February 2019 arXiv papers — page 8

David J. Wu

By introducing several improvements to the AlphaZero process and architecture, we greatly accelerate self-play learning in Go, achieving a 50x reduction in computation over comparable methods. Like AlphaZero and replications such as ELF OpenGo and Leela Zero, our bot KataGo only learns from neural-net-guided Monte Carlo tree search self-play. But whereas Alp

Dominique Malicet, Emmanuel Militon

Let K be a Cantor set embedded in the real line R. Following Funar and Neretin, we define the diffeomorphism group of K as the group of homeomorphisms of K which locally look like a diffeomorphism between two intervals of R. Higman-Thompson's groups Vn appear as subgroups of such groups. In this article, we prove some properties of this group. First, we

A. A. Alikhanov, A. M. Apekov, Z. A. Kokov, A. O. Belyaev

A new method for solving systems of linear algebraic equations of a special type arising in solving problems of image reconstruction has been proposed. This method, due to a certain symmetry of the matrix and the choice of the voxel numeration method for two-dimensional problems, allows us to divide the initial system of algebraic equations into two independ

Thomas Dreyfus, Charlotte Hardouin

In the present paper, we use difference Galois theory to study the nature of the generating function counting walks with small steps in the quarter plane. These series are trivariate formal power series $Q(x,y,t)$ that count the number of walks confined in the first quadrant of the plane with a fixed set of admissible steps, called the model of the walk. Whi

Subhajit Pal, Colin Benjamin

A popular evidence of the existence of Majorana bound states(MBS) is a quantized zero-bias conductance peak(ZBCP) which is robust to scattering by impurities, a consequence of its topological protection. In this work we examine the stability of this MBS induced ZBCP in a metal-superconductor junction in the vicinity of a spin flipper. We analytically calcula

Gérald Tenenbaum

Let $\{p_j(n)\}_{j=1}^{ω(n)}$ denote the increasing sequence of distinct prime factors of an integer $n$. We provide details for the proof of a statement of Erdős implying that, for any function $ξ(n)$ tending to infinity with $n$, we have $$f(n):=\max_{1\leqslant j<ω(n)}\log \Big({\log p_{j+1}(n)\over \log p_j(n)}\Big)=\log_3n+O(ξ(n))$$ for almost all integ

Yan Xiao, Yang-Hui He, Cyril Matti

We study the vacuum geometry prescribed by the gauge invariant operators of the MSSM via the Plethystic Programme. This is achieved by using several tricks to perform the highly computationally challenging Molien-Weyl integral, from which we extract the Hilbert series, encoding the invariants of the geometry at all degrees. The fully refined Hilbert series i

Anja Gemeinhardt, Miriam Martinsons, Michael Schmiedeberg

We explore the behavior of two-dimensional patchy colloidal particles with 8 or 10 symmetrically arranged patches by employing Monte-Carlo simulations. The particles interact according to an isotropic pair potential that possesses only one typical length. The patches lead to additional attractions that are anisotropic and depend on the relative orientation o

Tatjana Z. Mirkovic, Slobodan B. Trickovic, Miomir S. Stankovic

We have fundamentally corrected the proofs of the theorems from our paper [9] by giving an entirely different approach, using quite a simple method based on applications of some elementary inequalities, well-known Hölder&#39;s inequality, and the Gauchman $q$-restricted integral.

Alexandra Chronopoulou, Christos Baziotis, Alexandros Potamianos

A growing number of state-of-the-art transfer learning methods employ language models pretrained on large generic corpora. In this paper we present a conceptually simple and effective transfer learning approach that addresses the problem of catastrophic forgetting. Specifically, we combine the task-specific optimization function with an auxiliary language mo

Dmitri Burago, Dong Chen

We show that given any Liouville direction and flat Finsler torus, one can make a $C^{\infty}$-small perturbation on an arbitrarily small disc to get a nondense geodesic in the given direction.

François Le Maître

We pursue the study of $\mathrm L^1$ full groups of graphings and of the closures of their derived groups, which we call derived $\mathrm L^1$ full groups. Our main result shows that aperiodic probability measure-preserving actions of finitely generated groups have finite Rokhlin entropy if and only if their derived $\mathrm L^1$ full group has finite topolo

Margaret Hawton

We extend classical Maxwell field theory to a first quantized theory of the photon by deriving a conserved Lorentz four-current whose zero component is a positive definite number density. Fields are real and their positive (negative) frequency parts are interpreted as absorption (emission) of a positive energy photon. With invariant plane wave normalization,

Masahiko Ito, Masatoshi Noumi

We establish a determinant formula for the bilinear form associated with the elliptic hypergeometric integrals of type $BC_n$ by studying the structure of $q$-difference equations to be satisfied by them. The determinant formula is proved by combining the $q$-difference equations of the determinant and its asymptotic analysis along the singularities. The ell

Theodor Bjorkmo

We prove the existence of a general class of rapidly turning two-field inflationary attractors. By only requiring a large, slowly varying turn rate, we solve the system completely without specifying any metric or potential, and prove the linear stability of the solution. Several recently studied turning inflation models, including hyperinflation, side-tracke

Rohit Tripathy, Ilias Bilionis

A problem of considerable importance within the field of uncertainty quantification (UQ) is the development of efficient methods for the construction of accurate surrogate models. Such efforts are particularly important to applications constrained by high-dimensional uncertain parameter spaces. The difficulty of accurate surrogate modeling in such systems, i

Mor Geva, Eric Malmi, Idan Szpektor, Jonathan Berant

Sentence fusion is the task of joining several independent sentences into a single coherent text. Current datasets for sentence fusion are small and insufficient for training modern neural models. In this paper, we propose a method for automatically-generating fusion examples from raw text and present DiscoFuse, a large scale dataset for discourse-based sent

Hikaru Ueki, Ryota Tamura, Jun Goryo

We discuss the type of pairing in the hexagonal pnictide superconductor SrPtAs, taking into account its multiband structure. The topological chiral $d$-wave state with time-reversal-symmetry breaking has been anticipated from the spontaneous magnetization observed by the muon-spin-relaxation experiment. We point out in this paper that the recent experimental

Shinichi Deguchi, Kazuo Fujikawa

The monopole-like singularity of Berry&#39;s adiabatic phase in momentum space and associated anomalous Poisson brackets have been recently discussed in various fields. With the help of the results of an exactly solvable version of Berry&#39;s model, we show that Berry&#39;s phase does not lead to the deformation of the principle of quantum mechanics in the

K. Rama Koteswara Rao, Yihua Wang, Zingfu Zhang, Dieter Suter

Initializing quantum registers with high fidelity is a fundamental precondition for many applications like quantum information processing and sensing. The electronic and nuclear spins of a Nitrogen-Vacancy (NV) center in diamond form an interesting hybrid quantum register that can be initialized by a combination of laser, microwave, and radio-frequency pulse

Md Helal Ahmed, Jagmohan Tanti

The congruences for Jacobi sums of some lower orders has been treated by many authors in the literature. In this paper we establish the congruences for Jacobi sums of order 2l^2 with odd prime l. These congruences are useful to obtain algebraic and arithmetic characterizations for Jacobi sums of order 2l^2 .

Bernd Illing, Wulfram Gerstner, Johanni Brea

Training deep neural networks with the error backpropagation algorithm is considered implausible from a biological perspective. Numerous recent publications suggest elaborate models for biologically plausible variants of deep learning, typically defining success as reaching around 98% test accuracy on the MNIST data set. Here, we investigate how far we can g

Yoshinori Sakamoto

We consider the random-field O($N$) spin model with long-range exchange interactions which decay with distance $r$ between spins as $r^{-d-σ}$ and/or random fields which correlate with distance $r$ as $r^{-d+ρ}$, and reexamine the critical phenomena near the lower critical dimension by use of the perturbative functional renormalization group. We compute the

Daniel Severt, Ulf-G. Meißner, Jambul Gegelia

We re-analyze the flavor decomposition of the pion-nucleon $σ$-term in the framework of baryon chiral perturbation to fourth order. We employ a covariant and the heavy baryon framework including also the low-lying decuplet. Using only continuum data, we find a small strangeness content of the proton. The uncertainties are, however, large and might be overcom

Michalina Strzyz, David Vilares, Carlos Gómez-Rodríguez

We recast dependency parsing as a sequence labeling problem, exploring several encodings of dependency trees as labels. While dependency parsing by means of sequence labeling had been attempted in existing work, results suggested that the technique was impractical. We show instead that with a conventional BiLSTM-based model it is possible to obtain fast and

Pedro G. Ferreira

Cosmological observations are beginning to reach a level of precision that allow us to test some of the most fundamental assumptions in our working model of the Universe. One such an assumption is that gravity is governed by the General Theory of Relativity. In this review we discuss how one might go about extending General Relativity and how such extensions

Dharmesh Jain, Augniva Ray

We study 3d $\mathcal{N}=2$ Chern-Simons (CS) quiver theories on $S^3$ and $Σ_{\mathfrak{g}}\times S^1$. Using localization results, we examine their partition functions in the large rank limit and requiring the resulting matrix models to be local, find a large class of quiver theories that include quivers in one-to-one correspondence with the $\widehat{ADE}

Ludovic Berthier, Giulio Biroli, Patrick Charbonneau, Eric I. Corwin

One of the most remarkable predictions to emerge out of the exact infinite-dimensional solution of the glass problem is the Gardner transition. Although this transition was first theoretically proposed a generation ago for certain mean-field spin glass models, its materials relevance was only realized when a systematic effort to relate glass formation and ja

Börge Göbel, Jürgen Henk, Ingrid Mertig

When two magnetic skyrmions - whirl-like, topologically protected quasiparticles - form a bound pair, a biskyrmion state with a topological charge of $N_\mathrm{Sk}=\pm 2$ is constituted. Recently, especially the case of two partially overlapping skyrmions has brought about great research interest. Since for its formation the individual skyrmions need to pos

Fadhel Ayed, Marco Battiston, Federico Camerlenghi, Stefano Favaro

Feature allocation models generalize species sampling models by allowing every observation to belong to more than one species, now called features. Under the popular Bernoulli product model for feature allocation, given $n$ samples, we study the problem of estimating the missing mass $M_{n}$, namely the expected number hitherto unseen features that would be

Tom Broadhurst, Ivan de Martino, Hoang Nhan Luu, George F. Smoot

The large dark cores of common dwarf galaxies are unexplained by the standard heavy particle interpretation of dark matter. This puzzle is exacerbated by the discovery of a very large but barely visible, dark matter dominated galaxy Antlia II orbiting the Milky Way, uncovered by tracking star motions with the {\t Gaia} satellite. Although Antlia II has a low

Arslan Chaudhry, Marcus Rohrbach, Mohamed Elhoseiny, Thalaiyasingam Ajanthan

In continual learning (CL), an agent learns from a stream of tasks leveraging prior experience to transfer knowledge to future tasks. It is an ideal framework to decrease the amount of supervision in the existing learning algorithms. But for a successful knowledge transfer, the learner needs to remember how to perform previous tasks. One way to endow the lea

Andrew Everall, Payel Das

Selection functions are vital for understanding the observational biases of spectroscopic surveys. With the wide variety of multi-object spectrographs currently in operation and becoming available soon, we require easily generalisable methods for determining the selection functions of these surveys. Previous work, however, has largely been focused on generat

Christopher Daw, Martin Orr

The Zilber-Pink conjecture predicts that an algebraic curve in $\mathcal{A}_2$ has only finitely many intersections with the special curves, unless it is contained in a proper special subvariety. Under a large Galois orbits hypothesis, we prove the finiteness of the intersection with the special curves parametrising abelian surfaces isogenous to the product

Ruiying Geng, Binhua Li, Yongbin Li, Xiaodan Zhu

Text classification tends to struggle when data is deficient or when it needs to adapt to unseen classes. In such challenging scenarios, recent studies have used meta-learning to simulate the few-shot task, in which new queries are compared to a small support set at the sample-wise level. However, this sample-wise comparison may be severely disturbed by the

A K Dmitriev, A K Vershovskii

The methods for controlling spin states of negatively charged nitrogen-vacancy centers using a combination of microwave (MW) or radiofrequency (RF) excitation field for electron spin transitions and RF excitation field for nuclear spin transitions are most effective in strong magnetic fields where level anti-crossing (LAC) occurs. However, LAC in zero field

Kenji Harada, Naoki Kawashima

We investigate the informational aspect of (1+1)-dimensional directed percolation, a canonical model of a nonequilibrium continuous transition to a phase dominated by a single special state called the &#34;absorbing&#34; state. Using a tensor network scheme, we numerically calculate the time evolution of state probability distribution of directed percolation

Sangwoo S. Chung, Philippe Corboz

SU(N) symmetric fermions on a lattice, which can be realized in ultracold-atom-based quantum simulators, have very promising prospects for realizing exotic states of matter. Here we present the ground state phase diagram of the repulsive SU(3) Hubbard model on a honeycomb lattice at 1/3 filling obtained from infinite projected entangled pair states tensor ne

James Tyrrell, David J Smith, Rosemary J Dyson

The method of regularized Stokeslets, based on the divergence-free exact solution to the equations of highly viscous flow due to a spatially-smoothed concentrated force, is widely employed in biological fluid mechanics. Many problems of interest are axisymmetric, motivating the study of the azimuthally-integrated form of the Stokeslet which physically corres

Ki-Young Choi, Takeo Inami, Kenji Kadota, Inwoo Park

We investigate the detectability of axino-like particle, which is defined as a supersymmetric partner of axion-like particle and can be a good candidate for dark matter in our Universe. Especially, we consider the fixed target experiments to search for the light axino-like particle with a neutralino as the next-to-lightest supersymmetric particle. We calcula

Christian Pedersen, John Niven, Thomas Salez, Kari Dalnoki-Veress

An elastic sheet that deforms near a solid substrate in a viscous fluid is a situation relevant to various dynamical processes in biology, geophysics and engineering. Here, we study the relaxation dynamics of an elastic plate resting on a thin viscous film that is supported by a solid substrate. By combining scaling analysis, numerical simulations and experi

Johann Cigler

In this (mostly expository) paper I want to share some observations prompted by a class of matrices whose determinants are Catalan numbers. Considering different methods of proof we obtain some generalizations and q-analogues and connections with Hankel determinants. Finally we state some conjectures.

Mohamed El Amine Seddik, Mohamed Tamaazousti, John Lin

A common issue of deep neural networks-based methods for the problem of Single Image Super-Resolution (SISR), is the recovery of finer texture details when super-resolving at large upscaling factors. This issue is particularly related to the choice of the objective loss function. In particular, recent works proposed the use of a VGG loss which consists in mi

Pablo Villegas, Serena di Santo, Raffaella Burioni, Miguel A. Muñoz

Avalanches whose sizes and durations are distributed as power laws appear in many contexts. Here, we show that there is a hidden peril in thresholding continuous times series --either from empirical or synthetic data-- for the detection of avalanches. In particular, we consider two possible alternative definitions of avalanche size used e.g. in the empirical

Marco Pozzetta

Under suitable regularity assumptions the $p$-elastic energy of a planar set $E\subset\mathbb{R}^2$ is defined to be $\int_{\partial E} 1 + |k_{\partial E}|^p \,\, d\mathcal{H}^1,$ where $k_{\partial E}$ is the curvature of the boundary $\partial E$. In this work we use a varifold approach to investigate this energy, that can be well defined on varifolds wit

Xu Tan, Yi Ren, Di He, Tao Qin

Multilingual machine translation, which translates multiple languages with a single model, has attracted much attention due to its efficiency of offline training and online serving. However, traditional multilingual translation usually yields inferior accuracy compared with the counterpart using individual models for each language pair, due to language diver

Michele Allegra, Elena Facco, Francesco Denti, Alessandro Laio

One of the founding paradigms of machine learning is that a small number of variables is often sufficient to describe high-dimensional data. The minimum number of variables required is called the intrinsic dimension (ID) of the data. Contrary to common intuition, there are cases where the ID varies within the same data set. This fact has been highlighted in

Oleg Andreev

We use gauge/string duality to model a heavy quark-antiquark pair in a color singlet moving through a thermal plasma. In particular, we explore the effect of velocity on the string tension and Debye screening mass. Then we apply the results to the analysis of heavy quarkonium bound states. With some assumptions, we estimate the characteristic size of quarkon

Dominic Enders, Tatiana Shulman

We investigate which relations for families of commuting matrices are stable under small perturbations, or in other words, which commutative $C^*$-algebras $C(X)$ are matricially semiprojective. Extending the works of Davidson, Eilers-Loring-Pedersen, Lin and Voiculescu on almost commuting matrices, we identify the precise dimensional and cohomological restr

Sarod Yatawatta

Calibration is an essential step in radio interferometric data processing that corrects the data for systematic errors and in addition, subtracts bright foreground interference to reveal weak signals hidden in the residual. These weak and unknown signals are much sought after to reach many science goals but the effect of calibration on such signals is an eve

Manuel Friedrich, Leonard Kreutz

We present two-dimensional crystallization results in the square lattice for finite particle systems consisting of two different atomic types. We identify energy minimizers of configurational energies featuring two-body short-ranged particle interactions which favor some reference distance between different atomic types and contain repulsive contributions fo

Federico Pernici, Matteo Bruni, Claudio Baecchi, Alberto Del Bimbo

Neural networks are widely used as a model for classification in a large variety of tasks. Typically, a learnable transformation (i.e. the classifier) is placed at the end of such models returning a value for each class used for classification. This transformation plays an important role in determining how the generated features change during the learning pr

Daniel Huybrechts, Chenyang Xu

The base surface $B$ of a Lagrangian fibration $X\twoheadrightarrow B$ of a projective, irreducible symplectic fourfold $X$ is shown to be isomorphic to ${\mathbb P}^2$.

M. Querejeta, E. Schinnerer, A. Schruba, E. Murphy

It remains unclear what sets the efficiency with which molecular gas transforms into stars. Here we present a new VLA map of the spiral galaxy M51 in 33GHz radio continuum, an extinction-free tracer of star formation, at 3&#34; scales (~100pc). We combined this map with interferometric PdBI/NOEMA observations of CO(1-0) and HCN(1-0) at matched resolution for

Alexander Korotin, Vladimir V&#39;yugin, Evgeny Burnaev

The article is devoted to investigating the application of hedging strategies to online expert weight allocation under delayed feedback. As the main result, we develop the General Hedging algorithm $\mathcal{G}$ based on the exponential reweighing of experts&#39; losses. We build the artificial probabilistic framework and use it to prove the adversarial loss

Raphael Schiedung, Marvin Tegeler, Dmitry Medvedev, Fathollah Varnik

We propose a combined computational approach based on the multi-phase-field and the lattice Boltzmann method for the motion of solid particles under the action of capillary forces. The accuracy of the method is analyzed by comparison with the analytic solutions for the motion of two parallel plates of finite extension connected by a capillary bridge. The met

Vahid Jamali, Nariman Farsad, Robert Schober, Andrea Goldsmith

This paper focuses on molecular communication (MC) systems using two types of signaling molecules which may participate in a reversible bimolecular reaction in the channel. The motivation for studying these MC systems is that they can realize the concept of constructive and destructive signal superposition, which leads to favorable properties such as inter-s

Thomas Nedelec, Noureddine El Karoui, Vianney Perchet

We consider the problem of the optimization of bidding strategies in prior-dependent revenue-maximizing auctions, when the seller fixes the reserve prices based on the bid distributions. Our study is done in the setting where one bidder is strategic. Using a variational approach, we study the complexity of the original objective and we introduce a relaxation

Atul Mohan, Patrick I. McCauley, Alpha Mastrano, Divya Oberoi

Weak heating events are frequent and ubiquitous in solar corona. They derive their energy from the local magnetic field and form a major source of local heating, signatures of which are seen in EUV and X-ray bands. Their radio emission arise from various plasma instabilities that lead to coherent radiation, making even a weak flare appear very bright. The ra

Hongmin Xu, Qiang Li, Wenbo Zhang, Wen Zheng

Multi-Style Transfer (MST) intents to capture the high-level visual vocabulary of different styles and expresses these vocabularies in a joint model to transfer each specific style. Recently, Style Embedding Learning (SEL) based methods represent each style with an explicit set of parameters to perform MST task. However, most existing SEL methods either lear

Gabriel Eilertsen, Rafał K. Mantiuk, Jonas Unger

Convolutional neural networks (CNNs) can model complicated non-linear relations between images. However, they are notoriously sensitive to small changes in the input. Most CNNs trained to describe image-to-image mappings generate temporally unstable results when applied to video sequences, leading to flickering artifacts and other inconsistencies over time.

Xiao-Ling Pang, Ai-Lin Yang, Chao-Ni Zhang, Jian-Peng Dou

Unconditionally secure communication, being pursued for thousands of years, however, hasn&#39;t been reached yet due to continuous competitions between encryption and hacking. Quantum key distribution (QKD), harnessing the quantum mechanical nature of superposition and non-cloning, may promise unconditional security by incorporating the one-time pad algorith

Jungbeom Lee, Eunji Kim, Sungmin Lee, Jangho Lee

The main obstacle to weakly supervised semantic image segmentation is the difficulty of obtaining pixel-level information from coarse image-level annotations. Most methods based on image-level annotations use localization maps obtained from the classifier, but these only focus on the small discriminative parts of objects and do not capture precise boundaries

Bruno Ordozgoiti, Aristides Gionis

We propose a new variant of the k-median problem, where the objective function models not only the cost of assigning data points to cluster representatives, but also a penalty term for disagreement among the representatives. We motivate this novel problem by applications where we are interested in clustering data while avoiding selecting representatives that

Alain Deville, Yannick Deville

If a pure state of a qubit pair is developed over the four basis states, it is known that an equality between the four coefficients of that development exists if and only if that state is unentangled. This paper considers an arbitrary pure state of an N-qubit system, developed over the 2^N basis states. It is shown that the state is unentangled if and only i

Maria Vittoria Minniti, Farbod Farshidian, Ruben Grandia, Marco Hutter

Autonomous mobile manipulation offers a dual advantage of mobility provided by a mobile platform and dexterity afforded by the manipulator. In this paper, we present a whole-body optimal control framework to jointly solve the problems of manipulation, balancing and interaction as one optimization problem for an inherently unstable robot. The optimization is

Alexander Kreuzer, Claudia Czado

Latent autoregressive processes are a popular choice to model time varying parameters. These models can be formulated as nonlinear state space models for which inference is not straightforward due to the high number of parameters. Therefore maximum likelihood methods are often infeasible and researchers rely on alternative techniques, such as Gibbs sampling.

Ling-Yun Dai, Javier Fuentes-Martin, Jorge Portoles

We analyse within the framework of resonance chiral theory the $\langle SA_μA_ν\rangle$ and $\langle SV_μV_ν\rangle$ three-point Green functions, where $S$, $A_μ $ and $V_μ$ are short for scalar, axial-vector and vector $SU(3)$ hadronic currents. We construct the necessary Lagrangian such that the Green functions fulfill the asymptotic constraints, at large

Geewook Kim, Akifumi Okuno, Kazuki Fukui, Hidetoshi Shimodaira

We propose $\textit{weighted inner product similarity}$ (WIPS) for neural network-based graph embedding. In addition to the parameters of neural networks, we optimize the weights of the inner product by allowing positive and negative values. Despite its simplicity, WIPS can approximate arbitrary general similarities including positive definite, conditionally

el Houcein el Abdalaoui, Issam Naghmouchi

It is shown that the restriction of the action of any group with finite orbit on the minimal sets of dendrites is equicontinuous. Consequently, we obtain that the action of any amenable group and Thompson group on dendrite restricted on minimal sets is equicontinuous. We further provide a class of non-amenable groups whose action on dendrites has finite orbi

Abdelkerim Amari, Xiang Lin, Octavia A. Dobre, Ramachandran Venkatesan

We investigate the performance of a machine learning classification technique, called the Parzen window, to mitigate the fiber nonlinearity in the context of dispersion managed and dispersion unmanaged systems. The technique is applied for detection at the receiver side, and deals with the non-Gaussian nonlinear effects by designing improved decision boundar

Romuald Elie, Emma Hubert, Thibaut Mastrolia, Dylan Possamaï

We study the problem of demand response contracts in electricity markets by quantifying the impact of considering a mean-field of consumers, whose consumption is impacted by a common noise. We formulate the problem as a Principal-Agent problem with moral hazard in which the Principal - she - is an electricity producer who observes continuously the consumptio

Wankai Tang, Jun Yan Dai, Mingzheng Chen, Xiang Li

In this letter, a wireless transmitter using the new architecture of programmable metasurface is presented. The proposed transmitter does not require any filter, nor wideband mixer or wideband power amplifier, thereby making it a promising hardware architecture for cost-effective wireless communications systems in the future. Using experimental results, we d

Sylwester Klocek, Łukasz Maziarka, Maciej Wołczyk, Jacek Tabor

Motivated by the human way of memorizing images we introduce their functional representation, where an image is represented by a neural network. For this purpose, we construct a hypernetwork which takes an image and returns weights to the target network, which maps point from the plane (representing positions of the pixel) into its corresponding color in the

Hai-Xiao Wang, Guang-Yu Guo, Jian-Hua Jiang

The rapid development of topological photonics and acoustics calls for accurate understanding of band topology in classical waves, which is not yet achieved in many situations. Here, we present the Wilson-loop approach for exact numerical calculation of the topological invariants for several photonic/sonic crystals. We demonstrate that these topological phot

Indranil Biswas, Anoop Singh

We investigate relative connections on a sheaf of modules. A sufficient condition is given for the existence of a relative holomorphic connection on a holomorphic vector bundle over a complex analytic family. We show that the relative Chern classes of a holomorphic vector bundle admitting relative holomorphic connection vanish, if each of the fiber of the co

C. -H. Luke Ong, Dominik Wagner

We present a simple resolution proof system for higher-order constrained Horn clauses (HoCHC) - a system of higher-order logic modulo theories - and prove its soundness and refutational completeness w.r.t. the standard semantics. As corollaries, we obtain the compactness theorem and semi-decidability of HoCHC for semi-decidable background theories, and we pr

David J. Nott, Max Seah, Luai Al-Labadi, Michael Evans

Any Bayesian analysis involves combining information represented through different model components, and when different sources of information are in conflict it is important to detect this. Here we consider checking for prior-data conflict in Bayesian models by expanding the prior used for the analysis into a larger family of priors, and considering a margi

Fabian Steiner, Emna Ben Yacoub, Balazs Matuz, Gianluigi Liva

Low complexity decoding algorithms are necessary to meet data rate requirements in excess of 1 Tbps. In this paper, we study one and two bit message passing algorithms for belief propagation decoding of low-density parity-check (LDPC) codes and analyze them by density evolution. The variable nodes (VNs) exploit soft information from the channel output. To de

Wen Chen

In this paper, the reduction of Feynman integrals in the parametric representation is considered. This method proves to be more efficient than the integration-by-part (IBP) method in the momentum space. Tensor integrals can directly be parametrized without performing tensor reductions. The integrands of parametric integrals are functions of Lorentz scalars,

Donatella Iacono, Marco Manetti

We investigate the deformations of pairs $(X,L)$, where $L$ is a line bundle on a smooth projective variety $X$, defined over an algebraically closed field $\mathbb{K}$ of characteristic 0. In particular, we prove that the DG-Lie algebra controlling the deformations of the pair $(X,L)$ is homotopy abelian whenever $X$ has trivial canonical bundle, and so the

Andreas Maurischat

In this short note, we answer a question raised by M. Papikian on a universal upper bound for the degree of the extension of $K_\infty$ given by adjoining the periods of a Drinfeld module of rank 2. We show that contrary to the rank 1 case such a universal upper bound does not exist, and the proof generalises to higher rank. Moreover, we give an upper and lo

Hao Zhang, Xiuyan Yang, Jianwei Ma

We propose a convolutional neural network (CNN) denoising based method for seismic data interpolation. It provides a simple and efficient way to break though the lack problem of geophysical training labels that are often required by deep learning methods. The new method consists of two steps: (1) Train a set of CNN denoisers from natural image clean-noisy pa

Gisa Schäfer, Yasuyoshi Yonezawa

We consider colored operads and their actions on categories. As a special example we construct a cobordism category with a colored operad action arising from oriented planar arc diagrams. This is used to construct an invariant of oriented tangle diagrams with values in the homotopy category attached to the cobordism category. Motivated by Bar-Natan&#39;s cat

Vincenzo Macrí, Franco Nori, Salvatore Savasta, David Zueco

It has been shown elsewhere that two spatially separated atoms can jointly absorb one photon, whose frequency is equal to the sum of the transition frequencies of the two atoms. We describe this process in the presence of an ensemble of many two-level atoms, and show that it can be used to generate spin squeezing and entanglement. This resonant collective pr

F. Chu, F. Skiff

Extensive information, such as ion temperature and wave-particle interactions, can be obtained by direct measurement of ion distribution functions using laser-induced fluorescence (LIF). This nonintrusive plasma diagnostic provides an important window into the ion motions in phase-space. Previous simulation results suggest that LIF measurements, which are pe

Tomoyuki Obuchi, Ayaka Sakata

We investigate the signal reconstruction performance of sparse linear regression in the presence of noise when piecewise continuous nonconvex penalties are used. Among such penalties, we focus on the SCAD penalty. The contributions of this study are three-fold: We first present a theoretical analysis of a typical reconstruction performance, using the replica

Yael Hitron, Merav Parter

We consider the task of measuring time with probabilistic threshold gates implemented by bio-inspired spiking neurons. In the model of spiking neural networks, network evolves in discrete rounds, where in each round, neurons fire in pulses in response to a sufficiently high membrane potential. This potential is induced by spikes from neighboring neurons that

Rabin Banerjee

In his 1963 paper, Dirac [1] gave `A Remarkable Representation of the 3 + 2 de- Sitter Group&#39;. We reproduce this representation using a generalised Jordan map which is motivated by the infinitesimal symplectic transformations related to the four dimensional symplectic group Sp(4). A physical picture of Dirac&#39;s representation is also discussed.

Soumajyoti Sarkar, Hamidreza Alvari, Paulo Shakarian

Information diffusion mechanisms based on social influence models are mainly studied using likelihood of adoption when active neighbors expose a user to a message. The problem arises primarily from the fact that for the most part, this explicit information of who-exposed-whom among a group of active neighbors in a social network, before a susceptible node is

Masoud Badiei Khuzani, Hongyi Ren, Md Tauhidul Islam, Lei Xing

We propose a novel data-driven method to learn a mixture of multiple kernels with random features that is certifiabaly robust against adverserial inputs. Specifically, we consider a distributionally robust optimization of the kernel-target alignment with respect to the distribution of training samples over a distributional ball defined by the Kullback-Leible

Malte Gerhold, Orr Shalit

Let $q = e^{i θ} \in \mathbb{T}$ (where $θ\in \mathbb{R}$), and let $u,v$ be $q$-commuting unitaries, i.e., $u$ and $v$ are unitaries such that $vu = quv$. In this paper we find the optimal constant $c = c_θ$ such that $u,v$ can be dilated to a pair of operators $c U, c V$, where $U$ and $V$ are commuting unitaries. We show that \[ c_θ= \frac{4}{\|u_θ+u_θ^*+

Alban Urvoy, Zachary Vendeiro, Joshua Ramette, Albert Adiyatullin

We present a method for producing three-dimensional Bose-Einstein condensates using only laser cooling. The phase transition to condensation is crossed with $2.5 {\times} 10^{4}$ $^{87}\mathrm{Rb}$ atoms at a temperature of $T_{\mathrm{c}} = 0.6\ μ\mathrm{K}$ after 1.4 s of cooling. Atoms are trapped in a crossed optical dipole trap and cooled using Raman co

Xinsheng Xuan, Bo Peng, Wei Wang, Jing Dong

Recently the GAN generated face images are more and more realistic with high-quality, even hard for human eyes to detect. On the other hand, the forensics community keeps on developing methods to detect these generated fake images and try to guarantee the credibility of visual contents. Although researchers have developed some methods to detect generated ima

Manuel de la Cruz, Néstor Gaspar, Román Linares

In this paper we revisit the construction by which the $SL(2,\mathbb{R})$ symmetry of the Euler equations allows to obtain the simple pendulum from the rigid body. We begin reviewing the original relation found by Holm and Marsden in which, starting from the two Casimir functions of the extended rigid body with Lie algebra $ISO(2)$ and introducing a proper m

Tal Ben-Nun, Johannes de Fine Licht, Alexandros Nikolaos Ziogas, Timo Schneider

The ubiquity of accelerators in high-performance computing has driven programming complexity beyond the skill-set of the average domain scientist. To maintain performance portability in the future, it is imperative to decouple architecture-specific programming paradigms from the underlying scientific computations. We present the Stateful DataFlow multiGraph

Tejaswi Venumadhav, Barak Zackay, Javier Roulet, Liang Dai

In this paper, we report on the construction of a new and independent pipeline for analyzing the public data from the first observing run of advanced LIGO for mergers of compact binary systems. The pipeline incorporates different techniques and makes independent implementation choices in all its stages including the search design, the method to construct tem

Wenhu Chen, Yu Su, Yilin Shen, Zhiyu Chen

With the rapid development in deep learning, deep neural networks have been widely adopted in many real-life natural language applications. Under deep neural networks, a pre-defined vocabulary is required to vectorize text inputs. The canonical approach to select pre-defined vocabulary is based on the word frequency, where a threshold is selected to cut off

Richeng Jin, Xiaofan He, Huaiyu Dai

The recent advances in sensor technologies and smart devices enable the collaborative collection of a sheer volume of data from multiple information sources. As a promising tool to efficiently extract useful information from such big data, machine learning has been pushed to the forefront and seen great success in a wide range of relevant areas such as compu

Sofiya Ostrovska, Mikhail Ostrovskii

The paper is devoted to the geometry of transportation cost spaces and their generalizations introduced by Melleray, Petrov, and Vershik (2008). Transportation cost spaces are also known as Arens-Eells, Lipschitz-free, or Wasserstein $1$ spaces. In this work, the existence of metric spaces with the following properties is proved: (1) uniformly discrete infin