Boeing Co. will acquire floating-drone maker Liquid Robotics, the aerospace giant said Tuesday. Based in Sunnyvale, Liquid Robotics developed the Wave Glider, a surfboard-shaped drone that floats on the ocean surface and collects data, propelling itself for up to a year using wave and solar power. In 2014, Liquid Robotics formed a partnership with Boeing to develop a military version of the Wave Glider called SHARC -- the Sensor Hosting Autonomous Remote Craft -- that combines Liquid Robotics' platform with Boeing's sensor technology. Boeing said it sees the SHARC as a way to connect intelligence-gathering efforts between underwater vehicles, aircraft and satellites. Liquid Robotics' headquarters will remain in Sunnyvale, though the company will be part of Boeing's autonomous systems unit, which is based in St. Louis.

Oculus Touch has two pretty good reasons to stay inside this winter, and Amazon is killing checkout lines. Is this the future of retail? Amazon premieres the "Just Walk Out Shopping experience" Amazon already has internet shopping boiled down to a single click or voice command, so what's next? It's opened an employees-only shopping location in Seattle that uses " computer vision, sensor fusion, and deep learning" to track what people take. There's no check-out lane here -- just take your stuff and go, while Amazon bills your account and emails a receipt.

Experiments in particle physics produce enormous quantities of data that must be analyzed and interpreted by teams of physicists. This analysis is often exploratory, where scientists are unable to enumerate the possible types of signal prior to performing the experiment. Thus, tools for summarizing, clustering, visualizing and classifying high-dimensional data are essential. In this work, we show that meaningful physical content can be revealed by transforming the raw data into a learned high-level representation using deep neural networks, with measurements taken at the Daya Bay Neutrino Experiment as a case study. We further show how convolutional deep neural networks can provide an effective classification filter with greater than 97% accuracy across different classes of physics events, significantly better than other machine learning approaches.

Apple FINALLY reveals the cause of its iPhone 6s'unexpected shutdown' bug - but only on its Chinese website The issue has seen some iPhone 6s devices powering down unexpectedly It affects a relatively small batch of the devices manufactured last year Apple is blaming the flaw on a battery component that was exposed to'ambient air' during the manufacturing process for longer than intended Apple is blaming the flaw on a battery component that was exposed to'ambient air' during the manufacturing process for longer than intended Apple has finally revealed the details behind a technical glitch that saw some iPhone 6s handsets shutting down unexpectedly. Creepy life-like sex robots modelled on Hollywood... Now Apple invests in driverless cars: Firm finally admits... Watch the world's biggest cities evolve: Google's timelapse... Is this Amazon's top-secret delivery drone? Creepy life-like sex robots modelled on Hollywood... Now Apple invests in driverless cars: Firm finally admits... Watch the world's biggest cities evolve: Google's timelapse... Is this Amazon's top-secret delivery drone? An Apple information website based in Japan said the company might have plans to bring out a'pure white' or'jet white' version of the iPhone 7 and 7 Plus. The website cited the source as a'supplier'.

Knowing where the consumer is spending is normally not public information. Google Trends tracks the frequency of terms searched on their website and reports it as an index. Although web search terms are not a guarantee that a purchase was made, it's a good insight into the consumer's thoughts. And a great indication of when and where advertising companies should advertise. Could there be a possible relationship between some web search terms and gasoline prices?

This paper presents the development of a hybrid learning system based on Support Vector Machines (SVM), Adaptive Neuro-Fuzzy Inference System (ANFIS) and domain knowledge to solve prediction problem. The proposed two-stage Domain Knowledge based Fuzzy Information System (DKFIS) improves the prediction accuracy attained by ANFIS alone. The proposed framework has been implemented on a noisy and incomplete dataset acquired from a hydrocarbon field located at western part of India. Here, oil saturation has been predicted from four different well logs i.e. gamma ray, resistivity, density, and clay volume. In the first stage, depending on zero or near zero and non-zero oil saturation levels the input vector is classified into two classes (Class 0 and Class 1) using SVM. The classification results have been further fine-tuned applying expert knowledge based on the relationship among predictor variables i.e. well logs and target variable - oil saturation. Second, an ANFIS is designed to predict non-zero (Class 1) oil saturation values from predictor logs. The predicted output has been further refined based on expert knowledge. It is apparent from the experimental results that the expert intervention with qualitative judgment at each stage has rendered the prediction into the feasible and realistic ranges. The performance analysis of the prediction in terms of four performance metrics such as correlation coefficient (CC), root mean square error (RMSE), and absolute error mean (AEM), scatter index (SI) has established DKFIS as a useful tool for reservoir characterization.

Support vector machines (SVMs) have been recognized as a potential tool for supervised classification analyses in different domains of research. In essence, SVM is a binary classifier. Therefore, in case of a multiclass problem, the problem is divided into a series of binary problems which are solved by binary classifiers, and finally the classification results are combined following either the one-against-one or one-against-all strategies. In this paper, an attempt has been made to classify lithology using a multiclass SVM based framework using well logs as predictor variables. Here, the lithology is classified into four classes such as sand, shaly sand, sandy shale and shale based on the relative values of sand and shale fractions as suggested by an expert geologist. The available dataset consisting well logs (gamma ray, neutron porosity, density, and P-sonic) and class information from four closely spaced wells from an onshore hydrocarbon field is divided into training and testing sets. We have used one-against-all strategy to combine the results of multiple binary classifiers. The reported results established the superiority of multiclass SVM compared to other classifiers in terms of classification accuracy. The selection of kernel function and associated parameters has also been investigated here. It can be envisaged from the results achieved in this study that the proposed framework based on multiclass SVM can further be used to solve classification problems. In future research endeavor, seismic attributes can be introduced in the framework to classify the lithology throughout a study area from seismic inputs.

Water saturation is an important property in reservoir engineering domain. Thus, satisfactory classification of water saturation from seismic attributes is beneficial for reservoir characterization. However, diverse and non-linear nature of subsurface attributes makes the classification task difficult. In this context, this paper proposes a generalized Support Vector Data Description (SVDD) based novel classification framework to classify water saturation into two classes (Class high and Class low) from three seismic attributes seismic impedance, amplitude envelop, and seismic sweetness. G-metric means and program execution time are used to quantify the performance of the proposed framework along with established supervised classifiers. The documented results imply that the proposed framework is superior to existing classifiers. The present study is envisioned to contribute in further reservoir modeling.

Evaluation of hydrocarbon reservoir requires classification of petrophysical properties from available dataset. However, characterization of reservoir attributes is difficult due to the nonlinear and heterogeneous nature of the subsurface physical properties. In this context, present study proposes a generalized one class classification framework based on Support Vector Data Description (SVDD) to classify a reservoir characteristic water saturation into two classes (Class high and Class low) from four logs namely gamma ray, neutron porosity, bulk density, and P sonic using an imbalanced dataset. A comparison is carried out among proposed framework and different supervised classification algorithms in terms of g metric means and execution time. Experimental results show that proposed framework has outperformed other classifiers in terms of these performance evaluators. It is envisaged that the classification analysis performed in this study will be useful in further reservoir modeling.

Experts say we must maintain reactions for over a long period of time Also, devise a material structure to harness the fusion power for electricity We also need to research the tokamak and make fusion more attractive Once these issues are solved we'll have an unlimited source of energy Once these issues are solved we'll have an unlimited source of energy If we're able to solve an extremely complex set of scientific and engineering problems, fusion energy promises a green, safe, unlimited source of energy. Pictured is the plasma inside a fusion reactor. Mystery as researchers find extreme tornado outbreaks are... Is BRAIN HACKING the future of war? Experts predict drone... Google's humanoid robot goes off road (and this time,... The'time bomb' under our feet: Researchers warn global... Mystery as researchers find extreme tornado outbreaks are... Is BRAIN HACKING the future of war?