"Just like software, and the Internet from previous decades, public cloud and now AI are the megatrends of our generation." Artificial intelligence and machine learning (AI/ML) is driving breakthrough developments across industries such as Healthcare, Energy, Logistics, and more. Heliogen is using AI to optimize the next generation of solar technology to power energy intensive processes such as manufacturing steel which in the past was only possible with fossil fuels. Another example is Boston Dynamics' HANDLE – an agile mobile robot that uses deep learning to autonomously unload trucks and move boxes in warehouses. If someone tells you that AI/ML is hype, remind them that cloud computing was once called hype.

The field of machine learning has continued to accelerate through 2019, moving at light speed with compelling new results coming out of academia and the research arms of large tech firms like Google, Microsoft, Yahoo, Facebook and many more. It's a daunting task for the down-in-the-trenches data scientist to keep pace. I advise my data science students at UCLA to be up on the latest research results in order to keep ahead of the pack. I recount how industry luminary Andrew Ng keeps his head above water by toting around a file of research papers (so when he has a free moment, like riding on an Uber, he can consume part of a paper). It does take time to add the research realm to your everyday duties, but I think it's fun to know what technologies are fertile areas of research.

A plan to remove fuel debris from the primary containment vessel of a reactor at the Fukushima No. 1 nuclear power plant is expected to be further pushed back after it became apparent that Tokyo Electric Power Company Holdings Ltd. will not be able to conduct an internal probe -- a key step to start removing the fuel debris -- by the end of March as planned. The internal probe would involve using remote-controlled robots to collect fuel debris inside the No. 1 reactor so Tepco can examine its composition and form. Tepco's plan is to open three holes in both the outer and inner doors of the primary containment vessel using pressurized water mixed with a polishing agent. After it succeeded in opening three holes in the outer door, Tepco started drilling a hole in the inner door in June 2019. But that procedure caused the concentration of radioactive dust to increase temporarily, prompting staff to suspend work.

In many application domains, time series are monitored to detect extreme events like technical faults, natural disasters, or disease outbreaks. Unfortunately, it is often non-trivial to select both a time series that is informative about events and a powerful detection algorithm: detection may fail because the detection algorithm is not suitable, or because there is no shared information between the time series and the events of interest. In this work, we thus propose a non-parametric statistical test for shared information between a time series and a series of observed events. Our test allows identifying time series that carry information on event occurrences without committing to a specific event detection methodology. In a nutshell, we test for divergences of the value distributions of the time series at increasing lags after event occurrences with a multiple two-sample testing approach. In contrast to related tests, our approach is applicable for time series over arbitrary domains, including multivariate numeric, strings or graphs. We perform a large-scale simulation study to show that it outperforms or is on par with related tests on our task for univariate time series. We also demonstrate the real-world applicability of our approach on datasets from social media and smart home environments.

An innovative physics-guided learning algorithm for predicting the mechanical response of materials and structures is proposed in this paper. The key concept of the proposed study is based on the fact that physics models are governed by Partial Differential Equation (PDE), and its loading/ response mapping can be solved using Finite Element Analysis (FEA). Based on this, a special type of deep convolutional neural network (DCNN) is proposed that takes advantage of our prior knowledge in physics to build data-driven models whose architectures are of physics meaning. This type of network is named as FEA-Net and is used to solve the mechanical response under external loading. Thus, the identification of a mechanical system parameters and the computation of its responses are treated as the learning and inference of FEA-Net, respectively. Case studies on multi-physics (e.g., coupled mechanical-thermal analysis) and multi-phase problems (e.g., composite materials with random micro-structures) are used to demonstrate and verify the theoretical and computational advantages of the proposed method.

--Solving power flow (PF) equations is the basis of power flow analysis, which is important in determining the best operation of existing systems, performing security analysis, etc. However, PF equations can be out-of-date or even unavailable due to system dynamics and uncertainties, making traditional numerical approaches infeasible. T o address these concerns, researchers have proposed data-driven approaches to solve the PF problem by learning the mapping rules from historical system operation data. Nevertheless, prior data-driven approaches suffer from poor performance and generalizability, due to overly simplified assumptions of the PF problem or ignorance of physical laws governing power systems. In this paper, we propose a physics-guided neural network to solve the PF problem, with an auxiliary task to rebuild the PF model. By encoding different granularity of Kirchhoff's laws and system topology into the rebuilt PF model, our neural-network based PF solver is regularized by the auxiliary task and constrained by the physical laws. The simulation results show that our physics-guided neural network methods achieve better performance and generalizability compared to existing unconstrained data-driven approaches. Furthermore, we demonstrate that the weight matrices of our physics-guided neural networks embody power system physics by showing their similarities with the bus admittance matrices. OWER flow (PF) analysis aims at obtaining complete voltage angle and magnitude information for each bus in a power system, given specified loads, generator real power and voltage conditions [1].

--One problem found when working with satellite images is the radiometric variations across the image and different images. Intending to improve remote sensing models for the classification of burnt areas, we set two objectives. The first is to understand the relationship between feature spaces and the predictive ability of the models, allowing us to explain the differences between learning and generalization when training and testing in different datasets. We find that training on datasets built from more than one image provides models that generalize better . These results are explained by visualizing the dispersion of values on the feature space. The second objective is to evolve hyper-features that improve the performance of different classifiers on a variety of test sets. We find the hyper-features to be beneficial, and obtain the best models with XGBoost, even if the hyper-features are optimized for a different method. Deforestation has serious implications on biodiversity, on rural communities that depend on forests for survival, and on greenhouse gas emissions that drive the global climate. The machine learning (ML) community can help by providing predictive models that, after learning from a small sample of an image, can automatically classify the whole image. Although previous ML work in forest monitoring has shown good results, the predictive models are often applied on the same location where they were learnt, i.e., the models are trained and tested in samples from the same dataset (e.g., [1]) or time series from the same area (e.g., [2]).

-- We introduce for the first time the utilization of Long short - term memory (LSTM) neural network architectures for the compensation of fiber nonlinearities in digital coherent systems. We conduct numerical simulations considering either C - band or O - band transmission systems for single channel and multi - channel 16 - QAM modulation format with polarization multiplexing . A detailed analysis regarding the effect of the number of hidden units and the length of the word of sym bols that trains the LSTM algorithm and corresponds to the considered channel memory is conducted in order to reveal the limits of LSTM based receiver with respect to performance and complexity. The numerical results show that LSTM Neural Networks can be v ery efficient as post processors of optical receivers which clas sify data that have undergone non - linear impairments in fiber and provide superior performance compared to digital back propagation, especially in the multi - channel transmission scenario. The complexity analysis shows that LSTM becomes more complex as the number of hidden units and the channel memory increase can be less complex than DBP in long distances ( 1000 km). There is a huge effort in fiber - optic communication industry to cope with the exponentially increasing capacity demands coming from next generation mobile networks and high bandwidth internet applica tions [1]. New trends such as internet of things especially in the context of tactile internet increase the requirements for real - time, high bandwidth, high availability connectivity in the access network domain, thus enhancing the capacity needs in metro and long - haul transmission networks. Optical fiber communication community predicted the imminent explosion of capacity needs ten years ago and started working intensively on techniques that can leverage fiber capabilities in this respect.

Interpreting 3D seismic data correctly helps identify geological features that may hold or trap oil and gas deposits. In this post, I use these services to build and train a custom deep-learning model for the interpretation of geological features on 3D seismic data. The purpose of this post is to show oil and gas data scientists how they can quickly and easily create customized semantic-segmentation models. Amazon SageMaker is a fully managed service that enables data scientists to build, train, tune, and deploy machine learning models at any scale. This service provides a powerful and scalable compute environment that is also easy to use.

Design starts are up significantly thanks to increased investment in areas such as AI, a plethora of new communications standards, buildout of the Cloud, the race toward autonomous driving and continued advancements in mobile phones. Many designs demand the latest technologies and push the limits of complexity. Low power is becoming more than just reducing wasted power at the chip level. It is becoming an important environmental consideration. The focus of environmentalists increasingly will be toward the semiconductor industry. Systems are becoming more distributed, driven by the IoT, requiring new approaches to both design and verification.