3D body scanning and healthcare applications

Developed largely for the clothing industry, 3D body-surface scanners are transforming our ability to accurately measure and visualize a person's body size, shape, and skin-surface area. Advancements in 3D whole-body scanning seem to offer even greater potential for healthcare applications

Anthropometry and body composition of 18 year old men according to duration of breast feeding: birth cohort study from Brazil

Objective: To assess the association between duration of breast feeding and measures of adiposity in adolescence. Design: Population based birth cohort study. Setting: Pelotas, a city of 320 000 inhabitants in a relatively developed area in southern Brazil. Participants: All newborn infants in the city's hospitals were enrolled in 1982; 78.8% (2250) of all male participants were located at age 18 years when enrolling in the national army. Main outcome measures: Weight, height, sitting height, subscapular and triceps skinfolds, and body composition (body fat, lean mass). Results: Neither the duration of total breast feeding nor that of predominant breast feeding (breast milk plus non-nutritive fluids) showed consistent associations with anthropometric or body composition indices. After adjustment for confounding factors, the only significant associations were a greater than 50% reduction in obesity among participants breast fed for three to five months compared with all other breastfeeding categories (P = 0.007) and a linear decreasing trend in obesity with increasing duration of predominant breast feeding (P = 0.03). Similar significant effects were not observed for other measures of adiposity. Borderline direct associations also occurred between total duration of breast feeding and adult height (P = 0.06). Conclusions: The significant reduction in obesity among children breast fed for three to five months is difficult to interpret, as no a priori hypothesis existed regarding a protective effect of intermediate duration of breast feeding. The findings indicate that, in this population, breast feeding has no marked protective effect against adolescent adiposity

Future Computers: Digital, Quantum, Biological

Quantum computers offer huge potential performance, and biological computers can revolutionize pharmaceuticals. However, simple “engineering” descriptions and standardized approaches are needed. We provide “layperson” descriptions of quantum and biological computer architectures, comparisons with digital computers, and discussions of industry-standard models

Machine Understandable Contracts with Deep Learning

This research investigates the automatic translation of contracts to computer understandable rules trough Natural Language Processing. The most challenging aspect, which is studied throughout this paper, is to understand the meaning of the contract and express it into a structured format. This problem can be reduced to the Named Entity Recognition and Rule Extraction tasks, the latter handles the extraction of terms and conditions. These two problems are difficult, but deep learning models can tackle them. We think that this paper is the first work to approach Rule Extraction with deep learning. This method is data-hungry, so the research also introduces data sets for these two tasks. Additionally, it contributes to the literature by introducing Law-Bert, a model based on BERT which is pre-trained on unlabelled contracts. The results obtained on Named Entity Recognition and Rule Extraction show that pre-training on contracts has a positive effect on performance for the downstream tasks

Generative adversarial networks for financial trading strategies fine-tuning and combination

Systematic trading strategies are algorithmic procedures that allocate assets aiming to optimize a certain performance criterion. To obtain an edge in a highly competitive environment, an analyst needs to appropriately fine-tune their strategy, or discover how to combine weak signals in novel alpha creating manners. Both aspects, namely fine-tuning and combination, have been extensively researched using several methods, but emerging techniques such as Generative Adversarial Networks can have an impact on such aspects. Therefore, our work proposes the use of Conditional Generative Adversarial Networks (cGANs) for trading strategy calibration and aggregation. To this end, we provide a full methodology on: (i) the training and selection of a cGAN for time series data; (ii) how each sample is used for strategy calibration; and (iii) how all generated samples can be used for ensemble modelling. To provide evidence that our approach is well grounded, we have designed an experiment with multiple trading strategies, encompassing 579 assets. We compared cGAN with an ensemble scheme and model validation methods, both suited for time series. Our results suggest that cGANs are a suitable alternative for strategy calibration and combination, providing outperformance when the traditional techniques fail to generate any alpha

Algorithms in future capital markets: A survey on AI, ML and associated algorithms in capital markets

This paper reviews Artificial Intelligence (AI), Machine Learning (ML) and associated algorithms in future Capital Markets. New AI algorithms are constantly emerging, with each 'strain' mimicking a new form of human learning, reasoning, knowledge, and decisionmaking. The current main disrupting forms of learning include Deep Learning, Adversarial Learning, Transfer and Meta Learning. Albeit these modes of learning have been in the AI/ML field more than a decade, they now are more applicable due to the availability of data, computing power and infrastructure. These forms of learning have produced new models (e.g., Long Short-Term Memory, Generative Adversarial Networks) and leverage important applications (e.g., Natural Language Processing, Adversarial Examples, Deep Fakes, etc.). These new models and applications will drive changes in future Capital Markets, so it is important to understand their computational strengths and weaknesses. Since ML algorithms effectively self-program and evolve dynamically, financial institutions and regulators are becoming increasingly concerned with ensuring there remains a modicum of human control, focusing on Algorithmic Interpretability/Explainability, Robustness and Legality. For example, the concern is that, in the future, an ecology of trading algorithms across different institutions may 'conspire' and become unintentionally fraudulent (cf. LIBOR) or subject to subversion through compromised datasets (e.g. Microsoft Tay). New and unique forms of systemic risks can emerge, potentially coming from excessive algorithmic complexity. The contribution of this paper is to review AI, ML and associated algorithms, their computational strengths and weaknesses, and discuss their future impact on the Capital Markets

Algorithm Auditing: Managing the Legal, Ethical, and Technological Risks of Artificial Intelligence, Machine Learning, and Associated Algorithms

Algorithms are becoming ubiquitous. However, companies are increasingly alarmed about their algorithms causing major financial or reputational damage. A new industry is envisaged: auditing and assurance of algorithms with the remit to validate artificial intelligence, machine learning, and associated algorithms

Federated Learning: The Pioneering Distributed Machine Learning and Privacy-Preserving Data Technology

Federated learning (pioneered by Google) is a new class of machine learning models trained on distributed data sets, and equally important, a key privacy-preserving data technology. The contribution of this article is to place it in perspective to other data science technologies