Что это такое FMCG (fast moving consumer goods)

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FGCM and civilian convicts

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THIS is apropos the news item in Dawn of March 9. The division bench of the Lahore High Court has rejected the appeal of the five civilians convicted by a Field General Court Martial (FGCM) who wanted to have a copy of the proceedings to help them file appeals against their convictions.

I am not questioning the decision of the army to deny the request as it must have obviously been based on some hackneyed provision of the Army Act and the Rules.

I am also not questioning the decision of the division bench in view of the notorious article 199(3) of the Constitution.

But I do question whether the poor civilians are getting a fair deal. My questions are as follows: The FGCM, as the name suggests, is meant when the army is in the field engaged in some operations against an enemy .

The FGCM can be composed of fewer officers than a normal General Court Martial . The proceedings can be shortened so that the court members return to field duty early.

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What in God’s name is there need for an FGCM in a peace time? Why can’t the army spare five officers to try civilians by a normal General Court Martial, if they are so keen to try them, and why shorten the proceedings?. No one is rushing to fight a war.

Then, while a convict is allowed to appeal against the conviction, he is denied to have a copy of the proceedings.

Now, pray, what is there for him to challenge? If there is no record, how one can show any illegality in the proceedings or whether the conviction is based on competent evidence, the points generally urged in appeals.

There is a crying need to have a critical look at these unjust provisions not only in respect of civilians but also in respect of army personnel who are tried by a truncated and obsolete FGCM in peace time, just for the convenience or lethargy of the commanders.

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README.md

fgcmcal: Global Photometric Calibration in LSST with FGCM

This is the LSST stack code to interface with the Forward Global Calibration Method (FGCM) third-party package to perform global photometric survey calibration. Please see Burke, Rykoff, et al. 2018 for the paper describing the method, and https://github.com/lsst/fgcm/tree/lsst-dev (the LSST fork of https://github.com/erykoff/fgcm) for the Python implementation that is used by this fgcmcal package.

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FGCM performs a global photometric calibration, starting with instrumental fluxes and producing top-of-the-atmosphere standard fluxes by forward modeling the atmosphere and instrument, including chromatic corrections. Overall absolute calibration is reduced to the computation of one offset per band, which fgcmcal can optionally compute based on a reference catalog.

Currently, the fgcmcal package will only run on HSC data within the stack, but this is being actively worked on. In general, the FGCM code should be compatible with any survey with the following requirements:

  • Visit/CCD based observations
  • Transmission curves for each filter, preferably for each CCD
  • MODTRAN4 is required to generate new atmosphere tables.
  • Atmosphere tables for the following telescope locations are included:
    • Blanco telescope at CTIO (DES)
    • Subaru telescope at Mauna Kea (HSC)
    • LSST telescope at Cerro Pachon (LSST)
  • Enough memory to hold all the observations in memory at once.
    • A full run of four years of DES survey data can be run on a machine with 128 Gb RAM and 32 processors in less than a day.

Installing the fgcm and fgcmcal Packages

As of DM-16128, both fgcm (the third-party package) and fgcmcal (the stack interface) are distributed with lsst_distrib . Therefore, with a stack installation after w_2018_47 or v17.0 or later, setting up fgcmcal is as simple as:

Or, to only get fgcmcal and required dependencies:

Please see the FGCM Cookbook for doing a test run on HSC RC data on lsst-dev and to learn the control flow of fgcmcal .

Liver tumor segmentation using kernel-based FGCM and PGCM

School of Computer Sciences, Universiti Sains Malaysia, Penang, Malaysia

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School of Computer Sciences, Universiti Sains Malaysia, Penang, Malaysia

School of Computer Sciences, Universiti Sains Malaysia, Penang, Malaysia

School of Computer Sciences, Universiti Sains Malaysia, Penang, Malaysia

School of Computer Sciences, Universiti Sains Malaysia, Penang, Malaysia

School of Computer Sciences, Universiti Sains Malaysia, Penang, Malaysia

School of Computer Sciences, Universiti Sains Malaysia, Penang, Malaysia

School of Computer Sciences, Universiti Sains Malaysia, Penang, Malaysia

School of Computer Sciences, Universiti Sains Malaysia, Penang, Malaysia

School of Computer Sciences, Universiti Sains Malaysia, Penang, Malaysia

Advanced Medical and Dental Institute, Bertam, Penang, Malaysia

Advanced Medical and Dental Institute, Bertam, Penang, Malaysia

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MICCAI’11: Proceedings of the Third international conference on Abdominal Imaging: computational and Clinical Applications

ABSTRACT

Low contrast between tumor and healthy liver tissue is one of the significant and challenging features among others in the automated tumor delineation process. In this paper we propose kernel based clustering algorithms that incorporate Tsallis entropy to resolve long range interactions between tumor and healthy tissue intensities. This paper reports the algorithm and its encouraging results of evaluation with MICCAI liver Tumor Segmentation Challenge 08 (LTS08) dataset. Work in progress involves incorporating additional features and expert knowledge into clustering algorithm to improve the accuracy.

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