GSA-Forecaster: Forecasting Graph-Based Time-Dependent Data with Graph Sequence Attention

Forecasting graph-based time-dependent data has many practical applications. This task is challenging as models need not only to capture spatial dependency and temporal dependency within the data, but also to leverage useful auxiliary information for accurate predictions. In this paper, we analyze limitations of state-of-the-art models on dealing with temporal dependency. To address this limitation, we propose GSA-Forecaster, a new deep learning model for forecasting graph-based time-dependent data. GSA-Forecaster leverages graph sequence attention (GSA), a new attention mechanism proposed in this paper, for effectively capturing temporal dependency. GSA-Forecaster embeds the graph structure of the data into its architecture to address spatial dependency. GSA-Forecaster also accounts for auxiliary information to further improve predictions. We evaluate GSA-Forecaster with large-scale real-world graph-based time-dependent data and demonstrate its effectiveness over state-of-the-art models with 6.7% RMSE and 5.8% MAPE reduction

Delocalization in harmonic chains with long-range correlated random masses

We study the nature of collective excitations in harmonic chains with masses exhibiting long-range correlated disorder with power spectrum proportional to $1/k^{\alpha}$, where $k$ is the wave-vector of the modulations on the random masses landscape. Using a transfer matrix method and exact diagonalization, we compute the localization length and participation ratio of eigenmodes within the band of allowed energies. We find extended vibrational modes in the low-energy region for $\alpha > 1$. In order to study the time evolution of an initially localized energy input, we calculate the second moment $M_2(t)$ of the energy spatial distribution. We show that $M_2(t)$, besides being dependent of the specific initial excitation and exhibiting an anomalous diffusion for weakly correlated disorder, assumes a ballistic spread in the regime $\alpha>1$ due to the presence of extended vibrational modes.Comment: 6 pages, 9 figure

Low velocity impact modelling in laminate composite panels with discrete interface elements

A model enabling the detection of damages developing during a low velocity/low energy impact test on laminate composite panels has been elaborated. The ply model is composed of interface type elements to describe matrix cracks and volumic finite elements. This mesh device allows to respect the material orthotropy of the ply and accounts for the discontinuity experimentally observed. Afterwards delaminations are described with interfaces similar to the ones observed with matrix cracks and the coupling between these two damages are established. In the first step, simple stress criteria are used to drive these interface type elements in order to assess the relevance of model principle. Nevertheless, the well known problem of mesh sensitivity of these criteria prevents the use of this model for now as a predictive tool but rather as a qualitative tool. An experimental validation is carried out thanks to impact experimental tests performed by Aboissiere (2003) and a very good match has been found. However, this model could predictivelly be used and would allow to foresee an original method to detect delaminations during an experimental test. This modelling has been successfully tested experimentally and compared to a C-Scan ultrasonic investigation

Empirical analysis of the worldwide maritime transportation network

In this paper we present an empirical study of the worldwide maritime transportation network (WMN) in which the nodes are ports and links are container liners connecting the ports. Using the different representation of network topology namely the space $L$ and $P$, we study the statistical properties of WMN including degree distribution, degree correlations, weight distribution, strength distribution, average shortest path length, line length distribution and centrality measures. We find that WMN is a small-world network with power law behavior. Important nodes are identified based on different centrality measures. Through analyzing weighted cluster coefficient and weighted average nearest neighbors degree, we reveal the hierarchy structure and "rich-club" phenomenon in the network.Comment: 10 pages, 11 figure

Cephalometric And Anthropometric Data Of Obstructive Apnea In Different Age Groups

Introduction: Patients with obstructive sleep apnea syndrome usually present with changes in syndromes; upper airway morphology and/or body fat distribution, which may occur throughout life and Cephalometry; increase the severity of obstructive sleep apnea syndrome with age. Body mass index; Objective: To correlate cephalometric and anthropometric measures with obstructive sleep Anthropometry; apnea syndrome severity in different age groups. Cervical rib; Methods: A retrospective study of cephalometric and anthropometric measures of 102 patients Obesity with obstructive sleep apnea syndrome was analyzed. Patients were divided into three age groups (>= 20 and = 40 and = 60 years). Pearson's correlation was performed for these measures with the apnea-hypopnea index in the full sample, and subsequently by age group. Results: The cephalometric measures MP-H (distance between the. mandibular plane and the hyoid bone) and PNS-P (distance between the posterior nasal spine and the tip of the soft palate) and the neck and waist circumferences showed a statistically significant correlation with apnea-hypopnea index in both the full sample and in the >= 40 and = 60 years). Conclusion: Cephalometric measurements MP-H and PNS-P and cervical and waist circumferences correlated with obstructive sleep apnea syndrome severity in patients in the >= 40 and <60 age group. (C) 2014 Associacao Brasileira de Otorrinolaringologia e Cirurgia Cervico-Facial. Published by Elsevier Editora Ltda. All rights reserved.811798

A compactness theorem for scalar-flat metrics on manifolds with boundary

Let (M,g) be a compact Riemannian manifold with boundary. This paper is concerned with the set of scalar-flat metrics which are in the conformal class of g and have the boundary as a constant mean curvature hypersurface. We prove that this set is compact for dimensions greater than or equal to 7 under the generic condition that the trace-free 2nd fundamental form of the boundary is nonzero everywhere.Comment: 49 pages. Final version, to appear in Calc. Var. Partial Differential Equation

Automatic Abstraction in SMT-Based Unbounded Software Model Checking

Software model checkers based on under-approximations and SMT solvers are very successful at verifying safety (i.e. reachability) properties. They combine two key ideas -- (a) "concreteness": a counterexample in an under-approximation is a counterexample in the original program as well, and (b) "generalization": a proof of safety of an under-approximation, produced by an SMT solver, are generalizable to proofs of safety of the original program. In this paper, we present a combination of "automatic abstraction" with the under-approximation-driven framework. We explore two iterative approaches for obtaining and refining abstractions -- "proof based" and "counterexample based" -- and show how they can be combined into a unified algorithm. To the best of our knowledge, this is the first application of Proof-Based Abstraction, primarily used to verify hardware, to Software Verification. We have implemented a prototype of the framework using Z3, and evaluate it on many benchmarks from the Software Verification Competition. We show experimentally that our combination is quite effective on hard instances.Comment: Extended version of a paper in the proceedings of CAV 201

An optimal charging algorithm to minimise solid electrolyte interface layer in lithium-ion battery

This article presents a novel control algorithm for online optimal charging of lithium-ion battery by explicitly incorporating degradation mechanism into control, to reduce the degradation process. The health of battery directly relates to degradation and capacity fade in cycles of charging. We mainly focus on the growth of the solid electrolyte interface (SEI) layer, which is the primary source of degradation of batteries. This article addresses the challenge of minimising SEI layer growth during charging by incorporating the first-order SEI layer growth rate model into a non-linear model predictive control approach. A single particle model (SPM) is used for optimal charging using orthogonal projection-based model reformulation. Gauss pseudo-spectral method is used for the optimisation of charging trajectories. Results of the optimal algorithm are compared with the traditional constant current constant voltage (CCCV) approach without considering SEI layer growth. It is ensured that overpotential caused by lithium plating remains in a healthy regime which is another feature of the proposed strategy. Simulation results are presented to demonstrate the advantages of the proposed charging method

Transport spectroscopy in a time-modulated open quantum dot

We have investigated the time-modulated coherent quantum transport phenomena in a ballistic open quantum dot. The conductance $G$ and the electron dwell time in the dots are calculated by a time-dependent mode-matching method. Under high-frequency modulation, the traversing electrons are found to exhibit three types of resonant scatterings. They are intersideband scatterings: into quasibound states in the dots, into true bound states in the dots, and into quasibound states just beneath the subband threshold in the leads. Dip structures or fano structures in $G$ are their signatures. Our results show structures due to 2$\hbar\omega$ intersideband processes. At the above scattering resonances, we have estimated, according to our dwell time calculation, the number of round-trip scatterings that the traversing electrons undertake between the two dot openings.Comment: 8 pages, 5 figure