We present a technique for efficiently synthesizing images of atmospheric clouds using a combination of Monte Carlo integration and neural networks. The intricacies of Lorenz-Mie scattering and the high albedo of cloud-forming aerosols make rendering of clouds---e.g. the characteristic silverlining and the "whiteness" of the inner body---challenging for methods based solely on Monte Carlo integration or diffusion theory. We approach the problem differently. Instead of simulating all light transport during rendering, we pre-learn the spatial and directional distribution of radiant flux from tens of cloud exemplars. To render a new scene, we sample visible points of the cloud and, for each, extract a hierarchical 3D descriptor of the cloud geometry with respect to the shading location and the light source. The descriptor is input to a deep neural network that predicts the radiance function for each shading configuration. We make the key observation that progressively feeding the hierarchical descriptor into the network enhances the network's ability to learn faster and predict with high accuracy while using few coefficients. We also employ a block design with residual connections to further improve performance. A GPU implementation of our method synthesizes images of clouds that are nearly indistinguishable from the reference solution within seconds interactively. Our method thus represents a viable solution for applications such as cloud design and, thanks to its temporal stability, also for high-quality production of animated content.

Outliers are extreme values that deviate from other observations on data, they may indicate a variability in a measurement, experimental errors or a novelty. In other words, an outlier is an observation that diverges from an overall pattern on a sample. Outliers can be of two kinds: univariate and multivariate. Univariate outliers can be found when looking at a distribution of values in a single feature space. Multivariate outliers can be found in a n-dimensional space (of n-features).

Summary: With only slight tongue in cheek about the road ahead we report on the just passed House of Representative's new "Federal Automated Vehicle Policy" as well as similar policy just emerging in Germany. As a model of regulation on emerging AI technology we think they got this just about right. Just today (9/6/17) the US House of Representatives released its 116 page "Federal Automated Vehicles Policy". This still has to be reconciled and approved by the Senate but word is that shouldn't take long. Equally as interesting is that just two weeks ago the German federal government published its guidelines for Highly Automated Vehicles (HAV being the new name of choice for these vehicles).

Answer Set Programming (ASP) is a well-known paradigm of declarative programming with roots in logic programming and non-monotonic reasoning. Similar to other closely related problem-solving technologies, such as SAT/SMT, QBF, Planning and Scheduling, advancements in ASP solving are assessed in competition events. In this paper, we report about the design and results of the Sixth ASP Competition, which was jointly organized by the University of Calabria (Italy), Aalto University (Finland), and the University of Genoa (Italy), in affiliation with the 13th International Conference on Logic Programming and Non-Monotonic Reasoning. This edition maintained some of the design decisions introduced in 2014, e.g., the conception of sub-tracks, the scoring scheme, and the adherence to a fixed modeling language in order to push the adoption of the ASP-Core-2 standard. On the other hand, it featured also some novelties, like a benchmark selection stage classifying instances according to their empirical hardness, and a "Marathon" track where the top-performing systems are given more time for solving hard benchmarks.

FinTech is a lucrative, yet quite saturated market. In order to stay competitive, businesses should keep track of the emerging trends and be able to capitalize on them before their competitors do. Artificial Intelligence is currently among the most promising FinTech trends. Leading financial brands such as Capital One, MasterCard, as well as hundreds of startups have set the pace for the adoption of virtual financial advisors. If you want to stay ahead of your competition or simply explore the opportunities for AI in fintech, this article is for you.

My current work revolves around using machine learning for the study of criminal behavior, so I read Introduction to Machine Learning with Python by Andreas Muller and Sarah Guido with great interest. The book comprises a complete documentation of the scikit-learn library, and provides a comprehensive overview of the machine learning models and the fundamental theory needed to get started in applying ML tools in practice. Each chapter contains Python source code that cover a wide range of interesting and practical data science problems. In addition to the basic theory, scikit-learn tools and code samples, the book also includes many useful hints, tricks and words of wisdom that can save you a lot of time by avoiding issues that invariably arise in your learning process. This is an excellent book that I highly recommend both to machine learning experts who want to be proficient in scikit-learn and also to beginners who want to learn machine learning basics and how to apply them on data.

Department of Informatics, King's College London; osvaldo.simeone@kcl.ac.uk ABSTRACT This monograph aims at providing an introduction to key concepts, algorithms, and theoretical frameworks in machine learning, including supervised and unsupervised learning, statistical learning theory, probabilistic graphical models and approximate inference. The intended readership consists of electrical engineers with a background in probability and linear algebra. The treatment builds on first principles, and organizes the main ideas according to clearly defined categories, such as discriminative and generative models, frequentist and Bayesian approaches, exact and approximate inference, directed and undirected models, and convex and non-convex optimization. The mathematical framework uses information-theoretic measures as a unifying tool. The text offers simple and reproducible numerical examples providing insights into key motivations and conclusions. Rather than providing exhaustive details on the existing myriad solutions in each specific category, for which the reader is referred to textbooks and papers, this monograph is meant as an entry point for an engineer into the literature on machine learning.

Becoming a specialist in a subject requires a highly tuned learning experience connecting multiple related courses. Programs unlock exclusive content that helps you develop a deep understanding of your subject. From your first course to your final summative assessment, our thoughtfully curated curriculum enables you to demonstrate your newly acquired skills.

In essence, people who interact with advanced technology want to be able to trust it appropriately, and then act on that trust. In interpersonal relationships, and otherwise, humans act largely based on trust. For example, a supervisor asks a subordinate to accomplish a task based on several factors that indicate they can trust them to accomplish that task. When consumers make purchases, they do so with trust that the product will perform as promised. Likewise, when using something like an autonomous vehicle, the user must be able to trust it appropriately in order to use it properly. With the rapid advancement of the capabilities of intelligent computing technology to do tasks that were previously assumed to be too complicated for computers, there has been much recent discussion regarding how humans can trust this technology - although the connection to trust is not always made explicit, per se.

Detecting and segmenting salient objects in natural scenes, often referred to as salient object detection, has attracted a lot of interest in computer vision. While many models have been proposed and several applications have emerged, yet a deep understanding of achievements and issues is lacking. We aim to provide a comprehensive review of the recent progress in salient object detection and situate this field among other closely related areas such as generic scene segmentation, object proposal generation, and saliency for fixation prediction. Covering 228 publications, we survey i) roots, key concepts, and tasks, ii) core techniques and main modeling trends, and iii) datasets and evaluation metrics in salient object detection. We also discuss open problems such as evaluation metrics and dataset bias in model performance and suggest future research directions.