That afternoon, he and his wife would leave their vacation home on the Caspian Sea and drive to their country house in Absard, a bucolic town east of Tehran, where they planned to spend the weekend. Iran's intelligence service had warned him of a possible assassination plot, but the scientist, Mohsen Fakhrizadeh, had brushed it off. Convinced that Mr. Fakhrizadeh was leading Iran's efforts to build a nuclear bomb, Israel had wanted to kill him for at least 14 years. But there had been so many threats and plots that he no longer paid them much attention. Despite his prominent position in Iran's military establishment, Mr. Fakhrizadeh wanted to live a normal life. And, disregarding the advice of his security team, he often drove his own car to Absard instead of having bodyguards drive him in an armored vehicle. It was a serious breach of security protocol, but he insisted. So shortly after noon on Friday, Nov. 27, he slipped behind the wheel of his black Nissan Teana sedan, his wife in the passenger seat beside him, and hit the road. Since 2004, when the Israeli government ordered its foreign intelligence agency, the Mossad, to prevent Iran from obtaining nuclear weapons, the agency had been carrying out a campaign of sabotage and cyberattacks on Iran's nuclear fuel enrichment facilities.

The research was sparked by the pandemic and set out to build an AI tool to predict how much extra oxygen a Covid-19 patient may need in the first days of hospital care, using data from across four continents. The technique, known as federated learning, used an algorithm to analyse chest x-rays and electronic health data from hospital patients with Covid symptoms. To maintain strict patient confidentiality, the patient data was fully anonymised and an algorithm was sent to each hospital so no data was shared or left its location. Once the algorithm had'learned' from the data, the analysis was brought together to build an AI tool which could predict the oxygen needs of hospital Covid patients anywhere in the world. Published today in Nature Medicine, the study dubbed EXAM (for EMR CXR AI Model), is one of the largest, most diverse clinical federated learning studies to date.

Addenbrooke's Hospital in Cambridge along with 20 other hospitals from across the world and healthcare technology leader, NVIDIA, have used artificial intelligence (AI) to predict Covid patients' oxygen needs on a global scale. The research was sparked by the pandemic and set out to build an AI tool to predict how much extra oxygen a Covid-19 patient may need in the first days of hospital care, using data from across four continents. The technique, known as federated learning, used an algorithm to analyse chest x-rays and electronic health data from hospital patients with Covid symptoms. To maintain strict patient confidentiality, the patient data was fully anonymised and an algorithm was sent to each hospital so no data was shared or left its location. Once the algorithm had'learned' from the data, the analysis was brought together to build an AI tool which could predict the oxygen needs of hospital Covid patients anywhere in the world.

This document focuses on modeling a complex situations to achieve an advantage within a competitive context. Our goal is to devise the characteristics of games to teach and exercise non-easily quantifiable tasks crucial to the math-modeling process. A computerized game to exercise the math-modeling process and optimization problem formulation is introduced. The game is named The Formula 1 Championship, and models of the game were developed in the computerized simulation platform MoNet. It resembles some situations in which team managers must make crucial decisions to enhance their racing cars up to the feasible, most advantageous conditions. This paper describes the game's rules, limitations, and five Formula 1 circuit simulators used for the championship development. We present several formulations of this situation in the form of optimization problems. Administering the budget to reach the best car adjustment to a set of circuits to win the respective races can be an approach. Focusing on the best distribution of each Grand Prix's budget and then deciding how to use the assigned money to improve the car is also the right approach. In general, there may be a degree of conflict among these approaches because they are different aspects of the same multi-scale optimization problem. Therefore, we evaluate the impact of assigning the highest priority to an element, or another, when formulating the optimization problem. Studying the effectiveness of solving such optimization problems turns out to be an exciting way of evaluating the advantages of focusing on one scale or another. Another thread of this research directs to the meaning of the game in the teaching-learning process. We believe applying the Formula 1 Game is an effective way to discover opportunities in a complex-system situation and formulate them to finally extract and concrete the related benefit to the context described.

Identification of user's opinions from natural language text has become an exciting field of research due to its growing applications in the real world. The research field is known as sentiment analysis and classification, where aspect category detection (ACD) and aspect category polarity (ACP) are two important sub-tasks of aspect-based sentiment analysis. The goal in ACD is to specify which aspect of the entity comes up in opinion while ACP aims to specify the polarity of each aspect category from the ACD task. The previous works mostly propose separate solutions for these two sub-tasks. This paper focuses on the ACD and ACP sub-tasks to solve both problems simultaneously. The proposed method carries out multi-label classification where four different deep models were employed and comparatively evaluated to examine their performance. A dataset of Persian reviews was collected from CinemaTicket website including 2200 samples from 14 categories. The developed models were evaluated using the collected dataset in terms of example-based and label-based metrics. The results indicate the high applicability and preference of the CNN and GRU models in comparison to LSTM and Bi-LSTM.

Data augmentation (DA) algorithms are widely used for Bayesian inference due to their simplicity. In massive data settings, however, DA algorithms are prohibitively slow because they pass through the full data in any iteration, imposing serious restrictions on their usage despite the advantages. Addressing this problem, we develop a framework for extending any DA that exploits asynchronous and distributed computing. The extended DA algorithm is indexed by a parameter $r \in (0, 1)$ and is called Asynchronous and Distributed (AD) DA with the original DA as its parent. Any ADDA starts by dividing the full data into $k$ smaller disjoint subsets and storing them on $k$ processes, which could be machines or processors. Every iteration of ADDA augments only an $r$-fraction of the $k$ data subsets with some positive probability and leaves the remaining $(1-r)$-fraction of the augmented data unchanged. The parameter draws are obtained using the $r$-fraction of new and $(1-r)$-fraction of old augmented data. For many choices of $k$ and $r$, the fractional updates of ADDA lead to a significant speed-up over the parent DA in massive data settings, and it reduces to the distributed version of its parent DA when $r=1$. We show that the ADDA Markov chain is Harris ergodic with the desired stationary distribution under mild conditions on the parent DA algorithm. We demonstrate the numerical advantages of the ADDA in three representative examples corresponding to different kinds of massive data settings encountered in applications. In all these examples, our DA generalization is significantly faster than its parent DA algorithm for all the choices of $k$ and $r$. We also establish geometric ergodicity of the ADDA Markov chain for all three examples, which in turn yields asymptotically valid standard errors for estimates of desired posterior quantities.

In this article, we summarise the content from our focus series on the UN Sustainable Development Goal (SDG) number 14: life below water, and we highlight further interesting research in the field. The UN write that the aim of this goal is to: "Conserve and sustainably use the oceans, seas and marine resources for sustainable development." This includes topics such as reducing marine pollution, protecting and restoring ecosystems, reducing ocean acidification, and sustainable fishing. The aim of the OcéanIA project is to develop new artificial intelligence and mathematical modelling tools to contribute to the understanding of the oceans and their role in regulating and sustaining the biosphere, and tackling climate change. We interviewed Nayat Sánchez-Pi, Director of the Inria Chile Research Center, who told us more about this important and exciting project.

Artificial intelligence is changing the game for cybersecurity across several industries by providing cutting-edge security technologies that analyze massive quantities of data. AI technology uses its ability to improve network security over time. Today, several organizations are increasingly implementing AI-powered intelligent security solutions & services to understand and reuse threat patterns to identify new coercions. AI technology provides wider security solutions and simplifies complete recognition and acknowledgment procedures related to cyberattacks. Thus, there is a growing demand for AI-based solutions in the end-use industry for cybersecurity.

Recommender system has been proven to be significantly crucial in many fields and is widely used by various domains. Most of the conventional recommender systems rely on the numeric rating given by a user to reflect his opinion about a consumed item; however, these ratings are not available in many domains. As a result, a new source of information represented by the user-generated reviews is incorporated in the recommendation process to compensate for the lack of these ratings. The reviews contain prosperous and numerous information related to the whole item or a specific feature that can be extracted using the sentiment analysis field. This paper gives a comprehensive overview to help researchers who aim to work with recommender system and sentiment analysis. It includes a background of the recommender system concept, including phases, approaches, and performance metrics used in recommender systems. Then, it discusses the sentiment analysis concept and highlights the main points in the sentiment analysis, including level, approaches, and focuses on aspect-based sentiment analysis.

First, technology always creates winners and losers. While automation poses risks to some workers and firms, for others – especially in developing countries – it presents opportunities to upgrade quality, reach new export markets, and create productive employment. Policy should allow such firms to grow, for example by lowering labor market rigidities, which may help to offset any negative effects of declining firms and sectors. Second, our findings warn that growing trade protectionism may slow cross-border technology diffusion. It may constrain the ability of firms in developing countries to upgrade production processes, move into higher value-added activities and produce the high-quality products demanded by consumers.