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Thus, we model + (1) these properties as below: − Success ( ) : the probability that B will where α,β,γ,δ,∈, and ε are weighting factors such successfully execute the task that α+β+γ+δ+∈+ε=1. However, calculating these − Cost ( ) : the probability that the cost of executing weighting factors are computationally costly and not practical due to infinite number of possibilities. the task by B is not more than expected Hence, we suggest to apply machine learning (ML) − Completeness ( ): the probability of complete techniques to combine all TAs, which we have data records over total data records discussed in our previous work [7]. − Uniqueness ( ): the probability of expected records over total records noted − Experience DTM ( ) − Timeliness ( ): the difference between last = + (2) update to the current one − Validity ( ): the validity of data type, syntax and where σ and φ are weighting factors such that σ+φ range =1 and >0. The ML method discussed in [7] is − Accuracy ( ): the probability of accurate data preferable for TA combination in this case as well. records over total data records – 150 –     161     162     163     164     165     166     167     168     169     170     171          
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Score: 722466.9 - https://www.itu.int/en/publica.../files/basic-html/page166.html
Data Source: un
Consequently, we propose to remove the transient states 0-11 and to add some new states at the lower end of the probability space. Since the probability values of states 0-11 are a subset of the states 12-63, the accuracy of the original representation is maintained for that part of the probability space, which corresponds to higher (LPS) probabilities. New proposed probability states 0,00 0,05 0,10 0,15 0,20 0,25 0,30 0,35 0,40 0,45 0,50 051015202530354045505560 States Probability container, cif, 30 Hz -35 -30 -25 -20 -15 -10 -5 0 283032 qp loss relative to unconstrained CABAC [%] all framesb-frames Figure 2 shows the proposed probability states, which have been generated as follows. (...) These states represent an additional probability interval (0.0256, 0.0192]. As a consequence of this re-design of the probability estimator, there is also a simplification of the MPS/LPS switching mechanism.
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Score: 722182.6 - https://www.itu.int/wftp3/av-a...07_Klagenfurt/JVT-D019r1-L.doc
Data Source: un
Consequently, we propose to remove the transient states 0-11 and to add some new states at the lower end of the probability space. Since the probability values of states 0-11 are a subset of the states 12-63, the accuracy of the original representation is maintained for that part of the probability space, which corresponds to higher (LPS) probabilities. New proposed probability states 0,00 0,05 0,10 0,15 0,20 0,25 0,30 0,35 0,40 0,45 0,50 051015202530354045505560 States Probability container, cif, 30 Hz -35 -30 -25 -20 -15 -10 -5 0 283032 qp loss relative to unconstrained CABAC [%] all framesb-frames Figure 2 shows the proposed probability states, which have been generated as follows. (...) These states represent an additional probability interval (0.0256, 0.0192]. As a consequence of this re-design of the probability estimator, there is also a simplification of the MPS/LPS switching mechanism.
Language:English
Score: 722182.6 - https://www.itu.int/wftp3/av-a...07_Klagenfurt/JVT-D019r2-L.doc
Data Source: un
Such a combination of a single probability and interval means that the return period, e.g. 475 years, of such maps is uniform across the territory. (...) The average annual probability of collapse nationally is 9 × 10 -6 , which compares favourably to the risk level assumed to be acceptable for the French population by Douglas et al. (2013) (1.0 × 10 -5 ). (...) Similar large variations are noticeable in the annual probability of structural yield, which varies between 0.03% and 2.3% with an average of 0.3% (i.e. about 300 times higher than the annual probability of collapse).
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Score: 722167.14 - https://sdgs.un.org/sites/defa...ntribution_DRR_Douglasetal.pdf
Data Source: un
Mortality rate for 5-14 year-olds (probability of dying per 1000 children aged 5-14 years) Global Regions WHO Regional websites Africa Americas South-East Asia Europe Eastern Mediterranean Western Pacific When autocomplete results are available use up and down arrows to review and enter to select. (...) To unsubscribe click here . × Visualisations Data Metadata Related indicators Map Map Table Table Indicator name: Mortality rate for 5-14 year-olds (probability of dying per 1000 children aged 5-14 years) Short name: Mortality among children Data type: Rate Indicator Id: 4802 Topic: Mortality and burden of disease Definition: The probability that a child aged 5 dies before reaching its 15th birthday. (...) The full birth histories were used to estimate the probability of dying in children aged 5 to 14 (10q5) for three reference periods prior to each survey (0-3 years prior to the survey, 4-7 years, and 8-11 years).
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Score: 721830.6 - https://www.who.int/data/gho/d...1000-children-aged-5-14-years)
Data Source: un
Also, the time required to complete the exercise is shorter. 5.6 In probability sampling, the units are selected in such a way that each unit (an outlet or a product) has a known non-zero probability of selection. (...) We then go on to consider some non- probability techniques. Reasons for using non-probability sampling 5.28 No sampling frame is available. (...) So the absence of sampling frames is not a good enough excuse for not applying probability sampling. 5.29 Bias resulting from non-probability sampling is negligible.
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Score: 721458.06 - https://www.ilo.org/public/eng...reau/stat/download/cpi/ch5.pdf
Data Source: un
Risk Analyses If geological probability is understood as the geological probability (Pg) of project success we may have to consider that projects may fail in the appraisal phase due to geological reasons, too. (...) Pd (probability of the development’s technical success) should belong in this scheme to the Technical Feasibility (F) axis, while Pg (probability of geological success) is to be assigned to the Degree of Confidence criterion (G-Axis) if G-Axis classification follows the Version A approach to the Degree of Confidence discussed above. (...) Regarding risk (actually success probability) evaluation I suggest the refinement of Pg (geological probability) by its extension to appraisal project volumes, the limitation of Pd 10 (development probability) to the technological success, and the assignment of Pc (commercial probability) to the environmental, societal and economic factors.
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Score: 721363.6 - https://unece.org/sites/defaul...NFC%20PRSG_Imre%20Szilagyi.pdf
Data Source: un
In developing countries, the probability of participating in the workforce increases by 7.8 per cent; in emerging, by 6.4 per cent; in ASNA, two regions with the widest gap in participation rates, the probability increases further, at 12.9 per cent. (...) In ASNA countries, it decreases the probability to participate by 6.2 percentage points; in developing countries by 4.8 percentage points; and in developed countries by 4.0 percentage points. (...) In developing countries, the probability to participate is substantially reduced by religion, a proxy indicator for more restrictive gender role conformity.
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Score: 719400.8 - www.ilo.org/moscow/news...WCMS_566891/lang--en/index.htm
Data Source: un
Further, to reduce the error probability, a co‑ probability of bit error expressions were derived at the operative abnormality detection scheme was proposed destination using the probabilities of detection and false in [158]. (...) Authors’ analysis demonstrated that the opti‑ it detects an abnormality itself or it receives signaling mal decision threshold at the destination depends on the molecules from other sensors that detected abnormality. probabilities of detection and false alarm of the last coop‑ Finally, an FC collected responses from all the sensors and erative nano‑machine. checked the activation lag of all the sensors to decide the presence or absence of abnormality. Optimal threshold at A cooperative detection strategy was presented in [151], the FC was derived by minimizing the error probability. where several cooperative nano‑machines sent their de‑ cisions to an FC about the presence or absence of an ab‑ normality inside the blood vessel.
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Score: 719400.8 - https://www.itu.int/en/publica...3/files/basic-html/page72.html
Data Source: un
Thus, tak- city meet each other with a probability of 0.01. ing the number of contacts with all people into con- Thus, on average, a person meets around 10 people sideration could significantly improve the tracing. (...) Otherwise, they will be probability that the virus spreads from an infected quarantined for 14 days, and then will be back to person to a healthy person during a contact. (...) Over here, is the so-called base infection value. probability, which can either be a constant or de- pend on the proportion of confirmed cases of the • Policy 2 with = 0.2, where 0.2 is an example of population (i.e., adaptive).
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Score: 719400.8 - https://www.itu.int/en/publica.../files/basic-html/page117.html
Data Source: un