14,868 research outputs found
A machine learning route between band mapping and band structure
The electronic band structure (BS) of solid state materials imprints the
multidimensional and multi-valued functional relations between energy and
momenta of periodically confined electrons. Photoemission spectroscopy is a
powerful tool for its comprehensive characterization. A common task in
photoemission band mapping is to recover the underlying quasiparticle
dispersion, which we call band structure reconstruction. Traditional methods
often focus on specific regions of interests yet require extensive human
oversight. To cope with the growing size and scale of photoemission data, we
develop a generic machine-learning approach leveraging the information within
electronic structure calculations for this task. We demonstrate its capability
by reconstructing all fourteen valence bands of tungsten diselenide and
validate the accuracy on various synthetic data. The reconstruction uncovers
previously inaccessible momentum-space structural information on both global
and local scales in conjunction with theory, while realizing a path towards
integrating band mapping data into materials science databases
One for all and all for one: regression checks with many regressors
We develop a novel approach to build checks of parametric regression models when many regressors are present, based on a class of sufficiently rich semiparametric alternatives, namely single-index models. We propose an omnibus test based on the kernel method that performs against a sequence of directional nonparametric alternatives as if there was one regressor only, whatever the number of regressors. This test can be viewed as a smooth version of the integrated conditional moment (ICM) test of Bierens. Qualitative information can be easily incorporated into the procedure to enhance power. In an extensive comparative simulation study, we find that our test is little sensitive to the smoothing parameter and performs well in multidimensional settings. We then apply it to a cross-country growth regression model.Dimensionality, Hypothesis testing, Nonparametric methods
The Modified Bottleneck Assignment Problem in Vector Case ―An Idea to Apply a Clustering Method―
In this study, we deal with the bottleneck assignment problem in vector case. This problem is NP-complete. We show an idea that we use a clustering method to divide the original problem into sub problems. Each set of vertices is divided to subsets by a non-hierarchical clustering method. We make the optimal combination of the subsets, then vertices in the subset are corresponded according to the subsets’ combinations. We show the effect of this idea by the numerical experiments
Closing the loop between neural network simulators and the OpenAI Gym
Since the enormous breakthroughs in machine learning over the last decade,
functional neural network models are of growing interest for many researchers
in the field of computational neuroscience. One major branch of research is
concerned with biologically plausible implementations of reinforcement
learning, with a variety of different models developed over the recent years.
However, most studies in this area are conducted with custom simulation scripts
and manually implemented tasks. This makes it hard for other researchers to
reproduce and build upon previous work and nearly impossible to compare the
performance of different learning architectures. In this work, we present a
novel approach to solve this problem, connecting benchmark tools from the field
of machine learning and state-of-the-art neural network simulators from
computational neuroscience. This toolchain enables researchers in both fields
to make use of well-tested high-performance simulation software supporting
biologically plausible neuron, synapse and network models and allows them to
evaluate and compare their approach on the basis of standardized environments
of varying complexity. We demonstrate the functionality of the toolchain by
implementing a neuronal actor-critic architecture for reinforcement learning in
the NEST simulator and successfully training it on two different environments
from the OpenAI Gym
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