1,258 research outputs found
Open-architecture Implementation of Fragment Molecular Orbital Method for Peta-scale Computing
We present our perspective and goals on highperformance computing for
nanoscience in accordance with the global trend toward "peta-scale computing."
After reviewing our results obtained through the grid-enabled version of the
fragment molecular orbital method (FMO) on the grid testbed by the Japanese
Grid Project, National Research Grid Initiative (NAREGI), we show that FMO is
one of the best candidates for peta-scale applications by predicting its
effective performance in peta-scale computers. Finally, we introduce our new
project constructing a peta-scale application in an open-architecture
implementation of FMO in order to realize both goals of highperformance in
peta-scale computers and extendibility to multiphysics simulations.Comment: 6 pages, 9 figures, proceedings of the 2nd IEEE/ACM international
workshop on high performance computing for nano-science and technology
(HPCNano06
A Preliminary Study on Susceptibility of Mice of Various Strain to Hymenolepis nana Eggs
Mice of various strain were given orally eggs of Hymenolepis nana maintained by ddY mice and sacrificed for the adult worm recovery 2 weeks after the administration of the eggs. The mice of ddY, ICR, C3H and BALB/C were highly susceptible to the eggs while those of DBA/2 and C57BL/6 were less susceptible
Multi-physics Extension of OpenFMO Framework
OpenFMO framework, an open-source software (OSS) platform for Fragment
Molecular Orbital (FMO) method, is extended to multi-physics simulations (MPS).
After reviewing the several FMO implementations on distributed computer
environments, the subsequent development planning corresponding to MPS is
presented. It is discussed which should be selected as a scientific software,
lightweight and reconfigurable form or large and self-contained form.Comment: 4 pages with 11 figure files, to appear in the Proceedings of ICCMSE
200
Covariance Matrix Adaptation Evolutionary Strategy with Worst-Case Ranking Approximation for Min--Max Optimization and its Application to Berthing Control Tasks
In this study, we consider a continuous min--max optimization problem
whose objective
function is a black-box. We propose a novel approach to minimize the worst-case
objective function directly using a covariance matrix
adaptation evolution strategy (CMA-ES) in which the rankings of solution
candidates are approximated by our proposed worst-case ranking approximation
(WRA) mechanism. We develop two variants of WRA combined with CMA-ES and
approximate gradient ascent as numerical solvers for the inner maximization
problem. Numerical experiments show that our proposed approach outperforms
several existing approaches when the objective function is a smooth strongly
convex--concave function and the interaction between and is strong. We
investigate the advantages of the proposed approach for problems where the
objective function is not limited to smooth strongly convex--concave functions.
The effectiveness of the proposed approach is demonstrated in the robust
berthing control problem with uncertainty.ngly convex--concave functions. The
effectiveness of the proposed approach is demonstrated in the robust berthing
control problem with uncertainty
Revisiting a kNN-based Image Classification System with High-capacity Storage
In existing image classification systems that use deep neural networks, the
knowledge needed for image classification is implicitly stored in model
parameters. If users want to update this knowledge, then they need to fine-tune
the model parameters. Moreover, users cannot verify the validity of inference
results or evaluate the contribution of knowledge to the results. In this
paper, we investigate a system that stores knowledge for image classification,
such as image feature maps, labels, and original images, not in model
parameters but in external high-capacity storage. Our system refers to the
storage like a database when classifying input images. To increase knowledge,
our system updates the database instead of fine-tuning model parameters, which
avoids catastrophic forgetting in incremental learning scenarios. We revisit a
kNN (k-Nearest Neighbor) classifier and employ it in our system. By analyzing
the neighborhood samples referred by the kNN algorithm, we can interpret how
knowledge learned in the past is used for inference results. Our system
achieves 79.8% top-1 accuracy on the ImageNet dataset without fine-tuning model
parameters after pretraining, and 90.8% accuracy on the Split CIFAR-100 dataset
in the task incremental learning setting.Comment: 16 pages, 7 figures, 6 table
Studies on Alterations in Acid Phosphatase Activity, Body Weight and Ultrastructure of Adult Angiostrongylus cantonensis in Rats Treated with Flubendazole at a Subcurative Dose
Physiological effects of flubendazole on adult Angiostrongylus cantonensis were studied. Administration of flubendazole for 3 consecutive days (48-50 days post-infection) at 10 mg/kg/day did not affect the number of worms of and weight of adult female A. cantonensis recovered from rats 16 hr after termination of medication while it lowered the phosphatase activity by the intact worms. The possible modes of action of the drug were discussed together with electron-microscopic observation of the body wall of the worms recovered from the treated and non-treated rats
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