20 research outputs found
Learning Reservoir Dynamics with Temporal Self-Modulation
Reservoir computing (RC) can efficiently process time-series data by
transferring the input signal to randomly connected recurrent neural networks
(RNNs), which are referred to as a reservoir. The high-dimensional
representation of time-series data in the reservoir significantly simplifies
subsequent learning tasks. Although this simple architecture allows fast
learning and facile physical implementation, the learning performance is
inferior to that of other state-of-the-art RNN models. In this paper, to
improve the learning ability of RC, we propose self-modulated RC (SM-RC), which
extends RC by adding a self-modulation mechanism. The self-modulation mechanism
is realized with two gating variables: an input gate and a reservoir gate. The
input gate modulates the input signal, and the reservoir gate modulates the
dynamical properties of the reservoir. We demonstrated that SM-RC can perform
attention tasks where input information is retained or discarded depending on
the input signal. We also found that a chaotic state emerged as a result of
learning in SM-RC. This indicates that self-modulation mechanisms provide RC
with qualitatively different information-processing capabilities. Furthermore,
SM-RC outperformed RC in NARMA and Lorentz model tasks. In particular, SM-RC
achieved a higher prediction accuracy than RC with a reservoir 10 times larger
in the Lorentz model tasks. Because the SM-RC architecture only requires two
additional gates, it is physically implementable as RC, providing a new
direction for realizing edge AI
Fingerprinting of Materials: Technical Supplement
This supplement to the Guidelines for Maintaining a Chemical Fingerprinting Program has been developed to assist NASA personnel, contractors, and sub-contractors in defining the technical aspects and basic concepts which can be used in chemical fingerprinting programs. This material is not meant to be totally inclusive to all chemical fingerprinting programs, but merely to present current concepts. Each program will be tailored to meet the needs of the individual organizations using chemical fingerprinting to improve their quality and reliability in the production of aerospace systems
Handbook of Mathematical Geosciences
This Open Access handbook published at the IAMG's 50th anniversary, presents a compilation of invited path-breaking research contributions by award-winning geoscientists who have been instrumental in shaping the IAMG. It contains 45 chapters that are categorized broadly into five parts (i) theory, (ii) general applications, (iii) exploration and resource estimation, (iv) reviews, and (v) reminiscences covering related topics like mathematical geosciences, mathematical morphology, geostatistics, fractals and multifractals, spatial statistics, multipoint geostatistics, compositional data analysis, informatics, geocomputation, numerical methods, and chaos theory in the geosciences