We present a multi-filtering Graph Convolution Neural Network (GCN) framework for network embedding task. It uses multiple local GCN filters to do feature extraction in every propagation layer. We show this approach could capture different important aspects of node features against the existing attribute embedding based method. We also show that with multi-filtering GCN approach, we can achieve significant improvement against baseline methods when training data is limited. We also perform many empirical experiments and demonstrate the benefit of using multiple filters against single filter as well as most current existing network embedding methods for both the link prediction and node classification tasks.

The Indian automotive industry is on the edge of disruption due to increasing automation, new business models and digitization. This disruption is also through innovation and transformational change as industry players are adapting to shifting preferences on car ownership and new technological developments such as Autonomous Vehicles (AVs), IoT, cloud and proliferation electric and connected vehicles. Apart from electric and connected vehicles, the auto industry is also adopting technologies like cloud and IoT to improve the driving experience. From design and operation to servicing, cloud technology will be increasingly used at every stage to reduce costs and eliminate any scope for wastage. Cloud computing enables better vehicle engineering and thanks to advanced analytic capabilities, design teams can deliver exactly what customers want.

Recent surveys, studies, forecasts and other quantitative assessments of the progress and impact of AI highlight the precarious nature of the future of work (long after the coronavirus pandemic ends), the continuing mixed attitudes of consumers about data privacy, and the possible resilience of this year's investments in AI. The IT department's need for AI talent has tripled between 2015 and 2019, but the number of AI jobs posted by IT is still less than half of that stemming from other business units; departments recruiting AI talent in high volumes include marketing, sales, customer service, finance, and research and development. By 2025, at least two of the top 10 global retailers will establish robot resource organizations to manage nonhuman workers; 77% of retailers plan to deploy AI by 2021, with the deployment of robotics for warehouse picking as the No. 1 use case [Gartner] By 2024, AI, virtual personal assistants, and chatbots will replace almost 69% of the manager's workload [Gartner] "Supervised machine learning doesn't live up to the hype. It isn't actual artificial intelligence akin to C-3PO, it's a sophisticated pattern-matching tool… Rather than seeing exponential improvements in the quality of AI performance (a la Moore's Law), we're instead seeing exponential increases in the cost to improve AI systems"--Stefan Seltz-Axmacher, founder, Starsky Robotics "…why are we holding our hands behind our back trying to build AI without mechanisms that infants have?"--Gary "We haven't really gone to great depth with deep learning yet. We've had a limited amount of training data so far. We've had limited structures with limited compute power. But the key point is that deep learning learns the concept, it learns the features. "…such capabilities [as "deepfake" transformation of the human face] were called image processing 15 years ago, but are routinely termed AI today.

The Radiological Society of North America (RSNA) has received numerous inquiries seeking access to COVID-19 related imaging data, both from radiology sites interested in sharing such data for use in research and education and from researchers. RSNA is committed to accelerating open source collaborative research on the uses of medical imaging in addressing the COVID-19 pandemic, including the use of new tools like artificial intelligence (AI). This form will enable institutions with COVID-19 data to express interest in participating in a planned open data repository for international COVID-19 imaging research and education efforts. Please complete this form if your institution has COVID-19 data that you may be willing and able to share for research purposes. Completing this brief survey does not represent a final commitment to collaborate with us or to share your data.

Over 20 years ago, Kearns [1998] introduced statistical queries as a framework for designing machine learning algorithms that are tolerant to noise. The statistical query model restricts a learning algorithm to ask certain types of queries to an oracle that responds with approximately correct answers. This framework has has proven useful, not only for designing noise-tolerant algorithms, but also for its connections to other noise models, for its ability to capture many of our current techniques, and for its explanatory power about the hardness of many important problems. Researchers have also found many connections between statistical queries and a variety of modern topics, including to evolvability, differential privacy, and adaptive data analysis. Statistical queries are now both an important tool and remain a foundational topic with many important questions. The aim of this survey is to illustrate these connections and bring researchers to the forefront of our understanding of this important area.

In this manuscript, we provide a structured and comprehensive overview of techniques to integrate machine learning with physics-based modeling. First, we provide a summary of application areas for which these approaches have been applied. Then, we describe classes of methodologies used to construct physics-guided machine learning models and hybrid physics-machine learning frameworks from a machine learning standpoint. With this foundation, we then provide a systematic organization of these existing techniques and discuss ideas for future research.

In public media as well as in scientific publications, the term \emph{bias} is used in conjunction with machine learning in many different contexts, and with many different meanings. This paper proposes a taxonomy of these different meanings, terminology, and definitions by surveying the, primarily scientific, literature on machine learning. In some cases, we suggest extensions and modifications to promote a clear terminology and completeness. The survey is followed by an analysis and discussion on how different types of biases are connected and depend on each other. We conclude that there is a complex relation between bias occurring in the machine learning pipeline that leads to a model, and the eventual bias of the model (which is typically related to social discrimination). The former bias may or may not influence the latter, in a sometimes bad, and sometime good way.

Recommender systems are software applications that help users to find items of interest in situations of information overload. Current research often assumes a one-shot interaction paradigm, where the users' preferences are estimated based on past observed behavior and where the presentation of a ranked list of suggestions is the main, one-directional form of user interaction. Conversational recommender systems (CRS) take a different approach and support a richer set of interactions. These interactions can, for example, help to improve the preference elicitation process or allow the user to ask questions about the recommendations and to give feedback. The interest in CRS has significantly increased in the past few years. This development is mainly due to the significant progress in the area of natural language processing, the emergence of new voice-controlled home assistants, and the increased use of chatbot technology. With this paper, we provide a detailed survey of existing approaches to conversational recommendation. We categorize these approaches in various dimensions, e.g., in terms of the supported user intents or the knowledge they use in the background. Moreover, we discuss technological approaches, review how CRS are evaluated, and finally identify a number of gaps that deserve more research in the future.

Computer-aided diagnosis (CADx) algorithms in medicine provide patient-specific decision support for physicians. These algorithms are usually applied after full acquisition of high-dimensional multimodal examination data, and often assume feature-completeness. This, however, is rarely the case due to examination costs, invasiveness, or a lack of indication. A sub-problem in CADx, which to our knowledge has received very little attention among the CADx community so far, is to guide the physician during the entire peri-diagnostic workflow, including the acquisition stage. We model the following question, asked from a physician's perspective: ''Given the evidence collected so far, which examination should I perform next, in order to achieve the most accurate and efficient diagnostic prediction?''. In this work, we propose a novel approach which is enticingly simple: use dropout at the input layer, and integrated gradients of the trained network at test-time to attribute feature importance dynamically. We validate and explain the effectiveness of our proposed approach using two public medical and two synthetic datasets. Results show that our proposed approach is more cost- and feature-efficient than prior approaches and achieves a higher overall accuracy. This directly translates to less unnecessary examinations for patients, and a quicker, less costly and more accurate decision support for the physician.

Existing patient data analytics platforms fail to incorporate information that has context, is personal, and topical to patients. For a recommendation system to give a suitable response to a query or to derive meaningful insights from patient data, it should consider personal information about the patient's health history, including but not limited to their preferences, locations, and life choices that are currently applicable to them. In this review paper, we critique existing literature in this space and also discuss the various research challenges that come with designing, building, and operationalizing a personal health knowledge graph (PHKG) for patients.