78,539 research outputs found
Contact patterns among high school students
Face-to-face contacts between individuals contribute to shape social networks
and play an important role in determining how infectious diseases can spread
within a population. It is thus important to obtain accurate and reliable
descriptions of human contact patterns occurring in various day-to-day life
contexts. Recent technological advances and the development of wearable sensors
able to sense proximity patterns have made it possible to gather data giving
access to time-varying contact networks of individuals in specific
environments. Here we present and analyze two such data sets describing with
high temporal resolution the contact patterns of students in a high school. We
define contact matrices describing the contact patterns between students of
different classes and show the importance of the class structure. We take
advantage of the fact that the two data sets were collected in the same setting
during several days in two successive years to perform a longitudinal analysis
on two very different timescales. We show the high stability of the contact
patterns across days and across years: the statistical distributions of numbers
and durations of contacts are the same in different periods, and we observe a
very high similarity of the contact matrices measured in different days or
different years. The rate of change of the contacts of each individual from one
day to the next is also similar in different years. We discuss the interest of
the present analysis and data sets for various fields, including in social
sciences in order to better understand and model human behavior and
interactions in different contexts, and in epidemiology in order to inform
models describing the spread of infectious diseases and design targeted
containment strategies.Comment: Supplementary Information at
http://s3-eu-west-1.amazonaws.com/files.figshare.com/1677807/File_S1.pd
QueRIE: Collaborative Database Exploration
Interactive database exploration is a key task in information mining. However, users who lack SQL expertise or familiarity with the database schema face great difficulties in performing this task. To aid these users, we developed the QueRIE system for personalized query recommendations. QueRIE continuously monitors the user’s querying behavior and finds matching patterns in the system’s query log, in an attempt to identify previous users with similar information needs. Subsequently, QueRIE uses these “similar” users and their queries to recommend queries that the current user may find interesting. In this work we describe an instantiation of the QueRIE framework, where the active user’s session is represented by a set of query fragments. The recorded fragments are used to identify similar query fragments in the previously recorded sessions, which are in turn assembled in potentially interesting queries for the active user. We show through experimentation that the proposed method generates meaningful recommendations on real-life traces from the SkyServer database and propose a scalable design that enables the incremental update of similarities, making real-time computations on large amounts of data feasible. Finally, we compare this fragment-based instantiation with our previously proposed tuple-based instantiation discussing the advantages and disadvantages of each approach
Inferring Mechanisms for Global Constitutional Progress
Constitutions help define domestic political orders, but are known to be
influenced by two international mechanisms: one that reflects global temporal
trends in legal development, and another that reflects international network
dynamics such as shared colonial history. We introduce the provision space; the
growing set of all legal provisions existing in the world's constitutions over
time. Through this we uncover a third mechanism influencing constitutional
change: hierarchical dependencies between legal provisions, under which the
adoption of essential, fundamental provisions precedes more advanced
provisions. This third mechanism appears to play an especially important role
in the emergence of new political rights, and may therefore provide a useful
roadmap for advocates of those rights. We further characterise each legal
provision in terms of the strength of these mechanisms
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Remembering the future: An overview of co-evolution in musical improvisation
Musical improvisation is driven mainly by the unconscious mind, engaging the dialogic imagination to reference the entire cultural heritage of an improvisor in a single flash. This paper introduces a case study of evolutionary computation techniques, in particular genetic co-evolution, as applied to the frequency domain using MPEG7 techniques, in order to create an artificial agent that mediates between an improvisor and her unconscious mind, to probe and unblock improvisatory action in live music performance or practice
A shortest-path based clustering algorithm for joint human-machine analysis of complex datasets
Clustering is a technique for the analysis of datasets obtained by empirical
studies in several disciplines with a major application for biomedical
research. Essentially, clustering algorithms are executed by machines aiming at
finding groups of related points in a dataset. However, the result of grouping
depends on both metrics for point-to-point similarity and rules for
point-to-group association. Indeed, non-appropriate metrics and rules can lead
to undesirable clustering artifacts. This is especially relevant for datasets,
where groups with heterogeneous structures co-exist. In this work, we propose
an algorithm that achieves clustering by exploring the paths between points.
This allows both, to evaluate the properties of the path (such as gaps, density
variations, etc.), and expressing the preference for certain paths. Moreover,
our algorithm supports the integration of existing knowledge about admissible
and non-admissible clusters by training a path classifier. We demonstrate the
accuracy of the proposed method on challenging datasets including points from
synthetic shapes in publicly available benchmarks and microscopy data
On localized application-driven topology control for energy-efficient wireless peer-to-peer file sharing
Wireless Peer-to-Peer (P2P) file sharing Is widely envisioned as one of the major applications of ad hoc networks in the near future. This trend is largely motivated by the recent advances in high-speed wireless communication technologies and high traffic demand for P2P file sharing applications. To achieve the ambitious goal of realizing a practical wireless P2P network, we need a scalable topology control protocol to solve the neighbor discovery problem and network organization problem. Indeed, we believe that the topology control mechanism should be application driven in that we should try to achieve an efficient connectivity among mobile devices in order to better serve the file sharing application. We propose a new protocol, which consists of two components, namely, Adjacency Set Construction (ASC) and Community-Based Asynchronous Wakeup (CAW). Our proposed protocol is shown to be able to enhance the fairness and provide an incentive mechanism in wireless P2P file sharing applications. It is also capable of increasing the energy efficiency. © 2008 IEEE.published_or_final_versio
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