Daytime hypoglycemia should be accurately predicted to achieve normoglycemia and to avoid disastrous situations. Hypoglycemia, an abnormally low blood glucose level, is divided into daytime hypoglycemia and nocturnal hypoglycemia. Many studies of hypoglycemia prevention deal with nocturnal hypoglycemia. In this paper, we propose new predictor variables to predict daytime hypoglycemia using continuous glucose monitoring (CGM) data. We apply classification and regression tree (CART) as a prediction method. The independent variables of our prediction model are the rate of decrease from a peak and absolute level of the BG at the decision point. The evaluation results showed that our model was able to detect almost 80% of hypoglycemic events 15 min in advance, which was higher than the existing methods with similar conditions. The proposed method might achieve a real-time prediction as well as can be embedded into BG monitoring device.

In this paper a new Bayesian model for sparse linear regression with a spatio-temporal structure is proposed. It incorporates the structural assumptions based on a hierarchical Gaussian process prior for spike and slab coefficients. We design an inference algorithm based on Expectation Propagation and evaluate the model over the real data.

The process of liquidity provision in financial markets can result in prolonged exposure to illiquid instruments for market makers. In this case, where a proprietary position is not desired, pro-actively targeting the right client who is likely to be interested can be an effective means to offset this position, rather than relying on commensurate interest arising through natural demand. In this paper, we consider the inference of a client profile for the purpose of corporate bond recommendation, based on typical recorded information available to the market maker. Given a historical record of corporate bond transactions and bond meta-data, we use a topic-modelling analogy to develop a probabilistic technique for compiling a curated list of client recommendations for a particular bond that needs to be traded, ranked by probability of interest. We show that a model based on Latent Dirichlet Allocation offers promising performance to deliver relevant recommendations for sales traders.

Matrix completion has been well studied under the uniform sampling model and the trace-norm regularized methods perform well both theoretically and numerically in such a setting. However, the uniform sampling model is unrealistic for a range of applications and the standard trace-norm relaxation can behave very poorly when the underlying sampling scheme is non-uniform. In this paper we propose and analyze a max-norm constrained empirical risk minimization method for noisy matrix completion under a general sampling model. The optimal rate of convergence is established under the Frobenius norm loss in the context of approximately low-rank matrix reconstruction. It is shown that the max-norm constrained method is minimax rate-optimal and yields a unified and robust approximate recovery guarantee, with respect to the sampling distributions. The computational effectiveness of this method is also discussed, based on first-order algorithms for solving convex optimizations involving max-norm regularization.

In deep learning, performance is strongly affected by the choice of architecture and hyperparameters. While there has been extensive work on automatic hyperparameter optimization for simple spaces, complex spaces such as the space of deep architectures remain largely unexplored. As a result, the choice of architecture is done manually by the human expert through a slow trial and error process guided mainly by intuition. In this paper we describe a framework for automatically designing and training deep models. We propose an extensible and modular language that allows the human expert to compactly represent complex search spaces over architectures and their hyperparameters. The resulting search spaces are tree-structured and therefore easy to traverse. Models can be automatically compiled to computational graphs once values for all hyperparameters have been chosen. We can leverage the structure of the search space to introduce different model search algorithms, such as random search, Monte Carlo tree search (MCTS), and sequential model-based optimization (SMBO). We present experiments comparing the different algorithms on CIFAR-10 and show that MCTS and SMBO outperform random search. In addition, these experiments show that our framework can be used effectively for model discovery, as it is possible to describe expressive search spaces and discover competitive models without much effort from the human expert. Code for our framework and experiments has been made publicly available.

In collaboration with data scientists, industry experts and top counsellors, we have put together a list of general data science interview questions and answers to help you with your preparation in applying for data science jobs. This also includes a list of open ended questions that interviewers ask to get a feel of how often and how quickly you can think on your feet.There are some data analyst interview questions in this blog which can also be asked in a data science interview. These kind of analytics interview questions also measure if you were successful in applying data science techniques to real life problems. If you would like more information about Online Data Science course, please click the orange "Request Info" button on top of this page. Data Science is not an easy field to get into. This is something all data scientists will agree on. Apart from having a degree in mathematics/statistics or engineering, a data scientist also needs to go through intense training to develop all the skills required for this field. Apart from the degree/diploma and the training, it is important to prepare the right resume for a data science job, and to be well versed with the data science interview questions and answers. Consider our top 100 Data Science Interview Questions and Answers as a starting point for your data scientist interview preparation.

Almost a year ago QuantStart discussed deep learning and introduced the Theano library via a logistic regression example. Given the recent results of the QuantStart 2017 Content Survey it was decided that an up to date beginner-friendly article was needed to introduce deep learning from first principles. These days it is almost impossible to work in any technology-heavy field without hearing about the latest advances in the field of deep learning. Quantitative finance is no different. Many of the recent discussions in the latest quant finance conferences such as Quantopian's QuantCon and Newsweek's AI & Data Science - Capital Markets are largely focusing around the promise of deep learning as the next frontier in quantitative trading.

British politicians who examined the details of a drone strike which killed a British man in Syria said they were disappointed by the government's lack of transparency during investigations. On August 21, 2015, the UK conducted a drone strike in Raqqa for the first time outside the traditional theatre of war, killing 21-year-old British national Reyaad Khan, a suspected fighter with the Islamic State of Iraq and the Levant (ISIL, also known as ISIS), and two other people. "We are in no doubt that Reyaad Khan posed a very serious threat to the UK," the Intelligence and Security Committee in the UK said in a report on Wednesday. "There is nevertheless a question as to how the threat is quantified and in this instance whether the actions of Khan and his associates amounted to an'armed attack' against the UK or Iraq - which is clearly a subjective assessment," the committee said. "The [government's] failure to provide what we consider to be relevant documents is profoundly disappointing," the report added.

The Drum in partnership with intent marketing specialist, Iotec, has launched a report exploring the application of machine learning to solve commercial challenges. The report, Machine Learning: Empowering the Next Generation of Marketing, examines what implications machine learning has and will have on the marketing world, identifies common misconceptions and sheds light on applicable AI-driven marketing solutions. A growing body of research indicates that machine learning is moving to the top of the marketing agenda. A survey by Demandbase and Wakefield Research revealed that 80% of marketing executives believe that AI will revolutionise marketing over the next five years. But the same survey found that only 26% are confident in their understanding of AI technologies and its application to marketing.

The Applied AI Conference is a must-attend event for people who are working, researching, building, and investing in Applied Artificial Intelligence technologies and products. The event is focused on practical applications and current commercialization of AI technologies across industries such as Transportation & Logistics, Internet of Things (IoT), Future of Work (FoW), Financial Technologies (FinTech), CyberSecurity, and Healthcare Technologies (HealthTech). The 2017 conference agenda will provide insights into the present and future impact of AI on your organization, as well as in your daily life. It will also feature concrete ways, tools, and methods to prepare, organize, and tap AI's transformative power. As active early stage investors in Applied AI, BootstrapLabs will provide an overview of the investment and consolidation landscape at the conference.