The method of covariate adjustment is often used for estimation of population average treatment effects in observational studies. Graphical rules for determining all valid covariate adjustment sets from an assumed causal graphical model are well known. Restricting attention to causal linear models, a recent article derived two novel graphical criteria: one to compare the asymptotic variance of linear regression treatment effect estimators that control for certain distinct adjustment sets and another to identify the optimal adjustment set that yields the least squares treatment effect estimator with the smallest asymptotic variance among consistent adjusted least squares estimators. In this paper we show that the same graphical criteria can be used in non-parametric causal graphical models when treatment effects are estimated by contrasts involving non-parametrically adjusted estimators of the interventional means. We also provide a graphical criterion for determining the optimal adjustment set among the minimal adjustment sets, which is valid for both linear and non-parametric estimators. We provide a new graphical criterion for comparing time dependent adjustment sets, that is, sets comprised by covariates that adjust for future treatments and that are themselves affected by earlier treatments. We show by example that uniformly optimal time dependent adjustment sets do not always exist. In addition, for point interventions, we provide a sound and complete graphical criterion for determining when a non-parametric optimally adjusted estimator of an interventional mean, or of a contrast of interventional means, is as efficient as an efficient estimator of the same parameter that exploits the information in the conditional independencies encoded in the non-parametric causal graphical model.

Applying machine learning models to meteorological data brings many opportunities to the Geosciences field, such as predicting future weather conditions more accurately. In recent years, modeling meteorological data with deep neural networks has become a relevant area of investigation. These works apply either recurrent neural networks (RNNs) or some hybrid approach mixing RNNs and convolutional neural networks (CNNs). In this work, we propose STConvS2S (short for Spatiotemporal Convolutional Sequence to Sequence Network), a new deep learning architecture built for learning both spatial and temporal data dependencies in weather data, using fully convolutional layers. Computational experiments using observations of air temperature and rainfall show that our architecture captures spatiotemporal context and outperforms baseline models and the state-of-art architecture for weather forecasting task.

--We introduce L EARN2 FIX, the first human-in-the-loop, semiautomatic repair technique when no bug oracle-except for the user who is reporting the bug-is available. Our approach negotiates with the user the condition under which the bug is observed. Only when a budget of queries to the user is exhausted, it attempts to repair the bug. A query can be thought of as the following question: "When executing this alternative test input, the program produces the following output; is the bug observed"? Through systematic queries, L EARN2 FIX trains an automatic bug oracle that becomes increasingly more accurate in predicting the user's response. Our key challenge is to maximize the oracle's accuracy in predicting which tests are bug-revealing given a small budget of queries. From the alternative tests that were labeled by the user, test-driven automatic repair produces the patch. Our experiments demonstrate that L EARN2 FIX learns a sufficiently accurate automatic oracle with a reasonably low labeling effort (lt. Given L EARN2 FIX's test suite, the GenProg test-driven repair tool produces a higher-quality patch (i.e., passing a larger proportion of validation tests) than using manual test suites provided with the repair benchmark. I NTRODUCTION Automatic program repair (APR) [1], [2] holds the promise of automating the tedious, manual task of patching bugs. In their seminal paper, Le Goues and colleagues [3] demonstrated that APR is both feasible and cost-effective even at the scale of several million lines of code. Given a failing test suite, APR changes the buggy program such that all test cases pass. However, what if no such test suite is available? Suppose, a user reports a bug and provides a test input to reproduce the bug. We envision a semiautomatic approach that keeps the human-in-the-loop and negotiates the condition under which the bug is observed before repairing the bug. Strategically, the user is asked: " F or this other input, the program produces that output; is the bug observed "? While the user might not have the expertise to understand the source code or to produce a patch, it seems reasonable to ask to distinguish expected from unexpected program behavior. Iteratively, an automatic bug oracle is trained to predict the user's responses with increasing accuracy. Using the trained oracle, the user can be asked more strategically.

Chinese companies have exported artificial intelligence surveillance technology to more than 60 countries including Iran, Myanmar, Venezuela, Zimbabwe and others with dismal human rights records, according to a report by a U.S. think tank. With the technology involving facial recognition systems that the Communist Party uses to crack down on Uighurs and other Muslim minorities in China's far western Xinjiang region, the report calls Beijing a global driver of "authoritarian tech." The Carnegie Endowment for International Peace released the report amid concerns that authoritarian regimes would use the technology to boost their power and data could be sent back to China. "Technology linked to Chinese companies -- particularly Huawei, Hikvision, Dahua and ZTE -- supply AI surveillance technology in 63 countries, 36 of which have signed onto China's Belt and Road Initiative," it said. Critics say the BRI, President Xi Jinping's signature cross-border infrastructure project, is intended to draw countries in Asia, Africa and Europe deeper into Beijing's economic orbit.

I LOVE IT! CYBERTRUCK Grid Down: Collapse of Critical Infrastructure (PART 1) #RobertFoertsch #TRUTH 18435041776 #ArtificialIntelligence #AI #MATRIX #MKULTRA #CIA #MindControl #LocalSun #LocalMoon #StationaryLevelEarth #RobertFoertsch #TRUTH 18435041776 #Research WHAT IS THE MATRIX?: THE TRUTH ABOUT THIS WORLD The Matrix (1999) Retrospective / Review The Matrix Explained 20th Anniversary Of The Matrix by GameSpot Universe Is It TRUE? Whatever It Is... Seeing Is Believing, What? #AI #TRUTH #RobertFoertsch 18435041776 Crystal Skies - Nigel Stanford - 4k TimeLapse by Nigel John Stanford AUTOMATICA - Robots Vs. Music - Nigel Stanford by Nigel John Stanford Solar Fields - The Missing The Ultimate 432Hz VS 440Hz CONSPIRACY Comparison "I Guarantee Your Beliefs Will Change" The "Reprogramming" of Self-Sabotage The Minds of Men Official Documentary by Aaron & Melissa Dykes by Truthstream Media Ex Gang Stalking Operative Explains Tactics and Motives Why Norway is full of Teslas Why Tesla And Elon Musk Face Challenges In China How Tesla Builds Electric Cars Tesla Motors Part 2 (WIRED) How the Tesla Model S is Made Tesla Motors Part 1 (WIRED) Inside The High-Tech TESLA Factory Mind Blowing Facts by Mind Blowing Facts Tesla's Secret Plan (don't tell anyone) by Solving The Money Problem Tesla Model 3 Review: The TRUTH After 26,000 Miles How does an Electric Car work? Tesla Model S by Learn Engineering Hydrogen Torch - Hydrogen Car "This Could Be a Tragedy For Humanity" The First Brain Chip Implant We've Found The Magic Frequency (This Will Revolutionize Our Future) Nikola Tesla: Great Minds Elon Musk on Nikola Tesla – What He Said May Shock You... Thomas Edison Did Everything He Could To Stop Nikola Tesla Succeeding Tesla's Death Ray "It's like Magic" The Secret Papers of NIKOLA TESLA "It Will Be The End Of Energy Companies" The FORBIDDEN Technology of Nikola Tesla 10 Most Dangerous Ocean Phenomena in the World The World's Most Remote Buildings The B1M In the Age of AI (full film) FRONTLINE by FRONTLINE PBS Official #LocalSun #Job37 #Firmament #StationaryLevelEarth #AI #RobertFoertsch #TRUTH 18435041776 NIAGARA FALLS - ONTARIO, CANADA 2019 4K ALPS 8K - A Time-Lapse Adventure The Truth About Wireless Charging 50,000 Tesla Owners Speak & The Feature They Want MOST May Surprise You! The Matrix of Control Silent Weapons For Quiet Wars - DARPA - Agenda 21 - DNA Mapping Silent Weapons For Quiet Wars [Full Read] IS IT TRUE?

Times Documentaries 19:10 A.I. Is Making It Easier to Kill (You). Culture 10:49 Where Are All the Bob Ross Paintings? Times Documentaries 12:31 Chinese Cameras Come With Chinese Tactics Health 9:13 How China Creates Cancer Refugees Times Documentaries 9:15 Inside China's Predatory Health Care System Dispatches 2:39 What's Left in Laos After a Massive Dam Collapsed Immigration 9:30 'I Just Simply Did What He Wanted': Sexual Abuse Inside Immigrant Detention Facilities Immigration 6:06 The Texas Law Silencing Undocumented Women Dispatches 4:10 How to Win an Election in Venezuela: Control the Food Dispatches 2:59 In the Ambulance With Gaza's Paramedics Dispatches 5:07 What Life Is Like on Gaza's Side of the Fence Dispatches 3:13 Inside a Philippine City Seized by ISIS Loyalists Times Documentaries 19:10 A.I. Is Making It Easier to Kill (You). A.I. Is Making It Easier to Kill (You). Culture 10:49 Where Are All the Bob Ross Paintings?

Spike-and-wave discharge (SWD) pattern classification in electroencephalography (EEG) signals is a key problem in signal processing. It is particularly important to develop a SWD automatic detection method in long-term EEG recordings since the task of marking the patters manually is time consuming, difficult and error-prone. This paper presents a new detection method with a low computational complexity that can be easily trained if standard medical protocols are respected. The detection procedure is as follows: First, each EEG signal is divided into several time segments and for each time segment, the Morlet 1-D decomposition is applied. Then three parameters are extracted from the wavelet coefficients of each segment: scale (using a generalized Gaussian statistical model), variance and median. This is followed by a k-nearest neighbors (k-NN) classifier to detect the spike-and-wave pattern in each EEG channel from these three parameters. A total of 106 spike-and-wave and 106 non-spike-and-wave were used for training, while 69 new annotated EEG segments from six subjects were used for classification. In these circumstances, the proposed methodology achieved 100% accuracy. These results generate new research opportunities for the underlying causes of the so-called absence epilepsy in long-term EEG recordings.

AI having applications in various sectors including agriculture has completely transformed the approaches of the agriculture market. AI in Agriculture helps the farmers in examining weather, soil, and field data to improve farming operations and crop productivity. AI in the agriculture market seems to be driven by the Internet of Things (IoT) due to its ability to revolutionize and transform current farming methods to a new level. Although, collecting accurate field data requires high initial investments which may hamper the growth of AI in the agriculture market. Some of the leading companies influencing the market are Ag Leader Technology, Trimble, Agribotix, Granular, SAP, Mavrx, PrecisionHawk, aWhere, IBM and Prospera Technologies.

CHICAGO – Agribusiness is increasingly turning to natural and sustainable alternatives to chemicals as consumers rebuff genetically modified foods and concerns grow over Big Ag's role in climate change. At the heart of the trend are innovations that harness beneficial microorganizms in the soil, including seed-coatings of naturally occurring bacteria and fungi that can do the same work as traditional chemicals, from warding off pests to helping plants flourish, according to a global patent study by research firm GreyB Services. Much of the research in crop biotech is centered in the United States, China, Germany, Japan and South Korea, according to the U.N. agency WIPO. "Both entrepreneurs and investors are saying, 'Hey, the writing is on the wall, we're entering a post-chemical world,'" said Rob LeClerc, chief executive officer of AgFunder, an online venture-capital platform. "The seed companies who have billions in market cap are like'We need to do something,' and everyone recognizes the opportunity."

In recent years, interest in monitoring air quality has been growing. Traditional environmental monitoring stations are very expensive, both to acquire and to maintain, therefore their deployment is generally very sparse. This is a problem when trying to generate air quality maps with a fine spatial resolution. Given the general interest in air quality monitoring, low-cost air quality sensors have become an active area of research and development. Low-cost air quality sensors can be deployed at a finer level of granularity than traditional monitoring stations. Furthermore, they can be portable and mobile. Low-cost air quality sensors, however, present some challenges: they suffer from cross-sensitivities between different ambient pollutants; they can be affected by external factors such as traffic, weather changes, and human behavior; and their accuracy degrades over time. Some promising machine learning approaches can help us obtain highly accurate measurements with low-cost air quality sensors. In this article, we present low-cost sensor technologies, and we survey and assess machine learning-based calibration techniques for their calibration. We conclude by presenting open questions and directions for future research.