Spatial-Temporal Data Modeling with Graph Neural Networks

University of Technology Sydney. Faculty of Engineering and Information Technology.Spatial-temporal graph modeling is an important task to analyze the spatial relations and temporal trends of components in a system. It aims to model the dynamic node-level inputs by assuming inter-dependency between connected nodes. A basic assumption behind spatial-temporal graph modeling is that a node's future information is conditioned on its historical information as well as its neighbors' historical information. Therefore how to capture spatial and temporal dependencies simultaneously becomes a primary challenge. Current studies on spatial-temporal graph modeling face four major shortcomings: 1) Most graph neural networks only focus on the low frequency band of graph signals; 2) Current studies assume the graph structure of data reflects the genuine dependency relationships among nodes; 3) Existing studies on spatial-temporal graph neural networks are not applicable to pure multivariate time series data due to the absence of a predefined graph and lack of a general framework; 4) Existing approaches either model spatial-temporal dependencies locally or model spatial correlations and temporal correlations separately. The aim of this thesis is to study spatial-temporal data from the perspective of deep learning on graphs. I have studied the research objective in deep depth with four research questions: (1) How to coordinate the low, middle, and high frequency band of graph signals in graph convolution networks. (2) How to model spatial-temporal graph data effectively and efficiently; (3) How to handle spatial dependencies when a graph is totally missing, incomplete or inaccurate in spatial-temporal graph modeling; (4) In contrast to traditional spatial-temporal graph neural networks that handle spatial dependencies and temporal dependencies in separate, how to unify space and time as a whole in message passing. To address the aforementioned four research problems, I proposed four algorithms or models that can achieve satisfactory results. Specifically, I proposed an Automatic Graph Convolutional Network to learn graph frequency bands for graph convolution filters automatically; I introduced an efficient and effective framework that integrates diffusion graph convolution and dilated temporal convolution to capture spatial-temporal dependencies simultaneously. I developed a novel joint-learning algorithm that can capture spatial-temporal dependencies and learn latent graph structures at the same time; I designed a unified graph neural network that captures the inner spatial-temporal dependencies without compromising space-time integrity. To validate the proposed methods, I have conducted experiments on real-world datasets with a range of tasks including node classification, graph classification, and spatial-temporal graph forecasting. Experimental results demonstrate the effectiveness of the proposed methods

Effect of Si and C additions on the reaction mechanism and mechanical properties of FeCrNiCu high entropy alloy

FeCrNiCu based high entropy alloy matrix composites were fabricated with addition of Si and C by vacuum electromagnetic induction melting. The primary goal of this research was to analyze the reaction mechanism, microstructure, mechanical properties at room temperature and strengthening mechanism of the composites with addition of Si and C. The reaction mechanism of powders containing (Si, Ni and C) was analyzed, only one reaction occurred (i.e., Si + C → SiC) and its activation energy is 1302.8 kJ/mol. The new composites consist of a face centered cubic (FCC) structured matrix reinforced by submicron sized SiC particles. The addition of Si and C enhances the hardness from 351.4 HV to 626.4 HV and the tensile strength from 565.5 MPa to 846.0 MPa, accompanied by a slight decrease in the plasticity. The main strengthening mechanisms of SiC/FeCrNiCu composites were discussed based on dislocation strengthening, load bearing effect, Orowan mechanism and solid solution hardening, whose contributions to the tensile strength increase are 58.6%, 6.3%, 14.3% and 20.8%, respectively

Influence of Cr content on the microstructure and mechanical properties of CrxFeNiCu high entropy alloys

© 2020 Chinese Materials Research Society The effect of Cr content on the microstructure and mechanical properties of CrxFeNiCu high entropy alloys (HEAs) was firstly studied by first-principles calculations. The calculated results show that the hardness of the alloys increased with the expense of its plasticity decrease, if the content of Cr in the alloy increased. In order to verify the calculated results, CrxFeNiCu (x = 0.8, 1, 1.5 and 2) high entropy alloys were synthesized by vacuum induction melting in the present study. The results show that as the value of x increased from 0.8 to 2, the crystal structure changed from single phase face centered cubic (FCC) phase to a mixture of FCC and body centered cubic (BCC) phases. For the single phase FCC (x = 0.8) structure, both the tensile strength and hardness values were low, which were 491.6 MPa and 322.2 HV respectively, however, the plasticity was high, reaching 33.2%. With the formation and growth of BCC phase (x = 2) the tensile strength and hardness of the alloy were significantly improved, which were 872.6 MPa and 808 HV, respectively

Effect of Si and C additions on the reaction mechanism and mechanical properties of FeCrNiCu high entropy alloy

FeCrNiCu based high entropy alloy matrix composites were fabricated with addition of Si and C by vacuum electromagnetic induction melting. The primary goal of this research was to analyze the reaction mechanism, microstructure, mechanical properties at room temperature and strengthening mechanism of the composites with addition of Si and C. The reaction mechanism of powders containing (Si, Ni and C) was analyzed, only one reaction occurred (i.e., Si + C → SiC) and its activation energy is 1302.8 kJ/mol. The new composites consist of a face centered cubic (FCC) structured matrix reinforced by submicron sized SiC particles. The addition of Si and C enhances the hardness from 351.4 HV to 626.4 HV and the tensile strength from 565.5 MPa to 846.0 MPa, accompanied by a slight decrease in the plasticity. The main strengthening mechanisms of SiC/FeCrNiCu composites were discussed based on dislocation strengthening, load bearing effect, Orowan mechanism and solid solution hardening, whose contributions to the tensile strength increase are 58.6%, 6.3%, 14.3% and 20.8%, respectively

Influence of Cr content on the microstructure and mechanical properties of CrxFeNiCu high entropy alloys

© 2020 Chinese Materials Research Society The effect of Cr content on the microstructure and mechanical properties of CrxFeNiCu high entropy alloys (HEAs) was firstly studied by first-principles calculations. The calculated results show that the hardness of the alloys increased with the expense of its plasticity decrease, if the content of Cr in the alloy increased. In order to verify the calculated results, CrxFeNiCu (x = 0.8, 1, 1.5 and 2) high entropy alloys were synthesized by vacuum induction melting in the present study. The results show that as the value of x increased from 0.8 to 2, the crystal structure changed from single phase face centered cubic (FCC) phase to a mixture of FCC and body centered cubic (BCC) phases. For the single phase FCC (x = 0.8) structure, both the tensile strength and hardness values were low, which were 491.6 MPa and 322.2 HV respectively, however, the plasticity was high, reaching 33.2%. With the formation and growth of BCC phase (x = 2) the tensile strength and hardness of the alloy were significantly improved, which were 872.6 MPa and 808 HV, respectively

ADC and TDC implemented using FPGA

Several tests of FPGA devices programmed as analog waveform digitizers are discussed. The ADC uses the ramping-comparing scheme. A multi-channel ADC can be implemented with only a few resistors and capacitors as external components. A periodic logic levels are shaped by passive RC network to generate exponential ramps. The FPGA differential input buffers are used as comparators to compare the ramps with the input signals. The times at which these ramps cross the input signals are digitized by time-to-digital-converters (TDCs) implemented within the FPGA. The TDC portion of the logic alone has potentially a broad range of HEP/nuclear science applications. A 96-channel TDC card using FPGAs as TDCs being designed for the Fermilab MIPP electronics upgrade project is discussed. A deserializer circuit based on multisampling circuit used in the TDC, the 'Digital Phase Follower' (DPF) is also documented

Unsupervised Domain Adaptive Graph Convolutional Networks

Graph convolutional networks (GCNs) have achieved impressive success in many graph related analytics tasks. However, most GCNs only work in a single domain (graph) incapable of transferring knowledge from/to other domains (graphs), due to the challenges in both graph representation learning and domain adaptation over graph structures. In this paper, we present a novel approach, unsupervised domain adaptive graph convolutional networks (UDA-GCN), for domain adaptation learning for graphs. To enable effective graph representation learning, we first develop a dual graph convolutional network component, which jointly exploits local and global consistency for feature aggregation. An attention mechanism is further used to produce a unified representation for each node in different graphs. To facilitate knowledge transfer between graphs, we propose a domain adaptive learning module to optimize three different loss functions, namely source classifier loss, domain classifier loss, and target classifier loss as a whole, thus our model can differentiate class labels in the source domain, samples from different domains, the class labels from the target domain, respectively. Experimental results on real-world datasets in the node classification task validate the performance of our method, compared to state-of-the-art graph neural network algorithms

Severe Acute Respiratory Syndrome, Beijing, 2003

The largest outbreak of severe acute respiratory syndrome (SARS) struck Beijing in spring 2003. Multiple importations of SARS to Beijing initiated transmission in several healthcare facilities. Beijing’s outbreak began March 5; by late April, daily hospital admissions for SARS exceeded 100 for several days; 2,521 cases of probable SARS occurred. Attack rates were highest in those 20–39 years of age; 1% of cases occurred in children <10 years. The case-fatality rate was highest among patients >65 years (27.7% vs. 4.8% for those 20–64 years, p < 0.001). Healthcare workers accounted for 16% of probable cases. The proportion of case-patients without known contact to a SARS patient increased significantly in May. Implementation of early detection, isolation, contact tracing, quarantine, triage of case-patients to designated SARS hospitals, and community mobilization ended the outbreak