68,353 research outputs found
Daily Stress Recognition from Mobile Phone Data, Weather Conditions and Individual Traits
Research has proven that stress reduces quality of life and causes many
diseases. For this reason, several researchers devised stress detection systems
based on physiological parameters. However, these systems require that
obtrusive sensors are continuously carried by the user. In our paper, we
propose an alternative approach providing evidence that daily stress can be
reliably recognized based on behavioral metrics, derived from the user's mobile
phone activity and from additional indicators, such as the weather conditions
(data pertaining to transitory properties of the environment) and the
personality traits (data concerning permanent dispositions of individuals). Our
multifactorial statistical model, which is person-independent, obtains the
accuracy score of 72.28% for a 2-class daily stress recognition problem. The
model is efficient to implement for most of multimedia applications due to
highly reduced low-dimensional feature space (32d). Moreover, we identify and
discuss the indicators which have strong predictive power.Comment: ACM Multimedia 2014, November 3-7, 2014, Orlando, Florida, US
Communicability Graph and Community Structures in Complex Networks
We use the concept of the network communicability (Phys. Rev. E 77 (2008)
036111) to define communities in a complex network. The communities are defined
as the cliques of a communicability graph, which has the same set of nodes as
the complex network and links determined by the communicability function. Then,
the problem of finding the network communities is transformed to an all-clique
problem of the communicability graph. We discuss the efficiency of this
algorithm of community detection. In addition, we extend here the concept of
the communicability to account for the strength of the interactions between the
nodes by using the concept of inverse temperature of the network. Finally, we
develop an algorithm to manage the different degrees of overlapping between the
communities in a complex network. We then analyze the USA airport network, for
which we successfully detect two big communities of the eastern airports and of
the western/central airports as well as two bridging central communities. In
striking contrast, a well-known algorithm groups all but two of the continental
airports into one community.Comment: 36 pages, 5 figures, to appear in Applied Mathematics and Computatio
Stress induced polarization of immune-neuroendocrine phenotypes in Gallus gallus
Immune-neuroendocrine phenotypes (INPs) stand for population subgroups differing in immune-neuroendocrine interactions. While mammalian INPs have been characterized thoroughly in rats and humans, avian INPs were only recently described in Coturnix coturnix (quail). To assess the scope of this biological phenomenon, herein we characterized INPs in Gallus gallus (a domestic hen strain submitted to a very long history of strong selective breeding pressure) and evaluated whether a social chronic stress challenge modulates the individualsâ interplay affecting the INP subsets and distribution. Evaluating plasmatic basal corticosterone, interferon-Îł and interleukin-4 concentrations, innate/acquired leukocyte ratio, PHA-P skin-swelling and induced antibody responses, two opposite INP profiles were found: LEWIS-like (15% of the population) and FISCHER-like (16%) hens. After chronic stress, an increment of about 12% in each polarized INP frequency was found at expenses of a reduction in the number of birds with intermediate responses. Results show that polarized INPs are also a phenomenon occurring in hens. The observed inter-individual variation suggest that, even after a considerable selection process, the population is still well prepared to deal with a variety of immune-neuroendocrine challenges. Stress promoted disruptive effects, leading to a more balanced INPs distribution, which represents a new substrate for challenging situations.Fil: Nazar, Franco Nicolas. Consejo Nacional de Investigaciones CientĂficas y TĂ©cnicas. Centro CientĂfico TecnolĂłgico Conicet - CĂłrdoba. Instituto de Investigaciones BiolĂłgicas y TecnolĂłgicas. Universidad Nacional de CĂłrdoba. Facultad de Ciencias Exactas, FĂsicas y Naturales. Instituto de Investigaciones BiolĂłgicas y TecnolĂłgicas; ArgentinaFil: Estevez, Inma. Centro de InvestigaciĂłn. Neiker - Tecnalia; EspañaFil: Correa, Silvia Graciela. Consejo Nacional de Investigaciones CientĂficas y TĂ©cnicas. Centro CientĂfico TecnolĂłgico CĂłrdoba. Centro de Investigaciones en BioquĂmica ClĂnica e InmunologĂa; ArgentinaFil: Marin, Raul Hector. Consejo Nacional de Investigaciones CientĂficas y TĂ©cnicas. Centro CientĂfico TecnolĂłgico Conicet - CĂłrdoba. Instituto de Investigaciones BiolĂłgicas y TecnolĂłgicas. Universidad Nacional de CĂłrdoba. Facultad de Ciencias Exactas, FĂsicas y Naturales. Instituto de Investigaciones BiolĂłgicas y TecnolĂłgicas; Argentin
Communities in Networks
We survey some of the concepts, methods, and applications of community
detection, which has become an increasingly important area of network science.
To help ease newcomers into the field, we provide a guide to available
methodology and open problems, and discuss why scientists from diverse
backgrounds are interested in these problems. As a running theme, we emphasize
the connections of community detection to problems in statistical physics and
computational optimization.Comment: survey/review article on community structure in networks; published
version is available at
http://people.maths.ox.ac.uk/~porterm/papers/comnotices.pd
Emotions in context: examining pervasive affective sensing systems, applications, and analyses
Pervasive sensing has opened up new opportunities for measuring our feelings and understanding our behavior by monitoring our affective states while mobile. This review paper surveys pervasive affect sensing by examining and considering three major elements of affective pervasive systems, namely; âsensingâ, âanalysisâ, and âapplicationâ. Sensing investigates the different sensing modalities that are used in existing real-time affective applications, Analysis explores different approaches to emotion recognition and visualization based on different types of collected data, and Application investigates different leading areas of affective applications. For each of the three aspects, the paper includes an extensive survey of the literature and finally outlines some of challenges and future research opportunities of affective sensing in the context of pervasive computing
Detecting change points in the large-scale structure of evolving networks
Interactions among people or objects are often dynamic in nature and can be
represented as a sequence of networks, each providing a snapshot of the
interactions over a brief period of time. An important task in analyzing such
evolving networks is change-point detection, in which we both identify the
times at which the large-scale pattern of interactions changes fundamentally
and quantify how large and what kind of change occurred. Here, we formalize for
the first time the network change-point detection problem within an online
probabilistic learning framework and introduce a method that can reliably solve
it. This method combines a generalized hierarchical random graph model with a
Bayesian hypothesis test to quantitatively determine if, when, and precisely
how a change point has occurred. We analyze the detectability of our method
using synthetic data with known change points of different types and
magnitudes, and show that this method is more accurate than several previously
used alternatives. Applied to two high-resolution evolving social networks,
this method identifies a sequence of change points that align with known
external "shocks" to these networks
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