Deep reinforcement learning has gathered much attention recently. Impressive results were achieved in activities as diverse as autonomous driving, game playing, molecular recombination, and robotics. In all these fields, computer programs have taught themselves to solve difficult problems. They have learned to fly model helicopters and perform aerobatic manoeuvers such as loops and rolls. In some applications they have even become better than the best humans, such as in Atari, Go, poker and StarCraft. The way in which deep reinforcement learning explores complex environments reminds us of how children learn, by playfully trying out things, getting feedback, and trying again. The computer seems to truly possess aspects of human learning; this goes to the heart of the dream of artificial intelligence. The successes in research have not gone unnoticed by educators, and universities have started to offer courses on the subject. The aim of this book is to provide a comprehensive overview of the field of deep reinforcement learning. The book is written for graduate students of artificial intelligence, and for researchers and practitioners who wish to better understand deep reinforcement learning methods and their challenges. We assume an undergraduate-level of understanding of computer science and artificial intelligence; the programming language of this book is Python. We describe the foundations, the algorithms and the applications of deep reinforcement learning. We cover the established model-free and model-based methods that form the basis of the field. Developments go quickly, and we also cover advanced topics: deep multi-agent reinforcement learning, deep hierarchical reinforcement learning, and deep meta learning.

The Cosmology and Astrophysics with MachinE Learning Simulations (CAMELS) project was developed to combine cosmology with astrophysics through thousands of cosmological hydrodynamic simulations and machine learning. CAMELS contains 4,233 cosmological simulations, 2,049 N-body and 2,184 state-of-the-art hydrodynamic simulations that sample a vast volume in parameter space. In this paper we present the CAMELS public data release, describing the characteristics of the CAMELS simulations and a variety of data products generated from them, including halo, subhalo, galaxy, and void catalogues, power spectra, bispectra, Lyman-$\alpha$ spectra, probability distribution functions, halo radial profiles, and X-rays photon lists. We also release over one thousand catalogues that contain billions of galaxies from CAMELS-SAM: a large collection of N-body simulations that have been combined with the Santa Cruz Semi-Analytic Model. We release all the data, comprising more than 350 terabytes and containing 143,922 snapshots, millions of halos, galaxies and summary statistics. We provide further technical details on how to access, download, read, and process the data at \url{https://camels.readthedocs.io}.

As we all want to know the science behind virus severity to lead better well planned lives. If not through logics of biology then AI can come in to help. How a virus effects lungs while another just causes cold and cough, there must be some biology behind this. If not sure take help of AI and Machines which can Learn anything these days once data is collected and data fed in the machines. In this article I present my proposal to detect virus severity right after the virus structure has been detected.

Iran's President Ebrahim Raisi vowed revenge against Donald Trump unless the former US president is tried over the killing of Qassem Soleimani, as Tehran marked two years since the revered commander's death. The Islamic republic and its allies across the Middle East held emotional commemorations for General Soleimani and his Iraqi lieutenant who were assassinated in a US drone strike at Baghdad airport on January 3, 2020. Tehran's arch enemies were targeted on the day of the anniversary in unclaimed drone and cyber attacks -- with two armed unmanned aerial vehicles intercepted by the US-led coalition in Iraq over Baghdad airport, and hackers attacking Israeli media sites. Soleimani headed the Quds Force, the foreign operations arm of Iran's Revolutionary Guards, with links to armed groups in Iraq, Lebanon, the Palestinian territories, Syria and Yemen. Raisi, addressing Tehran's largest prayer hall, said: "The aggressor and the main assassin, the then president of the United States, must face justice and retribution" alongside former US secretary of state Mike Pompeo "and other criminals".

The staple of human intelligence is the capability of acquiring knowledge in a continuous fashion. In stark contrast, Deep Networks forget catastrophically and, for this reason, the sub-field of Class-Incremental Continual Learning fosters methods that learn a sequence of tasks incrementally, blending sequentially-gained knowledge into a comprehensive prediction. This work aims at assessing and overcoming the pitfalls of our previous proposal Dark Experience Replay (DER), a simple and effective approach that combines rehearsal and Knowledge Distillation. Inspired by the way our minds constantly rewrite past recollections and set expectations for the future, we endow our model with the abilities to i) revise its replay memory to welcome novel information regarding past data ii) pave the way for learning yet unseen classes. We show that the application of these strategies leads to remarkable improvements; indeed, the resulting method - termed eXtended-DER (X-DER) - outperforms the state of the art on both standard benchmarks (such as CIFAR-100 and miniImagenet) and a novel one here introduced. To gain a better understanding, we further provide extensive ablation studies that corroborate and extend the findings of our previous research (e.g. the value of Knowledge Distillation and flatter minima in continual learning setups).

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.

Fifth generation (5G) networks and beyond envisions massive Internet of Things (IoT) rollout to support disruptive applications such as extended reality (XR), augmented/virtual reality (AR/VR), industrial automation, autonomous driving, and smart everything which brings together massive and diverse IoT devices occupying the radio frequency (RF) spectrum. Along with spectrum crunch and throughput challenges, such a massive scale of wireless devices exposes unprecedented threat surfaces. RF fingerprinting is heralded as a candidate technology that can be combined with cryptographic and zero-trust security measures to ensure data privacy, confidentiality, and integrity in wireless networks. Motivated by the relevance of this subject in the future communication networks, in this work, we present a comprehensive survey of RF fingerprinting approaches ranging from a traditional view to the most recent deep learning (DL) based algorithms. Existing surveys have mostly focused on a constrained presentation of the wireless fingerprinting approaches, however, many aspects remain untold. In this work, however, we mitigate this by addressing every aspect - background on signal intelligence (SIGINT), applications, relevant DL algorithms, systematic literature review of RF fingerprinting techniques spanning the past two decades, discussion on datasets, and potential research avenues - necessary to elucidate this topic to the reader in an encyclopedic manner.

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.

Not so long ago, searching for information could lead to a library to scan endless volumes or even tediously sift through microfilm. Clearly, technology is making the world a better place. Scientists, researchers, developers and companies have been on a quest to solve some of the world's most pressing problems. Only now they're accelerating their efforts by putting NVIDIA GPU-driven AI to work. And the benefits can be seen across critical global issues such as COVID-19 and climate change, to education and employment.

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."