The CFA Society Keynote: Advancing Hedge Funds Chief Investment Officer Practices: Model Risk Management with Auto Machine Learning: JP Morgan and Goldman Sachs Practices Case Studies. (Presentation: 107 slides)

The Sendai Framework for Disaster Risk Reduction 2015–2030 placed human health at the centre of disaster risk reduction, calling for the global community to enhance local and national health emergency and disaster risk management (Health EDRM). The Health EDRM Framework, published in 2019, describes the functions required for comprehensive disaster risk management across prevention, preparedness, readiness, response, and recovery to improve the resilience and health security of communities, countries, and health systems. Evidence-based Health EDRM workforce development is vital. However, there are still significant gaps in the evidence identifying common competencies for training and education programmes, and the clarification of strategies for workforce retention, motivation, deployment, and coordination. Initiated in June 2020, this project includes literature reviews, case studies, and an expert consensus (modified Delphi) study. Literature reviews in English, Japanese, and Chinese aim to identify research gaps and explore core competencies for Health EDRM workforce training. Thirteen Health EDRM related case studies from six WHO regions will illustrate best practices (and pitfalls) and inform the consensus study. Consensus will be sought from global experts in emergency and disaster medicine, nursing, public health and related disciplines. Recommendations for developing effective health workforce strategies for low- and middle-income countries and high-income countries will then be disseminated.

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How to train your robot with deep reinforcement learning: lessons we have learned

The International Journal of Robotics Research ◽

10.1177/0278364920987859 ◽

2021 ◽

pp. 027836492098785

Author(s):

Julian Ibarz ◽

Jie Tan ◽

Chelsea Finn ◽

Mrinal Kalakrishnan ◽

Peter Pastor ◽

...

Keyword(s):

Machine Learning ◽

Reinforcement Learning ◽

Case Studies ◽

Real World ◽

Review Article ◽

The Real ◽

Complex Skills ◽

Real World Learning ◽

Level Sensor ◽

Embodied Agent

Deep reinforcement learning (RL) has emerged as a promising approach for autonomously acquiring complex behaviors from low-level sensor observations. Although a large portion of deep RL research has focused on applications in video games and simulated control, which does not connect with the constraints of learning in real environments, deep RL has also demonstrated promise in enabling physical robots to learn complex skills in the real world. At the same time, real-world robotics provides an appealing domain for evaluating such algorithms, as it connects directly to how humans learn: as an embodied agent in the real world. Learning to perceive and move in the real world presents numerous challenges, some of which are easier to address than others, and some of which are often not considered in RL research that focuses only on simulated domains. In this review article, we present a number of case studies involving robotic deep RL. Building off of these case studies, we discuss commonly perceived challenges in deep RL and how they have been addressed in these works. We also provide an overview of other outstanding challenges, many of which are unique to the real-world robotics setting and are not often the focus of mainstream RL research. Our goal is to provide a resource both for roboticists and machine learning researchers who are interested in furthering the progress of deep RL in the real world.

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Covid-19 Chest Radiography Images Analysis Based on Integration of Image Preprocess, Guided Grad-CAM, Machine Learning and Risk Management

Proceedings of the 4th International Conference on Medical and Health Informatics ◽

10.1145/3418094.3418096 ◽

2020 ◽

Author(s):

Tsung-Chieh Lin ◽

Hsi-Chieh Lee

Keyword(s):

Machine Learning ◽

Risk Management ◽

Chest Radiography

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Pattern discovery and disentanglement on relational datasets

Scientific Reports ◽

10.1038/s41598-021-84869-4 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Andrew K. C. Wong ◽

Pei-Yuan Zhou ◽

Zahid A. Butt

Keyword(s):

Machine Learning ◽

Knowledge Base ◽

Case Studies ◽

Prediction Accuracy ◽

Pattern Discovery ◽

Explicit Representation ◽

Human Cognition ◽

Source Level ◽

Low Volume

AbstractMachine Learning has made impressive advances in many applications akin to human cognition for discernment. However, success has been limited in the areas of relational datasets, particularly for data with low volume, imbalanced groups, and mislabeled cases, with outputs that typically lack transparency and interpretability. The difficulties arise from the subtle overlapping and entanglement of functional and statistical relations at the source level. Hence, we have developed Pattern Discovery and Disentanglement System (PDD), which is able to discover explicit patterns from the data with various sizes, imbalanced groups, and screen out anomalies. We present herein four case studies on biomedical datasets to substantiate the efficacy of PDD. It improves prediction accuracy and facilitates transparent interpretation of discovered knowledge in an explicit representation framework PDD Knowledge Base that links the sources, the patterns, and individual patients. Hence, PDD promises broad and ground-breaking applications in genomic and biomedical machine learning.

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