Iran vows retaliation; Lt. Col. Daniel Davis, Walid Phares, and Rep. Mark Green react. A giant question mark hangs over the Middle East as the world waits to see what action Iran will take to retaliate for the long-overdue killing Friday morning of Iranian Gen. Qassem Soleimani in a drone strike ordered by President Trump. President Trump made the right decision in ordering Soleimani killed in Iraq. I've been arguing for four years that we ought to take out this dangerous enemy of the United States, who was responsible for the deaths of hundreds of Americans and wanted to kill many more. Thankfully, his killing days are over.

We are often interested in clustering objects that evolve over time and identifying solutions to the clustering problem for every time step. Evolutionary clustering provides insight into cluster evolution and temporal changes in cluster memberships while enabling performance superior to that achieved by independently clustering data collected at different time points. In this paper we introduce evolutionary affinity propagation (EAP), an evolutionary clustering algorithm that groups data points by exchanging messages on a factor graph. EAP promotes temporal smoothness of the solution to clustering time-evolving data by linking the nodes of the factor graph that are associated with adjacent data snapshots, and introduces consensus nodes to enable cluster tracking and identification of cluster births and deaths. Unlike existing evolutionary clustering methods that require additional processing to approximate the number of clusters or match them across time, EAP determines the number of clusters and tracks them automatically. A comparison with existing methods on simulated and experimental data demonstrates effectiveness of the proposed EAP algorithm.

Neural networks have become increasingly prevalent within the geosciences for applications ranging from numerical model parameterizations to the prediction of extreme weather. A common limitation of neural networks has been the lack of methods to interpret what the networks learn and how they make decisions. As such, neural networks have typically been used within the geosciences to accurately identify a desired output given a set of inputs, with the interpretation of what the network learns being used - if used at all - as a secondary metric to ensure the network is making the right decision for the right reason. Network interpretation techniques have become more advanced in recent years, however, and we therefore propose that the ultimate objective of using a neural network can also be the interpretation of what the network has learned rather than the output itself. We show that the interpretation of a neural network can enable the discovery of scientifically meaningful connections within geoscientific data. By training neural networks to use one or more components of the earth system to identify another, interpretation methods can be used to gain scientific insights into how and why the two components are related. In particular, we use two methods for neural network interpretation. These methods project the decision pathways of a network back onto the original input dimensions, and are called "optimal input" and layerwise relevance propagation (LRP). We then show how these interpretation techniques can be used to reliably infer scientifically meaningful information from neural networks by applying them to common climate patterns. These results suggest that combining interpretable neural networks with novel scientific hypotheses will open the door to many new avenues in neural network-related geoscience research.

Recently, scientist Gagandeep Kang had to forcefully remind a room full of senior colleagues -- all men -- that she was the chair and that they should speak only when their turn comes. This kind of thing happens all the time, and you become so inured to it that you don't realise it," she says. Kang is the first Indian woman to be elected as a fellow of the Royal Society, but even that, evidently, does not protect you from microaggressions from men. It is a reminder of the kind of bias that women in science have to deal with. Prejudice at many levels is one reason why there are far fewer women scientists than men in the higher echelons of science in India. A 2016-17 report, "Status of Women in Science Among Select Institutions in India: Policy Implications", supported by NITI Aayog, found that while women constitute over a third of science graduates and postgraduates, they make up only 15-20% of tenured faculty across research institutions and universities in India. "As a group, it is not easy for women to stay in science. Only 14% of scientists are women," science writers Nandita Jayaraj and Aashima Dogra write in their recent book, 31 Fantastic Adventures in Science: Women Scientists in India. However, there are women who have beaten odds and shattered stereotypes and glass ceilings. This special feature looks at nine such women who are doing critical work in science and technology in India. They work on an array of complex problems -- in fields ranging from quantum computation to paleoecology. Neuroscientist Vidita Vaidya is looking to decode how experiences and the environment affect the circuits in our brain, which might offer a clue to how we develop psychiatric disorders. Aditi Sen De, the first woman to receive the Shanti Swarup Bhatnagar Prize in physical sciences, is working on different aspects of quantum communication, a field that uses the laws of quantum physics to protect data. This is by no means an exhaustive list of exceptional women scientists, but they are representative of the brilliant minds that have striven and made it to the top and become exemplars. As Kang says, "If you see role models, you see areas you can aspire to.

NEW DELHI – Japan and India agreed Saturday to conduct their first joint fighter aircraft exercise in Japan as part of efforts to promote bilateral security cooperation in the face of China's military buildup and regional assertiveness. In inaugural "two-plus-two" security talks, the nations' foreign and defense ministers also welcomed the significant progress in negotiations for a pact that would allow the sharing of defense capabilities and supplies including fuel and ammunition. They called for a speedy conclusion to the acquisition and cross-servicing agreement (ACSA), according to a joint statement issued after the talks in New Delhi. The two governments are planning to sign the deal when Prime Minister Shinzo Abe visits India for talks with Prime Minister Narendra Modi in mid-December, according to Japanese officials. Tokyo and New Delhi aim to have a joint exercise involving fighter jets from the Air Self-Defense Force and the Indian Air Force next year, the officials said.

European satellite missions Sentinel-1 (S1) and Sentinel-2 (S2) provide at highspatial resolution and high revisit time, respectively, radar and optical imagesthat support a wide range of Earth surface monitoring tasks such as LandUse/Land Cover mapping. A long-standing challenge in the remote sensingcommunity is about how to efficiently exploit multiple sources of information and leverage their complementary. In this particular case, get the most out ofradar and optical satellite image time series (SITS). Here, we propose to dealwith land cover mapping through a deep learning framework especially tailoredto leverage the multi-source complementarity provided by radar and opticalSITS. The proposed architecture is based on an extension of Recurrent NeuralNetwork (RNN) enriched via a customized attention mechanism capable to fitthe specificity of SITS data. In addition, we propose a new pretraining strategythat exploits domain expert knowledge to guide the model parameter initial-ization. Thorough experimental evaluations involving several machine learningcompetitors, on two contrasted study sites, have demonstrated the suitabilityof our new attention mechanism combined with the extend RNN model as wellas the benefit/limit to inject domain expert knowledge in the neural networktraining process.

Regularized least-squares (kernel-ridge / Gaussian process) regression is a fundamental algorithm of statistics and machine learning. Because generic algorithms for the exact solution have cubic complexity in the number of datapoints, large datasets require to resort to approximations. In this work, the computation of the least-squares prediction is itself treated as a probabilistic inference problem. We propose a structured Gaussian regression model on the kernel function that uses projections of the kernel matrix to obtain a low-rank approximation of the kernel and the matrix. A central result is an enhanced way to use the method of conjugate gradients for the specific setting of least-squares regression as encountered in machine learning. Our method improves the approximation of the kernel ridge regressor / Gaussian process posterior mean over vanilla conjugate gradients and, allows computation of the posterior variance and the log marginal likelihood (evidence) without further overhead.

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Ayatollah Khamenei doubles down on Iran's commitment not to engage in talks with the United States; Trey Yingst reports. The drone was reportedly hit in the early morning at the port city of Mahshahr, which is in the oil-rich Khuzestan province and lies on the Persian Gulf. "The downed droned definitely belonged to a foreign country. Its wreckage has been recovered and is being investigated," the governor of Khuzestan, Gholamreza Shariati, said, according to the official IRNA news agency. He said the drone violated Iran's airspace but did not provide any additional information, including whether it was a military or civilian drone.

Recurrent Neural Networks (RNNs) can be seriously impacted by the initial parameters assignment, which may result in poor generalization performances on new unseen data. With the objective to tackle this crucial issue, in the context of RNN based classification, we propose a new supervised layer-wise pretraining strategy to initialize network parameters. The proposed approach leverages a data-aware strategy that sets up a taxonomy of classification problems automatically derived by the model behavior. To the best of our knowledge, despite the great interest in RNN-based classification, this is the first data-aware strategy dealing with the initialization of such models. The proposed strategy has been tested on four benchmarks coming from two different domains, i.e., Speech Recognition and Remote Sensing. Results underline the significance of our approach and point out that data-aware strategies positively support the initialization of Recurrent Neural Network based classification models.