The seven-arm octopus, Haliphron atlanticus, weighs as much as a person and haunts deep, dark waters from New Zealand to Brazil and British Columbia. So few people have seen this creature alive that researchers must study it in death--typically, as a mound of purplish flesh that washes ashore or turns up in a net. A living seven-arm octopus was scooped up by a Norwegian fishing trawler in 1984, but "when laid on deck the body collapsed," a local zoologist wrote at the time. What remained of the creature, he added, was "sack-shaped, large and flappy." Another turned up in a South Pacific research trawl in the early two-thousands, but the preservation process turned it into a "frozen lump," the giant-squid expert Steve O'Shea wrote.

During her Ph.D., Dr. Qurrat Ul Ain developed a computer-aided diagnostic system that can identify certain characteristics of the disease from a photograph of a skin lesion. "Skin cancer has certain unique visual features that help to differentiate it from normal skin," Dr. Qurrat Ul Ain says. "These include color, texture, and the shape of lesions. By showing our artificial intelligence program images of cancerous skin, we were able to teach it to identify cancer when shown other photographs." Dr. Qurrat Ul Ain's diagnostic system achieved a 100% accuracy rating in identifying images of melanoma based on the more than 600 images tested so far.

My grandmother, Claire Hastings, was born in the 1920s on a farm in Armidale, northern New South Wales. That was a relatively common thing, with just 43% of the population living in cities, compared with more than 70% now. She lived in a small wooden hut, with a chicken coop out the front and fields out the back. When she and her siblings came home from school, they helped plow the fields with a horse-drawn plow until sundown. Little did she know this life would soon disappear.

My grandmother, Claire Hastings, was born in the 1920s on a farm in Armidale, northern New South Wales. That was a relatively common thing, with just 43% of the population living in cities, compared with more than 70% now. She lived in a small wooden hut, with a chicken coop out the front and fields out the back. When she and her siblings came home from school, they helped plough the fields with a horse-drawn plough until sundown. Little did she know this life would soon disappear.

My grandmother, Claire Hastings, was born in the 1920s on a farm in Armidale, northern New South Wales. That was a relatively common thing, with just 43% of the population living in cities, compared with more than 70% now. She lived in a small wooden hut, with a chicken coop out the front and fields out the back. When she and her siblings came home from school, they helped plow the fields with a horse-drawn plow until sundown. Little did she know this life would soon disappear.

The neural network is trained to match the distribution of a class of random graphs by way of a moment estimator. The features used for training are graphlets, subgraph counts of small order. The neural network accepts random noise as input and outputs vector representations for nodes in the graph. Random graphs are then realized by applying a kernel to the representations. Graphs produced this way are demonstrated to be able to imitate data from chemistry, medicine, and social networks. The produced graphs are similar enough to the target data to be able to fool discriminator neural networks otherwise capable of separating classes of random graphs.

It was undoubtedly one of the outperformers in the first month of the current stock market year. The shares of the Australian company BrainChip Holdings, which also has subsidiaries in the US, India and France, almost quadrupled from AUD 0.71 to AUD 2.25 within one month. The announcement that Mercedes intends to develop systems based on BrainChip's Akida hardware and software caused a veritable buying panic. Among other things, the technology makes the "Hey, Mercedes" voice control in the EQXX five to ten times more efficient than conventional voice control. Since February, the stock has been in a strong consolidation phase, which is not unusual for such an innovative technology company.

Before we consider the five technologies that are set to transform Fintech, consider what Fintech is. Fintech is the synthesis of technology and finance and the harmonic combination of two of the largest industries into a single field. Naturally, its impact is enormous. Regarded as cutting-edge innovations a few years ago, now Fintech solutions are a daily reality. According to McKinsey, 80% of traditional financial institutions were exploring innovations in 2018.

Probabilistic programming is a growing area that strives to make statistical analysis more accessible, by separating probabilistic modelling from probabilistic inference. In practice this decoupling is difficult. No single inference algorithm can be used as a probabilistic programming back-end that is simultaneously reliable, efficient, black-box, and general. Probabilistic programming languages often choose a single algorithm to apply to a given problem, thus inheriting its limitations. While substantial work has been done both to formalise probabilistic programming and to improve efficiency of inference, there has been little work that makes use of the available program structure, by formally analysing it, to better utilise the underlying inference algorithm. This dissertation presents three novel techniques (both static and dynamic), which aim to improve probabilistic programming using program analysis. The techniques analyse a probabilistic program and adapt it to make inference more efficient, sometimes in a way that would have been tedious or impossible to do by hand.

Predicting the results of matches in sport is a challenging and interesting task. In this paper, we review a selection of studies from 1996 to 2019 that used machine learning for predicting match results in team sport. Considering both invasion sports and striking/fielding sports, we discuss commonly applied machine learning algorithms, as well as common approaches related to data and evaluation. Our study considers accuracies that have been achieved across different sports, and explores whether evidence exists to support the notion that outcomes of some sports may be inherently more difficult to predict. We also uncover common themes of future research directions and propose recommendations for future researchers. Although there remains a lack of benchmark datasets (apart from in soccer), and the differences between sports, datasets and features makes between-study comparisons difficult, as we discuss, it is possible to evaluate accuracy performance in other ways. Artificial Neural Networks were commonly applied in early studies, however, our findings suggest that a range of models should instead be compared. Selecting and engineering an appropriate feature set appears to be more important than having a large number of instances. For feature selection, we see potential for greater inter-disciplinary collaboration between sport performance analysis, a sub-discipline of sport science, and machine learning.