Iranian soldiers carry part of a target drone used in air-defense exercises. Iran is also turning some target drones into low-tech weapons for its proxies. Iranian soldiers carry part of a target drone used in air-defense exercises. Iran is also turning some target drones into low-tech weapons for its proxies. In January, a group of high-level military commanders gathered at an air base in Yemen.

SANAA - The Saudi-led military coalition in Yemen carried out several airstrikes on the Houthi-held capital Sanaa on Thursday after the Iranian-aligned movement claimed responsibility for drone attacks on Saudi oil installations. The Sanaa strikes targeted nine military sites in and around the city, residents said, with humanitarian agencies reporting a number of casualties. Rubble filled a populated street lined by mud-brick houses, a Reuters journalist on the scene said. A crowd of men lifted the body of a women, wrapped in a white shroud, into an ambulance. Houthi-run Masirah television quoted the Houthi health ministry as saying six civilians, including four children, had been killed and 60 wounded, including two Russian women working in the health sector.

Vikram Mahidhar reminds us all that AI is only as good as the humans supervising it and programming it. The biases and artefacts that come out of the processing are reflective of the biases programmed in at the beginning. A program trained to recognise totalled car bodies for insurance purposes, for example, will need close supervision of its decision-making outputs, for regulatory and consumer confidence and acceptance of the decision. There is a call and a growth in a new class of AI--one that is explainable, and that builds trust by providing evidence. Vikram also reminds us that a governance strategy is key to engendering trust in our organisation, processes and people.

Carbon sequestration Cutting greenhouse-gas emissions alone won't be enough to prevent sharp increases in global temperatures. We'll also need to remove vast amounts of carbon dioxide from the atmosphere, which not only would be incredibly expensive but would present us with the thorny problem of what to do with all that CO2. A growing number of startups are exploring ways of recycling carbon dioxide into products, including synthetic fuels, polymers, carbon fiber, and concrete. That's promising, but what we'll really need is a cheap way to permanently store the billions of tons of carbon dioxide that we might have to pull out of the atmosphere. Grid-scale energy storage Renewable energy sources like wind and solar are becoming cheap and more widely deployed, but they don't generate electricity when the sun's not shining or wind isn't blowing. That limits how much power these sources can supply, and how quickly we can move away from steady sources like coal and natural gas.

Modern computing and communication technologies can make data collection procedures very efficient. However, our ability to analyze large data sets and/or to extract information out from them is hard-pressed to keep up with our capacities for data collection. Among these huge data sets, some of them are not collected for any particular research purpose. For a classification problem, this means that the essential label information may not be readily obtainable, in the data set in hands, and an extra labeling procedure is required such that we can have enough label information to be used for constructing a classification model. When the size of a data set is huge, to label each subject in it will cost a lot in both capital and time. Thus, it is an important issue to decide which subjects should be labeled first in order to efficiently reduce the training cost/time. Active learning method is a promising outlet for this situation, because with the active learning ideas, we can select the unlabeled subjects sequentially without knowing their label information. In addition, there will be no confirmed information about the essential variables for constructing an efficient classification rule. Thus, how to merge a variable selection scheme with an active learning procedure is of interest. In this paper, we propose a procedure for building binary classification models when the complete label information is not available in the beginning of the training stage. We study an model-based active learning procedure with sequential variable selection schemes, and discuss the results of the proposed procedure from both theoretical and numerical aspects.

The high seas are probably the most lawless place left on Earth. They're a portal back in time to the way the world looked for most of our history: fierce and open competition for resources and contested territories. Pirating continues to be a way to make a living. It's not a complete free-for-all--most countries require registration of fishing vessels and enforce environmental protocols. Cooperative agreements between countries oversee fisheries in international waters.

Multi-hop reading comprehension focuses on one type of factoid question, where a system needs to properly integrate multiple pieces of evidence to correctly answer a question. Previous work approximates global evidence with local coreference information, encoding coreference chains with DAG-styled GRU layers within a gated-attention reader. However, coreference is limited in providing information for rich inference. We introduce a new method for better connecting global evidence, which forms more complex graphs compared to DAGs. To perform evidence integration on our graphs, we investigate two recent graph neural networks, namely graph convolutional network (GCN) and graph recurrent network (GRN). Experiments on two standard datasets show that richer global information leads to better answers. Our method performs better than all published results on these datasets.

Scientists have developed a spear-wielding submersible robot to hunt invasive lionfish in the western Atlantic Ocean. The fish have become a major problem in the waters off the coastal US and Caribbean islands; originally from the South Pacific and Indian oceans, lionfish have no natural predators in the area and are now out-competing native species. Researchers are now hoping an autonomous robot can help solve the problem by weeding out the lionfish and harvesting them without causing further damage to struggling coral reefs. Scientists have developed a spear-wielding submersible robot to hunt invasive lionfish in the western Atlantic Ocean. 'There are economic and environmental benefits to this, and the fish are delicious,' says Brandon Kelly, an undergraduate student at Worcester Polytechnic Institute who developed the robot's computer vision system.

A self-organizing map (SOM) is a type of competitive artificial neural network, which projects the high-dimensional input space of the training samples into a low-dimensional space with the topology relations preserved. This makes SOMs supportive of organizing and visualizing complex data sets and have been pervasively used among numerous disciplines with different applications. Notwithstanding its wide applications, the self-organizing map is perplexed by its inherent randomness, which produces dissimilar SOM patterns even when being trained on identical training samples with the same parameters every time, and thus causes usability concerns for other domain practitioners and precludes more potential users from exploring SOM based applications in a broader spectrum. Motivated by this practical concern, we propose a deterministic approach as a supplement to the standard self-organizing map. In accordance with the theoretical design, the experimental results with satellite cloud data demonstrate the effective and efficient organization as well as simplification capabilities of the proposed approach.

Identifying current and future informal regions within cities remains a crucial issue for policymakers and governments in developing countries. The delineation process of identifying such regions in cities requires a lot of resources. While there are various studies that identify informal settlements based on satellite image classification, relying on both supervised or unsupervised machine learning approaches, these models either require multiple input data to function or need further development with regards to precision. In this paper, we introduce a novel method for identifying and predicting informal settlements using only street intersections data, regardless of the variation of urban form, number of floors, materials used for construction or street width. With such minimal input data, we attempt to provide planners and policy-makers with a pragmatic tool that can aid in identifying informal zones in cities. The algorithm of the model is based on spatial statistics and a machine learning approach, using Multinomial Logistic Regression (MNL) and Artificial Neural Networks (ANN). The proposed model relies on defining informal settlements based on two ubiquitous characteristics that these regions tend to be filled in with smaller subdivided lots of housing relative to the formal areas within the local context, and the paucity of services and infrastructure within the boundary of these settlements that require relatively bigger lots. We applied the model in five major cities in Egypt and India that have spatial structures in which informality is present. These cities are Greater Cairo, Alexandria, Hurghada and Minya in Egypt, and Mumbai in India. The predictSLUMS model shows high validity and accuracy for identifying and predicting informality within the same city the model was trained on or in different ones of a similar context.