We address the problem of estimating the mixing time $t_{\mathsf{mix}}$ of an arbitrary ergodic finite Markov chain from a single trajectory of length $m$. The reversible case was addressed by Hsu et al. [2017], who left the general case as an open problem. In the reversible case, the analysis is greatly facilitated by the fact that the Markov operator is self-adjoint, and Weyl's inequality allows for a dimension-free perturbation analysis of the empirical eigenvalues. As Hsu et al. point out, in the absence of reversibility (and hence, the non-symmetry of the pair probabilities matrix), the existing perturbation analysis has a worst-case exponential dependence on the number of states $d$. Furthermore, even if an eigenvalue perturbation analysis with better dependence on $d$ were available, in the non-reversible case the connection between the spectral gap and the mixing time is not nearly as straightforward as in the reversible case. Our key insight is to estimate the pseudo-spectral gap instead, which allows us to overcome the loss of self-adjointness and to achieve a polynomial dependence on $d$ and the minimal stationary probability $\pi_\star$. Additionally, in the reversible case, we obtain simultaneous nearly (up to logarithmic factors) minimax rates in $t_{\mathsf{mix}}$ and precision $\varepsilon$, closing a gap in Hsu et al., who treated $\varepsilon$ as constant in the lower bounds. Finally, we construct fully empirical confidence intervals for the pseudo-spectral gap, which shrink to zero at a rate of roughly $1/\sqrt m$, and improve the state of the art in even the reversible case.

Optimal transport ($\OT$) theory provides a useful set of tools to compare probability distributions. As a consequence, the field of $\OT$ is gaining traction and interest within the machine learning community. A few deficiencies usually associated with $\OT$ include its high computational complexity when comparing discrete measures, which is quadratic when approximating it through entropic regularization; or supercubic when solving it exactly. For some applications, the fact that $\OT$ distances are not usually negative definite also means that they cannot be used with usual Hilbertian tools. In this work, we consider a particular family of ground metrics, namely tree metrics, which yield negative definite $\OT$ metrics that can be computed in linear time. By averaging over randomly sampled tree metrics, we obtain a tree-sliced-Wasserstein distance. We illustrate that the proposed tree-sliced-Wasserstein distances compare favorably with other baselines on various benchmark datasets.

Scientists in China have launched a weather observation rocket from a semi-submersible drone ship in a world's first for meteorological research. The unmanned semi-submersible vehicle (USSV) was designed specifically to deploy instruments that can hit altitudes beyond the reach of weather balloons. The breakthrough could lead to networks of USSVs around the oceans for cheaper, more efficient data collection, the researchers say. Scientists in China have launched a weather observation rocket from a semi-submersible drone ship in a world's first for meteorological research. According to a team from the Institute of Atmospheric Physics at the Chinese Academy of Sciences, the USSV is built to launch a type of instrument called a rocketsonde.

China and the United States are leading the way in the quest to dominate the world of artificial intelligence, a study by the United Nations has found. It found US tech giants IBM and Microsoft had the largest AI portfolios, with 8,920 and 5,930 different patents, respectively. China however, accounted for 17 of the top 20 academic institutions involved in patenting AI and was particularly strong in the fast growing area of'deep learning'. This process was recently used to create a'self-aware' robot that is capable of imagining itself. Other machines have complex speech recognition systems.

A few years ago, we wrote about the tiniest little quadruped robot we'd ever seen--a mere 20 millimeters long, with a hip height of 5.6 mm and weight of about 1.6 gram. The designer, Ryan St. Pierre from Sarah Bergbreiter's lab at the University of Maryland, also showed us a picture of an even smaller version that weighed just 100 mg. "It's always a fun challenge to try to make robots as small as possible," he told us. "Currently, I am working on making a robot, of the same design, that would be 2.5 mm long, an order of magnitude smaller than the ones we presented at ICRA. Smaller robots can more easily go places that later robots can't, and having them in various sizes would increase their utility."

A new 5G network is being created now, which will not only offer faster downloading on cell phones. It will provide the kind of connectivity we need in the era of the Internet of Things - driverless cars, Internet-connected medical devices, smart TVs and virtual assistants. But there are dangers that could be lurking in the equipment needed to build the new network. The Chinese telecommunications equipment giant Huawei is dominating the creation of 5G networks around the world. For years, classified intelligence reports from the U.S. have warned that China would one day use Huawei to penetrate American networks for cyber-espionage or cyberattacks. In the U.S., the National Security Agency has banned AT&T and Verizon from using Huawei products in America's 5G network. And last month, the U.S. had a top executive from Huawei arrested in Canada so she could be extradited to the U.S. The growing cyberthreat posed by China was stressed in the Worldwide Threat Assessment - a report from the U.S. intelligence community - that was released this week. And all this is part of the backdrop for this week's trade negotiations between the U.S. and China. My guest David Sanger is the author of a book about cyberwar and cyber-sabotage called "The Perfect Weapon." Let's start with the 5G network. And how will it affect our phones, our devices and all our interconnectivity? DAVID SANGER: Well, at its simplest, the 5G network is an increase in speed and range for what you see on your cell phone. So 5G means just fifth generation. The hope is that when you're using your phone or some other device over Wi-Fi, you'll get no lag time and that you'll get near instantaneous download of data, webpages and so forth.

Where will and AI/ML workloads be executed and who should handle them? The industry-wide rising tide towards the public cloud is not a foregone conclusion, as we were reminded by a Micron-commissioned report by Forrester Consulting that surveyed 200 manage architecture, systems, or strategy for complex data at large enterprises in the US and China. As of mid-2018, 72 percent are analyzing complex data within on-premises data centers and 51 percent do so in a public cloud. Three years from now, on-premises-only use will drop to 44 percent and public cloud use for analytics will rise to 61 percent. Those using edge environments to analyze complex data sets will rise from 44 to 53 percent.

Jan. 31 (UPI) -- The number of global artificial intelligence inventions over a recent three-year span more than doubled all previous creations since the …

GENEVA, Jan. 31 (Xinhua) -- China, together with the United States, lead the global competition to dominate artificial intelligence (AI), a study …

An interview with the force behind Seeing AI, the talking camera app for people who are blind or with low vision. Saqib Shaikh lost his sight at the age of seven, fell in love with computers as a schoolboy in Britain and grew up to become a top software engineer with an inspirational mission. Standing at the intersection of artificial intelligence (AI) and inclusive design, he believes we can create intelligent machines to empower millions of people around the world with disabilities to achieve more and live enhanced lives. The knowledge gained from targeting and solving the problems of those with special needs, he says, can only drive technological innovation that benefits everyone across society. Saqib has had a lifelong relationship with advancing digital technology.