Artificial intelligence helped co-pilot a U-2 "Dragon Lady" spy plane during a test flight Tuesday, the first time artificial intelligence has been used in such a way aboard a US military aircraft. Mastering artificial intelligence or "AI" is increasingly seen as critical to the future of warfare and Air Force officials said Tuesday's training flight represented a major milestone. "The Air Force flew artificial intelligence as a working aircrew member onboard a military aircraft for the first time, December 15," the Air Force said in a statement, saying the flight signaled "a major leap forward for national defense in the digital age." The Artificial Intelligence algorithm, known as "ARTUµ," was developed by researchers at the Air Force's Air Combat Command U-2 Federal Laboratory. The AI system has been "trained ... to execute specific in-flight tasks that otherwise would be done by the pilot," the statement said.

A resilient and robust positioning, navigation, and timing (PNT) system is a necessity for the navigation of autonomous vehicles (AVs). Global Navigation Satelite System (GNSS) provides satellite-based PNT services. However, a spoofer can temper an authentic GNSS signal and could transmit wrong position information to an AV. Therefore, a GNSS must have the capability of real-time detection and feedback-correction of spoofing attacks related to PNT receivers, whereby it will help the end-user (autonomous vehicle in this case) to navigate safely if it falls into any compromises. This paper aims to develop a deep reinforcement learning (RL)-based turn-by-turn spoofing attack detection using low-cost in-vehicle sensor data. We have utilized Honda Driving Dataset to create attack and non-attack datasets, develop a deep RL model, and evaluate the performance of the RL-based attack detection model. We find that the accuracy of the RL model ranges from 99.99% to 100%, and the recall value is 100%. However, the precision ranges from 93.44% to 100%, and the f1 score ranges from 96.61% to 100%. Overall, the analyses reveal that the RL model is effective in turn-by-turn spoofing attack detection.

This paper presents a sensor fusion based Global Navigation Satellite System (GNSS) spoofing attack detection framework for autonomous vehicles (AV) that consists of two concurrent strategies: (i) detection of vehicle state using predicted location shift -- i.e., distance traveled between two consecutive timestamps -- and monitoring of vehicle motion state -- i.e., standstill/ in motion; and (ii) detection and classification of turns (i.e., left or right). Data from multiple low-cost in-vehicle sensors (i.e., accelerometer, steering angle sensor, speed sensor, and GNSS) are fused and fed into a recurrent neural network model, which is a long short-term memory (LSTM) network for predicting the location shift, i.e., the distance that an AV travels between two consecutive timestamps. This location shift is then compared with the GNSS-based location shift to detect an attack. We have then combined k-Nearest Neighbors (k-NN) and Dynamic Time Warping (DTW) algorithms to detect and classify left and right turns using data from the steering angle sensor. To prove the efficacy of the sensor fusion-based attack detection framework, attack datasets are created for four unique and sophisticated spoofing attacks-turn-by-turn, overshoot, wrong turn, and stop, using the publicly available real-world Honda Research Institute Driving Dataset (HDD). Our analysis reveals that the sensor fusion-based detection framework successfully detects all four types of spoofing attacks within the required computational latency threshold.

Though inverse approach is computationally efficient in aerodynamic design as the desired target performance distribution is specified, it has some significant limitations that prevent full efficiency from being achieved. First, the iterative procedure should be repeated whenever the specified target distribution changes. Target distribution optimization can be performed to clarify the ambiguity in specifying this distribution, but several additional problems arise in this process such as loss of the representation capacity due to parameterization of the distribution, excessive constraints for a realistic distribution, inaccuracy of quantities of interest due to theoretical/empirical predictions, and the impossibility of explicitly imposing geometric constraints. To deal with these issues, a novel inverse design optimization framework with a two-step deep learning approach is proposed. A variational autoencoder and multi-layer perceptron are used to generate a realistic target distribution and predict the quantities of interest and shape parameters from the generated distribution, respectively. Then, target distribution optimization is performed as the inverse design optimization. The proposed framework applies active learning and transfer learning techniques to improve accuracy and efficiency. Finally, the framework is validated through aerodynamic shape optimizations of the airfoil of a wind turbine blade, where inverse design is actively being applied. The results of the optimizations show that this framework is sufficiently accurate, efficient, and flexible to be applied to other inverse design engineering applications.

Undercurrent is an upcoming immersive art event featuring audiovisual installations from around 40 musicians, headlined by Bon Iver, Grimes and The 1975, designed to inspire climate activism. Before the physical exhibition arrives in Brooklyn on September 9th, a digital sister event is today launching online that showcases 3D interactive music videos from some of the support acts. The Undercurrent digital platform includes original, unreleased music from Nosaj Thing, Mount Kimbie, Actress, Aluna, and Jayda G. Again, the focus is on spurring change around environmental issues through immersive art. Each musician's work ends with a call to action, whether it be donating to or volunteering for a non-profit. The virtual event could also be a way for budding visitors to get a feel for the main exhibition.

During the spring, a troublesome pattern plays out as marine birds along the California coast die from domoic acid poisoning, which is caused by harmful algal blooms. An early clue indicates when and where this problem starts spreading: rescued California brown pelicans, red-throated loons, and other species start turning up at wildlife rehabilitation centers with signs of neurological disease. Yet, though they pepper the state map, these centers are not interconnected enough to nip the issue in the bud. When staffers at one center diagnose a sick bird, others another 40 miles up the road might not be privy to that information. So researchers at UC Davis recently tested an early detection system that uses artificial intelligence to classify admissions to rehabilitation centers, in the hope of sending wildlife agencies and researchers warnings about growing problems among marine birds and many other kinds of animals.

Accurate traffic state prediction is the foundation of transportation control and guidance. It is very challenging due to the complex spatiotemporal dependencies in traffic data. Existing works cannot perform well for multi-step traffic prediction that involves long future time period. The spatiotemporal information dilution becomes serve when the time gap between input step and predicted step is large, especially when traffic data is not sufficient or noisy. To address this issue, we propose a multi-spatial graph convolution based Seq2Seq model. Our main novelties are three aspects: (1) We enrich the spatiotemporal information of model inputs by fusing multi-view features (time, location and traffic states) (2) We build multiple kinds of spatial correlations based on both prior knowledge and data-driven knowledge to improve model performance especially in insufficient or noisy data cases. (3) A spatiotemporal attention mechanism based on reachability knowledge is novelly designed to produce high-level features fed into decoder of Seq2Seq directly to ease information dilution. Our model is evaluated on two real world traffic datasets and achieves better performance than other competitors.

Japan plans to revise its Medium Term Defense Program earlier than originally scheduled as it looks to boost spending to counter China's growing assertiveness in surrounding waters and prepare for contingencies in the Taiwan Strait, government sources said Friday. The program, which covers the five years through fiscal 2023, could be updated within the year, with Prime Minister Yoshihide Suga and Defense Minister Nobuo Kishi having agreed earlier this month that some changes are necessary, the sources said. Discussions between officials including at the Defense Ministry and the National Security Secretariat are already underway, with budget issues set to be reviewed by the Finance Ministry. The revision would seek to fulfill Suga's promise to U.S. President Joe Biden during their meeting in Washington in April that Japan would bolster its defense capabilities to strengthen the alliance between their countries and maintain security in the Indo-Pacific region. In a joint statement issued after the meeting, the leaders singled out China for actions that are "inconsistent with the international rules-based order, including the use of economic and other forms of coercion."

Let me tell you a little-known fact. Look at companies that use a gaming mindset to help you grow and monetize your company. Snapchat or Uber might be the next big thing. They will likely look more like a gaming studio that uses the best user acquisition, retention, and revenue strategies from the gaming industry. The video game industry is more important than the movie and music industries.

Motional, a joint autonomous vehicle venture between Aptiv and Hyundai, is expanding its operations in California. The company plans to start public road mapping and testing of its robotaxi in Los Angeles this month. Motional is currently testing the AV in Boston, Pittsburgh, Las Vegas (including driverless tests) and Singapore. The company and partner Lyft plan to start a robotaxi service in several US markets in 2023. Extensive road mapping and testing are essential precursors for that to happen.