InSight's primary instrument is a French-built seismometer, designed to detect the slightest vibrations from "marsquakes" around the planet. The device, to be placed on the surface by the lander's robot arm, is so sensitive it can measure a seismic wave just one-half the radius of a hydrogen atom.

Video Friday is your weekly selection of awesome robotics videos, collected by your Automaton bloggers. We'll also be posting a weekly calendar of upcoming robotics events for the next few months; here's what we have so far (send us your events!): Let us know if you have suggestions for next week, and enjoy today's videos. Nothing is more secure than a workplace protected by prowling robots. But are the fish okay?

Imbalanced data with a skewed class distribution are common in many real-world applications. Deep Belief Network (DBN) is a machine learning technique that is effective in classification tasks. However, conventional DBN does not work well for imbalanced data classification because it assumes equal costs for each class. To deal with this problem, cost-sensitive approaches assign different misclassification costs for different classes without disrupting the true data sample distributions. However, due to lack of prior knowledge, the misclassification costs are usually unknown and hard to choose in practice. Moreover, it has not been well studied as to how cost-sensitive learning could improve DBN performance on imbalanced data problems. This paper proposes an evolutionary cost-sensitive deep belief network (ECS-DBN) for imbalanced classification. ECS-DBN uses adaptive differential evolution to optimize the misclassification costs based on training data, that presents an effective approach to incorporating the evaluation measure (i.e. G-mean) into the objective function. We first optimize the misclassification costs, then apply them to deep belief network. Adaptive differential evolution optimization is implemented as the optimization algorithm that automatically updates its corresponding parameters without the need of prior domain knowledge. The experiments have shown that the proposed approach consistently outperforms the state-of-the-art on both benchmark datasets and real-world dataset for fault diagnosis in tool condition monitoring.

The AI revolution is quickly gaining momentum, with robots being deployed everywhere from offshore oil rigs to marine research centres. Now, a laboratory at Pittsburgh's Carnegie Mellon University is championing artificial intelligence by gradually outsourcing chemical work to robots. Armed with skill, precision and steadfast diligence, the robots lift bottles filled with chemical reagents, transport them to test tube banks and dispense an exact number of drops. The goal is to pinpoint the optimum chemical makeup for high-capacity electric car batteries, a project that's fronted by the Toyota Research Institute. While the robots are currently used only as manpower, the team assert that they could eventually devise the experiments.

If you combine two or three metals together, you will get an alloy that usually looks and acts like a metal, with its atoms arranged in rigid geometric patterns. But once in a while, under just the right conditions, you get something entirely new: a futuristic alloy called metallic glass. The amorphous material's atoms are arranged every which way, much like the atoms of the glass in a window. Its glassy nature makes it stronger and lighter than today's best steel, and it stands up better to corrosion and wear. Although metallic glass shows a lot of promise as a protective coating and alternative to steel, only a few thousand of the millions of possible combinations of ingredients have been evaluated over the past 50 years, and only a handful developed to the point that they may become useful.

Metallurgists have long sought the Holy Grail of alloys, metallic glass that is strong and won't shatter. It is amorphous, with its atoms arranged every which way, much like the atoms of the glass in a window. Its glassy nature makes it stronger and lighter than today's best steel, plus it stands up better to corrosion and wear. Even though metallic glass would be useful as a protective coating or an alternative to steel, only a few thousand of the millions of possible combinations of ingredients have been evaluated over the past 50 years, and of those, only a handful developed to the point that they may become useful. Now a group led by scientists at the Department of Energy's SLAC National Accelerator Laboratory, the National Institute of Standards and Technology (NIST), and Northwestern University has reported a shortcut for discovering and improving metallic glass–and, by extension, other elusive materials–at a fraction of the time and cost.

We propose a generic, Bayesian, information geometric approach to the exploration--exploitation trade-off in multi-armed bandit problems. Our approach, BelMan, uniformly supports pure exploration, exploration--exploitation, and two-phase bandit problems. The knowledge on bandit arms and their reward distributions is summarised by the barycentre of the joint distributions of beliefs and rewards of the arms, the \emph{pseudobelief-reward}, within the beliefs-rewards manifold. BelMan alternates \emph{information projection} and \emph{reverse information projection}, i.e., projection of the pseudobelief-reward onto beliefs-rewards to choose the arm to play, and projection of the resulting beliefs-rewards onto the pseudobelief-reward. It introduces a mechanism that infuses an exploitative bias by means of a \emph{focal distribution}, i.e., a reward distribution that gradually concentrates on higher rewards. Comparative performance evaluation with state-of-the-art algorithms shows that BelMan is not only competitive but can also outperform other approaches in specific setups, for instance involving many arms and continuous rewards.

This paper presents a brief introduction to the key points of the Grey Machine Learning (GML) based on the kernels. The general formulation of the grey system models have been firstly summarized, and then the nonlinear extension of the grey models have been developed also with general formulations. The kernel implicit mapping is used to estimate the nonlinear function of the GML model, by extending the nonparametric formulation of the LSSVM, the estimation of the nonlinear function of the GML model can also be expressed by the kernels. A short discussion on the priority of this new framework to the existing grey models and LSSVM have also been discussed in this paper. And the perspectives and future orientations of this framework have also been presented.

The power law has been observed in the degree distributions of many biological neural networks. Sparse deep neural networks, which learn an economical representation from the data, resemble biological neural networks in many ways. In this paper, we study if these artificial networks also exhibit properties of the power law. Experimental results on two popular deep learning models, namely, multilayer perceptrons and convolutional neural networks, are affirmative. The power law is also naturally related to preferential attachment. To study the dynamical properties of deep networks in continual learning, we propose an internal preferential attachment model to explain how the network topology evolves. Experimental results show that with the arrival of a new task, the new connections made follow this preferential attachment process.

A new approach to energy efficiency uses high tech tools to make many small adjustments rather than more costly tactics such as replacing big ticket items like windows and cooling equipment. Startups including Carbon Lighthouse and Redaptive are using data collection and machine learning to make a building's mechanical and electrical infrastructure use power more efficiently. Carbon Lighthouse has helped Tesla cut electricity use at the electric vehicle maker's headquarters by using sensors, data collection, software algorithms, and technical analysis. Carbon Lighthouse engineers focused on two large cooling towers, two chillers, and some pumps at Tesla headquarters. They found an error in how two systems were communicating with each other.