37,837 research outputs found
Wearable Sensor Data Based Human Activity Recognition using Machine Learning: A new approach
Recent years have witnessed the rapid development of human activity
recognition (HAR) based on wearable sensor data. One can find many practical
applications in this area, especially in the field of health care. Many machine
learning algorithms such as Decision Trees, Support Vector Machine, Naive
Bayes, K-Nearest Neighbor, and Multilayer Perceptron are successfully used in
HAR. Although these methods are fast and easy for implementation, they still
have some limitations due to poor performance in a number of situations. In
this paper, we propose a novel method based on the ensemble learning to boost
the performance of these machine learning methods for HAR
InAs-AlSb quantum wells in tilted magnetic fields
InAs-AlSb quantum wells are investigated by transport experiments in magnetic
fields tilted with respect to the sample normal. Using the coincidence method
we find for magnetic fields up to 28 T that the spin splitting can be as large
as 5 times the Landau splitting. We find a value of the g-factor of about 13.
For small even-integer filling factors the corresponding minima in the
Shubnikov-de Haas oscillations cannot be tuned into maxima for arbitrary tilt
angles. This indicates the anti-crossing of neighboring Landau and spin levels.
Furthermore we find for particular tilt angles a crossover from even-integer
dominated Shubnikov-de Haas minima to odd-integer minima as a function of
magnetic field
Models of Social Groups in Blogosphere Based on Information about Comment Addressees and Sentiments
This work concerns the analysis of number, sizes and other characteristics of
groups identified in the blogosphere using a set of models identifying social
relations. These models differ regarding identification of social relations,
influenced by methods of classifying the addressee of the comments (they are
either the post author or the author of a comment on which this comment is
directly addressing) and by a sentiment calculated for comments considering the
statistics of words present and connotation. The state of a selected blog
portal was analyzed in sequential, partly overlapping time intervals. Groups in
each interval were identified using a version of the CPM algorithm, on the
basis of them, stable groups, existing for at least a minimal assumed duration
of time, were identified.Comment: Gliwa B., Ko\'zlak J., Zygmunt A., Models of Social Groups in
Blogosphere Based on Information about Comment Addressees and Sentiments, in
the K. Aberer et al. (Eds.): SocInfo 2012, LNCS 7710, pp. 475-488, Best Paper
Awar
A new broken U(1)-symmetry in extreme type-II superconductors
A phase transition within the molten phase of the Abrikosov vortex system
without disorder in extreme type-II superconductors is found via large-scale
Monte-Carlo simulations. It involves breaking a U(1)-symmetry, and has a
zero-field counterpart, unlike vortex lattice melting. Its hallmark is the loss
of number-conservation of connected vortex paths threading the entire system
{\it in any direction}, driving the vortex line tension to zero. This tension
plays the role of a generalized ``stiffness'' of the vortex liquid, and serves
as a probe of the loss of order at the transition, where a weak specific heat
anomaly is found.Comment: 5 pages, 3 figure
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