Variational Bayes (VB) is a critical method in machine learning and statistics, underpinning the recent success of Bayesian deep learning. The natural gradient is an essential component of efficient VB estimation, but it is prohibitively computationally expensive in high dimensions. We propose a hybrid quantum-classical algorithm to improve the scaling properties of natural gradient computation and make VB a truly computationally efficient method for Bayesian inference in highdimensional settings. The algorithm leverages matrix inversion from the linear systems algorithm by Harrow, Hassidim, and Lloyd [Phys. Rev. Lett. 103, 15 (2009)] (HHL). We demonstrate that the matrix to be inverted is sparse and the classical-quantum-classical handoffs are sufficiently economical to preserve computational efficiency, making the problem of natural gradient for VB an ideal application of HHL. We prove that, under standard conditions, the VB algorithm with quantum natural gradient is guaranteed to converge.

Algorithms implementing populations of agents which interact with one another and sense their environment may exhibit emergent behavior such as self-organization and swarm intelligence. Here a swarm system, called Databionic swarm (DBS), is introduced which is able to adapt itself to structures of high-dimensional data characterized by distance and/or density-based structures in the data space. By exploiting the interrelations of swarm intelligence, self-organization and emergence, DBS serves as an alternative approach to the optimization of a global objective function in the task of clustering. The swarm omits the usage of a global objective function and is parameter-free because it searches for the Nash equilibrium during its annealing process. To our knowledge, DBS is the first swarm combining these approaches. Its clustering can outperform common clustering methods such as K-means, PAM, single linkage, spectral clustering, model-based clustering, and Ward, if no prior knowledge about the data is available. A central problem in clustering is the correct estimation of the number of clusters. This is addressed by a DBS visualization called topographic map which allows assessing the number of clusters. It is known that all clustering algorithms construct clusters, irrespective of the data set contains clusters or not. In contrast to most other clustering algorithms, the topographic map identifies, that clustering of the data is meaningless if the data contains no (natural) clusters. The performance of DBS is demonstrated on a set of benchmark data, which are constructed to pose difficult clustering problems and in two real-world applications.

Deployed in over 80 per cent of the gaming venues in South Australia, the new solution is part of the state's gambling law reforms to protect the community from the potential harmful effects of gambling. "We had several critical capabilities that our wireless network solution must meet to support our facial recognition devices," said Fraser Larcombe, Vix Vizion. "It must be robust, bullet-proof secure, with the ability to access each machine from anywhere. We got it all from Cradlepoint and LTE." The South Australian state government established a law reform to consolidate banned individuals lists into a single government-managed list.

Inside a 13th-floor boardroom in downtown San Francisco, the atmosphere was tense. It was November 2015, and Databricks, a two-year-old software company started by a group of seven Berkeley researchers, was long on buzz but short on revenue. The directors awkwardly broached subjects that had been rehashed time and again. The startup had been trying to raise funds for five months, but venture capitalists were keeping it at arm's length, wary of its paltry sales. Seeing no other option, NEA partner Pete Sonsini, an existing investor, raised his hand to save the company with an emergency $30 million injection. Founding CEO Ion Stoica had agreed to step aside and return to his professorship at the University of California, Berkeley. The obvious move was to bring in a seasoned Silicon Valley executive, which is exactly what Databricks' chief competitor Snowflake did twice on its way to a software-record $33 billion IPO in September 2020.

I have ADHD and am what many would consider "underemployed" so obviously spend most of my time on the internet arguing with people on Twitter and watching TikToks. I live and breathe the internet and unfortunately/sadly haven't been able to stop posting since I first created an account on LiveJournal in the year 2002. Me and the internet have had some crazy times together. Like when my OkCupid page ended up on 200 websites of "insane internet dating profiles", or when my website kept getting hacked and diverted to Russian porn for a year. A few years ago I made an online show called Overshare with my friend Jared Jekyll (edited by my fiance – thanks babe!) purely for discussing random weird stuff we find on the internet.

The new study, published in Movement Ecology, used machine learning algorithms to identify and distinguish between behaviors including courtship, feeding, escape, chafing, and swimming to showcase how technology can offer greater understanding of marine life. The results revealed spawning behavior of yellowtail kingfish within the Neptune Islands Group Marine Park and Thorny Passage Marine Park in South Australia. Researchers tagged captive kingfish and filmed their behavior in tanks to identify the acceleration signatures and applied artificial intelligence to identify behavior in free-ranging fish. Flinders University Ph.D. student, Thomas Clarke, in the College of Science & Engineering, says it's the first study to use machine learning to identify spawning behaviors in wild kingfish and demonstrates how artificial intelligence can be used to better understand reproductive patterns. "Through direct observations of courtship and spawning behaviors, our findings provide the first study to predict natural reproduction of yellowtail kingfish, via the use of accelerometers and machine learning. The new study, published in Movement Ecology, used machine learning algorithms to identify and distinguish between behaviors including courtship, feeding, escape, chafing, and swimming to showcase how technology can offer greater understanding of marine life. The results revealed spawning behavior of yellowtail kingfish within the Neptune Islands Group Marine Park and Thorny Passage Marine Park in South Australia. Researchers tagged captive kingfish and filmed their behavior in tanks to identify the acceleration signatures and applied artificial intelligence to identify behavior in free-ranging fish. Flinders University Ph.D. student, Thomas Clarke, in the College of Science & Engineering, says it's the first study to use machine learning to identify spawning behaviors in wild kingfish and demonstrates how artificial intelligence can be used to better understand reproductive patterns. "Through direct observations of courtship and spawning behaviors, our findings provide the first study to predict natural reproduction of yellowtail kingfish, via the use of accelerometers and machine learning.

This week, the New South Wales Police announced the introduction of upgrades to their Insights policing platform. This new technology is designed to provide further services to frontline officers through faster access to critical information in the course of their roles in identifying persons and criminal activity across the state. Powered by Microsoft Azure cognitive technologies, the machine learning and deep learning capabilities were fully deployed in February 2021, with the goal of reducing police labour hours on manual data processing tasks, such as reviewing video feeds. Examples of how the AI systems will be used include one case were NSW Police collected 14,000 pieces of CCTV footage as part of a murder and assault investigation which would previously have taken detectives months to analyse. Microsoft claims the AI/ML infused Insights platform ingested this huge volume of information in five hours and prepared it for analysis by NSW Police Force investigators, a process which would otherwise have taken many weeks to months.

Become an expert and make a difference to society. The University of South Australia (UniSA) is Australia's University of Enterprise. We are South Australia's largest university and one of the very best young universities in the world. At UniSA, we are authentic, resilient, and influential - and we deliver results. We pride ourselves on our dynamic and agile culture, which embraces challenges and thrives on breaking new ground.

An integrated clinical environment (ICE) enables the connection and coordination of the internet of medical things around the care of patients in hospitals. However, ransomware attacks and their spread on hospital infrastructures, including ICE, are rising. Often the adversaries are targeting multiple hospitals with the same ransomware attacks. These attacks are detected by using machine learning algorithms. But the challenge is devising the anti-ransomware learning mechanisms and services under the following conditions: (1) provide immunity to other hospitals if one of them got the attack, (2) hospitals are usually distributed over geographical locations, and (3) direct data sharing is avoided due to privacy concerns. In this regard, this paper presents a federated distributed integrated clinical environment, aka. FedDICE. FedDICE integrates federated learning (FL), which is privacy-preserving learning, to SDN-oriented security architecture to enable collaborative learning, detection, and mitigation of ransomware attacks. We demonstrate the importance of FedDICE in a collaborative environment with up to four hospitals and four popular ransomware families, namely WannaCry, Petya, BadRabbit, and PowerGhost. Our results find that in both IID and non-IID data setups, FedDICE achieves the centralized baseline performance that needs direct data sharing for detection. However, as a trade-off to data privacy, FedDICE observes overhead in the anti-ransomware model training, e.g., 28x for the logistic regression model. Besides, FedDICE utilizes SDN's dynamic network programmability feature to remove the infected devices in ICE.

Communication is a cornerstone of social interactions, be it with human or artificial intelligence (AI). Yet it can be harmful, depending on the honesty of the exchanged information. To study this, an agent based sociological simulation framework is presented, the reputation game. This illustrates the impact of different communication strategies on the agents' reputation. The game focuses on the trustworthiness of the participating agents, their honesty as perceived by others. In the game, each agent exchanges statements with the others about their own and each other's honesty, which lets their judgments evolve. Various sender and receiver strategies are studied, like sycophant, egocentricity, pathological lying, and aggressiveness for senders as well as awareness and lack thereof for receivers. Minimalist malicious strategies are identified, like being manipulative, dominant, or destructive, which significantly increase reputation at others' costs. Phenomena such as echo chambers, self-deception, deception symbiosis, clique formation, freezing of group opinions emerge from the dynamics. This indicates that the reputation game can be studied for complex group phenomena, to test behavioral hypothesis, and to analyze AI influenced social media. With refined rules it may help to understand social interactions, and to safeguard the design of non-abusive AI systems.