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About 10,000 papers for “code density”

cell density code

Lim, Donghwan · Zenodo (CERN European Organization for Nuclear Research) · 2025

Cited by 0Open access

cell density code

Lim, Donghwan · Zenodo (CERN European Organization for Nuclear Research) · 2025

Cited by 0Open access

Kernel Density Code

Yuriy Tchamourliyski · RePEc: Research Papers in Economics

The archive contains Gauss programs for weighted kernel density estimation, and cross-validation. The same procedures can be used for non-parametric regression, which is a special case of weighted kernel density estimate, with the weights being given by the dependent variable. Currently, there are three choices for the kernel function (normal, standardized quartic, and unstandardized quartic). Other options include rotating the data, and adjusting for the presence of discrete regressors. See the description at the beginning of each file for details. The code uses a direct implementation, and may be slow for large data sets.

Cited by 0

Jaguar density JAGS code

Eriksson, Charlotte, Kantek, Daniel L. Z., Miyazaki, Selma et al. · Figshare · 2021

JAGS code used to estimate jaguar density<br>

Cited by 1Open access

Jaguar density JAGS code

Eriksson, Charlotte, Kantek, Daniel L. Z., Miyazaki, Selma et al. · Figshare · 2021

JAGS code used to estimate jaguar density<br>

Cited by 1Open access

PROMDENS: Promoted Density Approach code

Janoš, Jiří, Hollas, Daniel · Zenodo (CERN European Organization for Nuclear Research) · 2025

Enhancing PDAW weights analysis in the code (the output weights remain the same). What's Changed Update README.md with testing info by @JanosJiri in https://github.com/PHOTOX/promdens/pull/56 Slight modification of README.md by @JanosJiri in https://github.com/PHOTOX/promdens/pull/57 Upgrading readme and PDAW weights analysis by @JanosJiri in https://github.com/PHOTOX/promdens/pull/59 Getting ready for releasing v1.0.2 by @JanosJiri in https://github.com/PHOTOX/promdens/pull/60 Full Changelog: https://github.com/PHOTOX/promdens/compare/v1.0.1...v1.0.2

Cited by 0Open access

Global functional connectivity density code

Tomasi, Dardo · Figshare · 2024

Global functional connectivity density (gFCD) refers to the number of connections that a specific grayordinate has with all other grayordinates in the brain network. It provides insight into the importance or centrality of a particular brain region within the network. Nodes with a high gFCD are considered hubs and may play crucial roles in integrating information across different brain regions. The matlab scripts were developed at NIAAA to map positive (gFCDp), negative (gFCDn), ipsilateral (igFCD) and contralateral (cgFCD) connectivity from fMRI datasets in CIFTI format such as those of the ABCD brain imaging data structure (BIDS) Community Collection (ABCC) (https://collection3165.readthedocs.io/en/stable/). The Code is provided "as is," without any warranty of any kind, express or implied, including but not limited to the warranties of merchantability, fitness for a particular purpose, and non-infringement. Developer/Owner makes no representations or warranties regarding the accuracy, reliability, or completeness of the Code or its suitability for any particular purpose. In no event shall Developer/Owner be liable for any direct, indirect, incidental, special, consequential, or punitive damages arising out of or in connection with the use or performance of the Code, even if Developer/Owner has been advised of the possibility of such damages.

Cited by 0Open access

Global functional connectivity density code

Tomasi, Dardo · Figshare · 2024

Global functional connectivity density (gFCD) refers to the number of connections that a specific grayordinate has with all other grayordinates in the brain network. It provides insight into the importance or centrality of a particular brain region within the network. Nodes with a high gFCD are considered hubs and may play crucial roles in integrating information across different brain regions. The matlab scripts were developed at NIAAA to map positive (gFCDp), negative (gFCDn), ipsilateral (igFCD) and contralateral (cgFCD) connectivity from fMRI datasets in CIFTI format such as those of the ABCD brain imaging data structure (BIDS) Community Collection (ABCC) (https://collection3165.readthedocs.io/en/stable/). The Code is provided "as is," without any warranty of any kind, express or implied, including but not limited to the warranties of merchantability, fitness for a particular purpose, and non-infringement. Developer/Owner makes no representations or warranties regarding the accuracy, reliability, or completeness of the Code or its suitability for any particular purpose. In no event shall Developer/Owner be liable for any direct, indirect, incidental, special, consequential, or punitive damages arising out of or in connection with the use or performance of the Code, even if Developer/Owner has been advised of the possibility of such damages.

Cited by 0Open access

PROMDENS: Promoted Density Approach code

Janoš, Jiří, Hollas, Daniel · Zenodo (CERN European Organization for Nuclear Research) · 2025

Enhancing PDAW weights analysis in the code (the output weights remain the same). What's Changed Update README.md with testing info by @JanosJiri in https://github.com/PHOTOX/promdens/pull/56 Slight modification of README.md by @JanosJiri in https://github.com/PHOTOX/promdens/pull/57 Upgrading readme and PDAW weights analysis by @JanosJiri in https://github.com/PHOTOX/promdens/pull/59 Getting ready for releasing v1.0.2 by @JanosJiri in https://github.com/PHOTOX/promdens/pull/60 Full Changelog: https://github.com/PHOTOX/promdens/compare/v1.0.1...v1.0.2

Cited by 0Open access

Novel parton density determination code

Francesca Capel, R. Aggarwal, M. Botje et al. · Physical review. D/Physical review. D. · 2024

We introduce our novel Bayesian parton density determination code, artonensity.jl. The motivation for this new code, the framework, and its validation are described. As we show, artonensity.jl provides both a flexible environment for the determination of parton densities and a wealth of information concerning the knowledge update provided by the analyzed dataset. Published by the American Physical Society 2024

Cited by 3Open access

Code for "evolving symbolic density functionals"

Ma, He, Narayanaswamy, Arunachalam, Riley, Patrick et al. · Zenodo (CERN European Organization for Nuclear Research) · 2022

This directory contains demonstration of GAS22 functional from paper "Evolving Symbolic Density Functionals" (https://arxiv.org/abs/2203.02540) The paper uses the MGCDB84 dataset, which can be downloaded from https://doi.org/10.1080/00268976.2017.1333644. One can perform SCF calculations on the dataset using helper functions in syfes/scf/scf.py and parse the results using the Dataset class in the syfes/dataset/dataset.py. The parsed results can be dumped to files using the Dataset.save method. In run.sh, a simple example is provided to perform evolutionary search of the B97 exchange functional. To run the example, one needs to set the config.dataset.dataset_directory in run.sh to a directory that contains the SCF results of MGCDB84. GitHub: https://github.com/google-research/google-research/tree/master/symbolic_functionals

Cited by 0Open access

Density-induced Consensus Protocol (source code)

Minakowski, Piotr · Zenodo (CERN European Organization for Nuclear Research) · 2019

This is minimal example to reproduce simulations presented in P. Minakowski, P.B. Mucha, J Peszek <em>Density-induced Consensus Protocol</em>. If you are interested in the extended version please contact piotr.minakowski@ovgu.de To run simulation type in your terminal: python run.py

Cited by 0Open access

SH3: high code density, low power

Atsushi Hasegawa, I. Kawasaki, Kazuji Yamada et al. · IEEE Micro · 1995

Hitachi's SH series microprocessors feature 32-bit RISC architecture with a 16-bit, fixed-length instruction set. We describe SH3, a pipelined implementation of the SH architecture with on-chip cache, MMU, and software-programmable power management. Its higher code density and corresponding improvement in instruction-fetch latency lead to higher performance than typical 32-bit RISC architectures achieve. These features, small die size, and low power consumption make SH3 an ideal microprocessor for portable computing systems or multimedia systems.

Cited by 123

Code Vector Density in Topographic Mappings

Lutrell, Stephen P. · Defense Technical Information Center (DTIC) · 1992

In this memorandum we present an informally argued derivation of the properties of topographic vector quantisers in the limit of a large codebook size. In particular, we prove that the code vector density does not depend on one's choice of neighbourhood function, provided that we use the minimum distortion (rather than the nearest neighbour) encoding prescription. This result suggests that widespread use of the nearest neighbour prescription in topographic mapping networks is fundamentally misguided. It would be advisable to remember that the nearest neighbour prescription is assumed not derived, so its adherents must accept defeat gracefully.

Cited by 2

HD-Code: End-to-End High Density Code for DNA Storage

Jianjun Wu, Shufang Zhang, Tao Zhang et al. · IEEE Transactions on NanoBioscience · 2021

With the rapid development of digital information techniques, the use of DNA media for information storage is considered as the future direction of data storage. Existing DNA storage schemes simply map compressed binary multimedia data into DNA base data, which has the disadvantages of data loss, low logical storage density and high cost of synthesis. This paper presents an end-to-end high density DNA encoding algorithm(referred to as HD-code, where HD stands for high density). The novelty and contributions of this work contain three parts. First, by taking full advantage of the statistical characteristics of the original multimedia data and considering the biological constraints on the DNA bases, the proposed scheme achieves higher logical storage density and improves the flexibility and consistency in data storage. Second, by performing data conversion, the proposed scheme can effectively encode extreme images with large proportion of single color. Third, the proposed method can reconstruct high quality images and reduce synthesis costs by yielding better rate-PSNR(Peak Signal to Noise Ratio).

Cited by 13