November 2019 arXiv papers — page 67

B. Rezaei, G. R. Boroun

We present a set of formulas to extract the longitudinal structure function from the proton structure function and its derivatives with respect to lnQ2 in the next-to-next-to-leading order of the perturbative theory at low x based on a hard poemron exchange. The behavior of the DIS cross section ratio R(x;Q2) and the ratio FL(x;Q2)=F2(x;Q2) studied and compa

Akul Malhotra, Sen Lu, Kezhou Yang, Abhronil Sengupta

Uncertainty plays a key role in real-time machine learning. As a significant shift from standard deep networks, which does not consider any uncertainty formulation during its training or inference, Bayesian deep networks are being currently investigated where the network is envisaged as an ensemble of plausible models learnt by the Bayes' formulation in

David Gamarnik, Aukosh Jagannath

We consider the algorithmic problem of finding a near ground state (near optimal solution) of a $p$-spin model. We show that for a class of algorithms broadly defined as Approximate Message Passing (AMP), the presence of the Overlap Gap Property (OGP), appropriately defined, is a barrier. We conjecture that when $p\ge 4$ the model does indeed exhibits OGP (a

Yu Yu, Jean-Marc Odobez

Although automatic gaze estimation is very important to a large variety of application areas, it is difficult to train accurate and robust gaze models, in great part due to the difficulty in collecting large and diverse data (annotating 3D gaze is expensive and existing datasets use different setups). To address this issue, our main contribution in this pape

Nathaniel Sagman

We generalize a well-known existence and uniqueness result for equivariant harmonic maps due to Corlette, Donaldson, and Labourie to a non-compact infinite energy setting and analyze the asymptotic behaviour of the harmonic maps. When the relevant representation is Fuchsian and has hyperbolic monodromy, our construction recovers a family of harmonic maps ori

Natasha Dobrinen, Daniel Hathaway

Henle, Mathias, and Woodin proved that, provided that $\omega\rightarrow(\omega)^{\omega}$ holds in a model $M$ of ZF, then forcing with $([\omega]^{\omega},\subseteq^*)$ over $M$ adds no new sets of ordinals, thus earning the name a "barren" extension. Moreover, under an additional assumption, they proved that this generic extension preserves all strong par

Samuel A. Ballas

In this paper we produce many examples of thin subgroups of special linear groups that are isomorphic to the fundamental groups of non-arithmetic hyperbolic manifolds. Specifically, we show that the non-arithmetic lattices in $\mathrm{SO}(n,1)$ constructed by Gromov and Piatetski-Shapiro can be embedded into $\mathrm{SL}_{n+1}(\mathbb{R})$ so that their imag

Brandur Thorgrimsson, Thomas McJunkin, E. R. MacQuarrie, S. N. Coppersmith

Motivated by potential transformative applications of nanoelectronic circuits that incorporate superconducting elements, and by the advantages of integrating these elements in a silicon materials platform, we investigate the properties of the superconductivity of silicon ion-implanted with gallium. Here we measure 40 different samples and explore both a vari

Tien Mai, Kennard Chan, Patrick Jaillet

We consider the problem of learning from demonstrated trajectories with inverse reinforcement learning (IRL). Motivated by a limitation of the classical maximum entropy model in capturing the structure of the network of states, we propose an IRL model based on a generalized version of the causal entropy maximization problem, which allows us to generate a cla

Raffaele Folino, César Hernández Melo, Luis López Ríos, Ramón Plaza

This paper considers a one-dimensional generalized Allen-Cahn equation of the form \[ u_t = \varepsilon^2 (D(u)u_x)_x - f(u), \] where $\varepsilon>0$ is constant, $D=D(u)$ is a positive, uniformly bounded below diffusivity coefficient that depends on the phase field $u$ and $f(u)$ is a reaction function that can be derived from a double-well potential with

Sebastian F. Sanchez

I review here the spatially-resolved spectroscopic properties of low-redshift star-forming galaxies (and their retired counter-parts), using results from the most recent Integral Field Spectroscopy galaxy surveys. First, I briefly summarise the global spectroscopic properties of these galaxies, discussing the main ionization processes, and the global relatio

Ramesh Krishnan S. Pallavoor, Sofya Raskhodnikova, Erik Waingarten

We design a nonadaptive algorithm that, given oracle access to a function $f: \{0,1\}^n \to \{0,1\}$ which is $α$-far from monotone, makes poly$(n, 1/α)$ queries and returns an estimate that, with high probability, is an $\widetilde{O}(\sqrt{n})$-approximation to the distance of $f$ to monotonicity. The analysis of our algorithm relies on an improvement to t

Nader Ghassemi, Yefan Tian, Xu Lu, Yanci Yan

$^{63}$Cu NMR measurements are reported for the Cu-rich phase of \ch{Cu_{12+x}Sb4S13} ($x \lesssim 2$) and compared to \ch{Cu12Sb4S13}. We identify the NMR signatures of the phase segregation into Cu-poor ($x \approx 0$) and Cu-rich ($x \lesssim 2$) phases, with the metal-insulator transition observed in \ch{Cu12Sb4S13} suppressed in the Cu-rich phase. Based

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

As Deep Learning (DL) models have been increasingly used in latency-sensitive applications, there has been a growing interest in improving their response time. An important venue for such improvement is to profile the execution of these models and characterize their performance to identify possible optimization opportunities. However, the current profiling t

Brian Gaudet, Richard Linares, Roberto Furfaro

We optimize a six degrees of freedom hovering policy using reinforcement meta-learning. The policy maps flash LIDAR measurements directly to on/off spacecraft body-frame thrust commands, allowing hovering at a fixed position and attitude in the asteroid body-fixed reference frame. Importantly, the policy does not require position and velocity estimates, and

Brandon Trabucco, Albert Qu, Simon Li, Ganeshkumar Ashokavardhanan

This paper investigates methods for estimating the optimal stochastic control policy for a Markov Decision Process with unknown transition dynamics and an unknown reward function. This form of model-free reinforcement learning comprises many real world systems such as playing video games, simulated control tasks, and real robot locomotion. Existing methods f

Pameli Saha, Ujjal Debnath

In this work, we get an idea of the existence of compact stars in the background of f(T) modified gravity where T is a scalar torsion. We acquire the equations of motion using anisotropic property within the spherically compact star with electromagnetic field, quintessence field and modified Chaplygin gas in the framework of modified f(T) gravity. Then by ma

Semyon Sinchenko, Dmitry Bazhanov

We present herein a new approach based on the simultaneous application of the deep learning and statistical physics methods to solve the combinatorial optimization problems. The recent modern advanced techniques, such as an artificial neural network, demonstrate their efficiency for solving various physical tasks for the quantum many-body systems, which may

Ahmed Abdalazeez, Ira Didenkulova, Denys Dutykh

Dispersive effects during long wave run-up on a plane beach are studied. We take an advantage of experimental data collection of different wave types (single pulses, sinusoidal waves, bi-harmonic waves, and frequency modulated wave trains) and simulate their run-up using two models: (i) non-dispersive nonlinear shallow water theory and (ii) dispersive Boussi

Chuan Liu, Liuming Liu, Ke-Long Zhang

Within the framework of three-channel Ross-Shaw effective range theory, we derive the constraints among different parameters of the theory in the case of a narrow resonance close to the threshold of the third channel, which is relevant for the resonance-like structure $Z_c(3900)$. The usage of these constraint relations, together with the multi-channel Lüsch

Debjyoti Bhattacharjee, Abdullah Ash Saki, Mahabubul Alam, Anupam Chattopadhyay

Rapid advancement in the domain of quantum technologies has opened up researchers to the real possibility of experimenting with quantum circuits and simulating small-scale quantum programs. Nevertheless, the quality of currently available qubits and environmental noise poses a challenge in the smooth execution of the quantum circuits. Therefore, efficient de

Ruo-Wei Hung, Fatemeh Keshavarz-Kohjerdi

In this paper, we continue the study of the Hamiltonian and longest $(s, t)$-paths of supergrid graphs. The Hamiltonian $(s, t)$-path of a graph is a Hamiltonian path between any two given vertices $s$ and $t$ in the graph, and the longest $(s, t)$-path is a simple path with the maximum number of vertices from $s$ to $t$ in the graph. A graph holds Hamiltoni

N. K. Tamanna, J. Akter, N. A. Chowdhury, A. A. Mamun

The modulational instability of dust-ion-acoustic (DIA) mode and associated rogue waves in a three component dusty plasma system (containing inertial warm ion and negatively charged dust fluids along with inertialess $q$-distributed electrons) has been theoretically investigated. A nonlinear Schrödinger equation (NLSE) has been derived by employing the reduc

Komal K. Teru, Aishik Chakraborty

Societal bias towards certain communities is a big problem that affects a lot of machine learning systems. This work aims at addressing the racial bias present in many modern gender recognition systems. We learn race invariant representations of human faces with an adversarially trained autoencoder model. We show that such representations help us achieve les

Sam Shleifer, Manish Chablani, Anitha Kannan, Namit Katariya

Generative seq2seq dialogue systems are trained to predict the next word in dialogues that have already occurred. They can learn from large unlabeled conversation datasets, build a deeper understanding of conversational context, and generate a wide variety of responses. This flexibility comes at the cost of control, a concerning tradeoff in doctor/patient in

Y. Muda, F. T. Akbar, R. Kusdiantara, B. E. Gunara

We consider a damped, parametrically driven discrete nonlinear Klein-Gordon equation, that models coupled pendula and micromechanical arrays, among others. To study the equation, one usually uses a small-amplitude wave ansatz, that reduces the equation into a discrete nonlinear Schrödinger equation with damping and parametric drive. Here, we justify the appr

Riccardo Mancini, Shreshth Tuli, Tommaso Cucinotta, Rajkumar Buyya

In recent years, the Internet of Things (IoT) has been growing in popularity, along with the increasingly important role played by IoT gateways, mediating the interactions among a plethora of heterogeneous IoT devices and cloud services. In this paper, we present iGateLink, an open-source Android library easing the development of Android applications acting

Gert Raskin, Dmytro Rogozin, Tom Mladenov, Christian Schwab

Fiber modal noise is a performance limiting factor in high-precision radial velocity measurements with multi-mode fiber-fed high-resolution spectrographs. Traditionally, modal noise is mitigated by agitating the fiber, this way redistributing the light that propagates in the fiber over many different modes. However, in case of fibers with only a limited numb

Sarafa Iyaniwura, Tony Wong, Michael J. Ward, Colin B. Macdonald

We develop novel numerical methods and perturbation approaches to determine the mean first passage time (MFPT) for a Brownian particle to be captured by either small stationary or mobile traps inside a bounded 2-D confining domain. Of particular interest is to identify optimal arrangements of small absorbing traps that minimize the average MFPT. Although the

Kowshik Bettadapura

By analytic deformations of complex structures, we mean perturbations of the Dolbeault operator. By algebraic deformations of complex structures, we mean deformations of holomorphic glueing data. For complex manifolds there is, infinitesimally, a correspondence between these two types deformations. In this article we argue that an analogous correspondence ho

Min Du, Ruoxi Jia, Dawn Song

Outlier detection and novelty detection are two important topics for anomaly detection. Suppose the majority of a dataset are drawn from a certain distribution, outlier detection and novelty detection both aim to detect data samples that do not fit the distribution. Outliers refer to data samples within this dataset, while novelties refer to new samples. In

Alison Jenkins, Vinika Gupta, Mary Lenoir

The aim of this project is to develop a code to discover the optimal sigma value that maximum the F1 score and the optimal sigma value that maximizes the accuracy and to find out if they are the same. Four algorithms which can be used to solve this problem are: Genetic Regression Neural Networks (GRNNs), Radial Based Function (RBF) Neural Networks (RBFNNs),

Jorge L. Suzuki, Yongtao Zhou, Marta D'Elia, Mohsen Zayernouri

We develop a thermodynamically consistent, fractional visco-elasto-plastic model coupled with damage for anomalous materials. The model utilizes Scott-Blair rheological elements for both visco-elastic/plastic parts. The constitutive equations are obtained through Helmholtz free-energy potentials for Scott-Blair elements, together with a memory-dependent frac

Muhammad Sirajo Abdullahi, Poom Kumam, Isah Abor Garba

The aim of this paper is to generalize some of the properties and results regarding both the coincidence point set and the common fixed point set of any two digitally continuous maps to the case of several (more than two) digitally continuous mappings. Moreover, we study how rigidity may affect these coincidence and homotopy coincidence point sets. Also, we

Alison Jenkins, Vinika Gupta, Alexis Myrick, Mary Lenoir

The Particle Swarm Optimization (PSO) algorithm is developed for solving the Schaffer F6 function in fewer than 4000 function evaluations on a total of 30 runs. Four variations of the Full Model of Particle Swarm Optimization (PSO) algorithms are presented which consist of combinations of Ring and Star topologies with Synchronous and Asynchronous updates. Th

Viktoria Kadar, Ferenc Kun

We investigate the effect of the amount of disorder on the statistics of breaking bursts during the quasi-static fracture of heterogeneous materials. We consider a fiber bundle model where the strength of single fibers is sampled from a power law distribution over a finite range, so that the amount of materials' disorder can be controlled by varying the

Michael P. Leung

This paper studies inference in models of discrete choice with social interactions when the data consists of a single large network. We provide theoretical justification for the use of spatial and network HAC variance estimators in applied work, the latter constructed by using network path distance in place of spatial distance. Toward this end, we prove new

Farzaneh Khoshnevisan, Zhewen Fan

Robust anomaly detection is a requirement for monitoring complex modern systems with applications such as cyber-security, fraud prevention, and maintenance. These systems generate multiple correlated time series that are highly seasonal and noisy. This paper presents a novel unsupervised deep learning architecture for multivariate time series anomaly detecti

Paula Cerejeiras, Uwe Kaehler, Teppo Mertens, Frank Sommen

This text is an exposition of a new approach into discrete differential geometry, called Script Geometry. In difference to classic approaches while scripts are based on complexes of cells we are not limited to simplicial complexes. One of the principal concepts of Script Geometry is the notion of tightness which is a minimality condition corresponds to the c

Sanjay Kariyappa, Moinuddin K Qureshi

Deep Neural Networks (DNNs) are susceptible to model stealing attacks, which allows a data-limited adversary with no knowledge of the training dataset to clone the functionality of a target model, just by using black-box query access. Such attacks are typically carried out by querying the target model using inputs that are synthetically generated or sampled

Isabella N Grabski, Roberta De Vito, Barbara E Engelhardt

Unlike standard linear regression, quantile regression captures the relationship between covariates and the conditional response distribution as a whole, rather than only the relationship between covariates and the expected value of the conditional response. However, while there are well-established quantile regression methods for continuous variables and so

Pablo Boixeda Alvarez

This note extends the Steinberg Tensor product theorem from the Frobenius kernel $G_{(r)}$ to the deformation $\mathcal{U}^{[r]}(\mathfrak{g})$ of its distribution algebra. As a Corollary we proof some conjectures from \cite{Wes}. Further it describes the graded representation theory of $\mathcal{U}^{[r]}(\mathfrak{g})$ at a generic semisimple p-central char

Thomas A. Trainor

The journal Nature recently published a letter titled "Creating small circular, elliptical, and triangular droplets of quark-gluon plasma" [1]. The basis for that claim is a combination of measured Fourier amplitudes $v_2$ and $v_3$ from collision systems $p$-Au, $d$-Au and $h$-Au (helion $h$ is the nucleus of atom $^3$He), Glauber Monte Carlo estima

Aaron J. Barclay, Andrea Pietropolli Charmet, Kirk H. Michaelian, A. R. W. McKellar

The weakly-bound molecular clusters (D2O)2-CO and (D2O)3-CO are observed in the C-O stretch fundamental region (~2150 cm-1), and their rotationally-resolved infrared spectra yield precise rotational parameters. The corresponding H2O clusters are also observed, but their bands are broadened by predissociation, preventing detailed analysis. The rotational para

Xiaoyuan Guo, Fusheng Wang, George Teodorou, Alton B. Farris

Liver steatosis is known as the abnormal accumulation of lipids within cells. An accurate quantification of steatosis area within the liver histopathological microscopy images plays an important role in liver disease diagnosis and trans-plantation assessment. Such a quantification analysis often requires a precise steatosis segmentation that is challenging d

Zhong Guan

The approximate Bernstein polynomial model, a mixture of beta distributions, is applied to obtain maximum likelihood estimates of the regression coefficients, and the baseline density and survival functions in an accelerated failure time model based on interval censored data including current status data. The rate of convergence of the proposed estimates are

Scott L. Fleming, Kuhan Jeyapragasan, Tony Duan, Daisy Ding

There is an emerging trend in the reinforcement learning for healthcare literature. In order to prepare longitudinal, irregularly sampled, clinical datasets for reinforcement learning algorithms, many researchers will resample the time series data to short, regular intervals and use last-observation-carried-forward (LOCF) imputation to fill in these gaps. Ty

Zoltán Péli, István Nándori, Zoltán Trócsányi

We investigate a particle physics model for cosmic inflation based on the following assumptions: (i) there are at least two complex scalar fields; (ii) the scalar potential is bounded from below and remains perturbative up to the Planck scale; (iii) we assume slow-roll inflation with maximally correlated adiabatic and entropy fluctuations 50--60 e-folds befo

Amira Hajjeji, Nawel Jmail, Abir Hadriche, Amal Ncibi

The analysis of electrophysiological signal of scalp: EEG (electroencephalography), MEG (magnetoencephalography) and depth (intracerebral EEG) IEEG is a way to delimit epileptogenic zone (EZ). These epileptic signals present two different activities (oscillations and spikes) which can be overlapped in the time frequency plane. Automatic recognition of epilep

Qays Hatem Imran

In this paper, we shall use the concepts of Na-open and NSa-open sets to define some new types of weakly nano continuity such as; Na-continuous, Na*-continuous, Na**-continuous, NSa-continuous, NSa*-continuous and NSa**-continuous maps. Also, we shall explain the relationships between these types of weakly nano continuity and the concepts of nano continuity.

Ridha Jarray, Nawel Jmail, Abir Hadriche, Tarek Frikha

To define the neural networks responsible of the epileptic seizure, we had to study the electrophysiological signal in a proper way. The early recognition of the seizure build up could also be defined through the time space mapping of the preictal gamma oscillations. The electrophysiological signals present three types of wave: oscillations, spikes, and a mi

Sergio Albeverio, Zdzisław Brzeźniak, Alexei Daletskii

We consider a stochastic Camassa-Holm equation driven by a one-dimensional Wiener process with a first order differential operator as diffusion coefficient. We prove the existence and uniqueness of local strong solutions of this equation. In order to do so, we transform it into a random quasi-linear partial differential equation and apply Kato's operator

Selma Mededovic Thagard, Mohan Sankaran, Mark J. Kushner

The science and technology of Low Temperature Plasmas (LTPs) harbor dynamic and versatile methods of converting the potential energy of electricity to chemical reactivity, thereby enabling the Future Based on Renewable Electricity (FBRE). Research on LTPs connects fields as diverse as engineering, plasma physics, biology and medicine, and so LTPs embody the

T. Cantat-Gaudin, F. Anders

Many open clusters listed in modern catalogues were initially reported by visual astronomers as apparent overdensities of bright stars. As observational techniques and analysis methods continue to improve, some of them have been shown to be chance alignments of stars and not true clusters. Recent publications have provided membership lists for over a thousan

Ismail Hakki Duru

Path integration for the potential V=αcosθ is performed. Satisfaction of the corresponding Schrödinger equation by the resulting Feynman kernel is demonstrated. Expressions for the related Green function are presented.

Mohammad Hosein Fakheri, Hooman Barati sedeh, Ali Abdolali

Acoustic cloaks derived by coordinate transformation have opened up a new field of considerable interest in the last two decades. However, since perfect omnidirectional acoustic cloak relies on inhomogeneous and anisotropic materials that posses extreme values in certain regions, this cloak was deemed impossible to be attained even with metamaterials. Recent

Xuefei Cao, Bor-Chun Chen, Ser-Nam Lim

Deep metric learning is an important area due to its applicability to many domains such as image retrieval and person re-identification. The main drawback of such models is the necessity for labeled data. In this work, we propose to generate pseudo-labels for deep metric learning directly from clustering assignment and we introduce unsupervised deep metric l

Stella Kutrovskaya, Igor Chestnov, Anton Osipov, Vlad Samyshkin

We stabilize monoatomic carbon chains in water by attaching them to gold nanoparticles (NPs) by means of the laser ablation process. Resulting nanoobjects represent pairs of NPs connected by multiple straight carbon chains of several nanometer lengths. If NPs at the opposite ends of a chain differ in size, the structure acquires a dipole moment due to the di

Francesco Scotognella

We design photonic structures embedding different functional molecular systems of photochromic switching and lasing. We study the light absorption of two photochromic molecules and of 4,4'-bis[(N-carbazole)styryl]biphenyl (BSB-Cz) with density functional theory. For the photochromic diarylethene we derivate the refractive index with Kramers-Kronig relati

Debesh Jha, Pia H. Smedsrud, Michael A. Riegler, Pål Halvorsen

Pixel-wise image segmentation is a highly demanding task in medical-image analysis. In practice, it is difficult to find annotated medical images with corresponding segmentation masks. In this paper, we present Kvasir-SEG: an open-access dataset of gastrointestinal polyp images and corresponding segmentation masks, manually annotated by a medical doctor and

Owain Evans

This article is about the cognitive science of visual art. Artists create physical artifacts (such as sculptures or paintings) which depict people, objects, and events. These depictions are usually stylized rather than photo-realistic. How is it that humans are able to understand and create stylized representations? Does this ability depend on general cognit

Debesh Jha, Pia H. Smedsrud, Michael A. Riegler, Dag Johansen

Accurate computer-aided polyp detection and segmentation during colonoscopy examinations can help endoscopists resect abnormal tissue and thereby decrease chances of polyps growing into cancer. Towards developing a fully automated model for pixel-wise polyp segmentation, we propose ResUNet++, which is an improved ResUNet architecture for colonoscopic image s

Andrew James Kelley, Elizabeth Ciorsdan Dwyer Wolfe

We give here the exact maximal subgroup growth of two classes of polycyclic groups. Let $G_k = \langle x_1, x_2, ..., x_k \mid x_ix_jx_i^{-1}x_j \text{ for all } i < j \rangle$. So $G_k = \mathbb{Z} \rtimes (\mathbb{Z} \rtimes (\mathbb{Z} \rtimes ... \rtimes \mathbb{Z})$. Then for all $k \geq 2$, we calculate $m_n(G_k)$, the number of maximal subgroups of $G

Tatsuki Ezawa, Yasunori Kimura

In this paper, we consider the Halpern iteration scheme for a finite family of quasinonexpansive mappings and then prove a strong convergence theorem to their common fixed point in a complete geodesic space with curvature bounded above by one.

Anastassia Baxevani, Krzysztof Podgórski

It has been observed that an interesting class of non-Gaussian stationary processes is obtained when in the harmonics of a signal with random amplitudes and phases, frequencies can also vary randomly. In the resulting models, the statistical distribution of frequencies determines the process spectrum while the distribution of amplitudes governs the process d

Adam Skowyrski

We introduce new symmetric and periodic algebras of period 4, which are tame of non-polynomial growth

František Štampach, Pavel Šťovíček

A complete characterization is provided of Hankel matrices commuting with Jacobi matrices which correspond to hypergeometric orthogonal polynomials from the Askey scheme. It follows, as the main result of the paper, that the generalized Hilbert matrix is the only prominent infinite-rank Hankel matrix which, if regarded as an operator on $\ell^{2}(\mathbb{N}_

Abhik Kumar Sanyal, Dipankar Ray

Cosmological solutions of Einstein&#39;s equation for fluids with heat flow in a generalized Robertson-Walker metric are obtained, generalizing the results of Bergmann.

Phil Scott, Jacques D Fleuriot

Hilbert&#39;s Foundations of Geometry was perhaps one of the most influential works of geometry in the 20th century and its axiomatics was the first systematic attempt to clear up the logical gaps of the Elements. But does it have gaps of its own? In this paper, we discuss a logical issue, asking how Hilbert is able to talk about natural numbers within a fou

Pattarawat Chormai, Ponrawee Prasertsom, Attapol Rutherford

Word segmentation is a fundamental pre-processing step for Thai Natural Language Processing. The current off-the-shelf solutions are not benchmarked consistently, so it is difficult to compare their trade-offs. We conducted a speed and accuracy comparison of the popular systems on three different domains and found that the state-of-the-art deep learning syst

Jean-Claude Tinguely, Anna Maria Steyer, Cristina Ionica Øie, Øystein Ivar Helle

Correlative light-electron microscopy (CLEM) unifies the versatility of light microscopy (LM) with the high resolution of electron microscopy (EM), allowing one to zoom into the complex organization of cells. Most CLEM techniques use ultrathin sections, and thus lack the 3D-EM structural information, and focusing on a very restricted field of view. Here, we

Zoltán Garai, Theodor Pribulla, Richard Komžík, Emil Kundra

Only a few exoplanets are known to orbit around fast rotating stars. One of them is XO-6b, which orbits an F5V-type star. Shortly after the discovery, we started multicolor photometric and radial-velocity follow-up observations of XO-6b, using the telescopes of Astronomical Institute of the Slovak Academy of Sciences. Our main scientific goals were to better

Bowen Zhao, Xi Xiao, Guojun Gan, Bin Zhang

Deep neural networks (DNNs) have been applied in class incremental learning, which aims to solve common real-world problems of learning new classes continually. One drawback of standard DNNs is that they are prone to catastrophic forgetting. Knowledge distillation (KD) is a commonly used technique to alleviate this problem. In this paper, we demonstrate it c

Yajing Li, Yejuan Wang, Weihua Deng

To model wave propagation in inhomogeneous media with frequency-dependent power-law attenuation, it is needed to use the fractional powers of symmetric coercive elliptic operators in space and the Caputo tempered fractional derivative in time. The model studied in this paper is semilinear stochastic space-time fractional wave equations driven by infinite dim

Kamran Ali, Charles E. Hughes

In this paper, we present a unified architecture known as Transfer-Editing and Recognition Generative Adversarial Network (TER-GAN) which can be used: 1. to transfer facial expressions from one identity to another identity, known as Facial Expression Transfer (FET), 2. to transform the expression of a given image to a target expression, while preserving the

Stéphane Guerrier, Juan Jurado, Mehran Khaghani, Gaetan Bakalli

The task of inertial sensor calibration has required the development of various techniques to take into account the sources of measurement error coming from such devices. The calibration of the stochastic errors of these sensors has been the focus of increasing amount of research in which the method of reference has been the so-called &#34;Allan variance slo

M. S. Chaves, T. G. Mattos, A. P. F. Atman

Understanding the topological characteristics of complex networks and how they affect navigability is one of the most important goals in science today, as it plays a central role in various economic, biological, ecological and social systems. Here, we apply First Passage analysis tools to investigate the properties and characteristics of random walkers in ne

Daniel M. Sussman, Daniel A. Beller

Numerical modeling of nematic liquid crystals using the tensorial Landau-de Gennes (LdG) theory provides detailed insights into the structure and energetics of the enormous variety of possible topological defect configurations that may arise when the liquid crystal is in contact with colloidal inclusions or structured boundaries. However, these methods can b

Zhijie Dong, Haitao Ma

We study the convolution algebra $H^{G\times \CC^{*}}_{*}(Z)$ of $G$-equivariant homology group on the Steinberg variety of type B/C and define an algebra $\widetilde{Y}$ that maps to $H^{G\times \CC^{*}}_{*}(Z)$. The Drinfeld new realization of the twisted Yangian associated to symmetric pairs is a quotient of $\widetilde{Y}$. We also study the $G$-equivari

Manoj Sukhavasi, Sainath Adapa

We present an efficient architecture to detect mood/themes in music tracks on autotagging-moodtheme subset of the MTG-Jamendo dataset. Our approach consists of two blocks, a CNN block based on MobileNetV2 architecture and a self-attention block from Transformer architecture to capture long term temporal characteristics. We show that our proposed model produc

Marcel Gehrke, Ralf Möller, Tanya Braun

Evidence often grounds temporal probabilistic relational models over time, which makes reasoning infeasible. To counteract groundings over time and to keep reasoning polynomial by restoring a lifted representation, we present temporal approximate merging (TAMe), which incorporates (i) clustering for grouping submodels as well as (ii) statistical significance

Giuseppe Ortolano, Ivano Ruo Berchera, Enrico Predazzi

In this work quantum metrology techniques are applied to the imaging of objects with a non-uniform refractive spatial profile. A sensible improvement on the classical accuracy is shown to be found when the &#34;Twin Beam State&#34; (TWB) is used. In particular exploiting the multimode spatial correlation, naturally produced in the Parametric Down Conversion

Mohammed Ali Saif

We study the phase transition from the persistence phase to the extinction phase for the SIRS (susceptible/ infected/ refractory/ susceptible) model of diseases spreading on small world network. We show the effects of all the parameters associated with this model on small world network and we create the full phase space. The results we obtained are consisten

Guang-Wei Li, Ian D. Howarth

The ON stars are a rare subtype of O stars, of uncertain origin. We report two new, rapidly-rotating ON stars found in data acquired with the Large Sky Area Multi-Object Fiber Spectroscopic Telescope, LAMOST. LS I +61 28 is an ON8.5 Vn dwarf with a projected equatorial rotational velocity of $v_{\textrm e}\sin{i} \simeq 298$ km$\;$s$^{-1}$, while HDE 236672

Mohammed Ali Saif

We study the phase transition from the persistence phase to the extinction phase for the SIRS (susceptible/ infected/ refractory/ susceptible) model of diseases spreading on random networks. By studying temporal evolution and synchronization parameter of this model on random networks, we find that, this model on random networks, shows a synchronization phase

Sadik Bessou

In this thesis, we address several important issues concerning the morphological analysis of Arabic language applied to textual data and machine translation. First, we provided an overview on machine translation, its history and its development, then we exposed human translation techniques for eventual inspiration in machine translation, and we exposed lingu

Tian Xu, Ziniu Li, Yang Yu

Imitation learning trains a policy from expert demonstrations. Imitation learning approaches have been designed from various principles, such as behavioral cloning via supervised learning, apprenticeship learning via inverse reinforcement learning, and GAIL via generative adversarial learning. In this paper, we propose a framework to analyze the theoretical

Yufei Wang, Lei Su, Shuai Wang, Limin Hua

We report on the experimental observation of period-doubling bifurcation of dissipative-soliton-resonance (DSR) pulses in a fiber laser passively mode-locked by using the nonlinear optical loop mirror. Increasing the pump power of the fiber laser, we show that temporally a stable, uniform DSR pulse train could be transformed into a period-doubling state, exh

Sören Bartels, Philipp Reiter

Aiming at simulating elastic rods, we discretize a rod model based on a general theory of hyperelasticity for inextensible and unshearable rods. After reviewing this model and discussing topological effects of periodic rods, we prove convergence of the discretized functionals and stability of a corresponding discrete flow. Our experiments numerically confirm

Helen L . Miller, Sonia Contera, Adam J. M. Wollman, Adam Hirst

Intercalation of drug molecules into synthetic DNA nanostructures formed through self-assembled origami has been postulated as a valuable future method for targeted drug delivery. This is due to the excellent biocompatibility of synthetic DNA nanostructures, and high potential for flexible programmability including facile drug release into or near to target

Hai-Peng Ren, Kun Tian, Chao Bai

Secure communication using hyperchaos has a better potential performance, but hyperchaotic impulse circuits synchronization is a challenging task. In this paper, an impulse control method is proposed for the synchronization of two hyperchaotic Chen circuits. The sufficient conditions for the synchronization of hyperchaotic systems using the impulse control a

Limei Cheng, Guo-Wei Wei, Tarek Leil

Hemostasis and thrombosis are often thought as two sides of the same clotting mechanism whereas hemostasis is a natural protective mechanism to prevent bleeding and thrombosis is a blood clot abnormally formulated inside a blood vessel, blocking the normal blood flow. The evidence to date suggests that at least arterial thrombosis results from the same criti

Jindan Xu, Wei Xu, Derrick Wing Kwan Ng, A. Lee Swindlehurst

In this paper, we investigate secure communication over sparse millimeter-wave (mm-Wave) massive multiple-input multiple-output (MIMO) channels by exploiting the spatial sparsity of legitimate user&#39;s channel. We propose a secure communication scheme in which information data is precoded onto dominant angle components of the sparse channel through a limit

Mahdi Ahmadi, Alexandre Popier, Ali Devin Sezer

We consider a class of Backward Stochastic Differential Equations with superlinear driver process $f$ adapted to a filtration supporting at least a $d$ dimensional Brownian motion and a Poisson random measure on ${\mathbb R}^m- \{0\}.$ We consider the following class of terminal conditions $ξ_1 = \infty \cdot 1_{\{τ_1 \le T\}}$ where $τ_1$ is any stopping ti

Anshuman Chhabra, Abhishek Roy, Prasant Mohapatra

Clustering algorithms are used in a large number of applications and play an important role in modern machine learning-- yet, adversarial attacks on clustering algorithms seem to be broadly overlooked unlike supervised learning. In this paper, we seek to bridge this gap by proposing a black-box adversarial attack for clustering models for linearly separable

Pengyu Gao, Siyu Xia, Joseph Robinson, Junkang Zhang

Visual kinship recognition aims to identify blood relatives from facial images. Its practical application-- like in law-enforcement, video surveillance, automatic family album management, and more-- has motivated many researchers to put forth effort on the topic as of recent. In this paper, we focus on a new view of visual kinship technology: kin-based face

Jingjing Xu, Xu Sun, Zhiyuan Zhang, Guangxiang Zhao

Layer normalization (LayerNorm) is a technique to normalize the distributions of intermediate layers. It enables smoother gradients, faster training, and better generalization accuracy. However, it is still unclear where the effectiveness stems from. In this paper, our main contribution is to take a step further in understanding LayerNorm. Many of previous s

Damian Berger, Emanuele Varriale, Laurens Michiels van Kessenich, Hans J. Herrmann

Stroke is one of the main causes of human disabilities. Experimental observations indicate that several mechanisms are activated during the recovery of functional activity after a stroke. Here we unveil how the brain recovers by explaining the role played by three mechanisms: Plastic adaptation, hyperexcitability and synaptogenesis. We consider two different

Sergey Finashin, Viatcheslav Kharlamov

We suggest a short proof of O.Benoist and O.Wittenberg theorem (arXiv:1907.10859) which states that for each real non-singular cubic hypersurface $X$ of dimension $\ge 2$ the real lines on $X$ generate the whole group $H_1(X(\Bbb R);\Bbb Z/2)$.

Shuai Li, Rui Yu, Jin-Hua Gao, X. C. Xie

Hydrogen adatoms induced magnetic moment on graphene has been observed in atomic scale in a recent experiment [Gonzalez-Herrero et al., Science 352, 437 (2016)]. Here, we demonstrate that all the experimental phenomena can be simply and well described by an equivalent Anderson impurity model, where the electronic correlations on both carbon and hydrogen atom