European smart specialization for Ukrainian regional development: path from creation to implementation

The focus of the research is to develop recommendations of smart specialization (SS) for Ukrainian policymakers using European approaches. The authors revealed that the main SS projects are presented in such sectors as agri-food, industrial modernization and energy. More than 12 EU countries were the plot for conducted analysis of SS, as a result of which the level of activity of each country was determined. The creation of consortiums, including SMEs, associations, universities and other participants, disclosed the successful way of SS realization. The structure of SME’s innovative potential in Ukraine was identified underlining their main characteristic features like types of innovations and innovative activity, differentiation according to enterprise size, their regional distribution. The authors explored lack of innovations on regional and national level and significant territorial disparities, which could be eliminated through policy implementation of regional SS. The existing legislative norms for possibility of SS implementation in Ukraine were analyzed due to correspondence with the EU ones. The analysis provides the opportunity to consider them only as general framework documents without any action plans and sectoral prioritization at all. The weak points of these law documents are emphasized. As a result of research, the authors developed recommendations presented by direct action plan for Ukrainian policymakers, which include such activities as underlining key priorities (especially ICT applicability in every SS project) and their correspondence with the EU ones; eliminating regional imbalances by focusing on innovation development and reorientation of some regions according to SS priorities; respecting regional existing capacities; providing organizational mechanism for cooperation of stakeholders and financial mechanism for SS support through the EU structural funds

A proposed nutrient density score that includes food groups and nutrients to better align with dietary guidance.

Current research on diets and health focuses on composite food patterns and their likely impact on health outcomes. The Dietary Guidelines for Americans (DGA) have likewise adopted a more food group-based approach. By contrast, most nutrient profiling (NP) models continue to assess nutrient density of individual foods, based on a small number of individual nutrients. Nutrients to encourage have included protein, fiber, and a wide range of vitamins and minerals. Nutrients to limit are typically saturated fats, total or added sugars, and sodium. Because current NP models may not fully capture the healthfulness of foods, there is a case for advancing a hybrid NP approach that takes both nutrients and desirable food groups and food ingredients into account. Creating a nutrient- and food-based NP model may provide a more integrated way of assessing a foods nutrient density. Hybrid nutrient density scores will provide for a better alignment between NP models and the DGA, a chief instrument of food and nutrition policy in the United States. Such synergy may lead ultimately to improved dietary guidance, sound nutrition policy, and better public health

Global Risks 2015, 10th Edition.

The 2015 edition of the Global Risks report completes a decade of highlighting the most significant long-term risks worldwide, drawing on the perspectives of experts and global decision-makers. Over that time, analysis has moved from risk identification to thinking through risk interconnections and the potentially cascading effects that result. Taking this effort one step further, this year's report underscores potential causes as well as solutions to global risks. Not only do we set out a view on 28 global risks in the report's traditional categories (economic, environmental, societal, geopolitical and technological) but also we consider the drivers of those risks in the form of 13 trends. In addition, we have selected initiatives for addressing significant challenges, which we hope will inspire collaboration among business, government and civil society communitie

Model Cards for Model Reporting

Trained machine learning models are increasingly used to perform high-impact tasks in areas such as law enforcement, medicine, education, and employment. In order to clarify the intended use cases of machine learning models and minimize their usage in contexts for which they are not well suited, we recommend that released models be accompanied by documentation detailing their performance characteristics. In this paper, we propose a framework that we call model cards, to encourage such transparent model reporting. Model cards are short documents accompanying trained machine learning models that provide benchmarked evaluation in a variety of conditions, such as across different cultural, demographic, or phenotypic groups (e.g., race, geographic location, sex, Fitzpatrick skin type) and intersectional groups (e.g., age and race, or sex and Fitzpatrick skin type) that are relevant to the intended application domains. Model cards also disclose the context in which models are intended to be used, details of the performance evaluation procedures, and other relevant information. While we focus primarily on human-centered machine learning models in the application fields of computer vision and natural language processing, this framework can be used to document any trained machine learning model. To solidify the concept, we provide cards for two supervised models: One trained to detect smiling faces in images, and one trained to detect toxic comments in text. We propose model cards as a step towards the responsible democratization of machine learning and related AI technology, increasing transparency into how well AI technology works. We hope this work encourages those releasing trained machine learning models to accompany model releases with similar detailed evaluation numbers and other relevant documentation

The State of American Indian & Alaska Native Education in California 2014

The findings from the California Indian Culture and Sovereignty Center's 2012 report confirmed the need for greater efforts to prepare, to recruit, to retain, and to graduate Native youth from institutions of higher education. In particular, the realization that AI/AN enrollment rates are declining across the CSUs was alarming. These results provided the basis to delve deeper into the program, outreach, and support of postsecondary institutions in the 2014 report to determine where enrollment and transfer numbers are decreasing or increasing; to determine what the best practices at state colleges and universities to attract, retain, and graduate AI/ANs are; and correspondingly to determine where we, as educators of AI/AN students in the state of California, need to improve