18,746 research outputs found
Global network structure of dominance hierarchy of ant workers
Dominance hierarchy among animals is widespread in various species and
believed to serve to regulate resource allocation within an animal group.
Unlike small groups, however, detection and quantification of linear hierarchy
in large groups of animals are a difficult task. Here, we analyse
aggression-based dominance hierarchies formed by worker ants in Diacamma sp. as
large directed networks. We show that the observed dominance networks are
perfect or approximate directed acyclic graphs, which are consistent with
perfect linear hierarchy. The observed networks are also sparse and random but
significantly different from networks generated through thinning of the perfect
linear tournament (i.e., all individuals are linearly ranked and dominance
relationship exists between every pair of individuals). These results pertain
to global structure of the networks, which contrasts with the previous studies
inspecting frequencies of different types of triads. In addition, the
distribution of the out-degree (i.e., number of workers that the focal worker
attacks), not in-degree (i.e., number of workers that attack the focal worker),
of each observed network is right-skewed. Those having excessively large
out-degrees are located near the top, but not the top, of the hierarchy. We
also discuss evolutionary implications of the discovered properties of
dominance networks.Comment: 5 figures, 2 tables, 4 supplementary figures, 2 supplementary table
Neural Collaborative Filtering
In recent years, deep neural networks have yielded immense success on speech
recognition, computer vision and natural language processing. However, the
exploration of deep neural networks on recommender systems has received
relatively less scrutiny. In this work, we strive to develop techniques based
on neural networks to tackle the key problem in recommendation -- collaborative
filtering -- on the basis of implicit feedback. Although some recent work has
employed deep learning for recommendation, they primarily used it to model
auxiliary information, such as textual descriptions of items and acoustic
features of musics. When it comes to model the key factor in collaborative
filtering -- the interaction between user and item features, they still
resorted to matrix factorization and applied an inner product on the latent
features of users and items. By replacing the inner product with a neural
architecture that can learn an arbitrary function from data, we present a
general framework named NCF, short for Neural network-based Collaborative
Filtering. NCF is generic and can express and generalize matrix factorization
under its framework. To supercharge NCF modelling with non-linearities, we
propose to leverage a multi-layer perceptron to learn the user-item interaction
function. Extensive experiments on two real-world datasets show significant
improvements of our proposed NCF framework over the state-of-the-art methods.
Empirical evidence shows that using deeper layers of neural networks offers
better recommendation performance.Comment: 10 pages, 7 figure
Herding and Social Pressure in Trading Tasks: A Behavioural Analysis
We extend the experimental literature on Bayesian herding using evidence from a financial decision-making experiment. We identify significant propensities to herd increasing with the degree of herd-consensus. We test various herding models to capture the differential impacts of Bayesian-style thinking versus behavioural factors. We find statistically significant associations between herding and individual characteristics such as age and personality traits. Overall, our evidence is consistent with explanations of herding as the outcome of social and behavioural factors. Suggestions for further research are outlined and include verifying these findings and identifying the neurological correlates of propensities to herd
Effects of in-class variation and student rank on the probability of withdrawal : cross-section and time-series analysis for UK university students
From individual-level data for nine entire cohorts of undergraduate students in UK
universities, we estimate the probability that an individual will drop out of university during
their first-year. We examine the 1984-85 to 1992-93 cohorts of students enrolling full-time for
a three or four-year course, and focus on the sensitivity of the probability of withdrawal to the
individualâs prior qualifications relative to those of the other students in their university course.
We show not only that weaker students are more likely to withdraw but also that the extent of
variation in prior qualifications within the studentâs university degree course exerts an
influence on the individual's probability of withdrawal in a way that varies with the individualâs
own in-class rank
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