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When adaptively estimated conditional probability based on context model provides larger probability of MPS (Most Probable Symbol), the coding efficiency will be relatively increased as long as the estimation of conditional probability is valid. (...) Based on our observation, probability of the transform coefficient which absolute value is greater than 1 is less 5%. (...) Table 5 shows the overall coding gain of the proposed method under restricted coding for probability update in arithmetic coding engine. Although the initial probability of context does not well match to actual statistic of symbol, we can found that the inaccurate probability models can gradually approach to actual probability of symbol due to probability update of arithmetic coding engine.
Language:English
Score: 680600.34 - https://www.itu.int/wftp3/av-a...te/2005_07_Poznan/JVT-P067.doc
Data Source: un
It is important to first carefully ponder the character and probability of "climate migration" before we learn to cope with it. (...) Based on the above scenarios, it is probable that most migration would remain local or regional. (...) Due to the quality of the migrations, it is less probable to see acute and full-scale crises but, rather, slower and more orderly movements.
Language:English
Score: 680178.6 - https://www.un.org/en/chronicl...e-be-climate-migrants-en-masse
Data Source: un
• Contrast the expected life of presently installed capacity with expectations about price evolution Sverrir.Olafsson@bt.com Probability of exceeding capacity • Probability of exceeding installed capacity • Probability density function • Cumulative probability function 0 500 1000 1500 2000 2500 0 0.005 0.01 0.015 0.02 0.025 0.03 0.035 Days P ro ba bi li ty d en si ty Parameter estimates, a = 6.2112, b = 141.9538 Empirical data Gamma fit 0 500 1000 1500 2000 2500 3000 0 0.2 0.4 0.6 0.8 1 Days C um ul at iv e pr ob ab il it y µ = 0.35, σ = 0.25, Initial demand = 50, Capacity = 150 Log-normal Empirical Gamma gmbreach.mgmbreach.m Sverrir.Olafsson@bt.com Impact of uncertainty 0 1000 2000 3000 4000 5000 6000 7000 8000 9000 10000 0 0.005 0.01 0.015 0.02 0.025 0.03 0.035 0.04 0.045 0.05 Days P ro ba bi li ty d en si ty µ = 0.5, σ = 0.2, Initial demand = 50, Capacity = 150 0 1000 2000 3000 4000 5000 6000 7000 8000 9000 10000 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 Days C um ul at iv e pr ob ab il it y µ = 0.5, σ = 0.2, Initial demand = 50, Capacity = 150 0 1000 2000 3000 4000 5000 6000 7000 8000 9000 10000 0 0.005 0.01 0.015 0.02 0.025 0.03 0.035 0.04 0.045 0.05 Days P ro ba bi li ty d en si ty µ = 0.5, σ = 0.4, Initial demand = 50, Capacity = 150 0 1000 2000 3000 4000 5000 6000 7000 8000 9000 10000 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 Days C um ul at iv e pr ob ab ili ty µ = 0.5, σ = 0.4, Initial demand = 50, Capacity = 150 0 1000 2000 3000 4000 5000 6000 7000 8000 9000 10000 0 0.01 0.02 0.03 0.04 0.05 0.06 0.07 0.08 0.09 Days P ro ba bi li ty d en si ty µ = 0.5, σ = 0.8, Initial demand = 50, Capacity = 150 0 1000 2000 3000 4000 5000 6000 7000 8000 9000 10000 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 Days C um ul at iv e pr ob ab il it y µ = 0.5, σ = 0.8, Initial demand = 50, Capacity = 150 µ = 0.5 , σ = 0.2 µ = 0.5 , σ = 0.4 µ = 0.5 , σ = 0.8 All scenarios have the same mean capacity life       = 0 0log 1 D C t µ Sverrir.Olafsson@bt.com Justification for log-normal modelling • The empirical cumulative probability is well approximated by the log-normal distribution 0 1000 2000 3000 4000 5000 6000 0 0.2 0.4 0.6 0.8 1 Days C um ul at iv e pr ob ab ili ty µ = 0.25, σ = 0.4, Initial demand = 50, Capacity = 150 Log-normal Empirical Gamma ( ) ( ) ( )          −−+=≤ t tDCerftCDCP t 2 2//log1 2 1, 2 0 σ σµ • Deviations are explained by demand exceeding installed capacity and then go down below installed capacity again Sverrir.Olafsson@bt.com Criteria for delaying instalment • There are benefits in delaying the acquirement of additional capacity – Cost – Efficient usage • Risks – QoS reduction – Loss of customers • Quantifying the criteria requires assumptions on the price evolution Sverrir.Olafsson@bt.com Bandwidth demand risk • Bandwidth evolution is a stochastic process D(t) • Match installed capacity optimally to demand • Upgrade sequence • The process C(t) should stochastically dominate D(t) • Approach – Dynamic programming, simulation, real options { };...,,...,, 11 kk CtCt ∆∆=Ω ( ) ( )( ) 1Pr →≥ tDtC Sverrir.Olafsson@bt.com Controlled instalments • Probability to exceed installed capacity • The instalment process will depend on – Expected growth – Expected volatility – Required QoS ( ) ( ) ( )             −− +=≤ t tDC erftCDCP t 2 2//log 1 2 1 , 2 00 0 σ σµ ][ ],[ 1 1 instalmentcontrolleddCCCC eduncontrollGBMdDDDD TTTT tttt +=→ +=→ + + Sverrir.Olafsson@bt.com Instalment strategy • Probability that demand does not exceed installed capacity for different instalment strategies Program:tempcapincrease.m0 1000 2000 3000 4000 5000 0.5 0.6 0.7 0.8 0.9 1 Initial demand = 50, Initial capacity = 100, µ = 0.2, σ = 0.50 DaysP ro ba bi li ty t ha t de m an d is b el ow t he in st al le d ca pa ci ty µ inc=0.05 µ inc=0.10 µ inc=0.15 µ inc=0.20 µ inc=0.25 µ inc=0.30 0 200 400 600 800 1000 0.65 0.7 0.75 0.8 0.85 0.9 0.95 1 Initial demand = 50, Initial capacity = 100, µ = 0.2, σ = 0.50 DaysP ro ba bi li ty t ha t de m an d is b el ow t he in st al le d ca pa ci ty µ inc=0.05 µinc=0.10 µ inc=0.15 µ inc=0.20 µ inc=0.25 µ inc=0.30 Sverrir.Olafsson@bt.com Time to capacity exhaustion • Assume additional capacity is installed at the average rate µi • Time to exhaustion ( ) ( )tCttWtDD int µσ σ µ exp 2 exp 0 2 0 =      +      −= ( ) ( ) ( ) 2 log 2 log 2 0 0 02 0 0 σ µµσ σ µµ −−       =⇒ +−−       = = i WE i D C tE tW D C t Sverrir.Olafsson@bt.com Different instalment strategies • Installing capacity at different – Rates – Time intervals 0 10 20 30 40 50 0 50 100 150 200 90 92 94 96 98 100 Excess instalment [%] Initial demand =50, Initial capacity = 75, µ = 0.35, σ = 0.25 Days between instalments C ap ac it y co ve ra ge [ % ] 0 10 20 30 40 50 0 200 400 600 75 80 85 90 95 100 Excess instalment [%] Initial demand =50, Initial capacity = 75, µ = 0.35, σ = 0.25 Days between instalments C ap ac it y co ve ra ge [ % ] capacityplot([0:0.05:0.45],[1:50:500],50,75,0.35,0.25,1/360,1000,1000,1.5)capacityplot([0:0.05:0.45],[1:50:200],50,75,0.35,0.25,1/360,1000,1000,1.5); Sverrir.Olafsson@bt.com Simple model for price of bandwidth • Price of bandwidth has been going down – [P] = $/year/mile/megabit • The real uncertainty is regarding the rate of decline in price ( ) ( ) 11 ++=+ ttaStS η ( ) { } ( ) { } 221 var,0,,0 ηη σηηηση ===∈ + ttttt EEN ( ) ( ) ∑ = + −+=+ k i it ikk atSaktS 1 η Sverrir.Olafsson@bt.com Simple model for price of bandwidth • Price expectations and variance • Therefore - even if the future expected spot price decreases its variance increases as long as a < 1 ( ){ } ( )tSaktSE kt =+ ( ) ( )( )σ σ ση ηS n n k k k S t k a a a 2 2 2 0 1 2 2 2 1 1 = + = = − −    = − ∑var ( ){ } ( ){ } ( ){ }E S t k E S t k E S t+ > + + > > + ∞1 ... ( ) ( ) ( )σ σ σS S Sk k2 2 21< + < < ∞... 0 5 0 100 150 200 250 300 3 5 0 400 0 2 4 6 0 5 0 100 150 200 250 300 3 5 0 400 4 0 5 0 6 0 7 0 8 0 9 0 100 Time V al ue 0 5 0 100 150 200 250 300 3 5 0 400 0 1 2 3 4 5 6 St an da rd d ev ia ti on s0 = 100, a = 0.99858, σ = 0.35, Expected annual price change [%] = -0.4 Sverrir.Olafsson@bt.com When to install additional capacity • Take into account the “damage” of capacity exhaustion • Develop analogies to efficient frontier in portfolio management • Optimal decisions » Attitudes to risk » Utility function » etc Sverrir.Olafsson@bt.com When to install additional capacity? • Delaying capacity instalment – Provides monetary benefits – Incurs risk 0 500 1000 1500 2000 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 Days C um ul at iv e pr ob ab il it y µ = 0.3, σ = 0.25, Initial demand = 50, Capacity = 100 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 Probability of exceeding capacity G ai ns f ro m d ec li ne in p ri ce µ = 0.3, σ = 0.25, In dem = 50, Cap = 100 , Pr decline = 0.3 Sverrir.Olafsson@bt.com When to install additional capacity? • The impact of volatility on expected benefits gmbreach.m 0 0.2 0.4 0.6 0.8 1 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 Probability of exceeding capacity G ai ns f ro m d ec li ne in p ri ce µ = 0.5, In dem = 50, Cap = 150 , Pr decline = 0.3 σ = 0.20 σ = 0.40 σ = 0.60 σ = 0.80 σ = 0.90 10000 iterations - days 1500 experiments 0 0.2 0.4 0.6 0.8 1 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 Probability of exceeding capacity G ai ns f ro m d ec li ne in p ri ce µ = 0.5, In dem = 50, Cap = 150 , Pr decline = 0.3 σ = 0.20 σ = 0.40 σ = 0.60 σ = 0.80 σ = 0.90 2500 iterations - days 1500 experiments Sverrir.Olafsson@bt.com Cost benefit analysis • Delaying capacity instalment is not only a question of making monetary savings • What are the implications for deterioration in QoS on customers?
Language:English
Score: 679332.3 - https://www.itu.int/ITU-D/fina...ment/olafsson-presentation.pdf
Data Source: un
I had one presenter once who told me, ‘I know I probably won't see the dead tomorrow morning. But I want to do it because it's bigger than me.’ (...) Maybe people were managing without me. And probably – not probably -- they were managing without me. (...) I think really the human contact, the human relationship is what matters and where I probably unwind and where I probably feel the balance with my humanitarian responsibilities, so to speak.
Language:English
Score: 678319.53 - https://www.un.org/en/node/126759
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.
Language:English
Score: 677720.8 - https://www.ilo.org/global/abo...WCMS_566891/lang--en/index.htm
Data Source: un
Infection prevention and control of epidemic- and pandemic-prone acute respiratory diseases in health care Management of case contacts Any person who has had close contact** with a probable or confirmed case while the probable or confirmed case was ill should be carefully monitored for the appearance of respiratory symptoms. (...) If laboratory data, including histopathological examination of fatal cases, cannot be obtained because the patient has died before specimens are taken, clinical specimens cannot otherwise be obtained, or appropriate laboratory testing for other pathogens is not available, then the patient may meet criteria for “Probable Case” as defined below. Case definitions for reporting Probable Case A person fitting the definition above of a “Patient Under Investigation” with clinical, radiological, or histopathological evidence of pulmonary parenchyma disease (e.g. pneumonia or ARDS) but no possibility of laboratory confirmation either because the patient or samples are not available or there is no testing available for other respiratory infections, AND close contact** with a laboratory confirmed case, AND not already explained by any other infection or aetiology, including all clinically indicated tests for community-acquired pneumonia according to local management guidelines. (...) Reporting: WHO requests that probable and confirmed cases be reported within 24 hours of being classified as such, through the regional focal point for International Health Regulations at the appropriate WHO Regional Office. * Currently, these areas would include only Qatar and Saudi Arabia (as of 29 September 2012). ** Close contact includes: anyone who provided care for the patient including a health care worker or family member, or had other similarly close physical contact; anyone who stayed at the same place (e.g. lived with, visited) as a probable or confirmed case while the case was symptomatic.
Language:English
Score: 677720.8 - https://www.who.int/csr/diseas...s/case_definition_29092012/en/
Data Source: un
NARROW DEFINITION: implies that a relatively limited number of animals will be tested/investigated; all tested animals have a high probability to be infected; Low number of negative test; Reduced costs; BUT reduced probability to (early) detect the infection 11 DETECTION OF ASF IN WILD BOAR USING TWO DIFFERENT SUSPECT CASE DEFINITIONS A) All individuals found dead => broad suspect case definition B) All individuals shot showing clinical sign of the diseases => narrow suspect case definition Expected number of cases? (...) It means that nobody reports them and thus the passive surveillance is not working; At present there are no magic recipes Form the experience gained in infected countries it appears that, in FREE AREAS 0,5-1% of the estimated wild boar population is found dead each year without any infection Wild boar natural mortality is about 10% (excluding hunting) The goal would be to find 10% of them 1% of the whole alive population 16 9 PASSIVE SURVEILLANCE: CRITICAL POINTS I Suspect case definition: Plays a pivotal role in determining the efficiency of any surveillance system BROAD: many samples, much work ($) , more probability to detect the virus NARROW: few samples, less work ($), less probability to detect the virus The suspect case definition could be adjusted according to the (perceived or assessed) risk of the area. Low risk areas => narrow case definition (possibly undetected positive cases) High risk areas => broad case definition (many negative animals investigated but high probability to early detect the virus) PASSIVE SURVEILLANCE: CRITICAL POINTS II Communication chain: passive surveillance is based on reporting, hence a person willing to report must know to whom to report and how (green lines, mobile of a responsible person, avoid reporting to “Veterinary Service”) To whom it should be reported the finding of a dead wild boar in the forest?
Language:English
Score: 677509 - https://www.fao.org/fileadmin/...events2017/ASF_Kaunas/8_en.pdf
Data Source: un
A quarter of thyroid cancer cases ‘probably’ due to Chernobyl: UN scientific committee | | UN News Skip to main content Welcome to the United Nations Toggle navigation Language: العربية 中文 English Français Русский Español Português Kiswahili Other Hindi हिंदी Global UN News Global perspective Human stories Search the United Nations Search Advanced Search Home Africa Americas Asia Pacific Middle East Europe UN Art and Gifts History Corner Topics Peace and Security Economic Development Humanitarian Aid Climate and Environment Human Rights UN Affairs Women Law and Crime Prevention Health Culture and Education SDGs Migrants and Refugees In depth Interviews Features Photo Stories News in Brief The Lid is On UN Gender Focus UN and Africa UN Podcasts Secretary-General Spokesperson All Statements Selected Speeches Press Encounters Official Travels Media UN Video UN Photo Meeting Coverage Media Accreditation Webtv Home Africa Americas Asia Pacific Middle East Europe UN Art and Gifts History Corner Topics Peace and Security Economic Development Humanitarian Aid Climate and Environment Human Rights UN Affairs Women Law and Crime Prevention Health Culture and Education SDGs Migrants and Refugees In depth Interviews Features Photo Stories News in Brief The Lid is On UN Gender Focus UN and Africa UN Podcasts Secretary-General Spokesperson All Statements Selected Speeches Press Encounters Official Travels Media UN Video UN Photo Meeting Coverage Media Accreditation Webtv   Subscribe Audio Hub A quarter of thyroid cancer cases ‘probably’ due to Chernobyl: UN scientific committee 25 April 2018 Interviews Transcript Introduction: A quarter of all thyroid cancer cases among patients who were children at the time of the Chernobyl accident 32 years ago, are “probably” due to high doses of radiation received during and after the event. (...) Download A quarter of all thyroid cancer cases among patients who were children at the time of the Chernobyl accident 32 years ago, are “probably” due to high doses of radiation received during and after the event.
Language:English
Score: 676736.13 - https://news.un.org/en/audio/2018/04/1008252
Data Source: un
Vitamin D, Lysine, Choline  Processed matrices derived from pig/wild boar origin used as food e.g. cooked cured meat, precooked products, raw cured meat, raw fermented meat or kitchen waste containing any of these listed  Contaminated matrices e.g. contaminated feed, water, aerosol, vehicles, bedding (e.g. straw, wood chips), cereals (e.g. barley, maize), forages (e.g. fresh grass, hay, silage,..) 7 2019: Ranking of the combined probability of ASF transmission through a given matrix Probability of infection / contamination Frequency of exposure to the matrix of pigs/wild boar during 1 year Probability of infectiousness at time of exposure Probability of transmission, given infectiousness when exposed Overall probability of ASF transmission by matrix per year Epidemiologists Farmers- associations Hunter-associations Virologists  Most important research gaps to address the needs of different stakeholders involved in the prevention and control of ASF.  Research priorities that should be addressed in a short time frame (< 1 year).
Language:English
Score: 675767.3 - https://www.fao.org/fileadmin/...ts/events2019/ASFBalkans/5.pdf
Data Source: un
Twenty-four of the cases were identified as having contact with a probable, single index case who was diagnosed with MERS in a hospital in Riyadh City, Riyadh Region. (...) Based on available information, the probable index case is a woman who presented on 10 June 2016 to the hospital with a critical health condition, not consistent with MERS symptoms. (...) In addition, one case has been diagnosed in a household contact of a hospital patient who was diagnosed with the disease after exposure to the probable index case. Twenty (20) of the twenty-four (24) have not exhibited any MERS symptoms.
Language:English
Score: 675767.3 - https://www.who.int/news/item/...by-the-kingdom-of-saudi-arabia
Data Source: un