A Comprehensive Survey on Deep Graph Representation Learning.

Graph representation learning aims to effectively encode high-dimensional sparse graph-structured data into low-dimensional dense vectors, which is a fundamental task that has been widely studied in a range of fields, including machine learning and data mining. Classic graph embedding methods follow the basic idea that the embedding vectors of interconnected nodes in the graph can still maintain a relatively close distance, thereby preserving the structural information between the nodes in the graph. However, this is sub-optimal due to: (i) traditional methods have limited model capacity which limits the learning performance; (ii) existing techniques typically rely on unsupervised learning strategies and fail to couple with the latest learning paradigms; (iii) representation learning and downstream tasks are dependent on each other which should be jointly enhanced. With the remarkable success of deep learning, deep graph representation learning has shown great potential and advantages over shallow (traditional) methods, there exist a large number of deep graph representation learning techniques have been proposed in the past decade, especially graph neural networks. In this survey, we conduct a comprehensive survey on current deep graph representation learning algorithms by proposing a new taxonomy of existing state-of-the-art literature. Specifically, we systematically summarize the essential components of graph representation learning and categorize existing approaches by the ways of graph neural network architectures and the most recent advanced learning paradigms. Moreover, this survey also provides the practical and promising applications of deep graph representation learning. Last but not least, we state new perspectives and suggest challenging directions which deserve further investigations in the future.

Slow-wave-enhanced on-chip Michelson interferometer sensor.

We experimentally demonstrated slow-wave-enhanced phase and spectral sensitivity in asymmetric Michelson interferometer (MI) sensors. Compared to Mach-Zehnder interferometers (MZI) that experimentally demonstrated a phase sensitivity of 84,000 rad/RIU-cm, the reflected path enhancement of the optical path length coupled with slow light enhancement with photonic crystal waveguides in on-chip slow light Michelson interferometer sensors resulted in experimentally demonstrated phase sensitivity of 277,750 rad/RIU-cm with theoretical phase sensitivity as high as 461,810 rad/RIU-cm, at the same form factor as the MZI of identical interferometer arm lengths.

Induction Motor Stator Winding Inter-Tern Short Circuit Fault Detection Based on Start-Up Current Envelope Energy.

Inter-turn short circuit (ITSC) is a common fault in induction motors. However, it is challenging to detect the early stage of ITSC fault. To address this issue, this paper proposes an ITSC fault detection method for three-phase induction motors based on start-up current envelope energy. This approach uses Akima interpolation to calculate the envelope of the measured start-up current of the induction motor. A Gaussian window weighting is applied to eliminate endpoint effects caused by the initial phase angle, and the enveloping energy is obtained using the energy formula as the fault feature. Finally, by combining this with the support vector machine (SVM) classification learner, fault detection of ITSC in induction motors is achieved. The experimental results show that the average accuracy of this method reaches 96.9%, which can quickly and accurately detect ITSC faults in asynchronous motors and determine the severity of the faults. Furthermore, the average accuracy of SVM in detecting early ITSC faults under no-load conditions is 98.8%, which is higher than other classification learners, including LR, KNN, and NN. This study provides a new idea for induction motor fault detection and can contribute to induction motor maintenance.

Multi-task Learning via Adaptation to Similar Tasks for Mortality Prediction of Diverse Rare Diseases.

The mortality prediction of diverse rare diseases using electronic health record (EHR) data is a crucial task for intelligent healthcare. However, data insufficiency and the clinical diversity of rare diseases make it hard for deep learning models to be trained. Mortality prediction for these patients with different diseases can be viewed as a multi-task learning problem with insufficient data but a large number of tasks. On the other hand, insufficient training data makes it difficult to train task-specific modules in multi-task learning models. To address the challenges of data insufficiency and task diversity, we propose an initialization-sharing multi-task learning method (Ada-SiT). Ada-Sit can learn the parameter initialization and dynamically measure the tasks' similarities, used for fast adaptation. We use Ada-SiT to train long short-term memory networks (LSTM) based prediction models on longitudinal EHR data. The experimental results demonstrate that the proposed model is effective for mortality prediction of diverse rare diseases.

A Lane Detection Method Based on a Ridge Detector and Regional G-RANSAC.

Lane detection plays an important role in improving autopilot's safety. In this paper, a novel lane-division-lines detection method is proposed, which exhibits good performances in abnormal illumination and lane occlusion. It includes three major components: First, the captured image is converted to aerial view to make full use of parallel lanes' characteristics. Second, a ridge detector is proposed to extract each lane's feature points and remove noise points with an adaptable neural network (ANN). Last, the lane-division-lines are accurately fitted by an improved random sample consensus (RANSAC), termed the (regional) gaussian distribution random sample consensus (G-RANSAC). To test the performances of this novel lane detection method, we proposed a new index named the lane departure index (LDI) describing the departure degree between true lane and predicted lane. Experimental results verified the superior performances of the proposed method over others in different testing scenarios, respectively achieving 99.02%, 96.92%, 96.65% and 91.61% true-positive rates (TPR); and 66.16, 54.85, 55.98 and 52.61 LDIs in four different types of testing scenarios.