We present a unified approach for learning the parameters of Sum-Product networks (SPNs). We prove that any complete and decomposable SPN is equivalent to a mixture of trees where each tree corresponds to a product of univariate distributions. Based on the mixture model perspective, we characterize the objective function when learning SPNs based on the maximum likelihood estimation (MLE) principle and show that the optimization problem can be formulated as a signomial program. We construct two parameter learning algorithms for SPNs by using sequential monomial approximations (SMA) and the concave-convex procedure (CCCP), respectively. The two proposed methods naturally admit multiplicative updates, hence effectively avoiding the projection operation. With the help of the unified framework, we also show that, in the case of SPNs, CCCP leads to the same algorithm as Expectation Maximization (EM) despite the fact that they are different in general.

This article provides a thorough meta-analysis of the anomaly detection problem. To accomplish this we first identify approaches to benchmarking anomaly detection algorithms across the literature and produce a large corpus of anomaly detection benchmarks that vary in their construction across several dimensions we deem important to real-world applications: (a) point difficulty, (b) relative frequency of anomalies, (c) clusteredness of anomalies, and (d) relevance of features. We apply a representative set of anomaly detection algorithms to this corpus, yielding a very large collection of experimental results. We analyze these results to understand many phenomena observed in previous work. First we observe the effects of experimental design on experimental results. Second, results are evaluated with two metrics, ROC Area Under the Curve and Average Precision. We employ statistical hypothesis testing to demonstrate the value (or lack thereof) of our benchmarks. We then offer several approaches to summarizing our experimental results, drawing several conclusions about the impact of our methodology as well as the strengths and weaknesses of some algorithms. Last, we compare results against a trivial solution as an alternate means of normalizing the reported performance of algorithms. The intended contributions of this article are many; in addition to providing a large publicly-available corpus of anomaly detection benchmarks, we provide an ontology for describing anomaly detection contexts, a methodology for controlling various aspects of benchmark creation, guidelines for future experimental design and a discussion of the many potential pitfalls of trying to measure success in this field.

BOULDER, Colo.--(BUSINESS WIRE)--Artificial intelligence (AI) is poised to have a transformative effect on consumer, enterprise, and government markets around the world. An umbrella term that refers to information systems inspired by biological systems, AI encompasses multiple technologies including machine learning, deep learning, computer vision, natural language processing (NLP), machine reasoning, and strong AI. According to a new report from Tractica, these technologies have use cases and applications in almost every industry and promise to significantly change existing business models while simultaneously creating new ones. The market intelligence firm forecasts that annual worldwide AI revenue will grow from 643.7 million in 2016 to 36.8 billion by 2025. In sizing and forecasting the total global AI market, Tractica has identified 191 real-world use cases for AI, organized into 27 different industry sectors and corresponding with six major technology categories, plus multiple combinations of technologies.

Dr. Kanade and DENSO have worked together from 2002-2009 on a joint research of image recognition technology. In addition, he has been a lecturer of DENSO's high talent program organized by DENSO E&TS Training Center. DENSO expects to use artificial intelligence technology in more areas of its business. Currently, it uses machine learning in its sensing technologies and applies them to its sensing products. DENSO has developed technologies and products to help create a society free from road traffic accidents.

Robots have been illustrated as humans' mechanical servants, but experts are determined to turn these cyborgs into emotional synthetic beings. Now, researchers brought the two of the world's most advanced robots together to test their reactions by showing them the trailer for the horror flick'Morgan'. Edi vocalizes its fear with phrases such as'Oh no, I can't watch' and although FACE is silent, it offers its'thoughts' by eerily moving its eyes, mouth and head. Edi (Electronic Deceptive Intelligence) is the brainchild of magicLab.ny, Edi is a fitted with a range sensors, has long robotic arms and a screen that displays a cartoon face.

During the E-step we are calculating the expected value of cluster assignments. During the M-step we are calculating a new maximum likelihood for our hypothesis. Bio: Elena Sharova is a data scientist, financial risk analyst and software developer. She holds an MSc in Machine Learning and Data Mining from University of Bristol.

The Greeks shared their beliefs with the Egyptians on how to interpret good and bad dreams, and the idea of incubating dreams. In that century, other cultures influenced Greeks to develop the belief that souls left the sleeping body.

A dream is successions of images, ideas, emotions, and sensations that usually occurs involuntarily in the mind during certain stages of sleep.[1] The content and purpose of dreams are not definitively understood, though they have been a topic of scientific speculation, as well as a subject of philosophical and religious interest, throughout recorded history. The scientific study of dreams is called oneirology.[2] Dreams mainly occur in the rapid-eye movement (REM) stage of sleep--when brain activity is high and resembles that of being awake. REM sleep is revealed by continuous movements of the eyes during sleep. At times, dreams may occur during other stages of sleep. However, these dreams tend to be much less vivid or memorable.[3] The length of a dream can vary; they may last for a few seconds, or approximately 20–30 minutes.[3] People are more likely to remember the dream if they are awakened during the REM phase. The average person has three to five dreams per night, and some may have up to seven;[4] however, most dreams are immediately or quickly forgotten.[5] Dreams tend to last longer as the night progresses. During a full eight-hour night sleep, most dreams occur in the typical two hours of REM.[6] In modern times, dreams have been seen as a connection to the unconscious mind. They range from normal and ordinary to overly surreal and bizarre. Dreams can have varying natures, such as being frightening, exciting, magical, melancholic, adventurous, or sexual. The events in dreams are generally outside the control of the dreamer, with the exception of lucid dreaming, where the dreamer is self-aware.[7]

Nasa has announced that it has found evidence of flowing water on Mars. Scientists have long speculated that Recurring Slope Lineae -- or dark patches -- on Mars were made up of briny water but the new findings prove that those patches are caused by liquid water, which it has established by finding hydrated salts. Several hundred camped outside the London store in Covent Garden. The 6s will have new features like a vastly improved camera and a pressure-sensitive "3D Touch" display

Lab rats suffer from laziness after being administered marijuana, a new study has found. According to a report by University of British Columbia researchers published in the Journal of Psychiatry and Neuroscience, after being given THC – the main psychoactive ingredient in marijuana – rodents were less likely to try cognitively demanding activities. Under the study, 29 lab rats were used to test the effect of THC and cannabidiol (CBD) and how it correlates to their willingness to exert cognitive effort. During the experiment, rats had to choose between the cognitively challenging task of responding to a light for 0.2 seconds by touching their nose. An easier task required them to touch their nose while the light stayed on for just one second.