We report on an empirical study of the main strategies for conditional quantile estimation in the context of stochastic computer experiments. To ensure adequate diversity, six metamodels are presented, divided into three categories based on order statistics, functional approaches, and those of Bayesian inspiration. The metamodels are tested on several problems characterized by the size of the training set, the input dimension, the quantile order and the value of the probability density function in the neighborhood of the quantile. The metamodels studied reveal good contrasts in our set of 480 experiments, enabling several patterns to be extracted. Based on our results, guidelines are proposed to allow users to select the best method for a given problem.

We propose local symplectic surgery, a two-timescale procedure for finding local Nash equilibria in two-player zero-sum games. We first show that previous gradient-based algorithms cannot guarantee convergence to local Nash equilibria due to the existence of non-Nash stationary points. By taking advantage of the differential structure of the game, we construct an algorithm for which the local Nash equilibria are the only attracting fixed points. We also show that the algorithm exhibits no oscillatory behaviors in neighborhoods of equilibria and show that it has the same per-iteration complexity as other recently proposed algorithms. We conclude by validating the algorithm on two numerical examples: a toy example with multiple Nash equilibria and a non-Nash equilibrium, and the training of a small generative adversarial network (GAN).

To overcome the oscillation problem in the classical momentum-based optimizer, recent work associates it with the proportional-integral (PI) controller, and artificially adds D term producing a PID controller. It suppresses oscillation with the sacrifice of introducing extra hyper-parameter. In this paper, we start by analyzing: why momentum-based method oscillates about the optimal point? and answering that: the fluctuation problem relates to the lag effect of integral (I) term. Inspired by the conditional integration idea in classical control society, we propose SPI-Optimizer, an integral-Separated PI controller based optimizer WITHOUT introducing extra hyperparameter. It separates momentum term adaptively when the inconsistency of current and historical gradient direction occurs. Extensive experiments demonstrate that SPIOptimizer generalizes well on popular network architectures to eliminate the oscillation, and owns competitive performance with faster convergence speed (up to 40% epochs reduction ratio ) and more accurate classification result on MNIST, CIFAR10, and CIFAR100 (up to 27.5% error reduction ratio) than the state-of-the-art methods.

We address the problem of inferring the causal direction between two variables by comparing the least-squares errors of the predictions in both possible directions. Under the assumption of an independence between the function relating cause and effect, the conditional noise distribution, and the distribution of the cause, we show that the errors are smaller in causal direction if both variables are equally scaled and the causal relation is close to deterministic. Based on this, we provide an easily applicable algorithm that only requires a regression in both possible causal directions and a comparison of the errors. The performance of the algorithm is compared with various related causal inference methods in different artificial and real-world data sets.

Black-box variational inference tries to approximate a complex target distribution though a gradient-based optimization of the parameters of a simpler distribution. Provable convergence guarantees require structural properties of the objective. This paper shows that for location-scale family approximations, if the target is M-Lipschitz smooth, then so is the objective, if the entropy is excluded. The key proof idea is to describe gradients in a certain inner-product space, thus permitting use of Bessel's inequality. This result gives insight into how to parameterize distributions, gives bounds the location of the optimal parameters, and is a key ingredient for convergence guarantees.

Recent developments in the field of deep learning have motivated many researchers to apply these methods to problems in quantum information. Torlai and Melko first proposed a decoder for surface codes based on neural networks. Since then, many other researchers have applied neural networks to study a variety of problems in the context of decoding. An important development in this regard was due to Varsamopoulos et al. who proposed a two-step decoder using neural networks. Subsequent work of Maskara et al. used the same concept for decoding for various noise models. We propose a similar two-step neural decoder using inverse parity-check matrix for topological color codes. We show that it outperforms the state-of-the-art performance of non-neural decoders for independent Pauli errors noise model on a 2D hexagonal color code. Our final decoder is independent of the noise model and achieves a threshold of $10 \%$. Our result is comparable to the recent work on neural decoder for quantum error correction by Maskara et al.. It appears that our decoder has significant advantages with respect to training cost and complexity of the network for higher lengths when compared to that of Maskara et al.. Our proposed method can also be extended to arbitrary dimension and other stabilizer codes.

SINGAPORE-BASED smart city investor GBCI Ventures has launched a US$100 million development fund for technologies such as robotics, artificial intelligence and virtual reality. The fund unveiled on Wednesday is backed by corporate capital, family offices and high-net worth individuals, among other sources, a spokesperson told The Business Times. It does not have a close date. This is the first fund from GBCI, which was set up in late 2018. GBCI, which is already in talks with some 50 startups, said that it aims to help nurture a smart cities ecosystem by providing startups both regional and global with support such as technology access and go-to-market support, on top of fund injections.

Five cloned monkeys have been born with a host of genetic mental health conditions in a controversial experiment in China. The monkeys - all clones of one primate - have been specially bred to create a'diseased' population of animals to use in laboratory tests. All five have the same DNA altered, which has resulted in symptoms similar to the human conditions of anxiety, depression and schizophrenia. The quintet were born at the Institute of Neuroscience (ION) of the Chinese Academy of Sciences (CAS) in Shanghai. Researchers used the same technique as was used last year to produce Zhong Zhong and Hua Hua – the first ever two cloned monkeys - and Dolly the sheep, famously cloned in the late 90s in Scotland.

Following a recent story with the top twenty companies in the area of AI drug development, this time we will make a flight over this new ecosystem looking for the bright lights of a rigorous developing market. In a recent article about new drugs approved, we found out that in the record year 2018, 61 new drugs were launched which means 20% more new pharmaceutical products compared with the previous record year 1996. It's no wonder if AI drug development realizes the promises, that we will witness an explosive growth in the coming years. Some of the 20 most prominent companies have some very fresh news that it is worth our attention. The most recent news for Atomwise is the formation of a strategic alliance with Charles River Laboratories International, Inc.

Instagram is not limiting the reach of its posts, it has said – but it will not explain exactly what decides how many people see them. Instagram's algorithm is still largely mysterious to everyone who uses the site: though it has said that it decides which posts are shown by using a wide range of different pieces of data, it's not clear what exactly those pieces of data are. That mystery has led to a series of conspiracy theories that attempt to explain what exactly is going on. And now Instagram has taken on one in particular: the idea that it is limiting posts so that only a certain proportion of followers can see them. The company says that isn't true: there is no limit to the number of people that see each post.