems deviations from the last economically optimal ope

Abhass Kumar

Inflation has long been the accepted paradigm for understanding the early universe. Most models of inflation have a scalar field acting as the inflaton particle which decays after inflation during a process called reheating into standard model (SM) particles. In this work, we consider a fermion as the inflaton field. Noether symmetry arguments show that an e

Wenfu Wang, Weijie Yang, An Chen, Zhijie Pan

Urban traffic systems worldwide are suffering from severe traffic safety problems. Traffic safety is affected by many complex factors, and heavily related to all drivers&#39; behaviors involved in traffic system. Drivers with aggressive driving behaviors increase the risk of traffic accidents. In order to manage the safety level of traffic system, we propose

Zhaidary Myrzakulova, Akbota Myrzakul, Gulgassyl Nugmanova, Ratbay Myrzakulov

In the present paper, we study the integrable 2-layer generalized Heisenberg ferromagnet equation (HFE). The relation between this generalized HFE and differential geometry of curves is established. Using this relation we found the geometrical equivalent counterpart of the 2-layer spin system which is the 2-component KdV equation. Finally, the gauge equivale

Qi Zhang, Jiang Zhu, Peter Gerstoft, Mihai-Alin Badiu

Line spectral estimation (LSE) from multi snapshot samples is studied utilizing the variational Bayesian methods. Motivated by the recently proposed variational line spectral estimation (VALSE) method for a single snapshot, we develop the multisnapshot VALSE (MVALSE) for multi snapshot scenarios, which is important for array processing. The MVALSE shares the

Chien-Yu Lu, Min-Xin Xue, Chia-Che Chang, Che-Rung Lee

Style transfer of polyphonic music recordings is a challenging task when considering the modeling of diverse, imaginative, and reasonable music pieces in the style different from their original one. To achieve this, learning stable multi-modal representations for both domain-variant (i.e., style) and domain-invariant (i.e., content) information of music in a

Jiangyi Liu, Chunping Wang, Wei Wang

Most multi-target tracking filters assume that one target and its observation follow a Hidden Markov Chain (HMC) model, but the implicit independence assumption of HMC model is invalid in many practical applications, and a Pairwise Markov Chain (PMC) model is more universally suitable than traditional HMC model. A particle probability hypothesis density filt

Ying Yang, Yiming Pan

In this article, we developed a generalized coupled-mode theory for mixing an isolated state with the gapped continuum to unify the engineering mechanism of gap-protected zero modes, Fano resonance and Tamm surface states, even though those phenomena are diverse in numerous branches of physics and optics. By tuning the on-site potential and coupling strength

Dabiao Ma, Zhiba Su, Yuhao Lu, Wenxuan Wang

Neural networks with Auto-regressive structures, such as Recurrent Neural Networks (RNNs), have become the most appealing structures for acoustic modeling of parametric text to speech synthesis (TTS) in ecent studies. Despite the prominent capacity to capture long-term dependency, these models consist of massive sequential computations that cannot be fully p

Hector Gisbert

A recent lattice prediction of $\varepsilon&#39;/\varepsilon$, with a 2.1 $σ$ deviation from the experimental value, has triggered several studies of possible contributions from physics beyond the Standard Model. We have recently updated the SM prediction, including all known short-distance and long-distance contributions, our result $\mbox{Re}\left(\varepsi

Fayçal Hammad, Étienne Massé, Patrick Labelle

The fate of black hole thermodynamics under Weyl transformations is investigated by going back to the laws of black hole mechanics. It is shown that the transformed surface gravity, that one would identify with the black hole temperature in the conformal frame, as well as the black hole entropy, that one would identify with the horizon area, cannot be invari

Marco Cavallo, Çağatay Demiralp

Dimensionality reduction is a common method for analyzing and visualizing high-dimensional data. However, reasoning dynamically about the results of a dimensionality reduction is difficult. Dimensionality-reduction algorithms use complex optimizations to reduce the number of dimensions of a dataset, but these new dimensions often lack a clear relation to the

Valentin Debarnot, Paul Escande, Pierre Weiss

We propose a scalable method to find a subspace $\widehat{\mathcal{H}}$ of low-rank tensors that simultaneously approximates a set of integral operators. The method can be seen as a generalization of the Tucker-2 decomposition model, which was never used in this context. In addition, we propose to construct a convex set $\widehat{\mathcal{C}} \subset \wideha

Abhisek Das, Satanik Panda, Suman Datta, Soumitra Naskar

In this paper authors are going to present a Markov Decision Process (MDP) based algorithm in Industrial Internet of Things (IIoT) as a safety compliance layer for human in loop system. Though some industries are moving towards Industry 4.0 and attempting to automate the systems as much as possible by using robots, still human in loop systems are very common

Sabah Al-Fedaghi, Yousef Atiyah

Object tracking systems play important roles in tracking moving objects and overcoming problems such as safety, security and other location-related applications. Problems arise from the difficulties in creating a well-defined and understandable description of tracking systems. Nowadays, describing such processes results in fragmental representation that most

Andrew J. Hesselbrock, David A. Minton

In coupled ring-satellite systems, satellites exchange angular momentum with both the primary through tides and with the ring through Lindblad torques, and may exchange material with the ring through accretion and tidal disruption. Here we show that these coupled ring-satellite systems fall into three distinct dynamical regimes, which we refer to as `Boomera

Rafia Malik, Mai Vu

We present a novel approach to maximizing the transmission rate in a MIMO relay system, where all nodes are equipped with multiple antennas and the relay is self-sustained by harvesting energy. We formulate an optimization problem and use dual-characterization to derive a closed-form solution for the optimal power splitting ratio and precoding design. We pro

M. E. Goossens, A. J. Bird, A. B. Hill, V. Sguera

We present the results of combined INTEGRAL and XMM-Newton observations of the supergiant fast X-ray transient (SFXT) IGR J17354$-$3255. Three XMM-Newton observations of lengths 33.4 ks, 32.5 ks and 21.9 ks were undertaken, the first an initial pointing to identify the correct source in the field of view and the latter two performed around periastron. Simult

Daniel Justus, John Brennan, Stephen Bonner, Andrew Stephen McGough

Deep learning is rapidly becoming a go-to tool for many artificial intelligence problems due to its ability to outperform other approaches and even humans at many problems. Despite its popularity we are still unable to accurately predict the time it will take to train a deep learning network to solve a given problem. This training time can be seen as the pro

Yulia Alexandr, Patricia Commins, Alexandra Embry, Sylvia Frank

We explore combinatorial formulas for deformations of highest weight characters of the odd orthogonal group $SO(2n+1)$. Our goal is to represent these deformations of characters as partition functions of statistical mechanical models -- in particular, two-dimensional solvable lattice models. In Cartan type $A$, Hamel and King [8] and Brubaker, Bump, and Frie

Bo Liu, Normand Paquin

Motion capture is a widely-used technology in robotics research thanks to its precise posi tional measurements with real-time performance. This paper presents ViconMAVLink, a cross-platform open-source software tool that provides indoor positioning services to networked robots. ViconMAVLink converts Vicon motion capture data into proper pose and motion data

Paolo Luchini

Something as simple as Couette and Poiseuille onedimensional flow of a newtonian fluid between infinite parallel walls provides an illuminating example of the contrasting physics of laminar and turbulent flow: the difference between their mean velocity profiles has in one regime the opposite sign than in the other. This easily verifiable but yet unnoticed be

Nathan Inkawhich, Matthew Inkawhich, Yiran Chen, Hai Li

The success of deep learning research has catapulted deep models into production systems that our society is becoming increasingly dependent on, especially in the image and video domains. However, recent work has shown that these largely uninterpretable models exhibit glaring security vulnerabilities in the presence of an adversary. In this work, we develop

Chen Gong, N. Benjamin Erichson, John P. Kelly, Laura Trutoiu

Retinal template matching and registration is an important challenge in teleophthalmology with low-cost imaging devices. However, the images from such devices generally have a small field of view (FOV) and image quality degradations, making matching difficult. In this work, we develop an efficient and accurate retinal matching technique that combines dimensi

Beka Ergemlidze, Abhishek Methuku

We introduce a new approach and prove that the maximum number of triangles in a $C_5$-free graph on $n$ vertices is at most $$(1 + o(1)) \frac{1}{3 \sqrt 2} n^{3/2}.$$ We also show a connection to $r$-uniform hypergraphs without (Berge) cycles of length less than six, and estimate their maximum possible size.

Sergio Vitale, Davide Cozzolino, Giuseppe Scarpa, Luisa Verdoliva

We propose a new method for SAR image despeckling which leverages information drawn from co-registered optical imagery. Filtering is performed by plain patch-wise nonlocal means, operating exclusively on SAR data. However, the filtering weights are computed by taking into account also the optical guide, which is much cleaner than the SAR data, and hence more

Robert Lahmann

The acoustic neutrino detection technique is a promising approach for future large-scale detectors with the aim of measuring the small expected flux of neutrinos at energies in the EeV-range and above. The technique is based on the thermo-acoustic model, which implies that the energy deposition by a particle cascade - resulting from a neutrino interaction in

I-Cheng Tung, Aravind Krishnamoorthy, Sridhar Sadasivam, Hua Zhou

X-ray scattering is one of the primary tools to determine crystallographic configuration with atomic accuracy. However, the measurement of ultrafast structural dynamics in monolayer crystals remains a long-standing challenge due to a significant reduction of diffraction volume and complexity of data analysis, prohibiting the application of ultrafast x-ray sc

Vieri Mastropietro

The energy spectrum of the Hofstadter model has a fractal structure with infinitely many gaps. We prove the persistence of each gap in presence of Hubbard interaction in the case of small transversal hopping, even when the coupling is much larger than the non interacting gaps. The proof relies on a subtle interplay of Renormalization Group arguments combined

Min Gyu Kim, Hu Miao, Bin Gao, S. -W. Cheong

Manipulating magnetic domains is essential for many technological applications. Recent breakthroughs in Antiferromagnetic Spintronics brought up novel concepts for electronic device development. Imaging antiferromagnetic domains is of key importance to this field. Unfortunately, some of the basic domain types, such as antiphase domains, cannot be imaged by c

S. M. Mahdi Seyednezhad, Kailey Nobuko Cozart, John Anthony Bowllan, Anthony O. Smith

The number of Internet users had grown rapidly enticing companies and cooperations to make full use of recommendation infrastructures. Consequently, online advertisement companies emerged to aid us in the presence of numerous items and users. Even as a user, you may find yourself drowned in a set of items that you think you might need, but you are not sure i

Kenneth G. Carpenter, Krister E. Nielsen, Gladys V. Kober, Thomas R. Ayres

The HST Treasury Program &#34;Advanced Spectral Library Project: Cool Stars&#34; was designed to collect representative, high quality ultraviolet spectra of eight evolved F-M type cool stars. The Space Telescope Imaging Spectrograph (STIS) echelle spectra of these objects enable investigations of a broad range of topics including stellar and interstellar ast

Ulrich Bunke, Daniel Kasprowski, Christoph Winges

We construct an equivariant coarse homology theory arising from the algebraic $K$-theory of spherical group rings and use this theory to derive split injectivity results for associated assembly maps. On the way, we prove that the fundamental structural theorems for Waldhausen&#39;s algebraic $K$-theory functor carry over to its nonconnective counterpart defi

Shima Fardad, Alessandro Salandrino

Exploiting the dynamics of Plasmonic Parametric Resonance (PPR), we introduce the theory of Plasmonic Parametric Absorbers (PPA). The key insight informing the PPA idea is that in the PPR process a pump field experiences an extinction rate that strongly depends on the intensity of the pump itself, creating two distinct regimes: one of weak absorption under l

Howard J. Schnitzer

It is shown, using level-rank duality that a universal topological quantum computer based on Chern-Simons theory for SU(2)$_3$ also implies an analogous universal quantum computer based on SU(3)$_2$. Suggestions are made for the possible role of level-rank duality in entanglement from topology.

Anne Ernst, Luca Di Luzio, Andreas Ringwald, Carlos Tamarit

We summarize recent studies of realistic nonsupersymmetric Grand Unified Theories (GUTs) extended with a global $U(1)_{\rm PQ}$ symmetry, so as to accommodate the axion solution to the strong CP problem. Aside from solving the CP problem and unifying the gauge structure of the SM, these models can also yield realistic spectra and mixings, including neutrino

Douglas Farenick, Remus Floricel, Sarah Plosker

A quantum probability measure--or quantum measurement--is said to be clean if it cannot be irreversibly connected to any other quantum probability measure via a quantum channel. The notion of a clean quantum measure was introduced by Buscemi et al (2005) for finite-dimensional Hilbert space, and was studied subsequently by Kahn (2007) and Pellonpää (2011). T

Jaewon Yang, Dookun Park, Jae Ho Sohn, Zhen Jane Wang

Deep convolutional neural networks (DCNN) have demonstrated its capability to convert MR image to pseudo CT for PET attenuation correction in PET/MRI. Conventionally, attenuated events are corrected in sinogram space using attenuation maps derived from CT or MR-derived pseudo CT. Separately, scattered events are iteratively estimated by a 3D model-based simu

Jesse G. Meyer

Identification of nearly all proteins in a system using data-dependent acquisition (DDA) mass spectrometry has become routine for simple organisms, such as bacteria and yeast. Still, quantification of the identified proteins may be a complex process and require multiple different software packages. This protocol describes identification and label-free quanti

Guoxin Fan, Huaqing Liu, Zhenhua Wu, Yufeng Li

Background and objective: Combined evaluation of lumbosacral structures (e.g. nerves, bone) on multimodal radiographic images is routinely conducted prior to spinal surgery and interventional procedures. Generally, magnetic resonance imaging is conducted to differentiate nerves, while computed tomography (CT) is used to observe bony structures. The aim of th

Nathan McClanahan, Nicholas Stegmeier, Rylee Sundermann, Jeffrey Doom

We present a 2D parallel implementation of the modified Cahn-Hilliard equation for the simulation of a biofilm in an aqueous enviroment. Biofilms are attached microbial communities made of many different components and can have both positive and negative effects. They can be used for bioremediation but also are the cause of the majority of chronic infections