We propose to use deep neural networks for generating samples in Monte Carlo integration. Our work is based on non-linear independent component analysis, which we extend in numerous ways to improve performance and enable its application to integration problems. First, we introduce piecewise-polynomial coupling transforms that greatly increase the modeling power of individual coupling layers. Second, we propose to preprocess the inputs of neural networks using one-blob encoding, which stimulates localization of computation and improves inference. Third, we derive a gradient-descent-based optimization for the KL and the $\chi^2$ divergence for the specific application of Monte Carlo integration with stochastic estimates of the target distribution. Our approach enables fast and accurate inference and efficient sample generation independent of the dimensionality of the integration domain. We demonstrate the benefits of our approach for generating natural images and in two applications to light-transport simulation. First, we show how to learn joint path-sampling densities in primary sample space and how to importance sample multi-dimensional path prefixes thereof. Second, we use our technique to extract conditional directional densities driven by the triple product of the rendering equation, and leverage them for path guiding. In all applications, our approach yields on-par or higher performance at equal sample count than competing techniques.

We consider the problem of ranking a set of items from pairwise comparisons in the presence of features associated with the items. Recent works have established that $O(n\log(n))$ samples are needed to rank well when there is no feature information present. However, this might be sub-optimal in the presence of associated features. We introduce a new probabilistic preference model called feature-Bradley-Terry-Luce (f-BTL) model that generalizes the standard BTL model to incorporate feature information. We present a new least squares based algorithm called fBTL-LS which we show requires much lesser than $O(n\log(n))$ pairs to obtain a good ranking -- precisely our new sample complexity bound is of $O(\alpha\log \alpha)$, where $\alpha$ denotes the number of `independent items' of the set, in general $\alpha << n$. Our analysis is novel and makes use of tools from classical graph matching theory to provide tighter bounds that sheds light on the true complexity of the ranking problem, capturing the item dependencies in terms of their feature representations. This was not possible with earlier matrix completion based tools used for this problem. We also prove an information theoretic lower bound on the required sample complexity for recovering the underlying ranking, which essentially shows the tightness of our proposed algorithms. The efficacy of our proposed algorithms are validated through extensive experimental evaluations on a variety of synthetic and real world datasets.

Chemical transport models (CTMs), which simulate air pollution transport, transformation, and removal, are computationally expensive, largely because of the computational intensity of the chemical mechanisms: systems of coupled differential equations representing atmospheric chemistry. Here we investigate the potential for machine learning to reproduce the behavior of a chemical mechanism, yet with reduced computational expense. We create a 17-layer residual multi-target regression neural network to emulate the Carbon Bond Mechanism Z (CBM-Z) gas-phase chemical mechanism. We train the network to match CBM-Z predictions of changes in concentrations of 77 chemical species after one hour, given a range of chemical and meteorological input conditions, which it is able to do with root-mean-square error (RMSE) of less than 1.97 ppb (median RMSE = 0.02 ppb), while achieving a 250x computational speedup. An additional 17x speedup (total 4250x speedup) is achieved by running the neural network on a graphics-processing unit (GPU). The neural network is able to reproduce the emergent behavior of the chemical system over diurnal cycles using Euler integration, but additional work is needed to constrain the propagation of errors as simulation time progresses.

In this technical report, we present jLDADMM---an easy-to-use Java toolkit for conventional topic models. jLDADMM is released to provide alternatives for topic modeling on normal or short texts. It provides implementations of the Latent Dirichlet Allocation topic model and the one-topic-per-document Dirichlet Multinomial Mixture model (i.e. mixture of unigrams), using collapsed Gibbs sampling. In addition, jLDADMM supplies a document clustering evaluation to compare topic models. jLDADMM is open-source and available to download at: https://github.com/datquocnguyen/jLDADMM

A capsule is a collection of neurons which represents different variants of a pattern in the network. The routing scheme ensures only certain capsules which resemble lower counterparts in the higher layer should be activated. However, the computational complexity becomes a bottleneck for scaling up to larger networks, as lower capsules need to correspond to each and every higher capsule. To resolve this limitation, we approximate the routing process with two branches: a master branch which collects primary information from its direct contact in the lower layer and an aide branch that replenishes master based on pattern variants encoded in other lower capsules. Compared with previous iterative and unsupervised routing scheme, these two branches are communicated in a fast, supervised and one-time pass fashion. The complexity and runtime of the model are therefore decreased by a large margin. Motivated by the routing to make higher capsule have agreement with lower capsule, we extend the mechanism as a compensation for the rapid loss of information in nearby layers. We devise a feedback agreement unit to send back higher capsules as feedback. It could be regarded as an additional regularization to the network. The feedback agreement is achieved by comparing the optimal transport divergence between two distributions (lower and higher capsules). Such an add-on witnesses a unanimous gain in both capsule and vanilla networks. Our proposed EncapNet performs favorably better against previous state-of-the-arts on CIFAR10/100, SVHN and a subset of ImageNet.

Chinese artificial-intelligence startup CloudWalk Technology signed a deal in March with the Zimbabwean government, providing the authoritarian regime an advanced facial-recognition system that it can use to identify, track, and monitor citizens. In exchange, CloudWalk gains access to the facial data of the demographically distinct country, which provides the company much-needed data for improving its recognition algorithms. Arrangements such as this are common under China's Artificial Intelligence (AI) strategy, whereby Chinese private and state-controlled companies take advantage of the weak legal systems and low privacy standards of developing nations as part of the country's effort to become a world leader in artificial intelligence by 2030. But the vision of artificial intelligence that China is creating is a thoroughly illiberal one. Constant surveillance of citizens is powering initiatives such as the Social Credit System, which will rate citizens on their social and economic performance, increasing the power of the state to enforce its cultural vision.

The report starts by an introduction about the company profiling and a comprehensive review about the strategy concept and the tools that can be used to assess and analyze strategy. Porter's Five Forces model is a powerful tool that combines five competitive forces which limit any industry's profit according to external factors. These forces are the threat of new entrants, the customer bargaining power, the supplier bargaining power, the substitution to an alternative product or service, and the intensity of competition among current rivals inside the industry. This market is expected to grow at XXX billion by the end of forecast period with XX.X% of CAGR. The future trends also introduced in the report which elaborates key factors of Global Artificial Intelligence in Medicine such as market opportunities, future market risk, benefit, loss and profit, customer perspective, Innovation, Short Term vs.

People who check their phones while eating or spending time with their friends are less likely to enjoy themselves, a study has found. Researchers at the University of British Columbia in Canada found that mobile phone use is making people more distracted, distant and drained as a result of its pervasiveness in our modern lives. Even having a mobile phone within easy access during a meal is enough to make diners not enjoy the experience as much as those who keep their devices out of reach while they eat. 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 giant human-like robot bears a striking resemblance to the military robots starring in the movie'Avatar' and is claimed as a world first by its creators from a South Korean robotic company Waseda University's saxophonist robot WAS-5, developed by professor Atsuo Takanishi and Kaptain Rock playing one string light saber guitar perform jam session A man looks at an exhibit entitled'Mimus' a giant industrial robot which has been reprogrammed to interact with humans during a photocall at the new Design Museum in South Kensington, London Electrification Guru Dr. Wolfgang Ziebart talks about the electric Jaguar I-PACE concept SUV before it was unveiled before the Los Angeles Auto Show in Los Angeles, California, U.S The Jaguar I-PACE Concept car is the start of a new era for Jaguar.

Chinese academic institutions are more prolific patent filers in the artificial intelligence (AI) area than domestic companies, according to China's State Intellectual Property Office (SIPO). SIPO shared the statement, based on a release from China IP News, on Wednesday, August 1. The release is based on "China's AI Development Report 2018", which was recently published by Tsinghua University, in Beijing. The university's report revealed that the most prolific filers in AI tend to come from research institutions, such as universities. Unlike in other countries, industry players in China file fewer patents in the AI sphere than those in research institutions. The country's "top IT giants" such as Alibaba and Tencent are "overwhelmed" by the filings of foreign companies, such as IBM and Microsoft, SIPO said.

Apple's much-rumoured, oft-revived car project might be back on the road. The company has re-hired a former employee who left to go and work at Tesla. And though Apple is not saying anything about its plans, he may have joined to help restart the electric vehicle project. Apple's car – said to be named Project Titan within the company – has been rumoured for years and appears to have changed status within the company. At various times, it has been rumoured to have been working on an entire car, shelving that plan, or simply creating a computer that other car manufacturers could include in their vehicles.