November 2019 arXiv papers — page 54

Saeed Mahdisoltani, Riccardo Ben Alì Zinati, Charlie Duclut, Andrea Gambassi

A generically observed mechanism that drives the self-organization of living systems is interaction via chemical signals among the individual elements -- which may represent cells, bacteria, or even enzymes. Here we propose a novel mechanism for such interactions, in the context of chemotaxis, which originates from the polarity of the particles and which gen

Jian-Hao Luo, Jianxin Wu

Filter level pruning is an effective method to accelerate the inference speed of deep CNN models. Although numerous pruning algorithms have been proposed, there are still two open issues. The first problem is how to prune residual connections. We propose to prune both channels inside and outside the residual connections via a KL-divergence based criterion. T

Jin Qiu, Jiangbin Lyu, Liqun Fu

Unmanned aerial vehicles (UAVs) can be utilized as aerial base stations (ABSs) to assist terrestrial infrastructure for keeping wireless connectivity in various emergency scenarios. To maximize the coverage rate of N ground users (GUs) by jointly placing multiple ABSs with limited coverage range is known to be a NP-hard problem with exponential complexity in

Shao-Jie Xiong, Zhe Sun, Xiaofeng Li, Qi-Ping Su

We experimentally investigate problems of one-shot coherence distillation [Regula, Fang, Wang, and Adesso, Phys. Rev. Lett. 121, 010401 (2018)]. Based on a set of optical devices, we design a type of strictly incoherent operation (SIO), which is applicable in high-dimensional cases and can be applied to accomplish the transformations from higher-dimensional

Yangfeng Su, Tianyi Lu, Zhaojun Bai

A two dimensional eigenvalue problem (2DEVP) of a Hermitian matrix pair $(A, C)$ is introduced in this paper. The 2DEVP can be viewed as a linear algebraic formulation of the well-known eigenvalue optimization problem of the parameter matrix $H(\mu) = A - \mu C$. We present fundamental properties of the 2DEVP such as the existence, the necessary and sufficie

Shao-Peng Tang, Jin-Liang Jiang, Wei-Hong Gao, Yi-Zhong Fan

For some neutron stars (NSs) in the binary systems, the masses have been accurately measured. While for the isolated neutron stars (INSs), no mass measurement has been reported yet. The situation will change soon thanks to the successful performance of the Neutron Star Interior Composition Explorer (NICER), with which the radius and mass of the isolated PSR

Mauricio Tec, Natalia Zuniga-Garcia, Randy B. Machemehl, James G. Scott

Ride-sourcing or transportation network companies (TNCs) provide on-demand transportation service for compensation, connecting drivers of personal vehicles with passengers through smartphone applications. In this study, we consider the problem of estimating a spatiotemporally varying probability distribution for the productivity of a TNC driver, using data o

Megumi Nakao, Keiho Imanishi, Nobuhiro Ueda, Yuichiro Imai

The reduction of metal artifacts in computed tomography (CT) images, specifically for strong artifacts generated from multiple metal objects, is a challenging issue in medical imaging research. Although there have been some studies on supervised metal artifact reduction through the learning of synthesized artifacts, it is difficult for simulated artifacts to

Grant T. Barkley, Ricky Ini Liu

Let $m_G$ denote the number of perfect matchings of the graph $G$. We introduce a number of combinatorial tools for determining the parity of $m_G$ and giving a lower bound on the power of 2 dividing $m_G$. In particular, we introduce certain vertex sets called channels, which correspond to elements in the kernel of the adjacency matrix of $G$ modulo $2$. A

Gorjan Alagic, Andrew M. Childs, Alex B. Grilo, Shih-Han Hung

In a recent breakthrough, Mahadev constructed an interactive protocol that enables a purely classical party to delegate any quantum computation to an untrusted quantum prover. In this work, we show that this same task can in fact be performed non-interactively and in zero-knowledge. Our protocols result from a sequence of significant improvements to the orig

Yevgeniy Kovchegov, Ilya Zaliapin

We give a description of invariants and attractors of the critical and subcritical Galton-Watson tree measures under the operation of Horton pruning (cutting tree leaves with subsequent series reduction). Under a regularity condition, the class of invariant measures consists of the critical binary Galton-Watson tree and a one-parameter family of critical Gal

Michael A. Fedderke, Peter W. Graham, Surjeet Rajendran

White dwarfs (WD) effectively act as high-gain amplifiers for relatively small energy deposits within their volume via their supernova instability. In this paper, we consider the ways a galactic abundance of $\mathcal{O}(1)$-charged massive relics (i.e., CHAMPs) could trigger this instability, thereby destroying old WD. The dense central core structure forme

Rica Gonen, Erel Segal-Halevi

In two-sided markets, Myerson and Satterthwaite's impossibility theorem states that one can not maximize the gain-from-trade while also satisfying truthfulness, individual-rationality and no deficit. Attempts have been made to circumvent Myerson and Satterthwaite's result by attaining approximately-maximum gain-from-trade: the double-sided auctions o

Tomonori Totani

Abiotic emergence of ordered information stored in the form of RNA is an important unresolved problem concerning the origin of life. A polymer longer than 40--100 nucleotides is necessary to expect a self-replicating activity, but the formation of such a long polymer having a correct nucleotide sequence by random reactions seems statistically unlikely. Howev

Masayuki Asaoka

In this paper, we construct (a) a pair of two regular Cantor sets in higher dimension which exhibits $C^1$-stable intersection and (b) a hyperbolic basic set which exhibits $C^2$-robust homoclinic tangency of the largest codimension for any higher dimensional manifold, using blenders. The former implies that an analog of Moreira's theorem on Cantor sets in t

Mark Sendak, Madeleine Elish, Michael Gao, Joseph Futoma

Machine learning technologies are increasingly developed for use in healthcare. While research communities have focused on creating state-of-the-art models, there has been less focus on real world implementation and the associated challenges to accuracy, fairness, accountability, and transparency that come from actual, situated use. Serious questions remain

Awadhesh Narayan

We systematically investigate the effect of strain and doping on the polar metal phase in lithium osmate, LiOsO$_3$, using first-principles calculations. We demonstrate that the polar metal phase in LiOsO$_3$ can be controlled by biaxial strain. Based on density functional calculations, we show that a compressive biaxial strain enhances the stability of the

Sixue Gong, Xiaoming Liu, Anil K. Jain

We address the problem of bias in automated face recognition and demographic attribute estimation algorithms, where errors are lower on certain cohorts belonging to specific demographic groups. We present a novel de-biasing adversarial network (DebFace) that learns to extract disentangled feature representations for both unbiased face recognition and demogra

Duc Minh Vo, Akihiro Sugimoto

Style transfer is to render given image contents in given styles, and it has an important role in both computer vision fundamental research and industrial applications. Following the success of deep learning based approaches, this problem has been re-launched recently, but still remains a difficult task because of trade-off between preserving contents and fa

Yu-Pin Hsu

We analyze a wireless line network employing wireless network coding. The two end nodes exchange their packets through relays. While a packet at a relay might not find its coding pair upon arrival, a transmission cost can be reduced by waiting for coding with a packet from the other side. To strike a balance between the reduced transmission cost and the cost

Yusupjan Habibulla

The directed L-distance minimal dominating set (MDS) problem has wide practical applications in the fields of computer science and communication networks. Here, we study this problem from the perspective of purely theoretical interest. We only give results for an Erd$\acute{o}$s R$\acute{e}$nyi (ER) random graph and regular random graph, but this work can be

Zhaoxu Ji, Peiru Fan, Huanguo Zhang, Houzhen Wang

By introducing a semi-honest third party (TP), we propose in this paper a novel QPC protocol using (n+1)- qubit (n \ge 2) Greenberger-Horne-Zeilinger (GHZ) states as information carriers. The parameter n not only determines the number of qubits contained in a GHZ state, but also determines the probability that TP can successfully steal the participants' data

Noah Mendelson, Marcus Doherty, Milos Toth, Igor Aharonovich

Quantum emitters in hexagonal boron nitride (hBN) are promising building blocks for the realization of integrated quantum photonic systems. However, their spectral inhomogeneity currently limits their potential applications. Here, we apply tensile strain to quantum emitters embedded in few-layer hBN films and realize both red and blue spectral shifts with tu

Keishu Utimula, Genki I. Prayogo, Kousuke Nakano, Kenta Hongo

We considered the problem how to handle the exploding number of possibilities to be sorted into irreducible classes by using a clustering tool when its input capacity cannot accommodate the total number of the possibility. Concrete situations are explained taking examples of atomic substitutions in the supercell modeling of alloys. The number of the possibil

HAWC Collaboration, A. Albert, R. Alfaro, C. Alvarez

Due to the high energies and long distances to the sources, astrophysical observations provide a unique opportunity to test possible signatures of Lorentz invariance violation (LIV). Superluminal LIV enables the decay of photons at high energy. The High Altitude Water Cherenkov (HAWC) Observatory is among the most sensitive gamma-ray instruments currently op

Jesse F. Giron, Scott D. Ramsey, Roy S. Baty

We investigate the inviscid compressible flow (Euler) equations constrained by an "isentropic" equation of state (EOS), whose functional form in pressure is an arbitrary function of density alone. Under the aforementioned condition, we interrogate using symmetry methods the scale-invariance of the homentropic inviscid Euler equations. We find that un

Yangchen Pan, Kirby Banman, Martha White

Recent work has shown that sparse representations -- where only a small percentage of units are active -- can significantly reduce interference. Those works, however, relied on relatively complex regularization or meta-learning approaches, that have only been used offline in a pre-training phase. In this work, we pursue a direction that achieves sparsity by

Vajresh Balaji, Olivia Edwards, Anne Marie Loftin, Solomon Mcharo

We address an analog of a problem introduced by Erdős and Fishburn, itself an inverse formulation of the famous Erdős distance problem, in which the usual Euclidean distance is replaced with the metric induced by the $\ell^1$-norm, commonly referred to as the $\textit{taxicab metric}$. Specifically, we investigate the following question: given $d,k\in \mathb

A. Augusto, L. Pellegrini

A bounded linear operator $T$ on a Banach space $X$ is called subspace-hypercyclic if there is a subspace $M \subsetneq X$ and a vector $x \in X$ such that $orb{(x,T)} \cap M$ is dense in $M$. We show that every Banach space supports subspace-hypercyclic operators and provide a new criteira for subspace-hypercyclic operators, generalizing a previous result f

De-Cheng Zou, Yun Soo Myung

We obtain scalar hairy black holes from Einstein-Maxwell-conformally coupled scalar (EMCS) theory with the scalar coupling parameter $α$ to the Maxwell term. In case of $α=0$, the $α=0$ EMCS theory provides constant (charged) scalar hairy black hole and charged BBMB (Bocharova-Bronnikov-Melnikov-Bekenstein) black hole where the former is stable against full

Jianlong Yang, Yan Hu, Liyang Fang, Jun Cheng

Retinal optical coherence tomography (OCT) and OCT angiography (OCTA) suffer from the degeneration of image quality due to speckle noise and bulk-motion noise, respectively. Because the cross-sectional retina has distinct features in OCT and OCTA B-scans, existing digital filters that can denoise OCT efficiently are unable to handle the bulk-motion noise in

Hwanjun Song, Minseok Kim, Dongmin Park, Jae-Gil Lee

Noisy labels are very common in real-world training data, which lead to poor generalization on test data because of overfitting to the noisy labels. In this paper, we claim that such overfitting can be avoided by "early stopping" training a deep neural network before the noisy labels are severely memorized. Then, we resume training the early stopped network

Pham Viet Hai, Joel A. Rosenfeld

Motivated by gradient methods in optimization theory, we give methods based on $\psi$-fractional derivatives of order $\alpha$ in order to solve unconstrained optimization problems. The convergence of these methods is analyzed in detail. This paper also presents an Adams-Bashforth-Moulton (ABM) method for the estimation of solutions to equations involving $\

Himank Yadav, Zhengxiao Du, Thorsten Joachims

While implicit feedback (e.g., clicks, dwell times, etc.) is an abundant and attractive source of data for learning to rank, it can produce unfair ranking policies for both exogenous and endogenous reasons. Exogenous reasons typically manifest themselves as biases in the training data, which then get reflected in the learned ranking policy and often lead to

Satyaki Mukherjee

Here we prove the following result. Let $A = \{a_{ij}\}_{i,j\in \mathbb{N}}$ be a bounded operator. Then there exists a signing of $A$ such that $$||A\circ S||_2 < 2||A||_{l_\infty(l_2)},$$ where $A\circ S$ denotes the matrix generated by the entry-wise product of $A$ and $S$.

Hwanjun Song, Minseok Kim, Sundong Kim, Jae-Gil Lee

The accuracy of deep neural networks is significantly affected by how well mini-batches are constructed during the training step. In this paper, we propose a novel adaptive batch selection algorithm called Recency Bias that exploits the uncertain samples predicted inconsistently in recent iterations. The historical label predictions of each training sample a

Pin Wang, Dapeng Liu, Jiayu Chen, Hanhan Li

Making decisions in complex driving environments is a challenging task for autonomous agents. Imitation learning methods have great potentials for achieving such a goal. Adversarial Inverse Reinforcement Learning (AIRL) is one of the state-of-art imitation learning methods that can learn both a behavioral policy and a reward function simultaneously, yet it i

K. Rajpurohit, M. Hoeft, F. Vazza, L. Rudnick

Radio relics are diffuse extended synchrotron sources that originate from shock fronts induced by galaxy cluster mergers. The actual particle acceleration mechanism at the shock fronts is still under debate. The galaxy cluster 1RXS J0603.3+4214 hosts one of the most intriguing examples of radio relics, known as the Toothbrush. We present new wideband radio c

Gianluca Brero, Benjamin Lubin, Sven Seuken

We present a machine learning-powered iterative combinatorial auction (MLCA). The main goal of integrating machine learning (ML) into the auction is to improve preference elicitation, which is a major challenge in large combinatorial auctions (CAs). In contrast to prior work, our auction design uses value queries instead of prices to drive the auction. The M

Timothy P. Bywaters

We study the scale function, space of directions and scale-multiplicative semigroups for restricted Burger-Mozes groups. We relate these general notions to intrinsic properties of the group. Among other things, we give a formula for the scale function; relate the space of directions to both the action on the tree and an action on a $\operatorname{CAT}(0)$ cu

Cheng Li, Abdul Dakkak, Jinjun Xiong, Wen-mei Hwu

The current Deep Learning (DL) landscape is fast-paced and is rife with non-uniform models, hardware/software (HW/SW) stacks, but lacks a DL benchmarking platform to facilitate evaluation and comparison of DL innovations, be it models, frameworks, libraries, or hardware. Due to the lack of a benchmarking platform, the current practice of evaluating the benef

Oded Stein, Eitan Grinspun, Alec Jacobson, Max Wardetzky

The biharmonic equation with Dirichlet and Neumann boundary conditions discretized using the mixed finite element method and piecewise linear (with the possible exception of boundary triangles) finite elements on triangular elements has been well-studied for domains in R2. Here we study the analogous problem on polyhedral surfaces. In particular, we provide

Mattia Bruno, Isabel Campos, Patrick Fritzsch, Jonna Koponen

We present a lattice QCD determination of light quark masses with three sea-quark flavours ($N_f = 2+1$). Bare quark masses are known from PCAC relations in the framework of CLS lattice computations with a non-perturbatively improved Wilson-Clover action and a tree-level Symanzik improved gauge action. They are fully non-perturbatively improved, including th

Liang-Liang Liu, Xian-Wei Kang, Zhen-Yang Wang, Xin-Heng Guo

We study the rare decays $\Lambda_b \rightarrow \Lambda l^+ l^-~(l=e,\mu, \tau)$ in the Bethe-Salpeter equation approach. We find that depending on the values of parameters in our model the branching ratio $Br(\Lambda_b \rightarrow \Lambda \mu^+ \mu^-)\times 10^{6}$ varies from $0.812$ to $1.445$ when $\kappa = 0.050 \sim 0.060$ GeV$^3$ and the binding energ

Timothy M. Pollington, Michael J. Tildesley, T. Déirdre Hollingsworth, Lloyd A. C. Chapman

The tau statistic $τ$ uses geolocation and, usually, symptom onset time to assess global spatiotemporal clustering from epidemiological data. We test different factors that could affect graphical hypothesis tests of clustering or bias clustering range estimates based on the statistic, by comparison with a baseline analysis of an open access measles dataset.

Kangyong You, Wenbin Guo, Tao Peng, Yueliang Liu

Received signal strength (RSS) based source localization method is popular due to its simplicity and low cost. However, this method is highly dependent on the propagation model which is not easy to be captured in practice. Moreover, most existing works only consider the single source and the identical measurement noise scenario, while in practice multiple co

Ao Ren, Tao Zhang, Yuhao Wang, Sheng Lin

The rapidly growing parameter volume of deep neural networks (DNNs) hinders the artificial intelligence applications on resource constrained devices, such as mobile and wearable devices. Neural network pruning, as one of the mainstream model compression techniques, is under extensive study to reduce the number of parameters and computations. In contrast to i

Lucas Caccia, Eugene Belilovsky, Massimo Caccia, Joelle Pineau

We introduce and study the problem of Online Continual Compression, where one attempts to simultaneously learn to compress and store a representative dataset from a non i.i.d data stream, while only observing each sample once. A naive application of auto-encoders in this setting encounters a major challenge: representations derived from earlier encoder state

Yueliang Liu, Wenbin Guo, Kangyong You, Lei Zhao

Mining natural associations from high-dimensional spatiotemporal signals plays an important role in various fields including biology, climatology, and financial analysis. However, most existing works have mainly studied time-independent signals without considering the correlations of spatiotemporal signals that achieve high learning accuracy. This paper aims

Neale Ratzlaff, Qinxun Bai, Li Fuxin, Wei Xu

Efficient exploration remains a challenging problem in reinforcement learning, especially for those tasks where rewards from environments are sparse. A commonly used approach for exploring such environments is to introduce some &#34;intrinsic&#34; reward. In this work, we focus on model uncertainty estimation as an intrinsic reward for efficient exploration.

Daniele Alessandrini, Olivier Guichard, Eugen Rogozinnikov, Anna Wienhard

We introduce coordinates on the spaces of framed and decorated representations of the fundamental group of a surface with nonempty boundary into the symplectic group $Sp(2n,\mathbf R)$. These coordinates provide a noncommutative generalization of the parametrizations of the spaces of representations into $SL(2,\mathbf R)$ or $PSL(2,\mathbf R)$ given by Thurs

Yu-Jung Tsai, Alexandre Bousse, Simon Arridge, Charles W. Stearns

Two algorithms for solving misalignment issues in penalized PET/CT reconstruction using anatomical priors are proposed. Both approaches are based on a recently published joint motion estimation and image reconstruction method. The first approach deforms the anatomical image to align it with the functional one while the second approach deforms both images to

Iman Niazazari, Hanif Livani

With the ever-increasing reliance on data for data-driven applications in power grids, such as event cause analysis, the authenticity of data streams has become crucially important. The data can be prone to adversarial stealthy attacks aiming to manipulate the data such that residual-based bad data detectors cannot detect them, and the perception of system o

Daouda Diouf, Djibril Seck, Mountaga Diop, Abdoulye Ba

Identifying and characterizing the patient's blood samples is indispensable in diagnostics of malignance suspicious. A painstaking and sometimes subjective task is used in laboratories to manually classify white blood cells. Neural mathematical methods as deep learnings can be very useful in the automated recognition of blood cells. This study uses a particu

Nuwanthika Rajapaksha, Nandana Rajatheva, Matti Latva-aho

End-to-end learning of a communications system using the deep learning-based autoencoder concept has drawn interest in recent research due to its simplicity, flexibility and its potential of adapting to complex channel models and practical system imperfections. In this paper, we have compared the bit error rate (BER) performance of autoencoder based systems

Bergoi Ibarlucea, Arnau Perez Roig, Dmitry Belyaev, Larysa Baraban

We demonstrate a flexible sensor for ascorbic acid detection in sweat based on single-step modified gold microelectrodes. The modification consists on the electrodeposition of alginate membrane with trapped CuO nanoparticles on top of the electrodes. The electrodes are fabricated at a thin polyimide support and the soft nature of the membrane can withstand m

Godfred Yaw Koi-Akrofi

The general objective of this work was to contribute to the general body of knowledge and research work in the area of managing IT projects successfully. The office of Government commerce of the UK Government in conjunction with the National Audit came out with a guideline in 2007 which list out eight causes of project failures. Six out of the eight causes w

Jon Hael Brenas, Eun Kyong Shin, Arash Shaban-Nejad

Background: Adverse Childhood Experiences (ACEs), a set of negative events and processes that a person might encounter during childhood and adolescence, have been proven to be linked to increased risks of a multitude of negative health outcomes and conditions when children reach adulthood and beyond. Objective: To better understand the relationship between A

Carl Poelking, Yehia Amar, Alexei Lapkin, Lucy Colwell

Capturing the microscopic interactions that determine molecular reactivity poses a challenge across the physical sciences. Even a basic understanding of the underlying reaction mechanisms can substantially accelerate materials and compound design, including the development of new catalysts or drugs. Given the difficulties routinely faced by both experimental

Tom White

This paper presents abstract art created by neural networks and broadly recognizable across various computer vision systems. The existence of abstract forms that trigger specific labels independent of neural architecture or training set suggests convolutional neural networks build shared visual representations for the categories they understand. Computer vis

Vijini Liyanage, Surangika Ranathunga

Existing approaches for automatically generating mathematical word problems are deprived of customizability and creativity due to the inherent nature of template-based mechanisms they employ. We present a solution to this problem with the use of deep neural language generation mechanisms. Our approach uses a Character Level Long Short Term Memory Network (LS

Nicholas Vincent, Yichun Li, Renee Zha, Brent Hecht

Identifying strategies to more broadly distribute the economic winnings of AI technologies is a growing priority in HCI and other fields. One idea gaining prominence centers on &#34;data dividends&#34;, or sharing the profits of AI technologies with the people who generated the data on which these technologies rely. Despite the rapidly growing discussion aro

Aldo Iván Ramírez Abarca, Jan Broersen

We characterize four types of agentive knowledge using a stit semantics over branching discrete-time structures. These are \emph{ex ante} knowledge, \emph{ex interim} knowledge, \emph{ex post} knowledge, and know-how. The first three are notions that arose from game-theoretical analyses on the stages of information disclosure across the decision making proce

Arnab Sen Sharma, Maruf Ahmed Mridul, Md Saiful Islam

Widespread of satirical news in online communities is an ongoing trend. The nature of satires is so inherently ambiguous that sometimes it&#39;s too hard even for humans to understand whether it&#39;s actually satire or not. So, research interest has grown in this field. The purpose of this research is to detect Bangla satirical news spread in online news po

Angelo Carollo, Davide Valenti, Bernardo Spagnolo

In this article we provide a review of geometrical methods employed in the analysis of quantum phase transitions and non-equilibrium dissipative phase transitions. After a pedagogical introduction to geometric phases and geometric information in the characterisation of quantum phase transitions, we describe recent developments of geometrical approaches based

Shashank Srivastava, Murali Damodaran, Boo Cheong Khoo

This study demonstrates the feasibility of developing machine learning (ML) surrogates based on Recurrent Neural Networks (RNN) for predicting the unsteady aeroelastic response of transonic pitching and plunging wing-fuel tank sloshing system by considering an approximate simplified model of an airfoil in transonic flow and sloshing loads from a partially fi

Alexandre Eremenko

A solution of Problem 184 from the Scottish Book is presented.

Daniele Bartoli, Marco Timpanella

Let $p>3$ and consider a prime power $q=p^h$. We completely characterize permutation polynomials of $\mathbb{F}_{q^2}$ of the type $f_{a,b}(X) = X(1 + aX^{q(q-1)} + bX^{2(q-1)}) \in \mathbb{F}_{q^2}[X]$. In particular, using connections with algebraic curves over finite fields, we show that the already known sufficient conditions are also necessary.

Suryansh Kumar, Yuchao Dai, Hongdong Li

This work addresses the task of dense 3D reconstruction of a complex dynamic scene from images. The prevailing idea to solve this task is composed of a sequence of steps and is dependent on the success of several pipelines in its execution. To overcome such limitations with the existing algorithm, we propose a unified approach to solve this problem. We assum

Carlos Tomei

This is a remark on a recent post by P. Denton, S. Parke, T. Tao, X. Zhang, Eigenvectors from eigenvalues, arXiv:1908.03795v1

Benoit Vermersch, Dominique Delande, Jean Claude Garreau

We study the dynamics of a Bose-Einstein condensate in the quasiperiodic kicked rotor described by a Gross-Pitaevskii equation with periodic boundary conditions. As the interactions are increased, Bogoliubov excitations appear and deplete the condensate; we characterize this instability by considering the population of the first Bogoliubov mode, and show tha

Simone Bonechi, Paolo Andreini, Monica Bianchini, Franco Scarselli

Providing pixel-level supervisions for scene text segmentation is inherently difficult and costly, so that only few small datasets are available for this task. To face the scarcity of training data, previous approaches based on Convolutional Neural Networks (CNNs) rely on the use of a synthetic dataset for pre-training. However, synthetic data cannot reprodu

Yuxuan Xia, Karl Granström, Lennart Svensson, Ángel F. García-Fernández

The Poisson multi-Bernoulli mixture (PMBM) is a multi-target distribution for which the prediction and update are closed. By applying the random finite set (RFS) framework to multi-target tracking with sets of trajectories as the variable of interest, the PMBM trackers can efficiently estimate the set of target trajectories. This paper derives two trajectory

Danylo Malyuta, Behcet Acikmese

This paper presents a novel convex optimization-based method for finding the globally optimal solutions of a class of mixed-integer non-convex optimal control problems. We consider problems with non-convex constraints that restrict the input norms to be either zero or lower- and upper-bounded. The non-convex problem is relaxed to a convex one whose optimal s

Fahd Jarad, Manar A. Alqudah, Thabet Abdeljawad

In this article, we propose new proportional fractional operators generated from local proportional derivatives of a function with respect to another function. We present some properties of these fractional operators which can be also called proportional fractional operators of a function with respect to another function or proportional fractional operators

Rishikesh Yadav, Ramakanta Meher, Vishnu Narayan Mishra

In this paper, bivariate Szasz-Mirakjan type operators are introduced along with the estimation of its approximation properties and its rate of convergence. Furthermore, to check the asymptotic behavior of the said bivariate operators, the Voronovskaya type theorem is proved and determined the simultaneous approximation of the operators for first-order parti

Kai Pua, Daisuke Yuhara, Sho Ayuba, Kenji Yasuoka

The study of molecular dynamics simulations is largely facilitated by analysis and visualization toolsets. However, these toolsets are often designed for specific use cases and those only, while scripting extensions to such toolsets is often exceedingly complicated. To overcome this problem, we designed a software application called AViS which focuses on the

Carl Corea, Matthias Thimm

We investigate the application of inconsistency measures to the problem of analysing business rule bases. Due to some intricacies of the domain of business rule bases, a straightforward application is not feasible. We therefore develop some new rationality postulates for this setting as well as adapt and modify existing inconsistency measures. We further ada

Siddharth Krishna, Alexander J. Summers, Thomas Wies

Separation logics are widely used for verifying programs that manipulate complex heap-based data structures. These logics build on so-called separation algebras, which allow expressing properties of heap regions such that modifications to a region do not invalidate properties stated about the remainder of the heap. This concept is key to enabling modular rea

I. Marković, C. A. Hooley, O. J. Clark, F. Mazzola

Band inversions are key to stabilising a variety of novel electronic states in solids, from topological surface states in inverted bulk band gaps of topological insulators to the formation of symmetry-protected three-dimensional Dirac and Weyl points and nodal-line semimetals. Here, we create a band inversion not of bulk states, but rather between manifolds

Rahul Duggal, Cao Xiao, Richard Vuduc, Jimeng Sun

We propose Cluster Pruning (CUP) for compressing and accelerating deep neural networks. Our approach prunes similar filters by clustering them based on features derived from both the incoming and outgoing weight connections. With CUP, we overcome two limitations of prior work-(1) non-uniform pruning: CUP can efficiently determine the ideal number of filters

Anna Kaminska

We present a direct proof of the fact that the weak-$L^1$ and weak-$\ell^1$ spaces do not have type $1$.

Emi Tanaka, Francis K. C. Hui

A statistical model is a mathematical representation of an often simplified or idealised data-generating process. In this paper, we focus on a particular type of statistical model, called linear mixed models (LMMs), that is widely used in many disciplines e.g.~agriculture, ecology, econometrics, psychology. Mixed models, also commonly known as multi-level, n

Filip Klaesson, Petter Nilsson, Aaron D. Ames, Richard M. Murray

Motivated by exploration of communication-constrained underground environments using robot teams, we study the problem of planning for intermittent connectivity in multi-agent systems. We propose a novel concept of information-consistency to handle situations where the plan is not initially known by all agents, and suggest an integer linear program for synth

Guansong Pang, Chunhua Shen, Anton van den Hengel

Although deep learning has been applied to successfully address many data mining problems, relatively limited work has been done on deep learning for anomaly detection. Existing deep anomaly detection methods, which focus on learning new feature representations to enable downstream anomaly detection methods, perform indirect optimization of anomaly scores, l

Julian Edward, Steve Hudson, Mark Leckband

Let $D$ be an bounded region in ${\bf R}^n$. The regularity of solutions of a family of quasilinear elliptic partial differential equations is studied, one example being $Δ_nu=Vu^{n-1}$. The coefficients are assumed to be in the space $L\log^{m}L(D)$ for $m>n-1$. Using a Moser iteration argument coupled with the Moser-Trudinger inequality, a local $L^{\infty

Federico Vázquez, Péter Ván, Róbert Kovács

There has been much interest in semiconductor superlattices because of showing very low thermal conductivities. This makes them especially suitable for applications in a variety of devices for thermoelectric generation of energy, heat control at the nanometric length scale, etc. Recent experiments have confirmed that the effective thermal conductivity of sup

Shuwen Deng, Wenjie Xiong, Jakub Szefer

Timing-based side or covert channels in processor caches continue to present a threat to computer systems, and they are the key to many of the recent Spectre and Meltdown attacks. Based on improvements to an existing three-step model for cache timing-based attacks, this work presents 88 Strong types of theoretical timing-based vulnerabilities in processor ca

Badri N. Patro, Anupriy, Vinay P. Namboodiri

In this paper, we aim to obtain improved attention for a visual question answering (VQA) task. It is challenging to provide supervision for attention. An observation we make is that visual explanations as obtained through class activation mappings (specifically Grad-CAM) that are meant to explain the performance of various networks could form a means of supe

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

Michael Neaton, Andreas Pieper, Catherine Ray

The crystalline period map is a tool for linearizing $p$-divisible groups. It has been applied to study the Langlands correspondences, and has possible applications to the homotopy groups of spheres. The original construction of the period map is inherently local. We present an alternative construction, giving a map on the entire moduli stack of $p$-divisbil

Casey A Cole, Christopher Ott, Diego Valdes, Homayoun Valafar

Large scale initiatives such as the Human Genome Project, Structural Genomics, and individual research teams have provided large deposits of genomic and proteomic data. The transfer of data to knowledge has become one of the existing challenges, which is a consequence of capturing data in databases that are optimally designed for archiving and not mining. In

Casey A Cole, Caleb Parks, Julian Rachele, Homayoun Valafar

Traditional approaches to elucidation of protein structures by NMR spectroscopy rely on distance restraints also known as nuclear Overhauser effects (NOEs). The use of NOEs as the primary source of structure determination by NMR spectroscopy is time consuming and expensive. Residual Dipolar Couplings (RDCs) have become an alternate approach for structure cal

Khushboo Suman, Mohit Mittal, Yogesh M Joshi

We investigate physical origin of ergodicity breaking in an aqueous colloidal dispersion of synthetic hectorite clay, LAPONITE(R), by performing dissolution and rheological experiments with monovalent salt and tetrasodium pyrophosphate solution. We also study the effect of interface, nitrogen and paraffin oil on the same. Dissolution experiments carried out

Borislav Mavrin, Daniel Graves, Alan Chan

Learning good representations is a long standing problem in reinforcement learning (RL). One of the conventional ways to achieve this goal in the supervised setting is through regularization of the parameters. Extending some of these ideas to the RL setting has not yielded similar improvements in learning. In this paper, we develop an online regularization f

Bing Xu, Andrew Tulloch, Yunpeng Chen, Xiaomeng Yang

We propose a new building block, IdleBlock, which naturally prunes connections within the block. To fully utilize the IdleBlock we break the tradition of monotonic design in state-of-the-art networks, and introducing hybrid composition with IdleBlock. We study hybrid composition on MobileNet v3 and EfficientNet-B0, two of the most efficient networks. Without

Riccardo Bonetto, Mattia Soldan, Alberto Lanaro, Simone Milani

Smartphones and wearable devices are fast growing technologies that, in conjunction with advances in wireless sensor hardware, are enabling ubiquitous sensing applications. Wearables are suitable for indoor and outdoor scenarios, can be placed on many parts of the human body and can integrate a large number of sensors capable of gathering physiological and b

Anilkumar Parsi, Andrea Iannelli, Mingzhou Yin, Mohammad Khosravi

A robust adaptive model predictive control (MPC) algorithm is presented for linear, time invariant systems with unknown dynamics and subject to bounded measurement noise. The system is characterized by an impulse response model, which is assumed to lie within a bounded set called the feasible system set. Online set-membership identification is used to reduce

Elena Gati, Li Xiang, Sergey L. Bud&#39;ko, Paul C. Canfield

Uniaxial stress, as well as hydrostatic pressure are often used to tune material properties in condensed matter physics. Here, we present a setup which allows for the study of the combined effects of quasi-uniaxial stress and hydrostatic pressure. Following earlier designs for measurements under finite stress at ambient pressure (e.g., Chu et al., Science 33

Wudi Wang, Stephan Kim, Minhao Liu, F. A. Cevallos

Edge supercurrents in superconductors have long been an elusive target. Interest in them has reappeared in the context of topological superconductivity. We report the observation of a robust edge supercurrent in the Weyl superconductor MoTe2. In a magnetic field B, fluxoid quantization generates a periodic modulation of the edge condensate observable as a &#