On the minimum stopping sets of product codes [PDF]
Transactions on Combinatorics, 2018 It is shown that the certain combinatorial structures called stopping sets have the important role in analysis of iterative decoding. In this paper, the number of minimum stopping sets of a product code is determined by the number of the minimum stopping
Morteza Hivadi, Akbar Zare Chavoshi
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Target-Session early stopping for cross-session EEG mental workload classification: a reusable deployment-oriented method [PDF]
MethodsXCross-session EEG mental workload classifiers degrade severely when applied to new recording sessions from the same individual. A key but overlooked cause is the model selection criterion: standard within-session validation rewards checkpoints that ...
Abinaya G
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Don't Stop Me Now, `Cause I'm Having a Good Time Screening: Evaluation of Stopping Methods for Safe Use of Priority Screening in Systematic Reviews [PDF]
Cochrane Evidence Synthesis and MethodsIntroduction Priority screening has the potential to reduce the number of records that need to be annotated in systematic literature reviews. So‐called technology‐assisted reviews (TAR) use machine‐learning with prior include/exclude annotations to ...
Tim Repke +7 more
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Multi-objective calibration of Tank model using multiple genetic algorithms and stopping criteria [PDF]
RBRH, 2022 Calibration of hydrologic models estimates parameter values that cannot be measured and enable the rainfall-runoff processes simulation. Multi-objective evolutionary algorithms can make the calibration faster and more efficient through an iterative ...
Juan Carlos Ticona Gutierrez +4 more
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Improvement of accident prevention measures in cases of spontaneous derailment of railway rolling stock [PDF]
E3S Web of Conferences, 2023 There has been researched on railway station tracks to determine the method of automatically stopping motion content in cases where Motion Content has gone away on its own.
Yusupov Aziz +3 more
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Impact of Stop Sets on Stopping Active Learning for Text Classification
2022 IEEE 16th International Conference on Semantic Computing (ICSC), 2022 Active learning is an increasingly important branch of machine learning and a powerful technique for natural language processing. The main advantage of active learning is its potential to reduce the amount of labeled data needed to learn high-performing models.
Luke Kurlandski, Michael Bloodgood
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New stopping criteria for iterative root finding [PDF]
Royal Society Open Science, 2014 A set of simple stopping criteria is presented, which improve the efficiency of iterative root finding by terminating the iterations immediately when no further improvement of the roots is possible.
Jorgen L. Nikolajsen
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Frontiers in Conservation Science, 2021 Familiarity with the landscape increases foraging efficiency and safety. Thus, when animals are confronted with a novel environment, either by natural dispersal or translocation, establishing a home range becomes a priority.
David Saltz +2 more
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Stopping criteria for ending autonomous, single detector radiological source searches.
PLoS ONE, 2021 While the localization of radiological sources has traditionally been handled with statistical algorithms, such a task can be augmented with advanced machine learning methodologies. The combination of deep and reinforcement learning has provided learning-
Gregory R Romanchek, Shiva Abbaszadeh
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On the Hardness of Approximating Stopping and Trapping Sets [PDF]
IEEE Transactions on Information Theory, 2010 We prove that approximating the size of stopping and trapping sets in Tanner graphs of linear block codes, and more restrictively, the class of low-density parity-check (LDPC) codes, is NP-hard. The ramifications of our findings are that methods used for estimating the height of the error-floor of moderate- and long-length LDPC codes, based on stopping
Mcgregor, A, Milenkovic, O
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