In this paper, we introduce stochastic block majorization-minimization, where the surrogates can now be only block multi-convex and a single block is optimized at a time within a diminishing radius. Relaxing the standard strong convexity requirements for surrogates in SMM, our framework gives wider applicability including online CANDECOMP/PARAFAC (CP) dictionary learning and yields greater computational efficiency especially when the problem dimension is large. We provide an extensive convergence analysis on the proposed algorithm, which we derive under possibly dependent data streams, relaxing the standard i.i.d. Our results provide first convergence rate bounds for various online matrix and tensor decomposition algorithms under a general Markovian data setting. Empirical loss minimization is a classical problem setting regarding parameter estimation with a growing number of observations, where one seeks to minimize a recursively defined empirical loss function as new data arrives. Some of its well-known applications include maximum likelihood estimation, or more generally, M-estimation [Gey94, GvdGW00, SB02], as well as the online dictionary learning literature [MBPS10, Mai13b, MMTV17, LNB20]. On the other hand, the expected loss minimization seeks to estimate a parameter by minimizing the loss function with respect to random data. It provides a general framework for stochastic optimization literature [SK07, Mar05, BB08, NJLS09]. Optimization algorithms for empirical or expected loss minimization are in nature'online', meaning that sampling new data points and adjusting the current estimation occurs recursively. Such onilne algorithms have proven to be particularly efficient in large-scale problems in statistics, optimization, and machine learning [Bot98, DS09, GL13, KB14].

Prior to this pandemic year of 2021, the term "supply chain" didn't raise many red flags for most consumers, frankly because they didn't have to think about it. Buyers were so accustomed to getting things on schedule that it rarely became a regular topic of conversation. That all changed in the second half of 2021. With the pandemic slowing down production lines and transportation in faraway places, the term "supply chain" is now regularly in headlines. This has been the greatest shock to global supply chains in modern history.

In public proceedings, the Legal Board of Appeal of the EPO confirmed that under the European Patent Convention (EPC), an inventor designated in a patent application must be a human being. This was the judgement in combined cases J 8/20 and J 9/20, where the board just dismissed the applicant's appeal. Here, both the applications were made by a Missouri physicist Stephen Thaler, whose AI-system DABUS had made the inventions. Device for the Autonomous Bootstrapping of Unified Sentience, or DABUS, is a computer system programmed to invent by itself. It is, basically, a swarm of disconnected neutral nets that can continuously generate thought processes and even memories that can, over time, generate new and inventive outputs independently.

When Shamim Nabuuma Kaliisa first had chest pain, she was in the second year of her medical degree at Makerere University (Kampala, Uganda). She was diagnosed with breast cancer when she was barely in her 20s. "Being told that you have cancer is one of the worst things anyone can hear", she told The Lancet Oncology. "It comes with a feeling of not having a future, with the imagination of pain until death." Luckily, at stage I, her breast cancer was treatable, but the pain she went through during the long treatment process was unbearable.

For the second time in 24 hours, a United States-led coalition fighting ISIL (ISIS) in Iraq says it has foiled a drone attack on a base hosting US troops. An official of the international military coalition said on Tuesday two armed drones were shot down as they approached the base in western Anbar province. "Two fixed-wing drones rigged with explosives were engaged and destroyed by defensive capabilities at the Iraqi Ain al-Asad airbase early this morning," the official was quoted as saying by news agencies. "The attempted attack was unsuccessful. All forces are accounted for."

One day after work last month, Tom Otieno* went to a shopping centre in Nairobi to pick up groceries before heading home. He got a call from someone he had been chatting to for a week on Grindr, a social networking app for gay, bi, trans and queer people. The man had already tried ringing several times during the day while Otieno was with colleagues and was keen to meet. Otieno, 29, mentioned where he was but said that he did not want to see the man. Then, as he was heading to his car, he got another call.

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".