China's Ministry of Industry and Information Technology is developing a time frame for ending the construction and sale of fuel cars as it makes the transition to electric vehicles (EVs), according to state media citing a Cabinet official. Deputy industry minister Xin Guobin said at an auto industry forum on Saturday that his ministry has begun "research on formulating a timetable to stop production and sales of traditional energy vehicles", according to Xinhua News Agency, such as gasoline and diesel cars. No specific target dates were given in the reports, however. China is the biggest auto market in the world by number of vehicles sold, meaning such a policy change could have a sizeable effect on the global industry. The country joins France and Britain in their plans to oust fuel cars, both of which announced goals in July to completely stop sales of gasoline and diesel automobiles by 2040 to reduce pollution and carbon emissions.

We generalize a support vector machine to a support spinor machine by using the mathematical structure of wedge product over vector machine in order to extend field from vector field to spinor field. The separated hyperplane is extended to Kolmogorov space in time series data which allow us to extend a structure of support vector machine to a support tensor machine and a support tensor machine moduli space. Our performance test on support spinor machine is done over one class classification of end point in physiology state of time series data after empirical mode analysis and compared with support vector machine test. We implement algorithm of support spinor machine by using Holo-Hilbert amplitude modulation for fully nonlinear and nonstationary time series data analysis.

We consider the problem of recovering a $d-$dimensional manifold $\mathcal{M} \subset \mathbb{R}^n$ when provided with noiseless samples from $\mathcal{M}$. There are many algorithms (e.g., Isomap) that are used in practice to fit manifolds and thus reduce the dimensionality of a given data set. Ideally, the estimate $\mathcal{M}_\mathrm{put}$ of $\mathcal{M}$ should be an actual manifold of a certain smoothness; furthermore, $\mathcal{M}_\mathrm{put}$ should be arbitrarily close to $\mathcal{M}$ in Hausdorff distance given a large enough sample. Generally speaking, existing manifold learning algorithms do not meet these criteria. Fefferman, Mitter, and Narayanan (2016) have developed an algorithm whose output is provably a manifold. The key idea is to define an approximate squared-distance function (asdf) to $\mathcal{M}$. Then, $\mathcal{M}_\mathrm{put}$ is given by the set of points where the gradient of the asdf is orthogonal to the subspace spanned by the largest $n - d$ eigenvectors of the Hessian of the asdf. As long as the asdf meets certain regularity conditions, $\mathcal{M}_\mathrm{put}$ is a manifold that is arbitrarily close in Hausdorff distance to $\mathcal{M}$. In this paper, we define two asdfs that can be calculated from the data and show that they meet the required regularity conditions. The first asdf is based on kernel density estimation, and the second is based on estimation of tangent spaces using local principal components analysis.

We consider the topic of multivariate regression on manifold-valued output, that is, for a multivariate observation, its output response lies on a manifold. Moreover, we propose a new regression model to deal with the presence of grossly corrupted manifold-valued responses, a bottleneck issue commonly encountered in practical scenarios. Our model first takes a correction step on the grossly corrupted responses via geodesic curves on the manifold, and then performs multivariate linear regression on the corrected data. This results in a nonconvex and nonsmooth optimization problem on manifolds. To this end, we propose a dedicated approach named PALMR, by utilizing and extending the proximal alternating linearized minimization techniques. Theoretically, we investigate its convergence property, where it is shown to converge to a critical point under mild conditions. Empirically, we test our model on both synthetic and real diffusion tensor imaging data, and show that our model outperforms other multivariate regression models when manifold-valued responses contain gross errors, and is effective in identifying gross errors.

In this paper we formally analyse the use of sparse filtering algorithms to perform covariate shift adaptation. We provide a theoretical analysis of sparse filtering by evaluating the conditions required to perform covariate shift adaptation. We prove that sparse filtering can perform adaptation only if the conditional distribution of the labels has a structure explained by a cosine metric. To overcome this limitation, we propose a new algorithm, named periodic sparse filtering, and carry out the same theoretical analysis regarding covariate shift adaptation. We show that periodic sparse filtering can perform adaptation under the looser and more realistic requirement that the conditional distribution of the labels has a periodic structure, which may be satisfied, for instance, by user-dependent data sets. We experimentally validate our theoretical results on synthetic data. Moreover, we apply periodic sparse filtering to real-world data sets to demonstrate that this simple and computationally efficient algorithm is able to achieve competitive performances.

The news of Rahul Gandhi addressing technology experts in Silicon Valley of the US has set the explosion of Jokes on Rahul Gandhi in the Twitter world and various social media domains.

Duckietown is a hands-on, project-based course at MIT that focuses on self-driving vehicles and high-level autonomy. In Spring 2016, Liam Paull served as Duckietown's CEO and Teddy Ort worked as a vehicle autonomy engineer in training. Since the course began at MIT, it has spread to other universities around the globe, and is now taught in universities from Beijing to Zurich.

Voice assistants, including Apple's Siri and Amazon's Alexa, can be controlled by hackers using inaudible voice commands, researchers at Zhejiang University in China have found. This can be done using a technique that translates voice commands into ultrasonic frequencies that are too high for the human ear to recognise. The technique, named DolphinAttack, could be used to download a virus, send fake messages and even add fake events to a calendar. It could also give hackers access to outgoing video or phone calls, allowing them to spy on their victims. The fault is due to vulnerabilities in the software and hardware of speech recognition systems.

THE human face is a remarkable piece of work. The astonishing variety of facial features helps people recognise each other and is crucial to the formation of complex societies. So is the face's ability to send emotional signals, whether through an involuntary blush or the artifice of a false smile. People spend much of their waking lives, in the office and the courtroom as well as the bar and the bedroom, reading faces, for signs of attraction, hostility, trust and deceit. They also spend plenty of time trying to dissimulate.

ARTIFICIAL intelligence (AI) could wipe out thousands of jobs in the country's fast-growing business process outsourcing (BPO) sector, but the industry can address this threat through innovation and training, Trade Secretary Ramon Lopez said on Wednesday. "One solution to this technology's threat is to modify and remodel the existing jobs' nature. Another way is the organization of groups necessary in the infusion of science and technology to other fields or areas for the realization of establishing a comprehensive network," Lopez said. The most important aspect, he said, is to conduct training and related activities. "In simpler terms, we will endeavor to make this new technology work for them," Lopez said.