April 2019 arXiv papers — page 103

Omar Mustafa

The n-dimensional extension of the one dimensional Position-dependent mass (PDM) Lagrangians under the nonlocal point transformations by Mustafa <cite>38</cite> is introduced. The invariance of the n-dimensional PDM Euler-Lagrange equations is examined using two possible/different PDM Lagrangian settings. Under the nonlocal point transformation of Mustafa <c

Raghwendra Kumar, S. Anantha Ramakrishna

A simple metamaterial absorber suitable for fabrication over large areas associated with a Fano like resonance is proposed. The proposed designed of the metamaterial absorber consists of photoresist disk arrays on a silicon substrate followed by the deposition of a tri layer of Au, ZnS and Au on the top of the structure. Due to the tri layer, there is a form

Junjie Hu, Yan Zhang, Takayuki Okatani

Recently, convolutional neural networks (CNNs) have shown great success on the task of monocular depth estimation. A fundamental yet unanswered question is: how CNNs can infer depth from a single image. Toward answering this question, we consider visualization of inference of a CNN by identifying relevant pixels of an input image to depth estimation. We form

Chang Chen, Zhiwei Xiong, Xinmei Tian, Zheng-Jun Zha

Existing methods for single image super-resolution (SR) are typically evaluated with synthetic degradation models such as bicubic or Gaussian downsampling. In this paper, we investigate SR from the perspective of camera lenses, named as CameraSR, which aims to alleviate the intrinsic tradeoff between resolution (R) and field-of-view (V) in realistic imaging

Jiancheng Yang, Qiang Zhang, Bingbing Ni, Linguo Li

Geometric deep learning is increasingly important thanks to the popularity of 3D sensors. Inspired by the recent advances in NLP domain, the self-attention transformer is introduced to consume the point clouds. We develop Point Attention Transformers (PATs), using a parameter-efficient Group Shuffle Attention (GSA) to replace the costly Multi-Head Attention.

O. L. Hernández Rosero, J. I. Melo, P. I. Tamborenea

The corrections to the $E_2^*$ energy level of hydrogenic impurities in semiconductors with wurtzite crystal structure are calculated using first-order perturbation theory in the envelope-function approximation. We consider the intrinsic (Dresselhaus) spin-orbit effective Hamiltonian in the conduction band and compare its effects to the renormalized extrinsi

Muming Zhao, Jian Zhang, Chongyang Zhang, Wenjun Zhang

High-density object counting in surveillance scenes is challenging mainly due to the drastic variation of object scales. The prevalence of deep learning has largely boosted the object counting accuracy on several benchmark datasets. However, does the global counts really count? Armed with this question we dive into the predicted density map whose summation o

Cong Liu, Feng An, Paria S. M. Gharavi, Qinwen Lu

Complex oxides with tunable structures have many fascinating properties, though high-quality complex oxide epitaxy with precisely controlled composition is still out of reach. Here we have successfully developed solution-based single crystalline epitaxy for multiferroic (1-x)BiTi(1-y)/2FeyMg(1-y)/2O3-(x)CaTiO3 (BTFM-CTO) solid solution in large area, confirm

Xing Huang, Wujun Lyu

The existence and uniqueness of the mild solutions for a class of degenerate functional SPDEs are obtained, where the drift is assumed to be Hölder-Dini continuous. Moreover, the non-explosion of the solution is proved under some reasonable conditions. In addition, the Harnack is derived by the coupling by change of measure. Finally, the shift Harnack inequa

Anthony Manchin, Ehsan Abbasnejad, Anton van den Hengel

Attention models have had a significant positive impact on deep learning across a range of tasks. However previous attempts at integrating attention with reinforcement learning have failed to produce significant improvements. We propose the first combination of self attention and reinforcement learning that is capable of producing significant improvements, i

Yatri Modi, Natalie Parde

Visual storytelling is an intriguing and complex task that only recently entered the research arena. In this work, we survey relevant work to date, and conduct a thorough error analysis of three very recent approaches to visual storytelling. We categorize and provide examples of common types of errors, and identify key shortcomings in current work. Finally,

Masamichi Ishihara

We gave a simple derivation of density operator with the quantum analysis. We dealt with the functional of a density operator, and applied maximum entropy principle. We obtained easily the density operators for the Tsallis entropy and Rényi entropy with the $q$-expectation value (escort average), and also obtained easily the density operators for the Boltzma

Aifang Qu, Hairong Yuan, Qin Zhao

We study high Mach number limit of the one dimensional piston problem for the full compressible Euler equations of polytropic gas, for both cases that the piston rushes into or recedes from the uniform still gas, at a constant speed. There are two different situations, and one needs to consider measure solutions of the Euler equations to deal with concentrat

Nelson Gutiérrez Jiménez, Sergii M. Torba

A representation in the form of spectral parameter power series (SPPS) is given for a general solution of a one dimension Dirac system containing arbitrary matrix coefficient at the spectral parameter, \[ B \frac{dY}{dx} + P(x)Y = λR(x)Y,\] where $Y=(y_1,y_2)^T$ is the unknown vector-function, $λ$ is the spectral parameter, $B = \begin{pmatrix}0 & 1 \\ -1 &

Aifang Qu, Hairong Yuan, Qin Zhao

We formulated a problem on hypersonic limit of two-dimensional steady non-isentropic compressible Euler flows passing a straight wedge. It turns out that Mach number of the upcoming uniform supersonic flow increases to infinite may be taken as the adiabatic exponent $γ$ of the polytropic gas decreases to $1$. We proposed a form of the Euler equations which i

Arnab Dhabal, Lee G. Mundy, Che-yu Chen, Peter Teuben

We use NH3 inversion transitions to trace the dense gas in the NGC 1333 region of the Perseus molecular cloud. NH3(1,1) and NH3(2,2) maps covering an area of 102 square arcminutes at an angular resolution of ~3.7&#34; are produced by combining VLA interferometric observations with GBT single dish maps. The combined maps have a spectral resolution of 0.14 km/

Wanhua Li, Jiwen Lu, Jianjiang Feng, Chunjing Xu

Age estimation is an important yet very challenging problem in computer vision. Existing methods for age estimation usually apply a divide-and-conquer strategy to deal with heterogeneous data caused by the non-stationary aging process. However, the facial aging process is also a continuous process, and the continuity relationship between different components

Shanjie Qian

The light curves of optical outbursts observed in blazar OJ287 during 1983-2015 are analyzed and model-simulated to investigate the nature of its optical radiation. It is shown that the December/2015 outburst has its multi-wavelength variability behavior very similar to that of the synchrotron outburst in March/2016, indicating that the 2015 outburst may ori

Seydi Battal Gazi Karakoc, Samir Kumar Bhowmik

The generalized regularized long wave (GRLW) equation has been developed to model a variety of physical phenomena such as ion-acoustic and magnetohydrodynamic waves in plasma, nonlinear transverse waves in shallow water and phonon packets in nonlinear crystals. This paper aims to develop and analyze a powerful numerical scheme for the nonlinear generalized r

Katsunori Iwasaki

The leading asymptotics of the truncation error for Gauss&#39;s continued fraction is determined exactly. Not only for this purpose but also for wider applicability elsewhere the discrete analogue of Laplace&#39;s method for hypergeometric series containing a large parameter, which was developed in a previous paper, is generalized in two directions.

Minyi Huang, Son Luu Nguyen

This paper considers a linear-quadratic (LQ) mean field control problem involving a major player and a large number of minor players, where the dynamics and costs depend on random parameters. The objective is to optimize a social cost as a weighted sum of the individual costs under decentralized information. We apply the person-by-person optimality principle

Asghar Aryanfar, William A. Goddard, Jaime Marian

Percolation phenomena are pervasive in nature, ranging from capillary flow, crack propagation, ionic transport, fluid permeation, etc. Modeling percolation in highly-branched media requires the use of numerical solutions, as problems can quickly become intractable due to the number of pathways available. This becomes even more challenging in dynamic scenario

Rahim K. Charania

R is a robust open-source programming language mainly used for statistical computing . Many areas of statistical research are experiencing rapid growth in the size of data sets. Methodological advances drive increased use of simulations. A common approach is to use parallel/concurrent computing. This paper presents an overview of techniques for parallel comp

Askold Khovanskii

In the preprint we present an outline of the one dimensional version of topological Galois theory. The theory studies topological obstruction to solvability of equations &#34;in finite terms&#34; (i.e. to their solvabilty by radicals, by elementary functions, by quadratures and so on). The preprint is based on the author&#39;s book on topological Galois theo

Marius Tărnăuceanu

In this paper, we introduce a new function related to the sum of element orders of finite groups. It is used to give some criteria for a finite group to be cyclic, abelian, nilpotent, supersolvable and solvable, respectively.

Cheoneum Park, Juae Kim, Hyeon-gu Lee, Reinald Kim Amplayo

This paper describes our system, Joint Encoders for Stable Suggestion Inference (JESSI), for the SemEval 2019 Task 9: Suggestion Mining from Online Reviews and Forums. JESSI is a combination of two sentence encoders: (a) one using multiple pre-trained word embeddings learned from log-bilinear regression (GloVe) and translation (CoVe) models, and (b) one on t

Thaisa Storchi-Bergmann, Allan Schnorr-Müller

Supermassive Black Holes grow at the center of galaxies in consonance with them. In this review we discuss the mass feeding mechanisms that lead to this growth in Active Galactic Nuclei (AGN), focusing on constraints derived from observations of their environment, from extragalactic down to galactic and nuclear scales. At high AGN luminosities, galaxy merger

Nicolas Garcia Trillos, Daniel Sanz-Alonso, Ruiyi Yang

Several data analysis techniques employ similarity relationships between data points to uncover the intrinsic dimension and geometric structure of the underlying data-generating mechanism. In this paper we work under the model assumption that the data is made of random perturbations of feature vectors lying on a low-dimensional manifold. We study two questio

Fedor Duzhin

The &#34;free rider&#34; problem has long plagued pedagogies based on collaborative learning. The most common solution to the free rider problem is peer evaluation. As well other existing methods of peer evaluation include self-evaluation --- and hence are prone to grade inflation or, as we show here, are inaccurate in that they do not fairly reward the most

Xiu Ye, Shangyou Zhang

A new finite element method with discontinuous approximation is introduced for solving second order elliptic problem. Since this method combines the features of both conforming finite element method and discontinuous Galerkin (DG) method, we call it conforming DG method. While using DG finite element space, this conforming DG method maintains the features of

Israt Nisa, Jiajia Li, Aravind Sukumaran-Rajam, Richard Vuduc

Sparse matricized tensor times Khatri-Rao product (MTTKRP) is one of the most computationally expensive kernels in sparse tensor computations. This work focuses on optimizing the MTTKRP operation on GPUs, addressing both performance and storage requirements. We begin by identifying the performance bottlenecks in directly extending the state-of-the-art CSF (c

Noah D Brenowitz, Christopher S Bretherton

General circulation models (GCMs) typically have a grid size of 25--200 km. Parametrizations are used to represent diabatic processes such as radiative transfer and cloud microphysics and account for sub-grid-scale motions and variability. Unlike traditional approaches, neural networks (NNs) can readily exploit recent observational datasets and global cloud-

William W. Hager, Dzung T. Phan, Jia-Jie Zhu

We consider concave minimization problems over non-convex sets.Optimization problems with this structure arise in sparse principal component analysis. We analyze both a gradient projection algorithm and an approximate Newton algorithm where the Hessian approximation is a multiple of the identity. Convergence results are established. In numerical experiments

Chaoyi Zhu, Haoren Wang, Kevin Kaufmann, Kenneth Vecchio

Characterization of the deformation of materials across different length scales has continuously attracted enormous attention from the mechanics and materials communities. In this study, the possibility of utilizing a computer vision algorithm to extract deformation information of materials has been explored, which greatly expands the use of computer vision

L. Arturo Ureña-López

Dark matter models in which the constituent particle is an ultra-light boson have become part of the mainstream discussion in cosmology and astrophysics. At the classical level, the models are represented by the dynamics of a (real or complex) scalar field endowed with a potential that contains its self-interactions, and for this reason are generically known

Zhao-Yu Li, Juntai Shen

The on-going vertical phase mixing, manifesting itself as a snail shell in the $Z-V_{Z}$ phase space, has been discovered with the Gaia DR2 data. To better understand the origin and properties of the phase mixing process, we study the vertical phase-mixing signatures in arches (including the classical ``moving groups&#39;&#39;) of the $V_{R}-V_ϕ$ phase space

Ahmed El Alaoui, Andrea Montanari

We consider the problem of estimating a vector of discrete variables $(θ_1,\cdots,θ_n)$, based on noisy observations $Y_{uv}$ of the pairs $(θ_u,θ_v)$ on the edges of a graph $G=([n],E)$. This setting comprises a broad family of statistical estimation problems, including group synchronization on graphs, community detection, and low-rank matrix estimation. A

Thomas Barthel

Stochastic dynamics of classical degrees of freedom, defined on vertices of locally tree-like graphs, can be studied in the framework of the dynamic cavity method which is exact for tree graphs. Such models correspond for example to spin-glass systems, Boolean networks, neural networks, and other technical, biological, and social networks. The central object

Karol W. Hajduk, James C. Robinson, Witold Sadowski

We prove a robustness of regularity result for the $3$D convective Brinkman-Forchheimer equations $$ \partial_tu -μΔu + (u \cdot \nabla)u + \nabla p + αu + β\abs{u}^{r - 1}u = f, $$ for the range of the absorption exponent $r \in [1, 3]$ (for $r > 3$ there exist global-in-time regular solutions), i.e. we show that strong solutions of these equations remain s

Oktay Karakuş, Igor Rizaev, Alin Achim

In order to analyse synthetic aperture radar (SAR) images of the sea surface, ship wake detection is essential for extracting information on the wake generating vessels. One possibility is to assume a linear model for wakes, in which case detection approaches are based on transforms such as Radon and Hough. These express the bright (dark) lines as peak (trou

Nargiza Nosirova, Mingbin Xu, Hui Jiang

In this paper, we explore a new approach to named entity recognition (NER) with the goal of learning from context and fragment features more effectively, contributing to the improvement of overall recognition performance. We use the recent fixed-size ordinally forgetting encoding (FOFE) method to fully encode each sentence fragment and its left-right context

De Huang

We introduce the notion of $k$-trace and use interpolation of operators to prove the joint concavity of the function $(A,B)\mapsto\text{Tr}_k\big[(B^\frac{qs}{2}K^*A^{ps}KB^\frac{qs}{2})^{\frac{1}{s}}\big]^\frac{1}{k}$, which generalizes Lieb&#39;s concavity theorem from trace to a class of homogeneous functions $\text{Tr}_k[\cdot]^\frac{1}{k}$. Here $\text{

Nargiza Nosirova, Mingbin Xu, Hui Jiang

Named entity recognition (NER) systems that perform well require task-related and manually annotated datasets. However, they are expensive to develop, and are thus limited in size. As there already exists a large number of NER datasets that share a certain degree of relationship but differ in content, it is important to explore the question of whether such d

Chao Tao, Qin Zhang, Yuan Zhou

Best arm identification (or, pure exploration) in multi-armed bandits is a fundamental problem in machine learning. In this paper we study the distributed version of this problem where we have multiple agents, and they want to learn the best arm collaboratively. We want to quantify the power of collaboration under limited interaction (or, communication steps

Alessandro Achille, Giovanni Paolini, Glen Mbeng, Stefano Soatto

We introduce an asymmetric distance in the space of learning tasks, and a framework to compute their complexity. These concepts are foundational for the practice of transfer learning, whereby a parametric model is pre-trained for a task, and then fine-tuned for another. The framework we develop is non-asymptotic, captures the finite nature of the training da

Mike Steel, Wim Hordijk, Stuart A. Kauffman

A feature of human creativity is the ability to take a subset of existing items (e.g. objects, ideas, or techniques) and combine them in various ways to give rise to new items, which, in turn, fuel further growth. Occasionally, some of these items may also disappear (extinction). We model this process by a simple stochastic birth--death model, with non-linea

Jason Li, Vitaly Lavrukhin, Boris Ginsburg, Ryan Leary

In this paper, we report state-of-the-art results on LibriSpeech among end-to-end speech recognition models without any external training data. Our model, Jasper, uses only 1D convolutions, batch normalization, ReLU, dropout, and residual connections. To improve training, we further introduce a new layer-wise optimizer called NovoGrad. Through experiments, w

Wenyuan Wang, Xiaowen Zhou

In this paper we study the draw-down related Parisian ruin problem for spectrally negative Lévy risk processes. We introduce the draw-down Parisian ruin time and solve the corresponding two-sided exit time via excursion theory. We also obtain an expression of the potential measure for the process killed at the draw-down Parisian time. As applications, new re

Arijit Ray, Yi Yao, Rakesh Kumar, Ajay Divakaran

While there have been many proposals on making AI algorithms explainable, few have attempted to evaluate the impact of AI-generated explanations on human performance in conducting human-AI collaborative tasks. To bridge the gap, we propose a Twenty-Questions style collaborative image retrieval game, Explanation-assisted Guess Which (ExAG), as a method of eva

Peng Hao, Xianbin Wang

Recent literature has demonstrated the improved data discovery and delivery efficiency gained through applying named data networking (NDN) to a variety of information-centric Internet of things (IoT) applications. However, from a data security perspective, the development of NDN-IoT raises several new authentication challenges. In particular, NDN-IoT authent

Niluthpol Chowdhury Mithun, Sujoy Paul, Amit K. Roy-Chowdhury

There have been a few recent methods proposed in text to video moment retrieval using natural language queries, but requiring full supervision during training. However, acquiring a large number of training videos with temporal boundary annotations for each text description is extremely time-consuming and often not scalable. In order to cope with this issue,

Ashwini Challa, Kartikeya Upasani, Anusha Balakrishnan, Rajen Subba

Neural approaches to Natural Language Generation (NLG) have been promising for goal-oriented dialogue. One of the challenges of productionizing these approaches, however, is the ability to control response quality, and ensure that generated responses are acceptable. We propose the use of a generate, filter, and rank framework, in which candidate responses ar

Hexiang Hu, Liyu Chen, Boqing Gong, Fei Sha

The ability to transfer in reinforcement learning is key towards building an agent of general artificial intelligence. In this paper, we consider the problem of learning to simultaneously transfer across both environments (ENV) and tasks (TASK), probably more importantly, by learning from only sparse (ENV, TASK) pairs out of all the possible combinations. We

Yanjun Han, Kedar Tatwawadi, Gowtham R. Kurri, Zhengqing Zhou

We study common randomness generation problems where $n$ players aim to generate same sequences of random coin flips where some subsets of the players share an independent common coin which can be tossed multiple times, and there is a publicly seen blackboard through which the players communicate with each other. We provide a tight representation of the opti

A. Aguilar-Arevalo, M. Aoki, M. Blecher, D. I. Britton

Heavy neutrinos were sought in pion decays $π^+ \rightarrow μ^+ ν$ by examining the observed muon energy spectrum for extra peaks in addition to the expected peak for a massless neutrino. No evidence for heavy neutrinos was observed. Upper limits were set on the neutrino mixing matrix $|U_{μi}|^2$ in the neutrino mass region of 15.7--33.8 MeV/c$^2$, improvin

Evgeny A. Poletsky

In this paper we prove the basic facts for pluricomplex Green functions on manifolds. The main goal is to establish properties of complex manifolds that make them analogous to relatively compact or hyperconvex domains in Stein manifolds. The final version to appear in JGA.

ZEUS collaboration, I. Abt, L. Adamczyk, R. Aggarwal

Charm production in charged current deep inelastic scattering has been measured for the first time in $e^{\pm}p$ collisions, using data collected with the ZEUS detector at HERA, corresponding to an integrated luminosity of $358 pb^{-1}$. Results are presented separately for $e^{+}p$ and $e^{-}p$ scattering at a centre-of-mass energy of $\sqrt{s} = 318 GeV$ w

Qiaoling Zhang, Fehmi Cirak

We introduce manifold-based basis functions for isogeometric analysis of surfaces with arbitrary smoothness, prescribed $C^0$ continuous creases and boundaries. The utility of the manifold-based surface construction techniques in isogeometric analysis was demonstrated in Majeed and Cirak (CMAME, 2017). The respective basis functions are derived by combining

Michael Roysdon

In this paper we address the following question: given a measure $μ$ on $\mathbb{R}^n$, does there exists a constant $C>0$ such that, for any $m$-dimensional subspace $H \subset \mathbb{R}^n$ and any convex body $K \subset \mathbb{R}^n$, the following sectional Rogers-Shephard type inequality holds: \[ μ((K-K) \cap H) \leq C \sup_{y \in \mathbb{R}^n} μ(K \ca

Will FitzGerald, Jon Warren

This paper proves an equality in law between the invariant measure of a reflected system of Brownian motions and a vector of point-to-line last passage percolation times in a discrete random environment. A consequence describes the distribution of the all-time supremum of Dyson Brownian motion with drift. A finite temperature version relates the point-to-lin

Volker Runde

For a locally compact group $G$, let $A(G)$ denote its Fourier algebra, $M_{cb}(A(G))$ the completely bounded multipliers of $A(G)$, and $A_{M_cb}(G)$ the closure of $A(G)$ in $M_{cb}(A(G))$. We show that, if $A_{M_cb}(G)$ is amenable, then $a(G_d)$, the almost periodic compactification of the discretization of $G$, has an abelian subgroup of finite index. A

Miao Liu, Xin Chen, Yun Zhang, Yin Li

We address the challenging problem of learning motion representations using deep models for video recognition. To this end, we make use of attention modules that learn to highlight regions in the video and aggregate features for recognition. Specifically, we propose to leverage output attention maps as a vehicle to transfer the learned representation from a

Nithin Varma, Yuichi Yoshida

In modern applications of graphs algorithms, where the graphs of interest are large and dynamic, it is unrealistic to assume that an input representation contains the full information of a graph being studied. Hence, it is desirable to use algorithms that, even when only a (large) subgraph is available, output solutions that are close to the solutions output

Boyan Sirakov

We describe a new method of proving a priori bounds for positive supersolutions and solutions of superlinear elliptic PDE, based on global weak Harnack inequalities and a quantitative Hopf lemma. Novel results based on the method include: (i) equations without a boundary condition on the whole boundary; (ii) equations with nonlinearities which do not have pr

Koushik Chatterjee, Matthew Liska, Alexander Tchekhovskoy, Sera B. Markoff

Accreting black holes produce collimated outflows, or jets, that traverse many orders of magnitude in distance, accelerate to relativistic velocities, and collimate into tight opening angles. Of these, perhaps the least understood is jet collimation due to the interaction with the ambient medium. In order to investigate this interaction, we carried out axisy

Kshitij Bansal, Sarah M. Loos, Markus N. Rabe, Christian Szegedy

We present an environment, benchmark, and deep learning driven automated theorem prover for higher-order logic. Higher-order interactive theorem provers enable the formalization of arbitrary mathematical theories and thereby present an interesting, open-ended challenge for deep learning. We provide an open-source framework based on the HOL Light theorem prov

Yu-An Chung, Wei-Ning Hsu, Hao Tang, James Glass

This paper proposes a novel unsupervised autoregressive neural model for learning generic speech representations. In contrast to other speech representation learning methods that aim to remove noise or speaker variabilities, ours is designed to preserve information for a wide range of downstream tasks. In addition, the proposed model does not require any pho

M. Goryca, J. Li, A. V. Stier, T. Taniguchi

In semiconductor physics, many essential optoelectronic material parameters can be experimentally revealed via optical spectroscopy in sufficiently large magnetic fields. For monolayer transition-metal dichalcogenide semiconductors, this field scale is substantial --tens of teslas or more-- due to heavy carrier masses and huge exciton binding energies. Here

António Caetano, Henning Kempka

We continue the study of the variable exponent Morreyfied Triebel-Lizorkin spaces introduced in a previous paper. Here we give characterizations by means of atoms and molecules. We also show that in some cases the number of zero moments needed for molecules, in order that an infinite linear combination of them (with coefficients in a natural sequence space)

A. Reyes, M. Curbelo, F. Tejera, A. Rivera

We report the emergence of Log-normal Superstatistics in the collective motion of ants confined in a quasi-2D arena and exposed to a panic-inducing stimulus. A data-driven superstatistical Langevin model accurately reproduces the transition from stationary behavior to an organized escape response, characterized by non-Gaussian velocity distributions and a st

Alexander Ruys de Perez, Laura Felicia Matusevich, Anne Shiu

We introduce the factor complex of a neural code, and show how intervals and maximal codewords are captured by the combinatorics of factor complexes. We use these results to obtain algebraic and combinatorial characterizations of max-intersection-complete codes, as well as a new combinatorial characterization of intersection-complete codes.

Jeroen van Dongen, Sebastian De Haro, Manus Visser, Jeremy Butterfield

This is one of a pair of papers that give a historical-\emph{cum}-philosophical analysis of the endeavour to understand black hole entropy as a statistical mechanical entropy obtained by counting string-theoretic microstates. Both papers focus on Andrew Strominger and Cumrun Vafa&#39;s ground-breaking 1996 calculation, which analysed the black hole in terms

N. C. Jackson, R. K. Hanley, M. Hill, F. Leroux

We demonstrate number-resolved detection of individual strontium atoms in a long working distance low numerical aperture (NA = 0.26) tweezer. Using a camera based on single-photon counting technology, we determine the presence of an atom in the tweezer with a fidelity of 0.989(6) (and loss of 0.13(5)) within a 200 $μ$s imaging time. Adding continuous narrow-

Sebastian De Haro, Jeroen van Dongen, Manus Visser, Jeremy Butterfield

The microscopic state counting of the extremal Reissner-Nordström black hole performed by Andrew Strominger and Cumrun Vafa in 1996 has proven to be a central result in string theory. Here, with a philosophical readership in mind, the argument is presented in its contemporary context and its rather complex conceptual structure is analysed. In particular, we

Alexander Moroz, Andrey E. Miroshnichenko

For the same potential as originally studied by Ma [Phys. Rev. {\bf 71}, 195 (1947)] we obtain analytic expressions for the Jost functions and the residui of the S-matrix of both (i) redundant poles and (ii) the poles corresponding to true bound states. This enables us to demonstrate that the Heisenberg condition is valid in spite of the presence of redundan

Art Hobson

The entangled Schrodinger cat state obtained immediately upon measurement of a superposed two-state quantum system is often considered paradoxical because it appears to predict two macroscopically different outcomes, such as an alive and dead cat. However, nonlocal interferometry experiments testing momentum-entangled photon pairs over all phases demonstrate

Venkata Vikram Orre, Elizabeth A. Goldschmidt, Abhinav Deshpande, Alexey V. Gorshkov

We demonstrate quantum interference of three photons that are distinguishable in time, by resolving them in the conjugate parameter, frequency. We show that the multiphoton interference pattern in our setup can be manipulated by tuning the relative delays between the photons, without the need for reconfiguring the optical network. Furthermore, we observe tha

Huu T. Do, Khagendra Adhikari, K. S. D. Beach

We address the problem of free fermions interacting with frozen gauge fields. In particular, we consider a tight-binding model of fermions on the square lattice in which (i) flux 0 or $π$ is threaded through each plaquette and (ii) each nearest-neighbor link is decorated with an Ising degree of freedom that describes the local modulation of the hopping ampli

Nengkun Yu

One of the main subjects of this paper is to study quantum property testing with local measurement. In particular, we establish a novel $\ell_2$ norm connection between quantum property testing problems and the corresponding distribution testing problems. This connection opens up the potential to derive efficient testing algorithms using techniques developed

Hermann Blum, Paul-Edouard Sarlin, Juan Nieto, Roland Siegwart

Deep learning has enabled impressive progress in the accuracy of semantic segmentation. Yet, the ability to estimate uncertainty and detect failure is key for safety-critical applications like autonomous driving. Existing uncertainty estimates have mostly been evaluated on simple tasks, and it is unclear whether these methods generalize to more complex scena

Andrei Krokhin, Jakub Opršal

We study the complexity of approximation on satisfiable instances for graph homomorphism problems. For a fixed graph $H$, the $H$-colouring problem is to decide whether a given graph has a homomorphism to $H$. By a result of Hell and Nešetřil, this problem is NP-hard for any non-bipartite graph $H$. In the context of promise constraint satisfaction problems,

Yi Zhang, Hanna Terletska, Ka-Ming Tam, Yang Wang

We present a new embedding scheme for the locally self-consistent method to study disordered electron systems. We test this method in a tight-binding basis and apply it to the single band Anderson model. The local interaction zone is used to efficiently compute the local Green&#39;s function of a supercell embeded into a local typical medium. We find a quick

Qing Liu, Lingxi Xie, Huiyu Wang, Alan Yuille

Sketch-based image retrieval (SBIR) is widely recognized as an important vision problem which implies a wide range of real-world applications. Recently, research interests arise in solving this problem under the more realistic and challenging setting of zero-shot learning. In this paper, we investigate this problem from the viewpoint of domain adaptation whi

F. Nur Ünal, André Eckardt, Robert-Jan Slager

We present a topological characterization of time-periodically driven two-band models in 2+1 dimensions as Hopf insulators. The intrinsic periodicity of the Floquet system with respect to both time and the underlying two-dimensional momentum space constitutes a map from a three dimensional torus to the Bloch sphere. As a result, we find that the driven syste

Mark C. Neyrinck, Miguel A. Aragon-Calvo, Bridget Falck, Alexander S. Szalay

The standard explanation for galaxy spin starts with the tidal-torque theory (TTT), in which an ellipsoidal dark-matter protohalo, which comes to host the galaxy, is torqued up by the tidal gravitational field around it. We discuss a complementary picture, using the relatively familiar velocity field, instead of the tidal field, whose intuitive connection to

C. J. Grier, Yue Shen, Keith Horne, W. N. Brandt

We present reverberation-mapping lags and black-hole mass measurements using the CIV 1549 broad emission line from a sample of 349 quasars monitored as a part of the Sloan Digital Sky Survey Reverberation Mapping Project. Our data span four years of spectroscopic and photometric monitoring for a total baseline of 1300 days. We report significant time delays

Ping Chen, Subo Dong, Boaz Katz, C. S. Kochanek

We report comprehensive multi-wavelength observations of a peculiar Type Ia-like supernova (&#34;SN Ia-pec&#34;) ASASSN-15pz. ASASSN-15pz is a spectroscopic &#34;twin&#34; of SN 2009dc, a so-called &#34;Super-Chandrasekhar-mass&#34; SN, throughout its evolution, but it has a peak luminosity M_B,peak = -19.69 +/- 0.12 mag that is \approx 0.6 mag dimmer and co

Christian W. Bauer, Wibe A. de Jong, Benjamin Nachman, Davide Provasoli

Particles produced in high energy collisions that are charged under one of the fundamental forces will radiate proportionally to their charge, such as photon radiation from electrons in quantum electrodynamics. At sufficiently high energies, this radiation pattern is enhanced collinear to the initiating particle, resulting in a complex, many-body quantum sys

Miguel F. Paulos, Bernardo Zan

We apply recently constructed functional bases to the numerical conformal bootstrap for 1D CFTs. We argue and show that numerical results in this basis converge much faster than the traditional derivative basis. In particular, truncations of the crossing equation with even a handful of components can lead to extremely accurate results, in opposition to hundr

Ankan Bansal, Sai Saketh Rambhatla, Abhinav Shrivastava, Rama Chellappa

We present an approach for detecting human-object interactions (HOIs) in images, based on the idea that humans interact with functionally similar objects in a similar manner. The proposed model is simple and efficiently uses the data, visual features of the human, relative spatial orientation of the human and the object, and the knowledge that functionally s

Fei Han, Nina Andrejevic, Thanh Nguyen, Vladyslav Kozii

Thermoelectrics are promising by directly generating electricity from waste heat. However, (sub-)room-temperature thermoelectrics have been a long-standing challenge due to vanishing electronic entropy at low temperatures. Topological materials offer a new avenue for energy harvesting applications. Recent theories predicted that topological semimetals at the

Victor Bapst, Alvaro Sanchez-Gonzalez, Carl Doersch, Kimberly L. Stachenfeld

Physical construction---the ability to compose objects, subject to physical dynamics, to serve some function---is fundamental to human intelligence. We introduce a suite of challenging physical construction tasks inspired by how children play with blocks, such as matching a target configuration, stacking blocks to connect objects together, and creating shelt

Moein Shakeri, Hong Zhang

Although low-rank and sparse decomposition based methods have been successfully applied to the problem of moving object detection using structured sparsity-inducing norms, they are still vulnerable to significant illumination changes that arise in certain applications. We are interested in moving object detection in applications involving time-lapse image se

Rameen Abdal, Yipeng Qin, Peter Wonka

We propose an efficient algorithm to embed a given image into the latent space of StyleGAN. This embedding enables semantic image editing operations that can be applied to existing photographs. Taking the StyleGAN trained on the FFHQ dataset as an example, we show results for image morphing, style transfer, and expression transfer. Studying the results of th

Roman Kaskman, Sergey Zakharov, Ivan Shugurov, Slobodan Ilic

Among the most important prerequisites for creating and evaluating 6D object pose detectors are datasets with labeled 6D poses. With the advent of deep learning, demand for such datasets is growing continuously. Despite the fact that some of exist, they are scarce and typically have restricted setups, such as a single object per sequence, or they focus on sp

Eric J. Hanson, Kiyoshi Igusa

In $\tau$-tilting theory, it is often difficult to determine when a set of bricks forms a 2-simple minded collection. The aim of this paper is to determine when a set of bricks is contained in a 2-simple minded collection for a $\tau$-tilting finite algebra. We begin by extending the definition of mutation from 2-simple minded collections to more general set

R. Ruffini, J. D. Melon Fuksman, G. V. Vereshchagin

Within the binary-driven hypernova I (BdHN I) scenario, the gamma-ray burst GRB190114C originates in a binary system composed of a massive carbon-oxygen core (CO$_{core}$), and a binary neutron star (NS) companion. As the CO$_{core}$ undergoes a supernova explosion with the creation of a new neutron star ($ν$NS), hypercritical accretion occurs onto the compa

Jaehyeok Lee, Jae-Suk Park

We develop a basic theory of affine group dg-schemes, their Lie algebraic counterparts and linear representations. We prove Tannaka type reconstruction theorems that an affine group dg-scheme can be recovered from the dg-tensor category of its linear representations as well as from the rigid dg-tensor category of its finite dimensional linear representations

Chengju Yu, Alexander S. Sokolov, Piotr Kulik, Vincent G. Harris

Barium hexaferrite (BaFe12O19 or BaM) films were grown on c-plane sapphire (0001) substrates by pulsed laser deposition (PLD) to evaluate the effects of the laser fluence on their composition, structure, and magnetic properties. Continuum&#39;s Surelite pulsed 266nm Nd:YAG laser was employed, and the laser fluence varied systemically between 1 and 5.7 [J/cm2

Leovigildo Alonso, Ana Jeremias, Marta Perez

We show that, for a pseudo-proper smooth noetherian formal scheme $\mathfrak{X}$ over a positive characteristic $p$ field, its truncated De Rham complex up to the characteristic $p$ is decomposable. Moreover, if the dimension of $\mathfrak{X}$ is exactly $p$, then the full De Rham complex is decomposable. Along the way we establish the Cartier isomorphism as