Beijing – China livestreamed footage of its new manned submersible parked at the bottom of the Mariana Trench on Friday, part of a historic mission into the deepest underwater valley on the planet. The "Fendouzhe," or "Striver," descended more than 10,000 meters (about 33,000 feet) into the submarine trench in the western Pacific Ocean with three researchers on board, state broadcaster CCTV said. Only a handful of people have ever visited the bottom of the Mariana Trench, a crescent-shaped depression in the Earth's crust that is deeper than Mount Everest is high and more than 2,550 km (1,600 miles) long. The first explorers visited the trench in 1960 on a brief expedition, after which there had been no missions until Hollywood director James Cameron made the first solo trip to the bottom in 2012. Cameron described a "desolate" and "alien" environment.

The optimal charging infrastructure planning problem over a large geospatial area is challenging due to the increasing network sizes of the transportation system and the electric grid. The coupling between the electric vehicle travel behaviors and charging events is therefore complex. This paper focuses on the demonstration of a scalable computational framework for the electric vehicle charging infrastructure planning over the tightly integrated transportation and electric grid networks. On the transportation side, a charging profile generation strategy is proposed leveraging the EV energy consumption model, trip routing, and charger selection methods. On the grid side, a genetic algorithm is utilized within the optimal power flow program to solve the optimal charger placement problem with integer variables by adaptively evaluating candidate solutions in the current iteration and generating new solutions for the next iterations.

The Chinese military is aiming to utilize cutting-edge technologies like private sector-developed artificial intelligence to enhance its offensive capability in domains such as cyberspace and outer space, a Japanese Defense Ministry think tank warned Friday. Beijing aspires to match the United States' overall military capacity by transforming its People's Liberation Army into a world-class fighting force with the help of advanced technologies, the National Institute for Defense Studies said in its annual report on China's security strategy. The report said that until the Chinese catch up with the American military, "the PLA will build up its interference and strike capabilities to prevent the United States' military use of both the cyber and space domains." The China Security Report 2021 was released as the rivalry between Washington and Beijing has been intensifying, as has competition for technological hegemony. The United States has restricted exports of semiconductors to Huawei Technologies Co., the Chinese telecom giant that is aiming to expand its dominance of next-generation 5G technology.

Eighteen months ago, Uber's self-driving car unit, Uber Advanced Technologies Group, was valued at $7.25 billion following a $1 billion investment from Toyota, DENSO and SoftBank's Vision Fund. Now, it's up for sale and a competing autonomous vehicle technology startup is in talks with Uber to buy it, according to three sources familiar with the deal. Aurora Innovation, the startup founded by three veterans of the autonomous vehicle industry who led programs at Google, Tesla and Uber, is in negotiations to buy Uber ATG. Terms of the deal are still unknown, but sources say the two companies have been in talks since October and it is far along in the process. An Uber spokesperson declined to comment, citing that the company's general policy is not to comment on these sorts of inquiries.

The Chinese military is aiming to utilize cutting-edge technologies like private sector-developed artificial intelligence to enhance its offensive capability in domains such as cyberspace and outer space, a Japanese Defense Ministry think tank warned Friday. Beijing aspires to match the United States' overall military capacity by transforming its People's Liberation Army into a world-class fighting force with the help of advanced technologies, the National Institute for Defense Studies said in its annual report on China's security strategy. The report said that until the Chinese catch up with the American military, "the PLA will build up its interference and strike capabilities to prevent the United States' military use of both the cyber and space domains." The China Security Report 2021 was released as the rivalry between Washington and Beijing has been intensifying, as has competition for technological hegemony. The United States has restricted exports of semiconductors to Huawei Technologies Co., the Chinese telecom giant that is aiming to expand its dominance of next-generation 5G technology.

Classification of polarimetric synthetic aperture radar (PolSAR) images is an active research area with a major role in environmental applications. The traditional Machine Learning (ML) methods proposed in this domain generally focus on utilizing highly discriminative features to improve the classification performance, but this task is complicated by the well-known "curse of dimensionality" phenomena. Other approaches based on deep Convolutional Neural Networks (CNNs) have certain limitations and drawbacks, such as high computational complexity, an unfeasibly large training set with ground-truth labels, and special hardware requirements. In this work, to address the limitations of traditional ML and deep CNN based methods, a novel and systematic classification framework is proposed for the classification of PolSAR images, based on a compact and adaptive implementation of CNNs using a sliding-window classification approach. The proposed approach has three advantages. First, there is no requirement for an extensive feature extraction process. Second, it is computationally efficient due to utilized compact configurations. In particular, the proposed compact and adaptive CNN model is designed to achieve the maximum classification accuracy with minimum training and computational complexity. This is of considerable importance considering the high costs involved in labelling in PolSAR classification. Finally, the proposed approach can perform classification using smaller window sizes than deep CNNs. Experimental evaluations have been performed over the most commonly-used four benchmark PolSAR images: AIRSAR L-Band and RADARSAT-2 C-Band data of San Francisco Bay and Flevoland areas. Accordingly, the best obtained overall accuracies range between 92.33 - 99.39% for these benchmark study sites.

Stealth is a 2005 American military science fiction action film directed by Rob Cohen and written by W. D. Richter, and starring Josh Lucas, Jessica Biel, Jamie Foxx, Sam Shepard, Joe Morton and Richard Roxburgh. The film follows three top fighter pilots as they join a project to develop an automated robotic stealth aircraft. Released on July 29, 2005 by Columbia Pictures, the film was a box office bomb, grossing $79 million worldwide against a budget of $135 million. It was one of the worst losses in cinematic history.[2][3] In the near future, the U.S. Navy develops the F/A-37 Talon, a single-seat fighter-bomber with advanced payload, range, speed, and stealth capabilities.

It is a statement that has been broadcasted and heard around the world: China intends to be the global leader of artificial intelligence by 2030. The country is putting its money where its mouth is, officials and analysts say, and making investments in AI that could threaten the United States and erode Washington's advantages in the technology. "The Chinese Communist Party recognizes the transformational power of AI," Defense Secretary Mark Esper recently said during remarks at the Defense Department's AI Symposium and Exposition. Beijing views the technology as a critical component to its future military and industrial power, said the Pentagon's recently released "Military and Security Developments Involving the People's Republic of China 2020" annual report to Congress. The country's "Next Generation AI Development Plan" details Beijing's strategy to employ commercial and military organizations to achieve major breakthroughs by 2025 and become the world leader by 2030, the report said.

At the outset of World War II, you'd have a better chance of finding a needle in a haystack with a camel stuck in its eye than you did shooting down an enemy aircraft in your first dozen or so shots. This is because anti-aircraft shells at the time used manual fuses that had to be dialed in for specific lengths of time to delay their explosion. The idea was that you'd estimate where the targeted plane would be in, say five seconds, based on its currently flight path, then time the shell for that length, fire the shell at the plane and hope that the timing and location were close enough that shrapnel from the exploding shell hits the plane. If your calculations were off by even a hair, the shell would miss by thousands of feet. And if shooting down piloted aircraft was this hard, intercepting Germany's terrifyingly fast V1 and V2 rockets required far more luck than skill. But that's exactly what the team at Section T set out to do.

The path toward realizing the potential of seasonal forecasting and its socioeconomic benefits depends heavily on improving general circulation model based dynamical forecasting systems. To improve dynamical seasonal forecast, it is crucial to set up forecast benchmarks, and clarify forecast limitations posed by model initialization errors, formulation deficiencies, and internal climate variability. With huge cost in generating large forecast ensembles, and limited observations for forecast verification, the seasonal forecast benchmarking and diagnosing task proves challenging. In this study, we develop a probabilistic deep neural network model, drawing on a wealth of existing climate simulations to enhance seasonal forecast capability and forecast diagnosis. By leveraging complex physical relationships encoded in climate simulations, our probabilistic forecast model demonstrates favorable deterministic and probabilistic skill compared to state-of-the-art dynamical forecast systems in quasi-global seasonal forecast of precipitation and near-surface temperature. We apply this probabilistic forecast methodology to quantify the impacts of initialization errors and model formulation deficiencies in a dynamical seasonal forecasting system. We introduce the saliency analysis approach to efficiently identify the key predictors that influence seasonal variability. Furthermore, by explicitly modeling uncertainty using variational Bayes, we give a more definitive answer to how the El Nino/Southern Oscillation, the dominant mode of seasonal variability, modulates global seasonal predictability.