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SUTTA specifications


Unique identifier OMICS_00034
Alternative name Scoring-and-Unfolding Trimmed Tree Assembler
Software type Package/Module
Interface Command line interface
Restrictions to use Academic or non-commercial use
Operating system Unix/Linux
Computer skills Advanced
Stability No
Maintained No


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Publication for Scoring-and-Unfolding Trimmed Tree Assembler

SUTTA in publications

PMCID: 5223166
PMID: 28119651
DOI: 10.3389/fpsyg.2016.02055

[…] events to some degree (). consistent with these works, buddhist doctrine also suggests that a well-trained practitioner may modulate the mind’s response to negative events. according to the sallatha sutta (the arrow sutra), a well-trained practitioner and an untrained layperson would both experience the initial feeling of pain in their primary states when they come across a harmful event, […]

PMCID: 5023750
PMID: 27688740
DOI: 10.1007/s11469-016-9653-7

[…] number of discourses or suttas (pali; sanskrit: sutras) in the buddhist canonical literature are specifically devoted to the practice of mindfulness, of which the principal ones are the ānāpānasati sutta, satipaṭṭhāna sutta, mahasatipaṭṭhāna sutta, and kāyagatāsati sutta. consequently, we would argue that ‘mindfulness’ – that is a direct translation of the buddhist pali term ‘sati’ – assumes […]

PMCID: 3277272
PMID: 22363278
DOI: 10.3389/fnhum.2012.00018

[…] practice as explained by gunaratana, (). at the same time it is a basic component of different traditional buddhist meditation systems, ranging from early buddhist sources like the anāpānasati sutta or the satipatthāna sutta (bhikkhu bodhi, ) to classical tibetan buddhist instructions (karmapa wangchug dorje, ). thus, the mindful breathing exercises used in this study bear relevance […]

PMCID: 3272011
PMID: 22319599
DOI: 10.1371/journal.pone.0031002

[…] structure of the feature-space is better understood, it is hoped that well-chosen feature-combinations would suggest good “score” functions that can be globally optimized (e.g., by algorithms like sutta ) to improve assembly accuracy (since they would minimize features in the resulting assemblies). in this way, sequence-assembly problem can then be formulated as a (supervised) machine-learning […]

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SUTTA institution(s)
Courant Institute of Mathematical Sciences, New York University, New York, NY, USA; NYU School of Medicine, New York, NY, USA
SUTTA funding source(s)
This work was supported by the NSF CDI program and Abraxis BioScience, LLC.

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