NEW DELHI: India on Friday sought to add meat to its strategic partnership with Germany by wooing industries to invest in defence corridors of Tamil Nadu and Uttar Pradesh. At their biennial summit in New Delhi, India and Germany also sought to give momentum to revive stalled negotiations for free-trade agreement with the European Union. Proposed in 2007, the negotiations hit a roadblock in 2013 when the two sides arrived at an impasse on tariffs and market access. Disagreements on standards and practices exacerbated the situation and negotiations were shelved for five years. Germany has been an advocate of the deal and welcomed the resumption of negotiations last year.

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We present local ensembles, a method for detecting extrapolation at test time in a pre-trained model. We focus on underdetermination as a key component of extrapolation: we aim to detect when many possible predictions are consistent with the training data and model class. Our method uses local second-order information to approximate the variance of predictions across an ensemble of models from the same class. We compute this approximation by estimating the norm of the component of a test point's gradient that aligns with the low-curvature directions of the Hessian, and provide a tractable method for estimating this quantity. Experimentally, we show that our method is capable of detecting when a pre-trained model is extrapolating on test data, with applications to out-of-distribution detection, detecting spurious correlates, and active learning.

Massive volumes of high-dimensional data that evolves over time is continuously collected by contemporary information processing systems, which brings up the problem of organizing this data into clusters, i.e. achieve the purpose of dimensional deduction, and meanwhile learning its temporal evolution patterns. In this paper, a framework for evolutionary subspace clustering, referred to as LSTM-ESCM, is introduced, which aims at clustering a set of evolving high-dimensional data points that lie in a union of low-dimensional evolving subspaces. In order to obtain the parsimonious data representation at each time step, we propose to exploit the so-called self-expressive trait of the data at each time point. At the same time, LSTM networks are implemented to extract the inherited temporal patterns behind data in an overall time frame. An efficient algorithm has been proposed based on MATLAB. Next, experiments are carried out on real-world datasets to demonstrate the effectiveness of our proposed approach. And the results show that the suggested algorithm dramatically outperforms other known similar approaches in terms of both run time and accuracy.

A fully autonomous ship tracing the journey of the Mayflower is being built by a UK-based team, with help from tech firm IBM. The Mayflower Autonomous Ship, or MAS, will launch from Plymouth in the UK in September 2020. Its voyage will mark the 400th anniversary of the pilgrim ship which brought European settlers to America in 1620. IBM is providing artificial intelligence systems for the ship. The vessel will make its own decisions on its course and collision avoidance, and will even make expensive satellite phone calls back to base if it deems it necessary.

A fully autonomous ship tracing the journey of the Mayflower is being built by a UK-based team, with help from tech firm IBM. The Mayflower Autonomous Ship, or MAS, will launch from Plymouth in the UK in September 2020. Its voyage will mark the 400th anniversary of the pilgrim ship which brought European settlers to America in 1620. IBM is providing artificial intelligence systems for the ship. The vessel will make its own decisions on its course and collision avoidance, and will even make expensive satellite phone calls back to base if it deems it necessary.

Fox News Flash top headlines for Oct. 14 are here. Check out what's clicking on Foxnews.com Attacking enemy lines as a heavily up-gunned armored robot, firing lasers, knocking enemy drones out of the air with "elevating" weapons, controlling air and ground drones as networked "nodes" in war and using AI to organize long-range targeting data -- are all desired attributes for the Army's new infantry vehicle - the Optionally Manned Fighting Vehicle. The new vehicle, slated to ultimately replace the decades-old Bradley, will achieve operational combat status as soon as 2026 -- and, according to Army documents, pave the way forward into a new era of major, high-powered, mechanized warfare. As it enters a new prototyping and test phase for the vehicle, the Army is further refining its ambitious and high-standard requirements.

Computing expected predictions has many interesting applications in areas such as fairness, handling missing values, and data analysis. Unfortunately, computing expectations of a discriminative model with respect to a probability distribution defined by an arbitrary generative model has been proven to be hard in general. In fact, the task is intractable even for simple models such as logistic regression and a naive Bayes distribution. In this paper, we identify a pair of generative and discriminative models that enables tractable computation of expectations of the latter with respect to the former, as well as moments of any order, in case of regression. Specifically, we consider expressive probabilistic circuits with certain structural constraints that support tractable probabilistic inference. Moreover, we exploit the tractable computation of high-order moments to derive an algorithm to approximate the expectations, for classification scenarios in which exact computations are intractable. We evaluate the effectiveness of our exact and approximate algorithms in handling missing data during prediction time where they prove to be competitive to standard imputation techniques on a variety of datasets. Finally, we illustrate how expected prediction framework can be used to reason about the behaviour of discriminative models.

A trio of researchers from Chonnam National University, Nanjing University of Information Science and Technology and the Chinese Academy of Sciences has found that a deep learning convolutional neural network was able to accurately predict El Niño events up to 18 months in advance. In their paper published in the journal Nature, Yoo-Geun Ham, Jeong-Hwan Kim and Jing-Jia Luo, describe their deep learning application, how it was trained and how well it worked in predicting El Niño events. El Niño-Southern Oscillation events are periods during which water warms above normal temperatures in tropical parts of the Pacific. When that warm water moves east, it leads to more rainfall and other weather events, such as hurricanes, in the Americas, and less rain in Australia and Indonesia. Current models can accurately predict such events using data from water temperature gauges spread across the globe up to a year in advance.

Speaking at his mid-term semiconductor industry forecast seminar in London this week, Malcolm Penn, chairman and CEO of industry analyst Future Horizons, assured attendees that industry fundamentals were sound, and after a fall of around 15% in 2019, the industry will rebound with around 4% revenue growth to $414 billion in 2020. He said, "The fundamentals are sound. In terms of IC unit growth, fab capacity and average selling price, they are all in in good shape. It's the timing of the upswing in the economy that puts it into doubt." He added, "Rebound is a certainty, but its timing is not."