Detecting individual activity on smartphones still seems to be a challenge given the limitations of resources such as battery life and computational workload capacity. Considering user activity and managing them, we can conceive low power consumption for mobile phones and other mobile devices, which requires a complete and rigorous program to recognize a ctivities and adjust device power consumption regarding their application at different times and places. However, with the rapid development of new and innovative applications for mobile devices such as smartphones, advances in battery technology do not ke ep up, especially in energy conservation. On the other hand, the use of activity recognition is increasing in active and preventive healthcare applications at home, learning environments of security systems, and a variety of human - computer interactions. Th is paper proposes and implements a system for activity recognition in the home environment with a set of switch sensors and a practical text - based sampling tool.

Nuclear fusion is regarded as the energy of the future since it presents the possibility of unlimited clean energy. One obstacle in utilizing fusion as a feasible energy source is the stability of the reaction. Ideally, one would have a controller for the reactor that makes actions in response to the current state of the plasma in order to prolong the reaction as long as possible. In this work, we make preliminary steps to learning such a controller. Since learning on a real world reactor is infeasible, we tackle this problem by attempting to learn optimal controls offline via a simulator, where the state of the plasma can be explicitly set. In particular, we introduce a theoretically grounded Bayesian optimization algorithm that recommends a state and action pair to evaluate at every iteration and show that this results in more efficient use of the simulator.

We present an Automatic Relevance Determination prior Bayesian Neural Network(BNN-ARD) weight l2-norm measure as a feature importance statistic for the model-x knockoff filter. We show on both simulated data and the Norwegian wind farm dataset that the proposed feature importance statistic yields statistically significant improvements relative to similar feature importance measures in both variable selection power and predictive performance on a real world dataset.

Its impact is drastic and real: Youtube's AIdriven recommendation system would present sports videos for days if one happens to watch a live baseball game on the platform [1]; email writing becomes much faster with machine learning (ML) based auto-completion [2]; many businesses have adopted natural language processing based chatbots as part of their customer services [3]. AI has also greatly advanced human capabilities in complex decision-making processes ranging from determining how to allocate security resources to protect airports [4] to games such as poker [5] and Go [6]. All such tangible and stunning progress suggests that an "AI summer" is happening. As some put it, "AI is the new electricity" [7]. Meanwhile, in the past decade, an emerging theme in the AI research community is the so-called "AI for social good" (AI4SG): researchers aim at developing AI methods and tools to address problems at the societal level and improve the wellbeing of the society.

In particular, we present a method for determining the coefficients of the degree distributions, characterizing the structure of an LDPC code. We refer to our RNN architecture as Neural Density Evolution (NDE) and determine the weights of the RNN that correspond to optimal designs by minimizing a loss function that enforces the properties of asymptotically optimal design, as well as the desired structural characteristics of the code. This renders the LDPC design process highly configurable, as constraints can be added to meet applications' requirements by means of modifying the loss function. In order to train the RNN, we generate data corresponding to the expected channel noise. We analyze the complexity and optimality of NDE theoretically, and compare it with traditional design methods that employ differential evolution. Simulations illustrate that NDE improves upon differential evolution both in terms of asymptotic performance and complexity. Although we focus on asymptotic settings, we evaluate designs found by NDE for finite codeword lengths and observe that performance remains satisfactory across a variety of channels.

Whether businesses are using machine learning to perform predictive maintenance or create better retail shopping experiences, ML has the power to unlock value across a myriad of use cases. We're constantly inspired by all the ways our customers use Google Cloud AI for image and video understanding--everything from eBay's use of image search to improve their shopping experience, to AES leveraging AutoML Vision to accelerate a greener energy future and help make their employees safer. Today, we're introducing a number of enhancements to our Vision AI portfolio to help even more customers take advantage of AI. Performing machine learning on edge devices like connected sensors and cameras can help businesses do everything from detect anomalies faster to efficiently predict maintenance. But optimizing machine learning models to run on the edge can be challenging because these devices often grapple with latency and unreliable connectivity.

AI - Deep Learning based platform is used for simulating the oil field, planning and predicting the oil produced in the Oil Industry supply chain using forecasting techniques. Machine Learning is used in predictive maintenance, forecasting, analysis, energy trading, buy/sell, trade, risk management, and optimization. The Oil & Gas industry is divided into divisions which are the upstream, downstream and midstream. Machine Learning Analytics is used in optimizations for upstream, downstream and midstream business process. The business processes are related to exploration, extraction, refining, transporting of oil and gas by oil tankers and pipelines, and marketing of petroleum products.

Fox Business reporter Jackie DeAngelis on how the U.S. strike will impact oil prices and the economy. The threat Friday by a top Iranian military leader to attack "vital American targets" in or near the Strait of Hormuz – the waterway through which about 20 percent of the world's oil is transported – illustrates why President Trump's pro-American energy policies are critical to our national security. Iranian leaders have promised military action to retaliate for the killing of terrorist Gen. Qassem Soleimani in a U.S. drone strike this week that was ordered by President Trump. Soleimani commanded the elite Quds Force of Iran's Islamic Revolutionary Guard Corps and was killed because he was planning deadly new attacks against Americans and others, President Trump and other U.S. officials have said. Senior Revolutionary Guards commander Gen. Gholamali Abuhamzeh said Friday that "the Strait of Hormuz is a vital point for the West and a large number of American destroyers and warships cross there."

--Multivariate time series prediction has applications in a wide variety of domains and is considered to be a very challenging task, especially when the variables have correlations and exhibit complex temporal patterns, such as seasonality and trend. Many existing methods suffer from strong statistical assumptions, numerical issues with high dimensionality, manual feature engineering efforts, and scalability. In this work, we present a novel deep learning architecture, known as T emporal T ensor Transformation Network, which transforms the original multivariate time series into a higher order of tensor through the proposed T emporal-Slicing Stack Transformation. This yields a new representation of the original multivariate time series, which enables the convolution kernel to extract complex and nonlinear features as well as variable interactional signals from a relatively large temporal region. Experimental results show that T emporal T ensor Transformation Network outperforms several state-of-the-art methods on window-based predictions across various tasks. The proposed architecture also demonstrates robust prediction performance through an extensive sensitivity analysis. Index T erms--multivariate time series, prediction, convolution, deep learning, tensor transformation I. I NTRODUCTION Multivariate time series analysis has gained wide spread applications in many fields, e.g., financial market prediction, weather forecasting, and energy consumption prediction. It is used to model and explain the underlying temporal patterns among a group of time series variables in dynamical systems. V arious methods have been proposed to predict multivariate time series based on statistical modeling and deep neural networks. Classical statistical models assume that the time series is stationary, i.e., the summary statistics of data points are consistent over time. Preprocessing procedures are usually needed to remove trend, seasonality, and other time-dependent structures from the raw series in order to make the data stationary. In addition, these models also assume the independence condition in the underlying linear regression problem, i.e., the random errors in the model are not correlated over time.

As we enter our third decade in the 21st century, it seems appropriate to reflect on the ways technology developed and note the breakthroughs that were achieved in the last 10 years. The 2010s saw IBM's Watson win a game of Jeopardy, ushering in mainstream awareness of machine learning, along with DeepMind's AlphaGO becoming the world's Go champion. It was the decade that industrial tools like drones, 3D printers, genetic sequencing, and virtual reality (VR) all became consumer products. And it was a decade in which some alarming trends related to surveillance, targeted misinformation, and deepfakes came online. For better or worse, the past decade was a breathtaking era in human history in which the idea of exponential growth in information technologies powered by computation became a mainstream concept.