Classification of polarimetric synthetic aperture radar (PolSAR) images is an active research area with a major role in environmental applications. The traditional Machine Learning (ML) methods proposed in this domain generally focus on utilizing highly discriminative features to improve the classification performance, but this task is complicated by the well-known "curse of dimensionality" phenomena. Other approaches based on deep Convolutional Neural Networks (CNNs) have certain limitations and drawbacks, such as high computational complexity, an unfeasibly large training set with ground-truth labels, and special hardware requirements. In this work, to address the limitations of traditional ML and deep CNN based methods, a novel and systematic classification framework is proposed for the classification of PolSAR images, based on a compact and adaptive implementation of CNNs using a sliding-window classification approach. The proposed approach has three advantages. First, there is no requirement for an extensive feature extraction process. Second, it is computationally efficient due to utilized compact configurations. In particular, the proposed compact and adaptive CNN model is designed to achieve the maximum classification accuracy with minimum training and computational complexity. This is of considerable importance considering the high costs involved in labelling in PolSAR classification. Finally, the proposed approach can perform classification using smaller window sizes than deep CNNs. Experimental evaluations have been performed over the most commonly-used four benchmark PolSAR images: AIRSAR L-Band and RADARSAT-2 C-Band data of San Francisco Bay and Flevoland areas. Accordingly, the best obtained overall accuracies range between 92.33 - 99.39% for these benchmark study sites.

Stealth is a 2005 American military science fiction action film directed by Rob Cohen and written by W. D. Richter, and starring Josh Lucas, Jessica Biel, Jamie Foxx, Sam Shepard, Joe Morton and Richard Roxburgh. The film follows three top fighter pilots as they join a project to develop an automated robotic stealth aircraft. Released on July 29, 2005 by Columbia Pictures, the film was a box office bomb, grossing $79 million worldwide against a budget of $135 million. It was one of the worst losses in cinematic history.[2][3] In the near future, the U.S. Navy develops the F/A-37 Talon, a single-seat fighter-bomber with advanced payload, range, speed, and stealth capabilities.

It is a statement that has been broadcasted and heard around the world: China intends to be the global leader of artificial intelligence by 2030. The country is putting its money where its mouth is, officials and analysts say, and making investments in AI that could threaten the United States and erode Washington's advantages in the technology. "The Chinese Communist Party recognizes the transformational power of AI," Defense Secretary Mark Esper recently said during remarks at the Defense Department's AI Symposium and Exposition. Beijing views the technology as a critical component to its future military and industrial power, said the Pentagon's recently released "Military and Security Developments Involving the People's Republic of China 2020" annual report to Congress. The country's "Next Generation AI Development Plan" details Beijing's strategy to employ commercial and military organizations to achieve major breakthroughs by 2025 and become the world leader by 2030, the report said.

At the outset of World War II, you'd have a better chance of finding a needle in a haystack with a camel stuck in its eye than you did shooting down an enemy aircraft in your first dozen or so shots. This is because anti-aircraft shells at the time used manual fuses that had to be dialed in for specific lengths of time to delay their explosion. The idea was that you'd estimate where the targeted plane would be in, say five seconds, based on its currently flight path, then time the shell for that length, fire the shell at the plane and hope that the timing and location were close enough that shrapnel from the exploding shell hits the plane. If your calculations were off by even a hair, the shell would miss by thousands of feet. And if shooting down piloted aircraft was this hard, intercepting Germany's terrifyingly fast V1 and V2 rockets required far more luck than skill. But that's exactly what the team at Section T set out to do.

The path toward realizing the potential of seasonal forecasting and its socioeconomic benefits depends heavily on improving general circulation model based dynamical forecasting systems. To improve dynamical seasonal forecast, it is crucial to set up forecast benchmarks, and clarify forecast limitations posed by model initialization errors, formulation deficiencies, and internal climate variability. With huge cost in generating large forecast ensembles, and limited observations for forecast verification, the seasonal forecast benchmarking and diagnosing task proves challenging. In this study, we develop a probabilistic deep neural network model, drawing on a wealth of existing climate simulations to enhance seasonal forecast capability and forecast diagnosis. By leveraging complex physical relationships encoded in climate simulations, our probabilistic forecast model demonstrates favorable deterministic and probabilistic skill compared to state-of-the-art dynamical forecast systems in quasi-global seasonal forecast of precipitation and near-surface temperature. We apply this probabilistic forecast methodology to quantify the impacts of initialization errors and model formulation deficiencies in a dynamical seasonal forecasting system. We introduce the saliency analysis approach to efficiently identify the key predictors that influence seasonal variability. Furthermore, by explicitly modeling uncertainty using variational Bayes, we give a more definitive answer to how the El Nino/Southern Oscillation, the dominant mode of seasonal variability, modulates global seasonal predictability.

A tiny California start-up is looking to printers to solve the housing crisis – actually, a very large 3D printer. The company, Mighty Buildings, has been showcasing small (350 square foot) studio apartment models of its new "ADU" units (Accessory Dwelling Units) aimed at backyards and selling for around $115,000. That is, if you do the work and deal with local governments to get all the permits, connect the utilities and install the unit. Have Mighty set it up for you, and you're looking around $184,000. Sam Ruben, the co-founder of the firm, says Mighty can have the home in place in just over two weeks.

Many important real-world settings contain multiple players interacting over an unknown duration with probabilistic state transitions, and are naturally modeled as stochastic games. Prior research on algorithms for stochastic games has focused on two-player zero-sum games, games with perfect information, and games with imperfect-information that is local and does not extend between game states. We present an algorithm for approximating Nash equilibrium in multiplayer general-sum stochastic games with persistent imperfect information that extends throughout game play. We experiment on a 4-player imperfect-information naval strategic planning scenario. Using a new procedure, we are able to demonstrate that our algorithm computes a strategy that closely approximates Nash equilibrium in this game.

Machine learning can be a valuable tool for speeding up elements of the research process. It can be used to analyze data and create knowledge graphs and to surface the most relevant research for a specific research community. But as Dr. Alex Ganose, a postdoctoral researcher at Lawrence Berkeley National Laboratory (LBNL), points out, it needs to be deployed wisely. Alex's research involves using data science and machine learning to solve problems in materials science. "Most recently, I've developed a new framework to calculate electron lifetimes from first principles," he explained.

Directional data consist of observations distributed on a (hyper)sphere, and appear in many applied fields, such as astronomy, ecology, and environmental science. This paper studies both statistical and computational problems of kernel smoothing for directional data. We generalize the classical mean shift algorithm to directional data, which allows us to identify local modes of the directional kernel density estimator (KDE). The statistical convergence rates of the directional KDE and its derivatives are derived, and the problem of mode estimation is examined. We also prove the ascending property of our directional mean shift algorithm and investigate a general problem of gradient ascent on the unit hypersphere. To demonstrate the applicability of our proposed algorithm, we evaluate it as a mode clustering method on both simulated and real-world datasets.

Modeling complex spatial and temporal correlations in the correlated time series data is indispensable for understanding the traffic dynamics and predicting the future status of an evolving traffic system. Recent works focus on designing complicated graph neural network architectures to capture shared patterns with the help of pre-defined graphs. In this paper, we argue that learning node-specific patterns is essential for traffic forecasting while the pre-defined graph is avoidable. To this end, we propose two adaptive modules for enhancing Graph Convolutional Network (GCN) with new capabilities: 1) a Node Adaptive Parameter Learning (NAPL) module to capture node-specific patterns; 2) a Data Adaptive Graph Generation (DAGG) module to infer the inter-dependencies among different traffic series automatically. We further propose an Adaptive Graph Convolutional Recurrent Network (AGCRN) to capture fine-grained spatial and temporal correlations in traffic series automatically based on the two modules and recurrent networks. Our experiments on two real-world traffic datasets show AGCRN outperforms state-of-the-art by a significant margin without pre-defined graphs about spatial connections.