Variational inference machines for semiparametric regression

Variational approximation methods are enjoying an increasing amount of development and use in statistical problems. In the Bayesian field, we develop mean field variational Bayes (MFVB) algorithms that perform variable selection and fit complicated regression models. We also produce a new Bayesian inference software, InferMachine(), which can perform the MFVB inference using BRugs model code. Finally, a new computational framework, Infer.NET, for approximate Bayesian inference in hierarchical Bayesian models is demonstrated. We assess the accuracy of MFVB via comparison with a Markov chain Monte Carlo (MCMC) baseline. The simulation results show that the results of the MFVB inference agree with those of the MCMC approach. In the non-Bayesian field, the precise asymptotic distributional behaviour of Gaussian variational approximate estimators in a single predictor Poisson mixed model is derived. A simulation study shows that the Gaussian variational approximate confidence intervals possess good to excellent coverage properties

Broad Structural Representation Learning

A broad spectrum of data from different sources and structures are widely existing, such as natural graph data (social network, IT/OT network, brain network), unnatural graph data (image, text, sphere), sequence data (stock). Modeling these data with heterogeneous sources and structures is a fundamental problem in data mining with diversified applications in many science and business fields. Given the intrinsic heterogeneous nature, broad visions and strategies for structural representation are required to derive competitive advantages and unlock the power of the big data. We investigate and develop novel deep learning approaches for structural pattern analysis and discovering in the graph. Specifically, we proposed new representation learning models from the graph data via graph neural networks. The graph data provides a generalized representation of many different types of inter-connected data collected from various disciplines. Besides the unique attributes possessed by individual nodes, the extensive connections among the nodes can convey very complicated yet important information. The graph data are very challenging to deal with because of their complex structures (containing multiple kinds of nodes and extensive connections), and diverse attributes (attached to the nodes and links). To address these issues, I will show how to develop structural preserving and heterogeneity preserving representation learning model taking the benefit of graph neural network. I also apply the board structural learning on multiple applications including, healthcare, cybersecurity, recommender system, and natural language processing

hollow_object_trained_model

Trained model for hollow object inside</p

A hybrid vehicular re-routing strategy with dynamic time constraints for road traffic congestion avoidance

Nowadays, the rapid rise of the number of vehicles on the roads has led to several challenging problems for road authorities, such as traffic congestion, increasing number of accidents and air pollution. According to recent statistics, road traffic jam leads to a huge economic loss due to the increasing delay on the roads and the extra fuel consumption. Intelligent Transportation System (ITS) provides a promising framework to alleviate the congestion on the roads. However, a lot of work needs to be done to improve its efficiency, such as in the area of vehicles re-routing strategies. The main focus of this paper is on designing novel vehicles re-routing strategy to reduce the traffic congestion in urban areas. The proposed strategy is a hybrid approach which takes full advantage of both exact and heuristic algorithms and meets the requirements of dynamic time constraints of real road traffic scenarios. The next step of our work is to evaluate the performance of our strategy and compare it with the existing algorithms based on several metrics and under a benchmark of road topologies and traffic scenarios

Spectrum-Dependent Spiro-OMeTAD Oxidization Mechanism in Perovskite Solar Cells

We propose a spectrum-dependent mechanism for the oxidation of 2,2′,7,7′-tetrakis­(<i>N</i>,<i>N</i>-di-<i>p</i>-methoxyphenylamine)-9,9′-spirobifluorene (Spiro-OMeTAD) with bis­(trifluoromethane)­sulfonimide lithium salt (LiTFSI), which is commonly used in perovskite solar cells as the hole transport layer. The perovskite layer plays different roles in the Spiro-OMeTAD oxidization for various spectral ranges. The effect of oxidized Spiro-OMeTAD on the solar cell performance was observed and characterized. With the initial long-wavelength illumination (>450 nm), the charge recombination at the TiO₂/Spiro-OMeTAD interface was increased due to the higher amount of the oxidized Spiro-OMeTAD. On the other hand, the increased conductivity of the Spiro-OMeTAD layer and enhanced charge transfer at the Au/Spiro-OMeTAD interface facilitated the solar cell performance

Efficacy of targeted therapy for advanced renal cell carcinoma: A systematic review and meta-analysis of randomized controlled trials

<div><p>ABSTRACT We conducted a systematic review and meta-analysis of the literature on the efficacy of the targeted therapies in the treatment of advanced RCC and, via an indirect comparison, to provide an optimal treatment among these agents. A systematic search of Medline, Scopus, Cochrane Library and Clinical Trials unpublished was performed up to Jan 1, 2015 to identify eligible randomized trials. Outcomes of interest assessing a targeted agent included progression free survival (PFS), overall survival (OS) and objective response rate (ORR). Thirty eligible randomized controlled studies, total twentyfourth trails (5110 cases and 4626 controls) were identified. Compared with placebo and IFN-α, single vascular epithelial growth factor (receptor) tyrosine kinase inhibitor and mammalian target of rapamycin agent (VEGF(r)-TKI & mTOR inhibitor) were associated with improved PFS, improved OS and higher ORR, respectively. Comparing sorafenib combination vs sorafenib, there was no significant difference with regard to PFS and OS, but with a higher ORR. Comparing single or combination VEGF(r)-TKI & mTOR inhibitor vs BEV + IFN-α, there was no significant difference with regard to PFS, OS, or ORR. Our network ITC meta-analysis also indicated a superior PFS of axitinib and everolimus compared to sorafenib. Our data suggest that targeted therapy with VEGF(r)-TKI & mTOR inhibitor is associated with superior efficacy for treating advanced RCC with improved PFS, OS and higher ORR compared to placebo and IFN-α. In summary, here we give a comprehensive overview of current targeted therapies of advanced RCC that may provide evidence for the adequate targeted therapy selecting.</p></div

Next road rerouting: a multiagent system for mitigating unexpected urban traffic congestion

During peak hours in urban areas, unpredictable traffic congestion caused by en route events (e.g., vehicle crashes) increases drivers’ travel time and, more seriously, decreases their travel time reliability. In this paper, an original and highly practical vehicle rerouting system, which is called Next Road Rerouting (NRR), is proposed to aid drivers in making the most appropriate next road choice to avoid unexpected congestions. In particular, this heuristic rerouting decision is made upon a cost function that takes into account the driver’s destination and local traffic conditions. In addition, the newly designed multiagent system architecture of NRR allows the positive rerouting impacts on local traffic to be disseminated to a larger area through the natural traffic flow propagation within connected local areas. The simulation results based on both synthetic and realistic urban scenarios demonstrate that, compared with the existing solutions, NRR can achieve a lower average travel time while guaranteeing a higher travel time reliability in the face of unexpected congestion. The impacts of NRR on the travel time of both rerouted and nonrerouted vehicles are also assessed, and the corresponding results reveal its higher practicability

Evaporation of Tiny Water Aggregation on Solid Surfaces with Different Wetting Properties

The evaporation of a tiny amount of water on the solid surface with different wettabilities has been studied by molecular dynamics simulations. From nonequilibrium MD simulations, we found that, as the surface changed from hydrophobic to hydrophilic, the evaporation speed did not show a monotonic decrease as intuitively expected, but increased first, and then decreased after it reached a maximum value. The analysis of the simulation trajectory and calculation of the surface water interaction illustrate that the competition between the number of water molecules on the water–gas surface from where the water molecules can evaporate and the potential barrier to prevent those water molecules from evaporating results in the unexpected behavior of the evaporation. This finding is helpful in understanding the evaporation on biological surfaces, designing artificial surfaces of ultrafast water evaporating, or preserving water in soil

Comprehensive performance analysis and comparison of vehicles routing algorithms in smart cities

Due to the severe impact of road traffic congestion on both economy and environment, several vehicles routing algorithms have been proposed to optimize travelers itinerary based on real-time traffic feeds or historical data. However, their evaluation methodologies are not as compelling as their key design idea because none of them had been tested under both real transportation map and real traffic data. In this paper, we conduct a deep performance analysis and comparison of four typical vehicles routing algorithms under various scalability levels (i.e. trip length and traffic load) based on realistic transportation simulation. The ultimate goal of this work is to suggest the most suitable routing algorithm to use in different transportation scenarios, so that it can provide a valuable reference for both traffic managers and researchers when they deploy or optimize a large scale centralized Traffic Management System (TMS). The obtained simulation results reveal that dynamic A* is the best routing algorithm if the TMS has sufficient memory or storage capacities, otherwise static A* is also a great alternative

Synthesis of 3‑Substituted 2‑Aminochromones via Sn(IV)-Promoted Annulation of Ynamides with 2‑Methoxyaroyl Chlorides

A Sn­(IV)-promoted annulation reaction of ynamides is described for the efficient synthesis of 3-substituted 2-aminochromones under mild conditions. This novel method allows for a concomitant construction of C–C and C–O bonds between ynamides and 2-methoxyaroyl chlorides by a tandem Friedel–Crafts acylation/oxo-Michael addition/elimination strategy