May 2019 arXiv papers — page 2

William T. Stephenson, Tamara Broderick

Leave-one-out cross-validation (LOOCV) can be particularly accurate among cross-validation (CV) variants for machine learning assessment tasks -- e.g., assessing methods' error or variability. But it is expensive to re-fit a model $N$ times for a dataset of size $N$. Previous work has shown that approximations to LOOCV can be both fast and accurate -- wh

Won Ik Cho, Seok Min Kim, Nam Soo Kim

Different from the writing systems of many Romance and Germanic languages, some languages or language families show complex conjunct forms in character composition. For such cases where the conjuncts consist of the components representing consonant(s) and vowel, various character encoding schemes can be adopted beyond merely making up a one-hot vector. Howev

Sanjeev Arora, Nadav Cohen, Wei Hu, Yuping Luo

Efforts to understand the generalization mystery in deep learning have led to the belief that gradient-based optimization induces a form of implicit regularization, a bias towards models of low "complexity." We study the implicit regularization of gradient descent over deep linear neural networks for matrix completion and sensing, a model referred to

Soufiane Hayou, Arnaud Doucet, Judith Rousseau

Recent work by Jacot et al. (2018) has shown that training a neural network using gradient descent in parameter space is related to kernel gradient descent in function space with respect to the Neural Tangent Kernel (NTK). Lee et al. (2019) built on this result by establishing that the output of a neural network trained using gradient descent can be approxim

Aris Anagnostopoulos, Luca Becchetti, Adriano Fazzone, Cristina Menghini

Reducing hidden bias in the data and ensuring fairness in algorithmic data analysis has recently received significant attention. We complement several recent papers in this line of research by introducing a general method to reduce bias in the data through random projections in a "fair" subspace. We apply this method to densest subgraph problem. For

Gary P. Centers, John W. Blanchard, Jan Conrad, Nataniel L. Figueroa

Numerous theories extending beyond the standard model of particle physics predict the existence of bosons that could constitute the dark matter (DM) permeating the universe. In the standard halo model (SHM) of galactic dark matter the velocity distribution of the bosonic DM field defines a characteristic coherence time $τ_c$. Until recently, laboratory exper

Sarthika Dhawan, Siva Charan Reddy Gangireddy, Shiv Kumar, Tanmoy Chakraborty

Online reviews play a crucial role in deciding the quality before purchasing any product. Unfortunately, spammers often take advantage of online review forums by writing fraud reviews to promote/demote certain products. It may turn out to be more detrimental when such spammers collude and collectively inject spam reviews as they can take complete control of

Ali Furkan Biten, Ruben Tito, Andres Mafla, Lluis Gomez

Current visual question answering datasets do not consider the rich semantic information conveyed by text within an image. In this work, we present a new dataset, ST-VQA, that aims to highlight the importance of exploiting high-level semantic information present in images as textual cues in the VQA process. We use this dataset to define a series of tasks of

Pascal Michaillat, Emmanuel Saez

At the zero lower bound, the New Keynesian model predicts that output and inflation collapse to implausibly low levels, and that government spending and forward guidance have implausibly large effects. To resolve these anomalies, we introduce wealth into the utility function; the justification is that wealth is a marker of social status, and people value sta

Simon Baker

Let $(f_n)_{n=1}^{\infty}$ be a sequence of polynomials and $α>1$. In this paper we study the distribution of the sequence $(f_n(α))_{n=1}^{\infty}$ modulo one. We give sufficient conditions for a sequence $(f_n)_{n=1}^{\infty}$ to ensure that for Lebesgue almost every $α>1$ the sequence $(f_n(α))_{n=1}^{\infty}$ has Poissonian pair correlations. In particul

M. Paoluzzi, L. Angelani

Besides the dynamical slowing down signaled by an enormous increase of the viscosity approaching the glass transition, structural glasses show interesting anomalous thermodynamic features at low temperatures that hint at peculiar deviations from Debye's law at low enough frequencies. Theory, numerical simulations, and experiments suggest that deviation f

Morten Kjaergaard, Mollie E. Schwartz, Jochen Braumüller, Philip Krantz

Superconducting qubits are leading candidates in the race to build a quantum computer capable of realizing computations beyond the reach of modern supercomputers. The superconducting qubit modality has been used to demonstrate prototype algorithms in the 'noisy intermediate scale quantum' (NISQ) technology era, in which non-error-corrected qubits are

Hui Shao, Wenan Guo, Anders W. Sandvik

We present a way to visualize and quantify renormalization group flows in a space of observables computed using Monte Carlo simulations. We apply the method to classical three-dimensional clock models, i.e., the planar (XY) spin model perturbed by a $Z_q$ symmetric anisotropy field. The method performs significantly better than standard techniques for determ

Julius Ross, Matei Toma

Let $X$ be a $d$ dimensional projective manifold, $E$ be an ample vector bundle on $X$ and $0\le λ_N\le λ_{N-1} \le \cdots \le λ_1 \le \operatorname{rank}(E)$ be a partition of $d-2$. We prove that the Schur class $s_λ(E)\in H^{d-2,d-2}(X)$ has the Hard Lefschetz property and satisfies the Hodge-Riemann bilinear relations. As a consequence we obtain various

Nikola Opačak, Benedikt Schwarz

Frequency modulated (FM) frequency combs constitute an exciting alternative to generate equidistant spectra. The full set of Maxwell-Bloch equations is reduced to a single master equation for FM combs with fast dynamics to provide insight into the governing mechanisms behind phase-locking. It reveals that the recently observed linear frequency chirp is cause

Gang Guo, Gabriel Martínez-Pinedo

We revisit the rates of neutrino pair emission and absorption from nucleon-nucleon bremsstrahlung in supernova matter using the $T$-matrix formalism in the long-wavelength limit. Based on two-body potentials of chiral effective field theory ($χ$EFT), we solve the Lippmann-Schwinger equation for the $T$-matrix including non-diagonal contributions. We consider

D. A. Tatarskiy, E. A. Karashtin

We propose a possible experimental setup for nonreciprocal electron transport in a lateral spin valve due to noncoplanar distribution of magnetic moment (and field) in the system. Some metals (Al, Cu) and semiconductors (GaAs, InSb etc.) demonstrate spin accumulation even at room temperatures due to large spin flip lengths (0.3--2.0 $μm$). The Hanle precessi

Ladislav Hlavatý, Ivo Petr

We investigate a special class of Poisson--Lie T-plurality transformations of Bianchi cosmologies invariant with respect to non-semisimple Bianchi groups. For six-dimensional semi-Abelian Manin triples $\mathfrak{b}\bowtie \mathfrak{a}$ containing Bianchi algebras $\mathfrak{b}$ we identify general forms of Poisson--Lie identities and dualities. We show that

NEXT Collaboration, P. Novella, B. Palmeiro, M. Sorel

Natural radioactivity represents one of the main backgrounds in the search for neutrinoless double beta decay. Within the NEXT physics program, the radioactivity-induced backgrounds are measured with the NEXT-White detector. Data from 37.9 days of low-background operations at the Laboratorio Subterráneo de Canfranc with xenon depleted in $^{136}$Xe are analy

Xiaomeng Liu, Oliver Kühn

Allochromatium (Alc.) vinosum has a double-peak structure of its absorption band around 800~nm. Previously, the excitonic origin of this feature has been demonstrated experimentally, but a detailed understanding still lacks a model Hamiltonian being able to reproduce absorption as well as exciton relaxation time scales. Here, we propose a system-bath model w

Yan Gao

In this paper, a relation between shadow price and the Lagrangian multiplier for nonsmooth problem is explored. It is shown that the Lagrangian Multiplier is the upper bound of shadow price for convex optimization and a class of Lipschtzian optimizations. This work can be used in shadow pricing for nonsmooth situation. The several nonsmooth functions involve

Wataru Fujiya, Peter Hoppe, Takayuki Ushikubo, Kohei Fukuda

Recent dynamical models of Solar System evolution and isotope studies of rock-forming elements in meteorites have suggested that volatile-rich asteroids formed in the outer Solar System beyond Jupiter's orbit, despite being currently located in the main asteroid belt. The ambient temperature under which asteroids formed is a crucial diagnostic to pinpoin

Amin Coja-Oghlan, Max Hahn-Klimroth

Guided by the theory of graph limits, we investigate a variant of the cut metric for limit objects of sequences of discrete probability distributions. Apart from establishing basic results, we introduce a natural operation called {\em pinning} on the space of limit objects and show how this operation yields a canonical cut metric approximation to a given pro

Thomas Bonis

We use Stein's method to bound the Wasserstein distance of order $2$ between a measure $ν$ and the Gaussian measure using a stochastic process $(X_t)_{t \geq 0}$ such that $X_t$ is drawn from $ν$ for any $t > 0$. If the stochastic process $(X_t)_{t \geq 0}$ satisfies an additional exchangeability assumption, we show it can also be used to obtain bounds o

Arnout Devos, Matthias Grossglauser

We propose regression networks for the problem of few-shot classification, where a classifier must generalize to new classes not seen in the training set, given only a small number of examples of each class. In high dimensional embedding spaces the direction of data generally contains richer information than magnitude. Next to this, state-of-the-art few-shot

Junxiang Wang, Fuxun Yu, Xiang Chen, Liang Zhao

Alternating Direction Method of Multipliers (ADMM) has been used successfully in many conventional machine learning applications and is considered to be a useful alternative to Stochastic Gradient Descent (SGD) as a deep learning optimizer. However, as an emerging domain, several challenges remain, including 1) The lack of global convergence guarantees, 2) S

Jungin Lee, Jeehoon Park

The goal of this paper is two-fold: we generalize the arithmetic Chern-Simons theory over totally imaginary number fields studied in [Kim15, CKK+16] to arbitrary number fields (with real places) and provide new examples of non-trivial arithmetic Chern-Simons invariant with coefficient $\mathbb{Z}/n\mathbb{Z}$ $(n \geq 2)$ associated to a non-abelian gauge gr

Sebastian Selter, Gaël Bastien, Anja U. B. Wolter, Saicharan Aswartham

In this work we present a comprehensive investigation on magnetic and thermodynamic properties of the two-dimensional layered honeycomb system Cr$_2$Ge$_2$Te$_6$. Using magnetization and specific heat measurements under magnetic field applied along two crystallographic directions we obtain the magnetic phase diagram for both directions. Cr$_2$Ge$_2$Te$_6$ is

Martin Averseng

We introduce two new classes of pseudo-differential operators on open curves. They correspond via a change of variables to subclasses of the periodic pseudo-differential operators, which respectively stabilize even and odd functions. The resulting symbolic calculus can be applied to the analysis of the Helmholtz weighted layer potentials on open curves. In p

François Alouges, Martin Averseng

The Helmholtz wave scattering problem by screens in 2D can be recast into first-kind integral equations which lead to ill-conditioned linear systems after discretization. We introduce two new preconditioners, in the form of square-roots of local operators respectively for the corresponding problems with Dirichlet and Neumann conditions on the arc. They gener

Hagit Attiya, Ohad Ben-Baruch, Panagiota Fatourou, Danny Hendler

This paper presents the tracking approach for deriving detectably recoverable (and thus also durable) implementations of many widely-used concurrent data structures. Such data structures, satisfying detectable recovery, are appealing for emerging systems featuring byte-addressable non-volatile main memory (NVRAM), whose persistence allows to efficiently resu

Grégoire Clarté, Christian P. Robert, Robin Ryder, Julien Stoehr

Approximate Bayesian computation methods are useful for generative models with intractable likelihoods. These methods are however sensitive to the dimension of the parameter space, requiring exponentially increasing resources as this dimension grows. To tackle this difficulty, we explore a Gibbs version of the ABC approach that runs component-wise approximat

G. Pieplow, C. E. Creffield, F. Sols

We investigate the behavior of a one-dimensional Bose-Hubbard gas in both a ring and a hard-wall box, whose kinetic energy is made to oscillate with zero time-average, which suppresses first-order particle hopping. For intermediate and large driving amplitudes the system in the ring has similarities to the Richardson model, but with a peculiar type of pairin

Alex Kaltenbach, Michael Růžička

In this note we develop a framework which allows to prove an existence result for non-linear evolution problems involving time-dependent, pseudo-monotone operators. This abstract existence result is applicable to a large class of concrete problems where the standard theorem on evolutionary pseudo-monotone operators is not applicable. To this end we introduce

D. Solovyev

In present paper the Quantum Electrodynamics theory at finite temperatures for the bound states is presented. To describe the thermal effects arising in a heat bath the Hadamard form of thermal photon propagator is employed. This form permits the simple introduction of thermal gauges in a way similar to the 'ordinary' Feynman propagator and, therefor

James Howie, Gerald Williams

A fundamental theorem in the study of Dunwoody manifolds is a classification of finite graphs on $2n$ vertices that satisfy seven conditions (concerning planarity, regularity, and a cyclic automorphism of order $n$). Its significance is that if the presentation complex of a cyclic presentation is a spine of a 3-manifold then its Whitehead graph satisfies the

Cihan Özgür

In $(2n+1)$-dimensional non-Sasakian contact metric manifolds, we consider Legendre curves whose mean curvature vector fields are $\mathcal{C}$-parallel or $\mathcal{C}$-proper in the tangent or normal bundles. We obtain the curvature characterizations of these curves. Moreover, we give some examples of these kinds of curves which satisfy the conditions of o

Lingxu Meng

We consider the $\partial\bar{\partial}$-lemma for complex manifolds under surjective holomorphic maps. Furthermore, using Deligne-Griffiths-Morgan-Sullivan's theorem, we prove that a product compact complex manifold satisfies the $\partial\bar{\partial}$-lemma if and only if so do all its components.

Artem Voronkov, Leonardo A. Martucci, Stefan Lindskog

A graphical user interface (GUI) represents the most common option for interacting with computer systems. However, according to the literature system administrators often favor command line interfaces (CLIs). The goal of our work is to investigate which interfaces system administrators prefer, and which they actually utilize in their daily tasks. We collecte

Manuel Drees, Yong Xu

The self coupling $λ$ of the Higgs boson in the Standard Model may show critical behavior, i.e. the Higgs potential may have a point at an energy scale $\sim 10^{17-18}$ GeV where both the first and second derivatives (almost) vanish. In this case the Higgs boson can serve as inflaton even if its nonminimal coupling to the curvature scalar is only ${\cal O}(

Petter Andreas Bergh, Peder Thompson

For a commutative ring $S$ and self-orthogonal subcategory $\mathsf{C}$ of $\mathsf{Mod}(S)$, we consider matrix factorizations whose modules belong to $\mathsf{C}$. Let $f\in S$ be a regular element. If $f$ is $M$-regular for every $M\in \mathsf{C}$, we show there is a natural embedding of the homotopy category of $\mathsf{C}$-factorizations of $f$ into a c

Simone Göttlich, Falk M. Hante, Andreas Potschka, Lars Schewe

We consider mixed-integer optimal control problems with combinatorial constraints that couple over time such as minimum dwell times. We analyze a lifting and decomposition approach into a mixed-integer optimal control problem without combinatorial constraints and a mixed-integer problem for the combinatorial constraints in the control space. Both problems ca

Benito A. Juárez-Aubry, Dimitris Moustos

We study the late-time asymptotic state of a stationary Unruh-DeWitt detector interacting with a field in a thermal state. We work in an open system framework, where the field plays the role of an environment for the detector. The long-time interaction between the detector and the field is modelled with the aid of a one-parameter family of switching function

Tibra Ali, Arpan Bhattacharyya, S. Shajidul Haque, Eugene H. Kim

We propose a new diagnostic for quantum chaos. We show that time evolution of complexity for a particular type of target state can provide equivalent information about the classical Lyapunov exponent and scrambling time as out-of-time-order correlators. Moreover, for systems that can be switched from a regular to unstable (chaotic) regime by a tuning of the

Luzie Helfmann, Natasa Djurdjevac Conrad, Ana Djurdjevac, Stefanie Winkelmann

Many real-world processes can naturally be modeled as systems of interacting agents. However, the long-term simulation of such agent-based models is often intractable when the system becomes too large. In this paper, starting from a stochastic spatio-temporal agent-based model (ABM), we present a reduced model in terms of stochastic PDEs that describes the e

Mohammed Brahimi, Said Mahmoudi, Kamel Boukhalfa, Abdelouhab Moussaoui

Recently, many works have been inspired by the success of deep learning in computer vision for plant diseases classification. Unfortunately, these end-to-end deep classifiers lack transparency which can limit their adoption in practice. In this paper, we propose a new trainable visualization method for plant diseases classification based on a Convolutional N

Markus Bachmayr, Ivan G. Graham, Van Kien Nguyen, Robert Scheichl

The periodization of a stationary Gaussian random field on a sufficiently large torus comprising the spatial domain of interest is the basis of various efficient computational methods, such as the classical circulant embedding technique using the fast Fourier transform for generating samples on uniform grids. For the family of Matérn covariances with smoothn

Kunimoto Komatsu, Tomotake Matsumura, Hiroaki Imada, Hirokazu Ishino

We report the development of the achromatic half-wave plate (AHWP) at millimeter wave for cosmic microwave background polarization experiments. We fabricate an AHWP consisting of nine a-cut sapphire plates based on the Pancharatnam recipe to cover a wide frequency range. The modulation efficiency and the phase are measured in a frequency range of 33 to 260 G

S. K. Maurya, Francisco Tello-Ortiz

This article is devoted to the study of high dense charged anisotropic compact structures in the framework of $f(R,\mathcal{T})$ gravity theory. The principal aims of this investigation, regard the extension of the isotropic Durgapal-Fuloria model within the context of charged isotropic $f(R,\mathcal{T})$ solutions. The second main goal of this work is to ap

Md Mansur Alam, Shruti Dubey

In this work, we consider time-fractional Navier-Stokes equations (NSE) with the external forces involving finite delay. Equations are considered on a bounded domain in 3-D space having sufficiently smooth boundary. We transform the system of equations (NSE) to an abstract Cauchy problem and then investigate local existence and uniqueness of the mild solutio

Margarita Baltakienė, Kęstutis Baltakys, Juho Kanniainen, Dino Pedreschi

The complex networks approach has been gaining popularity in analysing investor behaviour and stock markets, but within this approach, initial public offerings (IPO) have barely been explored. We fill this gap in the literature by analysing investor clusters in the first two years after the IPO filing in the Helsinki Stock Exchange by using a statistically v

V. Kravtsov, E. Khestanova, F. A. Benimetskiy, T. Ivanova

Optical bound states in the continuum (BICs) provide a way to engineer very narrow resonances in photonic crystals. The extended interaction time in such systems is particularly promising for enhancement of nonlinear optical processes and development of the next generation of active optical devices. However, the achievable interaction strength is limited by

Behnaz Pourmohseni, Fedor Smirnov, Stefan Wildermann, Jürgen Teich

Composable many-core systems enable the independent development and analysis of applications which will be executed on a shared platform where the mix of concurrently executed applications may change dynamically at run time. For each individual application, an off-line Design Space Exploration (DSE) is performed to compute several mapping alternatives on the

Wee Teck Gan, Xiaolei Wan

In this paper, we will determine the factorization of global period attached to the spherical variety $X:=\mathrm{SO}(n-1)\backslash \mathrm{SO}(n)$, which is a special case of the Sakellaridis-Venkatesh conjecture. The main idea is to build a connection between the periods of $X$ and the periods of the Whittaker case $N,ψ\backslash \mathrm{SL}_2$ (n even) o

Stephen M. Fleming

Humans have the ability to report the contents of their subjective experience - we can say to each other, "I am aware of X". The decision processes that support these reports about mental contents remain poorly understood. In this article I propose a computational framework that characterises awareness reports as metacognitive decisions (inference) a

David Andersson, Astrid S. de Wijn

Friction is a ubiquitous phenomenon that greatly affects our everyday lives and is responsible for large amounts of energy loss in industrialised societies. Layered materials such as graphene have interesting frictional properties and are often used as (additives to) lubricants to reduce friction and protect against wear. Experimental Atomic Force Microscopy

Sahand Mahmoodian

I introduce and analyse chiral light--matter interaction in the ultrastrong coupling limit where the rotating-wave approximation cannot be made. Within this limit, a two-level system (TLS) with a circularly polarized transition dipole interacts with a copolarized mode through rotating-wave terms. However, the counter-rotating terms allow the TLS to couple to

Eduardo Casas, Mariano Mateos, Arnd Rösch

In this paper we study optimal control problems governed by a semilinear elliptic equation. The equation is nonmonotone due to the presence of a convection term, despite the monotonocity of the nonlinear term. The resulting operator is neither monotone nor coervive. However, by using conveniently a comparison principle we prove existence and uniqueness of so

Magdalena Sieniawska, Michał Bejger, Andrzej Królak

Among promising sources of gravitational waves are long-lived nearly periodic signals produced by rotating, asymmetric neutron stars. Depending on the astrophysical scenario, the sources of asymmetry may have thermal, viscous, elastic and/or magnetic origin. In this work we introduce a follow-up procedure for an all-sky search for gravitational wave signals

Gilbert Moss

Let $F$ be a $p$-adic field and choose $k$ an algebraic closure of $\mathbb{F}_{\ell}$, with $\ell$ different from $p$. We define ``nilpotent lifts'' of irreducible generic $k$-representations of $GL_n(F)$, which take coefficients in Artin local $k$-algebras. We show that an irreducible generic $\ell$-modular representation $π$ of $GL_n(F)$ is unique

Jitesh R. Bhatt, Pravin Kumar Natwariya, Alekha C. Nayak, Arun Kumar Pandey

We have shown that in presence of a cosmic magnetic field the bounds on baryon dark matter cross-section ($\hat σ$), dark-matter mass ($m_d$) and values of the magnetic field ($B_0$) can strongly influence each other. This requires to rework the bounds on $\hat σ\,$, $m_d$ and $B_0$ which can explain the observed absorption signal by EDGES collaboration. The

Hau-Wen Huang

Let $X$ denote a connected $(q+1)$-regular undirected graph of finite order $n$. The graph $X$ is called Ramanujan whenever $$ |λ|\leq 2q^{\frac{1}{2}} $$ for all nontrivial eigenvalues $λ$ of $X$. We consider the variant $Ξ(u)$ of the Ihara zeta function $Z(u)$ of $X$ defined by \begin{gather*} Ξ(u)^{-1} = \left\{ \begin{array}{ll} (1-u)(1-qu)(1-q^{\frac{1}

Gyehyun Park, YounJoon Jung

Polymer brushes normally swell in a good solvent and collapse in a poor solvent. An abnormal response of polymer brushes, so-called co-nonsolvency, is the phenomenon where the brush counter-intuitively collapses in a good solvent mixture. In this work, we employed molecular dynamics simulations to investigate the structural properties of the grafted polymers

Odysseas Bakas, Salvador Rodríguez-López, Alan Sola

We obtain versions of some classical results of Zygmund and Stein for functions belonging to the Hardy space $H^{\log} (\mathbb{R}^d)$ introduced by Bonami, Grellier, and Ky. We present further applications in the context of more general Orlicz spaces.

Michał Dereziński, Daniele Calandriello, Michal Valko

We study the complexity of sampling from a distribution over all index subsets of the set $\{1,...,n\}$ with the probability of a subset $S$ proportional to the determinant of the submatrix $\mathbf{L}_S$ of some $n\times n$ p.s.d. matrix $\mathbf{L}$, where $\mathbf{L}_S$ corresponds to the entries of $\mathbf{L}$ indexed by $S$. Known as a determinantal po

Andrey Malinin, Mark Gales

Ensemble approaches for uncertainty estimation have recently been applied to the tasks of misclassification detection, out-of-distribution input detection and adversarial attack detection. Prior Networks have been proposed as an approach to efficiently \emph{emulate} an ensemble of models for classification by parameterising a Dirichlet prior distribution ov

ALICE Collaboration

The study of the strength and behaviour of the antikaon-nucleon ($\mathrm{\overline{K}N}$) interaction constitutes one of the key focuses of the strangeness sector in low-energy Quantum Chromodynamics (QCD). In this letter a unique high-precision measurement of the strong interaction between kaons and protons, close and above the kinematic threshold, is pres

Ben Deverett, Ryan Faulkner, Meire Fortunato, Greg Wayne

The measurement of time is central to intelligent behavior. We know that both animals and artificial agents can successfully use temporal dependencies to select actions. In artificial agents, little work has directly addressed (1) which architectural components are necessary for successful development of this ability, (2) how this timing ability comes to be

Tommi Gröndahl, N. Asokan

Stylometry can be used to profile or deanonymize authors against their will based on writing style. Style transfer provides a defence. Current techniques typically use either encoder-decoder architectures or rule-based algorithms. Crucially, style transfer must reliably retain original semantic content to be actually deployable. We conduct a multifaceted eva

Giuseppe Marra, Ondřej Kuželka

We introduce neural Markov logic networks (NMLNs), a statistical relational learning system that borrows ideas from Markov logic. Like Markov logic networks (MLNs), NMLNs are an exponential-family model for modelling distributions over possible worlds, but unlike MLNs, they do not rely on explicitly specified first-order logic rules. Instead, NMLNs learn an

Michael Oberguggenberger, Martin Schwarz

The paper proposes a method of damage detection in elastic materials, which is based on analyzing the time-dependent (dynamic) response of the material excited by an acoustic signal. A case study is presented consisting of experimental measurements and their mathematical analysis. The decisive parameters (wave speed and damping coefficient) of a mathematical

Giuseppe Gnocchi, Andrea Maselli, Tiziano Abdelsalhin, Nicola Giacobbo

We study the constraints on alternative theories of gravity that can be determined by multi-band observations of gravitational wave signals emitted from binary black hole coalescences. We focus on three types of General Relativity modifications induced by a generalised Brans-Dicke theory, and two classes of quadratic gravity, Einstein-dilaton-Gauss-Bonnet an

V. De Luca, V. Desjacques, G. Franciolini, A. Riotto

We discuss a novel mechanism to generate gravitational waves in the early universe. A standard way to produce primordial black holes is to enhance at small-scales the overdensity perturbations generated during inflation. The latter, upon horizon re-entry, collapse into black holes. They must be sizeable enough and are therefore associated to rare peaks. Ther

Changhee Han, Leonardo Rundo, Ryosuke Araki, Yudai Nagano

Convolutional Neural Networks (CNNs) achieve excellent computer-assisted diagnosis with sufficient annotated training data. However, most medical imaging datasets are small and fragmented. In this context, Generative Adversarial Networks (GANs) can synthesize realistic/diverse additional training images to fill the data lack in the real image distribution; r

Huan-Yu Ku, Neill Lambert, Fong-Ruei Jhan, Clive Emary

The Leggett-Garg inequality attempts to classify experimental outcomes as arising from one of two possible classes of physical theories: those described by macrorealism (which obey our intuition about how the macroscopic classical world behaves), and those that are not (e.g., quantum theory). The development of cloud-based quantum computing devices enables u

David G Flad, Andrea D Beck, Philipp Guthke

In this paper, we apply a specifically designed dissipative spatial filter as sub-grid scale model within the increasingly popular discontinuous Galerkin methods and the closely related flux reconstruction high order methods for large eddy simulation. The parameters of the filter kernel are optimized with data obtained from direct numerical simulation, that

Xuenan Xu, Heinrich Dinkel, Mengyue Wu, Kai Yu

Captioning has attracted much attention in image and video understanding while a small amount of work examines audio captioning. This paper contributes a Mandarin-annotated dataset for audio captioning within a car scene. A sentence-level loss is proposed to be used in tandem with a GRU encoder-decoder model to generate captions with higher semantic similari

Parminder Singh, Shafiq Ul Rehman, Selvakumar Manickam

A new variant of the DDoS attack, called Economic Denial of Sustainability attack has emerged. Since the cloud service is based on the pay-per-use model, the EDoS attack endeavors to scale up the resource usage over time to the point the purveyor of the server is financially incapable of sustaining the service due to the incurred unaffordable usage charges.

Paula Harring, Ralf Köhl

We determine the fundamental groups of symmetrizable algebraically simply connected split real Kac-Moody groups endowed with the Kac-Peterson topology. In analogy to the finite-dimensional situation, the Iwasawa decomposition $G = KAU_+$ provides a weak homotopy equivalence $K \hookrightarrow G$, implying $π_1(G) = π_1(K)$. It thus suffices to determine $π_1

Dao-Neng Gao

We study the long-distance contribution to $B_s\toμ^+μ^-$ decay, which is generated by the two-photon intermediate state via $B_s\toγ^*γ^*\toμ^+μ^-$ transition. It is found that the dispersive two-photon amplitude can interfere with the dominant short-distance amplitude, which gives rise to new theoretical uncertainty in the branching ratio of $B_s\toμ^+μ^-$

Tianyu Zhao, Shinsuke Mori, Tatsuya Kawahara

Various encoder-decoder models have been applied to response generation in open-domain dialogs, but a majority of conventional models directly learn a mapping from lexical input to lexical output without explicitly modeling intermediate representations. Utilizing language hierarchy and modeling intermediate information have been shown to benefit many languag

Masao Hayashi, Yusei Koyama, Tadayuki Kodama, Yutaka Komiyama

We present the large-scale structure over more than 50 comoving Mpc scale at z $\sim$ 0.9 where the CL1604 supercluster, which is one of the largest structures ever known at high redshifts, is embedded. The wide-field deep imaging survey by the Subaru Strategic Program with Hyper Suprime-Cam reveals that the already-known CL1604 supercluster is a mere part o

Kohei Miyaguchi

Empirically, the PAC-Bayesian analysis is known to produce tight risk bounds for practical machine learning algorithms. However, in its naive form, it can only deal with stochastic predictors while such predictors are rarely used and deterministic predictors often performs well in practice. To fill this gap, we develop a new generalization error bound, the P

Weiwei Kong, Renato D. C. Monteiro

This paper presents smoothing schemes for obtaining approximate stationary points of unconstrained or linearly-constrained composite nonconvex-concave min-max (and hence nonsmooth) problems by applying well-known algorithms to composite smooth approximations of the original problems. More specifically, in the unconstrained (resp. constrained) case, approxima

André Platzer, Yong Kiam Tan

This article proves the completeness of an axiomatization for differential equation invariants described by Noetherian functions. First, the differential equation axioms of differential dynamic logic are shown to be complete for reasoning about analytic invariants. Completeness crucially exploits differential ghosts, which introduce additional variables that

C. H. Wong, R. Lortz

We show that the superconducting transition temperature Tc of FeSe1-xTex can be computed to reasonable values in a modified McMillan approach in which the electron-phonon coupling is amplified by the antiferromagnetism and the out-of-plane phonons triggered by the tetrahedral lattice sites. This interplay is not only effective at ambient pressure, but also u

Xing Yan, Qi Wu, Wen Zhang

We propose a novel probabilistic model to facilitate the learning of multivariate tail dependence of multiple financial assets. Our method allows one to construct from known random vectors, e.g., standard normal, sophisticated joint heavy-tailed random vectors featuring not only distinct marginal tail heaviness, but also flexible tail dependence structure. T

B. Cessac

We review two examples where the linear response of a neuronal network submitted to an external stimulus can be derived explicitely, including network parameters dependence. This is done in a statistical physics-like approach where one associates to the spontaneous dynamics of the model a natural notion of Gibbs distribution inherited from ergodic theory or

Xingran Chen, Mohammad Hassan Lotfi, Saswati Sarkar

This paper investigates the incentives of mobile network operators (MNOs) for acquiring additional spectrum to offer mobile virtual network operators (MVNOs) and thereby inviting competition for a common pool of end users (EUs). We consider a base case and two generalizations: (i) one MNO and one MVNO, (ii) one MNO, one MVNO and an outside option, and (iii)

Younjoo Seo, Andreas Loukas, Nathanaël Perraudin

This paper focuses on the discrimination capacity of aggregation functions: these are the permutation invariant functions used by graph neural networks to combine the features of nodes. Realizing that the most powerful aggregation functions suffer from a dimensionality curse, we consider a restricted setting. In particular, we show that the standard sum and

Hiroshi Okada, Morimitsu Tanimoto

We study quark and lepton mass matrices in the $A_4$ modular symmetry towards the unification of the quark and lepton flavors. We adopt modular forms of weights $2$ and $6$ for quarks and charged leptons, while we use modular forms of weight $4$ for the neutrino mass matrix which is generated by the Weinberg operator. We obtain the successful quark mass matr

Elias Stehle, Hans-Arno Jacobsen

Parsing is essential for a wide range of use cases, such as stream processing, bulk loading, and in-situ querying of raw data. Yet, the compute-intense step often constitutes a major bottleneck in the data ingestion pipeline, since parsing of inputs that require more involved parsing rules is challenging to parallelise. This work proposes a massively paralle

Paul Wild, Lutz Schröder, Dirk Pattinson, Barbara König

The fuzzy modality `probably` is interpreted over probabilistic type spaces by taking expected truth values. The arising probabilistic fuzzy description logic is invariant under probabilistic bisimilarity; more informatively, it is non-expansive wrt. a suitable notion of behavioural distance. In the present paper, we provide a characterization of the express

Hisashi Morioka, Naotaka Shoji

In this paper, we consider the scattering theory for acoustic-type equations on non-compact manifolds with a single flat end. Our main purpose is to show an existence result of non-scattering energies. Precisely, we show a Weyl-type lower bound for the number of non-scattering energies. Usually a scattered wave occurs for every incident wave by the inhomogen

Te Juin Lester Tan, Reza Shokri

Deep learning models are vulnerable to various adversarial manipulations of their training data, parameters, and input sample. In particular, an adversary can modify the training data and model parameters to embed backdoors into the model, so the model behaves according to the adversary's objective if the input contains the backdoor features, referred to

Zhenwei Luo

We presented a new 3D refinement method for Cryo-EM single particle analysis which can improve the resolution of final electron density map in this paper. We proposed to enforce both sparsity and smoothness to improve the regularity of electron density map in the refinement process. To achieve this goal, we designed a novel type of real space penalty functio

Min Huang, Guo Luo

We present a simple, fast, and accurate method for pricing a variety of discretely monitored options in the Black-Scholes framework, including autocallable structured products, single and double barrier options, and Bermudan options. The method is based on a quadrature technique, and it employs only elementary calculations and a fixed one-dimensional uniform

Yuandong Tian, Tina Jiang, Qucheng Gong, Ari Morcos

We analyze the dynamics of training deep ReLU networks and their implications on generalization capability. Using a teacher-student setting, we discovered a novel relationship between the gradient received by hidden student nodes and the activations of teacher nodes for deep ReLU networks. With this relationship and the assumption of small overlapping teache

Jyun-Yi Wu, Cheng Yu, Szu-Wei Fu, Chih-Ting Liu

Most recent studies on deep learning based speech enhancement (SE) focused on improving denoising performance. However, successful SE applications require striking a desirable balance between denoising performance and computational cost in real scenarios. In this study, we propose a novel parameter pruning (PP) technique, which removes redundant channels in

Jesus A. De Loera, Jamie Haddock, Anna Ma, Deanna Needell

Machine learning algorithms typically rely on optimization subroutines and are well-known to provide very effective outcomes for many types of problems. Here, we flip the reliance and ask the reverse question: can machine learning algorithms lead to more effective outcomes for optimization problems? Our goal is to train machine learning methods to automatica