6,079 research outputs found
Second look at the spread of epidemics on networks
In an important paper, M.E.J. Newman claimed that a general network-based
stochastic Susceptible-Infectious-Removed (SIR) epidemic model is isomorphic to
a bond percolation model, where the bonds are the edges of the contact network
and the bond occupation probability is equal to the marginal probability of
transmission from an infected node to a susceptible neighbor. In this paper, we
show that this isomorphism is incorrect and define a semi-directed random
network we call the epidemic percolation network that is exactly isomorphic to
the SIR epidemic model in any finite population. In the limit of a large
population, (i) the distribution of (self-limited) outbreak sizes is identical
to the size distribution of (small) out-components, (ii) the epidemic threshold
corresponds to the phase transition where a giant strongly-connected component
appears, (iii) the probability of a large epidemic is equal to the probability
that an initial infection occurs in the giant in-component, and (iv) the
relative final size of an epidemic is equal to the proportion of the network
contained in the giant out-component. For the SIR model considered by Newman,
we show that the epidemic percolation network predicts the same mean outbreak
size below the epidemic threshold, the same epidemic threshold, and the same
final size of an epidemic as the bond percolation model. However, the bond
percolation model fails to predict the correct outbreak size distribution and
probability of an epidemic when there is a nondegenerate infectious period
distribution. We confirm our findings by comparing predictions from percolation
networks and bond percolation models to the results of simulations. In an
appendix, we show that an isomorphism to an epidemic percolation network can be
defined for any time-homogeneous stochastic SIR model.Comment: 29 pages, 5 figure
Computational Topology Techniques for Characterizing Time-Series Data
Topological data analysis (TDA), while abstract, allows a characterization of
time-series data obtained from nonlinear and complex dynamical systems. Though
it is surprising that such an abstract measure of structure - counting pieces
and holes - could be useful for real-world data, TDA lets us compare different
systems, and even do membership testing or change-point detection. However, TDA
is computationally expensive and involves a number of free parameters. This
complexity can be obviated by coarse-graining, using a construct called the
witness complex. The parametric dependence gives rise to the concept of
persistent homology: how shape changes with scale. Its results allow us to
distinguish time-series data from different systems - e.g., the same note
played on different musical instruments.Comment: 12 pages, 6 Figures, 1 Table, The Sixteenth International Symposium
on Intelligent Data Analysis (IDA 2017
A detector for continuous measurement of ultra-cold atoms in real time
We present the first detector capable of recording high-bandwidth real time
atom number density measurements of a Bose Einstein condensate. Based on a
two-color Mach-Zehnder interferometer, our detector has a response time that is
six orders of magnitude faster than current detectors based on CCD cameras
while still operating at the shot-noise limit. With this minimally destructive
system it may be possible to implement feedback to stabilize a Bose-Einstein
condensate or an atom laser.Comment: 3 pages, 3 figures, submitted to optics letter
Sparse Nerves in Practice
Topological data analysis combines machine learning with methods from
algebraic topology. Persistent homology, a method to characterize topological
features occurring in data at multiple scales is of particular interest. A
major obstacle to the wide-spread use of persistent homology is its
computational complexity. In order to be able to calculate persistent homology
of large datasets, a number of approximations can be applied in order to reduce
its complexity. We propose algorithms for calculation of approximate sparse
nerves for classes of Dowker dissimilarities including all finite Dowker
dissimilarities and Dowker dissimilarities whose homology is Cech persistent
homology. All other sparsification methods and software packages that we are
aware of calculate persistent homology with either an additive or a
multiplicative interleaving. In dowker_homology, we allow for any
non-decreasing interleaving function . We analyze the computational
complexity of the algorithms and present some benchmarks. For Euclidean data in
dimensions larger than three, the sizes of simplicial complexes we create are
in general smaller than the ones created by SimBa. Especially when calculating
persistent homology in higher homology dimensions, the differences can become
substantial
Robot-mediated interviews: : Do robots possess advantages over human interviewers when talking to children with special needs?
Wood L.J., Dautenhahn K., Lehmann H., Robins B., Rainer A., Syrdal D.S. (2013) 'Robot-Mediated Interviews: Do Robots Possess Advantages over Human Interviewers When Talking to Children with Special Needs?', In: Herrmann G., Pearson M.J., Lenz A., Bremner P., Spiers A., Leonards U. (eds) Social Robotics. ICSR 2013. Lecture Notes in Computer Science, vol 8239. Springer, Cham Available online at doi: 10.1007/978-3-319-02675-6-6 © Springer-Verlag Berlin Heidelberg 2013Children that have a disability are up to four times more likely to be a victim of abuse than typically developing children. However, the number of cases that result in prosecution is relatively low. One of the factors influencing this low prosecution rate is communication difficulties. Our previous research has shown that typically developing children respond to a robotic interviewer very similar compared to a human interviewer. In this paper we conduct a follow up study investigating the possibility of Robot-Mediated Interviews with children that have various special needs. In a case study we investigated how 5 children with special needs aged 9 to 11 responded to the humanoid robot KASPAR compared to a human in an interview scenario. The measures used in this study include duration analysis of responses, detailed analysis of transcribed data, questionnaire responses and data from engagement coding. The main questions in the interviews varied in difficulty and focused on the theme of animals and pets. The results from quantitative data analysis reveal that the children interacted with KASPAR in a very similar manner to how they interacted with the human interviewer, providing both interviewers with similar information and amounts of information regardless of question difficulty. However qualitative analysis suggests that some children may have been more engaged with the robotic interviewer
Tactile Interactions with a Humanoid Robot : Novel Play Scenario Implementations with Children with Autism
Acknowledgments: This work has been partially supported by the European Commission under contract number FP7-231500-ROBOSKIN. Open Access: This article is distributed under the terms of the Creative Commons Attribution License which permits any use, distribution, and reproduction in any medium, provided the original author(s) and the source are credited.The work presented in this paper was part of our investigation in the ROBOSKIN project. The project has developed new robot capabilities based on the tactile feedback provided by novel robotic skin, with the aim to provide cognitive mechanisms to improve human-robot interaction capabilities. This article presents two novel tactile play scenarios developed for robot-assisted play for children with autism. The play scenarios were developed against specific educational and therapeutic objectives that were discussed with teachers and therapists. These objectives were classified with reference to the ICF-CY, the International Classification of Functioning – version for Children and Youth. The article presents a detailed description of the play scenarios, and case study examples of their implementation in HRI studies with children with autism and the humanoid robot KASPAR.Peer reviewedFinal Published versio
Exponential Random Graph Modeling for Complex Brain Networks
Exponential random graph models (ERGMs), also known as p* models, have been
utilized extensively in the social science literature to study complex networks
and how their global structure depends on underlying structural components.
However, the literature on their use in biological networks (especially brain
networks) has remained sparse. Descriptive models based on a specific feature
of the graph (clustering coefficient, degree distribution, etc.) have dominated
connectivity research in neuroscience. Corresponding generative models have
been developed to reproduce one of these features. However, the complexity
inherent in whole-brain network data necessitates the development and use of
tools that allow the systematic exploration of several features simultaneously
and how they interact to form the global network architecture. ERGMs provide a
statistically principled approach to the assessment of how a set of interacting
local brain network features gives rise to the global structure. We illustrate
the utility of ERGMs for modeling, analyzing, and simulating complex
whole-brain networks with network data from normal subjects. We also provide a
foundation for the selection of important local features through the
implementation and assessment of three selection approaches: a traditional
p-value based backward selection approach, an information criterion approach
(AIC), and a graphical goodness of fit (GOF) approach. The graphical GOF
approach serves as the best method given the scientific interest in being able
to capture and reproduce the structure of fitted brain networks
A Pilot Study with a Novel Setup for Collaborative Play of the Humanoid Robot KASPAR with children with autism
This article is distributed under the terms of the Creative Commons Attribution License which permits any use, distribution, and reproduction in any medium, provided the original author(s) and the source are credited.This article describes a pilot study in which a novel experimental setup, involving an autonomous humanoid robot, KASPAR, participating in a collaborative, dyadic video game, was implemented and tested with children with autism, all of whom had impairments in playing socially and communicating with others. The children alternated between playing the collaborative video game with a neurotypical adult and playing the same game with the humanoid robot, being exposed to each condition twice. The equipment and experimental setup were designed to observe whether the children would engage in more collaborative behaviours while playing the video game and interacting with the adult than performing the same activities with the humanoid robot. The article describes the development of the experimental setup and its first evaluation in a small-scale exploratory pilot study. The purpose of the study was to gain experience with the operational limits of the robot as well as the dyadic video game, to determine what changes should be made to the systems, and to gain experience with analyzing the data from this study in order to conduct a more extensive evaluation in the future. Based on our observations of the childrens’ experiences in playing the cooperative game, we determined that while the children enjoyed both playing the game and interacting with the robot, the game should be made simpler to play as well as more explicitly collaborative in its mechanics. Also, the robot should be more explicit in its speech as well as more structured in its interactions. Results show that the children found the activity to be more entertaining, appeared more engaged in playing, and displayed better collaborative behaviours with their partners (For the purposes of this article, ‘partner’ refers to the human/robotic agent which interacts with the children with autism. We are not using the term’s other meanings that refer to specific relationships or emotional involvement between two individuals.) in the second sessions of playing with human adults than during their first sessions. One way of explaining these findings is that the children’s intermediary play session with the humanoid robot impacted their subsequent play session with the human adult. However, another longer and more thorough study would have to be conducted in order to better re-interpret these findings. Furthermore, although the children with autism were more interested in and entertained by the robotic partner, the children showed more examples of collaborative play and cooperation while playing with the human adult.Peer reviewe
Optically guided linear Mach Zehnder atom interferometer
We demonstrate a horizontal, linearly guided Mach Zehnder atom interferometer
in an optical waveguide. Intended as a proof-of-principle experiment, the
interferometer utilises a Bose-Einstein condensate in the magnetically
insensitive |F=1,mF=0> state of Rubidium-87 as an acceleration sensitive test
mass. We achieve a modest sensitivity to acceleration of da = 7x10^-4 m/s^2.
Our fringe visibility is as high as 38% in this optically guided atom
interferometer. We observe a time-of-flight in the waveguide of over half a
second, demonstrating the utility of our optical guide for future sensors.Comment: 6 pages, 3 figures. Submitted to Phys. Rev.
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