50,124 research outputs found
Common and Distinct Functional Brain Networks for Intuitive and Deliberate Decision Making
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
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
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
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
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
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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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