8,383 research outputs found
Neural-inspired sensors enable sparse, efficient classification of spatiotemporal data
Sparse sensor placement is a central challenge in the efficient
characterization of complex systems when the cost of acquiring and processing
data is high. Leading sparse sensing methods typically exploit either spatial
or temporal correlations, but rarely both. This work introduces a new sparse
sensor optimization that is designed to leverage the rich spatiotemporal
coherence exhibited by many systems. Our approach is inspired by the remarkable
performance of flying insects, which use a few embedded strain-sensitive
neurons to achieve rapid and robust flight control despite large gust
disturbances. Specifically, we draw on nature to identify targeted
neural-inspired sensors on a flapping wing to detect body rotation. This task
is particularly challenging as the rotational twisting mode is three
orders-of-magnitude smaller than the flapping modes. We show that nonlinear
filtering in time, built to mimic strain-sensitive neurons, is essential to
detect rotation, whereas instantaneous measurements fail. Optimized sparse
sensor placement results in efficient classification with approximately ten
sensors, achieving the same accuracy and noise robustness as full measurements
consisting of hundreds of sensors. Sparse sensing with neural inspired encoding
establishes a new paradigm in hyper-efficient, embodied sensing of
spatiotemporal data and sheds light on principles of biological sensing for
agile flight control.Comment: 21 pages, 19 figure
Finding Efficient Collective Variables: The Case of Crystallization
Several enhanced sampling methods such as umbrella sampling or metadynamics
rely on the identification of an appropriate set of collective variables.
Recently two methods have been proposed to alleviate the task of determining
efficient collective variables. One is based on linear discriminant analysis,
the other on a variational approach to conformational dynamics, and uses
time-lagged independent component analysis. In this paper, we compare the
performance of these two approaches in the study of the homogeneous
crystallization of two simple metals. We focus on Na and Al and search for the
most efficient collective variables that can be expressed as a linear
combination of X-ray diffraction peak intensities. We find that the
performances of the two methods are very similar. However, the method based on
linear discriminant analysis, in its harmonic version, is to be preferred
because it is simpler and much less computationally demanding
Exploiting Nonlinear Recurrence and Fractal Scaling Properties for Voice Disorder Detection
Background: Voice disorders affect patients profoundly, and acoustic tools can potentially measure voice function objectively. Disordered sustained vowels exhibit wide-ranging phenomena, from nearly periodic to highly complex, aperiodic vibrations, and increased "breathiness". Modelling and surrogate data studies have shown significant nonlinear and non-Gaussian random properties in these sounds. Nonetheless, existing tools are limited to analysing voices displaying near periodicity, and do not account for this inherent biophysical nonlinearity and non-Gaussian randomness, often using linear signal processing methods insensitive to these properties. They do not directly measure the two main biophysical symptoms of disorder: complex nonlinear aperiodicity, and turbulent, aeroacoustic, non-Gaussian randomness. Often these tools cannot be applied to more severe disordered voices, limiting their clinical usefulness.

Methods: This paper introduces two new tools to speech analysis: recurrence and fractal scaling, which overcome the range limitations of existing tools by addressing directly these two symptoms of disorder, together reproducing a "hoarseness" diagram. A simple bootstrapped classifier then uses these two features to distinguish normal from disordered voices.

Results: On a large database of subjects with a wide variety of voice disorders, these new techniques can distinguish normal from disordered cases, using quadratic discriminant analysis, to overall correct classification performance of 91.8% plus or minus 2.0%. The true positive classification performance is 95.4% plus or minus 3.2%, and the true negative performance is 91.5% plus or minus 2.3% (95% confidence). This is shown to outperform all combinations of the most popular classical tools.

Conclusions: Given the very large number of arbitrary parameters and computational complexity of existing techniques, these new techniques are far simpler and yet achieve clinically useful classification performance using only a basic classification technique. They do so by exploiting the inherent nonlinearity and turbulent randomness in disordered voice signals. They are widely applicable to the whole range of disordered voice phenomena by design. These new measures could therefore be used for a variety of practical clinical purposes.

The Economics and Psychology of Personality Traits
This paper explores the interface between personality psychology andeconomics. We examine the predictive power of personality and the stability ofpersonality traits over the life cycle. We develop simple analytical frameworksfor interpreting the evidence in personality psychology and suggest promisingavenues for future research.education, training and the labour market;
The Economics and Psychology of Personality Traits
This paper explores the interface between personality psychology and economics. We examine the predictive power of personality and the stability of personality traits over the life cycle. We develop simple analytical frameworks for interpreting the evidence in personality psychology and suggest promising avenues for future research.personality traits, lifecycle effects, psychology, economics
The Economics and Psychology of Personality Traits
This paper explores the interface between personality psychology and economics. We examine the predictive power of personality and the stability of personality traits over the life cycle. We develop simple analytical frameworks for interpreting the evidence in personality psychology and suggest promising avenues for future research.lifecycle effects, personality traits
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