3,399,203 research outputs found
Motivating physical activity at work: using persuasive social media extensions for simple mobile devices
Powerful behaviour change programmes can be developed through a combination of very simple, accessible technology, and an understanding of the psychological processes that drive behaviour change. We present a study in which very basic digital pedometers were used to record the number of steps taken by participants over the course of a normal working day. A Facebook application, named Step Matron, was utilised to provide a social and competitive context for pedometer readings. We were particularly interested in whether interactions between users via the application more successfully motivated physical activity than simply recording daily step counts in a similar application. Ten participants (1 male), all nurses working in a UK hospital, used the application across two conditions over the course of the study. In the socially-enabled condition, participants could view each other’s step data and make comparisons and comments. In the non-social condition, participants could only view their own personal step data. A significant increase in step activity was observed in the socially enabled condition. Our findings highlight the potential of social media as a means for generating positive behaviour change. They also suggest that simple mobile devices can function as an inexpensive, accessible and powerful trigger towards this behaviour change without necessitating the use of overly complex and expensive mobile applications or device
Common Representation Learning Using Step-based Correlation Multi-Modal CNN
Deep learning techniques have been successfully used in learning a common
representation for multi-view data, wherein the different modalities are
projected onto a common subspace. In a broader perspective, the techniques used
to investigate common representation learning falls under the categories of
canonical correlation-based approaches and autoencoder based approaches. In
this paper, we investigate the performance of deep autoencoder based methods on
multi-view data. We propose a novel step-based correlation multi-modal CNN
(CorrMCNN) which reconstructs one view of the data given the other while
increasing the interaction between the representations at each hidden layer or
every intermediate step. Finally, we evaluate the performance of the proposed
model on two benchmark datasets - MNIST and XRMB. Through extensive
experiments, we find that the proposed model achieves better performance than
the current state-of-the-art techniques on joint common representation learning
and transfer learning tasks.Comment: Accepted in Asian Conference of Pattern Recognition (ACPR-2017
Multi-view Metric Learning in Vector-valued Kernel Spaces
We consider the problem of metric learning for multi-view data and present a
novel method for learning within-view as well as between-view metrics in
vector-valued kernel spaces, as a way to capture multi-modal structure of the
data. We formulate two convex optimization problems to jointly learn the metric
and the classifier or regressor in kernel feature spaces. An iterative
three-step multi-view metric learning algorithm is derived from the
optimization problems. In order to scale the computation to large training
sets, a block-wise Nystr{\"o}m approximation of the multi-view kernel matrix is
introduced. We justify our approach theoretically and experimentally, and show
its performance on real-world datasets against relevant state-of-the-art
methods
Systematic Analysis of Majorization in Quantum Algorithms
Motivated by the need to uncover some underlying mathematical structure of
optimal quantum computation, we carry out a systematic analysis of a wide
variety of quantum algorithms from the majorization theory point of view. We
conclude that step-by-step majorization is found in the known instances of fast
and efficient algorithms, namely in the quantum Fourier transform, in Grover's
algorithm, in the hidden affine function problem, in searching by quantum
adiabatic evolution and in deterministic quantum walks in continuous time
solving a classically hard problem. On the other hand, the optimal quantum
algorithm for parity determination, which does not provide any computational
speed-up, does not show step-by-step majorization. Lack of both speed-up and
step-by-step majorization is also a feature of the adiabatic quantum algorithm
solving the 2-SAT ``ring of agrees'' problem. Furthermore, the quantum
algorithm for the hidden affine function problem does not make use of any
entanglement while it does obey majorization. All the above results give
support to a step-by-step Majorization Principle necessary for optimal quantum
computation.Comment: 15 pages, 14 figures, final versio
Non-equilibrium thermodynamical framework for rate- and state-dependent friction
Rate- and state-dependent friction law for velocity-step and healing are
analysed from a thermodynamic point of view. Assuming a logarithmic deviation
from steady-state a unification of the classical Dieterich and Ruina models of
rock friction is proposed.Comment: 12 pages, 5 figure
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