A non-linear model of information sharing practices in academic communities

A new model of information sharing practices in academic communities is based on Latour's circulatory system of scientific facts, and some elements of Foster's non-linear model of information-seeking behavior. The main proposition of this model is that information-sharing practices and context simultaneously shape each other. The proposed model supports Foster's conceptualization of information practices as non-linear processes, but its emphasis on the interdependence between context and information practices provides a more effective means to capture complex negotiations involved in information-sharing practices. The proposition is that the major reason for nonlinearity in information practices is a continuous shifting of actors' interests, pressures, and concerns. Capturing these dynamic relations becomes possible through this model. The model also offers a way to generate a number of research questions and hypotheses, and as such it could be a useful tool for empirical studies on information sharing in academic communities

Improving the performance of independent task assignment heuristics minmin, maxmin and mufferage

Cataloged from PDF version of article.MinMin, MaxMin, and Sufferage are constructive heuristics that are widely and successfully used in assigning independent tasks to processors in heterogeneous computing systems. All three heuristics are known to run in O(KN2) time in assigning N tasks to K processors. In this paper, we propose an algorithmic improvement that asymptotically decreases the running time complexity of MinMin to O(KN log N) without affecting its solution quality. Furthermore, we combine the newly proposed MinMin algorithm with MaxMin as well as Sufferage, obtaining two hybrid algorithms. The motivation behind the former hybrid algorithm is to address the drawback of MaxMin in solving problem instances with highly skewed cost distributions while also improving the running time performance of MaxMin. The latter hybrid algorithm improves the running time performance of Sufferage without degrading its solution quality. The proposed algorithms are easy to implement and we illustrate them through detailed pseudocodes. The experimental results over a large number of real-life data sets show that the proposed fast MinMin algorithm and the proposed hybrid algorithms perform significantly better than their traditional counterparts as well as more recent state-of-the-art assignment heuristics. For the large data sets used in the experiments, MinMin, MaxMin, and Sufferage, as well as recent state-of-the-art heuristics, require days, weeks, or even months to produce a solution, whereas all of the proposed algorithms produce solutions within only two or three minutes

Optimal Monetary Rules: The Case of Brazil

Within a dynamic programming approach we derive an optimal rule for the central bank to attain it's inflation targeting goals. The short-run nominal interest rate is used as an instrument to achieve monetary objectives. The model is tested for the Brazilian economy and compared with results found for other countries. Evidence for the estimated feedback interest rule for the Central Bank suggests that the cost of reducing inflation in an open economy is lower than that of a closed economy.

Can forest conservation and logging activities be reconciled for sustainable future? A case of Deramakot Forest Reserve in Borneo / Silverina Anabelle Kibat, Anderson Ngelambong and Vickson E Tabak

Sabah has a long history in the timber industry. While no doubt that logging is a significant source for local economies, it can also contribute to deforestation and forest degradation. Due to the critical importance of timber to human society has led to active logging activities that fail to consider the long-term impact on the forest ecosystems and all who depend on them. The forest in Sabah was fast depleting and the only option was to manage it sustainably or risk losing this economic resource. Because of this, Deramakot Forest Reserve was chosen for sustainable forest management by the Forest Department in Sabah with technical help from the German Agency for Technical Cooperation (GTZ). This paper identifies the success of Deramakot Forest Reserve in becoming the first and longest tropical rainforest in the world to receive the Forest Stewardship Council (FSC) gold standard in forest management, how their Forest Management Plan (FMP) has to produce ecologically sustainable timber and taking into consideration the needs of indigenous people who live within the area and today is one of the most diverse wildlife sanctuaries in world

Cognitive Systems For Revenge and Forgiveness

Minimizing the costs that others impose upon oneself and upon those in whom one has a fitness stake, such as kin and allies, is a key adaptive problem for many organisms. Our ancestors regularly faced such adaptive problems (including homicide, bodily harm, theft, mate poaching, cuckoldry, reputational damage, sexual aggression, and the infliction of these costs on one\u27s offspring, mates, coalition partners, or friends). One solution to this problem is to impose retaliatory costs on an aggressor so that the aggressor and other observers will lower their estimates of the net benefits to be gained from exploiting the retaliator in the future. We posit that humans have an evolved cognitive system that implements this strategy - deterrence - which we conceptualize as a revenge system. The revenge system produces a second adaptive problem: losing downstream gains from the individual on whom retaliatory costs have been imposed. We posit, consequently, a subsidiary computational system designed to restore particular relationships after cost-imposing interactions by inhibiting revenge and motivating behaviors that signal benevolence for the harmdoer. The operation of these systems depends on estimating the risk of future exploitation by the harmdoer and the expected future value of the relationship with the harmdoer. We review empirical evidence regarding the operation of these systems, discuss the causes of cultural and individual differences in their outputs, and sketch their computational architecture

Independent task assignment for heterogeneous systems

Ankara : The Department of Computer Engineering and the Graduate School of Engineering and Science of Bilkent Univ., 2013.Thesis (Ph. D.) -- Bilkent University, 2013.Includes bibliographical references leaves 136-150.We study the problem of assigning nonuniform tasks onto heterogeneous systems. We investigate two distinct problems in this context. The first problem is the one-dimensional partitioning of nonuniform workload arrays with optimal load balancing. The second problem is the assignment of nonuniform independent tasks onto heterogeneous systems. For one-dimensional partitioning of nonuniform workload arrays, we investigate two cases: chain-on-chain partitioning (CCP), where the order of the processors is specified, and chain partitioning (CP), where processor permutation is allowed. We present polynomial time algorithms to solve the CCP problem optimally, while we prove that the CP problem is NP complete. Our empirical studies show that our proposed exact algorithms for the CCP problem produce substantially better results than the state-of-the-art heuristics while the solution times remain comparable. For the independent task assignment problem, we investigate improving the performance of the well-known and widely used constructive heuristics MinMin, MaxMin and Sufferage. All three heuristics are known to run in O(KN2 ) time in assigning N tasks to K processors. In this thesis, we present our work on an algorithmic improvement that asymptotically decreases the running time complexity of MinMin to O(KN log N) without affecting its solution quality. Furthermore, we combine the newly proposed MinMin algorithm with MaxMin as well as Sufferage, obtaining two hybrid algorithms. The motivation behind the former hybrid algorithm is to address the drawback of MaxMin in solving problem instances with highly skewed cost distributions while also improving the running time performance of MaxMin. The latter hybrid algorithm improves the running time performance of Sufferage without degrading its solution quality. The proposed algorithms are easy to implement and we illustrate them through detailed pseudocodes. The experimental results over a large number of real-life datasets show that the proposed fast MinMin algorithm and the proposed hybrid algorithms perform significantly better than their traditional counterparts as well as more recent state-of-the-art assignment heuristics. For the large datasets used in the experiments, MinMin, MaxMin, and Sufferage, as well as recent state-of-the-art heuristics, require days, weeks, or even months to produce a solution, whereas all of the proposed algorithms produce solutions within only two or three minutes. For the independent task assignment problem, we also investigate adopting the multi-level framework which was successfully utilized in several applications including graph and hypergraph partitioning. For the coarsening phase of the multi-level framework, we present an efficient matching algorithm which runs in O(KN) time in most cases. For the uncoarsening phase, we present two refinement algorithms: an efficient O(KN)-time move-based refinement and an efficient O(K2N log N)-time swap-based refinement. Our results indicate that multi-level approach improves the quality of task assignments, while also improving the running time performance, especially for large datasets. As a realistic distributed application of the independent task assignment problem, we introduce the site-to-crawler assignment problem, where a large number of geographically distributed web servers are crawled by a multi-site distributed crawling system and the objective is to minimize the duration of the crawl. We show that this problem can be modeled as an independent task assignment problem. As a solution to the problem, we evaluate a large number of state-of-the-art task assignment heuristics selected from the literature as well as the improved versions and the newly developed multi-level task assignment algorithm. We compare the performance of different approaches through simulations on very large, real-life web datasets. Our results indicate that multi-site web crawling efficiency can be considerably improved using the independent task assignment approach, when compared to relatively easy-to-implement, yet naive baselines.Tabak, E KartalPh.D

Hamiltonian formalism and the Garrett-Munk spectrum of internal waves in the ocean

Wave turbulence formalism for long internal waves in a stratified fluid is developed, based on a natural Hamiltonian description. A kinetic equation appropriate for the description of spectral energy transfer is derived, and its self-similar stationary solution corresponding to a direct cascade of energy toward the short scales is found. This solution is very close to the high wavenumber limit of the Garrett-Munk spectrum of long internal waves in the ocean. In fact, a small modification of the Garrett-Munk formalism includes a spectrum consistent with the one predicted by wave turbulence.Comment: 4 pages latex fil

Enabling Personalized Decision Support with Patient-Generated Data and Attributable Components

Decision-making related to health is complex. Machine learning (ML) and patient generated data can identify patterns and insights at the individual level, where human cognition falls short, but not all ML-generated information is of equal utility for making health-related decisions. We develop and apply attributable components analysis (ACA), a method inspired by optimal transport theory, to type 2 diabetes self-monitoring data to identify patterns of association between nutrition and blood glucose control. In comparison with linear regression, we found that ACA offers a number of characteristics that make it promising for use in decision support applications. For example, ACA was able to identify non-linear relationships, was more robust to outliers, and offered broader and more expressive uncertainty estimates. In addition, our results highlight a tradeoff between model accuracy and interpretability, and we discuss implications for ML-driven decision support systems

Influence of home and school environments on specific dietary behaviors among postpartum, high-risk teens, 27 states, 2007-2009

INTRODUCTION: The objective of this study was to determine whether perceptions of the home and school food environments are related to food and beverage intakes of postpartum teens. METHODS: Our study was a baseline, cross-sectional analysis of 853 postpartum teens enrolled in a weight-loss intervention study across 27 states from 2007 through 2009. Eight-item scales assessed perceived accessibility and availability of foods and beverages in school and home environments. Associations between environments and intakes were assessed by using χ(2) and using logistic regression with generalized estimating equations (GEE), respectively. RESULTS: Overall, 52% of teens perceived their school food environment as positive, and 68% of teens perceived their home food environment as positive. A positive school environment was independently associated with fruit consumption and 100% fruit juice consumption. A positive home environment was independently associated with fruit, vegetable, and water consumption and infrequent consumption of soda and chips (χ(2) P < .05). Having only a positive school environment was associated with fruit consumption (GEE odds ratio [OR], 3.1; 95% confidence interval [CI], 1.5–6.5), and having only a positive home environment was associated with fruit (GEE OR, 2.9; 95% CI, 1.6–5.6), vegetable (GEE OR, 3.1; 95% CI, 1.5–6.2), and water (GEE OR, 2.6; 95% CI, 1.7–4.0) consumption and infrequent consumption of soda (GEE OR, 0.5; 95% CI, 0.3–0.7). Results for positive home and school environments were similar to those for positive home only. CONCLUSION: Home and school environments are related to dietary behaviors among postpartum teens, with a positive home environment more strongly associated with healthful behaviors