Glancing at Barcelona's still-unfinished Sagrada Família Roman Catholic basilica, with its famous sandcastle-like exterior, it is easy to get the wrong idea about its architect, Antoni Gaudí, as a carefree, loosey-goosey artist. The whimsical exterior hides a geometrically sophisticated, structurally advanced design--a big part of the reason this grand basilica, begun in 1882, has taken so many decades to build, remaining the world's longest-running ongoing architectural project. This complexity required an utterly different approach to modeling than what architects had typically deployed. Instead of using two-dimensional drawings to guide builders, Gaudí relied heavily on large, high-fidelity plaster models--models that needed to be reverse engineered and rebuilt after extensive damage during the Spanish Civil War. In a separate project, Gaudí pioneered the use of hanging-chain models that enable changes in real time; though he did not use these interactive models on the Sagrada Família, they guided his thinking and prefigured the so-called parametric design software that has been instrumental to the acceleration of the project's pace in recent years.

The past decade has seen a substantial rise in the amount of mis- and disinformation online, from targeted disinformation campaigns to influence politics, to the unintentional spreading of misinformation about public health. This development has spurred research in the area of automatic fact checking, from approaches to detect check-worthy claims and determining the stance of tweets towards claims, to methods to determine the veracity of claims given evidence documents. These automatic methods are often content-based, using natural language processing methods, which in turn utilise deep neural networks to learn higher-order features from text in order to make predictions. As deep neural networks are black-box models, their inner workings cannot be easily explained. At the same time, it is desirable to explain how they arrive at certain decisions, especially if they are to be used for decision making. While this has been known for some time, the issues this raises have been exacerbated by models increasing in size, and by EU legislation requiring models to be used for decision making to provide explanations, and, very recently, by legislation requiring online platforms operating in the EU to provide transparent reporting on their services. Despite this, current solutions for explainability are still lacking in the area of fact checking. This thesis presents my research on automatic fact checking, including claim check-worthiness detection, stance detection and veracity prediction. Its contributions go beyond fact checking, with the thesis proposing more general machine learning solutions for natural language processing in the area of learning with limited labelled data. Finally, the thesis presents some first solutions for explainable fact checking.

The Defense Ministry will seek another record budget of over ¥5.4 trillion ($49 billion) for fiscal 2022, aiming to beef up its capabilities around remote southwestern islands to counter China's growing naval activities, government sources have said. The request would exceed the ministry's highest-ever ¥5.3 trillion initial budget for fiscal 2021, which started in April, and also reflects an increase in the cost to develop cutting-edge technologies, such as unmanned aircraft using artificial intelligence, the sources said Thursday. The defense budget could further expand, possibly topping 1% of Japan's gross domestic product, when it is finalized in December, as the request excludes outlays linked to hosting U.S. military bases. Japan's defense budget has long stayed at around 1% of its GDP, in light of the country's postwar pacifist Constitution and since the Cabinet decided in 1976 that the outlays should not exceed 1%. The last time the defense expenditure exceeded 1% was in fiscal 2010, when the GDP shrank sharply following the 2008-2009 global financial crisis.

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.