22,576 research outputs found
On the Inability of Markov Models to Capture Criticality in Human Mobility
We examine the non-Markovian nature of human mobility by exposing the
inability of Markov models to capture criticality in human mobility. In
particular, the assumed Markovian nature of mobility was used to establish a
theoretical upper bound on the predictability of human mobility (expressed as a
minimum error probability limit), based on temporally correlated entropy. Since
its inception, this bound has been widely used and empirically validated using
Markov chains. We show that recurrent-neural architectures can achieve
significantly higher predictability, surpassing this widely used upper bound.
In order to explain this anomaly, we shed light on several underlying
assumptions in previous research works that has resulted in this bias. By
evaluating the mobility predictability on real-world datasets, we show that
human mobility exhibits scale-invariant long-range correlations, bearing
similarity to a power-law decay. This is in contrast to the initial assumption
that human mobility follows an exponential decay. This assumption of
exponential decay coupled with Lempel-Ziv compression in computing Fano's
inequality has led to an inaccurate estimation of the predictability upper
bound. We show that this approach inflates the entropy, consequently lowering
the upper bound on human mobility predictability. We finally highlight that
this approach tends to overlook long-range correlations in human mobility. This
explains why recurrent-neural architectures that are designed to handle
long-range structural correlations surpass the previously computed upper bound
on mobility predictability
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The role of empirical space-weather models (in a world of physics-based numerical simulations)
Advanced forecasting of space weather requires prediction of near-Earth solar-wind conditions on the basis of remote solar observations. This is typically achieved using numerical magnetohydrodynamic models initiated by photospheric magnetic field observations. The accuracy of such forecasts is being continually improved through better numerics, better determination of the boundary conditions and better representation of the underlying physical processes. Thus it is not unreasonable to conclude that simple, empirical solar-wind forecasts have been rendered obsolete. However, empirical models arguably have more to contribute now than ever before. In addition to providing quick, cheap, independent forecasts, simple empirical models aid in numerical model validation and verification, and add value to numerical model forecasts through parameterization, uncertainty estimation and ‘downscaling’ of sub-grid processes
Links between the personalities, styles and performance in computer programming
There are repetitive patterns in strategies of manipulating source code. For
example, modifying source code before acquiring knowledge of how a code works
is a depth-first style and reading and understanding before modifying source
code is a breadth-first style. To the extent we know there is no study on the
influence of personality on them. The objective of this study is to understand
the influence of personality on programming styles. We did a correlational
study with 65 programmers at the University of Stuttgart. Academic achievement,
programming experience, attitude towards programming and five personality
factors were measured via self-assessed survey. The programming styles were
asked in the survey or mined from the software repositories. Performance in
programming was composed of bug-proneness of programmers which was mined from
software repositories, the grades they got in a software project course and
their estimate of their own programming ability. We did statistical analysis
and found that Openness to Experience has a positive association with
breadth-first style and Conscientiousness has a positive association with
depth-first style. We also found that in addition to having more programming
experience and better academic achievement, the styles of working depth-first
and saving coarse-grained revisions improve performance in programming.Comment: 27 pages, 6 figure
Efficient Algorithm on a Non-staggered Mesh for Simulating Rayleigh-Benard Convection in a Box
An efficient semi-implicit second-order-accurate finite-difference method is
described for studying incompressible Rayleigh-Benard convection in a box, with
sidewalls that are periodic, thermally insulated, or thermally conducting.
Operator-splitting and a projection method reduce the algorithm at each time
step to the solution of four Helmholtz equations and one Poisson equation, and
these are are solved by fast direct methods. The method is numerically stable
even though all field values are placed on a single non-staggered mesh
commensurate with the boundaries. The efficiency and accuracy of the method are
characterized for several representative convection problems.Comment: REVTeX, 30 pages, 5 figure
"Mental Rotation" by Optimizing Transforming Distance
The human visual system is able to recognize objects despite transformations
that can drastically alter their appearance. To this end, much effort has been
devoted to the invariance properties of recognition systems. Invariance can be
engineered (e.g. convolutional nets), or learned from data explicitly (e.g.
temporal coherence) or implicitly (e.g. by data augmentation). One idea that
has not, to date, been explored is the integration of latent variables which
permit a search over a learned space of transformations. Motivated by evidence
that people mentally simulate transformations in space while comparing
examples, so-called "mental rotation", we propose a transforming distance.
Here, a trained relational model actively transforms pairs of examples so that
they are maximally similar in some feature space yet respect the learned
transformational constraints. We apply our method to nearest-neighbour problems
on the Toronto Face Database and NORB
Ensemble of Hankel Matrices for Face Emotion Recognition
In this paper, a face emotion is considered as the result of the composition
of multiple concurrent signals, each corresponding to the movements of a
specific facial muscle. These concurrent signals are represented by means of a
set of multi-scale appearance features that might be correlated with one or
more concurrent signals. The extraction of these appearance features from a
sequence of face images yields to a set of time series. This paper proposes to
use the dynamics regulating each appearance feature time series to recognize
among different face emotions. To this purpose, an ensemble of Hankel matrices
corresponding to the extracted time series is used for emotion classification
within a framework that combines nearest neighbor and a majority vote schema.
Experimental results on a public available dataset shows that the adopted
representation is promising and yields state-of-the-art accuracy in emotion
classification.Comment: Paper to appear in Proc. of ICIAP 2015. arXiv admin note: text
overlap with arXiv:1506.0500
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