"The air force announced the execution of air strikes with the Qasif 1 aircraft on Aramco in Jizan (province)," the channel said on its official Twitter account, referring to a drone the Houthis had previously unveiled.

You can create skimmers with the formula syntax from rlang! You can now control the object name output for topojson_write, and there's now an analog of geojson_sp for sf (geojson_sf) We accept community contributed packages via our onboarding system - an open software review system, sorta like scholarly paper review, but way better. We'll highlight newly onboarded packages here. A huge thanks to our reviewers, who do a lot of work reviewing (see the blog post on our review system), and the authors of the packages! If you want to be a reviewer fill out this short form, and we'll ping you when there's a submission that fits in your area of expertise.

In this paper, we investigate cost-aware joint learning and optimization for multi-channel opportunistic spectrum access in a cognitive radio system. We investigate a discrete time model where the time axis is partitioned into frames. Each frame consists of a sensing phase, followed by a transmission phase. During the sensing phase, the user is able to sense a subset of channels sequentially before it decides to use one of them in the following transmission phase. We assume the channel states alternate between busy and idle according to independent Bernoulli random processes from frame to frame. To capture the inherent uncertainty in channel sensing, we assume the reward of each transmission when the channel is idle is a random variable. We also associate random costs with sensing and transmission actions. Our objective is to understand how the costs and reward of the actions would affect the optimal behavior of the user in both offline and online settings, and design the corresponding opportunistic spectrum access strategies to maximize the expected cumulative net reward (i.e., reward-minus-cost). We start with an offline setting where the statistics of the channel status, costs and reward are known beforehand. We show that the the optimal policy exhibits a recursive double threshold structure, and the user needs to compare the channel statistics with those thresholds sequentially in order to decide its actions. With such insights, we then study the online setting, where the statistical information of the channels, costs and reward are unknown a priori. We judiciously balance exploration and exploitation, and show that the cumulative regret scales in O(log T). We also establish a matched lower bound, which implies that our online algorithm is order-optimal. Simulation results corroborate our theoretical analysis.

Machine learning techniques have been used in the past using Monte Carlo samples to construct predictors of the dynamic stability of power systems. In this paper we move beyond the task of prediction and propose a comprehensive approach to use predictors, such as Decision Trees (DT), within a standard optimization framework for pre- and post-fault control purposes. In particular, we present a generalizable method for embedding rules derived from DTs in an operation decision-making model. We begin by pointing out the specific challenges entailed when moving from a prediction to a control framework. We proceed with introducing the solution strategy based on generalized disjunctive programming (GDP) as well as a two-step search method for identifying optimal hyper-parameters for balancing cost and control accuracy. We showcase how the proposed approach constructs security proxies that cover multiple contingencies while facing high-dimensional uncertainty with respect to operating conditions with the use of a case study on the IEEE 39-bus system. The method is shown to achieve efficient system control at a marginal increase in system price compared to an oracle model.

With each industrial revolution mankind, has progressed by leaps and bounds. But that progress has also damaged our environment. Today, climate change, loss of biodiversity, water woes, and food sustainability are among the most pressing global issues. However, the advent of the Fourth Industrial Revolution is set to fundamentally change such trends. Characterized by advanced technologies such as Artificial Intelligence (AI), big data, automation, and quantum computing, the Fourth Industrial Revolution has the potential to heal the past and ensure a better future.

In early April, California's new rules that allow automakers, tech giants and just about anybody to test fully driverless cars on its roads finally took effect. But before those companies can realize their ride-hailing robot taxi ambitions, they have to wait for the state to adopt a proposal issued by the California Public Utilities Commission. The public utility regulator's proposed rules would allow autonomous vehicles to give rides to the public as part of a pilot program -- that is, so long as their creators meet a few conditions. To start with, only cars with backup drivers can initially take passengers. That won't be a problem, since only one (unnamed) company has applied for permission to test no-driver cars in California, thus far.

We recognize Earth as the blue planet, but it's not the only ocean world in our neighborhood. Oceans may be concealed beneath thick crusts of ice on moons orbiting Jupiter, Saturn, and Neptune, and on the dwarf planets Pluto and Eris. Saturn's moon Titan even boasts liquid seas right on its surface, although they are full of methane rather than water. If anywhere in our solar system holds signs of life, it is likely to be these frigid worlds. Scientists are determined to explore the distant seas of Titan and Jupiter's moon Europa, and are designing ice-gripping rovers and submarines to take the plunge into their mysterious depths.

When complete, the homes are autonomous and mobile, meaning they don't need to connect to external electrical and plumbing systems. Solar energy is stored in a battery connected to the houses, and water is collected and filtered from humidity in the air (or you can pour water into the system yourself). The houses also feature independent sewage systems.

Automated analysis methods are crucial aids for monitoring and defending a network to protect the sensitive or confidential data it hosts. This work introduces a flexible, powerful, and unsupervised approach to detecting anomalous behavior in computer and network logs; one that largely eliminates domain-dependent feature engineering employed by existing methods. By treating system logs as threads of interleaved ``sentences'' (event log lines) to train online unsupervised neural network language models, our approach provides an adaptive model of normal network behavior. We compare the effectiveness of both standard and bidirectional recurrent neural network language models at detecting malicious activity within network log data. Extending these models, we introduce a tiered recurrent architecture, which provides context by modeling sequences of users' actions over time. Compared to Isolation Forest and Principal Components Analysis, two popular anomaly detection algorithms, we observe superior performance on the Los Alamos National Laboratory Cyber Security dataset. For log-line-level red team detection, our best performing character-based model provides test set area under the receiver operator characteristic curve of 0.98, demonstrating the strong fine-grained anomaly detection performance of this approach on open vocabulary logging sources.

SENDAI – Municipalities and private firms are hoping robots and drones will be able to help with future disaster recovery efforts -- an initiative that incorporates lessons learned from the 2011 Great East Japan Earthquake -- by sending out warnings, gauging damage and accessing places were people cannot. To that end, the Sendai Municipal Government is testing a speaker-equipped drone for sending evacuation warnings during flight. Drones are quieter than helicopters, meaning messages would be easier for those on the ground to hear, city officials said. In such a system, the drone would automatically take flight after receiving a warning from the country's J-Alert early warning system and would issue evacuation messages to local residents. In the 2011 disaster, two city government workers and three volunteer fire department rescuers were killed in the tsunami while warning local residents to evacuate.