We develop a novel computationally efficient and general framework for robust hypothesis testing. The new framework features a new way to construct uncertainty sets under the null and the alternative distributions, which are sets centered around the empirical distribution defined via Wasserstein metric, thus our approach is data-driven and free of distributional assumptions. We develop a convex safe approximation of the minimax formulation and show that such approximation renders a nearly-optimal detector among the family of all possible tests. By exploiting the structure of the least favorable distribution, we also develop a tractable reformulation of such approximation, with complexity independent of the dimension of observation space and can be nearly sample-size-independent in general. Real-data example using human activity data demonstrated the excellent performance of the new robust detector.

We don't have to wait for the patient to get sick and present themselves anymore. Now, we can intervene before they end up in the ED or go to see the specialist. Illness is usually detectable to an algorithm before it is detectible to a patient. The fact that we wait long enough for someone to acknowledge that they should get some help is an artifact of the traditional notions we have about healthcare.

Unleash the power of your hidden, undocumented data with automatic discovery with Io-Tahoe. Io-Tahoe is recognized by Gartner as a smart data discovery and modern information stewardship solution. Big Data equals big need for robust Data Governance.

The U.S. Senate and House hearings last week on Facebook's use of data and foreign interference in the U.S. election raised important challenges concerning data privacy, security, ethics, transparency, and responsibility. They also illuminated what could become a vast chasm between traditional privacy and security laws and regulations and rapidly evolving internet-related business models and activities. To help close this gap, technologists need to seriously reevaluate their relationship with government. Here are four ways to start. Help to increase tech literacy in Washington.

Organizations around the world -- and not just those in the EU but also those consuming goods and services from Europe -- need to prepare now for the EU GDPR. If you're providing electronic goods or services to anyone who's in Europe, be they a citizen, a temporary resident, or even if they're passing through a European airport for half an hour, potentially, GDPR may apply, and you need to comply with that. It may also apply to anyone in the world, anywhere, if you are profiling or doing analytics on them. You have limited time and increasing pressure to get ready by 25 May 2018. GDPR practical data actions and accelerators from IBM can help your organization on its journey to compliance.

Determining how certain properties are related to other properties is fundamental to scientific discovery. As data collection rates accelerate, it is becoming increasingly difficult yet ever more important to determine whether one property of data (e.g., cloud density) is related to another (e.g., grass wetness). Only if two properties are related are further investigations into the geometry of the relationship warranted. While existing approaches can test whether two properties are related, they may require unfeasibly large sample sizes in real data scenarios, and do not address how they are related. Our key insight is that one can adaptively restrict the analysis to the "jointly local" observations---that is, one can estimate the scales with the most informative neighbors for determining the existence and geometry of a relationship. "Multiscale Graph Correlation" (MGC) is a framework that extends global procedures to be multiscale; consequently, MGC tests typically require far fewer samples than existing methods for a wide variety of dependence structures and dimensionalities, while maintaining computational efficiency. Moreover, MGC provides a simple and elegant multiscale characterization of the potentially complex latent geometry underlying the relationship. In several real data applications, MGC uniquely detects the presence and reveals the geometry of the relationships.

Io-Tahoe, a machine learning-driven smart data discovery company recently announced the launch of its smart data discovery platform at the Gartner Data & Analytics 2018 Summit, where it will showcase the product. The new version includes the addition of Data Catalog, a new feature designed to allow data owners and stewards to use a machine learning-based smart catalog to create, maintain and search business rules. Also, it would help define policies and provide governance workflow functionality. It reportedly enables a business user to govern the rules and define policies for critical data elements. It allows data-driven enterprises to enhance information about data automatically, regardless of the underlying technology and build a data catalog.

Io-Tahoe today announced the general availability of its smart data discovery solution, with the addition of a new machine-learning Data Catalog feature that enables the creation of business rules and policy definition.

Helping students develop skills in both critical thinking and scientific reasoning is fundamental to science education. However, the relationship between these two constructs remains largely unknown. Dowd et al. examined this issue by investigating how students' critical thinking skills related to scientific reasoning in the context of undergraduate thesis writing. The authors used the BioTAP rubric to assess scientific reasoning and the California Critical Thinking Skills Test to assess critical thinking. Results support the role of inference in scientific reasoning in writing, while also revealing other aspects of scientific reasoning (epistemological considerations and writing conventions) not related to critical thinking. In considering future implications for instruction, the authors suggest that further research into the impact of interventions focused on specific critical thinking skills (i.e., inference) for improved science reasoning in writing is needed.

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