September 2019 arXiv papers — page 20

Christina Niklaus, Andre Freitas, Siegfried Handschuh

We compiled a new sentence splitting corpus that is composed of 203K pairs of aligned complex source and simplified target sentences. Contrary to previously proposed text simplification corpora, which contain only a small number of split examples, we present a dataset where each input sentence is broken down into a set of minimal propositions, i.e. a sequenc

Takashi Kitazawa

This paper deals with a study of the rational elliptic surfaces whose $J$-invariant functions are of degree one. Almost all of these elliptic surfaces have four singular fibers, while the remaining surfaces have only three singular fibers. The moduli space of these elliptic surfaces is canonically isomorphic to the projective line by taking the $J$-values fo

Milan Kapolka, Enric Pardo, Francesco Grilli, Algirdas Baskys

Stacks of superconducting tapes can trap much higher magnetic fields than conventional magnets. This makes them very promising for motors and generators. However, ripple magnetic fields in these machines present a cross-field component that demagnetizes the stacks. At present, there is no quantitative agreement between measurements and modeling of cross-fiel

C. X. Zhang, M. A. Zubkov

Bloch theorem in ordinary quantum mechanics means the absence of the total electric current in equilibrium. In the present paper we analyze the possibility that this theorem remains valid within quantum field theory relevant for the description of both high energy physics and condensed matter physics phenomena. First of all, we prove that the total electric

Oleksandr Shchur, Marin Biloš, Stephan Günnemann

Temporal point processes are the dominant paradigm for modeling sequences of events happening at irregular intervals. The standard way of learning in such models is by estimating the conditional intensity function. However, parameterizing the intensity function usually incurs several trade-offs. We show how to overcome the limitations of intensity-based appr

Atsushi Hariki, Tatsuya Yamaguchi, Mathias Winder, Jan Kuneš

We study structurally-triggered metal-insulator transition in CaCu$_3$Fe$_4$O$_{12}$ by means of local density approximation (LDA) +$U$ and LDA+dynamical mean-field theory (DMFT). The ferrimagnetic insulating phase is essentially the same within both approaches. While LDA+$U$ describes the metal-insulator transition as a Peierls-like instability driven by Fe

Bireswar Basu-Mallick, Madhurima Sinha

As is well known, energy levels appearing in the highly degenerate spectra of the $A_{N-1}$ type of Haldane-Shastry and Polychronakos spin chains can be classified through the motifs, which are characterized by some sequences of the binary digits like `0' and `1'. In a similar way, at present we classify all energy levels appearing in the spectra of the $BC_

Zhao Wang

A gear effect is demonstrated at parallel and cross junctions between boron nitride nanotubes (BNNTs) via atomistic simulations. The atoms of neighboring BNNTs are meshed together at the junctions like gear teeth through long-range non-covalent interaction, which are shown to be able to transmit motion and power. The sliding motion of a BNNT can be spontaneo

Allan Gerrard, Vidas Regelskis

We present a nested algebraic Bethe ansatz for one-dimensional open so(2n)- and sp(2n)-symmetric spin chains with diagonal boundary conditions and described by the extended twisted Yangian. We use a generalization of the Bethe ansatz introduced by De Vega and Karowski which allows us to relate the spectral problem of a so(2n)- or sp(2n)-symmetric open spin c

Mingzhu Shen, Xianglong Liu, Ruihao Gong, Kai Han

Binary neural networks have attracted numerous attention in recent years. However, mainly due to the information loss stemming from the biased binarization, how to preserve the accuracy of networks still remains a critical issue. In this paper, we attempt to maintain the information propagated in the forward process and propose a Balanced Binary Neural Netwo

Zhao Wang

I demonstrate a directional motion-transmission behavior of aligned carbon nanotubes (CNTs) using atomistic simulations. The network of overlapping $\pi$ orbitals at the interface act as gear teeth to translate the sliding motion of a CNT into a rotating motion of the adjacent CNT, or \textit{viceversa}. The efficiency of this orthogonal motion transmission

Nirbhay Kumar Behera, Ranjit Kumar Nayak, Sadhana Dash

The multiplicity percentile dependence of cumulants, of net-proton number distributions in Au$-$Au collisions at $\sqrt{s_{NN}} = $ 200 GeV and Pb$-$Pb collisions at $\sqrt{s_{NN}} = $ 2.76 TeV has been investigated using the Angantyr model (the heavy-ion extension of the \textsc{Pythia 8} model). The effects of finite transverse momentum ($p_{\mathrm T}$) a

Alan Riva Palacio, Fabrizio Leisen

L\'evy copulas are an important tool which can be used to build dependent L\'evy processes. In a classical setting, they have been used to model financial applications. In a Bayesian framework they have been employed to introduce dependent nonparametric priors which allow to model heterogeneous data. This paper focuses on introducing a new class of L\'evy co

Jianhang Zhou, Shaoning Zeng, Bob Zhang

The two-stage strategy has been widely used in image classification. However, these methods barely take the classification criteria of the first stage into consideration in the second prediction stage. In this paper, we propose a novel two-stage representation method (TSR), and convert it to a Single-Teacher Single-Student (STSS) problem in our two-stage ima

Valentina Candiani, Jérémi Dardé, Henrik Garde, Nuutti Hyvönen

The monotonicity-based approach has become one of the fundamental methods for reconstructing inclusions in the inverse problem of electrical impedance tomography. Thus far the method has not been proven to be able to handle extreme inclusions that correspond to some parts of the studied domain becoming either perfectly conducting or perfectly insulating. The

Attila Szolnoki, Xiaojie Chen

According to the standard imitation protocol, a less successful player adopts the strategy of the more successful one faithfully for future success. This is the cornerstone of evolutionary game theory that explores the vitality of competing strategies in different social dilemma situations. In our present work we explore the possible consequences of two slig

Avraam Chatzimichailidis, Franz-Josef Pfreundt, Nicolas R. Gauger, Janis Keuper

Current training methods for deep neural networks boil down to very high dimensional and non-convex optimization problems which are usually solved by a wide range of stochastic gradient descent methods. While these approaches tend to work in practice, there are still many gaps in the theoretical understanding of key aspects like convergence and generalizatio

Nikolas P. Breuckmann, Benedikt Placke, Ananda Roy

The Ising model exhibits qualitatively different properties in hyperbolic space in comparison to its flat space counterpart. Due to the negative curvature, a finite fraction of the total number of spins reside at the boundary of a volume in hyperbolic space. As a result, boundary conditions play an important role even when taking the thermodynamic limit. We

Luca Scarpa

We prove existence of martingale solutions for the stochastic Cahn-Hilliard equation with degenerate mobility and multiplicative Wiener noise. The potential is allowed to be of logarithmic or double-obstacle type. By extending to the stochastic framework a regularization procedure introduced by C. M. Elliott and H. Garcke in the deterministic setting, we sho

Blai Bonet, Hector Geffner

Belief tracking is a basic problem in planning with sensing. While the problem is intractable, it has been recently shown that for both deterministic and non-deterministic systems expressed in compact form, it can be done in time and space that are exponential in the problem width. The width measures the maximum number of state variables that are all relevan

Thomas Botzung, David Hagenmüller, Guido Masella, Jérôme Dubail

We investigate the ground state properties of one-dimensional hard-core bosons interacting via a variable long-range potential using the density matrix renormalization group. We demonstrate that restoring energy extensivity in the system, which is done by rescaling the interaction potential with a suitable size-dependent factor known as Kac's prescription, h

Blai Bonet, Hector Geffner

In the presence of non-admissible heuristics, A* and other best-first algorithms can be converted into anytime optimal algorithms over OR graphs, by simply continuing the search after the first solution is found. The same trick, however, does not work for best-first algorithms over AND/OR graphs, that must be able to expand leaf nodes of the explicit graph t

B. I. Ermolaev, S. I. Troyan

We demonstrate how to calculate perturbative components of the structure functions F_1 (for unpolarized DIS) and g_1 (spin-dependent DIS) in Double-Logarithmic Approximation, studying separately the cases of fixed and running QCD coupling. We show that as long as only ladder graphs are accounted for (throughout the talk we use the Feynman gauge for virtual g

Kaleb Alway, Eric Blais, Semih Salihoglu

Recent beyond worst-case optimal join algorithms Minesweeper and its generalization Tetris have brought the theory of indexing and join processing together by developing a geometric framework for joins. These algorithms take as input an index $\mathcal{B}$, referred to as a box cover, that stores output gaps that can be inferred from traditional indexes, suc

Jonathan Vestin, Andreas Kassler, Deval Bhamare, Karl-Johan Grinnemo

In-Band Network Telemetry (INT) is a novel framework for collecting telemetry items and switch internal state information from the data plane at line rate. With the support of programmable data planes and programming language P4, switches parse telemetry instruction headers and determine which telemetry items to attach using custom metadata. At the network e

Mohamed Saïdi, Michael Tyler

We investigate the birational section conjecture for curves over function fields of characteristic zero and prove that the conjecture holds over finitely generated fields over Q if it holds over number fields.

Seyedsaman Emami, Gonzalo Martínez-Muñoz

This paper presents a novel technique based on gradient boosting to train the final layers of a neural network (NN). Gradient boosting is an additive expansion algorithm in which a series of models are trained sequentially to approximate a given function. A neural network can also be seen as an additive expansion where the scalar product of the responses of

Yihao Yin

This paper is a supplement to and extension of arXiv:1903.01399. In the internal twistor space of the 4D Vasiliev's higher-spin gravity, we study the star-product eigenfunctions of number operators with generic complex eigenvalues. In particular, we focus on a set of eigenfunctions represented by formulas with generalized Laguerre functions. This set of eige

Stefano Almi

This work is devoted to the analysis of convergence of an alternate (staggered) minimization algorithm in the framework of phase field models of fracture. The energy of the system is characterized by a nonlinear splitting of tensile and compressive strains, featuring non-interpenetration of the fracture lips. The alternating scheme is coupled with an $L^{2}$

Jinyul Hu, Juho Hong, KwangHoon Kim, Seonghoon Jung

PAL-XFEL (Pohang Accelerator Laboratory X-ray Free Electron Laser) started RF conditioning in October 2015 and has been operating reliably for ~ 4 years. The machine's LLRF and SSA systems contributed to the stable operation of PAL-XFEL with over 99% availability. The LLRF and SSA systems showed some problems in rare cases. The delay caused by the problem is

Hossein Noorazar, Kevin R. Vixie, Arghavan Talebanpour, Yunfeng Hu

In this age of Facebook, Instagram and Twitter, there is rapidly growing interest in understanding network-enabled opinion dynamics in large groups of autonomous agents. The phenomena of opinion polarization, the spread of propaganda and fake news, and the manipulation of sentiment are of interest to large numbers of organizations and people, some of whom ar

Zhimin Hu, Gang Xiong, Xiang Gao, Nobuyuki Nakamura

We present the observations of x-rays emitted from the $1s2s^{2}2p_{1/2}2p_{3/2}$ inner shell excited state of B-like W and Bi ions. The relative transition rates are obtained for two dominant radiative transitions to $1s^{2}2s^{2}2p_{1/2}$ and $1s^{2}2s^{2}2p_{3/2}$. The experimental results and the comparison with rigorous relativistic calculations show th

Jun Quan, Deyi Xiong, Bonnie Webber, Changjian Hu

Ellipsis and co-reference are common and ubiquitous especially in multi-turn dialogues. In this paper, we treat the resolution of ellipsis and co-reference in dialogue as a problem of generating omitted or referred expressions from the dialogue context. We therefore propose a unified end-to-end Generative Ellipsis and CO-reference Resolution model (GECOR) in

L. Coviello, M. Cristoforetti, G. Jurman, C. Furlanello

Digital technologies ignited a revolution in the agrifood domain known as precision agriculture: a main question for enabling precision agriculture at scale is if accurate product quality control can be made available at minimal cost, leveraging existing technologies and agronomists' skills. As a contribution along this direction we demonstrate a tool for ac

Zh. Devizorova, A. Buzdin

Recently the evidence of the helical magnetic structure modulated along $c$-axis with the period of four lattice parameters was obtained in easy $ab$ plane ferromagnetic superconductor EuRbFe$_4$As$_4$ [K. Iida et al., Phys. Rev. B 100, 014506 (2019)]. We argue that such structure may appear due to the presence of superconductivity. In spite of the very smal

Prabodh Shukla

We examine persistence in one dimensional Ising model under zero temperature Glauber dynamics for random initial states with unequal fraction of up and down spins. We find the persistence exponent varies continuously with the fraction of up spins in the initial state. Apparently this feature has been overlooked in the studies so far.

Hongliang Dai, Donghong Du, Xin Li, Yangqiu Song

Fine-grained entity typing is a challenging problem since it usually involves a relatively large tag set and may require to understand the context of the entity mention. In this paper, we use entity linking to help with the fine-grained entity type classification process. We propose a deep neural model that makes predictions based on both the context and the

K. Aoyama, H. Kawamura

We have theoretically investigated transport properties of the classical Heisenberg antiferromagnet on the triangular lattice in which a binding-unbinding topological transition of $\mathbb{Z}_2$ vortices is predicted to occur at a finite temperature $T_v$. It is shown by means of the hybrid Monte-Carlo and spin-dynamics simulations that the longitudinal spi

Kolyan Ray, Botond Szabo

Bayesian approaches have become increasingly popular in causal inference problems due to their conceptual simplicity, excellent performance and in-built uncertainty quantification ('posterior credible sets'). We investigate Bayesian inference for average treatment effects from observational data, which is a challenging problem due to the missing counterfactu

Yaofeng Desmond Zhong, Biswadip Dey, Amit Chakraborty

In this paper, we introduce Symplectic ODE-Net (SymODEN), a deep learning framework which can infer the dynamics of a physical system, given by an ordinary differential equation (ODE), from observed state trajectories. To achieve better generalization with fewer training samples, SymODEN incorporates appropriate inductive bias by designing the associated com

Deb Kumar Giri, Rama Rawat

Let $\Gamma$ be the hyperbola $\{(x,y)\in\mathbb R^2 : xy=1\}$ and $\Lambda_\beta$ be the lattice-cross defined by $\Lambda_\beta=\left(\mathbb Z\times\{0\}\right)\cup\left(\{0\}\times\beta\mathbb Z\right)$ in $\mathbb R^2,$ where $\beta$ is a positive real. A result of Hedenmalm and Montes-Rodr\'iguez says that $\left(\Gamma,\Lambda_\beta\right)$ is a Heise

Bryce Kerr

In this paper we obtain some new estimates for the number of large values of Dirichlet polynomials. Our results imply new zero density estimates for the Riemann zeta function which give a small improvement on results of Bourgain and Jutila.

Christopher M. Graney

This paper consists of a translation of Andre Tacquet's discussion of the question of sizes of stars in a heliocentric universe, as published in his posthumous Opera Mathematica of 1668, along with introductory material and analysis. While Robert Hooke mentions Tacquet as one of the "great Anti-copernicans", who argued the question of star sizes against the

Giovanni Mellace, Alessandra Pasquini

The synthetic control method (SCM) allows estimating the causal effect of an intervention in settings where panel data on a small number of treated and control units are available. We show that the existing SCM, as well as its extensions, can be easily modified to estimate how much of the ``total'' effect goes through observed causal channels. Our new mediat

Wojciech Samek, Klaus-Robert Müller

In recent years, machine learning (ML) has become a key enabling technology for the sciences and industry. Especially through improvements in methodology, the availability of large databases and increased computational power, today's ML algorithms are able to achieve excellent performance (at times even exceeding the human level) on an increasing number of c

Andreas Zacchi, Juergen Schaffner-Bielich

We study the impact of the fermion vacuum term in the SU(3) quark meson model on the equation of state and determine the vacuum parameters for various sigma meson masses. We examine its influence on the equation of state and on the resulting mass radius relations for compact stars. The tidal deformability $\Lambda$ of the stars is studied and compared to the

Jiaming Lu, Lei Zhao, Kairen Chen, Peipei Deng

Small animal Positron Emission Tomography (PET) is dedicated to small animal imaging, which requires high position and energy precision, as well as good flexibility and efficiency of the electronics. This paper presents the design of a digital signal processing logic for a marmoset brain PET system based on LYSO crystal arrays, SiPMs, and the resistive netwo

Hengxing Pan, Danail Obreschkow, Cullan Howlett, Claudia del P. Lagos

We model the large-scale linear galaxy bias $b_g(x,z)$ as a function of redshift $z$ and observed absolute magnitude threshold $x$ for broadband continuum emission from the far infrared to ultra-violet, as well as for prominent emission lines, such as the H$\alpha$, H$\beta$, Lya and [OII] lines. The modelling relies on the semi-analytic galaxy formation mod

A Núñez-Cascajero, R. Blasco, S Valdueza-Felip, D. Montero

High-quality Al0.37In0.63N layers have been grown by reactive radio-frequency (RF) sputtering on sapphire, glass and Si (111) at low substrate temperature (from room temperature to 300{\deg}C). Their structural, chemical and optical properties are investigated as a function of the growth temperature and type of substrate. X-ray diffraction measurements revea

Ronen Eldan, Renan Gross

We develop a new technique for proving concentration inequalities which relate between the variance and influences of Boolean functions. Using this technique, we 1. Settle a conjecture of Talagrand [Tal97] proving that $$\int_{\left\{ -1,1\right\} ^{n}}\sqrt{h_{f}\left(x\right)}d\mu\geq C\cdot\mathrm{var}\left(f\right)\cdot\left(\log\left(\frac{1}{\sum\mathr

Genqian Liu

For a compact, connected, oriented Riemannian $3$-manifold $(M, g)$ with smooth boundary $\partial M$, we explicitly give a local representation and a full symbol expression for the electromagnetic Dirichlet-to-Neumann map by factorizing Maxwell's equations and using an isometric transform. We prove that one can reconstruct a compact, connected, real-analyti

Jan Deriu, Mark Cieliebak

We present "AutoJudge", an automated evaluation method for conversational dialogue systems. The method works by first generating dialogues based on self-talk, i.e. dialogue systems talking to itself. Then, it uses human ratings on these dialogues to train an automated judgement model. Our experiments show that AutoJudge correlates well with the human ratings

Gebrehiwet Gebrekrstos Lema, Dawit Hadush Hailu

Wireless communication requires an effective antenna synthesis that characterizes adequate infrastructures to provide the broader bandwidth and reduced interference. Antenna design with minimal signal degradation, optimal gain directive main beam to sustain minimal loss has been a hot issue among many communication engineers for several years. In this paper,

Max Horn, Michael Moor, Christian Bock, Bastian Rieck

Despite the eminent successes of deep neural networks, many architectures are often hard to transfer to irregularly-sampled and asynchronous time series that commonly occur in real-world datasets, especially in healthcare applications. This paper proposes a novel approach for classifying irregularly-sampled time series with unaligned measurements, focusing o

Qi Deng

The AIBC is an Artificial Intelligence and blockchain technology based large-scale decentralized ecosystem that allows system-wide low-cost sharing of computing and storage resources. The AIBC consists of four layers: a fundamental layer, a resource layer, an application layer, and an ecosystem layer. The AIBC implements a two-consensus scheme to enforce upp

Blai Bonet, Hector Geffner

The problem of belief tracking in the presence of stochastic actions and observations is pervasive and yet computationally intractable. In this work we show however that probabilistic beliefs can be maintained in factored form exactly and efficiently across a number of causally closed beams, when the state variables that appear in more than one beam obey a f

K. Castillo, M. N. de Jesus, J. Petronilho

In a companion paper [On semiclassical orthogonal polynomials via polynomial mappings, J. Math. Anal. Appl. (2017)] we proved that the semiclassical class of orthogonal polynomials is stable under polynomial transformations. In this work we use this fact to derive in an unified way old and new properties concerning the sieved ultraspherical polynomials of th

J. Casal, M. Gómez-Ramos, A. M. Moro, A. Corsi

The Transfer to the Continuum method has been applied to describe the $^{11}\text{Li}(p,pn)$ and $^{14}\text{Be}(p,pn)$ reactions in inverse kinematics, using structure overlaps computed within a full three-body model for the projectile. Calculations agree with the available experimental data on the unbound $^{10}$Li and $^{13}$Be nuclei.

Enrico Calzavarini, Linfeng Jiang, Chao Sun

The orientational dynamics of inertialess anisotropic particles transported by two-dimensional convective turbulent flows display a coexistence of regular and chaotic features. We numerically demonstrate that very elongated particles (rods) align preferentially with the direction of the fluid flow, i.e., horizontally close to the isothermal walls and dominan

X. Marcano, A. Abada, C. Hati, A. M. Teixeira

Several beyond the Standard Model scenarios introduce new heavy neutrinos, whose Dirac or Majorana nature could be tested by comparing the rates of lepton number violating and lepton number conserving processes: a Dirac fermion induces only the latter, while a Majorana one predicts the same rate for both of them. Nevertheless, in the presence of more than on

Naohiko Kasuya

Ahern and Rudin have given an explicit construction of a totally real embedding of $S^3$ in $\mathbb{C}^3$. As a generalization of their example, we give an explicit example of a CR regular embedding of $S^{4n-1}$ in $\mathbb{C}^{2n+1}$. Consequently, we show that the odd dimensional sphere $S^{2m-1}$ with $m>1$ admits a CR regular embedding in $\mathbb{C}^{

Alessandro Ferreri, Michelangelo Domina, Lucia Rizzuto, Roberto Passante

We investigate the spontaneous emission of one atom placed near an oscillating reflecting plate. We consider the atom modeled as a two-level system, interacting with the quantum electromagnetic field in the vacuum state, in the presence of the oscillating mirror. We suppose that the plate oscillates adiabatically, so that the time-dependence of the interacti

Erik J Bekkers

Group convolutional neural networks (G-CNNs) can be used to improve classical CNNs by equipping them with the geometric structure of groups. Central in the success of G-CNNs is the lifting of feature maps to higher dimensional disentangled representations, in which data characteristics are effectively learned, geometric data-augmentations are made obsolete,

Peter R. Wiecha, Otto L. Muskens

Deep artificial neural networks are powerful tools with many possible applications in nanophotonics. Here, we demonstrate how a deep neural network can be used as a fast, general purpose predictor of the full near-field and far-field response of plasmonic and dielectric nanostructures. A trained neural network is shown to infer the internal fields of arbitra

Norman Do, Jian He, Daniel V. Mathews

Given a surface with boundary and some points on its boundary, a polygon diagram is a way to connect those points as vertices of non-overlapping polygons on the surface. Such polygon diagrams represent non-crossing permutations on a surface with any genus and number of boundary components. If only bigons are allowed, then it becomes an arc diagram. The count

Yupeng Jiang, Minchen Li, Chenfanfu Jiang, Fernando Alonso-marroquin

Capturing the interaction between objects that have an extreme difference in Young s modulus or geometrical scale is a highly challenging topic for numerical simulation. One of the fundamental questions is how to build an accurate multi-scale method with optimal computational efficiency. In this work, we develop a material-point-spheropolygon discrete elemen

Alexis Stoven-Dubois, Janos Botzheim, Naoyuki Kubota

Interaction with a robot partner requires many elements, including not only speech but also embodiment. Thus, gestural and facial expressions are important for communication. Furthermore, understanding human movements is essential for safe and natural interchange. This paper proposes an interactive fuzzy emotional model for the robot partner's gesture expres

Bao-Xi Sun

The collective excitation of nuclear matter is analyzed in a bosonized Landau Fermi liquid model. When the nonlinear self-interacting terms of scalar mesons are included in Walecka model, the collective excitation energy of nuclear matter can be obtained self-consistently, and the calculation results are consistent with the corresponding experimental data of

Bartosz Sobolewski

We show that polynomials associated with automatic sequences satisfy a certain recurrence relation when evaluated at a root of unity, which generalizes a result of Brillhart, Lomont and Morton on the Rudin--Shapiro polynomials. We study the minimal order of such a relation and the integrality of its coefficients.

Daniel Gissin, Shai Shalev-Shwartz, Amit Daniely

A leading hypothesis for the surprising generalization of neural networks is that the dynamics of gradient descent bias the model towards simple solutions, by searching through the solution space in an incremental order of complexity. We formally define the notion of incremental learning dynamics and derive the conditions on depth and initialization for whic

Luca Calderaro, Andrea Stanco, Costantino Agnesi, Marco Avesani

We propose Qubit4Sync, a synchronization method for Quantum Key Distribution (QKD) setups, based on the same qubits exchanged during the protocol and without requiring additional hardware other than the one necessary to prepare and measure the quantum states. Our approach introduces a new cross-correlation algorithm achieving the lowest computational complex

Simon Bang Kristensen, Bo Martin Bibby

Bivariate observations of binary and ordinal data arise frequently and require a bivariate modelling approach in cases where one is interested in aspects of the marginal distributions as separate outcomes along with the association between the two. We consider methods for constructing such bivariate models with logistic marginals and propose a model based on

I. A. Domagalska, M. W. Jarosik, A. P. Durajski, J. K. Kalaga

We analysed the dynamics of the positively charged ions of diatomic molecules (${\rm X_{2}^{+}}$ and ${\rm XY^{+}}$), in which the bond is realised by the single electron. We assumed that the atomic cores separated by the distance $R$ were subjected to the external excitation of the harmonic type with the amplitude $A$ and frequency $\Omega$. We found the gr

Max Argus, Cornelia Schaefer-Prokop, David A. Lynch, Bram van Ginneken

Chronic Obstructive Pulmonary Disease (COPD) is a leading cause of morbidity and mortality. While COPD diagnosis is based on lung function tests, early stages and progression of different aspects of the disease can be visible and quantitatively assessed on computed tomography (CT) scans. Many studies have been published that quantify imaging biomarkers relat

Cyril Pitrou, Alain Coc, Jean-Philippe Uzan, Elisabeth Vangioni

Precision on primordial abundances, deduced from observations, have now reached the percent level for 4He and deuterium. Precision on big bang nucleosynthesis (BBN) predictions should, hence, reach the same level. The uncertainty on the 4He mass fraction is strongly affected by theoretical uncertainties on the weak reaction rates that interconvert neutrons w

Salvatore Federico, Giorgio Ferrari, Patrick Schuhmann

In this paper we study a Markovian two-dimensional bounded-variation stochastic control problem whose state process consists of a diffusive mean-reverting component and of a purely controlled one. The main problem's characteristic lies in the interaction of the two components of the state process: the mean-reversion level of the diffusive component is an aff

Kexin Xie, Myron Hlynka

The choice of forward and reverse parking in a parking lot is studied as a stochastic process. An $M/M/c/c$ queueing system is used as an initial framework. We use Monte Carlo simulation to get the relationship between vehicle orientation and vehicle entry and exit rates, as well as the most likely parking states at each specific rate. We view the change in

Sándor Kisfaludi-Bak, Dániel Marx, Tom C. van der Zanden

Packing is a classical problem where one is given a set of subsets of Euclidean space called objects, and the goal is to find a maximum size subset of objects that are pairwise non-intersecting. The problem is also known as the Independent Set problem on the intersection graph defined by the objects. Although the problem is NP-complete, there are several sub

Parthajit Bhowal, Deiborlang Nongsiang, Rajat Kanti Nath

Let $G$ be a group and $Sol(G)=\{x \in G : \langle x,y \rangle \text{ is solvable for all } y \in G\}$. We associate a graph $\mathcal{NS}_G$ (called the non-solvable graph of $G$) with $G$ whose vertex set is $G \setminus Sol(G)$ and two distinct vertices are adjacent if they generate a non-solvable subgroup. In this paper we study many properties of $\math

Kirsi Tappura, Taneli Juntunen, Kaarle Jaakkola, Mikko Ruoho

A large-area thermoelectric generator (TEG) utilizing a folded thin-film concept is implemented and the performance evaluated for near room temperature applications having modest temperature gradients (< 50 K). The TEGs with the area of ~0.33 m^2 are shown capable of powering a wireless sensor node of multiple sensors suitable e.g. for monitoring environment

Surajit Mondal, Divya Oberoi, Angelos Vourlidas

Measuring the physical parameters of Coronal Mass Ejections (CMEs), particularly their entrained magnetic field, is crucial for understanding their physics and for assessing their geo-effectiveness. At the moment, only remote sensing techniques can probe these quantities in the corona, the region where CMEs form and acquire their defining characteristics. Ra

Otto Hauler, Frank Wackenhut, Lukas A. Jakob, Alexander Stuhl

In this work, we present a novel technique to directly measure the phase shift of the optical signal scattered by single plasmonic nanoparticles in a diffraction-limited laser focus. We accomplish this by equipping an inverted confocal microscope with a Michelson interferometer and scanning single nanoparticles through the focal volume while recording interf

Ajay Sharma, Dhiren Kumar Basnet

In this article, we introduce the concept of weakly $I$-clean ring, for any ideal $I$ of a ring $R$. We show that, for an ideal $I$ of a ring $R$, $R$ is uniquely weakly $I$-clean if and only if $R/I$ is semi boolean and idempotents can be lifted uniquely weakly modulo $I$ if and only if for each $a\in R$, there exists a central idempotent $e\in R$ such that

Qimai Li, Xiaotong Zhang, Han Liu, Quanyu Dai

Graph convolutional neural networks (GCN) have been the model of choice for graph representation learning, which is mainly due to the effective design of graph convolution that computes the representation of a node by aggregating those of its neighbors. However, existing GCN variants commonly use 1-D graph convolution that solely operates on the object link

Sidney Golstein, Trung-Hien Nguyen, François Horlin, Philippe De Doncker

A frequency domain (FD) time-reversal (TR) precoder is proposed to perform physical layer security (PLS) in single-input single-output (SISO) system using orthogonal frequency-division multiplexing (OFDM). To maximize the secrecy of the communication, the design of an artificial noise (AN) signal well-suited to the proposed FD TR-based OFDM SISO system is de

Jianqiang Wang, Hao Zhu, Zhan Ma, Tong Chen

This paper presents a novel end-to-end Learned Point Cloud Geometry Compression (a.k.a., Learned-PCGC) framework, to efficiently compress the point cloud geometry (PCG) using deep neural networks (DNN) based variational autoencoders (VAE). In our approach, PCG is first voxelized, scaled and partitioned into non-overlapped 3D cubes, which is then fed into sta

Daria Boratyn, Jarosław Flis, Wojciech Słomczyński, Dariusz Stolicki

On the basis of a formula for calculating seat shares and natural thresholds in multidistrict elections under the Jefferson-D'Hondt system and a probabilistic model of electoral behavior based on P\'{o}lya's urn model, we propose a new model of the relationship between the district magnitude and the number / effective number of relevant parties. We test that

Moustafa Ebada, Sebastian Cammerer, Ahmed Elkelesh, Stephan ten Brink

In this work, we introduce a deep learning-based polar code construction algorithm. The core idea is to represent the information/frozen bit indices of a polar code as a binary vector which can be interpreted as trainable weights of a neural network (NN). For this, we demonstrate how this binary vector can be relaxed to a soft-valued vector, facilitating the

Ivan P. Yamshchikov, Viacheslav Shibaev, Aleksander Nagaev, Jürgen Jost

This paper focuses on latent representations that could effectively decompose different aspects of textual information. Using a framework of style transfer for texts, we propose several empirical methods to assess information decomposition quality. We validate these methods with several state-of-the-art textual style transfer methods. Higher quality of infor

Thomas Probst, Danda Pani Paudel, Ajad Chhatkuli, Luc Van Gool

In this paper, we formulate a generic non-minimal solver using the existing tools of Polynomials Optimization Problems (POP) from computational algebraic geometry. The proposed method exploits the well known Shor's or Lasserre's relaxations, whose theoretical aspects are also discussed. Notably, we further exploit the POP formulation of non-minimal solver al

Kentaro Somiya

KAGRA is the first large-scale gravitational-wave detector with cryogenic test masses. Its target sensitivity is limited mostly by quantum noise in the observation frequency band owing to the remarkable reduction of thermal noise at cryogenic temperatures. It is thus essential to reduce quantum noise, and KAGRA is designed to implement two quantum noise redu

Mieczysław A. Kłopotek, Sławomir T. Wierzchoń

Valuation-Based~System can represent knowledge in different domains including probability theory, Dempster-Shafer theory and possibility theory. More recent studies show that the framework of VBS is also appropriate for representing and solving Bayesian decision problems and optimization problems. In this paper after introducing the valuation based system (V

Hong Liu, Mingsheng Long, Jianmin Wang, Michael I. Jordan

Deep neural networks trained on a wide range of datasets demonstrate impressive transferability. Deep features appear general in that they are applicable to many datasets and tasks. Such property is in prevalent use in real-world applications. A neural network pretrained on large datasets, such as ImageNet, can significantly boost generalization and accelera

Moustafa Ebada, Ahmed Elkelesh, Stephan ten Brink

Previous work showed that polar codes can be decoded using off-the-shelf LDPC decoders by imposing special constraints on the LDPC code structure, which, however, resulted in some performance degradation. In this paper we show that this loss can be mitigated; in particular, we demonstrate how the gap between LDPC-style decoding and Arikan's Belief Propagatio

Chao Li, Xi Zhang, QiZhi Zhao

In this paper, by using analytical methods we obtain a generalization of the famous Kodaira embedding theorem.

Ruoxi Yu, Samuel L. Charreyron, Quentin Boehler, Cameron Weibel

Electromagnetic Navigation Systems (eMNS) can be used to control a variety of multiscale devices within the human body for remote surgery. Accurate modeling of the magnetic fields generated by the electromagnets of an eMNS is crucial for the precise control of these devices. Existing methods assume a linear behavior of these systems, leading to significant m

Sadashige Matsuo, Kazuyuki Kuroyama, Shunsuke Yabunaka, Sascha R. Valentin

We investigate the full counting statistics (FCS) of spin-conserving and spin-flip charge transitions in Pauli-spin blockade regime of a GaAs double quantum dot. A theoretical model is proposed to evaluate all spin-conserving and spin-flip tunnel rates, and to demonstrate the fundamental relation between FCS and waiting time distribution. We observe the rema

Jaroslav Vrba, Ahmadjon Abdujabbarov, Arman Tursunov, Bobomurat Ahmedov

We study spherically symmetric magnetically charged generic black hole solutions of general relativity coupled to non-linear electrodynamics (NED). For characteristic values of the generic spacetime parameters we give the position of horizons in dependence on the charge parameter, demonstrating separation of the black hole and no-horizon solutions, and possi

Stefan Hougardy, Fabian Zaiser, Xianghui Zhong

The 2-Opt heuristic is one of the simplest algorithms for finding good solutions to the metric Traveling Salesman Problem. It is the key ingredient to the well-known Lin-Kernighan algorithm and often used in practice. So far, only upper and lower bounds on the approximation ratio of the 2-Opt heuristic for the metric TSP were known. We prove that for the met

Lars Kuger, Aleksandar Ichkov, Petri Mähönen, Ljiljana Simić

Exploiting multi-antenna technologies for robust beamsteering to overcome the effects of blockage and beam misalignment is the key to providing seamless multi-Gbps connectivity in millimeter-wave (mm-wave) networks. In this paper, we present the first large-scale outdoor mm-wave measurement study using a phased antenna array in a typical European town. We sy