PARIS – The government on Monday filed a candidacy for the city of Osaka to host the 2025 World Exposition with the Bureau International des Expositions in Paris. France has already submitted the bid of Paris, which is likely to be a tough rival for Osaka. The deadline for submitting bids is set at May 22, and the BIE will select the host city by a vote by member states at its general meeting in November 2018. The Japanese plan is based on the theme "Designing Future Society for Our Lives." The papers for Japan's bid to host the event were submitted by Japanese Ambassador to France Masato Kitera, Osaka Gov. Ichiro Matsui and Keidanren Chairman Sadayuki Sakakibara, who serves as head of the bidding committee for the envisaged Osaka exposition.

Dynamic Programming (DP) over tree decompositions is a well-established method to solve problems - that are in general NP-hard - efficiently for instances of small treewidth. Experience shows that (i) heuristically computing a tree decomposition has negligible runtime compared to the DP step; and (ii) DP algorithms exhibit a high variance in runtime when using different tree decompositions; in fact, given an instance of the problem at hand, even decompositions of the same width might yield extremely diverging runtimes. We thus propose here a novel and general method that is based on selection of the best decomposition from an available pool of heuristically generated ones. For this purpose, we require machine learning techniques that provide automated selection based on features of the decomposition rather than on the actual problem instance. Thus, one main contribution of this work is to propose novel features for tree decompositions. Moreover, we report on extensive experiments in different problem domains which show a significant speedup when choosing the tree decomposition according to this concept over simply using an arbitrary one of the same width.

In emotion recognition, it is difficult to recognize human's emotional states using just a single modality. Besides, the annotation of physiological emotional data is particularly expensive. These two aspects make the building of effective emotion recognition model challenging. In this paper, we first build a multi-view deep generative model to simulate the generative process of multi-modality emotional data. By imposing a mixture of Gaussians assumption on the posterior approximation of the latent variables, our model can learn the shared deep representation from multiple modalities. To solve the labeled-data-scarcity problem, we further extend our multi-view model to semi-supervised learning scenario by casting the semi-supervised classification problem as a specialized missing data imputation task. Our semi-supervised multi-view deep generative framework can leverage both labeled and unlabeled data from multiple modalities, where the weight factor for each modality can be learned automatically. Compared with previous emotion recognition methods, our method is more robust and flexible. The experiments conducted on two real multi-modal emotion datasets have demonstrated the superiority of our framework over a number of competitors.

In this paper, we study the stochastic gradient descent (SGD) method for the nonconvex nonsmooth optimization, and propose an accelerated SGD method by combining the variance reduction technique with Nesterov's extrapolation technique. Moreover, based on the local error bound condition, we establish the linear convergence of our method to obtain a stationary point of the nonconvex optimization. In particular, we prove that not only the sequence generated linearly converges to a stationary point of the problem, but also the corresponding sequence of objective values is linearly convergent. Finally, some numerical experiments demonstrate the effectiveness of our method. To the best of our knowledge, it is first proved that the accelerated SGD method converges linearly to the local minimum of the nonconvex optimization.

Tasks like code generation and semantic parsing require mapping unstructured (or partially structured) inputs to well-formed, executable outputs. We introduce abstract syntax networks, a modeling framework for these problems. The outputs are represented as abstract syntax trees (ASTs) and constructed by a decoder with a dynamically-determined modular structure paralleling the structure of the output tree. On the benchmark Hearthstone dataset for code generation, our model obtains 79.2 BLEU and 22.7% exact match accuracy, compared to previous state-of-the-art values of 67.1 and 6.1%. Furthermore, we perform competitively on the Atis, Jobs, and Geo semantic parsing datasets with no task-specific engineering.

Amazon may use driverless cars to help expedite deliveries. Elizabeth Keatinge (@elizkeatinge) has more. SAN FRANCISCO -- Amazon has a team studying the ramifications of self-driving car technology on its business, although at present the online retailing giant does not have plans to build a fleet of autonomous delivery vehicles. The team consists of a dozen employees and was formed more than a year ago, according to a Wall Street Journalreport Monday citing individuals briefed on the matter. The group amounts to an in-house think tank charged with studying how a range of fast-paced developments in the self-driving field can be applied to its vast shipping needs.

We all use user-generated reviews to figure out what points of interest are worth checking out. If you're traveling in a country where you don't speak the language, however, the reviews you rely on are usually in the local tongue. Google has a new feature to help you out. The company will now automatically translate reviews into your native language without any effort on your part. When you use Google Maps or Search to find a place you're interested in, the reviews will be translated on the fly into the language you have set on your phone.

Artificial intelligence and other technologies will cause people "more pain than happiness" over the next three decades, according to Jack Ma, the billionaire chairman and founder of Alibaba. "Social conflicts in the next three decades will have an impact on all sorts of industries and walks of life," said Ma, speaking at an entrepreneurship conference in China about the job disruptions that would be created by automation and the internet. A key social conflict will be the rise of artificial intelligence and longer life expectancy, which will lead to an aging workforce fighting for fewer jobs. Ma, who is usually more optimistic in his presentations, issued the warning to encourage businesses to adapt or face problems in the future. He said that 15 years ago he gave hundreds of speeches warning about the impact of e-commerce on traditional retailers and few people listened because he wasn't as well-known as he is now.

That's what one of China's most influential CEOs is predicting for the future. A link has been sent to your friend's email address. That's what one of China's most influential CEOs is predicting for the future.

It's been reckoned that about one third of large companies globally are using Artificial Intelligence as part of their marketing mechanics. The main goal of AI in this area is one of anticipating what customers will be buying in the short, medium and longer term – allowing special packages and promotions to be tailored to specific trends, on and offline. It also has the obvious advantages of improving media advertising placement, monitoring social media platforms and analysing brand loyalty. In essence, a strategy for informing what the buyer wants and when, which can enable dynamic pricing whilst at the same time vastly improving the automation of in-store or department selling function. AI is allowing clearer visibility into not only the retail sector, but is making its impact felt in the banking and telecom industries.