Autonomous vehicles are on the horizon and will be transforming transportation safety and comfort. These vehicles will be connected to various external systems and utilize advanced embedded systems to perceive their environment and make intelligent decisions. However, this increased connectivity makes these vehicles vulnerable to various cyber-attacks that can have catastrophic effects. Attacks on automotive systems are already on the rise in today's vehicles and are expected to become more commonplace in future autonomous vehicles. Thus, there is a need to strengthen cybersecurity in future autonomous vehicles. In this article, we discuss major automotive cyber-attacks over the past decade and present state-of-the-art solutions that leverage artificial intelligence (AI). We propose a roadmap towards building secure autonomous vehicles and highlight key open challenges that need to be addressed.

Diverse Natural Language Processing tasks employ constituency parsing to understand the syntactic structure of a sentence according to a phrase structure grammar. Many state-of-the-art constituency parsers are proposed, but they may provide different results for the same sentences, especially for corpora outside their training domains. This paper adopts the truth discovery idea to aggregate constituency parse trees from different parsers by estimating their reliability in the absence of ground truth. Our goal is to consistently obtain high-quality aggregated constituency parse trees. We formulate the constituency parse tree aggregation problem in two steps, structure aggregation and constituent label aggregation. Specifically, we propose the first truth discovery solution for tree structures by minimizing the weighted sum of Robinson-Foulds (RF) distances, a classic symmetric distance metric between two trees. Extensive experiments are conducted on benchmark datasets in different languages and domains. The experimental results show that our method, CPTAM, outperforms the state-of-the-art aggregation baselines. We also demonstrate that the weights estimated by CPTAM can adequately evaluate constituency parsers in the absence of ground truth.

Federated learning (FL) is a useful tool in distributed machine learning that utilizes users' local datasets in a privacy-preserving manner. When deploying FL in a constrained wireless environment; however, training models in a time-efficient manner can be a challenging task due to intermittent connectivity of devices, heterogeneous connection quality, and non-i.i.d. data. In this paper, we provide a novel convergence analysis of non-convex loss functions using FL on both i.i.d. and non-i.i.d. datasets with arbitrary device selection probabilities for each round. Then, using the derived convergence bound, we use stochastic optimization to develop a new client selection and power allocation algorithm that minimizes a function of the convergence bound and the average communication time under a transmit power constraint. We find an analytical solution to the minimization problem. One key feature of the algorithm is that knowledge of the channel statistics is not required and only the instantaneous channel state information needs to be known. Using the FEMNIST and CIFAR-10 datasets, we show through simulations that the communication time can be significantly decreased using our algorithm, compared to uniformly random participation.

The various methods that have been proposed for (1.1) have favorable complexity guarantees in certain special cases. The plethora of methods and results makes it difficult for both theoreticians and practitioners to choose the method best suited to particular instances of (1.1). In this paper, we focus on improving the theory for an existing method, the PURE-CD algorithm described in [2]. We show that this method achieves or improves best-known complexity results for interesting special cases of (1.1). The state-of-the-art results are currently dispersed around different methods. 1

We provide new insights into the finding that Medicaid increased emergency department (ED) use from the Oregon experiment. Using nonparametric causal machine learning methods, we find economically meaningful treatment effect heterogeneity in the impact of Medicaid coverage on ED use. The effect distribution is widely dispersed, with significant positive effects concentrated among high-use individuals. A small group - about 14% of participants - in the right tail with significant increases in ED use drives the overall effect. The remainder of the individualized treatment effects is either indistinguishable from zero or negative. The average treatment effect is not representative of the individualized treatment effect for most people. We identify four priority groups with large and statistically significant increases in ED use - men, prior SNAP participants, adults less than 50 years old, and those with pre-lottery ED use classified as primary care treatable. Our results point to an essential role of intensive margin effects - Medicaid increases utilization among those already accustomed to ED use and who use the emergency department for all types of care. We leverage the heterogeneous effects to estimate optimal assignment rules to prioritize insurance applications in similar expansions.

But the acquisition also shines a fresh light on how big and powerful Microsoft is, and how far its empire sprawls away from its old-school software products like Microsoft Office and Windows. In the two decades since the U.S. government sued Microsoft for using its dominance in operating systems to freeze out competitors, the company has rebounded in a spectacular way. It now owns household-name companies in a variety of fields, from social media to gaming to vital tools used by computer programmers all over the world.

A California motorist has become the first person to be charged over a fatal crash involving Tesla's Autopilot system. Kevin George Aziz Riad, 27, faces two counts of vehicular manslaughter after being behind the wheel of a Tesla when it ran a red light, crashing into another car and killing two people. It is the first time a motorist has been charged with a felony for an incident involving the electric car maker's partially automated driving system, according to the Associated Press. Los Angeles County prosecutors filed the charges in October, but details of the case have only just emerged. Mr Riad, who works as a limousine service driver, is out on bail while the case is pending.

California prosecutors have filed two counts of vehicular manslaughter against the driver of a Tesla on Autopilot that ran a red light, slammed into another car and killed two people in 2019. California prosecutors have filed two counts of vehicular manslaughter against the driver of a Tesla on Autopilot that ran a red light, slammed into another car and killed two people in 2019. DETROIT -- California prosecutors have filed two counts of vehicular manslaughter against the driver of a Tesla on Autopilot who ran a red light, slammed into another car and killed two people in 2019. The defendant appears to be the first person to be charged with a felony in the United States for a fatal crash involving a motorist who was using a partially automated driving system. Los Angeles County prosecutors filed the charges in October, but they came to light only last week.

Fox News Flash top headlines are here. Check out what's clicking on Foxnews.com. California prosecutors have filed two counts of vehicular manslaughter against the driver of a Tesla on Autopilot who ran a red light, slammed into another car and killed two people in 2019. All Tesla models, including the Model S, now come standard with Autopilot. The defendant appears to be the first person to be charged with a felony in the United States for a fatal crash involving a motorist who was using a partially automated driving system.

A shoebox-sized robot named Iris is among the final two vying for the chance to become the first US spacecraft to land on the moon in 50 years. NASA has been sending rovers to the surface of Mars for decades, with two, Perseverance and Curiosity, currently operating and sending back photographs. However, the US-based space agency hasn't sent a vehicle to explore the surface of the moon since the last Apollo landing in 1972 - 50 years ago this year. Two contractors funded by NASA have vehicles that could launch this year - including Houston-based Intuitive Machines, and Pittsburgh-based Astrobotic. With contracts worth more than $70 million each, the firms could launch for the moon this year on a mixture of SpaceX Falcon 9 and United Launch Alliance rockets.