February 2019 arXiv papers — page 96

Hirofumi Fujii, Kazuhiko Hara, Shogo Hashimoto, Kohei Hayashi

We studied the inner structure of the nuclear reactor of the Japan Atomic Power Company (JAPC) at Tokai, Japan, by the muon radiography. In this study, muon detectors were placed outside of the reactor building. By detecting cosmic muons penetrating through the wall of the reactor building, we could successfully identify the objects such as the containment v

Yaxing Li, Christian Diddens, Pengyu Lv, Herman Wijshoff

The flow in an evaporating glycerol-water binary sub-millimeter droplet with Bond number Bo $\ll$ 1 is studied both experimentally and numerically. First, we measure the flow fields near the substrate by micro-PIV for both sessile and pendant droplets during the evaporation process, which surprisingly show opposite radial flow directions -- inward and outwar

Milad Afzalan, Farrokh Jazizadeh

Spectral clustering algorithms typically require a priori selection of input parameters such as the number of clusters, a scaling parameter for the affinity measure, or ranges of these values for parameter tuning. Despite efforts for automating the process of spectral clustering, the task of grouping data in multi-scale and higher dimensional spaces is yet t

Jing-Zhao Qi, Shuo Cao, Chenfa Zheng, Yu Pan

In this paper we analyse the implications of the latest cosmological data sets to test the Etherington's distance duality relation (DDR), which connects the luminosity distance $D_L$ and angular diameter distance $D_A$ at the same redshift. For $D_L$ we consider the simulated data of gravitational waves from the third-generation gravitational wave detect

C. Ligtenberg, K. Heijhoff, Y. Bilevych, K. Desch

With the ultimate goal of developing a pixel-based readout for a TPC at the ILC, a GridPix readout system consisting of one Timepix3 chip with an integrated amplification grid was embedded in a prototype detector. The performance was studied in a testbeam with 2.5 GeV electrons at the ELSA accelerator in Bonn. The error on the track position measurement both

Yueru Chen, Yijing Yang, Min Zhang, C. -C. Jay Kuo

A semi-supervised learning framework using the feedforward-designed convolutional neural networks (FF-CNNs) is proposed for image classification in this work. One unique property of FF-CNNs is that no backpropagation is used in model parameters determination. Since unlabeled data may not always enhance semi-supervised learning, we define an effective quality

Sean Vaskov, Utkarsh Sharma, Shreyas Kousik, Matthew Johnson-Roberson

Trajectory planning is challenging for autonomous cars since they operate in unpredictable environments with limited sensor horizons. To incorporate new information as it is sensed, planning is done in a loop, with the next plan being computed as the previous plan is executed. The recent Reachability-based Trajectory Design (RTD) is a provably safe, real-tim

Tommaso Taddei

We present an offline/online computational procedure for computing the dual norm of parameterized linear functionals. The key elements of the approach are (i) an empirical test space for the manifold of Riesz elements associated with the parameterized functional, and (ii) an empirical quadrature procedure to efficiently deal with parametrically non-affine te

Andrea Barth, Andreas Stein

As a simplified model for subsurface flows elliptic equations may be utilized. Insufficient measurements or uncertainty in those are commonly modeled by a random coefficient, which then accounts for the uncertain permeability of a given medium. As an extension of this methodology to flows in heterogeneous\fractured\porous media, we incorporate jumps in the d

Gang Wang, Georgios B. Giannakis, Jie Chen

In today's cyber-enabled smart grids, high penetration of uncertain renewables, purposeful manipulation of meter readings, and the need for wide-area situational awareness, call for fast, accurate, and robust power system state estimation. The least-absolute-value (LAV) estimator is known for its robustness relative to the weighted least-squares (WLS) on

Dante Kalise, Karl Kunisch

A procedure for the numerical approximation of high-dimensional Hamilton-Jacobi-Bellman (HJB) equations associated to optimal feedback control problems for semilinear parabolic equations is proposed. Its main ingredients are a pseudospectral collocation approximation of the PDE dynamics, and an iterative method for the nonlinear HJB equation associated to th

Christoph Boeddeker, Patrick Hanebrink, Lukas Drude, Jahn Heymann

This report describes the computation of gradients by algorithmic differentiation for statistically optimum beamforming operations. Especially the derivation of complex-valued functions is a key component of this approach. Therefore the real-valued algorithmic differentiation is extended via the complex-valued chain rule. In addition to the basic mathematic

L. M. Briceño-Arias, D. Kalise, F. J. Silva

We address the numerical approximation of Mean Field Games with local couplings. For power-like Hamiltonians, we consider both unconstrained and constrained stationary systems with density constraints in order to model hard congestion effects. For finite difference discretizations of the Mean Field Game system, we follow a variational approach. We prove that

Alireza Javani, Marwen Zorgui, Zhiying Wang

Having timely and fresh knowledge about the current state of information sources is critical in a variety of applications. In particular, a status update may arrive at the destination much later than its generation time due to processing and communication delays. The freshness of the status update at the destination is captured by the notion of age of inform

Harish Kumar, Balaraman Ravindran

In the domain of algorithmic music composition, machine learning-driven systems eliminate the need for carefully hand-crafting rules for composition. In particular, the capability of recurrent neural networks to learn complex temporal patterns lends itself well to the musical domain. Promising results have been observed across a number of recent attempts at

Eric Chesebro

We describe the (P)SL(2,C) character varieties of all 2-bridge knots and the diagonal character varieties for all 2-bridge links in terms of a set of polynomials defined using Farey recursion.

Christopher Bender, Kevin O'Connor, Yang Li, Juan Jose Garcia

In this work, we develop a new approach to generative density estimation for exchangeable, non-i.i.d. data. The proposed framework, FlowScan, combines invertible flow transformations with a sorted scan to flexibly model the data while preserving exchangeability. Unlike most existing methods, FlowScan exploits the intradependencies within sets to learn both g

Kofi Edee, Maha Benrhouma, Mauro Antezza, Jonathan Albert Fan

We present a semi-analytical model of the resonance phenomena occurring in a hybrid system made of a 1D array of periodic subwavelength slits deposited on an insulator/graphene layer. We show that the spectral response of this hybrid system can be fully explained by a simple semi-analytical model based on a weak and strong couplings between two elementary su

Alexandros Haridis

A new methodological approach for the study of topology for shapes made of arrangements of lines, planes or solids is presented. Topologies for shapes are traditionally built on the classical theory of point-sets. In this paper, topologies are built with shapes, which are formalized without points, and with structures defined from their parts. An interpretat

Armin Taheri, Carlos Da Silva, Cristina H. Amon

The investigation of thermal properties of recently emerged two-dimensional (2D) materials is a necessary step towards fulfilling their potential applications in nano-electronics devices. In this study, the thermal conductivity of novel \b{eta}-NX (X=P, As, Sb) monolayers are investigated using a first-principles density functional theory (DFT) study based o

P. L. Krapivsky, L. I. Nazarov, M. V. Tamm

We investigate the growth of needles from a flat substrate. We focus on the situation when needles suddenly begin to grow from the seeds randomly distributed on the line. The width of needles is ignored and we additionally assume that (i) the growth rate is the same for all needles; (ii) the direction of the growth of each needle is randomly chosen from the

Lukas Oberreiter, Udo Seifert, Andre C. Barato

Subharmonic response is a well known phenomena in, e.g., deterministic nonlinear dynamical systems. We investigate the conditions under which such subharmonic oscillations can persist for a long time in open systems with stochastic dynamics due to thermal fluctuations. In contrast to stochastic autonomous systems in a stationary state, for which the number o

Daniel Lemire, Owen Kaser, Nathan Kurz

On common processors, integer multiplication is many times faster than integer division. Dividing a numerator n by a divisor d is mathematically equivalent to multiplication by the inverse of the divisor (n / d = n x 1/d). If the divisor is known in advance---or if repeated integer divisions will be performed with the same divisor---it can be beneficial to s

Talmaj Marinč, Vignesh Srinivasan, Serhan Gül, Cornelius Hellge

In low light or short-exposure photography the image is often corrupted by noise. While longer exposure helps reduce the noise, it can produce blurry results due to the object and camera motion. The reconstruction of a noise-less image is an ill posed problem. Recent approaches for image denoising aim to predict kernels which are convolved with a set of succ

Daniel Gibney, Sharma V. Thankachan

In recent years several compressed indexes based on variants of the Burrows-Wheeler transformation have been introduced. Some of these index structures far more complex than a single string, as was originally done with the FM-index [Ferragina and Manzini, J. ACM 2005]. As such, there has been an effort to better understand under which conditions such an inde

Alex Nowak-Vila, Francis Bach, Alessandro Rudi

In this work we provide a theoretical framework for structured prediction that generalizes the existing theory of surrogate methods for binary and multiclass classification based on estimating conditional probabilities with smooth convex surrogates (e.g. logistic regression). The theory relies on a natural characterization of structural properties of the tas

Kazuki Irie, Rohit Prabhavalkar, Anjuli Kannan, Antoine Bruguier

In conventional speech recognition, phoneme-based models outperform grapheme-based models for non-phonetic languages such as English. The performance gap between the two typically reduces as the amount of training data is increased. In this work, we examine the impact of the choice of modeling unit for attention-based encoder-decoder models. We conduct exper

Mike Wu, Kristy Choi, Noah Goodman, Stefano Ermon

Despite the recent success in probabilistic modeling and their applications, generative models trained using traditional inference techniques struggle to adapt to new distributions, even when the target distribution may be closely related to the ones seen during training. In this work, we present a doubly-amortized variational inference procedure as a way to

Masoud Shabaninezhad, Abubkr Abuhagr, Naga Arjun Sakthivel, Chanaka Kumara

The influence of passivating ligand on electron-phonon relaxation dynamics of the smallest sized gold clusters was studied using ultrafast transient absorption spectroscopy and theoretical modeling. The electron dynamics in Au279, Au329, and Au329 passivated with TBBT, SC2Ph and SC6, respectively, were investigated. Ultrafast transient absorption measurement

Ali Vahdat, Mouloud Belbahri, Vahid Partovi Nia

Erbium-doped fiber amplifier (EDFA) is an optical amplifier/repeater device used to boost the intensity of optical signals being carried through a fiber optic communication system. A highly accurate EDFA model is important because of its crucial role in optical network management and optimization. The input channels of an EDFA device are treated as either on

Le Ngoc Tram, Thiem Hoang

In a previous work, Hoang and Tram discovered a new mechanism for destruction of nanoparticles due to suprathermal rotation of grains in stationary C-shocks, which is termed rotational disruption. In this paper, we extend our previous study for non-stationary shocks driven by outflows and young supernovae remnants that have dynamical ages shorter than the ti

Vladimir A. Gavrichkov, Yury Shanko, Natalia G. Zamkova, Antonio Bianconi

Local and fast structural probes using synchrotron radiation have shown nanoscale striped puddles and nanoscale phase separation in doped perovskites.It is known that the striped phases in doped perovskites are due to competing interactions involving charge, spin and lattice degrees of freedom,but while many theoretical models for spin and charge stripes hav

Adrián Lozano-Durán, Michael Karp, Navid. C. Constantinou

We study the mechanism of energy injection from the mean flow to the fluctuating velocity necessary to maintain wall turbulence. This process is believed to be correctly represented by the linearized Navier--Stokes equations, and three potential linear mechanisms have been considered, namely, modal instability of the streamwise mean cross-flow $U(y,z,t)$, no

Musema Sinamuli, Robert B. Mann

We construct a time-dependent expression of the computational complexity of a quantum system which consists of two conformal complex scalar field theories in d dimensions coupled to constant electric potentials and defined on the boundaries of a charged AdS black hole in (d+1) dimensions. Using a suitable choice of the reference state, Hamiltonian gates and

Andreas Maurer, Massimiliano Pontil

The method to derive uniform bounds with Gaussian and Rademacher complexities is extended to the case where the sample average is replaced by a nonlinear statistic. Tight bounds are obtained for U-statistics, smoothened L-statistics and error functionals of l2-regularized algorithms.

J. A. Hofer, Nestor Haberkorn

We report on the structural and superconducting properties of nanocrystalline tungsten thin films growth by sputtering at room temperature with an N2:Ar mixture (N2 from 3% to 50%). The crystalline phases were identified by comparing pristine and thermal annealed thin films. For N2/(Ar+N2) mixtures between 3 and 10 %, the films display nanocrystalline beta-W

E. L. Mustapha Ait Benhassi, Mohamed Fadili, Lahcen Maniar

This paper is devoted to the null controllability of a one-dimensional linear parabolic equation with a time-varying state delay and a diffusion coefficient that degenerates at one endpoint. Both the weakly and strongly degenerate cases are considered. When the delay map $g(t)=t-τ(t)$ is strictly increasing, the adjoint equation contains an advanced term wei

Prateek Jaiswal, Vinayak A. Rao, Harsha Honnappa

We study the asymptotic consistency properties of $α$-Rényi approximate posteriors, a class of variational Bayesian methods that approximate an intractable Bayesian posterior with a member of a tractable family of distributions, the member chosen to minimize the $α$-Rényi divergence from the true posterior. Unique to our work is that we consider settings wit

Chula J. Jayawardene, W. Chandanie W. Navaratna, J. N. Senadheera

Let $G$ and $H$ be finite graphs. If for any two-coloring of the edges of a complete graph $K_n$, there is a copy of $G$ in the first color, red, or a copy of $H$ in the second color, blue, we will say $K_n\rightarrow (G,H)$. The Ramsey number $r(G, H)$ is defined as the smallest positive integer $n$ such that $K_{n} \rightarrow (G, H)$. A two-coloring of $K

Sepideh Aghamolaei, Mohammad Ghodsi

A set of points $P$ in a metric space and a constant integer $k$ are given. The $k$-center problem finds $k$ points as centers among $P$, such that the maximum distance of any point of $P$ to their closest centers $(r)$ is minimized. Doubling metrics are metric spaces in which for any $r$, a ball of radius $r$ can be covered using a constant number of balls

Ang Li, Aleksandra Spyra, Sagi Perel, Valentin Dalibard

Population Based Training (PBT) is a recent approach that jointly optimizes neural network weights and hyperparameters which periodically copies weights of the best performers and mutates hyperparameters during training. Previous PBT implementations have been synchronized glass-box systems. We propose a general, black-box PBT framework that distributes many

Micha Livne, David Fleet

We formulate a new class of conditional generative models based on probability flows. Trained with maximum likelihood, it provides efficient inference and sampling from class-conditionals or the joint distribution, and does not require a priori knowledge of the number of classes or the relationships between classes. This allows one to train generative models

Jonathan Colen, Eugene B. Kolomeisky

According to Kelvin, a point pressure source uniformly traveling over the surface of deep calm water leaves behind universal wake pattern confined within $39^{\circ}$ sector and consisting of the so-called transverse and diverging wavefronts. Actual ship wakes differ in their appearance from both each other and Kelvin's prediction. The difference can be

Joshua Romoff, Peter Henderson, Ahmed Touati, Emma Brunskill

In many finite horizon episodic reinforcement learning (RL) settings, it is desirable to optimize for the undiscounted return - in settings like Atari, for instance, the goal is to collect the most points while staying alive in the long run. Yet, it may be difficult (or even intractable) mathematically to learn with this target. As such, temporal discounting

Weiyan Chen

We prove that the space of complex irreducible polynomials of degree $d$ in $n$ variables satisfies two forms of homological stability: first, its cohomology stabilizes as $d$ increases, and second, its compactly supported cohomology stabilizes as $n$ increases. Our topological results are inspired by counting results over finite fields due to Carlitz and Hy

Alessio Zappone, Marco Di Renzo, Mérouane Debbah

This work deals with the use of emerging deep learning techniques in future wireless communication networks. It will be shown that data-driven approaches should not replace, but rather complement traditional design techniques based on mathematical models. Extensive motivation is given for why deep learning based on artificial neural networks will be an indis

Seyran Saeedi, Tom Arodz

We analyze the computational complexity of Quantum Sparse Support Vector Machine, a linear classifier that minimizes the hinge loss and the $L_1$ norm of the feature weights vector and relies on a quantum linear programming solver instead of a classical solver. Sparse SVM leads to sparse models that use only a small fraction of the input features in making d

Sagar Sharma, Keke Chen

Deep learning model developers often use cloud GPU resources to experiment with large data and models that need expensive setups. However, this practice raises privacy concerns. Adversaries may be interested in: 1) personally identifiable information or objects encoded in the training images, and 2) the models trained with sensitive data to launch model-base

Nolan R. Wallach

The first main result is that the Casselman-Wallach Globalization of a real analytic family of Harish-Chandra modules is continuous in the parameter. Our proof of this result uses results from the thesis of Vincent van der Noort in several critical ways. In his thesis the holomorphic version of the result was proved in the case when the parameter space is a

Gabriel-Dominique Marleau, Gavin A. L. Coleman, Adrien Leleu, Christoph Mordasini

A low-mass companion to the two-solar mass star HIP65426 has recently been detected by SPHERE at around 100 au from its host. Explaining the presence of super-Jovian planets at large separations, as revealed by direct imaging, is currently an open question. We want to derive statistical constraints on the mass and initial entropy of HIP65426b and to explore

David S. Berman, Chris D. A. Blair, Ray Otsuki

We construct a background for M-theory that is moduli free. This background is then shown to be related to a topological phase of the $\mathrm{E}_{8(8)}$ exceptional field theory (ExFT). The key ingredient in the construction is the embedding of non-Riemannian geometry in ExFT. This allows one to describe non-relativistic geometries, such as Newton-Cartan or

Brian D. Metzger, Ben Margalit, Lorenzo Sironi

Fast radio bursts (FRB) can arise from synchrotron maser emission at ultra-relativistic magnetized shocks, such as produced by flare ejecta from young magnetars. We combine PIC simulation results for the synchrotron maser with the dynamics of self-similar shock deceleration, as commonly applied to GRBs, to explore the implications for FRB emission. We assume

Hang Liu, Ziyu Tang, Kyriakos Destounis, Bin Wang

It was recently shown that Strong Cosmic Censorship might be violated for near-extremally-charged black holes in 4-dimensional de Sitter space under scalar perturbations. Here, we extend the study of neutral massless scalar perturbations in higher dimensions and discuss the dimensional influence on the validity of Strong Cosmic Censorship hypothesis. By givi

Brooke Dicenzo Emily M. Levesque

Red supergiants (RSGs) are evolved massive stars that represent extremes, in both their physical sizes and their cool temperatures, of the massive star population. Effective temperature ($T_{\rm eff}$) is the most critical physical property needed to place a RSG on the Hertzsprung-Russell Diagram, due to the stars' cool temperatures and resulting large b

M. E. Ravasio, G. Oganesyan, O. S. Salafia, G. Ghirlanda

GRB 190114C is the first gamma-ray burst detected at Very High Energies (VHE, i.e. >300 GeV) by the MAGIC Cherenkov telescope. The analysis of the emission detected by the Fermi satellite at lower energies, in the 10 keV -- 100 GeV energy range, up to ~ 50 seconds (i.e. before the MAGIC detection) can hold valuable information. We analyze the spectral evolut

J. M. D. Kruijssen, J. E. Dale, S. N. Longmore, D. L. Walker

The evolution of molecular clouds in galactic centres is thought to differ from that in galactic discs due to a significant influence of the external gravitational potential. We present a set of numerical simulations of molecular clouds orbiting on the 100-pc stream of the Central Molecular Zone (the central $\sim500$ pc of the Galaxy) and characterise their

Bingtian Ye, Francisco Machado, Christopher David White, Roger S. K. Mong

A tremendous amount of recent attention has focused on characterizing the dynamical properties of periodically driven many-body systems. Here, we use a novel numerical tool termed `density matrix truncation' (DMT) to investigate the late-time dynamics of large-scale Floquet systems. We find that DMT accurately captures two essential pieces of Floquet phy

Jonah Herzog-Arbeitman, Sebastian Mantilla, Inti Sodemann

The nature and the very existence of the resonant plaquette valence bond state that separates the classical columnar phase and the Rokhsar and Kivelson point in the quantum dimer model remains unsettled. Here we take a different line of attack on this model, and on the closely related six vertex model, by exploiting the global conservation law of the number

L. Kotsedi, V. Furlan, V. Bharadwaj, K. Kaviyarasu

Thin coatings of Chromium oxide have been used for applications as absorbing material in solar cells, as protections for magnetic data recording devices and as shields in flexible solar cells. Thin coatings of pure chromium were vacuum deposited on a glass substrate using hot electrons from tungsten filament. These coatings were then treated with a nanosecon

Ilaria Pallecchi, Marco Monni, Pietro Manfrinetti, Marina Putti

By a combined experimental and theoretical approach, we investigate normal state thermoelectric transport in MgB2, as a probe of selective disorder and doping in the sigma and pi bands. We calculate the temperature dependent diffusive Seebeck coefficient Sdiff(T) with the Boltzmann equation resolved in relaxation time approximation, taking into account the s

Grigory Bednik

Machine learning methods are being actively considered as a new tool of describing many body physics. However, so far, their capabilities has been only demonstrated in previously studied models, such as e.g. Ising model. Here, we consider a simple problem, demonstrating that neural networks can be successfully used to give new insights in statistical physics

Olivier Glorieux, Daniel Monclair, Nicolas Tholozan

We study the relation between critical exponents and Hausdorff dimensions of limit sets for projective Anosov representations. We prove that the Hausdorff dimension of the symmetric limit set in $\mathbf{P}(\mathbb{R}^{n}) \times \mathbf{P}({\mathbb{R}^{n}}^*)$ is bounded between two critical exponents associated respectively to a highest weight and a simple

Grant Rotskoff, Samy Jelassi, Joan Bruna, Eric Vanden-Eijnden

Neural networks with a large number of parameters admit a mean-field description, which has recently served as a theoretical explanation for the favorable training properties of "overparameterized" models. In this regime, gradient descent obeys a deterministic partial differential equation (PDE) that converges to a globally optimal solution for netwo

Amritanshu Agrawal, Wei Fu, Di Chen, Xipeng Shen

Machine learning techniques applied to software engineering tasks can be improved by hyperparameter optimization, i.e., automatic tools that find good settings for a learner's control parameters. We show that such hyperparameter optimization can be unnecessarily slow, particularly when the optimizers waste time exploring "redundant tunings"',

Wanpeng Tan

A model of $n-n'$ (neutron-mirror neutron) oscillations is proposed under the framework of the mirror matter theory with slightly broken mirror symmetry. It resolves the neutron lifetime discrepancy, i.e., the 1% difference in neutron lifetime between measurements from "beam" and "bottle" experiments. In consideration of the early univers

G. M. Bosyk, G. Bellomo, F. Holik, H. Freytes

We address the problem of finding the optimal common resource for an arbitrary family of target states in quantum resource theories based on majorization, that is, theories whose conversion law between resources is determined by a majorization relationship, such as it happens with entanglement, coherence or purity. We provide a conclusive answer to this prob

Cinzia Casagrande

We study (smooth, complex) Fano 4-folds X having a rational contraction of fiber type, that is, a rational map X-->Y that factors as a sequence of flips followed by a contraction of fiber type. The existence of such a map is equivalent to the existence of a non-zero, non-big movable divisor on X. Our main result is that if Y is not P^1 or P^2, then the Picar

Yasha Savelyev

We first partially extend a theorem of Topping, on the relation between mean curvature and intrinsic diameter, from immersed submanifolds of $\mathbb{R} ^{n} $ to almost everywhere immersed, closed submanifolds of a compact Riemannian manifold. We use this to prove quantization of energy for pseudo-holomorphic closed curves, of all genus, in a compact locall

Fabricio Valencia

We give a new characterization of flat affine manifolds in terms of an action of the Lie algebra of classical infinitesimal affine transformations on the bundle of linear frames. We characterize flat affine symplectic Lie groups using symplectic étale affine representations and as a consequence of this, we show that a flat affine symplectic Lie group with bi

Roberto Valle, José M. Buenaposada, Antonio Valdés, Luis Baumela

Face alignment algorithms locate a set of landmark points in images of faces taken in unrestricted situations. State-of-the-art approaches typically fail or lose accuracy in the presence of occlusions, strong deformations, large pose variations and ambiguous configurations. In this paper we present 3DDE, a robust and efficient face alignment algorithm based

Jesse Chan

High order entropy stable discontinuous Galerkin (DG) methods for nonlinear conservation laws satisfy an inherent discrete entropy inequality. The construction of such schemes has relied on the use of carefully chosen nodal points or volume and surface quadrature rules to produce operators which satisfy a summation-by-parts (SBP) property. In this work, we s

Rena Elkin, Saad Nadeem, Hedok Lee, Helene Benveniste

In this work, a unified representation of all the time-varying dynamics is accomplished with a Lagrangian framework for analyzing Fisher-Rao regularized dynamical optimal mass transport (OMT) derived flows. While formally equivalent to the Eulerian based Schrödinger bridge OMT regularization scheme, the Fisher-Rao approach allows a simple and interpretable m

Michael O'Connor, Simon J. Bennie, Helen M. Deeks, Alexander Jamieson-Binnie

As molecular scientists have made progress in their ability to engineer nano-scale molecular structure, we are facing new challenges in our ability to engineer molecular dynamics (MD) and flexibility. Dynamics at the molecular scale differs from the familiar mechanics of everyday objects, because it involves a complicated, highly correlated, and three-dimens

Marco Polo Jimenez-Segura, Tomohiro Takayama, David Bérardan, Andreas Hoser

We report the magnetic properties of Mg$_{0.2}$Co$_{0.2}$Ni$_{0.2}$Cu$_{0.2}$Zn$_{0.2}$O, a high-entropy oxide with rocksalt structure, and the influence of substitutions on these properties. From the magnetic susceptibility and neutron diffraction measurements, we found that this compound exhibits long-range magnetic order below 120 K despite the substantia

Olaf Parczyk

A graph $G$ is called universal for a family of graphs $\mathcal{F}$ if it contains every element $F \in \mathcal{F}$ as a subgraph. Let $\mathcal{F}(n,2)$ be the family of all graphs with maximum degree $2$. Ferber, Kronenberg, and Luh [Optimal Threshold for a Random Graph to be 2-Universal, to appear in Transactions of the American Mathematical Society] pr

Rama Mishra, Ross Staffeldt

We investigate several conjectures in geometric topology by assembling computer data obtained by studying weaving knots, a doubly infinite family $W(p,n)$ of examples of hyperbolic knots. In particular, we compute some important polynomial knot invariants, as well as knot homologies, for the subclass $W(3,n)$ of this family. We use these knot invariants to c

Stefan Takacs

Isogeometric Analysis is a high-order discretization method for boundary value problems that uses a number of degrees of freedom which is as small as for a low-order method. Standard isogeometric discretizations require a global parameterization of the computational domain. In non-trivial cases, the domain is decomposed into patches having separate parameter

Diego A. Carranza, Adem E. Hursit, Juan A. Valiente Kroon

Inspired by a similar analysis for the vacuum conformal Einstein field equations by Paetz [Ann. H. Poincaré 16, 2059 (2015)], in this article we show how to construct a system of quasilinear wave equations for the geometric fields associated to the conformal Einstein field equations coupled to matter models whose energy-momentum tensor has vanishing trace. I

Xiao Xiao, Laurent Buse, Fehmi Cirak

The need to compute the intersections between a line and a high-order curve or surface arises in a large number of finite element applications. Such intersection problems are easy to formulate but hard to solve robustly. We introduce a non-iterative method for computing intersections by solving a matrix singular value decomposition (SVD) and an eigenvalue pr

Felix Dangel, Stefan Harmeling, Philipp Hennig

We propose a modular extension of backpropagation for the computation of block-diagonal approximations to various curvature matrices of the training objective (in particular, the Hessian, generalized Gauss-Newton, and positive-curvature Hessian). The approach reduces the otherwise tedious manual derivation of these matrices into local modules, and is easy to

Manuel Wieland, Walter Arne, Nicole Marheineke, Raimund Wegener

In melt-blowing processes mico- and nanofibers are produced by the extrusion of polymeric jets into a directed, turbulent high-speed airflow. Up to now the physical mechanism for the drastic jet thinning is not fully understood, since in the existing literature the numerically computed/predicted fiber thickness differs several orders of magnitude from those

Moritz Mühlenthaler, Alexander Raß, Manuel Schmitt, Rolf Wanka

Meta-heuristics are powerful tools for solving optimization problems whose structural properties are unknown or cannot be exploited algorithmically. We propose such a meta-heuristic for a large class of optimization problems over discrete domains based on the particle swarm optimization (PSO) paradigm. We provide a comprehensive formal analysis of the perfor

Shumaila Yousaf, Akhlaq Ahmad Bhatti, Akbar Ali

The modified Albertson index, denoted by $A\!^*\!$, of a graph $G$ is defined as $A\!^*\!(G)=\sum_{uv\in E(G)} |(d_{u})^{2}- (d_{v})^{2}|$, where $d_u$, $d_v$ denote the degrees of the vertices $u$, $v$, respectively, of $G$ and $E(G)$ is the edge set of $G$. In this note, a sharp lower bound of $A\!^*$ in terms of the maximum degree for the case of trees is

Alexander Heinemann

This paper studies the joint inference on conditional volatility parameters and the innovation moments by means of bootstrap to test for the existence of moments for GARCH(p,q) processes. We propose a residual bootstrap to mimic the joint distribution of the quasi-maximum likelihood estimators and the empirical moments of the residuals and also prove its val

Lorenzo M. Procopio, Francisco Delgado, Marco Enriquez, Nadia Belabas

In quantum Shannon theory, transmission of information is enhanced by quantum features. Up to very recently, the trajectories of transmission remained fully classical. Recently, a new paradigm was proposed by playing quantum tricks on two completely depolarizing quantum channels i.e. using coherent control in space or time of the two quantum channels. We ext

Mohammad Mohammadi

For a real nonlinear Klein-Gordon Lagrangian density with a special solitary wave solution (SSWS), which is essentially unstable, it is shown how adding a proper additional massless term could guarantee the energetically stability of the SSWS, without changing its dominant dynamical equation and other properties. In other words, it is a stability catalyzer.

Alysson Gold, Sami Tantawi

Finite element representations of Maxwell's equations pose unusual challenges inherent to the variational representation of the `curl-curl' equation for the fields. We present a variational formulation based on classical field theory. Borrowing from QED, we modify the Lagrangian by adding an implicit gauge-fixing term. Our formulation, in the languag

Andreas Čap, Karin Melnick

We construct a family of $(2,n)$-almost Grassmannian structures of regularity $C^1$, each admitting a one-parameter group of strongly essential automorphisms, and each not flat on any neighborhood of the higher-order fixed point. This shows that Theorem 1.3 of [9] does not hold assuming only $C^1$ regularity of the structure (see also [2, Prop 3.5]).

James A. Goodchild, Danielle L. Walsh, Simon D. Connell

Supported Lipid Bilayers (SLBs) are model membranes formed at solid substrate surfaces. This architecture renders the membrane experimentally accessible to surface sensitive techniques used to study their properties, including Atomic Force Microscopy (AFM), optical fluorescence microscopy, Quartz Crystal Microbalance (QCM) and X-Ray/Neutron Reflectometry, an

Paul E. O'Keeffe, Sebastian Wieczorek

We discuss tipping phenomena (critical transitions) in nonautonomous systems using an example of a bistable ecosystem model with environmental changes represented by time-varying parameters [Scheffer et al., Ecosystems, 11 (2008), pp. 275--279]. We give simple testable criteria for the occurrence of nonautonomous tipping from the herbivore-dominating equilib

Ben Brubaker, Valentin Buciumas, Daniel Bump, Henrik P. A. Gustafsson

Type A Demazure atoms are pieces of Schur functions, or sets of tableaux whose weights sum to such functions. Inspired by colored vertex models of Borodin and Wheeler, we will construct solvable lattice models whose partition functions are Demazure atoms; the proof of this makes use of a Yang-Baxter equation for a colored five-vertex model. As a biproduct, w

Christopher Voll

We produce explicit formulae for various ideal zeta functions associated to the members of an infinite family of class-$2$-nilpotent Lie rings, introduced in [1], in terms of Igusa functions. As corollaries we obtain information about analytic properties of global ideal zeta functions, local functional equations, topological, reduced, and graded ideal zeta f

Tianwei Hou, Yuanwei Liu, Zhengyu Song, Xin Sun

This paper advocates a pair of strategies in non-orthogonal multiple access (NOMA) in unmanned aerial vehicles (UAVs) communications, where multiple UAVs play as new aerial communications platforms for serving terrestrial NOMA users. A new multiple UAVs framework with invoking stochastic geometry technique is proposed, in which a pair of practical strategies

Moon-Jin Kang, Alexis Vasseur

We prove the uniqueness and stability of entropy shocks to the isentropic Euler systems among all vanishing viscosity limits of solutions to associated Navier-Stokes systems. To take into account the vanishing viscosity limit, we show a contraction property for any large perturbations of viscous shocks to the Navier-Stokes system. The contraction estimate do

D. G. Cerdeno, A. Cheek, P. Martin-Ramiro, J. M. Moreno

We study an extension of the Standard Model that addresses the hints of lepton flavour universality violation observed in $B\to K^{(*)} l^+l^-$ decays at LHCb, while providing a viable candidate for dark matter. The model incorporates two new scalar fields and a Majorana fermion that induce one-loop contributions to $B$ meson decays. We show that agreement w

A. M. Tsvelik, O. M. Yevtushenko

We study the phase diagram and transport properties of arbitrarily doped quantum wires functionalized by magnetic adatoms. The appropriate theoretical model for these systems is a dense one-dimensional Kondo Lattice (KL) which consists of itinerant electrons interacting with localized quantum magnetic moments. We discover the novel phase of the locally helic

Ricardo Heras

The existence of the Aharonov-Bohm phase shows that the magnetic field may produce nonlocal effects in quantum mechanics. It is generally believed that such a nonlocal behavior of the magnetic field is not possible in classical physics and that this represents a clear difference between classical and quantum mechanics. Contrary to these beliefs, we argue tha

Thomas Frerix, Matthias Nießner, Daniel Cremers

We propose a method to impose homogeneous linear inequality constraints of the form $Ax\leq 0$ on neural network activations. The proposed method allows a data-driven training approach to be combined with modeling prior knowledge about the task. One way to achieve this task is by means of a projection step at test time after unconstrained training. However,

Stephan Alaniz, Diego Marcos, Bernt Schiele, Zeynep Akata

Integrated interpretability without sacrificing the prediction accuracy of decision making algorithms has the potential of greatly improving their value to the user. Instead of assigning a label to an image directly, we propose to learn iterative binary sub-decisions, inducing sparsity and transparency in the decision making process. The key aspect of our mo

Jonathan Niles-Weed, Quentin Berthet

We study nonparametric density estimation problems where error is measured in the Wasserstein distance, a metric on probability distributions popular in many areas of statistics and machine learning. We give the first minimax-optimal rates for this problem for general Wasserstein distances, and show that, unlike classical nonparametric density estimation, th