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Embedded Ridge Approximations
[article]
2020
arXiv
pre-print
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Our paper offers analytical and simulation-based examples that expose different facets of embedded ridge approximations. ...
We formalise our ideas by assigning ridge approximations for the field at select nodes. ...
The authors would like to thank the anonymous reviewer for useful feedback, which helped improve the manuscript. ...
arXiv:1907.07037v3
fatcat:raax2bwl4fevbip3ab4pw37xkq
Polynomial Ridge Flowfield Estimation
[article]
2021
arXiv
pre-print
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Their dimension reducing nature alleviates the problems associated with visualising high dimensional datasets, enabling improved understanding of design spaces and potentially providing valuable physical ...
Dimension reducing ridge functions are obtained for numerous points within training flowfields. The functions can then be used to predict flow variables for new, previously unseen, flowfields. ...
Turing Institute, and by the Lloyd's Register Foundation-Alan Turing Institute programme on Data-Centric Engineering under the LRF grant G0095. ...
arXiv:2107.07547v1
fatcat:4vxb65vkkrbxppfemh5sntrhy4
Standard Model Physics and the Digital Quantum Revolution: Thoughts about the Interface
[article]
2021
arXiv
pre-print
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Beyond catalyzing these technological advances, entanglement is enabling parallel progress as a diagnostic for quantum correlations and as an organizational tool, both guiding improved understanding of ...
quantum many-body systems and quantum field theories defining and emerging from the Standard Model. ...
ACKNOWLEDGEMENTS We would like to thank our friends and collaborators for providing a stimulating and thriving quantum village from which this article emerged, and ask for their forgiveness in failing ...
arXiv:2107.04769v1
fatcat:reuyovhocbc2vgiygwig2mjhca
Enabling particle applications for exascale computing platforms
2021
The international journal of high performance computing applications
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2021 pre-print arXiv:2109.09056v1 fatcat:pyasu4ztq5fn5bfm44g2o4mul4
The Exascale Computing Project (ECP) is invested in co-design to assure that key applications are ready for exascale computing. ...
Success is measured by identifiable "lessons learned" that are translated either directly into parent production application codes or into libraries, with demonstrated performance and/or productivity improvement ...
Acknowledgements This work was performed as part of the Co-design Center for Particle Applications, supported by the Exascale Computing Project This paper describes objective technical results and analysis ...
doi:10.1177/10943420211022829
fatcat:7goiighkmjgjnosvzjubskjplq
Dynamic Neural Fields as Building Blocks of a Cortex-Inspired Architecture for Robotic Scene Representation
2011
IEEE Transactions on Autonomous Mental Development
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These three-dimensional fields couple into lower dimensional fields, which provide the links to the sensory surface and to the motor systems. ...
At the core of this architecture are three-dimensional dynamic neural fields (DNFs) that link feature to spatial information. ...
Spencer from the University of Iowa for the extensive discussion and sharing of ideas, and S. Schneegans from the Institut für Neuroinformatik, University of Bochum, Germany. ...
doi:10.1109/tamd.2011.2109714
fatcat:yzdhs3qbirc7voi4qamly2capq
Reproduction of patterns in melanocytic proliferations by agent-based simulation and geometric modeling
2021
PLoS Computational Biology
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Here we present an agent-based model for simulating the emergence of the main biologic patterns found in melanocytic proliferations. ...
Our model portrays the extracellular matrix of the dermo-epidermal junction as a two-dimensional manifold and we simulate cellular migration in terms of geometric translations driven by adhesive, repulsive ...
We further thank the reviewers for constructive feedback and ideas. ...
doi:10.1371/journal.pcbi.1008660
pmid:33539342
pmcid:PMC7888658
fatcat:l5kfcyod4ba2nn2shdiyzbfema
Mean-Field Density Matrix Decompositions
[article]
2020
arXiv
pre-print
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We introduce new and robust decompositions of mean-field Hartree-Fock (HF) and Kohn-Sham density functional theory (KS-DFT) relying on the use of localized molecular orbitals and physically sound charge ...
This is made possible by improving upon the granularity of the underlying data. ...
Specifically, the IAO transformation, C → B, proceeds through the initial construction of a reduced-dimension basis, the functions of which are constructed by a projection operation from a set of free-atom ...
arXiv:2009.10837v1
fatcat:g3ybi26hyrfxjeto6bxejeacve
On closures for reduced order models - A spectrum of first-principle to machine-learned avenues
[article]
2021
arXiv
pre-print
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For over a century, reduced order models (ROMs) have been a fundamental discipline of theoretical fluid mechanics. ...
Finally, we outline our vision on how state-of-the-art data-driven modeling can continue to reshape the field of reduced order modeling. ...
(20) For clarity, we introduce the POD procedure for a scalar field (POD is normally applied to the velocity vector field). ...
arXiv:2106.14954v2
fatcat:q6jzbxfjabc3vg3nsn24z4lbyy
2022 Review of Data-Driven Plasma Science
[article]
2022
arXiv
pre-print
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It is now becoming impractical for humans to analyze all the data manually. ...
This review article highlights latest development and progress in the interdisciplinary field of data-driven plasma science (DDPS). ...
locally linear embedding (HLLE) [41] is another type of sparse spectral method that learns manifolds in high-dimensional data, maintaining the balance between local geometric information and over-fitting ...
arXiv:2205.15832v1
fatcat:fxsl6gl3fncnhpoj76defxoc3a
A Comprehensive Survey on Secure Outsourced Computation and its Applications
2019
IEEE Access
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With the ever-increasing requirement of storage and computation resources, it is unrealistic for local devices (with limited sources) to implement large-scale data processing. ...
Finally, we discuss the security and performance of existing works and give future directions in this field. ...
Besides, the authors also applied the method of PCA (principal component analysis) for dimensionality reduction of the face templates. ...
doi:10.1109/access.2019.2949782
fatcat:ternbyhqezgd5cvhtfqfggqdqq
Scalable algorithms for physics-informed neural and graph networks
2022
Data-Centric Engineering
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Physics-informed machine learning (PIML) has emerged as a promising new approach for simulating complex physical and biological systems that are governed by complex multiscale processes for which some ...
with GNNs based on graph exterior calculus to construct differential operators; we refer to these architectures as physics-informed graph networks (PIGNs). ...
., for the U.S. Department of Energy's National Nuclear Security Administration under contract DE-NA0003525. This paper describes objective technical results and analysis. ...
doi:10.1017/dce.2022.24
fatcat:f54c6flff5cu3nvd6jull6wxyu
Explaining heterogeneity in medial entorhinal cortex with task-driven neural networks
[article]
2021
bioRxiv
pre-print
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of grid cells themselves --- despite not being explicitly trained for this purpose. ...
We found that recently developed task-optimized neural network models are substantially better than traditional grid cell-centric models at matching most MEC neuronal response profiles --- including those ...
Wen for helpful discussions. We thank the anonymous reviewers for their feedback on a draft of this manuscript. ...
doi:10.1101/2021.10.30.466617
fatcat:kcwyt4gznnhmllinlxqml6x3uy
Learning Representations by Humans, for Humans
[article]
2021
arXiv
pre-print
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2020 pre-print arXiv:1905.12686v3 fatcat:jyrk56fznjfblkg7xreyhqbexyOther Versions
Inspired by the success of representation learning in improving performance of machine predictors, our framework learns human-facing representations optimized for human performance. ...
When machine predictors can achieve higher performance than the human decision-makers they support, improving the performance of human decision-makers is often conflated with improving machine accuracy ...
Sinceĥ is trained to be locally rather than globally accurate,ĥ need not exactly match h for learning to improve. ...
arXiv:1905.12686v4
fatcat:3rto3t3qbvegjevfr6kvyqi6ci
Nonlinear decoding of a complex movie from the mammalian retina
2018
PLoS Computational Biology
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We constructed nonlinear (kernelized and neural network) decoders that improved significantly over linear results. ...
Theoretically, this procedure is possible for any stimulus, but in practice model inference is feasible only if it incorporates strong dimensionality reduction assumptions (e.g., that neurons respond to ...
Acknowledgments We thank Matthew Chalk, Cristina Savin, and Jonathan D Victor for helpful comments on the manuscript. We also thank Christoph Lampert for useful discussions on kernel methods. ...
doi:10.1371/journal.pcbi.1006057
pmid:29746463
pmcid:PMC5944913
fatcat:a4dssvu4xfh3pg62eemcxtqq6a
HUMAN EYE DETECTION SYSTEM FOR RECOGNIZE EYE DISEASES USING IMAGE PROCESSING
2021
Zenodo
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HUMAN EYE DETECTION SYSTEM FOR RECOGNIZE EYE DISEASES USING IMAGE PROCESSING: D.Sc Thesis. ...
MODEL SIM SIMULATION ModelSim could be a authentication and simulation tool for Verilog
MEDIAN FILTER SIMULATION OUTPUT Simulation results showed the performance of improved median filter. ...
MATLAB has developed as a vital tool in various fields over a
number of years by many users. ...
doi:10.5281/zenodo.5148811
fatcat:4pzov2mwtzd2hbgqsxib46snae
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