Cultural adaptation and Rasch psychometrics of the Substance Addiction Consequences scale

This study aims to adapt and evaluate the validity of the Substance Addiction Consequences scale for the Brazilian community-based addiction setting. This is a psychometric study, conducted in two stages: (1) cultural adaptation and (2) validation using the psychometric Rasch model. The Substance Addiction Consequences derived from the Nursing Outcome Classification comprises 16 items and four domains in the original instrument. We applied the original scale with 200 outpatients at two Psychosocial Care Centers for Alcohol and Drugs in São Paulo, Brazil. The four subscales are suitable for the Rasch model. In 13 of the 16 items, infits and outfits are between 0.5 and 1.5, corresponding to the model's optimal parameters. In addition, we removed one item that distorted the measurement. The psychometrics suggested that the SAC scale is valid with its 15 items and four domains. Therefore, it can be considered appropriate to use in the Brazilian community-based addiction setting.info:eu-repo/semantics/publishedVersio

Data mining Techniques for Health Care: AReview

Data mining is gaining popularity in disparate research fields due to its boundless applications and approaches to mine the data in an appropriate manner. Owing to the changes, the current world acquiring, it is one of the optimal approach for approximating the nearby future consequences. Along with advanced researches in healthcare monstrous of data are available, but the main difficulty is how to cultivate the existing information into a useful practices. To unfold this hurdle the concept of data mining is the best suited. Data mining have a great potential to enable healthcare systems to use data more efficiently and effectively. Hence, it improves care and reduces costs. This paper reviews various Data Mining techniques such as classification, clustering, association, regression in health domain. It also highlights applications, challenges and future work of Data Mining in healthcare

Policy Search: Any Local Optimum Enjoys a Global Performance Guarantee

Local Policy Search is a popular reinforcement learning approach for handling large state spaces. Formally, it searches locally in a paramet erized policy space in order to maximize the associated value function averaged over some predefined distribution. It is probably commonly b elieved that the best one can hope in general from such an approach is to get a local optimum of this criterion. In this article, we show th e following surprising result: \emph{any} (approximate) \emph{local optimum} enjoys a \emph{global performance guarantee}. We compare this g uarantee with the one that is satisfied by Direct Policy Iteration, an approximate dynamic programming algorithm that does some form of Poli cy Search: if the approximation error of Local Policy Search may generally be bigger (because local search requires to consider a space of s tochastic policies), we argue that the concentrability coefficient that appears in the performance bound is much nicer. Finally, we discuss several practical and theoretical consequences of our analysis

Rapid Invasion of Spartina Alterniflora in the Coastal Zone of Mainland China: Spatiotemporal Patterns and Human Prevention

Given the extensive spread and ecological consequences of exotic Spartina alterniflora (S. alterniflora) over the coast of mainland China, monitoring its spatiotemporal invasion patterns is important for the sake of coastal ecosystem management and ecological security. In this study, Landsat series images from 1990 to 2015 were used to establish multi-temporal datasets for documenting the temporal dynamics of S. alterniflora invasion. Our observations revealed that S. alterniflora had a continuous expansion with the area increasing by 50,204 ha during the considered 25 years. The largest expansion was identified in Jiangsu Province during the period of 1990-2000, and in Zhejiang Province during the periods 2000-2010 and 2010-2015. Three noticeable hotspots for S. alterniflora invasion were Yancheng of Jiangsu, Chongming of Shanghai, and Ningbo of Zhejiang, and each had a net area increase larger than 5000 ha. Moreover, an obvious shrinkage of S. alterniflora was identified in three coastal cities including the city of Cangzhou of Hebei, Dongguan, and Jiangmen of Guangdong. S. alterniflora invaded mostly into mudflats (>93%) and shrank primarily due to aquaculture (55.5%). This study sheds light on the historical spatial patterns in S. alterniflora distribution and thus is helpful for understanding its invasion mechanism and invasive species management

Теоретические аспекты построения оптимальной системы транспортного налогообложения

Целью данной статьи является анализ теоретико-методологических основ построения оптимальной системы транспортного налогообложения с выделением классификационных признаков, функций и принципов построения такой системы. В работе систематизированы экономические воззрения на природу транспортных налогов и представлен генезис транспортного налогообложения. Аргументируется, что генезис исследований в области транспортного налогообложения состоял в развитии экономических обоснований величин компенсаций, развивавшихся по логике от частного к общему, т. е. от компенсации за пользование отдельными объектами дорожно-транспортной сети до обоснования компенсации совокупности всех отрицательных экстерналий и всей дорожно-транспортной сети. Систематизируются функции транспортного налогообложения, проводится анализ двух основных функций: фискальной и регулирующей. Доказывается, что регулирующая функция в транспортном налогообложении является паритетно значимой, анализируются отрицательные внешние эффекты, связанные с форсированной автомобилизацией населения. Кроме того, обосновывается, что чистые общественные блага, используемые при эксплуатации автотранспорта, в процессе массовой автомобилизации трансформируются в смешанные блага, которые в свою очередь сохраняют свойство неисключаемости, но отличаются конкурентностью в потреблении. В результате исследования разработана оригинальная классификация транспортных налогов по основным классификационным признакам, представлены классификации по видам и характеру транспортных платежей, стадиям жизненного цикла транспортного средства, способу взимания платежа, характеру влияния на интенсивность использования транспортного средства и цели его использования. Предлагается система принципов оптимального транспортного налогообложения, включающая известные и оригинальные принципы, развиваются принцип выгоды в транспортном налогообложении и принцип социального оптимума. Сформулированы оригинальные принципы: комплексности, дифференциации, приближенности платежа к услуге, принцип маркировки.The purpose of this article is to analyze the theoretical and methodological basis of building an optimal transport taxation system. That includes establishing classification criteria, functions and principles of building the system. The article systematizes economic views on the nature of transport taxes and outlines the genesis of transport taxation. The article substantiates that the genesis of studies on transport taxation involved the development of economic measures of the size of compensation which followed the special-to-general model, that is, from compensation for the use of particular road network facilities to compensation for the entirety of negative externalities and the use of the whole road network. The article systemizes functions of transport taxation and analyses its two main functions: fiscal and regulatory ones. The article rationalizes that the regulatory function in transport taxation is equally significant. The article analyzes negative external effects resulting from accelerated growth in car ownership. In addition, it substantiates that pure public benefits relating to motor vehicle use tend to transform into mixed benefits in the course of mass car ownership, which, in turn, remain non-excludable, but become rivalrous in consumption. The work presents an original classification of transport taxes based on the main classification criteria. Transport taxes are classified based on types and designation of transport payments, stages of the life cycle of a motor vehicle, the way the tax is levied, the influence it has on the intensity of car use and the purpose of revenue spending. The work offers a system of principles of optimal transport taxation consisting of well-known and new ones. The article further develops the benefit principle in transport taxation as well as the social optimum principle. In addition, it provides definitions for original principles identified by the author: the principle of comprehensiveness, the principle of differentiation, the principle of payment collection at time of service, and the principle of designation

Comment on "Support Vector Machines with Applications"

Comment on "Support Vector Machines with Applications" [math.ST/0612817]Comment: Published at http://dx.doi.org/10.1214/088342306000000475 in the Statistical Science (http://www.imstat.org/sts/) by the Institute of Mathematical Statistics (http://www.imstat.org

The Disparate Effects of Strategic Manipulation

When consequential decisions are informed by algorithmic input, individuals may feel compelled to alter their behavior in order to gain a system's approval. Models of agent responsiveness, termed "strategic manipulation," analyze the interaction between a learner and agents in a world where all agents are equally able to manipulate their features in an attempt to "trick" a published classifier. In cases of real world classification, however, an agent's ability to adapt to an algorithm is not simply a function of her personal interest in receiving a positive classification, but is bound up in a complex web of social factors that affect her ability to pursue certain action responses. In this paper, we adapt models of strategic manipulation to capture dynamics that may arise in a setting of social inequality wherein candidate groups face different costs to manipulation. We find that whenever one group's costs are higher than the other's, the learner's equilibrium strategy exhibits an inequality-reinforcing phenomenon wherein the learner erroneously admits some members of the advantaged group, while erroneously excluding some members of the disadvantaged group. We also consider the effects of interventions in which a learner subsidizes members of the disadvantaged group, lowering their costs in order to improve her own classification performance. Here we encounter a paradoxical result: there exist cases in which providing a subsidy improves only the learner's utility while actually making both candidate groups worse-off--even the group receiving the subsidy. Our results reveal the potentially adverse social ramifications of deploying tools that attempt to evaluate an individual's "quality" when agents' capacities to adaptively respond differ.Comment: 29 pages, 4 figure

The Responsibility Quantification (ResQu) Model of Human Interaction with Automation

Intelligent systems and advanced automation are involved in information collection and evaluation, in decision-making and in the implementation of chosen actions. In such systems, human responsibility becomes equivocal. Understanding human casual responsibility is particularly important when intelligent autonomous systems can harm people, as with autonomous vehicles or, most notably, with autonomous weapon systems (AWS). Using Information Theory, we develop a responsibility quantification (ResQu) model of human involvement in intelligent automated systems and demonstrate its applications on decisions regarding AWS. The analysis reveals that human comparative responsibility to outcomes is often low, even when major functions are allocated to the human. Thus, broadly stated policies of keeping humans in the loop and having meaningful human control are misleading and cannot truly direct decisions on how to involve humans in intelligent systems and advanced automation. The current model is an initial step in the complex goal to create a comprehensive responsibility model, that will enable quantification of human causal responsibility. It assumes stationarity, full knowledge regarding the characteristic of the human and automation and ignores temporal aspects. Despite these limitations, it can aid in the analysis of systems designs alternatives and policy decisions regarding human responsibility in intelligent systems and advanced automation

The Measure and Mismeasure of Fairness: A Critical Review of Fair Machine Learning

The nascent field of fair machine learning aims to ensure that decisions guided by algorithms are equitable. Over the last several years, three formal definitions of fairness have gained prominence: (1) anti-classification, meaning that protected attributes---like race, gender, and their proxies---are not explicitly used to make decisions; (2) classification parity, meaning that common measures of predictive performance (e.g., false positive and false negative rates) are equal across groups defined by the protected attributes; and (3) calibration, meaning that conditional on risk estimates, outcomes are independent of protected attributes. Here we show that all three of these fairness definitions suffer from significant statistical limitations. Requiring anti-classification or classification parity can, perversely, harm the very groups they were designed to protect; and calibration, though generally desirable, provides little guarantee that decisions are equitable. In contrast to these formal fairness criteria, we argue that it is often preferable to treat similarly risky people similarly, based on the most statistically accurate estimates of risk that one can produce. Such a strategy, while not universally applicable, often aligns well with policy objectives; notably, this strategy will typically violate both anti-classification and classification parity. In practice, it requires significant effort to construct suitable risk estimates. One must carefully define and measure the targets of prediction to avoid retrenching biases in the data. But, importantly, one cannot generally address these difficulties by requiring that algorithms satisfy popular mathematical formalizations of fairness. By highlighting these challenges in the foundation of fair machine learning, we hope to help researchers and practitioners productively advance the area