With the digital medicine revolution in full swing, just about every specialty will experience some form of health care technology impact. AI algorithms, deep learning systems, and neural networks are already being used to detect lung cancer, screen skin lesions, and predict acute kidney injury. In the surgical realm, technological advancements previously involved the use of computer-assisted surgery (CAS) to improve precision and facilitate minimally invasive approaches. The da Vinci Surgical System obtained FDA approval in 2000 and, according to the company website, has been used in more than 6 million procedures world-wide. In orthopedics, CAS was introduced in the 1990s with perhaps joint replacement surgery as its most popular and widespread application.

The UK's Department for International Trade (DIT) are proud to be the Lead Partner Country at CEBIT Australia 2019 in Sydney from 29 - 31 October 2019. Visit the UK Pavilion at stand F45 to find out more about UK's capability in technology or to meet with innovative British technology companies. DIT are running a series of activities throughout the week. More details to be announced shortly. DIT will be bringing 10 innovative UK tech companies to showcase on the UK pavilion.

The New Zealand Government has awarded NZ$13 million ($12.1 million) in funding to the University of Waikato for a new research project aimed at helping New Zealanders solve the nation's critical environmental problems. The funding from the Ministry of Business, Innovation and Employment's Strategic Science Investment Fund will be spread over seven years. It will be used to support the university's Time-Evolving Data Science/Artificial Intelligence for Advanced Open Environmental Science (TAIAO) project. The TAIAO project will involve the development of new machine learning methods for time series and data streams tailored to processing large quantities of data collected on the New Zealand environment. It is being conducted as part of a collaboration between the Universities of Waikato, Auckland and Canterbury, Beca and MetService. It has the participation of highly qualified data scientists, data engineers and environmental scientists.

The Software Engineering Institute is creating a movement to mature the discipline of AI Engineering for Defense and National Security. As our government customers adopt artificial intelligence and machine learning to provide leap-ahead mission capabilities, we are working to provide the processes, practices, and tools to support operationalizing AI for robust and trustworthy mission capabilities. Our work includes scalable AI and ML; the tailoring of modern software practices for AI-enabled systems; architectures, design patterns, modeling, and representations; building tools and processes for trustworthy development of AI-enabled capabilities; and test, evaluation, verification, and validation of AI systems. Of course, building and maturing the discipline of AI Engineering also requires applying AI technologies to build real-world, mission-scale capabilities through envisioning creative solutions, solving practical engineering problems, and learning from those experiences. Are you creative, curious, energetic, collaborative, technology-focused, and hard-working?

--Uncertainty quantification for forward and inverse problems is a central challenge across physical and biomedical disciplines. We address this challenge for the problem of modeling subsurface flow at the Hanford Site by combining stochastic computational models with observational data using physics-informed GAN models. The geographic extent, spatial heterogeneity, and multiple correlation length scales of the Hanford Site require training a computationally intensive GAN model to thousands of dimensions. We develop a hierarchical scheme for exploiting domain parallelism, map discriminators and generators to multiple GPUs, and employ efficient communication schemes to ensure training stability and convergence. We developed a highly optimized implementation of this scheme that scales to 27,500 NVIDIA V olta GPUs and 4584 nodes on the Summit supercomputer with a 93.1% scaling efficiency, achieving peak and sustained half-precision rates of 1228 PF/s and 1207 PF/s. Index T erms --Stochastic PDEs, GANs, Deep Learning I. O VERVIEW A. Parameter estimation and uncertainty quantification for subsurface flow models Mathematical models of subsurface flow and transport are inherently uncertain because of the lack of data about the distribution of geological units, the distribution of hydrological properties (e.g., hydraulic conductivity) within each unit, and initial and boundary conditions. Here, we focus on parameter-ization and uncertainty quantification (UQ) in the subsurface flow model at the Department of Energy's Hanford Site, one of the most contaminated sites in the western hemisphere. During the Hanford Site's 60-plus years history, there have been more than 1000 individual sources of contaminants distributed over 200 square miles mostly along Columbia River [1]. Accurate subsurface flow models with rigorous UQ are necessary for assessing risks of the contaminants reaching the Columbia river and numerous wells used by agriculture and as sources of drinking water, as well as for the design of efficient remediation strategies. B. UQ with Stochastic Partial Differential Equations Uncertain initial and boundary conditions and model parameters render the governing model equations stochastic. In this context, UQ becomes equivalent to solving stochastic PDEs (SPDEs). Forward solution of SPDEs requires that all model parameters as well as the initial/boundary conditions are prescribed either deterministically or stochastically, which is not possible unless experimental data are available to provide additional information for critical parameters, e.g. the field conductivity.

Quantum computing (QC) has gained popularity due to its unique capabilities that are quite different from that of classical computers in terms of speed and methods of operations. This paper proposes hybrid models and methods that effectively leverage the complementary strengths of deterministic algorithms and QC techniques to overcome combinatorial complexity for solving large-scale mixed-integer programming problems. Four applicatio ns, namely the molecular conformation problem, job-shop scheduling problem, manufacturin g cell formation problem, and the vehicle routing problem, are specifically addressed. Large-scale instances of these application problems across multiple scales ranging from molecular design t o logistics optimization are computationally challenging for deterministic optimization algorithms on classical computers. To address the computational challenges, hybrid QC-based algorithms are proposed and extensive computational experimental results are presented to demonstrate their applicability and efficiency. The proposed QC-based solution strategies enjoy high computatio nal efficiency in terms of solution quality and computation time, by utilizing the unique features of both classical and quantum computers.

This paper presents the Poisson-randomized gamma dynamical system (PRGDS), a model for sequentially observed count tensors that encodes a strong inductive bias toward sparsity and burstiness. The PRGDS is based on a new motif in Bayesian latent variable modeling, an alternating chain of discrete Poisson and continuous gamma latent states that is analytically convenient and computationally tractable. This motif yields closed-form complete conditionals for all variables by way of the Bessel distribution and a novel discrete distribution that we call the shifted confluent hypergeometric distribution. We draw connections to closely related models and compare the PRGDS to these models in studies of real-world count data sets of text, international events, and neural spike trains. We find that a sparse variant of the PRGDS, which allows the continuous gamma latent states to take values of exactly zero, often obtains better predictive performance than other models and is uniquely capable of inferring latent structures that are highly localized in time.

To reduce human error and prejudice, many high-stakes decisions have been turned over to machine algorithms. However, recent research suggests that this does not remove discrimination, and can perpetuate harmful stereotypes. While algorithms have been developed to improve fairness, they typically face at least one of three shortcomings: they are not interpretable, they lose significant accuracy compared to unbiased equivalents, or they are not transferable across models. To address these issues, we propose a geometric method that removes correlations between data and any number of protected variables. Further, we can control the strength of debi-asing through an adjustable parameter to address the tradeoff between model accuracy and fairness. The resulting features are interpretable and can be used with many popular models, such as linear regression, random forest and multilayer perceptrons. The resulting predictions are found to be more accurate and fair than several comparable fair AI algorithms across a variety of benchmark datasets. Our work shows that debiasing data is a simple and effective solution toward improving fairness.

While variable selection is essential to optimize the learning complexity by prioritizing features, automating the selection process is preferred since it requires laborious efforts with intensive analysis otherwise. However, it is not an easy task to enable the automation due to several reasons. First, selection techniques often need a condition to terminate the reduction process, for example, by using a threshold or the number of features to stop, and searching an adequate stopping condition is highly challenging. Second, it is uncertain that the reduced variable set would work well; our preliminary experimental result shows that well-known selection techniques produce different sets of variables as a result of reduction (even with the same termination condition), and it is hard to estimate which of them would work the best in future testing. In this paper, we demonstrate the potential power of our approach to the automation of selection process that incorporates well-known selection methods identifying important variables. Our experimental results with two public network traffic data (UNSW-NB15 and IDS2017) show that our proposed method identifies a small number of core variables, with which it is possible to approximate the performance to the one with the entire variables.

We consider the problem of training machine learning models in a risk-averse manner. In particular, we propose an adaptive sampling algorithm for stochastically optimizing the Conditional Value-at-Risk (CVaR) of a loss distribution. We use a distributionally robust formulation of the CVaR to phrase the problem as a zero-sum game between two players. Our approach solves the game using an efficient no-regret algorithm for each player. Critically, we can apply these algorithms to large-scale settings because the implementation relies on sampling from Determinantal Point Processes. Finally, we empirically demonstrate its effectiveness on large-scale convex and non-convex learning tasks.