50,124 research outputs found

    Common and Distinct Functional Brain Networks for Intuitive and Deliberate Decision Making

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    Reinforcement learning studies in rodents and primates demonstrate that goal-directed and habitual choice behaviors are mediated through different fronto-striatal systems, but the evidence is less clear in humans. In this study, functional magnetic resonance imaging (fMRI) data were collected whilst participants ( n = 20) performed a conditional associative learning task in which blocks of novel conditional stimuli (CS) required a deliberate choice, and blocks of familiar CS required an intuitive choice. Using standard subtraction analysis for fMRI event-related designs, activation shifted from the dorso-fronto-parietal network, which involves dorsolateral prefrontal cortex (DLPFC) for deliberate choice of novel CS, to ventro-medial frontal (VMPFC) and anterior cingulate cortex for intuitive choice of familiar CS. Supporting this finding, psycho-physiological interaction (PPI) analysis, using the peak active areas within the PFC for novel and familiar CS as seed regions, showed functional coupling between caudate and DLPFC when processing novel CS and VMPFC when processing familiar CS. These findings demonstrate separable systems for deliberate and intuitive processing, which is in keeping with rodent and primate reinforcement learning studies, although in humans they operate in a dynamic, possibly synergistic, manner particularly at the level of the striatum.Peer reviewedFinal Published versio

    Rank Centrality: Ranking from Pair-wise Comparisons

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    The question of aggregating pair-wise comparisons to obtain a global ranking over a collection of objects has been of interest for a very long time: be it ranking of online gamers (e.g. MSR's TrueSkill system) and chess players, aggregating social opinions, or deciding which product to sell based on transactions. In most settings, in addition to obtaining a ranking, finding `scores' for each object (e.g. player's rating) is of interest for understanding the intensity of the preferences. In this paper, we propose Rank Centrality, an iterative rank aggregation algorithm for discovering scores for objects (or items) from pair-wise comparisons. The algorithm has a natural random walk interpretation over the graph of objects with an edge present between a pair of objects if they are compared; the score, which we call Rank Centrality, of an object turns out to be its stationary probability under this random walk. To study the efficacy of the algorithm, we consider the popular Bradley-Terry-Luce (BTL) model (equivalent to the Multinomial Logit (MNL) for pair-wise comparisons) in which each object has an associated score which determines the probabilistic outcomes of pair-wise comparisons between objects. In terms of the pair-wise marginal probabilities, which is the main subject of this paper, the MNL model and the BTL model are identical. We bound the finite sample error rates between the scores assumed by the BTL model and those estimated by our algorithm. In particular, the number of samples required to learn the score well with high probability depends on the structure of the comparison graph. When the Laplacian of the comparison graph has a strictly positive spectral gap, e.g. each item is compared to a subset of randomly chosen items, this leads to dependence on the number of samples that is nearly order-optimal.Comment: 45 pages, 3 figure

    FIRMS' STRATEGIES FOR REDUCING THE EFFECTIVENESS OF CONSUMER PRICE SEARCH

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    This paper considers a simple model of competition based on some buyers making price comparisons between two suppliers. The difficulties of making appropriate comparisons are made greater by exclusive dealer agreements and restrictions, and by suppliers trading under more than one name. It is argued that suppliers will set prices using mixed strategies, and that prices become less competitive as price comparisons become more difficult. The implications for competition policy are considered in the light of recent judgements of the UK’s Office of Fair Trading and the European Court of Justice.

    A Parallel Divide-and-Conquer based Evolutionary Algorithm for Large-scale Optimization

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    Large-scale optimization problems that involve thousands of decision variables have extensively arisen from various industrial areas. As a powerful optimization tool for many real-world applications, evolutionary algorithms (EAs) fail to solve the emerging large-scale problems both effectively and efficiently. In this paper, we propose a novel Divide-and-Conquer (DC) based EA that can not only produce high-quality solution by solving sub-problems separately, but also highly utilizes the power of parallel computing by solving the sub-problems simultaneously. Existing DC-based EAs that were deemed to enjoy the same advantages of the proposed algorithm, are shown to be practically incompatible with the parallel computing scheme, unless some trade-offs are made by compromising the solution quality.Comment: 12 pages, 0 figure

    Development of titanium dioxide nanoparticles/nanosolution for photocatalytic activity

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    Biological and chemical contaminants by man-made activities have been serious global issue. Exposure of these contaminants beyond the limits may result in serious environmental and health problem. Therefore, it is important to develop an effective solution that can be easily utilized by mankind. One of the effective ways to overcome this problem is by using titanium dioxide (TiO2). TiO2 is a well-known photocatalyst that widely used for environmental clean-up due to its ability to decompose organic pollutant and kill bacteria. Although it is proven TiO2 has an advantage to solve this concern, its usefulness unfortunately is limited only under UV light irradiation. Therefore, the aim of this work was to investigate the potential of TiO2 that can be activated under visible light by the incorporation of metal ions (Fe, Ag, Zr and Ag-Zr). In this study, sol-gel method was employed for the synthesis of metal ions incorporated TiO2. XRD analysis revealed that all samples content biphasic anatase-brookite TiO2 of size 3 nm to 5 nm. It was found that the incorporation of these metal ions did not change the morphology of TiO2 but the crystallinity and optical properties were affected. The crystallinity of anatase in the biphasic TiO2 was found to be decreased and favored brookite formation. PL analysis showed metal ions incorporation suppressed the recombination of electron-hole pairs while the band gap energy of TiO2 (3.2 eV) was decreased by the incorporation of Fe (2.46 eV) and Ag (2.86 eV). Among this incorporation, Ag-Zr incorporated TiO2 showed highest performance for methyl orange degradation (93%) under fluorescent xxv light irradiation for 10 h. This follows by Zr-TiO2 (82%), Fe-TiO2 (75%) and Ag�TiO2 (43%). Meanwhile, the highest antibacterial performance was exhibited by Ag�TiO2. TEM images showed that E.coli bacterium was killed within 12 h after treated with Ag-TiO2. The results obtained from the fieldwork study established that Ag-Zr incorporation have excellent performances for VOC removal and antibacterial test. The VOC content after treated with Ag-Zr-TiO2 fulfilled the Industry Code of Practice on Indoor Air Quality 2010 which is lower than 3 ppm. In addition, the percentage of microbes also found to be decrease around 45 % within 5 days of monitoring
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