However, performing k-means on SPD matrices may correspond bijectively to mean centered Gaussian distributions, be difficult, without a computationally efficient form for the and are used to model Brownian motion in Diffusion Fréchet mean [13]. Tensor Imaging (DTI), where they are referred to as tensors [1]. The finite-lag autocovariance matrices of time-series are In Section II, we introduce the log-Cholesky distance and SPD, and have been used in compression based clustering closed-form expression for the corresponding Fréchet mean.

The importance of algorithmic fairness grows with the increasing impact machine learning has on people's lives. Recent work on fairness metrics shows the need for causal reasoning in fairness constraints. In this work, a practical method named FairTrade is proposed for creating flexible prediction models which integrate fairness constraints on sensitive causal paths. The method uses recent advances in variational inference in order to account for unobserved confounders. Further, a method outline is proposed which uses the causal mechanism estimates to audit black box models. Experiments are conducted on simulated data and on a real dataset in the context of detecting unlawful social welfare. This research aims to contribute to machine learning techniques which honour our ethical and legal boundaries.

Rocket Lab has made a remarkable recovery after losing a payload during a mission failure on July 4 – just eight weeks later, the company has set a launch window for its next dedicated commercial mission that spans 12 days beginning August 27 at 3:05 PM local New Zealand time. At the end of July, Rocket Lab revealed that it had received crucial FAA clearance to resume its launch activities, following an internal investigation that lasted a month and identified the root cause – a component that had performed fine previously, but that somehow hadn't undergone rigorous and thorough testing. Rocket Lab founder and CEO Peter Beck noted that they'd be able to mitigate the problem with a relatively simple change to their production process, and even remedy the component on existing, already-produced Electron launch vehicles. Rocket Lab's quick turnaround on this resolution and return to active launch status also has to do with the nature of the problem – the error actually resulted in an early, but safe shutdown of the Electron's engines, which meant that it didn't reach its target orbit. The rocket didn't explode, however, or cause any kind of safety risk.

IMAGE: Covax-19 is an Australian-developed COVID-19 vaccine developed with the help of computational and artificial intelligence (AI)-based technologies. Given the global urgency of the COVID-19 pandemic, Microsoft's AI for Health program has stepped in to support the development and potential deployment of Vaxine's COVAX-19 vaccine with a philanthropic grant. Vaxine Pty Ltd, a biotechnology company based in South Australia, uses computational and artificial intelligence (AI)-based technologies to accelerate pandemic vaccine and drug development with the aim to reduce drug development processes that normally take decades down to just weeks. The Microsoft AI and Azure cloud capabilities will help the company accelerate clinical testing of its COVAX-19 vaccine. "Large international Phase 3 vaccine trials are extraordinarily complex and generate vast amounts of data that needs to be efficiently processed", says Vaxine Research Director, Flinders University Professor Nikolai Petrovsky.

The power of artificial intelligence has transformed health care by using massive datasets to improve diagnostics, treatment, records management, and patient outcomes. Complex decisions that once took hours -- such as making a breast or lung cancer diagnosis based on imaging studies, or deciding when patients should be discharged -- are now resolved within seconds by machine learning and deep learning applications. Any technology, of course, will have its limitations and flaws. And over the past few years, a steady stream of evidence has demonstrated that some of these AI-powered medical technologies are replicating racial bias and exacerbating historic health care inequities. Now, amid the SARS-CoV-2 pandemic, some researchers are asking whether these new technologies might be contributing to the disproportionately high rates of virus-related illness and death among African Americans. African Americans aged 35 to 44 experience Covid-19 mortality rates that are nine times higher than their White counterparts.

Image colorization is an essential image processing and computer vision branch to colorize images and videos. Recently, deep learning techniques progressed notably for image colorization. This article presents a comprehensive survey of recent state-of-the-art colorization using deep learning algorithms, describing their fundamental block architectures in terms of skip connections, input \etc as well as optimizers, loss functions, training protocols, and training data \etc Generally, we can roughly categorize the existing colorization techniques into seven classes. Besides, we also provide some additional essential issues, such as benchmark datasets and evaluation metrics. We also introduce a new dataset specific to colorization and perform an experimental evaluation of the publicly available methods. In the last section, we discuss the limitations, possible solutions, and future research directions of the rapidly evolving topic of deep image colorization that the community should further address. Dataset and Codes for evaluation will be publicly available at https://github.com/saeed-anwar/ColorSurvey

Precision health leverages information from various sources, including omics, lifestyle, environment, social media, medical records, and medical insurance claims to enable personalized care, prevent and predict illness, and precise treatments. It extensively uses sensing technologies (e.g., electronic health monitoring devices), computations (e.g., machine learning), and communication (e.g., interaction between the health data centers). As health data contain sensitive private information, including the identity of patient and carer and medical conditions of the patient, proper care is required at all times. Leakage of these private information affects the personal life, including bullying, high insurance premium, and loss of job due to the medical history. Thus, the security, privacy of and trust on the information are of utmost importance. Moreover, government legislation and ethics committees demand the security and privacy of healthcare data. Herein, in the light of precision health data security, privacy, ethical and regulatory requirements, finding the best methods and techniques for the utilization of the health data, and thus precision health is essential. In this regard, firstly, this paper explores the regulations, ethical guidelines around the world, and domain-specific needs. Then it presents the requirements and investigates the associated challenges. Secondly, this paper investigates secure and privacy-preserving machine learning methods suitable for the computation of precision health data along with their usage in relevant health projects. Finally, it illustrates the best available techniques for precision health data security and privacy with a conceptual system model that enables compliance, ethics clearance, consent management, medical innovations, and developments in the health domain.

Attempts to render deep learning models interpretable, data-efficient, and robust have seen some success through hybridisation with rule-based systems, for example, in Neural Theorem Provers (NTPs). These neuro-symbolic models can induce interpretable rules and learn representations from data via back-propagation, while providing logical explanations for their predictions. However, they are restricted by their computational complexity, as they need to consider all possible proof paths for explaining a goal, thus rendering them unfit for large-scale applications. We present Conditional Theorem Provers (CTPs), an extension to NTPs that learns an optimal rule selection strategy via gradient-based optimisation. We show that CTPs are scalable and yield state-of-the-art results on the CLUTRR dataset, which tests systematic generalisation of neural models by learning to reason over smaller graphs and evaluating on larger ones. Finally, CTPs show better link prediction results on standard benchmarks in comparison with other neural-symbolic models, while being explainable. All source code and datasets are available online, at https://github.com/uclnlp/ctp.

We propose a general semantics for strategic abilities of agents in asynchronous systems, with and without perfect information. Based on the semantics, we show some general complexity results for verification of strategic abilities in asynchronous interaction. More importantly, we develop a methodology for partial order reduction in verification of agents with imperfect information. We show that the reduction preserves an important subset of strategic properties, with as well as without the fairness assumption. We also demonstrate the effectiveness of the reduction on a number of benchmarks. Interestingly, the reduction does not work for strategic abilities under perfect information.

Recent work has shown that deep learning models can be used to classify land-use data from geospatial satellite imagery. We show that when these deep learning models are trained on data from specific continents/seasons, there is a high degree of variability in model performance on out-of-sample continents/seasons. This suggests that just because a model accurately predicts land-use classes in one continent or season does not mean that the model will accurately predict land-use classes in a different continent or season. We then use clustering techniques on satellite imagery from different continents to visualize the differences in landscapes that make geospatial generalization particularly difficult, and summarize our takeaways for future satellite imagery-related applications.