Abstract-- We investigate the problem of dynamic portfolio optimization in continuous-time, finite-horizon setting for a portfolio of two stocks and one risk-free asset. The stocks follow the Cointelation model recently introduced [7]. The proposed optimization methods are twofold. In what we call an Stochastic Differential Equation approach, we compute the optimal weights using mean-variance criterion and power utility maximization. We show that dynamically switching between these two optimal strategies by introducing a triggering function can further improve the portfolio returns. We contrast this with the machine learning clustering methodology inspired by the band-wise Gaussian mixture model [9]. The first benefit of the machine learning over the Stochastic Differential Equation approach is that we were able to achieve the same results though a simpler channel. The second advantage is a flexibility to regime change.

Stock market volatility forecasting is a task relevant to assessing market risk. We investigate the interaction between news and prices for the one-day-ahead volatility prediction using state-of-the-art deep learning approaches. The proposed models are trained either end-to-end or using sentence encoders transfered from other tasks. We evaluate a broad range of stock market sectors, namely Consumer Staples, Energy, Utilities, Heathcare, and Financials. Our experimental results show that adding news improves the volatility forecasting as compared to the mainstream models that rely only on price data. In particular, our model outperforms the widely-recognized GARCH(1,1) model for all sectors in terms of coefficient of determination $R^2$, $MSE$ and $MAE$, achieving the best performance when training from both news and price data.

Artificial Intelligence is nowadays a hot topic in many industries, from banking to travel, including retail, manufacturing, tech… But, what about artificial intelligence in the energy industry? Are utilities and ESCOs already applying it? What shall you know about it? Join our Data Science team and discover what is AI and how it's been used in the Energy industry. What is data science, artificial intelligence and other cool words like machine learning or neuronal networks.

Which company did you think would be most likely to offer an autonomous ride service in California? The state's Public Utilities Commission has revealed that self-driving car startup Zoox will be the first to transport the public. It's joining a pilot program where it will offer free rides with a backup driver in the front. It's not certain when the pilot will start, although Zoox has already stated plans to launch paid service in late 2020. Zoox has already been conducting internal tests, but hasn't talked much about its plans until very recently. The company likely won't be alone for long.

In 1994, soon after Jeff Bezos incorporated what would become Amazon, the entrepreneur briefly contemplated changing the company's name. The nascent firm had been dubbed "Cadabra," but Bezos wanted a less playful, more accurate alternative: "Relentless." Twenty-four years later, perhaps no adjective better describes Bezos' empire than the name he once wanted to give it. The company is known as the "everything store," but in its dogged pursuit of growth, Amazon has come to dominate more than just ecommerce. Amazon is a fashion designer, advertising business, television and movie producer, book publisher, and the owner of a sprawling platform for crowdsourced micro-labor tasks.

Future advancement of engineering applications is dependent on design of novel materials with desired properties. Enormous size of known chemical space necessitates use of automated high throughput screening to search the desired material. The high throughput screening uses quantum chemistry calculations to predict material properties, however, computational complexity of these calculations often imposes prohibitively high cost on the search for desired material. This critical bottleneck is resolved by using deep machine learning to emulate the quantum computations. However, the deep learning algorithms require a large training dataset to ensure an acceptable generalization, which is often unavailable a-priory. In this paper, we propose a deep Gaussian process based approach to develop an emulator for quantum calculations. We further propose a novel molecular descriptor that enables implementation of the proposed approach. As demonstrated in this paper, the proposed approach can be implemented using a small dataset. We demonstrate efficacy of our approach for prediction of formation energy of inorganic molecules.

In structured output prediction tasks, labeling ground-truth training output is often expensive. However, for many tasks, even when the true output is unknown, we can evaluate predictions using a scalar reward function, which may be easily assembled from human knowledge or non-differentiable pipelines. But searching through the entire output space to find the best output with respect to this reward function is typically intractable. In this paper, we instead use efficient truncated randomized search in this reward function to train structured prediction energy networks (SPENs), which provide efficient test-time inference using gradient-based search on a smooth, learned representation of the score landscape, and have previously yielded state-of-the-art results in structured prediction. In particular, this truncated randomized search in the reward function yields previously unknown local improvements, providing effective supervision to SPENs, avoiding their traditional need for labeled training data.

Price responsiveness is a major feature of end use customers (EUCs) that participate in demand response (DR) programs, and has been conventionally modeled with static demand functions, which take the electricity price as the input and the aggregate energy consumption as the output. This, however, neglects the inherent temporal correlation of the EUC behaviors, and may result in large errors when predicting the actual responses of EUCs in real-time pricing (RTP) programs. In this paper, we propose a dynamical DR model so as to capture the temporal behavior of the EUCs. The states in the proposed dynamical DR model can be explicitly chosen, in which case the model can be represented by a linear function or a multi-layer feedforward neural network, or implicitly chosen, in which case the model can be represented by a recurrent neural network or a long short-term memory unit network. In both cases, the dynamical DR model can be learned from historical price and energy consumption data. Numerical simulation illustrated how the states are chosen and also showed the proposed dynamical DR model significantly outperforms the static ones.

This paper develops a novel graph convolutional network (GCN) framework for fault location in power distribution networks. The proposed approach integrates multiple measurements at different buses while takes system topology into account. The effectiveness of the GCN model is corroborated by the IEEE 123-bus benchmark system. Simulation results show that the GCN model significantly outperforms other widely-used machine learning schemes with very high fault location accuracy. In addition, the proposed approach is robust to measurement noise and errors, missing entries, as well as multiple connection possibilities. Finally, data visualization results of two competing neural networks are presented to explore the mechanism of GCN's superior performance.

NASA's InSight lander has deployed its first instrument onto the surface of Mars. New images from the lander show the seismometer on the ground, after it was lifted onto the surface by the lander's robotic arm. It will record the waves traveling through the interior structure of the planet, and could help explain mysterious'marsquakes' scientists believe occur regularly. New images from the lander show the seismometer on the ground, after it was lifted onto the surface by the lander's robotic arm. It will record the waves traveling through the interior structure of the planet, and could help explain mysterious'marsquakes' scientists believe occur regularly.