BEIJING - Chinese gay dating app Blued is halting new user registration for a week, it said Sunday, following media reports that underage users caught HIV after going on dates set up via the world's largest networking app for the LGBT community. China has a vibrant lesbian, gay, bisexual and transgender scene, though activists say conservative attitudes among some groups in society have prompted occasional government clampdowns. On Saturday, citing academic research, financial magazine Caixin said juveniles were heavily involved in the gay dating app, where some teenagers had even hosted live-streaming. It added that many gay teenagers had unprotected sex through the app and contracted HIV, the virus that causes AIDS. In response, Blued vowed to launch a "comprehensive content audit and regulation," and crack down on juvenile users posing as adults and on texts, pictures and groups that involve minors.

The education ministry plans to set up a new subsidy system for prefectures and large cities that offer detailed support to foreign students attending public elementary and junior high schools and their parents by using multilingual translation systems. The subsidies will be offered to prefectural governments, ordinance-designated major cities and other core cities that use tablet computers with multilingual speech translation functions in teaching Japanese to students from abroad at school and providing school guidance to their parents. The ministry has set aside ¥20 million for the subsidy system, which is designed to cover one-third of related costs, under the government's fiscal 2019 budget, with 100 language support programs likely to become eligible for the financial aid, informed sources said. The launch of the new subsidy system comes in line with the government's policy of allowing more foreign workers to come here. The number of foreign students in Japan needing Japanese language education totaled 43,947 in fiscal 2016, up 70 percent from 26,281 in fiscal 2006.

Any organizations that are scaling up in size, locations, operations or products face exponential complexities. In this process of scaling up, data also grows in volume and variety, demanding innovation to more effectively derive value from quickly growing data. Data analytics is becoming table stakes as solutions have become more open and commoditized. Many organizations in the Asia Pacific region have adopted or experimented with emerging data solutions. Yet, according to a Data & Analytics Report by MIT and SAS in 2016, only 51% of respondents in 2015 believed analytics creates competitive advantages, down from 66% in 2012.

BEIJING (Reuters) - Chinese gay dating app Blued is halting new user registration for a week, it said on Sunday, following media reports that underage users caught HIV after going on dates set up via the world's largest networking app for the LGBT community.

Toyota Motor Corp. has sold enough cars to put one outside every Japanese home. Now it wants to put robots inside. Well-known for its automated assembly lines, Toyota sees a not-so-far-off future in which robots transcend the factory and become commonplace in homes, helping with chores -- and even offering companionship -- in an aging society where a quarter of the population is over 65 and millions of seniors live alone. Machines have become much smarter in the last decade or so. Yet, every attempt to build one that can do simple things like load a washing machine or carry groceries encounters the same basic, physical problem: the stronger a robot gets, the heavier and more dangerous it becomes.

Waves of disruptive technologies have fostered creativity, innovation and ease of communication, but they are also a catalyst of potentially massive job losses, with machines set to take over jobs once done by humans. SCB workers walk past the front of the bank's head office on Ratchadaphisek Road. While forecasts of future job losses from artificial intelligence (AI) and automation have mushroomed, tangible damage has already been done to certain industries. In a massive restructuring, US auto giant General Motors has announced it will cut 15% of its 180,000-strong workforce to save US$6 billion and adapt to "changing market conditions". Similarly, Swedish build-it-yourself furniture chain Ikea plans to cut 7,500 positions over the next two years, representing about 5% of the company's global workforce.

"Killer robots" are taking over. Also known as autonomous weapons, these devices, once activated, can destroy targets without human intervention. The technology has been with us for years. In 1959, the US Navy started using the Phalanx Close-In Weapon System, an autonomous defense device that can spot and attack anti-ship missiles, helicopters and similar threats. In 2014, Russia announced that killer robots would guard five of its ballistic missile installations. That same year, Israel deployed the Harpy, an autonomous weapon that can stay airborne for nine hours to identify and pick off enemy targets from enormous distances.

Artificial Intelligence frameworks should allow for ever more autonomous and general systems in contrast to very narrow and restricted (human pre-defined) domain systems, in analogy to how the brain works. Self-constructive Artificial Intelligence ($SCAI$) is one such possible framework. We herein propose that $SCAI$ is based on three principles of organization: self-growing, self-experimental and self-repairing. Self-growing: the ability to autonomously and incrementally construct structures and functionality as needed to solve encountered (sub)problems. Self-experimental: the ability to internally simulate, anticipate and take decisions based on these expectations. Self-repairing: the ability to autonomously re-construct a previously successful functionality or pattern of interaction lost from a possible sub-component failure (damage). To implement these principles of organization, a constructive architecture capable of evolving adaptive autonomous agents is required. We present Schema-based learning as one such architecture capable of incrementally constructing a myriad of internal models of three kinds: predictive schemas, dual (inverse models) schemas and goal schemas as they are necessary to autonomously develop increasing functionality. We claim that artificial systems, whether in the digital or in the physical world, can benefit very much form this constructive architecture and should be organized around these principles of organization. To illustrate the generality of the proposed framework, we include several test cases in structural adaptive navigation in artificial intelligence systems in Paper II of this series, and resilient robot motor control in Paper III of this series. Paper IV of this series will also include $SCAI$ for problem structural discovery in predictive Business Intelligence.

Dimensionality reduction is an essential technique for multi-way large-scale data, i.e., tensor. Tensor ring (TR) decomposition has become popular due to its high representation ability and flexibility. However, the traditional TR decomposition algorithms suffer from high computational cost when facing large-scale data. In this paper, taking advantages of the recently proposed tensor random projection method, we propose two TR decomposition algorithms. By employing random projection on every mode of the large-scale tensor, the TR decomposition can be processed at a much smaller scale. The simulation experiment shows that the proposed algorithms are $4-25$ times faster than traditional algorithms without loss of accuracy, and our algorithms show superior performance in deep learning dataset compression and hyperspectral image reconstruction experiments compared to other randomized algorithms.

Nonconvex matrix recovery is known to contain no spurious local minima under a restricted isometry property (RIP) with a sufficiently small RIP constant $\delta$. If $\delta$ is too large, however, then counterexamples containing spurious local minima are known to exist. In this paper, we introduce a proof technique that is capable of establishing sharp thresholds on $\delta$ to guarantee the inexistence of spurious local minima. Using the technique, we prove that in the case of a rank-1 ground truth, an RIP constant of $\delta<1/2$ is both necessary and sufficient for exact recovery from any arbitrary initial point (such as a random point). We also prove a local recovery result: given an initial point $x_{0}$ satisfying $f(x_{0})\le(1-\delta)^{2}f(0)$, any descent algorithm that converges to second-order optimality guarantees exact recovery.