Get raw data. It would be better to automate this, but to keep things simple, we exported as csv from https://data.sfgov.org/COVID-19/COVID-19-Hospitalizations/nxjg-bhem. This is COVID-19_Hospitalizations_SFMay4.csv.
Estimate Hospitilization Data bounds LEMMA
needs lower and upper bounds on the true number of people hospitalized with COVID-19 on each day. In other words, if all hospital patients in San Francisco were given a perfect COVID-19 test on Day \(X\), how many would test positive for COVID-19? San Francisco has number of confirmed COVID+ and PUI (persons under investigation - suspected COVID-19). We currently assume a lower bound of 100% of confirmed and an upper bound of 100% of confirmed + 30% of PUI (adding together DPHCategory = Med/Surg and DPHCategory = ICU).
A quick overview of the data:
dt <- fread("COVID-19_Hospitalizations_SFMay4.csv") dt[, date := as.Date(reportDate)] dt[, .(meanAcrossDays = mean(PatientCount)), by = c("Hospital", "DPHCategory", "CovidStatus")] #> Hospital DPHCategory CovidStatus meanAcrossDays #> 1: All SF Hospitals Med/Surg COVID+ 46.951220 #> 2: All SF Hospitals ICU COVID+ 28.146341 #> 3: All SF Hospitals Med/Surg PUI 44.195122 #> 4: All SF Hospitals ICU PUI 8.487805
Calculate bounds:
dt[CovidStatus == "COVID+", mult.low := 1] dt[CovidStatus == "COVID+", mult.high := 1] dt[CovidStatus == "PUI", mult.low := 0] dt[CovidStatus == "PUI", mult.high := 0.3] hosp.bounds <- dt[, .(LowerBound = sum(PatientCount * mult.low), UpperBound = sum(PatientCount * mult.high)), by=date] hosp.bounds #> date LowerBound UpperBound #> 1: 2020-03-23 26 62.6 #> 2: 2020-03-24 35 68.3 #> 3: 2020-03-25 40 62.2 #> 4: 2020-03-26 41 65.0 #> 5: 2020-03-27 52 75.7 #> 6: 2020-03-28 53 76.7 #> 7: 2020-03-29 58 81.1 #> 8: 2020-03-30 29 38.9 #> 9: 2020-03-31 57 69.6 #> 10: 2020-04-01 79 103.9 #> 11: 2020-04-02 76 92.2 #> 12: 2020-04-03 69 79.2 #> 13: 2020-04-04 68 84.8 #> 14: 2020-04-05 82 118.6 #> 15: 2020-04-06 93 128.7 #> 16: 2020-04-07 91 107.8 #> 17: 2020-04-08 89 105.2 #> 18: 2020-04-09 86 98.0 #> 19: 2020-04-10 83 94.7 #> 20: 2020-04-11 94 104.5 #> 21: 2020-04-12 90 102.6 #> 22: 2020-04-13 88 100.9 #> 23: 2020-04-14 88 113.8 #> 24: 2020-04-15 89 100.4 #> 25: 2020-04-16 84 94.5 #> 26: 2020-04-17 77 85.1 #> 27: 2020-04-18 78 88.8 #> 28: 2020-04-19 84 93.6 #> 29: 2020-04-20 81 94.8 #> 30: 2020-04-21 80 88.4 #> 31: 2020-04-22 82 90.4 #> 32: 2020-04-23 77 84.5 #> 33: 2020-04-24 78 84.6 #> 34: 2020-04-25 85 97.6 #> 35: 2020-04-26 87 111.9 #> 36: 2020-04-27 89 100.7 #> 37: 2020-04-28 84 92.4 #> 38: 2020-04-29 90 96.6 #> 39: 2020-04-30 90 96.0 #> 40: 2020-05-01 87 95.7 #> 41: 2020-05-02 90 96.0 #> date LowerBound UpperBound
There may be some data quality problems in March 30 - April 4:
ggplot(hosp.bounds, aes(x = date)) + geom_line(aes(y = LowerBound)) + geom_line(aes(y = UpperBound)) + ylab("Hospitalizations")
At this point we would suggest investigating this data further with your DPH. For this vignette, we will omit the days March 30, March 31, April 3, April 4.
hosp.bounds <- hosp.bounds[!date %in% as.Date(c("2020-03-30", "2020-03-31", "2020-04-03", "2020-04-04"))]
Open the template Excel file (the original is system.file("extdata", "SF-April13.xlsx", package = "LEMMA")
but the installation instructions on GitHub suggest copying it to example.xlsx in your local directory). In Excel, Save As as “SF-May4-v1.xlsx”.
Copy and paste the bounds to the Hospitilization Data tab.
write.table(hosp.bounds, sep = ",", row.names = F, col.names = F) #> 2020-03-23,26,62.6 #> 2020-03-24,35,68.3 #> 2020-03-25,40,62.2 #> 2020-03-26,41,65 #> 2020-03-27,52,75.7 #> 2020-03-28,53,76.7 #> 2020-03-29,58,81.1 #> 2020-04-01,79,103.9 #> 2020-04-02,76,92.2 #> 2020-04-05,82,118.6 #> 2020-04-06,93,128.7 #> 2020-04-07,91,107.8 #> 2020-04-08,89,105.2 #> 2020-04-09,86,98 #> 2020-04-10,83,94.7 #> 2020-04-11,94,104.5 #> 2020-04-12,90,102.6 #> 2020-04-13,88,100.9 #> 2020-04-14,88,113.8 #> 2020-04-15,89,100.4 #> 2020-04-16,84,94.5 #> 2020-04-17,77,85.1 #> 2020-04-18,78,88.8 #> 2020-04-19,84,93.6 #> 2020-04-20,81,94.8 #> 2020-04-21,80,88.4 #> 2020-04-22,82,90.4 #> 2020-04-23,77,84.5 #> 2020-04-24,78,84.6 #> 2020-04-25,85,97.6 #> 2020-04-26,87,111.9 #> 2020-04-27,89,100.7 #> 2020-04-28,84,92.4 #> 2020-04-29,90,96.6 #> 2020-04-30,90,96 #> 2020-05-01,87,95.7 #> 2020-05-02,90,96
Save the Excel file.
LEMMA::CredibilityIntervalFromExcel("SF-May4-v1.xlsx")
Note that the “Best Guess” scenario is not compatible with the given bounds. See FAQ.md for more details on what this means.
Examine the .pdf and/or .xlsx output. The first page of the .pdf says we have 342 iterations in the posterior distribution. This is also shown at the top of the Notes column on each .xlsx sheet.
Adjust and rerun. We suggest at least 1000 iterations in the posterior distribution for inference about the 5th-95th quantiles. On the Internal sheet to, change main.iterations
to 35000. The current output only projects until May 1. On the Model Inputs sheet, change Projection End Date to 9/1/2020. Save As SF-May4-v2.xlsx and rerun CredibilityIntervalFromExcel (takes ~5 minutes). This gives 1101 posterior iterations.
LEMMA::CredibilityIntervalFromExcel("SF-May4-v2.xlsx")
dt <- fread("Alameda_County_COVID-19_HospitalizationsMay4.csv") head(dt) #> Date Hospitalized_COVID_19_Positive_ ICU_COVID_19_Positive_Patients #> 1: 4/1/20 52 27 #> 2: 4/2/20 57 29 #> 3: 4/3/20 64 32 #> 4: 4/4/20 60 35 #> 5: 4/5/20 72 39 #> 6: 4/6/20 79 43 #> Hospitalized_COVID_19_Positive1 ObjectId #> 1: 25 1 #> 2: 28 2 #> 3: 32 3 #> 4: 25 4 #> 5: 33 5 #> 6: 36 6
hosp.bounds <- dt[, .(date = as.Date(Date, format = "%m/%d/%y"), LowerBound = Hospitalized_COVID_19_Positive_)]
The data looks somewhat noisy.
ggplot(hosp.bounds, aes(x=date, y=LowerBound)) + geom_point()
Let’s see what happens if we use it as-is.
In San Francisco, the UpperBound is roughly 115% of the LowerBound, so we could use than as a first approximation.
hosp.bounds[, UpperBound := LowerBound * 1.15] ggplot(hosp.bounds, aes(x = date)) + geom_line(aes(y = LowerBound)) + geom_line(aes(y = UpperBound)) + ylab("Hospitalizations")
Open the template Excel file. In Excel, Save As as “Alameda-May4-v1.xlsx”.
On the Model Inputs sheet, change Number of People in Area to 1671000
Copy and paste the bounds to the Hospitilization Data tab.
write.table(hosp.bounds, sep = ",", row.names = F, col.names = F) #> 2020-04-01,52,59.8 #> 2020-04-02,57,65.55 #> 2020-04-03,64,73.6 #> 2020-04-04,60,69 #> 2020-04-05,72,82.8 #> 2020-04-06,79,90.85 #> 2020-04-07,76,87.4 #> 2020-04-08,84,96.6 #> 2020-04-09,80,92 #> 2020-04-10,93,106.95 #> 2020-04-11,87,100.05 #> 2020-04-12,91,104.65 #> 2020-04-13,85,97.75 #> 2020-04-14,92,105.8 #> 2020-04-15,81,93.15 #> 2020-04-16,73,83.95 #> 2020-04-17,85,97.75 #> 2020-04-18,82,94.3 #> 2020-04-19,84,96.6 #> 2020-04-20,85,97.75 #> 2020-04-21,85,97.75 #> 2020-04-22,84,96.6 #> 2020-04-23,81,93.15 #> 2020-04-24,79,90.85 #> 2020-04-25,83,95.45 #> 2020-04-26,84,96.6 #> 2020-04-27,81,93.15 #> 2020-04-28,76,87.4 #> 2020-04-29,76,87.4 #> 2020-04-30,74,85.1 #> 2020-05-01,75,86.25 #> 2020-05-02,73,83.95 #> 2020-05-03,73,83.95
Choose your prior distributions in the Parameters with Distributions tab. As an example, let’s use the numbers in the template.
Run CredibilityIntervalFromExcel
LEMMA::CredibilityIntervalFromExcel("Alameda-May4-v1.xlsx")
The April 16 data is clearly an outlier. Delete that row in Excel. (Because the sheet is protected, it’s probably easiest to cut and paste everything else.)
Save As Alameda-May4-v2.xlsx.
LEMMA::CredibilityIntervalFromExcel("Alameda-May4-v2.xlsx")
Running CredibilityIntervalFromExcel still gives 0 iterations.
hosp.bounds <- hosp.bounds[date != as.Date("2020-04-16")] hosp.bounds[, orig.lower := LowerBound] hosp.bounds[, orig.upper := UpperBound] hosp.bounds[, date.index := 1:.N] m1 <- loess(LowerBound ~ date.index, data = hosp.bounds, span = 0.4) hosp.bounds$LowerBound <- predict(m1) hosp.bounds[, UpperBound := LowerBound * 1.15] ggplot(hosp.bounds, aes(x = date)) + geom_line(aes(y = LowerBound)) + geom_line(aes(y = UpperBound)) + geom_point(aes(y=orig.upper, shape = "Upper Bound"), fill = "black", na.rm = T) + geom_point(aes(y=orig.lower, shape = "Lower Bound"), fill = "black", na.rm = T)
Copy and paste the bounds to the Hospitilization Data tab.
write.table(hosp.bounds[, .(date, LowerBound, UpperBound)], sep = ",", row.names = F, col.names = F) #> 2020-04-01,51.8832100479336,59.6656915551236 #> 2020-04-02,56.8656337161158,65.3954787735331 #> 2020-04-03,61.6836235450605,70.9361670768195 #> 2020-04-04,66.2661963266626,76.206125775662 #> 2020-04-05,70.6055708802349,81.1964065122702 #> 2020-04-06,74.6986166982751,85.9034092030164 #> 2020-04-07,78.8235965067849,90.6471359828027 #> 2020-04-08,82.5039201267141,94.8795081457212 #> 2020-04-09,85.2057746641351,97.9866408637554 #> 2020-04-10,87.1980945186538,100.277808696452 #> 2020-04-11,88.6691138013258,101.969480871525 #> 2020-04-12,89.3486444947334,102.750941168943 #> 2020-04-13,88.2526493524055,101.490546755266 #> 2020-04-14,86.6398958063131,99.63588017726 #> 2020-04-15,85.2359394345269,98.0213303497059 #> 2020-04-17,84.0066495413416,96.6076469725428 #> 2020-04-18,83.8374332435815,96.4130482301187 #> 2020-04-19,83.8650557177177,96.4448140753753 #> 2020-04-20,83.8966648374278,96.481164563042 #> 2020-04-21,83.852700370669,96.4306054262694 #> 2020-04-22,83.1471546642847,95.6192278639274 #> 2020-04-23,82.4014298322305,94.7616443070651 #> 2020-04-24,82.1764297279875,94.5028941871856 #> 2020-04-25,81.8007613978311,94.0708756075057 #> 2020-04-26,81.0403022884185,93.1963476316813 #> 2020-04-27,80.0703485307944,92.0809008104135 #> 2020-04-28,78.3822879321543,90.1396311219774 #> 2020-04-29,76.6430719942722,88.139532793413 #> 2020-04-30,75.3139130680928,86.6110000283068 #> 2020-05-01,74.1529051109775,85.2758408776241 #> 2020-05-02,73.3223639925667,84.3207185914517 #> 2020-05-03,72.7339972989177,83.6440968937553
To get fast feedback on just the Best Guess scenario, set main.iterations to 1.
Save As Alameda-May4-v3.xlsx.
LEMMA::CredibilityIntervalFromExcel("Alameda-May4-v3.xlsx")
It may be more convenient to plot within RStudio so we don’t have to refer to the .pdf output.
z <- LEMMA::CredibilityIntervalFromExcel("Alameda-May4-v4.xlsx") print(z$gplot)
Try fitting E0 (the initial number of exposed) using only the days before peak hospitalization. On the Interal sheet, set max.obs.date.to.fit to 4/10/2020.
z <- LEMMA::CredibilityIntervalFromExcel("Alameda-May4-v5.xlsx") print(z$gplot)
Now the beginning looks better, but then the multiplier for one of the interventions needs to be stronger. Change the first Re multiplier from 0.6 to 0.5.
z <- LEMMA::CredibilityIntervalFromExcel("Alameda-May4-v6.xlsx") print(z$gplot)
We might be getting in the ballpark. Let’s try a flatter prior distribution. Set all Priors to 20%. Set Re multiplier to 0.35 0.4 0.45 0.55 0.7. Set main.iterations to 10000. Set required.in.bounds to 95%.
LEMMA::CredibilityIntervalFromExcel("Alameda-May4-v7.xlsx")
Now we at least have 34 iterations. Based on the histograms in the .pdf output, we might need a longer hospital length of stay (if you have data on this, that would be even better!).
LEMMA::CredibilityIntervalFromExcel("Alameda-May4-v8.xlsx")
Now we have 117 iterations.
We continued this process and eventually were able to come up with priors that fit the data well. We then increased main.iterations to 200000 to get a large number of posterior iterations.
This was not shown above, but another thing to try is increasing upper.bound.multiplier and/or decreasing lower.bound.multiplier. This will include combinations of parameters that give projections slightly (more) outside the given LowerBound and UpperBound.
In this case study, an important question is: how confident are we in the UpperBound of the most recent days? These are important because hospitalization appears to be declining. If we force the model to be lower than exactly UpperBound (upper.bound.multiplier = 1) on every day (required.in.bounds = 100%), projections will be more optimistic.
There are several reasons why it may be more prudent to use less strict bounds (perhaps lower.bound.multiplier = 0.95; upper.bound.multiplier = 1.10) and to allow the projections to be outside the bounds for some days (perhaps required.in.bounds = 90%) . For Alameda County, PUI data was not available so it is especially difficult to know the true number of COVID-19 positive. Even if PUI data were available, it is also possible that the recent decline is due to random fluctuation or reasons not captured by our model.
#This will take at least 30 minutes LEMMA::CredibilityIntervalFromExcel("Alameda-May4-v9-very strict criteria.xlsx")
#This will take at least 30 minutes LEMMA::CredibilityIntervalFromExcel("Alameda-May4-v9-less strict criteria.xlsx")
Future changes in public health interventions (for example, relaxing/replacing current Shelter In Place ordinances) can be modelled as a future intervention which multiplies Re by a factor greater than 1. This is already set in the existing xlsx inputs. We just need to change Model Inputs -> Projection End Date to a later date to see the effect. Below shows the effect of the third intervention which multiplies Re by 1.1 to 2 (C19:G19 in Alameda-May4-v9-less strict criteria.xlsx or Alameda-May4-v9-very strict criteria.xlsx) on June 1.


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{% comment 'LICENSE' %} Copyright (C) 2023 by eHealth Africa : http://www.eHealthAfrica.org See the NOTICE file distributed with this work for additional information regarding copyright ownership. Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0 Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License. {% endcomment %}
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This application is built as a re-usable Hortonworks Assembly Framework. The purpose is to quickly be able to create compelling web applications that take advantage of the key real-time components of the Hortonworks Data Platform. Such as Apache HBase, Apache Solr, and Apache Phoenix. The following use cases have been included for an example.


"It was wrong" - McDonald stops short of condemning attack on Foster's father
by Michael StainesMary Lou McDonald has said the attempted murder of Arlene Foster’s father during the Troubles was “wrong.”
Speaking to Pat Kenny on Newstalk this morning, the Sinn Féin leader stopped short of condemning the attack.
She said that singling out one act of violence would lead to ongoing calls for comment on others.

“I think that serves nobody. We need to deal with the past with compassion.”
The issue was raised after Deputy McDonald called on unionist leaders to condemn a number of loyalist banners that appeared in Belfast making allegations against Sinn Féin candidate for North Belfast, John Finucane.
Mrs Foster said the call was a “bit rich” and urged Sinn Féin to comment on the attempted murder of her father in 1979 and an attack on her party’s current deputy leader Nigel Dodds in 1996.
Deputy McDonald said the attack was “wrong” and the violence during the Troubles was “absolutely tragic.”
“It was wrong that Arlene Foster as a child – that any child – witnessed that kind of violence visited on their father,” she said.
“It was wrong that John Finucane – which is where this conversation started, this run of things started – that he saw his father shot dead and his mother wounded.
“All of that is just absolutely awful and tragic and cannot happen again.”
The PSNI is investigating the banners which made a series of allegations against Mr Finucane and his family – including his father who was murdered by loyalist paramilitaries.
Mr Finucane, the current Lord Mayor of Belfast, is running against Mr Dodds in one of the North’s closest constituencies.
Mr Dodds has condemned the posters and insisted that they had nothing to do with the DUP.
Mrs Foster’s father was a member of the Royal Ulster Constabulary police force when he was shot at his family farm by Republican paramilitaries in 1979.
He was shot in the head but survived the attack.
A policeman was injured in an attack on Mr Dodds as he visited his son at the Royal Children’s Hospital in Belfast in 1996.


Class: One2Influx::Config
- Inherits:
-
Object
- Object
- One2Influx::Config
- Defined in:
- lib/one2influx/config.rb
Overview
Class for holding all configuration data, it is accessed by its only instance
in global variable $CFG
Constant Summary
- @@fetch_interval =
Data fetch interval. Allowed values are number of seconds, minutes, hours
separated by space
'30 seconds'
- @@one =
OpenNebula connection configuration
{ # Login credentials separated by semicolon credentials: 'oneadmin:opennebula', # XML_RPC endpoint where OpenNebula is listening endpoint: 'http://localhost:2633/RPC2' }
- @@influx =
InfluxDB connection configuration
{ # Whether to use basic authentication authenticate: false, # Login credentials separated by semicolon credentials: 'user:password', # InfluxDB HTTP API endpoint endpoint: 'http://localhost:8086', # Database name database: 'test', # Retention policy for records with the smallest granularity policy: 'ten_hours' }
- @@log =
Logging configuration
{ # Level to use. Logger::INFO, Logger::WARN, Logger::ERROR are supported level: Logger::INFO, # Path to log file, when empty, one2influx.log is written into directory # where the binary is run from path: '' }
- @@storage =
Configuration of metrics and tags to be stored to InfluxDB Comment or uncomment metrics, tags or whole object depending on what you want to store.
{ # Host host: { tags: { HOST_NAME: 'NAME', CLUSTER_ID: 'CLUSTER_ID', CLUSTER_NAME: 'CLUSTER', #HOST_STATE: 'STATE', # [int] host state #IM_MAD: 'IM_MAD', # [string] monitoring manager driver #VM_MAD: 'VM_MAD', # [string] virtualization manager driver #VN_MAD: 'VN_MAD', # [string] virtual network manager driver DSS_IDS: '' # [string] IDs of datastores that this host uses # encoded in form ,,ID_1,,ID_2...,, }, metrics: [ 'MEM_USAGE', # [kB] memory requested by VMs 'MAX_MEM', # [kB] total memory available in host 'FREE_MEM', # [kB] free memory in host, MAX_MEM - USED_MEM 'USED_MEM', # [kB] used memory in host -(buffers+cache) 'CPU_USAGE', # [%] usage of CPU calculated by ONE as the sum of # CPU requested by all VMs running in the host 'MAX_CPU', # [%] total CPU in the host (number of cores * 100) 'FREE_CPU', # [%] free CPU as returned by the probes 'USED_CPU' # [%] CPU used by all host processes over a total # of # cores * 100 ], cust_metrics: [ #'HOST_MEM_ALOC', # [%] MEM_USAGE / MAX_MEM #'HOST_MEM_LOAD', # [%] USED_MEM / MAX_MEM #'HOST_MEM_WASTE', # [] HOST_MEM_ALOC / HOST_MEM_LOAD #'HOST_CPU_ALOC', # [%] CPU_USAGE / MAX_CPU #'HOST_CPU_LOAD', # [%] USED_CPU / MAX_CPU #'HOST_CPU_WASTE', # [] HOST_CPU_ALOC / HOST_CPU_LOAD ] }, # Virtual machine vm: { tags: { CLUSTER_ID: 'CLUSTER_ID', CLUSTER_NAME: 'CLUSTER_NAME', HOST_ID: 'HOST_ID', HOST_NAME: 'HOST_NAME', VM_NAME: 'NAME', UID: 'UID', # [int] user's ID GID: 'GID', # [int] group's ID UNAME: 'UNAME', # [string] user's name GNAME: 'GNAME', # [string] group's name #VM_STATE: 'STATE', # [int] virtual machine state #LCM_STATE: 'LCM_STATE' # [int] substates for ACTIVE state }, metrics: [ 'MEMORY', # [kB] memory consumption 'CPU', # [%] 1 VCPU consumed (two fully consumed cpu is 200) #'NET_TX', # [B] sent to the networ #'NET_RX' # [B] received from the network ], cust_metrics: [ #'MEMORY_PERC' # Computes percentage usage of memory for VM ] }, # Datastore ds: { tags: { DS_NAME: 'NAME', CLUSTER_ID: 'CLUSTER_ID', CLUSTER_NAME: 'CLUSTER', TM_MAD: 'TM_MAD', # [shared|ssh|qcow2|vmfs|ceph|lvm|fs_lvm|dev] # transfer manager DS_MAD: 'DS_MAD', # [fs|vmfs|lvm|ceph|dev] datastore type UID: 'UID', # [int] user's ID GID: 'GID', # [int] group's ID UNAME: 'UNAME', # [string] user's name GNAME: 'GNAME', # [string] group's name HOSTS_IDS: '' # [string] IDs of hosts that are using this # datastore encoded in form ,,ID_1,,ID_2...,, }, metrics: [ 'TOTAL_MB', # [MB] total space available in datastore 'FREE_MB', # [MB] free space 'USED_MB' # [MB] used space ], cust_metrics: [ #'DS_LOAD' # [%] USED_MB / TOTAL_MB ] }, # Cluster cluster: { tags: { CLUSTER_NAME: 'NAME' }, metrics: [ 'CLUSTER_MEM_USAGE', # [kB] sum of MEM_USAGE for whole cluster 'CLUSTER_MAX_MEM', # [kB] sum of MAX_MEM for whole cluster 'CLUSTER_FREE_MEM', # [kB] sum of FREE_MEM for whole cluster 'CLUSTER_USED_MEM', # [kB] sum of USED_MEM for whole cluster 'CLUSTER_CPU_USAGE', # [%] sum of CPU_USAGE for whole cluster 'CLUSTER_MAX_CPU', # [%] sum of MAX_CPU for whole cluster 'CLUSTER_FREE_CPU', # [%] sum of FREE_CPU for whole cluster 'CLUSTER_USED_CPU' # [%] sum of USED_CPU for whole cluster ], cust_metrics: [ #'CLUSTER_MEM_ALOC', # [%] CLUSTER_MEM_USAGE / CLUSTER_MAX_MEM #'CLUSTER_MEM_LOAD', # [%]CLUSTER_USED_MEM / CLUSTER_MAX_MEM #'CLUSTER_MEM_WASTE', # [] CLUSTER_MEM_ALOC / CLUSTER_MEM_LOAD #'CLUSTER_CPU_ALOC', # [%] CLUSTER_CPU_USAGE / CLUSTER_MAX_CPU #'CLUSTER_CPU_LOAD', # [%] CLUSTER_USED_CPU / CLUSTER_MAX_CPU #'CLUSTER_CPU_WASTE', # [] CLUSTER_CPU_ALOC / CLUSTER_CPU_LOAD ] } }
Instance Attribute Summary (collapse)
-
- (Object) sec_interval
readonly
DO NOT EDIT BELOW THIS LINE ##.
Instance Method Summary (collapse)
- - (Object) influx
-
- (Config) initialize
constructor
Initializes logging, converts fetch interval from human readable form, adds tag ID to all metrics and converts VM's tags from human readable form.
-
- (Boolean) is_one_available?
Checks it is possible to connect to ONE with provided credentials.
-
- (Object) one
Group of getter methods for configuration class variables.
- - (Object) storage
Constructor Details
- (Config) initialize
Initializes logging, converts fetch interval from human readable form,
adds tag ID to all metrics and converts VM's tags from human readable form
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# File 'lib/one2influx/config.rb', line 193 def initialize log_path = @@log[:path] + 'one2influx.log' begin $LOG = Logger.new(log_path, 'daily', 30) rescue Exception => e raise "Unable to create log file. #{e.}" end $LOG.level = @@log[:level] convert_to_sec prepare_storage_ids prepare_vm_config end |
Instance Attribute Details
- (Object) sec_interval (readonly)
DO NOT EDIT BELOW THIS LINE ##
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# File 'lib/one2influx/config.rb', line 173 def sec_interval @sec_interval end |
Instance Method Details
- (Object) influx
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# File 'lib/one2influx/config.rb', line 183 def influx @@influx end |
- (Boolean) is_one_available?
Checks it is possible to connect to ONE with provided credentials.
208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 |
# File 'lib/one2influx/config.rb', line 208 def is_one_available? begin client = OpenNebula::Client.new(@@one[:credentials], @@one[:endpoint]) rescue Exception => e $LOG.error "Unable to connect to ONE with message: #{e.}" return false end version = client.get_version # Try to get ONE version just to check if it's possible to connect to ONE if version.is_a? OpenNebula::Error $LOG.error 'Unable to find out ONE version with message: '+version. return false end $LOG.info 'Connection with ONE verified.' return true end |
- (Object) one
Group of getter methods for configuration class variables
179 180 181 |
# File 'lib/one2influx/config.rb', line 179 def one @@one end |
- (Object) storage
187 188 189 |
# File 'lib/one2influx/config.rb', line 187 def storage @@storage end |


{% extends "global/Page.html" %} {% load staticfiles otree %} {% block title %} Instructions {% endblock %} {% block content %}
Welcome to the experiment! General notice. In this experiment, you can earn money. To make this experiment a success, please do not talk to the other participants at all or draw any other attention to yourself. Please read the following rules of the experiment attentively. Should you have any questions, please signal us. At the end of the instructions, you will find several control questions. Please answer all questions, and signal us when you have finished. We will then come to you and check your answers.
Energy Conservation. Now, we will introduce you to a game simulating a household energy conservation challenge. Household energy use is an important contributor to many environmental problems including local and international air pollution and global warming. Many groups are seeking ways to encourage communities to use less energy. This experiment will simulate this type of exercise.
Rules of play. In this game, there are {{ players_in_group }} players who represent people living in the same apartment building. Thus, in addition to you, there are {{ other_players}} other players. The building is trying to meet a collective energy conservation goal of reducing its annual energy use by {{ goal }}% through the actions of residents. Every player faces the same decision-making problem. At the beginning of the experiment, you will receive a {{ endowment }} in your private account. During the experiment, you can use money from your account or not. In the end, your account balance will be paid out to you in cash. You will be making your decisions anonymously. During the course of the experiment, you will be playing exactly {{ Constants.num_rounds }} rounds equivalent to {{ Constants.num_rounds }} months in a year. In each round, you can invest some of your money into the community’s attempt to reduce energy consumption. Thus, in each round of the game, all {{ players_in_group }} players will be asked simultaneously, “How much do you want to invest into energy conservation?”
Possible answers are $0, $.50, or $1. Only when each player has made his choice will all decisions be displayed simultaneously. After that, the computer will credit all invested amounts to an account for energy conservation (“conservation account”). At the end of the game (after exactly {{ Constants.num_rounds }} rounds), the computer will compare the conservation account balance with the predetermined goal ($120). This amount is required to make the investments needed to reduce the buildings energy consumption by {{ goal }}%. It will be earned if every player pays, on average, $.50 per round into the conservation account. All payouts will be made anonymously. Your fellow players will not learn your identity.


This package contains contains all the classes that make up the menu system,
and is used by all the menu applications. The menu items are contained in 4 tables
in the dba2 Oracle schema, namely DMN_MENU_ITEMS, DMN_PARAMETERS, DMN_PARAM_SELECT
and DMN_SCRIPT_TYPE. Any application that uses tables out of another Oracle schema,
must have synonyms with the same names for these 4 tables.
All applications using this package should have the following:
- A args2 String array defined, e.g.
String[] args2 = {
"papplication=EdmMenuP",
"powner=edm",
"pttitle=EDM",
"put=1", // 1 == admin user, 2 == normal user
"pdate=27 May 2002"
};
with
- papplication = the name of the application using the package
- powner = the name of the Oracle schema
- pttitle = the title that will be displayed in the top bar
- put = the user type of the menu system
- pdate = the date the application was last updated which appears in the bottom bar
- The Tables.userid and Tables.password variables must be set, e.g.
menusp.Tables.userid = "edm";
menusp.Tables.password = "ter91qes";
-


|
||||||||||
| PREV CLASS NEXT CLASS | FRAMES NO FRAMES | |||||||||
| SUMMARY: INNER | FIELD | CONSTR | METHOD | DETAIL: FIELD | CONSTR | METHOD |
This interface specifies how the parser and the application interact with respect to Grammar objects that the application possesses--either by having precompiled them or by having stored them from a previous validation of an instance document. It makes no assumptions about the kind of Grammar involved, or about how the application's storage mechanism works.
The interaction works as follows:
retrieveInitialGrammarSet
method. retrieveGrammar
method. cacheGrammars
method; for ease of implementation it may make other Grammars it holds references to as well (i.e.,
it may return some grammars that were retrieved from the GrammarPool in earlier operations).
| Method Summary | |
|---|---|
void |
cacheGrammars(java.lang.String grammarType,
Grammar[] grammars)
return the final set of grammars that the validator ended up with. |
void |
clear()
Removes all grammars from the pool. |
void |
lockPool()
Causes the XMLGrammarPool not to store any grammars when the cacheGrammars(String, Grammar[[]) method is called. |
Grammar |
retrieveGrammar(XMLGrammarDescription desc)
This method requests that the application retrieve a grammar corresponding to the given GrammarIdentifier from its cache. |
Grammar[] |
retrieveInitialGrammarSet(java.lang.String grammarType)
retrieve the initial known set of grammars. |
void |
unlockPool()
Allows the XMLGrammarPool to store grammars when its cacheGrammars(String, Grammar[]) method is called. |
| Method Detail |
public Grammar[] retrieveInitialGrammarSet(java.lang.String grammarType)
retrieve the initial known set of grammars. this method is called by a validator before the validation starts. the application can provide an initial set of grammars available to the current validation attempt.
grammarType
- the type of the grammar, from the
org.apache.xerces.xni.grammars.Grammar
interface.public void cacheGrammars(java.lang.String grammarType, Grammar[] grammars)
return the final set of grammars that the validator ended up with. This method is called after the validation finishes. The application may then choose to cache some of the returned grammars.
grammarType
- the type of the grammars being returned;grammars
- an array containing the set of grammars being
returned; order is not significant.public Grammar retrieveGrammar(XMLGrammarDescription desc)
This method requests that the application retrieve a grammar corresponding to the given GrammarIdentifier from its cache. If it cannot do so it must return null; the parser will then call the EntityResolver. An application must not call its EntityResolver itself from this method; this may result in infinite recursions.
desc
- The description of the Grammar being requested.public void lockPool()
public void unlockPool()
public void clear()
|
||||||||||
| PREV CLASS NEXT CLASS | FRAMES NO FRAMES | |||||||||
| SUMMARY: INNER | FIELD | CONSTR | METHOD | DETAIL: FIELD | CONSTR | METHOD |


We already have a functioning bot from the last post. If you have not read it please follow the steps in it and build a working bot as this post is a follow up to it. What is the use of bot if it is not linked to any platform, so here we will integrate our bot to Facebook messenger and access it from messenger. Lets get started.
First we need to create a Facebook app. Go to Facebook Developer page and create a new app.
Go to “Add a product” and add Facebook messenger and you will be taken to the settings page of messenger.
You need to generate the Page Access Token. You can either create a new Facebook page or select an existing page(Make sure the page is not published if you are just testing).
Once you provide a page, page access token is generated.

Thats all we need from Facebook for now.

Go to the Channel taband choose Facebook channel.
Provide the name, description and the Alias you created in the settings.
Provide a random text as Verify Token. This will be used as authentication for the calls from Facebook. You need to provide the same Verify Token in Facebook.
Get the Page Access Token and App Secret key from Facebook app as mentioned and fill in them.
ClickActivate and a callback url should be generated for you.
Click Build and build the bot .
The settings should look something like below:
Now that we got the callback url we need to configure it in Facebook app we created.
Go to the Facebook app we created.
Copy the callback url from Lex and provide it in Webhook settings in messenger.
Provide the same Verify Tokenas you gave in Lex channel settings.
Provide the events messages, messaging_postbacks, messaging_optinsand verify and save.
Select the page and click on subscribe in the Webhook window to listen to chat from the page.
Go to Settings in your Facebook page.
Select Messenger Platform from the left tab.
You will see your Facebook app being listed there.
Set the role as Primary Receiver.
That’s it, now we are all set to chat with our bot. You can go to the Facebook page you created and click on Send Message to start chatting.
If all well well, Facebook messenger will be able to communicate with our Lex Bot and you will get response as below.
If you liked this story, feel free to reach out to me at Kaizen Coder.


@* * Copyright 2019 HM Revenue & Customs * * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. * You may obtain a copy of the License at * * http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. *@ @import agent.models.ExitSurveyModel @import play.api.data.Form @import agent.forms.ExitSurveyForm @import uk.gov.hmrc.play.views.html.helpers.form @import core.views.html.helpers._ @import core.config.AppConfig @(exitSurveyForm: Form[ExitSurveyModel], postAction: Call)(implicit request: Request[_], messages: Messages, appConfig: AppConfig) @question(text: String) = {
@Messages("agent.exit-survey.line_1")
@textAreaHelper( field = exitSurveyForm(ExitSurveyForm.improvements), maxLength = ExitSurveyForm.improvementsMaxLength, label = Messages("agent.exit-survey.q2"), showLabel = false )@Messages("agent.exit-survey.line_2")
@continueButton(Messages("agent.exit-survey.submit")) } }


thursday
PIE donation days
We really need money.
Turkey feast
We promised you first class seating and instead forced you to stand in the rain.
Día de los Muertos
A Japanese holiday celebrated in Sweden.
Period B
3:00am – 6:00pm
started
5 minutes
ago
ended in
5 minutes
ago
lasts for
5 minutes
Brunch
3:00am – 6:00pm
started
5 minutes
ago
ending in
5 minutes
lasts for
5 minutes
Period C
3:00am – 6:00pm
starting
5 minutes
ending in
5 minutes
lasts for
5 minutes
Period D
3:00am – 6:00pm
starting
5 minutes
ending in
5 minutes
lasts for
5 minutes
Lunch
3:00am – 6:00pm
starting
5 minutes
ending in
5 minutes
lasts for
5 minutes
very long class name muahaha
3:00am – 6:00pm
starting
5 minutes
ending in
5 minutes
lasts for
5 minutes


Integral Transformations¶
In this section the integral transformations will be described.
AO to MO basis 2 electron integrals¶
The integral transformation of the AO two electron integrals to the MO two electron integrals is given as:
\[\left(ij\left.\right|kl\right)=\sum_{\mu}\sum_{\nu}\sum_{\lambda}\sum_{\sigma}C_{\mu}^{i}C_{\nu}^{j}\left(\mu\nu\left.\right|\lambda\sigma\right)C_{\lambda}^{k}C_{\sigma}^{l}\]
FUNCTION:
- IntegralTransform.Transform2eMO(C, Vee)
- return VeeMO
Input:
- C, MO coefficients from SCF calculation
- Vee, two electron integrals in AO basis
Output:
- VeeMO, two electron integrals in MO basis
References:
MO spatial to MO spin basis 2 electron integrals¶
The integral transformation from MO spatial to MO spin orbitals is given by the following equation:
\[\left\langle \left.ij\right|kl\right\rangle =\left(\left.\sigma_{1}i\sigma_{2}k\right|\sigma_{3}j\sigma_{4}l\right)\delta_{\sigma_{1}\sigma_{2}}\delta_{\sigma_{3}\sigma_{4}}\]
FUNCTION:
- IntegralTransform.Transform2eSPIN(Vee)
- return VeeSpin
Input:
- Vee, two electron integrals
Output:
- VeeSpin, two electron integrals in spinbasis
References:
- None


Rylan Peery
Owner, Co-Founder
Rylan began working in the technology industry during the late 90s when he graduated from Stanford University and began a stint in venture capital. Compelled to get his hands deeper into technology, Rylan subsequently worked as a front-end engineer, designing and coding user interfaces for enterprise clients like Bizrate. Rylan has over 10 years of experience working in technology for Fortune 500 clients.


Name:
Email:
Lo needs help! Recycle as many items as you can and save the trees!
Sign in to play!
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Play


| F_1_panel.qqmath {lattice} | R Documentation |
This is the default panel function for qqmath
.
panel.qqmath(x, f.value = NULL, distribution = qnorm, qtype = 7, groups = NULL, ..., tails.n = 0, identifier = "qqmath")
x |
vector (typically numeric, coerced if not) of data values to be used in the panel. |
f.value, distribution |
Defines how quantiles are calculated. See |
qtype |
The |
groups |
An optional grouping variable. Within each panel, one Q-Q plot is produced for every level of this grouping variable, differentiated by different graphical parameters. |
... |
Further arguments, often graphical parameters, eventually passed on
to |
tails.n |
number of data points to represent exactly on each tail of the
distribution. This reproduces the effect of |
identifier |
A character string that is prepended to the names of grobs that are created by this panel function. |
Creates a Q-Q plot of the data and the theoretical distribution given
by distribution
. Note that most of the arguments controlling
the display can be supplied directly to the high-level qqmath
call.
Deepayan Sarkar<EMAIL_ADDRESS>set.seed(0) xx <- rt(10000, df = 10) qqmath(~ xx, pch = "+", distribution = qnorm, grid = TRUE, abline = c(0, 1), xlab.top = c("raw", "ppoints(100)", "tails.n = 50"), panel = function(..., f.value) { switch(panel.number(), panel.qqmath(..., f.value = NULL), panel.qqmath(..., f.value = ppoints(100)), panel.qqmath(..., f.value = ppoints(100), tails.n = 50)) }, layout = c(3, 1))[c(1,1,1)]


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| # | Drive Name | Package | Qualified Rounds | Drive Date | Status | |
|---|---|---|---|---|---|---|
| {{i+1}} | {{data.company}} | {{data.package}} | {{data.rounds}} | {{data.inserted_at | date:'dd-MMM-yyyy'}} | better luck next time | Placed |
| # | Drive Name | Drive Date | Package | Result |
|---|---|---|---|---|
| {{i+1}} | {{data.company}} | {{data.inserted_at | date:'dd-MMM-yyyy'}} | {{data.package}} | Placed |
| # | Drive Name | Drive Date | Package | Status |
|---|---|---|---|---|
| {{i+1}} | {{data.company}} | {{data.inserted_at | date:'dd-MMM-yyyy'}} | {{data.package}} | Not Attended |
| # |
|
|
|
|
|
|
|
|||
|---|---|---|---|---|---|---|---|---|---|---|
| {{all1.indexOf(a) + 1}} | {{a.company}} | {{a.location}} | {{a.c_date | date:'dd-MMM-yyyy'}} | {{a.package}} | {{a.backlogs}} | {{a.percentage}} | You are conformed | You are not Conformed | you are not eligible | |
| No Data Found | ||||||||||
|
|
| # |
|
|
|
|
|
|
|
|||
|---|---|---|---|---|---|---|---|---|---|---|
| {{plannedcompany.indexOf(item) + 1}} | {{item.company}} | {{item.location}} | {{item.c_date | date: 'dd-MMM-yyyy' }} | {{item.package}} | {{item.backlogs}} | {{item.percentage}} | You are conformed | You are not conformed | you are not eligible | |
| No Data Found | ||||||||||
|
|


Installation¶
The VortexFitting code works only on Python 3.6 and superior. The following packages are used:
- NumPy
- SciPy
- Matplotlib
- netCDF4
Using PIP¶
We recommend creating a new virtual environment for your projects
$ python3 -m venv NAME
$ source NAME/bin/activate
$ pip install vortexfitting
Simply run “pip install vortexfitting”
Documentation¶
In order to generate the documentation you need the sphinx package.
To perform changes on the documentation, you should modify directly the .rst files, located in the docs folder. After modifying the desired files, run the script update_docs.py (for Linux and Windows):
$ python update_docs.py
After this, the html files will be generated in the same folder.


The seq/to_array.h
header defines a macro that converts a sequence to an array.
#include <chaos/preprocessor/seq/to_array.h>
The CHAOS_PP_SEQ_TO_ARRAY
macro converts a sequence to an array.
CHAOS_PP_SEQ_TO_ARRAY
(seq)
The sequence to be converted.
CHAOS_PP_SEQ_TO_ARRAY
(seq)
The sequence to be converted.
This macro expands to a array containing the elements of seq.
In C99, sequences can contain elements of arities other than one. If such a sequence is converted to an array, the resulting array will be invalid.
CHAOS_PP_SEQ_TO_ARRAY
((a, b)(c)) // invalid
This macro makes no attempt to diagnose that situation.
#include<chaos/preprocessor/seq/to_array.h>
CHAOS_PP_SEQ_TO_ARRAY
((a)(b)(c)(d)) // (4, (a, b, c, d))
The CHAOS_PP_SEQ_TO_ARRAY_ID
macro indirectly expands to CHAOS_PP_SEQ_TO_ARRAY
.
CHAOS_PP_SEQ_TO_ARRAY_ID
()
This macro effectively allows CHAOS_PP_SEQ_TO_ARRAY
to pass through itself without becoming disabled.
The CHAOS_PP_SEQ_TO_ARRAY_
macro expands to a λ expression representing CHAOS_PP_SEQ_TO_ARRAY
.
CHAOS_PP_SEQ_TO_ARRAY_
This macro is a shorthand form of the following λ binding:


Watcher of Antharas Gilmore:
Ask away! What do you want to know about?
"Do I have a Emerald Horn skull?"
"Do I have a Dust Rider skull?"
"Do I have a Bleeding Fly skull?"
"Do I have a Black Dagger Wyrm skull?"
"Do I have a Shadow Summoner skull?"
"Do I have a Spike Slasher skull?"
"Do I have a Muscle Bomber skull?"
"No thanks. Goodbye."


Mike D'Antoni rips team for 'soft' effort in embarrassing loss to Heat
by TSD StaffIt’s unclear if the Rockets came down a case of the infamous “South Beach flu,” or if they simply didn’t execute well in Sunday’s 129-100 blowout loss to the Heat.
Whatever the reason, Houston got dominated from start to finish, and simply did not play well.
The Rockets fell behind 46-14 after just one quarter of play, leading many to wonder if the “South Beach flu” played a part. For those that don’t know, the angle involves teams that play Sunday afternoon or early evening games in Miami, with players struggling because they hit up the South Beach clubs on Saturday night. And given who the Rockets have on their roster, it’s certainly possible.
Regardless, the Rockets got dominated on both ends of the floor, and head coach Mike D’Antoni was not happy about it. He even called the team “soft” in speaking to reporters after the game.
Maybe Russell Westbrook caught the “South Beach flu,” because this stat line is historically bad.
The Rockets have a great opportunity to rebound on Monday night, as they’ll square off against the young, tanking Grizzlies in Memphis.


In case you are not interested in reading, here is the conclusion: Based on past analytics and Google updates, a subdomain on a well branded domain is better than a new domain. The subdomain will have the links and the branded domain. Plus, a subdomain will protect the site from future updates (many of the past updates have targeted less well-known domains with fewer links).
*Not in consideration is subdirectories. That is a completely different topic.
The Case for a Subdomain over a New Domain
Pros
- Subdomains SOMETIMES inherit and pass link/trust/quality/ranking metrics between one another (via Fishkin and Dr. Pete Myers).
- Webmaster tools (2011) “If you add either example.com or www.example.com as a site, links from both the www and non-www versions of the domain will be categorized as internal links. We’ve also extended this idea to include other subdomains (cats.example.com or pets.example.com will also be categorized as internal links for www.example.com.)
- Subdomain move towards one unified brand (Emphasized in Vince Update – 2009) / Panda 2013 also promoted branded results. Having a subdomain on a brand will help keep branding consistent. You won’t need new social profiles, tons more links, etc…
- Being branded on one domain could lead to more links.
Cons
- A subdomain could create a Long URL. By the time you get to a post, the url and main keywords in the URL are far off the domain.
- No one is really sure how Google sees subdomains.
- Some say it is a separate site. Cutts has only said that subdomains should be used to separate out distinct content.
What Do We Know for Sure?
- Since links can SOMETIMES pass credibility from domain to subdomain, we know that Google could see domains and subdomains as connected content (and not separate sites as previously thought). This will be great for ranking content quickly.
- Since content is linked – bots have the ability to bounce back and forth from domain to subdomain to domain. This will help content to be found more quickly.
Quick Synopsis:
You’ll benefit from brand, links, and the updates.
Don’t think of a subdomain as a separate site. Think of a subdomain as a closet in a house. You put things in a closet that are not necessarily what you want in the house, but they can still be seen. Same with a subdomain – it can be found, and although separate, links will pass.
The Case for a New Domain Over a Subdomain
Pros
- If you are under scrutiny for a penalty, your site will be pulled out from the old domain and separated to a new domain.
- Site may need a fresh start, and this is perfect.
Cons
- A new domain is hard to rank.
- The site will be starting completely from scratch. Also, it is not wise to move tons of content from domain to new domain with 301s. You could potentially hurt both sites.
- New back links (besides those garnered from 301s you do bring over) will be needed.
- Ranking will be extremely difficult because you are not competing against your legacy domain.
- Some of the old content that you brought over may never show up in the SERPs.
- Lack of branding has the potential to become a “networking site” if there are constant links to and from new domain to old domain. If you site is constantly interlinking – highly optimized anchor text links could hurt SEO (Panda).
Quick Synopsis:
The domain gets a fresh start, but you are building from scratch. Based on past Google Updates, it may not be the best option to have a smaller with no links. Enjoy that ranking dilemma.


Preparation time
less than 30 mins
Cooking time
30 mins to 1 hour
Serves
Serves 4
An Indian-style omelette served with a fresh salad. Take care when cooking the chicken livers - it's essential they are properly cooked through, but will taste bitter if overcooked.
For the fennel and red onion salad, mix all of the ingredients together and set aside until ready to serve.
Clean the chicken livers, taking care to remove all the membranes. Cut them into small pieces and put in a bowl.
Mix the ginger, garlic, chilli, turmeric, cumin, coriander and lime juice in with the livers. You can set this aside in the refrigerator for a few hours if you like.
Put the oil in a heavy-bottomed frying pan set over a high heat. When the oil begins to smoke, add the livers, spread them out and do not agitate the pan. After a few seconds shake the pan around or toss them, then let them settle again. (The pan has got to be really hot to prevent the livers from releasing too much liquid and losing their texture and flavour. Too much stirring introduces colder air to the base of the pan, thereby cooling it down.) If the pan remains hot enough the livers will cook within 2 minutes.
Season, to taste, with salt. Stir in half the coriander and mint (reserve the rest for the garnish). Immediately transfer to a colander set over a bowl to prevent any further cooking and to prevent the omelette from getting soggy from the juices from the livers.
For the spiced omelette, break the eggs into a bowl and beat. Add the red onion, chillies, tomatoes and coriander. Season with salt and pepper and beat again.
Heat two small frying pans and add a tablespoon of oil to each. When the oil is hot, add half the egg mixture to each pan. Cook until browned on one side and cooked through. Remove from the heat. Divide the liver mixture between the two omelettes, sprinkle the sliced onion on top and garnish with the reserved coriander and mint. Roll up the omelettes.
Cut the omelettes into two and serve with the salad alongside.


Examples¶
In this page you can find examples for several of the algorithms in the library. You can find the data used for the examples in the Github repository.
MajorityVoting¶
The example below shows how to use the MajorityVoting algorithm for estimating the ground truth for a binary target variable.
import com.enriquegrodrigo.spark.crowd.methods.MajorityVoting
import com.enriquegrodrigo.spark.crowd.types.BinaryAnnotation
val exampleFile = "data/binary-ann.parquet"
val exampleDataBinary = spark.read.parquet(exampleFile).as[BinaryAnnotation]
val muBinary = MajorityVoting.transformBinary(exampleDataBinary)
muBinary.show()
The method returns a result similar to this one:
+-------+-----+
|example|value|
+-------+-----+
| 26| 0|
| 29| 1|
| 474| 0|
| 964| 1|
| 65| 0|
| 191| 0|
| 418| 1|
....
MajorityVoting algorithms assume that all annotators are equally accurate, so they choose the most frequent annotation as the ground truth label. Because of this, they only return the ground truth for the problem.
The data file in this example follow the format from the BinaryAnnotation
type:
example, annotator, value
0, 0, 1
0, 1, 0
0, 2, 1
...
In this example, we use a .parquet
data file, which is usually a good option in terms of
efficiency. However, we do not limit the types of files you can use, as long as they can be
converted to typed datasets of BinaryAnnotation
, MulticlassAnnotation
or RealAnnotation
.
However, algorithms will suppose that there are no missing examples or annotators.
Specifically, MajorityVoting can make predictions both for discrete classes (BinaryAnnotation
and
MulticlassAnnotation
) and continuous-valued target variables. (RealAnnotation
). You can find
information about these methods in the API Docs.
DawidSkene¶
This algorithm is one of the most recommended both for its simplicity and its good results generally.
import com.enriquegrodrigo.spark.crowd.methods.DawidSkene
import com.enriquegrodrigo.spark.crowd.types.MulticlassAnnotation
val exampleFile = "examples/data/multi-ann.parquet"
val exampleData = spark.read.parquet(exampleFile).as[MulticlassAnnotation]
val mode = DawidSkene(exampleData, eMIters=10, emThreshold=0.001)
val pred = mode.getMu().as[MulticlassLabel]
val annprec = mode.getAnnotatorPrecision()
In the implementation, two parameters are used for controlling the algorithm execution, the maximum number of EM iterations and the threshold for the likelihood change. The execution stops if the number of iterations reaches the established maximum or if the change in likelihood is less than the threshold. You do not need to provide these parameters, as they have default values.
One executed, the model provides an estimation of the ground truth, and an estimation of the quality of each annotator, in the form of a confusion matrix. This information can be obtained as shown on the example.
GLAD¶
The GLAD algorithm is interesting as it provides both annotator accuracies and example difficulties obtained solely from the annotations. An example of how to use it can be found below.
import com.enriquegrodrigo.spark.crowd.methods.Glad
import com.enriquegrodrigo.spark.crowd.types.BinaryAnnotation
val annFile = "data/binary-ann.parquet"
val annData = spark.read.parquet(annFile).as[BinaryAnnotation]
val mode = Glad(annData,
eMIters=5, //Maximum number of iterations of EM algorithm
eMThreshold=0.1, //Threshold for likelihood changes
gradIters=30, //Gradient descent max number of iterations
gradTreshold=0.5, //Gradient descent threshold
gradLearningRate=0.01, //Gradient descent learning rate
alphaPrior=1, //Alpha first value (GLAD specific)
betaPrior=1) //Beta first value (GLAD specific)
val pred = mode.getMu().as[BinarySoftLabel]
val annprec = mode.getAnnotatorPrecision()
val annprec = mode.getInstanceDifficulty()
This model as implemented in the library is only compatible with binary class problems. It has a higher number of free parameters in comparison with the previous algorithm, but we provided default values for all of them for convenience. The meaning of each of these parameters is commented in the example above, as it is on the API Docs. The annotator precision is given as a vector, with an entry for each annotator. The difficulty is given in the form of a DataFrame, returning a difficulty value for each example. For more information, you can consult the documentation and/or the paper.
RaykarBinary, RaykarMulti and RaykarCont¶
We implement the three variants of this algorithm, two for discrete target variables (RaykarBinary
and
RaykarMulti
) and one for continuous variables (RaykarCont
).
These algorithms have in common that they are able to use features to estimate the ground truth
and even learn a linear model. The model also is able to use prior information about annotators,
which can be useful to add more confidence to certain annotators. The next example shows
how to use this priors to indicate that the trust put in the first annotator is higher and
that the second annotator is not reliable.
import com.enriquegrodrigo.spark.crowd.methods.RaykarBinary
import com.enriquegrodrigo.spark.crowd.types.BinaryAnnotation
val exampleFile = "data/binary-data.parquet"
val annFile = "data/binary-ann.parquet"
val exampleData = spark.read.parquet(exampleFile)
val annData = spark.read.parquet(annFile).as[BinaryAnnotation]
//Preparing priors
val nAnn = annData.map(_.annotator).distinct.count().toInt
val a = Array.fill[Double](nAnn,2)(2.0) //Uniform prior
val b = Array.fill[Double](nAnn,2)(2.0) //Uniform prior
//Give first annotator more confidence
a(0)(0) += 1000
b(0)(0) += 1000
//Give second annotator less confidence
//Annotator 1
a(1)(1) += 1000
b(1)(1) += 1000
//Applying the learning algorithm
val mode = RaykarBinary(exampleData, annData,
eMIters=5,
eMThreshold=0.001,
gradIters=100,
gradThreshold=0.1,
gradLearning=0.1
a_prior=Some(a), b_prior=Some(b))
//Get MulticlassLabel with the class predictions
val pred = mode.getMu().as[BinarySoftLabel]
//Annotator precision matrices
val annprec = mode.getAnnotatorPrecision()
Apart form the features matrix and the priors, the meaning of the parameters is the same as in the previous examples.
The priors are matrices of dimension A by 2, where A is the number of annotators. In each row we have the hyperparameters of a Beta distribution for each annotator.
The a_prior
gives prior information about the ability of annotators to correctly classify a positive example. The
b_prior
does the same thing but for the negative examples. More information about this method as well as the methods
for discrete and continuous target variables can be found in the API Docs.
CATD¶
This method allows to estimate continuous-value target variables from annotations.
import com.enriquegrodrigo.spark.crowd.methods.CATD
import com.enriquegrodrigo.spark.crowd.types.RealAnnotation
sc.setCheckpointDir("checkpoint")
val annFile = "examples/data/cont-ann.parquet"
val annData = spark.read.parquet(annFile).as[RealAnnotation]
//Applying the learning algorithm
val mode = CATD(annData, iterations=5,
threshold=0.1,
alpha=0.05)
//Get MulticlassLabel with the class predictions
val pred = mode.mu
//Annotator precision matrices
val annprec = mode.weights
It returns a model from which you can get the ground truth estimation and
also the annotator weight used (more weight means a better annotator).
The algorithm uses parameters such as iterations
and threshold
for
controlling the execution, and also alpha
, which is a parameter of the model
(check the API Docs for more information).


Sunshine **
Rated on a 4-star scale Screening venue: Cornerhouse (Manchester) Released in the UK by Alliance Atlantis on April 28, 2000; certificate 15; 180 minutes; countries of origin Austria/Canada/Germany/Hungary/UK; aspect ratio 1.85:1
Directed by István Szabó; produced by Adrás Hámori, Robert Lantos. Written by Israel Horovitz, István Szabó; based on an original story by István Szabó. Photographed by Lajos Koltai; edited by Michel Arcand, Dominique Fortin.
CAST..... Ralph Fiennes..... Ignatz Sonnenschein/Adam Sors/Ivan Sors Rosemary Harris..... Old Valerie Rachel Weisz..... Greta Sors Jennifer Ehle..... Young Valerie Deborah Kara Unger..... Major Carole Kovacs Molly Parker..... Hannah Wippler James Frain..... Gustave Sonnenschein David de Keyser..... Emmanuel Sonnenschein John Neville..... Gustave Sors Miriam Margolyes..... Rose Sonnenschein
Too many movies use the words "based on a true story" as an excuse for blandness. What they don't realise is that just because a story might be true doesn't make it interesting. Then again, sometimes the fact that a tale really happened might give it more importance or context. If only that had been the case with István Szabó's "Sunshine". The film is a silly fiction, but if it had really happened, well, that would have been something.
"Sunshine" follows three generations in a Hungarian family in three different times of revolution, dividing its three-hour running time evenly between each of its three stories. The first main character is Ignatz Sonnenschein, a lawyer in the late 19th Century who allows himself to change his Jewish name to Sors for purposes of conformity, marries a girl who had been raised as his sister, and finds that his work for the imperial government puts him in danger when communists come to power. Next we focus on his son Adam Sors, who allows himself to change his religion for purposes of conformity, marries a girl he hardly knows, has an affair with his sister-in-law, and finds that his heritage puts him in danger when the Nazis seem set to take over Europe. Finally comes the tale of Adam's son Ivan, who allows himself to get entangled in Stalinist persecutions, has an affair with an important army general's wife, and finds himself in danger during the Purges.
The connections are obvious: Each man somehow sells out, dives into troublesome romance and then winds up in over his head in a deadly political situation. What's more, Ignatz, Adam and Ivan are all played by the same actor, Ralph Fiennes. Subtle. The theory that the mistakes and sins of fathers can be repeated by their sons was present in the background of the "Godfather" and "Jean de Florette" movies, through vital situations and interesting characters. "Sunshine" thinks it's some kind of profound parable where the drama doesn't need to be fulfilling in itself. The people and plights are all just one-dimensional cardboard, existing to prove points. Nobody is convincing as a living, breathing person, and so we never care, and the point of the film is clear but carries no power.
Much of the movie has the silliness of "Legends of the Fall", without any of the gusto that made that movie enjoyable. When I saw it, people in the audience were laughing in key moments. Consider the amateurish attempts to mix the movie's own footage with old newsreel clips, just by developing bits in black and white. Or the exaggerated reaction shots of people rolling their eyes. Miriam Margolyes, as Ignatz's mother, breaks down crying so often that eventually the woman sitting next to me started doing a countdown whenever she appeared onscreen.
But even when the film does work on the level of performances and technical beauty, that doesn't change how simple-minded it is. Director Szabó's own "Mephisto" (1981) took its title from a fairly obvious parallel -- the one between its lead character and the story of "Dr. Faustus" -- but the content of the film didn't rely on it, and introduced it up front and kept it as a side-issue. "Sunshine" spends 180 minutes reiterating the same message, one that we've heard before and could be reminded of in a single line.
COPYRIGHT(c) 2000 Ian Waldron-Mantgani Please visit, and encourage others to visit, the UK Critic's website, which is located at http://members.aol.com/ukcritic
The review above was posted to the
rec.arts.movies.reviews newsgroup (de.rec.film.kritiken for German reviews).
The Internet Movie Database accepts no responsibility for the contents of the
review and has no editorial control. Unless stated otherwise, the copyright
belongs to the author.
Please direct comments/criticisms of the review to relevant newsgroups.
Broken URLs inthe reviews are the responsibility of the author.
The formatting of the review is likely to differ from the original due
to ASCII to HTML conversion.
Related links: index of all rec.arts.movies.reviews reviews


|
API C Reference
4.0
|
NAPI is an asynchronous API that operates by sending stringified JSON objects to NAPI and receiving stringified JSON objects from NAPI.
Making a request to NAPI (nymi::jsonNapiPut) is a non-blocking call returning immediately, and it always succeeds if NAPI has been started (nymi::JsonPutOutcome). This success even after nymi::jsonNapiTerminate has been called is an historical detail that will be addressed in a future release.
Responses to requests are obtained by calling nymi::jsonNapiGet. Both responses to NEA requests and NAPI-generated notifications are returned from this function. NAPI will include the exchange
attribute when defined in the request in all responses to the request. This is intended to allow the NEA to match responses to requests.
Calls to the nymi::jsonNapiGet are blocking – they will wait for a result unless interrupted. There are three versions of nymi::jsonNapiGet included in NAPI – the standard call, and two deprecated variants that should not be used. These provide different mechanisms to return early (without a JSON object). The standard call returns early when nymi::jsonNapiTerminate (or nymi::jsonNapiFinish) has been called. The variants provide mechanisms the NEA can control to return early (timeout, and signaling).
exchange
attribute to identify responses to requests.NAPI is an asynchronous API that works by an interchange of JSON strings between the NEA and NAPI. The details of the JSON are discussed here.
There are three common approaches to writing an NEA using NAPI:
exchange
.exchange
.The first approach is most useful in simple or short running NEAs. The second, and especially third, approaches are more useful in longer running or complex NEAs.
It is not possible for nymi::jsonNapiGet to succeed before nymi::jsonNapiConfigure has completed successfully. During the startup phase nymi::jsonNapiGet will return JsonGetOutcome::napiNotRunning. If the NEA is trying to call both simultaneously, or nearly, then if the NEA receives a JsonGetOutcome::napiNotRunning it is reasonable to wait for a few milliseconds and try again. If the NEA is trying to call nymi::jsonNapiGet well before nymi::jsonNapiConfigure you can use the same strategy, or choose some other kind of coordination mechanism. Either way no JSON messages are dropped.
LogLevel::normal reports only warnings, errors, and fatal log messages. There is no sensitive information in these messages. However if the LogLevel::info, LogLevel::debug, LogLevel::verbose levels are used then sensitive information almost certainly will be written to the logs. This may be an acceptable risk when developing a NEA, it is never an acceptable risk when the NEA is deployed.
The log files are crated in the config directory specified in nymi::jsonNapiConfigure
LogLevel::normal
, write sensitive material, such as symmetric keys, to the log in clear text.LogLevel::normal
should be used in a deployed NEA.


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December 20, 2002
Baptist school strips boys in search for pooper
New York Daily News | Submitted by: Penfold
"Two employees at the Concord Baptist Elementary School were supsended for
strip searching 8-10 year olds, looking to find a phantom pooper who took a
shit on the bathroom floor. The teachers, one of whom was caught wearing
rubber gloves, ordered the boys to drop their pants and bend over so they
could inspect them for signs of defecation. "Mommy, get me out of here,"
David told his mother when she stormed into the classroom to rescue her
son. "They're making us take off our underwear."
|
|
Read article...
|
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Wang cuts off wang
WDIV-TV 4 Detroit | Submitted by: PISTdetroit
"An Oak Park has pleaded guilty to castrating another man in his kitchen...
A Birmingham man went to Wang's home after he discovered the service in an
advertisement on the Internet."
|
|
Read article...
|
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Ex-con fabricates a worse, not better, past
Sacramento Bee | Submitted by: queasytick
"I have been living a lie," he told the hushed crowd seated in the community
meeting room. "I am truly sorry for violating the trust that you've shown me."
But those who know the conflicting cultures of life on the street and life in
recovery say Bonacci was trapped somewhere in between, boasting of violent
stints in prison that made him seem tougher to some and more inspirational to
others."
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Read article...
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Reindeer can't join in games
Charlotte Observer | Submitted by: SeMeN SPeRmS
"Half a dozen reindeer herds that usually spend the Christmas season
prancing in parades and around shopping malls have been grounded this
year not by bad weather at the North Pole, but by government officials
who fear the animals could spread chronic wasting disease... "I don't
know what we're going to do," said Cindy Gillaspie. "Maybe we'll
turn them all into sausage." Delicious!
|
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Read article...
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Welcome to the New World Order
The Memory Hole | Submitted by: Meerkat
"John Poindexter's Information Awareness Office has... removed its eye-death-ray logo, which was denounced far and wide as being Orwellian, Masonic, and just plain creepy as hell." The old logo has been preserved here.
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Read article...
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Bush wants to spy on people through internet
Toronto Star | Submitted by: Meerkat
"The Bush administration wants Internet service providers to help build a
centralized system to enable broad monitoring of the World Wide Web and,
potentially, real-time surveillance of its users." Too bad the article is
written by John Markoff.
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Read article...
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Science project results in refereed journal
Reuters | Submitted by: Rev. Soror Lucy Cannon
They were investigating brain freeze: "The subjects for Maya's
experiment were 145 students from Dalewood Middle School in Hamilton, Canada.
Half of the kids were instructed to eat about two scoops of vanilla ice cream
so that some was still left in the bowl after 30 seconds. The other students
were told to eat all of their ice cream in less than 5 seconds. Then all kids
were asked to report whether or not they developed a headache."
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Fetus Bingo
Time | Submitted by: Sketch
A priceless photo of wayward teen girls who are knocked up, playing
Fetus Bingo. Yeah baby.
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Author of deadbeat dad book facing jail
Philadelphia Inquirer | Submitted by: anonymous
"The author of a book on how to keep from paying child support could be
headed for prison for being a deadbeat dad... Morrison wrote the book
"Divorce Dirty Tricks," which provides advice on avoiding child support
obligations."
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Santa is a narc
Norman Transcript | Submitted by: Dandy Rieger
"Santa Claus still makes a list and checks it twice, but sometimes his methods
are a little unconventional. For example, [he] recently used a police radio at
Normanís Dimensions Academy, the cityís alternative elementary school, to
verify that students had been good rather than bad during the past year. Once
he got a 'clean report' on all the children, he and some assistants, all from
the Norman Police Department, distributed gifts..."
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End of the line for Meinhof terrorist brain
New Jersey Online | Submitted by: Son of Benway
"The brain of a notorious Red Army Faction terrorist was buried alongside
the rest of her body after a university that performed research on the organ
was forced to return it, officials said Friday... Meinhof was considered the
intellectual head of the Red Army Faction, a left-wing revolutionary group
that spread fear across West Germany in the 1970s and into the 1980s
after her death."
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Germans brace for nationwide DT's
Stuff NZ | Submitted by: Son of Benway
"German drinkers could be about to face their worst nightmare, a beer
shortage. The potential trickle in the supply of the nation's favourite
tipple is due to new government regulations restricting deposits of
disposable cans and bottles... Beer is among the most important drinks in
Germany, accounting for as much as half of the daily intake of fluids
for some people... Vending machines often have beer and the drink
can be ordered at fast-food restaurants."
 The woman, the apparent victim of a homicide, had been
dead for several weeks, said Lt. Gary Gilmond of the Beverly Hills Police
Department...  May God rest
her soul."
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Read article...
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Catholic Church covers up for IRA
Guardian | Submitted by: Tobermory
Good to see dodgy Catholic Priests have hobbies other than paedophilia: "The
British government and the Catholic church were involved in an astonishing
cover-up to shield a priest suspected of leading the IRA team responsible for
one of Northern Ireland's worst ever bomb atrocities... (A) letter (after the
attack) indicated that the cardinal knew that the priest was behaving
improperly." No shit.
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S.F. may soon see psychics regulated
San Francisco Chronicle | Submitted by: Brown Finger
"The future looked cloudy for dozens of fortune-tellers and psychics in
San Francisco on Thursday after legislation was proposed to require them to
obtain permits, post their rates and stop tricking their clients."
Nobody saw this coming.
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Nobody wants Hitler
Wire Services | Submitted by: Milk Bone
"Adolf Hitler might have been born in Germany and not, as long believed, in
the small Austrian border town of Braunau, a German author says. Egon Fein, a
historian who has just published a book on the Nazi dictator, said on Friday
that in his research he found indications Hitler was born north of the
Austrian town of Braunau in the German town Simbach."
 The big ships routinely dump
raw sewage into the ocean. Ships following behind then draw in the polluted
sea water for their desalinization plants, which take out the salt, but
not all the bacteria."
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The Irish Question solved: Fucking
MSNBC | Submitted by: harley
Ireland's new plan to recieve independence from England: have lots of
unprotected sex. A united Ireland will happen when there is a majority
of Catholics living in Northern Ireland, and the Catholics are so far
winning. "Sinn Feinís goal is to have a Catholic majority by 2016, the
100-year anniversary of the Easter Rising."
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Certificate Validator 0.1
(First visit will init the cache)
Cache Manager (provides a view of what is in the cache)
Cache as PEM


{{- $masonry_columns_count := 3 }} {{ $photos := slice }} {{/* The default behaviour is to use photos from all pages, so we set the intial value of our $pages array. */}} {{ $pages := .Pages }} {{ $show_all_photos := .Show_all_photos}} {{/* We then proceed to list all the photos from the pages. */}} {{- $list := where $pages ".Params.photos_with_metadata" "!=" nil -}} {{ range $index, $value := $list }} {{- $post_url := .Permalink -}} {{- $post_photos := cond $show_all_photos .Params.photos_with_metadata (first 1 .Params.photos_with_metadata) -}} {{ range $post_photo := $post_photos }} {{- $photo_with_post_url := merge $post_photo (dict "post_url" $post_url) -}} {{- $photos = $photos | append $photo_with_post_url -}} {{ end }} {{ end }} {{/* With the array of photos, we can now render them based on the style the user has chosen. */}} {{ if (eq .Grid_Style "grid") }}
{{ else if (eq .Grid_Style "masonry") }} {{ partial "masonry.html" (dict "columns" 3 "photos" $photos "class" "photos-grid--visible-desktop") }} {{ partial "masonry.html" (dict "columns" 1 "photos" $photos "class" "photos-grid--visible-mobile") }} {{ else }}The grid style "{{ .Grid_Style }}" is not supported.
{{ end }}


As the New Year rolls around, many of us set out to make promises to better ourselves. Goals play a powerful force as they give us direction in life.
Yet many people fail to follow through with their resolutions. But do not let their failure and lack of determination discourage you from achieving your goals. It is important to note that setting resolutions will help you grow significantly in both your personal and professional life.
I am dedicating this blog to show you how to set your goals and how to achieve them. It is important to note that it is not just about setting goals and accomplishing them – you have to pick the right and realistic goals. Additionally, I will also share with you my goals and how I break them up into smaller chunks.
New Year, New Feels, New Chances. Same Dreams, Fresh Starts.
 What would you like to accomplish? What particular areas of growth would you like to improve on? What blocks would you like to remove?
What little things would you like to achieve? What big things would you like to achieve? What would you like to have happen in your friendship, love life, and family life? What would you like to have happen in your career?
Would you like to improve your health and lifestyle? Do you want to change the way you look? Do you want to learn something new?
Take a piece of paper and write it all down. This process will help you organize your overall thoughts into concrete wants and needs to help you easily determine which goals you would like to set for yourself for the year.
Once you have an overall transformation in mind, break it down into concrete set of goals. For instance, one of my goals for the coming year is to improve my health and lifestyle. As such, I may choose to cut back on carbs and boba, eat more vegetables and fruits, hit the gym regularly, and use the sauna more often.
Set Goals
When setting your goals, focus on the following:
- Make your goals Specific, Measurable, Assignable, Realistic, and Time-related (SMART)
- Break the big picture into smaller goals (i.e. monthly goals)
- Do something every day to bring you closer to your goals
- Review and update your goals once per week
Your success and happiness lies in you. Resolve to keep happy, and your joy and you shall form an invincible host against difficulties.
I like to emphasize that the most crucial aspect to achieving your goals is to “do something every day.” It is a bad and unacceptable habit to think that postponing an activity for the next day will not make a huge difference.
You will accomplish a lot more and be more productive if you do something every day as opposed to trying to do everything altogether over a weekend.
My New Year's Resolutions
Here are the tentative goals I have set for myself for the year:
- Write cleaner code
- Improve skin care routine
- Adopt a healthier lifestyle
- Shop less...seriously
- Use keyboard shortcuts more often
- Spend more quality time with family
Every day is a chance to begin again. Don’t focus on the failures of yesterday, start today with positive thoughts and expectations.
And here is how I would organize my resolutions into monthly goals. For instance, in the first month, I set the following goals:
- Buy Clean Code by Robert C. Martin
- Read at least two chapters of Clean Code
- Dedicate a Friday for a one-day cleanse
- Incorporate more greens into meal prep
- Treat yourself to fried foods only 2 times
- Limit boba consumption to 5 times
- Exercise at least 4 times every week
- Sauna at least 3 times every week
- Do facial mask once every 2 weeks
- Do 10 sit ups for every mouse reliance instead of keyboard shortcuts
For every month thereafter, I may make slight changes to my list to get closer to my goals.
Final Message
Do not think about what will happen in the next few months or in a year – just focus on the 24 hours in front of you and do what you can to get closer to where you want to be.
Do not forget to review and update your goals weekly and continue working towards your goals. You will be surprised at how much you can accomplish and grow into a better human being in a year.
Challenge yourself. Be ambitious. Put in the work. Watch yourself change for the better. Best of luck!
I hope you enjoyed my blog. Have a MEGA AWESOME day! 😎
Outfit Details
👚 Top: Tobi 👖 Bottom: Zara 🧥 Coat: H&M 👢 Shoes: Forever 21


Multisubunit enzyme complexes that synthesize ADENOSINE TRIPHOSPHATE from energy sources such as ions traveling through channels. (mesh:D025181)
| Synonym | Reference | Specificity |
| ATP synthase | Exact |
| Identifier | Name | Relation |
| HGNC:14247 | ATP5MG | partof |
| HGNC:7415 | MT-ATP8 | partof |
| HGNC:845 | ATP5PD | partof |
| HGNC:850 | ATP5PO | partof |
| HGNC:848 | ATP5MF | partof |
| HGNC:823 | ATP5F1A | partof |
| FPLX:ATP5G | ATP5G | partof |
| HGNC:833 | ATP5F1C | partof |
| HGNC:838 | ATP5F1E | partof |
| HGNC:7414 | MT-ATP6 | partof |
| HGNC:1188 | ATP5MJ | partof |
| HGNC:846 | ATP5ME | partof |
| HGNC:840 | ATP5PB | partof |
| HGNC:847 | ATP5PF | partof |
| HGNC:871 | ATP5IF1 | partof |
| HGNC:837 | ATP5F1D | partof |
| HGNC:30889 | ATP5MK | partof |
| HGNC:830 | ATP5F1B | partof |


joon's favorite movies
home
classes
hobbies
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favorite movies
guestbook
really great movies that i like


| 1 | package fr.sii.ogham.html.inliner; | |
| 2 | ||
| 3 | import java.util.ArrayList; | |
| 4 | import java.util.Arrays; | |
| 5 | import java.util.List; | |
| 6 | ||
| 7 | import fr.sii.ogham.email.attachment.Attachment; | |
| 8 | ||
| 9 | public class ContentWithImages { | |
| 10 | private String content; | |
| 11 | | |
| 12 | private List<Attachment> attachments; | |
| 13 | ||
| 14 | public ContentWithImages(String content, Attachment... attachments) { | |
| 15 | this(content, new ArrayList<>(Arrays.asList(attachments))); | |
| 16 | } | |
| 17 | ||
| 18 | public ContentWithImages(String content, List<Attachment> attachments) { | |
| 19 | super(); | |
| 20 | this.content = content; | |
| 21 | this.attachments = attachments; | |
| 22 | } | |
| 23 | ||
| 24 | public String getContent() { | |
| 25 |
1
1. getContent : replaced return value with "" for fr/sii/ogham/html/inliner/ContentWithImages::getContent → RUN_ERROR |
return content; |
| 26 | } | |
| 27 | ||
| 28 | public List<Attachment> getAttachments() { | |
| 29 |
1
1. getAttachments : replaced return value with Collections.emptyList for fr/sii/ogham/html/inliner/ContentWithImages::getAttachments → RUN_ERROR |
return attachments; |
| 30 | } | |
| 31 | ||
| 32 | public void setContent(String content) { | |
| 33 | this.content = content; | |
| 34 | } | |
| 35 | ||
| 36 | public void setAttachments(List<Attachment> attachments) { | |
| 37 | this.attachments = attachments; | |
| 38 | } | |
| 39 | | |
| 40 | public void addAttachments(List<Attachment> attachments) { | |
| 41 | this.attachments.addAll(attachments); | |
| 42 | } | |
| 43 | } | |
Mutations | ||
| 25 |
1.1 |
|
| 29 |
1.1 |


YouTube and Vimeo videos can be wrapped with
 ..
.

Adding an element with the ID of scrollToTop makes the element clickable, once clicked the page is scrolled to the top.
The default is to make the element visible once the window is scrolled past 600 pixels, this can by changed by adding data-showat attribute to the element with the number of pixels of scroll after which to show the element.

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[§147-115] Certificate as evidence. A certificate issued under this part and all certificates issued under the authority of the Congress of the United States relating to matters covered by this part shall be acceptable in any court of this State as prima facie evidence of the status of a business or product at the time of its audit. [L 2003, c 49, pt of §1]