Probabilistic Shortest Time Queries Over Uncertain Road Networks

In many real applications such as location-based services (LBS), map utilities, trip planning, and transportation systems, it is very useful and important to provide query services over spatial road networks. Nowadays we can easily obtain rich traffic information such as the speeds of vehicles on roads. However, due to the inaccuracy of devices or integration in consistencies, the traffic data (i.e., speeds) are often imprecise and uncertain. In this paper, we model road networks by uncertain graphs, which contain edges that are associated with probabilistic velocities. We formalize the problem of probabilistic shortest time query, and we propose time bound pruning and probabilistic bound pruning to filter out false alarms. Moreover, we design offline pre-computation to facilitate PSTQ processing

Enhanced Gas-Flow-Induced Voltage in Graphene

We show by systemically experimental investigation that gas-flow-induced voltage in monolayer graphene is more than twenty times of that in bulk graphite. Examination over samples with sheet resistances ranging from 307 to 1600 {\Omega}/sq shows that the induced voltage increase with the resistance and can be further improved by controlling the quality and doping level of graphene. The induced voltage is nearly independent of the substrate materials and can be well explained by the interplay of Bernoulli's principle and the carrier density dependent Seebeck coefficient. The results demonstrate that graphene has great potential for flow sensors and energy conversion devices

Wasserstein Regression

The analysis of samples of random objects that do not lie in a vector space is gaining increasing attention in statistics. An important class of such object data is univariate probability measures defined on the real line. Adopting the Wasserstein metric, we develop a class of regression models for such data, where random distributions serve as predictors and the responses are either also distributions or scalars. To define this regression model, we utilize the geometry of tangent bundles of the space of random measures endowed with the Wasserstein metric for mapping distributions to tangent spaces. The proposed distribution-to-distribution regression model provides an extension of multivariate linear regression for Euclidean data and function-to-function regression for Hilbert space valued data in functional data analysis. In simulations, it performs better than an alternative transformation approach where one maps distributions to a Hilbert space through the log quantile density transformation and then applies traditional functional regression. We derive asymptotic rates of convergence for the estimator of the regression operator and for predicted distributions and also study an extension to autoregressive models for distribution-valued time series. The proposed methods are illustrated with data on human mortality and distributional time series of house prices

A genetic linkage map of Japanese scallop Mizuhopecten yessoensis based on amplified fragment length polymorphism (AFLP) and microsatellite (SSR) markers

A genetic linkage map of the Japanese scallop Mizuhopecten yessoensis was constructed based on 302 markers, including 263 amplified fragment length polymorphism (AFLP) markers and 39 microsatellite (SSR) markers. The two parental maps were constructed according to the double pseudo-test cross strategy with an F1 progeny of 115 individuals. In the maternal parent, 163 markers were assigned in 20 linkage groups, spanning a total coverage of 2184.9 cM with the average spacing between two adjacent markers was 15.3 cM. In the paternal parent, 155 markers were also mapped into 20 linkage groups, spanning a genetic length of 1882.4 cM with the average marker density of 13.9 cM, respectively. The coverage estimated for the framework maps were 78.3% for the female and 77% for the male without minor linkage groups. Five full alignment linkage groups and four homologous linkage groups could be identified based on the position of 16 high information content SSRs which segregated in the parents. The construction of the M. yessoensis genetic linkage maps here was a part of a genetic breeding program. This linkage map will contribute to the discovery of genes, comparative genomics and quantitative trait loci in Japanese scallop.Keywords: SSR, AFLP, genetic linkage map, Mizuhopecten yessoensi

Efficient and Joint Hyperparameter and Architecture Search for Collaborative Filtering

Automated Machine Learning (AutoML) techniques have recently been introduced to design Collaborative Filtering (CF) models in a data-specific manner. However, existing works either search architectures or hyperparameters while ignoring the fact they are intrinsically related and should be considered together. This motivates us to consider a joint hyperparameter and architecture search method to design CF models. However, this is not easy because of the large search space and high evaluation cost. To solve these challenges, we reduce the space by screening out usefulness yperparameter choices through a comprehensive understanding of individual hyperparameters. Next, we propose a two-stage search algorithm to find proper configurations from the reduced space. In the first stage, we leverage knowledge from subsampled datasets to reduce evaluation costs; in the second stage, we efficiently fine-tune top candidate models on the whole dataset. Extensive experiments on real-world datasets show better performance can be achieved compared with both hand-designed and previous searched models. Besides, ablation and case studies demonstrate the effectiveness of our search framework.Comment: Accepted by KDD 202