Japan's space agency is preparing to deploy two robotic explorers to the surface of an asteroid. On Friday, the Hayabusa-2 spacecraft will despatch a pair of "rovers" to the 1km-wide space rock known as Ryugu. Rover 1A and Rover 1B will move around by hopping in Ryugu's low gravity; they will capture images of the surface and measure temperatures. Hayabusa-2 reached the asteroid Ryugu in June this year after a three-and-a-half-year journey. If all goes well, Hayabusa-2 will be the first spacecraft to successfully place robot rovers on the surface of an asteroid.

It wouldn't be an Amazon event without a slew of new Echo devices, and this time is no different. The company announced not one, not two, but seven new Echo products today at an event today in Seattle. Not only are there now new versions of the Echo Dot, the Echo Plus and the Echo Show, Amazon also introduced several new Echo companions that you can use to bring Alexa to every room in your house -- and even your car. Like the new Echo Show, the new Echo Dot and Echo Plus look a lot better this year than last. The Dot, for example, no longer looks like an oversized hockey puck.

And now she'll tell you if she doesn't like what she'll hear. Amazon has announced that its Echo speakers will now be able to go on guard in your house when you're not there, keep an ear out for anything untoward. If the microphones in the smart speakers hear the sound of smashing glass or a smoke detector going off, for instance, they'll record that sound and send it to its owner. All of that is done using the same kinds of artificial intelligence that power the voice tools and other smarts of the Echo. The I.F.O. is fuelled by eight electric engines, which is able to push the flying object to an estimated top speed of about 120mph.

The science-fiction writer Robert Heinlein once wrote, "Each generation thinks it invented sex." He was presumably referring to the pride each generation takes in defining its own sexual practices and ethics. But his comment hit the mark in another sense: Every generation has to reinvent sex because the previous generation did a lousy job of teaching it. In the United States, the conversations we have with our children about sex are often awkward, limited, and brimming with euphemism. At school, if kids are lucky enough to live in a state that allows it, they'll get something like 10 total hours of sex education.1

Machine learning methods such as convolutional neural networks (CNNs) are becoming an integral part of scientific research in many disciplines, spatial vector data often fail to be analyzed using these powerful learning methods because of its irregularities. With the aid of graph Fourier transform and convolution theorem, it is possible to convert the convolution as a point-wise product in Fourier domain and construct a learning architecture of CNN on graph for the analysis task of irregular spatial data. In this study, we used the classification task of building patterns as a case study to test this method, and experiments showed that this method has achieved outstanding results in identifying regular and irregular patterns, and has significantly improved in comparing with other methods.

We consider the clustering problem of attributed graphs. Our challenge is how we can design an effective and efficient clustering method that precisely captures the hidden relationship between the topology and the attributes in real-world graphs. We propose Non-linear Attributed Graph Clustering by Symmetric Non-negative Matrix Factorization with Positive Unlabeled Learning. The features of our method are three holds. 1) it learns a non-linear projection function between the different cluster assignments of the topology and the attributes of graphs so as to capture the complicated relationship between the topology and the attributes in real-world graphs, 2) it leverages the positive unlabeled learning to take the effect of partially observed positive edges into the cluster assignment, and 3) it achieves efficient computational complexity, $O((n^2+mn)kt)$, where $n$ is the vertex size, $m$ is the attribute size, $k$ is the number of clusters, and $t$ is the number of iterations for learning the cluster assignment. We conducted experiments extensively for various clustering methods with various real datasets to validate that our method outperforms the former clustering methods regarding the clustering quality.

Mammographic breast density, a parameter used to describe the proportion of breast tissue fibrosis, is widely adopted as an evaluation characteristic of the likelihood of breast cancer incidence. Existing methods of breast density classification either requires steps of manual operations or achieves only moderate classification accuracies due to the limited model capacity. In this study, we present a radiomics approach based on residual learning for the classification of mammographic breast densities. Different from those established approaches, our method possesses several encouraging properties including being almost fully automatic, possessing big model capacity, and having high flexibility. As a result, it can obtain outstanding classification results without the necessity of result compensation using mammographs taken from different views. The proposed method was instantiated with the INbreast dataset and classification accuracies of 92.6% and 96.8% were obtained for the four BI-RADS (Breast Imaging and Reporting Data System) category task and the two BI-RADS category task, respectively. Both values are significantly higher than the classification results of the current state-of-the-art methods, including the eight-layer convolutional neural network and the high throughput-derived multilayer visual representations. The superior performances achieved with its encouraging properties indicate that our method has a great potential to be applied as a computer-aided diagnosis tool.

ARTICLE HISTORY Compiled September 25, 2018 ABSTRACT Lin et al. (Reports, 7 September 2018, p. 1004) reported a remarkable proposal that employs a passive, strictly linear optical setup to perform pattern classifications. But interpreting the multilayer diffractive setup as a deep neural network and advocating it as an all-optical deep learning framework are not well justified and represent a mischaracterization of the system by overlooking its defining characteristics of perfect linearity and strict passivity. Lin et al. [1] proposed a combination of methods for creating a computer-generated volumetric hologram (CGVH) made of multiple planar diffractive elements, and using such hologram to scatter and directionally focus each of a multitude of patternimprinted coherent light fields into a designated spatial region on an image sensor, effectively realizing a functionality of pattern recognition and classification. Their alloptical multi-planed setup bears a certain resemblance to the multi-layered structure of a deep neural network (DNN) [2], but that is about as far as the similarity goes. It is a mischaracterization to interpret the CGVH construct as a DNN, when its functionality is strictly limited to linear transformations of the input light field, thus unable to perform any task of statistical inference/prediction beyond the capacity of a single layer perceptron [2,3].

Physics-informed neural networks (PINNs) have recently emerged as an alternative way of solving partial differential equations (PDEs) without the need of building elaborate grids, instead, using a straightforward implementation. In particular, in addition to the deep neural network (DNN) for the solution, a second DNN is considered that represents the residual of the PDE. The residual is then combined with the mismatch in the given data of the solution in order to formulate the loss function. This framework is effective but is lacking uncertainty quantification of the solution due to the inherent randomness in the data or due to the approximation limitations of the DNN architecture. Here, we propose a new method with the objective of endowing the DNN with uncertainty quantification for both sources of uncertainty, i.e., the parametric uncertainty and the approximation uncertainty. We first account for the parametric uncertainty when the parameter in the differential equation is represented as a stochastic process. Multiple DNNs are designed to learn the modal functions of the arbitrary polynomial chaos (aPC) expansion of its solution by using stochastic data from sparse sensors. We can then make predictions from new sensor measurements very efficiently with the trained DNNs. Moreover, we employ dropout to correct the over-fitting and also to quantify the uncertainty of DNNs in approximating the modal functions. We then design an active learning strategy based on the dropout uncertainty to place new sensors in the domain to improve the predictions of DNNs. Several numerical tests are conducted for both the forward and the inverse problems to quantify the effectiveness of PINNs combined with uncertainty quantification. This NN-aPC new paradigm of physics-informed deep learning with uncertainty quantification can be readily applied to other types of stochastic PDEs in multi-dimensions.

Simulation plays an essential role in comprehending a target system in many fields of social and industrial sciences. A major task in simulation is the estimation of parameters, and optimal parameters to express the observed data need to directly elucidate the properties of the target system as the design of the simulator is based on the expert's domain knowledge. However, skilled human experts struggle to find the desired parameters.Data assimilation therefore becomes an unavoidable task in simulator design to reduce the cost of simulator optimization. Another necessary task is extrapolation; in many practical cases, the prediction based on simulation results will be often outside of the dominant range of the given data area, and this is referred to as the covariate shift. This paper focuses on the regression problem with the covariate shift. While the parameter estimation for the covariate shift has been studied thoroughly in parametric and nonparametric settings, conventional statistical methods of parameter searching are not applicable in the data assimilation of the simulation owing to the properties of the likelihood function: intractable or nondifferentiable. To address these problems, we propose a novel framework of Bayesian inference based on kernel mean embedding that comprises an extended kernel approximate Bayesian computation (ABC) of the importance weighted regression, kernel herding, and the kernel sum rule. This framework makes the prediction available in covariate shift situations, and its effectiveness is evaluated in both synthetic numerical experiments and a widely used production simulator.