While standard generative adversarial networks (GANs) rely solely on training data to learn unknown probability distributions, physics-informed GANs (PI-GANs) encode physical laws in the form of stochastic partial differential equations (PDEs) using auto differentiation. By relating observed data to unobserved quantities of interest through PDEs, PI-GANs allow for the estimation of underlying probability distributions without their direct measurement (i.e. inverse problems). The scalable nature of GANs allows high-dimensional, spatially-dependent probability distributions (i.e., random fields) to be inferred, while incorporating prior information through PDEs allows the training datasets to be relatively small. In this work, PI-GANs are demonstrated for the application of elastic modulus estimation in mechanical testing. Given measured deformation data, the underlying probability distribution of spatially-varying elastic modulus (stiffness) is learned. Two feed-forward deep neural network generators are used to model the deformation and material stiffness across a two dimensional domain. Wasserstein GANs with gradient penalty are employed for enhanced stability. In the absence of explicit training data, it is demonstrated that the PI-GAN learns to generate realistic, physically-admissible realizations of material stiffness by incorporating the PDE that relates it to the measured deformation. It is shown that the statistics (mean, standard deviation, point-wise distributions, correlation length) of these generated stiffness samples have good agreement with the true distribution.

Modern speech enhancement algorithms achieve remarkable noise suppression by means of large recurrent neural networks (RNNs). However, large RNNs limit practical deployment in hearing aid hardware (HW) form-factors, which are battery powered and run on resource-constrained microcontroller units (MCUs) with limited memory capacity and compute capability. In this work, we use model compression techniques to bridge this gap. We define the constraints imposed on the RNN by the HW and describe a method to satisfy them. Although model compression techniques are an active area of research, we are the first to demonstrate their efficacy for RNN speech enhancement, using pruning and integer quantization of weights/activations. We also demonstrate state update skipping, which reduces the computational load. Finally, we conduct a perceptual evaluation of the compressed models to verify audio quality on human raters. Results show a reduction in model size and operations of 11.9$\times$ and 2.9$\times$, respectively, over the baseline for compressed models, without a statistical difference in listening preference and only exhibiting a loss of 0.55dB SDR. Our model achieves a computational latency of 2.39ms, well within the 10ms target and 351$\times$ better than previous work.

Traditional robust estimation assumes that the data are corrupted, and studies methods of estimation that are immune to these corruptions or outliers in the data. In contrast, we explore the setting where the data are "clean" but a statistical model is corrupted after it has been estimated using the data. We study methods for recovering the model that do not require re-estimation from scratch, using only the design and not the original response values. The problem of model repair is motivated from several different perspectives. First, it can be formulated as a well-defined statistical problem that is closely related to, but different from, traditional robust estimation, and that deserves study in its own right. From a more practical perspective, modern machine learning practice is increasingly working with very large statistical models. For example, artificial neural networks having several million parameters are now routinely estimated. It is anticipated that neural networks having trillions of parameters will be built in the coming years, and that large models will be increasingly embedded in systems, where they may be subject to errors and corruption of the parameter values. In this setting, the maintenance of models in a fault tolerant manner becomes a concern.

This paper presents a novel meta learning framework for feature selection (FS) based on fuzzy similarity. The proposed method aims to recommend the best FS method from four candidate FS methods for any given dataset. This is achieved by firstly constructing a large training data repository using data synthesis. Six meta features that represent the characteristics of the training dataset are then extracted. The best FS method for each of the training datasets is used as the meta label. Both the meta features and the corresponding meta labels are subsequently used to train a classification model using a fuzzy similarity measure based framework. Finally the trained model is used to recommend the most suitable FS method for a given unseen dataset. This proposed method was evaluated based on eight public datasets of real-world applications. It successfully recommended the best method for five datasets and the second best method for one dataset, which outperformed any of the four individual FS methods. Besides, the proposed method is computationally efficient for algorithm selection, leading to negligible additional time for the feature selection process. Thus, the paper contributes a novel method for effectively recommending which feature selection method to use for any new given dataset.

Past few years have witnessed the artificial intelligence inspired evolution in various medical fields. The diagnosis and treatment of gliomas -- one of the most commonly seen brain tumors with low survival rate -- rely heavily on the computer assisted segmentation process undertaken on the magnetic resonance imaging (MRI) scans. Although the encoder-decoder shaped deep learning networks have been the de facto standard style for semantic segmentation tasks in medical imaging analysis, enormous effort is still required to be spent on designing the detailed architecture of the down-sampling and up-sampling blocks. In this work, we propose a neural architecture search (NAS) based solution to brain tumor segmentation tasks on multimodal volumetric MRI scans. Three sets of candidate operations are composed respectively for three kinds of basic building blocks in which each operation is assigned with a specific probabilistic parameter to be learned. Through alternately updating the weights of operations and the other parameters in the network, the searching mechanism ends up with two optimal structures for the upward and downward blocks. Moreover, the developed solution also integrates normalization and patching strategies tailored for brain MRI processing. Extensive comparative experiments on the BraTS 2019 dataset demonstrate that the proposed algorithm not only could relieve the pressure of fabricating block architectures but also possesses competitive feasibility and scalability.

Google says it will no longer build custom artificial intelligence tools for speeding up oil and gas extraction, separating itself from cloud computing rivals Microsoft and Amazon. A statement from the company Tuesday followed a Greenpeace report that documents how the three tech giants are using AI and computing power to help oil companies find and access oil and gas deposits in the U.S. and around the world. The environmentalist group says Amazon, Microsoft and Google have been undermining their own climate change pledges by partnering with major oil companies including Shell, BP, Chevron and ExxonMobil that have looked for new technology to get more oil and gas out of the ground. But the group applauded Google on Tuesday for taking a step away from those deals. "While Google still has a few legacy contracts with oil and gas firms, we welcome this indication from Google that it will no longer build custom solutions for upstream oil and gas extraction," said Elizabeth Jardim, senior corporate campaigner for Greenpeace USA.

AI has become a key part of our day-to-day lives and business operations. A report from Microsoft and EY that analysed the outlook for AI in 2019 and beyond, stated that "65% of organisations in Europe expect AI to have a high or a very high impact on the core business." In the banking and financial industries alone, the potential that AI has to improve the customer experience is vast. Important decisions are already made by AI on credit risk, wealth management and even financial crime risk assessments. Other applications include robo-advisory, intelligent pricing, product recommendation, investment services and debt-collection.

By Equipment the market for lab automation is segmented into automated liquid handlers, automated plate handlers, robotic arm, automated storage and retrieval systems. By software the lab automation market is segmented into laboratory information management system, laboratory information system, chromatography data system, electronic lab notebook, scientific data management system. On the basis of analyzer the market is segmented into biochemistry analyzers, immuno-based analyzers, hematology analyzers segments. By application the segmentation of the market is drug discovery, genomics, proteomics, protein engineering, bio analysis, analytical chemistry, system biology, clinical diagnostics, lyophilization. Based on end user the lab automation market is segmented into biotechnology & pharmaceuticals, hospitals, research institutions, academics, private labs. On the basis of geography, lab automation market report covers data points for 28 countries across multiple geographies such as North America & South America, Europe, Asia-Pacific, and Middle East & Africa. Some of the major countries covered in this report are U.S., Canada, Germany, France, U.K., Netherlands, Switzerland, Turkey, Russia, China, India, South Korea, Japan, Australia, Singapore, Saudi Arabia, South Africa, and Brazil among others. In 2017, North America is expected to dominate the market.

ODSC's first virtual conference is a wrap, and now we've started planning for our next one, the ODSC Europe 2020 Virtual Conference from September 17th to the 19th. We're thrilled to announce the first group of expert speakers to join. During the event, speakers will cover topics such as NLP machine learning quant finance deep learning data visualization data science for good image classification transfer learning recommendation systems and much, much more. Dr. Jiahong Zhong is the Head of Data Science at Zopa LTD, which facilitates peer-to-peer lending and is one of the United Kingdom's earliest fintech companies. Before joining Zopa, Zhong worked as a researcher on the Large Hadron Collider Project at CERN, focusing on statistics, distributed computing, and data analysis.

It is hard right now to think about how emerging markets and international companies are dealing with the slowdown, especially when you think of how integrated global supply chains are, and whether that is going to continue. Thankfully with the help of our Artificial Intelligence ("AI") deep learning algorithms, that study fundamental and price data in addition to alternative data like article sentiment and social sentiment, we are able to narrow down the top buys and top shorts in international ETFs. Going global can help with overall diversification if you know the smart moves to make. One of the top buys as rated by our AI system is the Invesco China Technology ETF (CQQQ). Investors agree that this is an interesting play, as China is well on its way to be a global powerhouse in technology, politics aside.