We present a simple and effective approach for posterior uncertainty quantification in deep operator networks (DeepONets); an emerging paradigm for supervised learning in function spaces. We adopt a frequentist approach based on randomized prior ensembles, and put forth an efficient vectorized implementation for fast parallel inference on accelerated hardware. Through a collection of representative examples in computational mechanics and climate modeling, we show that the merits of the proposed approach are fourfold. (1) It can provide more robust and accurate predictions when compared against deterministic DeepONets. (2) It shows great capability in providing reliable uncertainty estimates on scarce data-sets with multi-scale function pairs. (3) It can effectively detect out-of-distribution and adversarial examples. (4) It can seamlessly quantify uncertainty due to model bias, as well as noise corruption in the data. Finally, we provide an optimized JAX library called {\em UQDeepONet} that can accommodate large model architectures, large ensemble sizes, as well as large data-sets with excellent parallel performance on accelerated hardware, thereby enabling uncertainty quantification for DeepONets in realistic large-scale applications.

Meta learning aims at learning a model that can quickly adapt to unseen tasks. Widely used meta learning methods include model agnostic meta learning (MAML), implicit MAML, Bayesian MAML. Thanks to its ability of modeling uncertainty, Bayesian MAML often has advantageous empirical performance. However, the theoretical understanding of Bayesian MAML is still limited, especially on questions such as if and when Bayesian MAML has provably better performance than MAML. In this paper, we aim to provide theoretical justifications for Bayesian MAML's advantageous performance by comparing the meta test risks of MAML and Bayesian MAML. In the meta linear regression, under both the distribution agnostic and linear centroid cases, we have established that Bayesian MAML indeed has provably lower meta test risks than MAML. We verify our theoretical results through experiments.

For more than 20 years, the Landsat 7 satellite circled Earth every 99 minutes or so, capturing images of almost all the planet's surface each 16 days. One of many craft that observed the changing globe, it revealed melting glaciers in Greenland, the growth of shrimp farms in Mexico, and the extent of deforestation in Papua New Guinea. But after Landsat 7 ran short on fuel, its useful life effectively ended. In space, regular servicing has not been an option. Now, though, NASA has a potential fix for such enfeebled satellites.

New Zealand is developing an approach to supporting the ethical adoption of AI -- one that is focused on building an AI ecosystem on a foundation of trust, equity and accessibility right from the onset. A crucial part of this approach is to involve key stakeholders in the planning. And that is exactly the reason why the government has designed the system so every New Zealander and every technology expert who matters can contribute. The success of this ITP requires us to form a consensus view on the scope of our ambition and how this can be achieved with actions and initiatives that are sufficiently realistic to bring about meaningful change – both short and longer-term. Wellington published a draft that should jumpstart its pursuit of an ethical AI ecosystem: the Industry Transformation Plan (ITP) which covers its overall digital transformation road map.

Abalone is a common name for any of a group of small to very large sea snails, marine gastropod molluscs in the family Haliotidae. Other common names are ear shells, sea ears, and muttonfish or muttonshells in Australia, ormer in the UK, perlemoen in South Africa, and paua in New Zealand. The age of abalone is determined by cutting the shell through the cone, staining it, and counting the number of rings through a microscope a boring and time consuming task. Other measurements, which are easier to obtain, are used to predict the age. Given is the attribute name, attribute type, the measurement unit and a brief description.

We establish a direct connection between general tensor networks and deep feed-forward artificial neural networks. The core of our results is the construction of neural-network layers that efficiently perform tensor contractions, and that use commonly adopted non-linear activation functions. The resulting deep networks feature a number of edges that closely matches the contraction complexity of the tensor networks to be approximated. In the context of many-body quantum states, this result establishes that neural-network states have strictly the same or higher expressive power than practically usable variational tensor networks. As an example, we show that all matrix product states can be efficiently written as neural-network states with a number of edges polynomial in the bond dimension and depth logarithmic in the system size. The opposite instead does not hold true, and our results imply that there exist quantum states that are not efficiently expressible in terms of matrix product states or PEPS, but that are instead efficiently expressible with neural network states.

Given a deep learning model trained on data from a source site, how to deploy the model to a target hospital automatically? How to accommodate heterogeneous medical coding systems across different hospitals? Standard approaches rely on existing medical code mapping tools, which have significant practical limitations. To tackle this problem, we propose AutoMap to automatically map the medical codes across different EHR systems in a coarse-to-fine manner: (1) Ontology-level Alignment: We leverage the ontology structure to learn a coarse alignment between the source and target medical coding systems; (2) Code-level Refinement: We refine the alignment at a fine-grained code level for the downstream tasks using a teacher-student framework. We evaluate AutoMap using several deep learning models with two real-world EHR datasets: eICU and MIMIC-III. Results show that AutoMap achieves relative improvements up to 3.9% (AUC-ROC) and 8.7% (AUC-PR) for mortality prediction, and up to 4.7% (AUC-ROC) and 3.7% (F1) for length-of-stay estimation. Further, we show that AutoMap can provide accurate mapping across coding systems. Lastly, we demonstrate that AutoMap can adapt to the two challenging scenarios: (1) mapping between completely different coding systems and (2) between completely different hospitals.

Alphabet's drone service Wing this morning announced another milestone, as it hit 200,000 commercial deliveries. The number, which the firm says excludes test flights, comes half-a-year after it hit 100,000. Australia, which has been the primary market for testing and commercial deployment, comprises 30,000 of those deliveries in the first two months of this year. Broken down further, Wing says it was up to more than 1,000 deliveries in a day, or one delivery every 25 seconds or so. The big round number arrives as it announces a commercial partnership with Coles, one of Australia's leading supermarket chains.

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SARDI scientist Rhiannon Schilling showcases a demonstration application created by using paddock data and machine learning models. PINPOINTING the cause of paddock-yield variability using large data sets and innovative machine-learning models is the focus of a project led by the University of Adelaide and funded by the Grains Research and Development Corporation (GRDC). South Australian Research and Development Institute (SARDI) agriculture scientist Rhiannon Schilling gave an update of the project at ABARES Outlook online this week. Ms Schilling said the research looked at the challenge of working out what is behind variability in crop growth and yield across paddocks. "Often there has been a focus on improving grain yields of our varieties; but when we drive around and have a look at our paddocks, we can see that we are not always achieving uniform crop growth and yield across our paddocks," Ms Schilling said.