Uplift Modeling based on Graph Neural Network Combined with Causal Knowledge

Uplift modeling is a fundamental component of marketing effect modeling, which is commonly employed to evaluate the effects of treatments on outcomes. Through uplift modeling, we can identify the treatment with the greatest benefit. On the other side, we can identify clients who are likely to make favorable decisions in response to a certain treatment. In the past, uplift modeling approaches relied heavily on the difference-in-difference (DID) architecture, paired with a machine learning model as the estimation learner, while neglecting the link and confidential information between features. We proposed a framework based on graph neural networks that combine causal knowledge with an estimate of uplift value. Firstly, we presented a causal representation technique based on CATE (conditional average treatment effect) estimation and adjacency matrix structure learning. Secondly, we suggested a more scalable uplift modeling framework based on graph convolution networks for combining causal knowledge. Our findings demonstrate that this method works effectively for predicting uplift values, with small errors in typical simulated data, and its effectiveness has been verified in actual industry marketing data.Comment: 6 pages, 6 figure

Prediction of Suspect Location Based on Spatiotemporal Semantics

The prediction of suspect location enables proactive experiences for crime investigations and offers essential intelligence for crime prevention. However, existing studies have failed to capture the complex social location transition patterns of suspects and lack the capacity to address the issue of data sparsity. This paper proposes a novel location prediction model called CMoB (Crime Multi-order Bayes model) based on the spatiotemporal semantics to enhance the prediction performance. In particular, the model groups suspects with similar spatiotemporal semantics as one target suspect. Then, their mobility data are applied to estimate Markov transition probabilities of unobserved locations based on a KDE (kernel density estimating) smoothing method. Finally, by integrating the total transition probabilities, which are derived from the multi-order property of the Markov transition matrix, into a Bayesian-based formula, it is able to realize multi-step location prediction for the individual suspect. Experiments with the mobility dataset covering 210 suspects and their 18,754 location records from January to June 2012 in Wuhan City show that the proposed CMoB model significantly outperforms state-of-the-art algorithms for suspect location prediction in the context of data sparsity

The Local Colocation Patterns of Crime and Land-Use Features in Wuhan, China

Most studies of spatial colocation patterns of crime and land-use features in geographical information science and environmental criminology employ global measures, potentially obscuring spatial inhomogeneity. This study investigated the relationships of three types of crime with 22 types of land-use in Wuhan, China. First, global colocation patterns were examined. Then, local colocation patterns were examined based on the recently-proposed local colocation quotient, followed by a detailed comparison of the results. Different types of crimes were encouraged or discouraged by different types of land-use features with varying intensity, and the local colocation patterns demonstrated spatial inhomogeneity

Etch and growth rates of GaN for surface orientations in the <0001> crystallographic zone: Step flow and terrace erosion/filling via the Continuous Cellular Automaton

Compared to other methods, we present the benefits of the Continuous Cellular Automaton (CCA) to describe in a simple and flexible manner anisotropic wet chemical etching of GaN as a combination of step flow and terrace erosion. In fact, the simplicity of the approach enables accounting for epitaxial growth of GaN based on the equivalent perspective of step flow and terrace filling (or build-up). A key ingredient is the direct removal/deposition of GaN groups of atoms, which directly enables describing etching/growth via the removal/deposition of a small number of different crystallographic cell types, each with a different removal/deposition rate. We focus on the derivation of mathematical expressions for the etch rates of the surface orientations contained in the crystallographic zone as a function of the removal rates of the various cell types, then describing their use also for growth. A least squares approach is used to determine optimal values for the removal/deposition rates of the various cell types. For the first time, Focused Ion Beam (FIB) etching is used to manufacture a well-known, vertically micromachined wagon-wheel structure for subsequent use in wet etching, thus enabling a comparison between the CCA-derived etch rates and the experimental values. For growth, the comparison is performed against data from the literature. The combination of average step flow and average terrace erosion/filling, as inherently contained in the CCA method, explains well the anisotropy of both etching and growth of GaN in the crystallographic zone.This work was supported by the National Natural Science Foundation of China (Grant No.51875104).Peer reviewe

Comparative analysis of bacterial community structure in the rhizosphere of maize by high-throughput pyrosequencing.

In this study, we designed a microcosm experiment to explore the composition of the bacterial community in the rhizosphere of maize and bulk soil by sequencing the V3-V4 region of the 16S rRNA gene on the Illumina system. 978-1239 OTUs (cut off level of 3%) were found in rhizosphere and bulk soil samples. Rhizosphere shared features with the bulk soil, such as predominance of Acidobacteria, Proteobacteria, Actinobacteria, Bacteroidetes, Chloroflexi, Firmicutes, Gemmatimonadetes and TM7. At genus level, many of the dominant rhizosphere genera (Chitinophaga, Nitrospira, Flavobacterium, etc.) displayed different patterns of temporal changes in the rhizosphere as opposed to the bulk soil, showing rhizosphere has more impact on soil microorganisms. Besides, we found that significant growth-related dynamic changes in bacterial community structure were mainly associated with phylum Bacteroidetes, Proteobacteria and Actinobacteria (mainly genera Burkholderia, Flavisolibacter and Pseudomonas), indicating that different growth stages affected the bacterial community composition in maize soil. Furthermore, some unique genera in especial Plant-Growth Promoting Rhizobacteria (PGPR) such as Nonomuraea, Thiobacillus and Bradyrhizobium etc., which were beneficial for the plant growth appeared to be more abundant in the rhizosphere than bulk soil, indicating that the selectivity of root to rhizosphere microbial is an important mechanism leading to the differences in the bacteria community structure between rhizosphere and bulk soil

Detecting Inspection Objects of Power Line from Cable Inspection Robot LiDAR Data

Power lines are extending to complex environments (e.g., lakes and forests), and the distribution of power lines in a tower is becoming complicated (e.g., multi-loop and multi-bundle). Additionally, power line inspection is becoming heavier and more difficult. Advanced LiDAR technology is increasingly being used to solve these difficulties. Based on precise cable inspection robot (CIR) LiDAR data and the distinctive position and orientation system (POS) data, we propose a novel methodology to detect inspection objects surrounding power lines. The proposed method mainly includes four steps: firstly, the original point cloud is divided into single-span data as a processing unit; secondly, the optimal elevation threshold is constructed to remove ground points without the existing filtering algorithm, improving data processing efficiency and extraction accuracy; thirdly, a single power line and its surrounding data can be respectively extracted by a structured partition based on a POS data (SPPD) algorithm from “layer” to “block” according to power line distribution; finally, a partition recognition method is proposed based on the distribution characteristics of inspection objects, highlighting the feature information and improving the recognition effect. The local neighborhood statistics and the 3D region growing method are used to recognize different inspection objects surrounding power lines in a partition. Three datasets were collected by two CIR LIDAR systems in our study. The experimental results demonstrate that an average 90.6% accuracy and average 98.2% precision at the point cloud level can be achieved. The successful extraction indicates that the proposed method is feasible and promising. Our study can be used to obtain precise dimensions of fittings for modeling, as well as automatic detection and location of security risks, so as to improve the intelligence level of power line inspection

A Novel Method to Reconstruct Overhead High-Voltage Power Lines Using Cable Inspection Robot LiDAR Data

Overhead high-voltage power lines are key components of power transmission and their monitoring has a very significant influence on security and reliability of power system. Advanced laser scanning techniques have been widely used to capture three-dimensional (3D) point clouds of power system scenes. Nevertheless, power line corridors are found in increasingly complex environments (e.g., mountains and forests), and the multi-loop structure on the same power line tower raises great challenges to process light detection and ranging (LiDAR) data. This paper addresses these challenges by constructing a new collection mode of LiDAR data for power lines using cable inspection robot (CIR). A novel method is proposed to extract and reconstruct power line using CIR LiDAR data, which has two advantages: (1) rapidly extracts power line point by position and orientation system (POS) extraction model; and (2) better solves pseudo-line during reconstruction of power line by structured partition. The proposed method mainly includes four steps: CIR LiDAR data generation, POS-based crude extraction, voxel-based accurate extraction and power line reconstruction. The feasibility and validity of the proposed method are verified by test site experiment and actual line experiment, demonstrating a fast and reliable solution to accurately reconstruct power line

Field study on energy economic assessment of office buildings envelope retrofitting in southern China

Energy consumption of buildings accounts for more than 37.3% of total energy consumption while the proportion of energy-saving buildings is just 5% in China. In this paper, in order to save potential energy, the building envelope retrofitting is considered. An office building in Southern China was selected as a test example for energy consumption characteristics. The base building model was developed by TRNSYS software and validated against the recorded data from the field work in six days out of August-September in 2013. In addition, with a design of numerical simulation, sensitivity analysis was conducted for energy performance of building envelope retrofitting; six envelope parameters were analyzed for assessing the thermal responses. Results show that significant energy savings are obtained for the thermal transmittance (U-value) of exterior walls, infiltration rate, ventilation and shading coefficient, of which the sum relative sensitivity is about 91.06%. The results are evaluated in terms of energy and economic analysis. On the other hand, it appears that the cost-effective method improves the efficiency of investment management in building energy. (C) 2016 Elsevier Ltd. All rights reserved

FS2You: Peer-Assisted Semipersistent Online Hosting at a Large Scale

It has been widely acknowledged that online file hosting systems within the "cloud" of the Internet have provided valuable services to end users who wish to share files of any size. Such online hosting services are typically provided by dedicated servers, either in content distribution networks (CDNs) or large data centers. Server bandwidth costs, however, are prohibitive in these cases, especially when serving large volumes of files to a large number of users. Though it seems intuitive to take advantage of peer upload bandwidth to mitigate such server bandwidth costs in a complementary fashion, it is not trivial to design and fine-tune important aspects of such peer-assisted online hosting in a real-world large-scale deployment. This paper presents FS2You, a large-scale and real-world online file hosting system with peer assistance and semipersistent file availability. FS2You is designed to dramatically mitigate server bandwidth costs. In this paper, we show a number of key challenges involved in such a design objective, our architectural and protocol design in response to these challenges, as well as an extensive measurement study at a large scale to demonstrate the effectiveness of our design, using real-world traces that we have collected. To our knowledge, this paper represents the first attempt to design, implement, and evaluate a new peer-assisted semipersistent online file hosting system at a realistic scale. Since the launch of FS2You, it has quickly become one of the most popular online file hosting systems in mainland China, and a favorite in many online forums across the country