The artificial intelligence (AI) race between the global powers has countries everywhere hurriedly rummaging up AI applications. A quick glance at magazine headlines, popular culture, and even peer-reviewed academic literature shows the many grand predictions about AI and the eventual winner of its race. But is that race something to be celebrated or feared? And where does the Middle East and North Africa (Mena) region stand? Today, algorithms, deep learning and AI have emerged as unparalleled forces of power and have made their way into the everyday world.

In this letter, we propose an integrated autonomous flight and semantic SLAM system that can perform long-range missions and real-time semantic mapping in highly cluttered, unstructured, and GPS-denied under-canopy environments. First, tree trunks and ground planes are detected from LIDAR scans. We use a neural network and an instance extraction algorithm to enable semantic segmentation in real time onboard the UAV. Second, detected tree trunk instances are modeled as cylinders and associated across the whole LIDAR sequence. This semantic data association constraints both robot poses as well as trunk landmark models. The output of semantic SLAM is used in state estimation, planning, and control algorithms in real time. The global planner relies on a sparse map to plan the shortest path to the global goal, and the local trajectory planner uses a small but finely discretized robot-centric map to plan a dynamically feasible and collision-free trajectory to the local goal. Both the global path and local trajectory lead to drift-corrected goals, thus helping the UAV execute its mission accurately and safely.

It is increasingly common to encounter prediction tasks in the biomedical sciences for which multiple datasets are available for model training. Common approaches such as pooling datasets and applying standard statistical learning methods can result in poor out-of-study prediction performance when datasets are heterogeneous. Theoretical and applied work has shown $\textit{multi-study ensembling}$ to be a viable alternative that leverages the variability across datasets in a manner that promotes model generalizability. Multi-study ensembling uses a two-stage $\textit{stacking}$ strategy which fits study-specific models and estimates ensemble weights separately. This approach ignores, however, the ensemble properties at the model-fitting stage, potentially resulting in a loss of efficiency. We therefore propose $\textit{optimal ensemble construction}$, an $\textit{all-in-one}$ approach to multi-study stacking whereby we jointly estimate ensemble weights as well as parameters associated with each study-specific model. We prove that limiting cases of our approach yield existing methods such as multi-study stacking and pooling datasets before model fitting. We propose an efficient block coordinate descent algorithm to optimize the proposed loss function. We compare our approach to standard methods by applying it to a multi-country COVID-19 dataset for baseline mortality prediction. We show that when little data is available for a country before the onset of the pandemic, leveraging data from other countries can substantially improve prediction accuracy. Importantly, our approach outperforms multi-study stacking and other standard methods in this application. We further characterize the method's performance in data-driven and other simulations. Our method remains competitive with or outperforms multi-study stacking and other earlier methods across a range of between-study heterogeneity levels.

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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.

Tensor decomposition has become an essential tool in many applications in various domains, including machine learning. Sparse Matricized Tensor Times Khatri-Rao Product (MTTKRP) is one of the most computationally expensive kernels in tensor computations. Despite having significant computational parallelism, MTTKRP is a challenging kernel to optimize due to its irregular memory access characteristics. This paper focuses on a multi-faceted memory system, which explores the spatial and temporal locality of the data structures of MTTKRP. Further, users can reconfigure our design depending on the behavior of the compute units used in the FPGA accelerator. Our system efficiently accesses all the MTTKRP data structures while reducing the total memory access time, using a distributed cache and Direct Memory Access (DMA) subsystem. Moreover, our work improves the memory access time by 3.5x compared with commercial memory controller IPs. Also, our system shows 2x and 1.26x speedups compared with cache-only and DMA-only memory systems, respectively.

An artificial-intelligence program is able to predict missing words from cuneiform tablets that are up to 4,500 years old with stunning accuracy. The tablets include information about Mesopotamia from between 2500 BC and 100 AD, but missing text has hindered scientists' abilities to uncover the secrets of the ancient civilization. The AI, which was taught how to read 104 languages, was fed transcriptions of 10,000 cuneiform tablets. It accurately predicted the missing words, phrases and sentences, similarly to how the autosuggest feature on your phone suggests the next line. Mesopotamia is one of the world's oldest known civilizations and gave rise to the Sumerian, Assyrian and Babylonian empires.

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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.

Esophageal cancer (EC) is the eighth most common cancer and the sixth leading cause of cancer death worldwide[1]. EC mainly consists of esophageal adenocarcinoma (EAC) and esophageal squamous cell carcinoma (ESCC). EAC is the most common pathological type in Western countries, more than 40% of patients with EAC are diagnosed after the disease has metastasized, and the 5-year survival rate is less than 20%[2,3]. Although the incidence of EAC has been increasing globally, ESCC remains the most common pathological type (80%) of all ECs with the highest incidence across a'cancer belt' extending from East Africa and across the Middle East to Asia. Only 20% of patients with ESCC survive longer than 3 years, primarily due to late-stage diagnosis[4].