November 2019 arXiv papers — page 51

Junjie Hu, Takayuki Okatani

In this paper, we consider adversarial attacks against a system of monocular depth estimation (MDE) based on convolutional neural networks (CNNs). The motivation is two-fold. One is to study the security of MDE systems, which has not been actively considered in the community. The other is to improve our understanding of the computational mechanism of CNNs pe

M. Zacharias, M. Boettcher, F. Jankowsky, J. -P. Lenain

The FSRQ CTA 102 (z=1.032) has been tremendously active over the last few years. During its peak activity lasting several months in late 2016 and early 2017, the gamma-ray and optical fluxes rose by up to a factor 100 above the quiescence level. We have interpreted the peak activity as the ablation of a gas cloud by the relativistic jet, which can nicely acc

Angelo Capossele, Sebastian Mueller, Andreas Penzkofer

This paper investigates leaderless binary majority consensus protocols with low computational complexity in noisy Byzantine infrastructures. Using computer simulations, we show that explicit randomization of the consensus protocol can significantly increase the robustness towards faulty and malicious nodes. We identify the optimal amount of randomness for va

Ankur Srivastav, Debabrata Ghorai, Sunandan Gangopadhyay

In this paper, we have studied the effect of Born-Infeld electrodynamics in holographic $p$-wave superconductors with massive vector condensation. We have analysed this model in the probe limit using a variational method known as the Stürm-Liouville eigenvalue approach. For this $p$-wave holographic superconductor model, we have calculated the critical tempe

Qun Liu, Lihua Fu, Meng Zhang

Reconstruction of seismic data with missing traces is a long-standing issue in seismic data processing. In recent years, rank reduction operations are being commonly utilized to overcome this problem, which require the rank of seismic data to be a prior. However, the rank of field data is unknown; usually it requires much time to manually adjust the rank and

Donglin Chen, Jianbin Fang, Chuanfu Xu, Shizhao Chen

Understanding the scalability of parallel programs is crucial for software optimization and hardware architecture design. As HPC hardware is moving towards many-core design, it becomes increasingly difficult for a parallel program to make effective use of all available processor cores. This makes scalability analysis increasingly important. This paper presen

Michael F. Bietenholz, Raffaella Margutti, Deanne Coppejans, Kate D. Alexander

We report on VLBI observations of the fast and blue optical transient (FBOT), AT 2018cow. At ~62 Mpc, AT 2018cow is the first relatively nearby FBOT. The nature of AT 2018cow is not clear, although various hypotheses from a tidal disruption event to different kinds of supernovae have been suggested. It had a very fast rise time (3.5 d) and an almost featurel

Roni Haecki, Lukas Humbel, Reto Achermann, David Cock

We present CleanQ, a high-performance operating-system interface for descriptor-based data transfer with rigorous formal semantics, based on a simple, formally-verified notion of ownership transfer, with a fast reference implementation. CleanQ aims to replace the current proliferation of similar, but subtly diverse, and loosely specified, descriptor-based in

Shaohuai Shi, Xiaowen Chu, Ka Chun Cheung, Simon See

Distributed stochastic gradient descent (SGD) algorithms are widely deployed in training large-scale deep learning models, while the communication overhead among workers becomes the new system bottleneck. Recently proposed gradient sparsification techniques, especially Top-$k$ sparsification with error compensation (TopK-SGD), can significantly reduce the co

Jingzhi Hu, Hongliang Zhang, Lingyang Song, Zhu Han

Unmanned aerial vehicles (UAVs) can be powerful Internet-of-Things components to execute sensing tasks over the next-generation cellular networks, which are generally referred to as the cellular Internet of UAVs. However, due to the high mobility of UAVs and the shadowing in the air-to-ground channels, UAVs operate in an environment with dynamics and uncerta

Andrea Montoli, Diana Rodelo, Tim Van der Linden

In the context of regular unital categories we introduce an intrinsic version of the notion of a Schreier split epimorphism, originally considered for monoids. We show that such split epimorphisms satisfy the same homological properties as Schreier split epimorphisms of monoids do. This gives rise to new examples of S-protomodular categories, and allows us t

Syeda Noor Jaha Azim, Md. Aminur Rab Ratul

There are many award-winning pre-trained Convolutional Neural Network (CNN), which have a common phenomenon of increasing depth in convolutional layers. However, I inspect on VGG network, which is one of the famous model submitted to ILSVRC-2014, to show that slight modification in the basic architecture can enhance the accuracy result of the image classific

Ming-Qiang Ren, Sha Han, Shu-Ze Wang, Jia-Qi Fan

Alkali-fulleride superconductors with a maximum critical temperature Tc of 40 K exhibit similar electronic phase diagram with unconventional high-Tc superconductors where the superconductivity resides proximate to a magnetic Mott-insulating state. However, distinct from cuprate compounds, which superconduct through two-dimensional (2D) CuO2 planes, alkali fu

E. Celeghini, M. Gadella, M. A. del Olmo

This paper is a contribution to the study of the relations between special functions, Lie algebras and rigged Hilbert spaces. The discrete indices and continuous variables of special functions are in correspondence with the representations of their algebra of symmetry, that induce discrete and continuous bases coexisting on a rigged Hilbert space supporting

Matteo Testa, Arslan Ali, Tiziano Bianchi, Enrico Magli

We propose a novel architecture for generic biometric authentication based on deep neural networks: RegNet. Differently from other methods, RegNet learns a mapping of the input biometric traits onto a target distribution in a well-behaved space in which users can be separated by means of simple and tunable boundaries. More specifically, authorized and unauth

Preslav Nakov

An important challenge for the automatic analysis of English written text is the abundance of noun compounds: sequences of nouns acting as a single noun. In our view, their semantics is best characterized by the set of all possible paraphrasing verbs, with associated weights, e.g., malaria mosquito is carry (23), spread (16), cause (12), transmit (9), etc. U

Fei Shi, Xiande Zhang, Lin Chen

We construct two mutually unbiased bases by maximally entangled states (MUMEB$s$) in $\mathbb{C}^{2}\otimes \mathbb{C}^{3}$. This is the first example of MUMEB$s$ in $\mathbb{C}^{d}\otimes \mathbb{C}^{d'}$ when $d\nmid d'$, namely $d'$ is not divisible by $d$. We show that they cannot be extended to four MUBs in $\mathbb{C}^6$. We propose a recur

Wen Deng, Yiguang Hong, Brian D. O. Anderson, Guodong Shi

In this paper, we study distributed methods for solving a Sylvester equation in the form of AX+XB=C for matrices A, B, C$\in R^{n\times n}$ with X being the unknown variable. The entries of A, B and C (called data) are partitioned into a number of pieces (or sometimes we permit these pieces to overlap). Then a network with a given structure is assigned, whos

Lina Zhao, Eric Chung, Ming Fai Lam

In this paper we propose a novel staggered discontinuous Galerkin method for the Brinkman problem on general quadrilateral and polygonal meshes. The proposed method is robust in the Stokes and Darcy limits, in addition, hanging nodes can be automatically incorporated in the construction of the method, which are desirable features in practical applications. T

Shafiq Joty, Alberto Barrón-Cedeño, Giovanni Da San Martino, Simone Filice

Community question answering, a recent evolution of question answering in the Web context, allows a user to quickly consult the opinion of a number of people on a particular topic, thus taking advantage of the wisdom of the crowd. Here we try to help the user by deciding automatically which answers are good and which are bad for a given question. In particul

Shi-fan Qi, Jun Jing

The NOON states are valuable quantum resources, which have a wide range of applications in quantum communication, quantum metrology, and quantum information processing. Here we propose a fast, concise and reliable protocol for deterministically generating the NOON states of two resonators coupled to a single $\triangle$-type superconducting qutrit. In partic

S. Kalra, C. Choi, S. Shah, L. Pantanowitz

With the emergence of digital pathology, searching for similar images in large archives has gained considerable attention. Image retrieval can provide pathologists with unprecedented access to the evidence embodied in already diagnosed and treated cases from the past. This paper proposes a search engine specialized for digital pathology, called Yottixel, a p

Keyu An, Hongyu Xiang, Zhijian Ou

In this paper, we present a new open source toolkit for automatic speech recognition (ASR), named CAT (CRF-based ASR Toolkit). A key feature of CAT is discriminative training in the framework of conditional random field (CRF), particularly with connectionist temporal classification (CTC) inspired state topology. CAT contains a full-fledged implementation of

Weijin Chen, Qingdong Yang, Yuntian Chen, Wei Liu

Scattering activities are generally manifest through different optical responses of scattering bodies to circularly polarized light of opposite handedness. Similar to the ubiquitous roles played by scattering theory across different branches of photonics, scattering activities can serve as a fundamental concept to clarify underlying mechanisms of various chi

Todor Mihaylov, Preslav Nakov

We describe our system for finding good answers in a community forum, as defined in SemEval-2016, Task 3 on Community Question Answering. Our approach relies on several semantic similarity features based on fine-tuned word embeddings and topics similarities. In the main Subtask C, our primary submission was ranked third, with a MAP of 51.68 and accuracy of 6

J. S. Dhanya, L. K. Dewangan, D. K. Ojha, S. Mandal

In order to understand the formation of massive OB stars, we report a multi-wavelength observational study of a giant molecular cloud hosting the S147/S153 complex (size ~90 pc X 50 pc). The selected complex is located in the Perseus arm, and contains at least five HII regions (S147, S148, S149, S152, and S153) powered by massive OB stars having dynamical ag

Nikhil Thakurdesai, Anupam Tripathi, Dheeraj Butani, Smita Sankhe

Blind people face a lot of problems in their daily routines. They have to struggle a lot just to do their day-to-day chores. In this paper, we have proposed a system with the objective to help the visually impaired by providing audio aid guiding them to avoid obstacles, which will assist them to move in their surroundings. Object Detection using YOLO will he

Shivam Kalra, H. R. Tizhoosh, Sultaan Shah, Charles Choi

The emergence of digital pathology has opened new horizons for histopathology and cytology. Artificial-intelligence algorithms are able to operate on digitized slides to assist pathologists with diagnostic tasks. Whereas machine learning involving classification and segmentation methods have obvious benefits for image analysis in pathology, image search repr

Erik van Heumen, Maarten Berben, Linda Neubrand, Yingkai Huang

We present optical measurements of the transition metal dichalcogenide PdTe$_{2}$. The reflectivity displays an unusual temperature and energy dependence in the far-infrared, which we show can only be explained by a collapse of the scattering rate at low temperature, resulting from the vicinity of a van Hove singularity near the Fermi energy. An analysis of

Saeed Afshar, Andrew P Nicholson, Andre van Schaik, Gregory Cohen

In this work, we present optical space imaging using an unconventional yet promising class of imaging devices known as neuromorphic event-based sensors. These devices, which are modeled on the human retina, do not operate with frames, but rather generate asynchronous streams of events in response to changes in log-illumination at each pixel. These devices ar

Robin M. Gubela, Stefan Lessmann, Szymon Jaroszewicz

Uplift models support decision-making in marketing campaign planning. Estimating the causal effect of a marketing treatment, an uplift model facilitates targeting communication to responsive customers and efficient allocation of marketing budgets. Research into uplift models focuses on conversion models to maximize incremental sales. The paper introduces upl

Yujie Liu, Junping Wang

This article presents a $P_0$ finite element method for boundary value problems for linear elasticity equations. The new method makes use of piecewise constant approximating functions on the boundary of each polytopal element, and is devised by simplifying and modifying the weak Galerkin finite element method based on $P_1/P_0$ approximations for the displac

Xuejun Yu, David J. Nott, Minh-Ngoc Tran, Nadja Klein

A common method for assessing validity of Bayesian sampling or approximate inference methods makes use of simulated data replicates for parameters drawn from the prior. Under continuity assumptions, quantiles of functions of the simulated parameter values within corresponding posterior distributions are uniformly distributed. Checking for uniformity when a p

Shunta Maeda

Fast and flexible processing are two essential requirements for a number of practical applications of image denoising. Current state-of-the-art methods, however, still require either high computational cost or limited scopes of the target. We introduce an efficient ensemble network trained via a competition of expert networks, as an application for image bli

Lirong He, Ziyi Guo, Kaizhu Huang, Zenglin Xu

Deep neural networks enjoy a powerful representation and have proven effective in a number of applications. However, recent advances show that deep neural networks are vulnerable to adversarial attacks incurred by the so-called adversarial examples. Although the adversarial example is only slightly different from the input sample, the neural network classifi

Fengqiao Luo, Sanjay Mehrotra

We investigate robust optimization problems defined for maximizing convex functions. For finite uncertainty set, we develop a geometric branch-and-bound algorithmic approach to solve this problem. The geometric branch-and-bound algorithm performs sequential piecewise-linear approximations of the convex objective, and solves linear programs to determine lower

Amirata Ghorbani, Vivek Natarajan, David Coz, Yuan Liu

Despite the recent success in applying supervised deep learning to medical imaging tasks, the problem of obtaining large and diverse expert-annotated datasets required for the development of high performant models remains particularly challenging. In this work, we explore the possibility of using Generative Adverserial Networks (GAN) to synthesize clinical i

Fatmatulzehra Uslu

A fundus image usually contains the optic disc, pathologies and other structures in addition to vessels to be segmented. This study proposes a deep network for vessel segmentation, whose architecture is inspired by inception modules. The network contains three sub-networks, each with a different filter size, which are connected in the last layer of the propo

Fengqiao Luo, Sanjay Mehrotra

We develop a decomposition algorithm for distributionally-robust two-stage stochastic mixed-integer convex cone programs, and its important special case of distributionally-robust two-stage stochastic mixed-integer second order cone programs. This generalizes the algorithm proposed by Sen and Sherali~[Mathematical Programming 106(2): 203-223, 2006]. We show

Chih-Chung Hsu, Chia-Hsiang Lin

Deep learning-based single image super-resolution enables very fast and high-visual-quality reconstruction. Recently, an enhanced super-resolution based on generative adversarial network (ESRGAN) has achieved excellent performance in terms of both qualitative and quantitative quality of the reconstructed high-resolution image. In this paper, we propose to ad

Bing Gao, Haixia Liu, Yang Wang

Generally, phase retrieval problem can be viewed as the reconstruction of a function/signal from only the magnitude of the linear measurements. These measurements can be, for example, the Fourier transform of the density function. Computationally the phase retrieval problem is very challenging. Many algorithms for phase retrieval are based on i.i.d. Gaussian

Wenlin Wang, Hongteng Xu, Zhe Gan, Bai Li

We propose a novel graph-driven generative model, that unifies multiple heterogeneous learning tasks into the same framework. The proposed model is based on the fact that heterogeneous learning tasks, which correspond to different generative processes, often rely on data with a shared graph structure. Accordingly, our model combines a graph convolutional net

Takuya Hatomura

We consider a classical and superadiabatic version of an iterative quantum adiabatic algorithm to solve combinatorial optimization problems. This algorithm is deterministic because it is based on purely classical dynamics, that is, it does not rely on any stochastic approach to mimic quantum dynamics. Moreover, use of shortcuts to adiabaticity makes the algo

Xin He, Shihao Wang, Shaohuai Shi, Zhenheng Tang

Skin disease is one of the most common types of human diseases, which may happen to everyone regardless of age, gender or race. Due to the high visual diversity, human diagnosis highly relies on personal experience; and there is a serious shortage of experienced dermatologists in many countries. To alleviate this problem, computer-aided diagnosis with state-

Tom Blau, Lionel Ott, Fabio Ramos

Balancing exploration and exploitation is a fundamental part of reinforcement learning, yet most state-of-the-art algorithms use a naive exploration protocol like $ε$-greedy. This contributes to the problem of high sample complexity, as the algorithm wastes effort by repeatedly visiting parts of the state space that have already been explored. We introduce a

Teng Zhang

This paper studies an optimization problem on the sum of traces of matrix quadratic forms on $m$ orthogonal matrices, which can be considered as a generalization of the synchronization of rotations. While the problem is nonconvex, the paper shows that its semidefinite programming relaxation can solve the original nonconvex problems exactly, under an additive

Jingfeng Zhang, Bo Han, Gang Niu, Tongliang Liu

Deep neural networks (DNNs) are incredibly brittle due to adversarial examples. To robustify DNNs, adversarial training was proposed, which requires large-scale but well-labeled data. However, it is quite expensive to annotate large-scale data well. To compensate for this shortage, several seminal works are utilizing large-scale unlabeled data. In this paper

X. G. Wang, A. W. Thomas

We explore the application of a two-component model of proton structure functions in the analysis of deep-inelastic scattering (DIS) data at low $Q^2$ and small $x$. This model incorporates both vector meson dominance and the correct photo-production limit. The CJ15 parameterization is applied to the QCD component, in order to take into account effects of or

Sheng-Wen Li, Fu Li, Tao Peng, G. S. Agarwal

When a laser beam passes through a rotating ground glass (RGG), the scattered light exhibits thermal statistics. This is extensively used in speckle imaging. This scattering process has not been addressed in photon picture and is especially relevant if non-classical light is scattered by the RGG. We develop the photon picture for the scattering process using

Anupam Garg

The spin-$j$ extension of Bohm's version of the Einstein-Podolsky-Rosen experiment is is analysed in terms of the Wigner function when the two spins are in a singlet state. This function is calculated for all $j$, and it is shown that just as Bell inequalities are violated with undiminished range and magnitude for arbitarirly large $j$, this function doe

Anshuman Pradhan, Huy Q. Le, Nader C. Dutta, Biondo Biondi

We present a methodology for quantifying seismic velocity and pore pressure uncertainty that incorporates information regarding the geological history of a basin, rock physics, well log, drilling and seismic data. In particular, our approach relies on linking velocity models to the basin modeling outputs of porosity, mineral volume fractions and pore pressur

Sheng Jin, Shangchen Zhou, Yao Liu, Chao Chen

Deep hashing methods have been proved to be effective and efficient for large-scale Web media search. The success of these data-driven methods largely depends on collecting sufficient labeled data, which is usually a crucial limitation in practical cases. The current solutions to this issue utilize Generative Adversarial Network (GAN) to augment data in semi

Zhao Guo, Avi Shporer, Kelly Hambleton, Howard Isaacson

Tidal forces in eccentric binary stars known as heartbeat stars excite detectable oscillations that shed light on the processes of tidal synchronization and circularization. We examine the pulsation phases of tidally excited oscillations (TEOs) in heartbeat binary systems. The target list includes four published heartbeat binaries and four additional systems

Bradley Elliott, Ronald Gould, Kazuhide Hirohata

In this paper, we consider a general degree sum condition sufficient to imply the existence of $k$ vertex-disjoint chorded cycles in a graph $G$. Let $σ_t(G)$ be the minimum degree sum of $t$ independent vertices of $G$. We prove that if $G$ is a graph of sufficiently large order and $σ_t(G)\geq 3kt-t+1$ with $k\geq 1$, then $G$ contains $k$ vertex-disjoint

Alexander J. Dittmann, M. Coleman Miller

Accretion disks around active galactic nuclei are potentially unstable to star formation at large radii. We note that when the compact objects formed from some of these stars spiral into the central supermassive black hole, there is no radiative feedback and therefore the accretion rate is not limited by radiation forces. Using a set of accretion disk models

Kaiqun Fu, Taoran Ji, Liang Zhao, Chang-Tien Lu

Critical incident stages identification and reasonable prediction of traffic incident duration are essential in traffic incident management. In this paper, we propose a traffic incident duration prediction model that simultaneously predicts the impact of the traffic incidents and identifies the critical groups of temporal features via a multi-task learning f

Yulin Sun, Zhao Zhang, Weiming Jiang, Zheng Zhang

In this paper, we propose a structured Robust Adaptive Dic-tionary Pair Learning (RA-DPL) framework for the discrim-inative sparse representation learning. To achieve powerful representation ability of the available samples, the setting of RA-DPL seamlessly integrates the robust projective dictionary pair learning, locality-adaptive sparse representations an

Chang Eon Shin, Qiyu Sun

In this paper, we consider the norm-controlled inversion for differential $*$-subalgebras of a symmetric $*$-algebra with common identity and involution.

Huan Zhang, Zhao Zhang, Mingbo Zhao, Qiaolin Ye

The graph-based semi-supervised label propagation algorithm has delivered impressive classification results. However, the estimated soft labels typically contain mixed signs and noise, which cause inaccurate predictions due to the lack of suitable constraints. Moreover, available methods typically calculate the weights and estimate the labels in the original

Sha Han, Meng-Xue Guan, Can-Li Song, Yi-Lin Wang

Alkali-doped fullerides exhibit a wealth of unusual phases that remain controversial by nature. Here we report a cryogenic scanning tunneling microscopy study of the sub-molecular structural and electronic properties of expanded fullerene C60 films with various cesium (Cs) doping. By varying the discrete charge states and film thicknesses, we reveal a large

Alin Voskanian-Kordi, Ashley Funai, Maricel G. Kann

Protein domains are highly conserved functional units of proteins. Because they carry functionally significant information, the majority of the coding disease variants are located on domains. Additionally, domains are specific units of the proteins that can be targeted for drug delivery purposes. Here, using information about variants sites associated with d

Douglas A. Barros, Angeles Pérez-Villegas, Jacques R. D. Lépine, Tatiana A. Michtchenko

The present paper is the culminating one of a series aimed to contribute to the understanding of the kinematic structures of the solar neighbourhood (SN), explaining the origin of the Local Arm and relating the moving groups with the spiral-arms resonances in the disk. With a model for the Galactic potential, with the Sun inside the spiral corotation resonan

Ngoc Han Tu, Katsumasa Yoshioka, Satoshi Sasaki, Makoto Takamura

Graphene offers a possibility for actively controlling plasmon confinement and propagation by tailoring its spatial conductivity pattern. However, implementation of this concept has been hampered because uncontrollable plasmon reflection is easily induced by inhomogeneous dielectric environment. In this work, we demonstrate full electrical control of plasmon

Chen Ercai, He Shen, Zhou Xiaoyao

In this paper, we showed that the Pesin pressure of any subset under a mistake function is equal to the classical Pesin pressure of the subset in dynamical systems. Our result extended the result of [1] in additive case, which proved the topological pressure of the whole system is self adaptable under a mistake function. As an application, we showed that the

Niclas Boehmer, Edith Elkind

In hedonic diversity games (HDGs), recently introduced by Bredereck, Elkind and Igarashi (2019), each agent belongs to one of two classes (men and women, vegetarians and meat-eaters, junior and senior researchers), and agents' preferences over coalitions are determined by the fraction of agents from their class in each coalition. Bredereck et al. show th

Byung Hee An, Youngjin Bae, Tamás Kálmán

We define ruling invariants for even-valence Legendrian graphs in standard contact three-space. We prove that rulings exist if and only if the DGA of the graph, introduced by the first two authors, has an augmentation. We set up the usual ruling polynomials for various notions of gradedness and prove that if the graph is four-valent, then the ungraded ruling

Vibhavari Dasagi, Robert Lee, Jake Bruce, Jürgen Leitner

Deep reinforcement learning has been shown to solve challenging tasks where large amounts of training experience is available, usually obtained online while learning the task. Robotics is a significant potential application domain for many of these algorithms, but generating robot experience in the real world is expensive, especially when each task requires

Gretchen L. H. Harris, Iurii V. Babyk, William E. Harris, Brian R. McNamara

We compare the empirical relationships between the mass of a galaxy's globular system M_GCS, the gas mass in the hot X-ray atmosphere M_X within a fiducial radius of 5 r_e, the total gravitational mass M_grav within 5 r_e, and lastly the total halo mass M_h calibrated from weak lensing. We use a sample of 45 early-type galaxies (ETGs) for which both GCS

Tamitake Itoh, Yuko S. Yamamoto, Takayuki Okamoto

The evidence of strong coupling between plasmons and molecular excitons for plasmonic nanoparticle (NP) dimers exhibiting ultra-sensitive surface enhanced resonant Raman scattering is the observation of anti-crossing in the coupled resonance. However, it is not easy to experimentally tune plasmon resonance of such dimers for the observation. In this work, we

Yuri Netto, Adriana Valio

Context. The study of young solar type stars is fundamental for a better understanding of the magnetic activity of the Sun. As a planet in transit crosses in front of its host star, a darkspot on the stellar surface may be occulted, causing a detectable variation in the light curve. Kepler-63 is a young solar-like star withan age of only 210 Myr that exhibit

Rajendra R. Zope, Yoh Yamamoto, Carlos Diaz, Tunna Baruah

Self-interaction (SI) error, which results when exchange-correlation contributions to the total energy are approximated, limits the reliability of many density functional approximations. The Perdew-Zunger SI correction (PZSIC), when applied in conjunction with the local spin density approximation (LSDA), improves the description of many properties, but overa

Phillip Pope, Yogesh Balaji, Soheil Feizi

Flow-based generative models leverage invertible generator functions to fit a distribution to the training data using maximum likelihood. Despite their use in several application domains, robustness of these models to adversarial attacks has hardly been explored. In this paper, we study adversarial robustness of flow-based generative models both theoreticall

Yuming Zhang, Xinmin Hou

Let $T$ be a tournament with nondecreasing score sequence $R$ and $A$ be its tournament matrix. An upset of $T$ corresponds to an entry above the main diagonal of $A$. Given a feasible score sequence $R$, Fulkerson~(1965) gave a simple recursive construction for a tournament with score sequence $R$ and the minimum number of upsets, and Hacioglu et al. (2019)

Jordan MacLachlan, Yi Mei, Juergen Branke, Mengjie Zhang

Due to its direct relevance to post-disaster operations, meter reading and civil refuse collection, the Uncertain Capacitated Arc Routing Problem (UCARP) is an important optimisation problem. Stochastic models are critical to study as they more accurately represent the real-world than their deterministic counterparts. Although there have been extensive studi

Jack O. Burns, Gregg Hallinan, Jim Lux, Lawrence Teitelbaum

This is the final report submitted to NASA for a Probe-class concept study of the "Farside Array for Radio Science Investigations of the Dark ages and Exoplanets" (FARSIDE), a low radio frequency interferometric array on the farside of the Moon. The design study focused on the instrument, a deployment rover, the lander and base station, and delivered

Xiaorui Zhou, Senlin Luo, Yunfang Wu

In reading comprehension, generating sentence-level distractors is a significant task, which requires a deep understanding of the article and question. The traditional entity-centered methods can only generate word-level or phrase-level distractors. Although recently proposed neural-based methods like sequence-to-sequence (Seq2Seq) model show great potential

Jonathan Sadighian

This paper sets forth a framework for deep reinforcement learning as applied to market making (DRLMM) for cryptocurrencies. Two advanced policy gradient-based algorithms were selected as agents to interact with an environment that represents the observation space through limit order book data, and order flow arrival statistics. Within the experiment, a forwa

Hiromu Yakura, Youhei Akimoto, Jun Sakuma

In adversarial attacks intended to confound deep learning models, most studies have focused on limiting the magnitude of the modification so that humans do not notice the attack. On the other hand, during an attack against autonomous cars, for example, most drivers would not find it strange if a small insect image were placed on a stop sign, or they may over

Adam Eck, Maulik Shah, Prashant Doshi, Leen-Kiat Soh

In open agent systems, the set of agents that are cooperating or competing changes over time and in ways that are nontrivial to predict. For example, if collaborative robots were tasked with fighting wildfires, they may run out of suppressants and be temporarily unavailable to assist their peers. We consider the problem of planning in these contexts with the

Jianming Deng, Yanyu Liu, Mingqiang Li, Sheng Xu

Van der Waals (vdW) layered materials have rather weaker interlayer bonding than the intra-layer bonding, therefore the exfoliation along the stacking direction enables the achievement of monolayer or few layers vdW materials with emerging novel physical properties and functionalities. The ferroelectricity in vdW materials recently attracts renewed interest

Steffen Borgwardt, Charles Viss

Generalizing the simplex method, circuit augmentation schemes for linear programs follow circuit directions through the interior of the underlying polyhedron. Steepest-descent augmentation is especially promising, but an implementation of the iterative scheme is a significant challenge. We work towards a viable implementation through a model in which a singl

Ankur Mali, Alexander G. Ororbia, Clyde Lee Giles

For lossy image compression, we develop a neural-based system which learns a nonlinear estimator for decoding from quantized representations. The system links two recurrent networks that \help" each other reconstruct same target image patches using complementary portions of spatial context that communicate via gradient signals. This dual agent system bui

Gray C. Thomas, Binghan He, Luis Sentis

We introduce a hybrid (discrete--continuous) safety controller which enforces strict state and input constraints on a system---but only acts when necessary, preserving transparent operation of the original system within some safe region of the state space. We define this space using a Min-Quadratic Barrier function, which we construct along the equilibrium m

Peng Luo

The present paper is devoted to the study of the well-posedness of mean field BSDEs with mean reflection and nonlinear resistance. By the contraction mapping argument, we first prove that the mean-field BSDE with mean reflection and nonlinear resistance admits a unique deterministic flat local solution on a small time interval. Moreover, we build the global

R. Theler, P. Jablonka, R. Lucchesi, C. Lardo

We present the analysis of the FLAMES dataset targeting the central 25 arcmin region of the Sextans dSph. This dataset is the third major part of the high resolution spectroscopic section of the ESO large program 171.B-0588(A) obtained by the Dwarf galaxy Abundances and Radial-velocities Team (DART). Our sample is composed of red giant branch stars down to t

Narayan Mohanta, A. Taraphder, Elbio Dagotto, Satoshi Okamoto

We present a new switching mechanism that utilizes the proximity coupling between the surface spin texture of a Weyl semimetal and a superconductor, in a Weyl semimetal/superconductor/Weyl semimetal trilayer heterostructure. We show that the superconductivity in the middle layer can be fully suppressed by the surface spin texture of the Weyl semimetals in th

I. P. Lobo, V. B. Bezerra, J. P. Morais Graça, Luis C. N. Santos

Considering corrections produced by modified dispersion relations on the equation of state parameter of radiation, we study the induced black hole metric inspired by Kiselev's ansatz, thus defining a deformed Reissner-Nordström metric. In particular, we consider thermodynamic properties of such a black hole from the combined viewpoints of the modified eq

William Thong, Pascal Mettes, Cees G. M. Snoek

This paper addresses cross-domain visual search, where visual queries retrieve category samples from a different domain. For example, we may want to sketch an airplane and retrieve photographs of airplanes. Despite considerable progress, the search occurs in a closed setting between two pre-defined domains. In this paper, we make the step towards an open set

P. L. Krapivsky, Kirone Mallick, Dries Sels

We analyze the time evolution of an open quantum system driven by a localized source of bosons. We consider non-interacting identical bosons that are injected into a single lattice site and and perform a continuous time quantum walks on a lattice. We show that the average number of bosons grows exponentially with time when the input rate exceeds a certain la

Shashanka Venkataramanan, Kuan-Chuan Peng, Rajat Vikram Singh, Abhijit Mahalanobis

Anomaly localization is an important problem in computer vision which involves localizing anomalous regions within images with applications in industrial inspection, surveillance, and medical imaging. This task is challenging due to the small sample size and pixel coverage of the anomaly in real-world scenarios. Most prior works need to use anomalous trainin

Gideon Amir, Rangel Baldasso, Nissan Beilin

We consider the median dynamics process in general graphs. In this model, each vertex has an independent initial opinion uniformly distributed in the interval [0,1] and, with rate one, updates its opinion to coincide with the median of its neighbors. This process provides a continuous analog of binary majority dynamics. We deduce properties of median dynamic

Daniele Bartoli, Matteo Bonini, Burçin Güneş

We provide new families of minimal codes in any characteristic. Also, an inductive construction of minimal codes is presented.

Luca Mocerino, Andrea Calimera

Fixed-point quantization and binarization are two reduction methods adopted to deploy Convolutional Neural Networks (CNN) on end-nodes powered by low-power micro-controller units (MCUs). While most of the existing works use them as stand-alone optimizations, this work aims at demonstrating there is margin for a joint cooperation that leads to inferential eng

Hung-Hsun Hans Yu, Yufei Zhao

In $d$-dimensional space (over any field), given a set of lines, a joint is a point passed through by $d$ lines not all lying in some hyperplane. The joints problem asks to determine the maximum number of joints formed by $L$ lines, and it was one of the successes of the Guth--Katz polynomial method. We prove a new upper bound on the number of joints that ma

Hayato Waki, Yoshio Ebihara, Noboru Sebe

We consider the linear matrix inequality (LMI) problem of $H_\infty$ output feedback control problem for a generalized plant whose control input, measured output, disturbance input, and controlled output are scalar. We provide an explicit form of the optimal value. This form is the unification of some results in the literature of $H_\infty$ performance limit

Thilo Hagendorff

Certain research strands can yield "forbidden knowledge". This term refers to knowledge that is considered too sensitive, dangerous or taboo to be produced or shared. Discourses about such publication restrictions are already entrenched in scientific fields like IT security, synthetic biology or nuclear physics research. This paper makes the case for transfe

Brien C. Nolan

We revisit the issue of causality violations in G\"{o}del's universe, restricting to geodesic motions. It is well-known that while there are closed timelike curves in this spacetime, there are no closed causal geodesics. We show further that no observer can communicate directly (i.e.\ using a single causal geodesic) with their own past. However, we show that

Vincent Lahoche, Dine Ousmane Samary

This paper aims at giving a novel approach to investigate the behavior of the renormalization group flow for tensorial group field theories to all orders of the perturbation theory. From an appropriate choice of the kinetic kernel, we build an infinite family of just-renormalizable models, for tensor fields with arbitrary rank $d$. Investigating the large $

David A. Cohen, Martin C. Cooper, Artem Kaznatcheev, Mark Wallace

We investigate the complexity of local search based on steepest ascent. We show that even when all variables have domains of size two and the underlying constraint graph of variable interactions has bounded treewidth (in our construction, treewidth 7), there are fitness landscapes for which an exponential number of steps may be required to reach a local opti

LHCb collaboration, R. Aaij, C. Abellán Beteta, T. Ackernley

A measurement of the $Ξ_{cc}^{++}$ mass is performed using data collected by the LHCb experiment between 2016 and 2018 in $pp$ collisions at a centre-of-mass energy of 13 TeV, corresponding to an integrated luminosity of 5.6 $\mathrm{fb}^{-1}$. The $Ξ_{cc}^{++}$ candidates are reconstructed via the decay modes $Ξ_{cc}^{++}\toΛ_c^+K^-π^+π^+$ and $Ξ_{cc}^{++}\