Recently, graph neural networks have become a hot topic in machine learning community. This paper presents a Scopus based bibliometric overview of the GNNs research since 2004, when GNN papers were first published. The study aims to evaluate GNN research trend, both quantitatively and qualitatively. We provide the trend of research, distribution of subjects, active and influential authors and institutions, sources of publications, most cited documents, and hot topics. Our investigations reveal that the most frequent subject categories in this field are computer science, engineering, telecommunications, linguistics, operations research and management science, information science and library science, business and economics, automation and control systems, robotics, and social sciences. In addition, the most active source of GNN publications is Lecture Notes in Computer Science. The most prolific or impactful institutions are found in the United States, China, and Canada. We also provide must read papers and future directions. Finally, the application of graph convolutional networks and attention mechanism are now among hot topics of GNN research.

People's decisions about how to allocate their limited computational resources are essential to human intelligence. An important component of this metacognitive ability is deciding whether to continue thinking about what to do and move on to the next decision. Here, we show that people acquire this ability through learning and reverse-engineer the underlying learning mechanisms. Using a process-tracing paradigm that externalises human planning, we find that people quickly adapt how much planning they perform to the cost and benefit of planning. To discover the underlying metacognitive learning mechanisms we augmented a set of reinforcement learning models with metacognitive features and performed Bayesian model selection. Our results suggest that the metacognitive ability to adjust the amount of planning might be learned through a policy-gradient mechanism that is guided by metacognitive pseudo-rewards that communicate the value of planning.

Instead of the traditional Knowledge Graph Embedding (KGE) represents entities and Negative Sampling, we design a new loss function based on relations of knowledge graphs (KGs) in the semantic vector space, query sampling that can balance two important training targets, and has shown great potential in automatic KG completion and Alignment and Uniformity. Furthermore, we analyze the hardnessaware knowledge-driven tasks [15, 16, 31, 33]. Given a query having an ability of recent low-dimensional hyperbolic models and entity and the relation of a triple, a typical KGE model learns propose a lightweight hardness-aware activation mechanism, which embedding vectors by predicting the missing entity from the can help the KGE models focus on hard instances and speed up whole entity set [30]. However, the existing KGE models have convergence. The experimental results show that in the limited limited practicality in real-world applications [19, 23]. To improve training time, HaLE can effectively improve the performance and the prediction accuracy, recent KGE models utilize complicated training speed of KGE models on five commonly-used datasets. The computational structures and high-dimensional vectors up to 500 or HaLE-trained models can obtain a high prediction accuracy after even 1,000 dimensions [7, 12, 22]. Training such high-dimensional training few minutes and are competitive compared to the state-ofthe-art models demands prohibitive training costs and storage space, yet models in both low-and high-dimensional conditions.

In this paper, we introduce zero-shot cost models which enable learned cost estimation that generalizes to unseen databases. In contrast to state-of-the-art workload-driven approaches which require to execute a large set of training queries on every new database, zero-shot cost models thus allow to instantiate a learned cost model out-of-the-box without expensive training data collection. To enable such zero-shot cost models, we suggest a new learning paradigm based on pre-trained cost models. As core contributions to support the transfer of such a pre-trained cost model to unseen databases, we introduce a new model architecture and representation technique for encoding query workloads as input to those models. As we will show in our evaluation, zero-shot cost estimation can provide more accurate cost estimates than state-of-the-art models for a wide range of (real-world) databases without requiring any query executions on unseen databases. Furthermore, we show that zero-shot cost models can be used in a few-shot mode that further improves their quality by retraining them just with a small number of additional training queries on the unseen database.

According to a report from Apollo Hospitals, there are only about 10,000 trained radiologists for the current population of India, and AI-based radiology reporting could be a saviour to address the shortage of radiologists. Synapsica, an AI-based radiology reporting start-up, has been using AI-powered technology that automates several aspects of radiology workflow, improves the quality of reports, and increases transparency between patients and doctors. Meenakshi Singh, Co-Founder and CEO of Synapsica, saw the effect of the shortage of radiologists in her hometown in UP and decided to use her AI chops to find a faster and more efficient method to reduce the workload of doctors and radiologists, to help patients get their diagnoses sooner. How is AI used in radiology workflow? Explaining how AI is used in radiology workflow, Meenakshi said, "Once a patient scan is taken, and the information reaches our secure cloud server, the AI engine automatically creates biomarkers of pathologies that are shared with the reporting radiologist in a viewer that presents the patient's scan. A layer of NLP on top of AI-generated bio-markers also pre-fills radiology reports with relevant clinical findings that can be edited by the radiologist, saving typing time. The AI engine also generates reporting instructions that automate tasks normally performed by back-office support personnel."

Entity alignment is a crucial step in integrating knowledge graphs (KGs) from multiple sources. Previous attempts at entity alignment have explored different KG structures, such as neighborhood-based and path-based contexts, to learn entity embeddings, but they are limited in capturing the multi-context features. Moreover, most approaches directly utilize the embedding similarity to determine entity alignment without considering the global interaction among entities and relations. In this work, we propose an Informed Multi-context Entity Alignment (IMEA) model to address these issues. In particular, we introduce Transformer to flexibly capture the relation, path, and neighborhood contexts, and design holistic reasoning to estimate alignment probabilities based on both embedding similarity and the relation/entity functionality. The alignment evidence obtained from holistic reasoning is further injected back into the Transformer via the proposed soft label editing to inform embedding learning. Experimental results on several benchmark datasets demonstrate the superiority of our IMEA model compared with existing state-of-the-art entity alignment methods.

It is well known the sample covariance has a consistent bias in the spectrum, for example spectrum of Wishart matrix follows the Marchenko-Pastur law. We in this work introduce an iterative algorithm 'Concent' that actively eliminate this bias and recover the true spectrum for small and moderate dimensions.

Bayesian optimization (BO) is a framework for global optimization of expensive-to-evaluate objective functions. Classical BO methods assume that the objective function is a black box. However, internal information about objective function computation is often available. For example, when optimizing a manufacturing line's throughput with simulation, we observe the number of parts waiting at each workstation, in addition to the overall throughput. Recent BO methods leverage such internal information to dramatically improve performance. We call these "grey-box" BO methods because they treat objective computation as partially observable and even modifiable, blending the black-box approach with so-called "white-box" first-principles knowledge of objective function computation. This tutorial describes these methods, focusing on BO of composite objective functions, where one can observe and selectively evaluate individual constituents that feed into the overall objective; and multi-fidelity BO, where one can evaluate cheaper approximations of the objective function by varying parameters of the evaluation oracle.

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A new spy-in-the-sky camera which identified 15,000 cases of drivers using mobile phones could also catch motorists eating, drinking, or not wearing a seatbelt, its makers say. Smart cameras linked to a new, automated system using artificial intelligence (AI) are being trialled on an undisclosed motorway - ahead of a blanket ban on holding a mobile device while driving which comes into force in early 2022. The cameras instantly analyse high-definition photos taken through the windscreen of passing cars, and Jenoptik, the enforcement technology firm testing the cameras in the UK, believes they will be crucial in providing evidence to prosecute offenders. The pilot scheme has been running since spring and it is hoped a wider rollout across the country will be possible next year. But Acusensus, the Australian firm who designed the cameras, admits that they can be used to catch motorists doing anything from eating, drinking, smoking, adjusting the radio or using navigation devices in a holder.