July 2019 arXiv papers — page 78

Rui Li, Tao Wang

DP-coloring was introduced by Dvo\v{r}\'{a}k and Postle as a generalization of list coloring and signed coloring. A new coloring, strictly $f$-degenerate transversal, is a further generalization of DP-coloring and $L$-forested-coloring. In this paper, we present some structural results on planar and toroidal graphs with forbidden configurations, and establis

Yusuke Kawamoto

We introduce a modal logic for describing statistical knowledge, which we call statistical epistemic logic. We propose a Kripke model dealing with probability distributions and stochastic assignments, and show a stochastic semantics for the logic. To our knowledge, this is the first semantics for modal logic that can express the statistical knowledge depende

Zachary Pagel, Weicheng Zhong, Richard H. Parker, Christopher T. Olund

Cold atoms in an optical lattice provide an ideal platform for studying Bloch oscillations. Here, we extend Bloch oscillations to two superposed optical lattices that are accelerated away from one another, and for the first time show that these symmetric Bloch oscillations can split, reflect and recombine matter waves coherently. Using the momentum parity-sy

Fangyao Lu, Qianqian Wang, Tao Wang

Let $f$ be a nonnegative integer valued function on the vertex set of a graph. A graph is \textbf{strictly $f$-degenerate} if each nonempty subgraph $Γ$ has a vertex $v$ such that $\mathrm{deg}_Γ(v) < f(v)$. In this paper, we define a new concept, strictly $f$-degenerate transversal, which generalizes list coloring, signed coloring, DP-coloring, $L$-forested

Yusuke Kawamoto, Takao Murakami

We introduce a general model for the local obfuscation of probability distributions by probabilistic perturbation, e.g., by adding differentially private noise, and investigate its theoretical properties. Specifically, we relax a notion of distribution privacy (DistP) by generalizing it to divergence, and propose local obfuscation mechanisms that provide div

Myung-Geun Han, Joseph A. Garlow, Yu Liu, Huiqin Zhang

Two-dimensional (2D) van der Waals (vdW) materials show a range of profound physical properties that can be tailored through their incorporation in heterostructures and manipulated with external forces. The recent discovery of long-range ferromagnetic order down to atomic layers provides an additional degree of freedom in engineering 2D materials and their h

René Carmona, Mathieu Laurière

We propose two algorithms for the solution of the optimal control of ergodic McKean-Vlasov dynamics. Both algorithms are based on approximations of the theoretical solutions by neural networks, the latter being characterized by their architecture and a set of parameters. This allows the use of modern machine learning tools, and efficient implementations of s

X. Huo, G. Yossifon

Due to the reversibility of viscous flow it is not expected to obtain a fluidic rectifier simply from geometrical asymmetry without any moving mechanical parts. Here, we found a counter example by using spatial asymmetry combined with electric field to inject memory effects that render the flow irreversible. This stems from the strong dependency of the elect

Song-Nam Hong, Chol-Jun Yua, Un-Song Hwang, Chung-Hyok Kim

The thermal conductivity of porous jennite, as the major component of cement paste, and its porosity and temperature dependences are simulated by molecular dynamics methods using ClayFF force field. The porous jennite models with different porosities are created by removing atoms within the sphere from bulk jennite model. The thermal conductivity elements of

Hossein Zeinali, Themos Stafylakis, Georgia Athanasopoulou, Johan Rohdin

In this paper, we present the system description of the joint efforts of Brno University of Technology (BUT) and Omilia -- Conversational Intelligence for the ASVSpoof2019 Spoofing and Countermeasures Challenge. The primary submission for Physical access (PA) is a fusion of two VGG networks, trained on single and two-channels features. For Logical access (LA

Mahabubul Alam, Abdullah Ash-Saki, Swaroop Ghosh

The quantum approximate optimization algorithm (QAOA) is a promising quantum-classical hybrid technique to solve combinatorial optimization problems in near-term gate-based noisy quantum devices. In QAOA, the objective is a function of the quantum state, which itself is a function of the gate parameters of a multi-level parameterized quantum circuit (PQC). A

Adamantios Ntakaris, Juho Kanniainen, Moncef Gabbouj, Alexandros Iosifidis

Stock price prediction is a challenging task, but machine learning methods have recently been used successfully for this purpose. In this paper, we extract over 270 hand-crafted features (factors) inspired by technical and quantitative analysis and tested their validity on short-term mid-price movement prediction. We focus on a wrapper feature selection meth

Jing Zhang, Jing Tian, Tao Wen, Xiaohui Yang

Early and accurately detecting faults in rotating machinery is crucial for operation safety of the modern manufacturing system. In this paper, we proposed a novel Deep fault diagnosis (DFD) method for rotating machinery with scarce labeled samples. DFD tackles the challenging problem by transferring knowledge from shallow models, which is based on the idea t

Amin Mosayyebzadeh, Gerardo Ravago, Apoorve Mohan, Ali Raza

Bolted is a new architecture for a bare metal cloud with the goal of providing security-sensitive customers of a cloud the same level of security and control that they can obtain in their own private data centers. It allows tenants to elastically allocate secure resources within a cloud while being protected from other previous, current, and future tenants o

Anwei Zhang, Xianfeng Chen, Vladislav V. Yakovlev, Luqi Yuan

Single-photon super- and subradiance are important for the quantum memory and quantum information. We investigate one-dimensional atomic arrays under the spatially periodic magnetic field with a tunable phase, which provides a distinctive physics aspect of revealing exotic two-dimensional topological phenomena with a synthetic dimension. A butterfly-like non

Mirco Richter

A framework for asynchronous, signature free, fully local and probabilistically converging total order algorithms is developed, that may survive in high entropy, unstructured Peer-to-Peer networks with near optimal communication efficiency. Regarding the natural boundaries of the CAP-theorem, Crisis chooses different compromises for consistency and availabil

Ibrahim Al-Nahhal, Octavia A. Dobre, Ertugrul Basar, Cecilia Moloney

The Gaussian function (GF) is widely used to explain the behavior or statistical distribution of many natural phenomena as well as industrial processes in different disciplines of engineering and applied science. For example, the GF can be used to model an approximation of the Airy disk in image processing, laser heat source in laser transmission welding [1]

Hossein Zeinali, Lukáš Burget, Jan &#34;Honza&#39;&#39; Černocký

In this report, the Brno University of Technology (BUT) team submissions for Task 1 (Acoustic Scene Classification, ASC) of the DCASE-2019 challenge are described. Also, the analysis of different methods is provided. The proposed approach is a fusion of three different Convolutional Neural Network (CNN) topologies. The first one is a VGG like two-dimensional

Pablo Shmerkin

We present a self-contained proof of a formula for the $L^q$ dimensions of self-similar measures on the real line under exponential separation (up to the proof of an inverse theorem for the $L^q$ norm of convolutions). This is a special case of a more general result of the author from [Shmerkin, Pablo. On Furstenberg&#39;s intersection conjecture, self-simil

Md Rushdie Ibne Islam, Chong Peng

Conventional smoothed particle hydrodynamics based on Eulerian kernels (CESPH) is widely-used in large deformation analysis in geomaterials. Despite being popular, it suffers from tensile instability and rank-deficiency; thus, it needs several numerical treatments to be stable. In this work, we present a stabilized total-Lagrangian SPH method (TLSPH), which

Abdelkader Ben Hassine, Taoufik Chtioui, Sami Mabrouk, Othmen Ncib

The aim of this work is to introduce representations of BiHom-left-symmetric algebras. and develop its cohomology theory. As applications, we study linear deformations of BiHom-left-symmetric algebras, which are characterized by its second cohomology group with the coefficients in the adjoint representation. The notion of a Nijenhuis operator on a BiHom-left

Anurag Dwarakanath, Manish Ahuja, Sanjay Podder, Silja Vinu

In this paper, we present the Metamorphic Testing of an in-use deep learning based forecasting application. The application looks at the past data of system characteristics (e.g. `memory allocation&#39;) to predict outages in the future. We focus on two statistical / machine learning based components - a) detection of co-relation between system characteristi

Sangeeta Das, Arkajyoti De, Balaram Dey, Sathi Sharma

Time-stamped data have been used to estimate the decay half-lives of radioactive 118mSb and 64,66,68Ga nuclei. These nuclei are populated through the reaction 4He (Elab = 32 MeV) + nat In and p (Elab = 10 MeV) + natZn, respectively. The g -rays emitted by the excited daughter nuclei are detected by a single high purity germanium (HPGe) detector and the data

Jawad Abuhlail, Rangga Ganzar Noegraha

We investigate left k-Noetherian and left k-Artinian semirings. We characterize such semirings using i-injective semimodules. We prove in particular, a partial version of the celebrated Bass-Papp Theorem for semiring. We illustrate our main results by examples and counter examples.

D. Ngoduy, S. Lee, M. Treiber, M. Keyvan-Ekbatani

In car-following models, the driver reacts according to his physical and psychological abilities which may change over time. However, most car-following models are deterministic and do not capture the stochastic nature of human perception. It is expected that purely deterministic traffic models may produce unrealistic results due to the stochastic driving be

Sohaib Ahmad, Benjamin Fuller

Most iris recognition pipelines involve three stages: segmenting into iris/non-iris pixels, normalization the iris region to a fixed area, and extracting relevant features for comparison. Given recent advances in deep learning it is prudent to ask which stages are required for accurate iris recognition. Lojez et al. (IWBF 2019) recently concluded that the se

Peiyuan Gao, Xiu Yang, Alexandre M. Tartakovsky

Even though atomistic and coarse-grained (CG) models have been used to simulate liquid nanodroplets in vapor, very few rigorous studies of the liquid-liquid interface structure are available, and most of them are limited to planar interfaces. In this work, we evaluate several existing force fields (FF)s, including two atomistic and three CG FFs, with respect

Lingzhi Zhang, Andong Cao, Rui Li, Jianbo Shi

In common real-world robotic operations, action and state spaces can be vast and sometimes unknown, and observations are often relatively sparse. How do we learn the full topology of action and state spaces when given only few and sparse observations? Inspired by the properties of grid cells in mammalian brains, we build a generative model that enforces a no

Linfeng Song

How to properly model graphs is a long-existing and important problem in NLP area, where several popular types of graphs are knowledge graphs, semantic graphs and dependency graphs. Comparing with other data structures, such as sequences and trees, graphs are generally more powerful in representing complex correlations among entities. For example, a knowledg

Adnan Quadri, Huacheng Zeng, Y. Thomas Hou

As the spectrum under 6 GHz is being depleted, pushing wireless communications onto millimeter wave (mmWave) frequencies is a trend that promises multi-Gbps data rate. mmWave is therefore considered as a key technology for 5G wireless systems and has attracted tremendous research efforts. The booming research on mmWave necessitates a reconfigurable mmWave te

Boris S. Mordukhovich

This chapter presents a self-contained approach of variational analysis and generalized differentiation to deriving necessary optimality in problems of bilevel optimization with Lipschitzian data. We mainly concentrate on optimistic models, although the developed machinery also applies to pessimistic versions. Some open problems are posed and discussed.

Santiago Coelho, Jose M. Pozo, Sune N. Jespersen, Alejandro F. Frangi

Computational models of biophysical tissue properties have been widely used in diffusion MRI (dMRI) research to elucidate the link between microstructural properties and MR signal formation. For brain tissue, the research community has developed the so-called Standard Model (SM) that has been widely used. However, in clinically applicable acquisition protoco

Peiye Zhuang, Alexander G. Schwing, Sanmi Koyejo

We present an empirical evaluation of fMRI data augmentation via synthesis. For synthesis we use generative mod-els trained on real neuroimaging data to produce novel task-dependent functional brain images. Analyzed generative mod-els include classic approaches such as the Gaussian mixture model (GMM), and modern implicit generative models such as the genera

Lewis Coburn, Michael Hitrik, Johannes Sjoestrand, Francis White

We study Toeplitz operators on the Bargmann space, with Toeplitz symbols that are exponentials of inhomogeneous quadratic polynomials. It is shown that the boundedness of such operators is implied by the boundedness of the corresponding Weyl symbols.

Pavol Harar, Zoltan Galaz, Jesus B. Alonso-Hernandez, Jiri Mekyska

Automatic objective non-invasive detection of pathological voice based on computerized analysis of acoustic signals can play an important role in early diagnosis, progression tracking and even effective treatment of pathological voices. In search towards such a robust voice pathology detection system we investigated 3 distinct classifiers within supervised l

Yunhan Huang, Veeraruna Kavitha, Quanyan Zhu

In this paper, we study a continuous-time discounted jump Markov decision process with both controlled actions and observations. The observation is only available for a discrete set of time instances. At each time of observation, one has to select an optimal timing for the next observation and a control trajectory for the time interval between two observatio

Sun Kwok

The late stages of stellar evolution from asymptotic giant branch stars to planetary nebulae are now known to be an active phase of molecular synthesis. Over 80 gas-phase molecules have been detected through rotational transitions in the mm/submm region. Infrared spectroscopy has also detected inorganic minerals, fullerenes, and organic solids. The synthesis

A. González-Tudela, J. I. Cirac

The possibility of creating crystal bilayers twisted with respect to each other has led to the discovery of a wide range of novel electron correlated phenomena whose full understanding is still under debate. Here we propose and analyze a method to simulate twisted bilayers using cold atoms in state-dependent optical lattices. Our proposed setup can be used a

Darij Grinberg

We study integrality over rings (all commutative in this paper) and over ideal semifiltrations (a generalization of integrality over ideals). We begin by reproving classical results, such as a version of the &#34;faithful module&#34; criterion for integrality over a ring, the transitivity of integrality, and the theorem that sums and products of integral ele

Thomas Beck, Barbara Brandolini, Krzysztof Burdzy, Antoine Henrot

Let $Ω\subset \mathbb{R}^n$ be a convex domain and let $f:Ω\rightarrow \mathbb{R}$ be a positive, subharmonic function (i.e. $Δf \geq 0$). Then $$ \frac{1}{|Ω|} \int_Ω{f dx} \leq \frac{c_n}{ |\partial Ω| } \int_{\partial Ω}{ f dσ},$$ where $c_n \leq 2n^{3/2}$. This inequality was previously only known for convex functions with a much larger constant. We also

Yonghong Ma, Quanzhen Ding, Ting Yu

We investigate spin squeezing for a Lipkin-Meshkov-Glick (LMG) model coupled to a general non-Markovian environment in a finite temperature regime. Using the non-Markovian quantum state diffusion and master equation approach, we numerically study non-Markovian spin squeezing generation in LMG model. Our results show that the total spin number N, energy kBT,

Swarnendu Ghosh, Nibaran Das, Ishita Das, Ujjwal Maulik

The machine learning community has been overwhelmed by a plethora of deep learning based approaches. Many challenging computer vision tasks such as detection, localization, recognition and segmentation of objects in unconstrained environment are being efficiently addressed by various types of deep neural networks like convolutional neural networks, recurrent

Priyanka Nehla, V. K. Anand, Bastian Klemke, Bella Lake

We study the magnetocaloric effect and critical behavior of Co$_2$Cr$_{1-x}$Mn$_x$Al ($x=$ 0.25, 0.5, 0.75) Heusler alloys across the ferromagnetic (FM) transition (T$_{\rm C}$). The Rietveld refinement of x-ray diffraction patterns exhibit single phase cubic structure for all the samples. The temperature dependent magnetic susceptibility $χ$(T) data show a

Hossein Zeinali, Pavel Matějka, Ladislav Mošner, Oldřich Plchot

This is a description of our effort in VOiCES 2019 Speaker Recognition challenge. All systems in the fixed condition are based on the x-vector paradigm with different features and DNN topologies. The single best system reaches 1.2% EER and a fusion of 3 systems yields 1.0% EER, which is 15% relative improvement. The open condition allowed us to use external

Nooshin Maghsoodi, Hossein Sameti, Hossein Zeinali, Themos~Stafylakis

In this paper, we combine Hidden Markov Models (HMMs) with i-vector extractors to address the problem of text-dependent speaker recognition with random digit strings. We employ digit-specific HMMs to segment the utterances into digits, to perform frame alignment to HMM states and to extract Baum-Welch statistics. By making use of the natural partition of inp

Amin Mosayyebzadeh, Apoorve Mohan, Sahil Tikale, Mania Abdi

Bolted is a new architecture for bare-metal clouds that enables tenants to control tradeoffs between security, price, and performance. Security-sensitive tenants can minimize their trust in the public cloud provider and achieve similar levels of security and control that they can obtain in their own private data centers. At the same time, Bolted neither impo

Wenrui Xu, James M. Stone

We use 2D (axisymmetric) and 3D hydrodynamic simulations to study Bondi-Hoyle-Lyttleton (BHL) accretion with and without transverse upstream gradients. We mainly focus on the regime of high (upstream) Mach number, weak upstream gradients and small accretor size, which is relevant to neutron star (NS) accretion in wind-fed Supergiant X-ray binaries (SgXBs). W

Arno van den Essen

This is the note for the four lectures given by the author in the ``International Short-School/Conference on Affine Algebraic Geometry and the Jacobian Conjecture&#34; at Chern Institute of Mathematics, Nankai University, Tianjin, China. July 14-25, 2014. The aim of this lectures is to give an introduction to the theory of Mathieu subspaces. We will not trea

Arno van den Essen, Loes van Hove

We describe all Mathieu-Zhao spaces of $k[x_1,\cdots,x_n]$ ($k$ is an algebraically closed field of characteristic zero) which contains an ideal of finite codimension. Furthermore we give an algorithm to decide if a subspace of the form $I+kv_1+\cdots+kv_r$ is a Mathieu-Zhao space, in case the ideal $I$ has finite codimension.

Raphael Hellwig, Martin Uphoff, Yiqi Zhang, Mateusz Paszkiewicz

Organometallic nanostructures are promising candidates for applications in optoelectronics, magnetism and catalysis. Our bottom-up approach employs a cyano-functionalized terminal alkyne species (CN-DETP) on the Ag(110) surface to fabricate 2D domains of regularly stacked Ag-acetylide nanowires. We unravel their adsorption properties and give evidence to the

Andrea Condoluci, Beniamino Accattoli, Claudio Sacerdoti Coen

The $λ$-calculus is a handy formalism to specify the evaluation of higher-order programs. It is not very handy, however, when one interprets the specification as an execution mechanism, because terms can grow exponentially with the number of $β$-steps. This is why implementations of functional languages and proof assistants always rely on some form of sharin

Yueming Jin, Huaxia Li, Qi Dou, Hao Chen

Surgical tool presence detection and surgical phase recognition are two fundamental yet challenging tasks in surgical video analysis and also very essential components in various applications in modern operating rooms. While these two analysis tasks are highly correlated in clinical practice as the surgical process is well-defined, most previous methods tack

Brian Gaudet, Richard Linares, Roberto Furfaro

Current practice for asteroid close proximity maneuvers requires extremely accurate characterization of the environmental dynamics and precise spacecraft positioning prior to the maneuver. This creates a delay of several months between the spacecraft&#39;s arrival and the ability to safely complete close proximity maneuvers. In this work we develop an adapti

Pavan Nukala, Jordi Antoja-Lleonart, Yingfen Wei, Lluis Yedra

Ultra-thin Hf1-xZrxO2 films have attracted tremendous interest owing to their Si-compatible ferroelectricity arising from polar polymorphs. While these phases have been grown on Si as polycrystalline films, epitaxial growth was only achieved on non-Si substrates. Here we report direct epitaxy of polar phases on Si using pulsed laser deposition enabled via in

Felix V. E. Hensling, Christoph Baeumer, Marc-André Rose, Felix Gunkel

We provide insights into the influence of surface termination on the oxygen vacancy incorporation for the perovskite model material SrTiO3 during annealing in reducing gas environments. We present a novel approach to control to tailor the oxygen vacancy formation by controlling the termination. We prove that a SrO-termination can inhibit the incorporation of

William Pourmajidi, Andriy Miranskyy, John Steinbacher, Tony Erwin

The stability and performance of Cloud platforms are essential as they directly impact customers&#39; satisfaction. Cloud service providers use Cloud monitoring tools to ensure that rendered services match the quality of service requirements indicated in established contracts such as service-level agreements. Given the enormous number of resources that need

Erez Posner, Rami Hagege

We present a novel method for motion segmentation called LAAV (Locally Affine Atom Voting). Our model&#39;s main novelty is using sets of features to segment motion for all features in the scene. LAAV acts as a pre-processing pipeline stage for features in the image, followed by a fine-tuned version of the state-of-the-art Random Voting (RV) method. Unlike s

Jesse Clifton, Lili Wu, Eric Laber

We introduce Parameterized Exploration (PE), a simple family of methods for model-based tuning of the exploration schedule in sequential decision problems. Unlike common heuristics for exploration, our method accounts for the time horizon of the decision problem as well as the agent&#39;s current state of knowledge of the dynamics of the decision problem. We

Nada Cvetković, Han Cheng Lie

We consider a quantity that measures the roundness of a bounded, convex $d$-polytope in $\mathbb{R}^d$. We majorise this quantity in terms of the smallest singular value of the matrix of outer unit normals to the facets of the polytope.

Congcong Wang, Faouzi Alaya Cheikh, Azeddine Beghdadi, Ole Jakob Elle

The object sizes in images are diverse, therefore, capturing multiple scale context information is essential for semantic segmentation. Existing context aggregation methods such as pyramid pooling module (PPM) and atrous spatial pyramid pooling (ASPP) design different pooling size or atrous rate, such that multiple scale information is captured. However, the

Rana D. Parshad, Matthew A. Beauregard, Eric M. Takyi, Thomas Griffin

The Trojan Y Chromosome Strategy (TYC) is an extremely well investigated biological control method for controlling invasive populations with an XX-XY sex determinism. In \cite{GP12, WP14} various dynamical properties of the system are analyzed, including well posedness, boundedness of solutions, and conditions for extinction or recovery. These results are de

Alexander Gajewski, Jeff Clune, Kenneth O. Stanley, Joel Lehman

Designing evolutionary algorithms capable of uncovering highly evolvable representations is an open challenge; such evolvability is important because it accelerates evolution and enables fast adaptation to changing circumstances. This paper introduces evolvability ES, an evolutionary algorithm designed to explicitly and efficiently optimize for evolvability,

Subhabrata Dutta, Dipankar Das, Tanmoy Chakraborty

Persuasion and argumentation are possibly among the most complex examples of the interplay between multiple human subjects. With the advent of the Internet, online forums provide wide platforms for people to share their opinions and reasonings around various diverse topics. In this work, we attempt to model persuasive interaction between users on Reddit, a p

Jacob H. Marlow, Franck C. G. A. Nicolleau, Wernher Brevis

In this research article we study the wake formation behind 3-Dimensional Multi-scale Porous Obstacles (3DMPOs). Particle Imaging Velocimetry (PIV) is used in a non-shallow ($B/h =1.5$) water flume, with measurements carried out across the $x$ - $y$ plane (at $z$ = 130 mm) and with $Re=70,000$ based on the free-stream velocity ($U_{\infty}$). To characterise

Alexander Kolnogorov

We consider a continuous time two-armed bandit problem in which incomes are described by Poissonian processes. We develop Bayesian approach with arbitrary prior distribution. We present two versions of recursive equation for determination of Bayesian piece-wise constant strategy and Bayesian risk and partial differential equation in the limiting case. Unlike

Karoly Urmossy

We calculate the azimuthal anisotropy $v_2$ extracted from the large $Δy$ region of two particle $Δy-Δϕ$ correlations in a two-jet system, in which, the masses of the jets are not negligible compared to their energies. As the virtualities of the leading partons, initiating these jets are not negligible either, we use a recently developed, off-shell fragmenta

M. K. Basher, R. Mishan, S. Biswas, M. Khalid Hossain

Monocrystalline silicon solar cells with photo-absorbing morphology can amplify light-trapping properties within the absorber layer and help to fabricate cost-effective solar cells. In this paper, the effect of different parameters namely temperature and time of Cu-assisted chemical etching was thoroughly investigated for the optimization of the light absorp

Serhii E. Shafraniuk, Ivan P. Nevirkovets, Oleg A. Mukhanov

A model of superconducting computer memory exploiting the orthogonal spin transfer (OST) in the pseudospin valve (PS) that is controlled by the three-terminal Josephson superconducting-ferromagnetic transistor (SFT) is developed. The building blocks of the memory are hybrid PS and SFT structures. The memory model is formulated in terms of the equation-define

Evgenii Razinkov, Iuliia Saveleva, Jiři Matas

We propose ALFA - a novel late fusion algorithm for object detection. ALFA is based on agglomerative clustering of object detector predictions taking into consideration both the bounding box locations and the class scores. Each cluster represents a single object hypothesis whose location is a weighted combination of the clustered bounding boxes. ALFA was eva

Simo Särkkä

Gaussian processes are used in machine learning to learn input-output mappings from observed data. Gaussian process regression is based on imposing a Gaussian process prior on the unknown regressor function and statistically conditioning it on the observed data. In system identification, Gaussian processes are used to form time series prediction models such

Can Gafuroglu, Islem Rekik

Patients initially diagnosed with early mild cognitive impairment (eMCI) are known to be a clinically heterogeneous group with very subtle patterns of brain atrophy. To examine the boarders between normal controls (NC) and eMCI, Magnetic Resonance Imaging (MRI) was extensively used as a non-invasive imaging modality to pin-down subtle changes in brain images

Bodhisatwa Mandal, Swarnendu Ghosh, Ritesh Sarkhel, Nibaran Das

Capsule networks have gained a lot of popularity in short time due to its unique approach to model equivariant class specific properties as capsules from images. However the dynamic routing algorithm comes with a steep computational complexity. In the proposed approach we aim to create scalable versions of the capsule networks that are much faster and provid

Behzad Ghanbarian

Modeling fluid flow in dual-porosity media with bi-modal pore size distributions has practical applications to understanding transport in multi-scale systems such as natural soils. Dual-porosity media are typically formed of two domains: (1) structure and (2) texture. The former mainly incorporates macropores, while the latter contains micropores. Although t

M. Abu-Shady

By using conformable fractional of the Nikiforov-Uvarov (CF-NU) method, the radial Schrodinger equation is analytically solved. The energy eigenvalues and corresponding functions are obtained, in which the dependent temperature potential is employed. The effect of fraction-order parameter is studied on heavy-quarkonium masses such as charmonium and bottomoni

Maria Bampa, Panagiotis Papapetrou

We study the problem of detecting adverse drug events in electronic healthcare records. The challenge in this work is to aggregate heterogeneous data types involving diagnosis codes, drug codes, as well as lab measurements. An earlier framework proposed for the same problem demonstrated promising predictive performance for the random forest classifier by usi

Beniamino Accattoli, Andrea Condoluci, Giulio Guerrieri, Claudio Sacerdoti Coen

Extending the lambda-calculus with a construct for sharing, such as let expressions, enables a special representation of terms: iterated applications are decomposed by introducing sharing points in between any two of them, reducing to the case where applications have only values as immediate subterms. This work studies how such a crumbled representation of t

Hassan Fathivavsari

We have searched the Sloan Digital Sky Survey Data Release 12 for ghostly Damped Ly$α$ (DLA) systems. These systems, located at the redshift of the quasars, show strong absorption from low-ionization atomic species but reveal no HI Ly$α$ absorption. Our search has, for the first time, resulted in a sample of 30 homogeneously selected ghostly DLAs with $z_{\r

Gen Li, Xingyu Xu, Yuantao Gu

Restricted Isometry Property (RIP) is of fundamental importance in the theory of compressed sensing and forms the base of many exact and robust recovery guarantees in this field. A quantitative description of RIP involves bounding the so-called RIP constants of measurement matrices. In this respect, it is noteworthy that most results in the literature concer

Marek Kopicki, Dominik Belter, Jeremy L. Wyatt

This paper concerns the problem of how to learn to grasp dexterously, so as to be able to then grasp novel objects seen only from a single view-point. Recently, progress has been made in data-efficient learning of generative grasp models which transfer well to novel objects. These generative grasp models are learned from demonstration (LfD). One weakness is

D. Anish Roshi, L. D. Anderson, E. Araya, D. Balser

The white paper discusses Arecibo Observatory&#39;s plan for facility improvements and activities over the next decade. The facility improvements include: (a) improving the telescope surface, pointing and focusing to achieve superb performance up to ~12.5 GHz; (b) equip the telescope with ultrawide-band feeds; (c) upgrade the instrumentation with a 4 GHz ban

Abhijit Mahalunkar, John D. Kelleher

In order to successfully model Long Distance Dependencies (LDDs) it is necessary to understand the full-range of the characteristics of the LDDs exhibited in a target dataset. In this paper, we use Strictly k-Piecewise languages to generate datasets with various properties. We then compute the characteristics of the LDDs in these datasets using mutual inform

Ivan Chajda, Helmut Länger

We study the so-called closed and splitting subsemimodules and submodules of a given semimodule or module, respectively. We describe lattices of subsemimodules and of closed subsemimodules and posets of splitting subsemimodules and submodules. In the case of modules a natural bijective correspondence between these posets and posets of projections is establis

A. Pontin, N. P. Bullier, M. Toroš, P. F. Barker

Levitated nano-oscillators are seen as promising platforms for testing fundamental physics and testing quantum mechanics in a new high mass regime. Levitation allows extreme isolation from the environment, reducing the decoherence processes that are crucial for these sensitive experiments. A fundamental property of any oscillator is its line width and mechan

A. S. Detinko, D. L. Flannery

We develop methods for computing with matrix groups defined over a range of infinite domains, and apply those methods to the design of algorithms for nilpotent groups. In particular, we provide a practical algorithm to test nilpotency of matrix groups over an infinite field. We also provide algorithms that answer a number of structural questions for a given

Weixin Zhang, Chengde Yu, Zhifeng Shen, Shu Liu

Soil has been recognized as an indirect driver of global warming by regulating atmospheric greenhouse gases. However, in view of the higher heat capacity and CO2 concentration in soil than those in atmosphere, the direct contributions of soil to greenhouse effect may be non-ignorable. Through field manipulation of CO2 concentration both in soil and atmospher

Chia-Hsuan Lee, Hung-Yi Lee

Deep learning based question answering (QA) on English documents has achieved success because there is a large amount of English training examples. However, for most languages, training examples for high-quality QA models are not available. In this paper, we explore the problem of cross-lingual transfer learning for QA, where a source language task with plen

Qunsong Zeng, Yuqing Du, Kin K. Leung, Kaibin Huang

Edge machine learning involves the development of learning algorithms at the network edge to leverage massive distributed data and computation resources. Among others, the framework of federated edge learning (FEEL) is particularly promising for its data-privacy preservation. FEEL coordinates global model training at a server and local model training at edge

Andreas Christ Sølvsten Jørgensen, Achim Weiss

State-of-the-art one-dimensional (1D) stellar evolution codes rely on simplifying assumptions, such as mixing length theory, in order to describe superadiabatic convection. As a result, 1D stellar structure models do not correctly recover the surface layers of the Sun and other stars with convective envelopes. We present a method that overcomes this structur

Wenhuo Su, Aibin Zang

In the paper, the limit behavior of solutions to the second-grade fluid system with no-slip boundary conditions is studied as both $ν$ and $α$ tend to zero. More precisely, it is verified that the convergence from second-grade fluid system to Euler system holds as $ν$ and $α$ tend to zero independently under the radial symmetry case.

Fan Mo, Ali Shahin Shamsabadi, Kleomenis Katevas, Andrea Cavallaro

Pre-trained Deep Neural Network (DNN) models are increasingly used in smartphones and other user devices to enable prediction services, leading to potential disclosures of (sensitive) information from training data captured inside these models. Based on the concept of generalization error, we propose a framework to measure the amount of sensitive information

Filippo M. Smaldone, Nicola Scianca, Valerio Modugno, Leonardo Lanari

Maintaining balance while walking is not a simple task for a humanoid robot because of its complex dynamics. The presence of a persistent disturbance makes this task even more challenging, as it can cause a loss of balance and ultimately lead the the robot to a fall. In this paper, we extend our previously proposed Intrinsically Stable MPC (IS-MPC), which gu

Wendy Lowen, Michel Van den Bergh

Consider a monoidal category which is at the same time abelian with enough projectives and such that projectives are flat on the right. We show that there is a $B_{\infty}$-algebra which is $A_{\infty}$-quasi-isomorphic to the derived endomorphism algebra of the tensor unit. This $B_{\infty}$-algebra is obtained as the co-Hochschild complex of a projective r

Xiaotian Chen, Xuejin Chen, Zheng-Jun Zha

Monocular depth estimation is an essential task for scene understanding. The underlying structure of objects and stuff in a complex scene is critical to recovering accurate and visually-pleasing depth maps. Global structure conveys scene layouts, while local structure reflects shape details. Recently developed approaches based on convolutional neural network

Marc Dambrine, Helmut Harbrecht

This article combines shape optimization and homogenization techniques by looking for the optimal design of the microstructure in composite materials and of scaffolds. The development of materials with specific properties is of huge practical interest, for example, for medical applications or for the development of light weight structures in aeronautics. In

Zhendong Li, Matthew J. O&#39;Rourke, Garnet Kin-Lic Chan

In one dimension (1D), a general decaying long-range interaction can be fit to a sum of exponential interactions $e^{-λr_{ij}}$ with varying exponents $λ$, each of which can be represented by a simple matrix product operator (MPO) with bond dimension $D=3$. Using this technique, efficient and accurate simulations of 1D quantum systems with long-range interac

Ye Bai, Jiangyan Yi, Jianhua Tao, Zhengkun Tian

Integrating an external language model into a sequence-to-sequence speech recognition system is non-trivial. Previous works utilize linear interpolation or a fusion network to integrate external language models. However, these approaches introduce external components, and increase decoding computation. In this paper, we instead propose a knowledge distillati

BESIII Collaboration, M. Ablikim, M. N. Achasov, P. Adlarson

We measure the Born cross sections of the process $e^{+}e^{-} \to K^{+}K^{-}K^{+}K^{-}$ at center-of-mass (c.m.) energies, $\sqrt{s}$, between 2.100 and 3.080 GeV. The data were collected using the BESIII detector at the BEPCII collider. An enhancement at $\sqrt{s}= 2.232$ GeV is observed, very close to the $e^{+}e^{-} \to Λ\overlineΛ$ production threshold.

Xinyu Du, Chao Wang, Tianze Wang, Zeyu Gao

In this paper, we study reversibility of one-dimensional(1D) linear cellular automata(LCA) under null boundary condition, whose core problems have been divided into two main parts: calculating the period of reversibility and verifying the reversibility in a period. With existing methods, the time and space complexity of these two parts are still too expensiv

George D. Montanez, Jonathan Hayase, Julius Lauw, Dominique Macias

Building on the view of machine learning as search, we demonstrate the necessity of bias in learning, quantifying the role of bias (measured relative to a collection of possible datasets, or more generally, information resources) in increasing the probability of success. For a given degree of bias towards a fixed target, we show that the proportion of favora

O. V. Ageev, R. A. Sharipov

The three-dimensional linear regression problem is a problem of finding a spacial straight line best fitting a group of points in three-dimensional Euclidean space. This problem is considered in the present paper and a solution to it is given in a coordinate-free form.