We present a frame-invariant method for detecting coherent structures from Lagrangian flow trajectories that can be sparse in number, as is the case in many fluid mechanics applications of practical interest. The method, based on principles used in graph coloring and spectral graph drawing algorithms, examines a measure of the kinematic dissimilarity of all pairs of fluid trajectories, either measured experimentally, e.g. using particle tracking velocimetry; or numerically, by advecting fluid particles in the Eulerian velocity field. Coherence is assigned to groups of particles whose kinematics remain similar throughout the time interval for which trajectory data is available, regardless of their physical proximity to one another. Through the use of several analytical and experimental validation cases, this algorithm is shown to robustly detect coherent structures using significantly less flow data than is required by existing spectral graph theory methods.

We study a stochastic and distributed algorithm for nonconvex problems whose objective consists of a sum of $N$ nonconvex $L_i/N$-smooth functions, plus a nonsmooth regularizer. The proposed NonconvEx primal-dual SpliTTing (NESTT) algorithm splits the problem into $N$ subproblems, and utilizes an augmented Lagrangian based primal-dual scheme to solve it in a distributed and stochastic manner. With a special non-uniform sampling, a version of NESTT achieves $\epsilon$-stationary solution using $\mathcal{O}((\sum_{i=1}^N\sqrt{L_i/N})^2/\epsilon)$ gradient evaluations, which can be up to $\mathcal{O}(N)$ times better than the (proximal) gradient descent methods. It also achieves Q-linear convergence rate for nonconvex $\ell_1$ penalized quadratic problems with polyhedral constraints. Further, we reveal a fundamental connection between primal-dual based methods and a few primal only methods such as IAG/SAG/SAGA.

An upcoming TV interview with the Australian man acquitted of the murder of a New Zealand woman during a Tinder date has met with an online backlash. Gable Tostee, 30, was charged after Warriena Wright, 26, fell in 2014 from his balcony in Queensland's Gold Coast. After a high-profile, week-long trial last month, a jury found him not guilty of murder and manslaughter. The Nine Network's 60 Minutes programme has arranged the exclusive interview which will air on 13 November. "I restrained her to stop her from attacking me," Mr Tostee said in a preview of the interview.

Back in March of this year, Google DeepMind had its AI system, AlphaGo, "sit down" with international Go champion Lee Sedol in a 5-game Go match with a purse of a cool $1 million. Despite Go being far more difficult than say, chess, to program for (due in part to number of possible moves), it destroyed Sedol 4-1. Now, the same company that took down the best in what many consider to be the most difficult board game in the world, is turning its sights on Starcraft II. The company made the announcement at this year's BlizzCon 2016 in Anaheim, California, and in an associated press release, says that it has established a "collaboration with Blizzard Entertainment to open up StarCraft II to AI and Machine Learning researchers around the world." For anybody paying attention to Google DeepMind, or one of its central driving forces, Demis Hassabis, the leap to 3-D video games has been expected for some time.

As machine learning becomes ubiquitous, robots will be tasked with handling increasingly more sensitive and private data. In order to help protect this personal information, computer scientists at Google have developed neural networks that teach themselves how to encrypt the information they process. A team from Google Brain, the organisation's deep learning research project, taught neural networks how to encrypt and decrypt messages. In a research paper published online the scientists created three neural networks: Alice, Bob, and Eve. Each was assigned its own job.

Samsung Electronics announced Sunday that it will include an artificial intelligence assistance service in its upcoming Galaxy S8 smartphones. In addition to the Galaxy smartphone, the virtual assistance will also be incorporated into other products, including home appliances and wearable technology devices, according to media reports. "Our Galaxy smartphones don't provide services that enable consumers to order pizza or coffee, but does provide third party applications. But the new AI platform will enable consumers to do things that they would usually do through a separate third party application," Samsung reportedly said in a statement. Samsung, which plans to launch the Galaxy S8 early next year, recently announced the acquisition of the San Jose, California-based AI company Viv Labs, which was founded by the creators of Apple's Siri service.

The overall goal is to target treatment specifically to each individual so that clinical outcomes for that individual are optimized. One direction of attack is to use patient data to discover decision rules which specify the treatment to use as a function of a vector of features from the patient. Regression and classification are important statistical tools for estimating such rules based on either observational data or data from a randomized trial, and machine learning can help with this because of its ability to artfully handle high dimensional feature spaces with potentially complex interactions.

We investigate deep generative models that can exchange multiple modalities bi-directionally, e.g., generating images from corresponding texts and vice versa. Recently, some studies handle multiple modalities on deep generative models, such as variational autoencoders (VAEs). However, these models typically assume that modalities are forced to have a conditioned relation, i.e., we can only generate modalities in one direction. To achieve our objective, we should extract a joint representation that captures high-level concepts among all modalities and through which we can exchange them bi-directionally. As described herein, we propose a joint multimodal variational autoencoder (JMVAE), in which all modalities are independently conditioned on joint representation. In other words, it models a joint distribution of modalities. Furthermore, to be able to generate missing modalities from the remaining modalities properly, we develop an additional method, JMVAE-kl, that is trained by reducing the divergence between JMVAE's encoder and prepared networks of respective modalities. Our experiments show that our proposed method can obtain appropriate joint representation from multiple modalities and that it can generate and reconstruct them more properly than conventional VAEs. We further demonstrate that JMVAE can generate multiple modalities bi-directionally.

The growing penetration of distributed energy resources (DERs) in urban areas raises multiple reliability issues. The topology reconstruction is a critical step to ensure the robustness of distribution grid operation. However, the bus connectivity and network topology reconstruction are hard in distribution grids. The reasons are that 1) the branches are challenging and expensive to monitor due to underground setup; 2) the inappropriate assumption of radial topology in many studies that urban grids are mesh. To address these drawbacks, we propose a new data-driven approach to reconstruct distribution grid topology by utilizing the newly available smart meter data. Specifically, a graphical model is built to model the probabilistic relationships among different voltage measurements. With proof, the bus connectivity and topology estimation problems are formulated as a linear regression problem with least absolute shrinkage on grouped variables (Group Lasso) to deal with meshed network structures. Simulation results show highly accurate estimation in IEEE standard distribution test systems with and without loops using real smart meter data.

Generalising the idea of the classical EM algorithm that is widely used for computing maximum likelihood estimates, we propose an EM-Control (EM-C) algorithm for solving multi-period finite time horizon stochastic control problems. The new algorithm sequentially updates the control policies in each time period using Monte Carlo simulation in a forward-backward manner; in other words, the algorithm goes forward in simulation and backward in optimization in each iteration. Similar to the EM algorithm, the EM-C algorithm has the monotonicity of performance improvement in each iteration, leading to good convergence properties. We demonstrate the effectiveness of the algorithm by solving stochastic control problems in the monopoly pricing of perishable assets and in the study of real business cycle.