Changes in invertebrate assemblage composition in benthic and hyporheic zones during a severe supraseasonal drought

Droughts are unpredictable disturbances characterized in streams by declining flow, reduced habitat availability, and deteriorating abiotic conditions. Such events typically reduce benthic invertebrate taxon richness and modify assemblage composition, but little is known about how hyporheic invertebrate assemblages respond to drought or how these responses relate to changes in benthic assemblages. We hypothesized that taxon richness (diversity) and variability (as within-site diversity) in benthic assemblage composition would decline as drought proceeded, whereas concurrent changes in hyporheic assemblages would be lower in this more stable environment. We predicted that benthic assemblage composition between sites would converge as epigean taxa were selectively eliminated, whereas between-site hyporheic diversity would change little. We sampled benthic and hyporheic invertebrates concurrently from 4 sites along a groundwater-fed stream during the final stages of a severe supraseasonal drought punctuated by a record heat wave. Abiotic conditions in benthic habitats deteriorated as flow declined, but changes were less pronounced in the hyporheic zone. Benthic diversity declined during drought, whereas hyporheic diversity changed little. However, benthic within-site diversity increased as the drought progressed because of localized variation in the abundance of common taxa. Temporal trends in hyporheic diversity were less consistent. Benthic assemblages at individual sites became more similar, especially during the heat wave, reflecting low diversity and abundance. Hyporheic assemblages changed markedly because of temporary increases in abundances of epigean and hypogean amphipods. These contrasting responses of benthic and hyporheic assemblages to drought should be recognized when developing management strategies for drought-impacted streams

Transport consumption inequalities and redistributive effects of taxes: A repeated cross-sectional evaluation on French household data

This paper evaluates transport consumption inequalities among French households, investigates their temporal dynamics, and estimates the redistributive effects of taxes on different commodity categories. A decomposition by expenditure component of the Gini coefficient is applied, using household-level data from five expenditure surveys conducted between the end of the 1970’s and the early 2000’s. The results highlight the effect of car social diffusion. Indeed, the relative contribution to global inequality of car use items, especially fuels, decreased regularly over time, reflecting the more and more widespread use of the car. Moreover, fuel taxes become regressive (i.e. they affect the poor more than the rich), while the progressive character of taxes on the remaining car use commodities weakens over time. Therefore, the design of policy measures to reduce car use and thus attenuate its nuisances for the environment should also account for the imperative of equity. The case of local public transport underlines the necessity of accounting for disparities in terms of availability of alternatives to the car. Taxes on these services appear to be neutral (i.e. neither progressive nor regressive) at national level, but this result conceals a diversity of local conditions in terms of supply of these transport means according to the degree of urbanization and population density. Effectively, these taxes prove to be regressive when focusing on the Greater Paris region, a large urban area very well endowed with public transport infrastructure. Hence, a distinction by degree of urbanization is to be considered.Inequality, transport consumption, household expenditure surveys, Gini index, decomposition by component, redistributive effects of taxes

Transport consumption inequalities and redistributive effects of taxes: A comparison of France, Denmark and Cyprus

We evaluate household transport consumption inequalities in France, Denmark and Cyprus, investigate their temporal dynamics and estimate the redistributive effects of taxes on different commodity categories. A comparative analysis is carried out in light of the differences between these countries, most notably in terms of car taxation systems and car ownership levels. A decomposition by expenditure component of the Gini index is applied, using household-level data from repeated cross-sections of expenditure surveys spanning long time periods. The results highlight the effect of car social diffusion. The relative contribution of vehicle use items to total expenditure inequality decreases over time, thus reflecting the more and more widespread use of the car. Moreover, fuel taxes become regressive (i.e. they affect the poor more than the rich), while the progressive character of taxes on the remaining car use commodities weakens with time. Taxes on transport goods and services as a whole are progressive (i.e. they affect the rich more than the poor). However, this is principally due to the progressivity of taxes on automobile purchases. The progressivity of taxes on car purchases is by far much stronger in Denmark. In this country, these taxes are so high that car purchase costs can be afforded only by high incomes. These findings underline the fact that equity issues should not be overlooked when designing policies to attenuate the environmental impact of cars. Increasing car use costs, notably fuel prices, through an increase of uniform taxes would be particularly inequitable.Inequality; transport consumption; household expenditure surveys; Gini index; decomposition by component; redistributive effects of taxes

A probabilistic model to resolve diversity-accuracy challenge of recommendation systems

Recommendation systems have wide-spread applications in both academia and industry. Traditionally, performance of recommendation systems has been measured by their precision. By introducing novelty and diversity as key qualities in recommender systems, recently increasing attention has been focused on this topic. Precision and novelty of recommendation are not in the same direction, and practical systems should make a trade-off between these two quantities. Thus, it is an important feature of a recommender system to make it possible to adjust diversity and accuracy of the recommendations by tuning the model. In this paper, we introduce a probabilistic structure to resolve the diversity-accuracy dilemma in recommender systems. We propose a hybrid model with adjustable level of diversity and precision such that one can perform this by tuning a single parameter. The proposed recommendation model consists of two models: one for maximization of the accuracy and the other one for specification of the recommendation list to tastes of users. Our experiments on two real datasets show the functionality of the model in resolving accuracy-diversity dilemma and outperformance of the model over other classic models. The proposed method could be extensively applied to real commercial systems due to its low computational complexity and significant performance.Comment: 19 pages, 5 figure

USING FILTERS IN TIME-BASED MOVIE RECOMMENDER SYSTEMS

On a very high level, a movie recommendation system is one which uses data about the user, data about the movie and the ratings given by a user in order to generate predictions for the movies that the user will like. This prediction is further presented to the user as a recommendation. For example, Netflix uses a recommendation system to predict movies and generate favorable recommendations for users based on their profiles and the profiles of users similar to them. In user-based collaborative filtering algorithm, the movies rated highly by the similar users of a particular user are considered as recommendations to that user. But users’ preferences vary with time, which often affects the efficacy of the recommendation, especially in a movie recommendation system. Because of the constant variation of the preferences, there has been research on using time of rating or watching the movie as a significant factor for recommendation. If time is considered as an attribute in the training phase of building a recommendation model, the model might get complex. Most of the research till now does this in the training phase, however, we study the effect of using time as a factor in the post training phase and study it further by applying a genre-based filtering mechanism on the system. Employing this in the post training phase reduces the complexity of the method and also reduces the number of irrelevant recommendations

Immanent Anthropology: A Comparative Study of 'Process' in Contemporary France

This paper presents a comparative critique of the ‘processual temporalities’ which infuse both social-scientific theorizing and selected Western cultural practices. Through study of a public-private partnership which emerged from a biotechnology project devised for producing ‘self-cloning’ maize for resource-poor farmers, I analyse how processual temporalities were central to re-gearing knowledge practices towards market-orientated solutions. In a study of characterizations of the ‘state of flux’ which affects life in a French peri-urban village, I explore how processualism is identified as a component of a metropolitan hegemony which villagers ‘resist’ through idealizing ‘enduring temporalities’ of cultural practice. Drawing on Arendt and Deleuze, I analyse processualism as a dominant contemporary chronotope, mediating and disciplining conflictive temporalities and practices, underwriting economic projects of deterritorialization and restructuring – whose idiom is also prominent in social-scientific paradigms. I substitute an ‘immanent anthropology’, which advocates a non-transcendental ontology of cultural practice and analysis – displacing anthropological analysis onto a polychronic temporal foundation