The Deep Learning on Supercomputers workshop is with ISC'20 on June 25th, 2020 in Frankfurt, Germany. It is the fourth workshop in the Deep Learning on Supercomputers series. The workshop provides a forum for practitioners working on any and all aspects of DL for scientific research in the High Performance Computing (HPC) context to present their latest research results and development, deployment, and application experiences. The general theme of this workshop series is the intersection of DL and HPC, while the theme of this particular workshop is centered around the applications of deep learning methods in scientific research: novel uses of deep learning methods, e.g., convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial network (GAN), and reinforcement learning (RL), for both natural and social science research, and innovative applications of deep learning in traditional numerical simulation. Its scope encompasses application development in scientific scenarios using HPC platforms; DL methods applied to numerical simulation; fundamental algorithms, enhanced procedures, and software development methods to enable scalable training and inference; hardware changes with impact on future supercomputer design; and machine deployment, performance evaluation, and reproducibility practices for DL applications with an emphasis on scientific usage. Topics include but are not limited to: - DL as a novel approach of scientific computing - Emerging scientific applications driven by DL methods - Novel interactions between DL and traditional numerical simulation - Effectiveness and limitations of DL methods in scientific research - Algorithms and procedures to enhance reproducibility of scientific DL applications - DL for science workflows - Data management through the life cycle of scientific DL applications - General algorithms and procedures for efficient and scalable DL training - Scalable DL methods to address the challenges of demanding scientific applications - General algorithms and systems for large scale model serving for scientific use cases - New software, and enhancements to existing software, for scalable DL - DL communication optimization at scale - I/O optimization for DL at scale - DL performance evaluation and analysis on deployed systems - DL performance modeling and tuning of DL on supercomputers - DL benchmarks on supercomputers - Novel hardware designs for more efficient DL - Processors, accelerators, memory hierarchy, interconnect changes with impact on deep learning in the HPC context As part of the reproducibility initiative, the workshop requires authors to provide information such as the algorithms, software releases, datasets, and hardware configurations used.

This revisit gives a survey on the analytical methods for the inverse exponential Radon transform which has been investigated in the past three decades from both mathematical interests and medical applications such as nuclear medicine emission imaging. The derivation of the classical inversion formula is through the recent argument developed for the inverse attenuated Radon transform. That derivation allows the exponential parameter to be a complex constant, which is useful to other applications such as magnetic resonance imaging and tensor field imaging. The survey also includes the new technique of using the finite Hilbert transform to handle the exact reconstruction from 180 degree data. Special treatment has been paid on two practically important subjects. One is the exact reconstruction from partial measurements such as half-scan and truncated-scan data, and the other is the reconstruction from diverging-beam data. The noise propagation in the reconstruction is touched upon with more heuristic discussions than mathematical inference. The numerical realizations of several classical reconstruction algorithms are included. In the conclusion, several topics are discussed for more investigations in the future.

Knowledge Graphs (KGs) have found many applications in industry and academic settings, which in turn, have motivated considerable research efforts towards large-scale information extraction from a variety of sources. Despite such efforts, it is well known that even state-of-the-art KGs suffer from incompleteness. Link Prediction (LP), the task of predicting missing facts among entities already a KG, is a promising and widely studied task aimed at addressing KG incompleteness. Among the recent LP techniques, those based on KG embeddings have achieved very promising performances in some benchmarks. Despite the fast growing literature in the subject, insufficient attention has been paid to the effect of the various design choices in those methods. Moreover, the standard practice in this area is to report accuracy by aggregating over a large number of test facts in which some entities are over-represented; this allows LP methods to exhibit good performance by just attending to structural properties that include such entities, while ignoring the remaining majority of the KG. This analysis provides a comprehensive comparison of embedding-based LP methods, extending the dimensions of analysis beyond what is commonly available in the literature. We experimentally compare effectiveness and efficiency of 16 state-of-the-art methods, consider a rule-based baseline, and report detailed analysis over the most popular benchmarks in the literature.

With the development of deep representation learning, the domain of reinforcement learning (RL) has become a powerful learning framework now capable of learning complex policies in high dimensional environments. This review summarises deep reinforcement learning (DRL) algorithms, provides a taxonomy of automated driving tasks where (D)RL methods have been employed, highlights the key challenges algorithmically as well as in terms of deployment of real world autonomous driving agents, the role of simulators in training agents, and finally methods to evaluate, test and robustifying existing solutions in RL and imitation learning.

Human knowledge provides a formal understanding of the world. Knowledge graphs that represent structural relations between entities have become an increasingly popular research direction towards cognition and human-level intelligence. In this survey, we provide a comprehensive review on knowledge graph covering overall research topics about 1) knowledge graph representation learning, 2) knowledge acquisition and completion, 3) temporal knowledge graph, and 4) knowledge-aware applications, and summarize recent breakthroughs and perspective directions to facilitate future research. We propose a full-view categorization and new taxonomies on these topics. Knowledge graph embedding is organized from four aspects of representation space, scoring function, encoding models and auxiliary information. For knowledge acquisition, especially knowledge graph completion, embedding methods, path inference and logical rule reasoning are reviewed. We further explore several emerging topics including meta relational learning, commonsense reasoning, and temporal knowledge graphs. To facilitate future research on knowledge graphs, we also provide a curated collection of datasets and open-source libraries on different tasks. In the end, we have a thorough outlook on several promising research directions.

Communications drive business and when you also strategically use artificial intelligence and machine learning it can help improve your business results. RingCentral's open platform allows for a wide variety of AI and ML solutions to integrate with our cloud communications platform to help businesses drive results. This article will provide an overview of major use cases that have been successfully built and deployed so you can get an idea of where the opportunities are. Sales conversations are often measured for effectiveness and with RingCentral partners you'll be able to use specific post-call analysis, which will allow you to help teams provide coaching to improve the performance of their agents. Post-call analysis compares each agent with the best agents, providing personalized recommendations for each agent and also a dashboard for managers.

"Just like software, and the Internet from previous decades, public cloud and now AI are the megatrends of our generation." Artificial intelligence and machine learning (AI/ML) is driving breakthrough developments across industries such as Healthcare, Energy, Logistics, and more. Heliogen is using AI to optimize the next generation of solar technology to power energy intensive processes such as manufacturing steel which in the past was only possible with fossil fuels. Another example is Boston Dynamics' HANDLE – an agile mobile robot that uses deep learning to autonomously unload trucks and move boxes in warehouses. If someone tells you that AI/ML is hype, remind them that cloud computing was once called hype.

"Just like software, and the Internet from previous decades, public cloud and now AI are the megatrends of our generation." Artificial intelligence and machine learning (AI/ML) is driving breakthrough developments across industries such as Healthcare, Energy, Logistics, and more. Heliogen is using AI to optimize the next generation of solar technology to power energy intensive processes such as manufacturing steel which in the past was only possible with fossil fuels. Another example is Boston Dynamics' HANDLE – an agile mobile robot that uses deep learning to autonomously unload trucks and move boxes in warehouses. If someone tells you that AI/ML is hype, remind them that cloud computing was once called hype.

Not only is data coming in faster and at higher volumes, but it is also coming in messier. Such "non-Euclidean domains" can be imagined as complicated graphs comprised of data points with specified relationships or dependencies with other data points. Deep learning research is now working hard to figure out how to approach these data-as-spaghetti sources through the notion of GNNs, or graph neural networks. With so much happening in this emerging field recently, this survey paper took the top of the list as the most saved article in users' collections on arXiv.org,

Every day, global trends are reshaping society and the business landscape. Today's infographic from McKinsey Global Institute (MGI) presents a snapshot of 10 insights into how the world is changing, based on its research work from 2019. How did we get here, and where are we going? Globalization is making the world "shrink" every day, as humans and trade become increasingly connected. However, there are signs that point to a new phase of globalization that is leading to different outcomes than prior years.