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Record W2116036098 · doi:10.1142/s0218126611008031

ERROR RECOVERY IN CONTINUOUS VALUED NUMBER SYSTEM

2011· article· en· W2116036098 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueJournal of Circuits Systems and Computers · 2011
Typearticle
Languageen
FieldEngineering
TopicLow-power high-performance VLSI design
Canadian institutionsUniversity of CalgaryUniversity of Windsor
Fundersnot available
KeywordsRedundancy (engineering)Computer scienceImplementationError detection and correctionComputer hardwareElectronic circuitSet (abstract data type)ArithmeticAlgorithmMathematicsEngineeringElectrical engineering

Abstract

fetched live from OpenAlex

Continuous valued number system is a novel number system which can be employed for developing analog signal processing units. It is a continuous number system which is represented by a set of continuous digits. One of the main features of this system is that digits share information, and have a digit-level redundancy. This redundancy is used to protect the digits against environment imperfections when implemented by analog circuits. In this paper, integrity of this number system in representing real values is explored. The study is required to show the effect of implementation imperfections which is an indicative of its feasibility. Effects of possible errors and error threshold for implementing this system are studied in this paper. An error recovery method is proposed, which enhances this number system representation. An efficient error recovery method extends the application of this number system in high density memory and storage devices for hardware implementations of neural networks.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.327
Threshold uncertainty score0.610

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.017
GPT teacher head0.192
Teacher spread0.175 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it