While the amount of data stored in current information systems continuously grows, and the processes making use of such data become more and more complex, extracting knowledge and getting insights from these data, as well as governing both data and the associated processes, are still challenging tasks. The problem is complicated by the proliferation of data sources and services both within a single organization, and in cooperating environments. Effectively accessing, integrating and managing data in complex organizations is still one of the main issues faced by the information technology industry today. Indeed, it is not surprising that data scientists spend a comparatively large amount of time in the data preparation phase of a project, compared with the data minining and knowledge discovery phase. Whether you call it data wrangling, data munging, or data integration, it is estimated that 50%-80% of a data scientists time is spent on collecting and organizing data for analysis. If we consider that in any complex organization, data governance is also essential for tasks other than data analytics, we can conclude that the challenge of identifying, gathering, retaining, and providing access to all relevant data for the business at an acceptable cost, is huge.

New technologies have enabled the investigation of biology and human health at an unprecedented scale and in multiple dimensions. These dimensions include myriad properties describing genome, epigenome, transcriptome, microbiome, phenotype, and lifestyle. No single data type, however, can capture the complexity of all the factors relevant to understanding a phenomenon such as a disease. Integrative methods that combine data from multiple technologies have thus emerged as critical statistical and computational approaches. The key challenge in developing such approaches is the identification of effective models to provide a comprehensive and relevant systems view. An ideal method can answer a biological or medical question, identifying important features and predicting outcomes, by harnessing heterogeneous data across several dimensions of biological variation. In this Review, we describe the principles of data integration and discuss current methods and available implementations. We provide examples of successful data integration in biology and medicine. Finally, we discuss current challenges in biomedical integrative methods and our perspective on the future development of the field.

'Insider Threat' is a formidable risk to business because it threatens both customer and employee trust. Accidental or malicious misuse of a firm's most sensitive and valuable data can result in customer identity theft, financial fraud, intellectual property theft, or damage to infrastructure. Because insiders have privileged access to data in order to do their jobs, it's usually quite difficult for security professionals to detect suspicious activity; a process even more challenging to automate (and deploy at scale across the large organisations that most need it) as – so I will suggest – computers fundamentally lack semantic understanding of the meaning of the'bits' they so adroitly process. Conversely, in this talk I will outline a new approach to'Insider Threat' detection that draws inspiration from the Traffic Analysis' of encrypted Axis signal traffic' undertaken at Bletchley Park in WW2. A novel approach that (i) conceives companies as complex autonomous autopoietic entities and (ii) deploys state of art computational analysis of the communication flows that so define the company to flag potentially aberrant employee behaviour; intelligence that can be leveraged to help detect HR problematics before they arise.

Posted By C. M. Rubin on Jun 11, 2018 "Through rapid genetic sequencing, scientists can identify many different strains of Borrelia burgdorferi as well as new tick-borne microbial infections, such as Borrelia miyamotoi, Borrelia mayonii, and the Heartland virus." Most likely, you or someone you know has been affected by Lyme disease, the most common tick-borne illness in the US with more than 300,000 cases diagnosed each year. In a timely new book, Conquering Lyme Disease (Columbia University Press), Columbia University Medical Center physicians Brian A. Fallon and Jennifer Sotsky reveal that despite the challenges to find a cure for this complex, debilitating disease, precision medicine and biotechnology are accelerating the discovery of new tools with which doctors will be able to diagnose it and treat patients. Could groundbreaking technologies that rapidly increase our understanding and open up new pathways mean a cure for Lyme disease one day soon? The Global Search for Education is pleased to welcome Dr. Brian Fallon to find out how tech is tackling the ticks.

In this paper, we propose a successive convex approximation framework for sparse optimization where the nonsmooth regularization function in the objective function is nonconvex and it can be written as the difference of two convex functions. The proposed framework is based on a nontrivial combination of the majorization-minimization framework and the successive convex approximation framework proposed in literature for a convex regularization function. The proposed framework has several attractive features, namely, i) flexibility, as different choices of the approximate function lead to different type of algorithms; ii) fast convergence, as the problem structure can be better exploited by a proper choice of the approximate function and the stepsize is calculated by the line search; iii) low complexity, as the approximate function is convex and the line search scheme is carried out over a differentiable function; iv) guaranteed convergence to a stationary point. We demonstrate these features by two example applications in subspace learning, namely, the network anomaly detection problem and the sparse subspace clustering problem. Customizing the proposed framework by adopting the best-response type approximation, we obtain soft-thresholding with exact line search algorithms for which all elements of the unknown parameter are updated in parallel according to closed-form expressions. The attractive features of the proposed algorithms are illustrated numerically.

Technology is an important driver of future business. Those who make use of the opportunities offered by new technologies gain competitive advantages through innovation and productivity. The earlier the potentials of trends for one's own business model are recognised, the better they can be implemented. The adaptation of artificial intelligence, machine learning or smart data massively and continuously changes not only products but business models and even markets. Customers want to use the advantages of technology experienced in all areas of life – including in the financial sector.

While a decade ago China was known to be the "world's factory," manufacturing everyday household goods for companies across the globe, in recent years tech and internet companies have redefined the face of Chinese industry. Both Tencent and Alibaba are now among the world's top 10 most valuable companies. Indeed, Chinese companies are now leading the way in the most disruptive global tech trends, including autonomous vehicles, machine learning and blockchain. According to Deloitte, global CFOs' optimism about the Chinese market has never been higher. But all this innovation has come with a side effect: Chinese consumers' expectations of brands and businesses have risen to match the market's optimism.

Integrated information theory provides a mathematical framework to fully characterize the cause-effect structure of a physical system. Here, we introduce PyPhi, a Python software package that implements this framework for causal analysis and unfolds the full cause-effect structure of discrete dynamical systems of binary elements. The software allows users to easily study these structures, serves as an up-to-date reference implementation of the formalisms of integrated information theory, and has been applied in research on complexity, emergence, and certain biological questions. We first provide an overview of the main algorithm and demonstrate PyPhi's functionality in the course of analyzing an example system, and then describe details of the algorithm's design and implementation. PyPhi can be installed with Python's package manager via the command 'pip install pyphi' on Linux and macOS systems equipped with Python 3.4 or higher. PyPhi is open-source and licensed under the GPLv3; the source code is hosted on GitHub at https://github.com/wmayner/pyphi . Comprehensive and continually-updated documentation is available at https://pyphi.readthedocs.io/ . The pyphi-users mailing list can be joined at https://groups.google.com/forum/#!forum/pyphi-users . A web-based graphical interface to the software is available at http://integratedinformationtheory.org/calculate.html .

This paper discusses technology challenges and opportunities to embrace artificial intelligence (AI) era in the design of wireless networks. We aim to provide readers with motivation and general methodology for adoption of AI in the context of next-generation networks. First, we discuss the rise of network intelligence and then, we introduce a brief overview of AI with machine learning (ML) and their relationship to self-organization designs. Finally, we discuss design of intelligent agent and it's functions to enable knowledge-driven wireless networks with AI.

Artificial intelligence has been a trending topic for quite some time, which comes as no surprise as the use of it is on the rise in every industry. According to a report by Salesforce, 51% of marketing leaders state that they currently use AI in some scope, with 27% planning to start using it in the next two years. As it continues to grow, it will progressively impact how our society functions and transform the way we work as marketers. As with any transformative technology, AI has marketing professionals on their toes; leaving them to wonder where the fate of their jobs lies in the era of AI. So, should you anticipate a massive disruption of AI threatening the security of your job?