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This repository is a monorepo (multiple projects in one repository) containing the software to build the CCF-EUI, CCF-RUI, and CCF-Organ-Info.
For more information about the Human Reference Atlas (HRA) and the Common Coordinate Framework (CCF), see the HuBMAP HRA Portal at https://humanatlas.io.
See the ChangeLog for the latest developments.
This software is developed by the Cyberinfrastructure for Network Science Center at Indiana University. It is funded by NIH Award OT2OD026671.


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{% extends 'base.html' %} {% load staticfiles i18n %} {% block container %}
After completing the instructions and comprehension check, you will enter a waiting room. You will remain here until {{ players_num }} players enter the waiting room. If 5 minutes elapse and there are not enough players of the game, the game will not begin and you will be taken to the exit survey. If there are enough players, then everyone will enter the game. The game consists of 20 rounds. Each round is composed of 3 stages:
1. Initial Guess: You will see a scatter plot and be asked to estimate the correlation of the X and Y variables. Correlation measures how closely two variables are related to each other. For example, the graph below depicts the correlation between age (the X variable), and height (the Y variable). As age increases, height tends to increase as well.
Note that many different graphs may have the same correlation value. All 4 of the graphs below have a correlation of 0.82.


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Finally, on January \(10\text{,}\) \(2023\text{,}\) President Biden proposed a novel income-based federal student loan repayment plan [9.11.146]. Why did President Biden feel the political impetus to forgive student loan debt? What were the political causes of and blowback to these debt forgiveness actions, and why were they so controversial? Whether or not Biden's loan forgiveness policy is overturned by the Supreme Court, what would its impacts be, and should future Presidents and legislators continue to advocate for student loan forgiveness?
in-context


| Modifier and Type | Method and Description |
|---|---|
DominoDateTime |
DominoClient.createDateTime(TemporalAccessor temporal)
Creates a
DominoDateTime instance for the provided TemporalAccessor object.Some examples of supported TemporalAccessor value:DominoDateTime
Instant.now()
LocalDate.now()
LocalTime.now()
ZoneId zone = ZoneId.systemDefault(); Instant now=Instant.now(); OffsetDateTime.ofInstant(now, zone);
|
| Modifier and Type | Method and Description |
|---|---|
DominoDateTime |
AdministrationProcess.getCertificateExpiration() |
DominoDateTime |
ServerAdmin.DominoSSOToken.getRenewalDate()
Gives a date and time when the SSO token will expire and needs to be renewed
|
| Modifier and Type | Method and Description |
|---|---|
void |
ServerAdmin.registerCrossCertificate(String certFilePath,
String certPW,
String certLogPath,
DominoDateTime expirationDateTime,
String regServer,
Path idFilePath,
String comment,
ServerAdmin.RegistrationMessageHandler msgHandler)
This function will cross-certify an ID with another organization's hierarchy.
It will open the Domino Directory (Server's Address book) on the specified registration server, verify write access to the book, and add a new Cross Certificate entry. |
| Modifier and Type | Method and Description |
|---|---|
DominoDateTime |
ReplicaInfo.getReplicaIDAsDate()
Returns the replication ID which is same for all replica files
|
DominoDateTime |
ReplicationHistorySummary.getReplicationTime() |
| Modifier and Type | Method and Description |
|---|---|
Optional<DominoDateTime> |
ReplicaInfo.getCutOff()
Replication cutoff date
|
| Constructor and Description |
|---|
ReplicationHistorySummary(DominoDateTime replicationTime,
AclLevel accessLevel,
Set<AclFlag> accessFlags,
ReplicationHistorySummary.ReplicationDirection direction,
String server,
String filePath) |
| Modifier and Type | Method and Description |
|---|---|
int |
Calendaring.getNewInvitations(Database dbMail,
TemporalAccessor start,
String uid,
TemporalAccessor since,
AtomicReference<DominoDateTime> retUntil,
List<Integer> retNoteIds,
List<String> retUNIDs)
Retrieve invitations in a mailfile that have not yet been responded to.
This returns the number of new invitations as well as optional NOTEID and/or UNID lists. This returns only invitations (and delegated invitations), and not reschedules, information updates, cancels, etc. This method does not filter out any invitations that have since been canceled/rescheduled, or are otherwise out of date. Once the invitation is accepted, other notices that apply to that meeting can be discovered with a call to Calendaring.getUnappliedNotices(Database, String, List, List)
must be used (on a per-UID level).Only invitations for meetings that are current (at least one instance starts within the last day or in the future) are returned, although the starting time can be specified by the caller to override the default.A caller can retrieve only invitations that have arrived since a prior call to Calendaring.getNewInvitations(com.hcl.domino.data.Database, java.time.temporal.TemporalAccessor, java.lang.String, java.time.temporal.TemporalAccessor, java.util.concurrent.atomic.AtomicReference<com.hcl.domino.data.DominoDateTime>, java.util.List<java.lang.Integer>, java.util.List<java.lang.String>)
by using tdSince and ptdretUntil.If uid is provided, invitations only for a
particular meeting will be returned.This is useful if you are looking for an invitation or invitations that correspond to an updated notice that has arrived. Note: Multiple invitations might exist for a particular UID if that meeting is recurring and you were added to an instance or instances after the initial creation. The returned notices are not guaranteed to be in any particular order. |
| Modifier and Type | Method and Description |
|---|---|
DominoDateTime |
DominoDateTime.clone() |
DominoDateTime |
Document.getAddedToFile()
Retrives the time when the document was added to this NSF, regardless of when it was
originally created.
|
DominoDateTime |
Document.getCreated()
Retrieves the creation date of this document.
|
DominoDateTime |
DominoDateRange.getEndDateTime() |
DominoDateTime |
DominoOriginatorId.getFile()
Returns the File part of the universal id
|
DominoDateTime |
Attachment.getFileCreated()
Gets the created datetime of the attachment.
|
DominoDateTime |
Attachment.getFileModified()
Gets the modified datetime of the attachment
|
DominoDateTime |
Document.getLastAccessed()
Retrieves the last-accessed time of the document, if that information is being tracked
in the database.
|
DominoDateTime |
DominoCollection.getLastAccessedTime()
Returns the
DominoDateTime when this view was last accessed |
DominoDateTime |
Document.getLastModified()
Retrieves the time when the document was last modified.
|
DominoDateTime |
Database.SearchMatch.getLastModified()
Returns the modified date of the note as an
DominoDateTime |
DominoDateTime |
Document.getModifiedInThisFile()
Retrieves the time when the document was last modified in this NSF, which may be later
than the last time it was modified on any replica.
|
DominoDateTime |
DominoCollection.getNextDiscardTime()
Returns the
DominoDateTime when the view index will be discarded |
DominoDateTime |
DominoOriginatorId.getNote()
Returns the Note part of the universal id
|
DominoDateTime |
Database.ModifiedNoteInfo.getSeqTime() |
DominoDateTime |
Database.SearchMatch.getSequenceTime()
Returns the sequence time of the note as a
DominoDateTime . |
DominoDateTime |
DominoOriginatorId.getSequenceTime()
Returns the sequence time
|
DominoDateTime |
DominoDateRange.getStartDateTime() |
DominoDateTime |
Database.ModifiedNoteInfos.getUntil() |
DominoDateTime |
FormulaQueryResult.getUntil()
Same as
IDTable.getDateTime() of the IDTable returned by
FormulaQueryResult.getNoteIds() . |
DominoDateTime |
Document.SignatureData.getWhenSigned() |
DominoDateTime |
Database.queryFormula(String selectionFormula,
Set<Integer> filter,
Set<DirectorySearchQuery.SearchFlag> searchFlags,
TemporalAccessor since,
Set<DocumentClass> docClass,
Map<String,String> computeValues,
Database.FormulaQueryCallback callback) |
| Modifier and Type | Method and Description |
|---|---|
Optional<DominoDateTime> |
Database.DocInfoExt.getAddedToFile()
Returns the value for "Added in this file"
|
Optional<DominoDateTime> |
IDTable.getDateTime()
Returns a
DominoDateTime set in this IDTable via
a database search (e.g. |
Optional<DominoDateTime> |
DominoUniversalNoteId.getFile()
Returns the File part of the universal id
|
Optional<DominoDateTime> |
DocumentSelection.getLastBuildTime()
Returns the last time, the
DocumentSelection.build() has been invoked. |
Optional<DominoDateTime> |
Database.getLastFTIndexTime()
This routine returns the last time a database was full text indexed.
|
Optional<DominoDateTime> |
Database.DocInfoExt.getModified()
Returns the value for "Modified in this file"
|
Optional<DominoDateTime> |
DominoUniversalNoteId.getNote()
Returns the Note part of the universal id
|
Optional<DominoDateTime> |
Database.DocInfo.getSequenceTime()
Returns the sequence time ( = "Modified (initially)")
|
Optional<DominoDateTime> |
DocumentSelection.getSinceTime()
Returns the since time so that the search only contains documents
with a "Modified in this file" date after the given date.
|
Optional<DominoDateTime> |
DocumentSelection.getUntilTime()
Returns a date that can be passed as "since time" to the next search
for incremental searches.
|
| Modifier and Type | Method and Description |
|---|---|
void |
Database.getModifiedTime(Ref<DominoDateTime> retDataModified,
Ref<DominoDateTime> retNonDataModified)
This function obtains the date/time of the last modified data and non-data documents
in the specified database.
|
void |
Database.getModifiedTime(Ref<DominoDateTime> retDataModified,
Ref<DominoDateTime> retNonDataModified)
This function obtains the date/time of the last modified data and non-data documents
in the specified database.
|
| Modifier and Type | Method and Description |
|---|---|
DominoDateTime |
DatabaseData.getCreated()
Returns the database creation date
|
DominoDateTime |
DatabaseData.getDesignModifiedDate()
Returns the date of the last design change
|
DominoDateTime |
DatabaseData.getModified()
Returns the database modification date
|
DominoDateTime |
FolderData.getModified()
Returns the folder modification date
|
DominoDateTime |
DirEntry.getModified()
Returns the file modification date
|
| Modifier and Type | Method and Description |
|---|---|
Optional<DominoDateTime> |
DatabaseData.getLastCompact()
Returns the date of the last compact
|
Optional<DominoDateTime> |
DatabaseData.getLastFixup()
Returns the date of the last fixup
|
| Modifier and Type | Method and Description |
|---|---|
DominoDateTime |
NamedFileElement.getFileModified() |
DominoDateTime |
ImageResource.getFileModified() |
| Modifier and Type | Method and Description |
|---|---|
Optional<DominoDateTime> |
DesignAgent.getEndDate()
Gets the last date that the agent is able to run, if applicable
|
Optional<DominoDateTime> |
DesignAgent.getStartDate()
Gets the earliest date that the agent is able to run, if applicable
|
| Modifier and Type | Method and Description |
|---|---|
Optional<DominoDateTime> |
ScheduleEntry.getFrom() |
Optional<DominoDateTime> |
Schedule.getFrom()
Lower bound of the interval
|
Optional<DominoDateTime> |
ScheduleEntry.getUntil() |
Optional<DominoDateTime> |
Schedule.getUntil()
Upper bound of the interval
|
| Modifier and Type | Method and Description |
|---|---|
DominoDateTime |
AssistStruct.getEndDate() |
DominoDateTime |
AssistStruct.getStartDate() |
| Modifier and Type | Method and Description |
|---|---|
AssistStruct |
AssistStruct.setEndDate(DominoDateTime endDate) |
AssistStruct |
AssistStruct.setStartDate(DominoDateTime startDate) |
Copyright © 2019–2021 HCL. All rights reserved.


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Don't Need Wings to Fly
just add faith, trust, and pixie dust
“I have no special talent, I am only passionately curious”, I also tend to procrastinate, be easily distracted, and am very indecisive. Given those attributes in proportionate amounts, I’d say I’ll be as human as any other human’ll ever be. I completed by Bachelor’s Degree in Mechanical Engineering at Taylor’s University, Malaysia, and was also a research assistant at the Taylor’s Unmanned Aerial Vehicles Research Group (TUAV). My primary interests include Unmanned Aerial Vehicles, Autonomous Navigation, Control and Machine Intelligence. Currently, I’m a simulation and software engineer at an unnamed company.
This website is a brief overview of me, my projects, and an overview of my professional work. Do note that the intention of a projects listing is less documentation and more blog posting.


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7.2. Real world datasets¶
scikit-learn provides tools to load larger datasets, downloading them if necessary.
They can be loaded using the following functions:
|
Load the Olivetti faces data-set from AT&T (classification). |
|
Load the filenames and data from the 20 newsgroups dataset (classification). |
|
Load and vectorize the 20 newsgroups dataset (classification). |
|
Load the Labeled Faces in the Wild (LFW) people dataset (classification). |
|
Load the Labeled Faces in the Wild (LFW) pairs dataset (classification). |
|
Load the covertype dataset (classification). |
|
Load the RCV1 multilabel dataset (classification). |
|
Load the kddcup99 dataset (classification). |
|
Load the California housing dataset (regression). |
7.2.1. The Olivetti faces dataset¶
This dataset contains a set of face images taken between April 1992 and
April 1994 at AT&T Laboratories Cambridge. The
sklearn.datasets.fetch_olivetti_faces
function is the data
fetching / caching function that downloads the data
archive from AT&T.
As described on the original website:
There are ten different images of each of 40 distinct subjects. For some subjects, the images were taken at different times, varying the lighting, facial expressions (open / closed eyes, smiling / not smiling) and facial details (glasses / no glasses). All the images were taken against a dark homogeneous background with the subjects in an upright, frontal position (with tolerance for some side movement).
Data Set Characteristics:
Classes
40
Samples total
400
Dimensionality
4096
Features
real, between 0 and 1
The image is quantized to 256 grey levels and stored as unsigned 8-bit integers; the loader will convert these to floating point values on the interval [0, 1], which are easier to work with for many algorithms.
The “target” for this database is an integer from 0 to 39 indicating the identity of the person pictured; however, with only 10 examples per class, this relatively small dataset is more interesting from an unsupervised or semi-supervised perspective.
The original dataset consisted of 92 x 112, while the version available here consists of 64x64 images.
When using these images, please give credit to AT&T Laboratories Cambridge.
7.2.2. The 20 newsgroups text dataset¶
The 20 newsgroups dataset comprises around 18000 newsgroups posts on 20 topics split in two subsets: one for training (or development) and the other one for testing (or for performance evaluation). The split between the train and test set is based upon a messages posted before and after a specific date.
This module contains two loaders. The first one,
sklearn.datasets.fetch_20newsgroups
,
returns a list of the raw texts that can be fed to text feature
extractors such as CountVectorizer
with custom parameters so as to extract feature vectors.
The second one, sklearn.datasets.fetch_20newsgroups_vectorized
,
returns ready-to-use features, i.e., it is not necessary to use a feature
extractor.
Data Set Characteristics:
Classes
20
Samples total
18846
Dimensionality
1
Features
text
<IP_ADDRESS>. Usage¶
The sklearn.datasets.fetch_20newsgroups
function is a data
fetching / caching functions that downloads the data archive from
the original 20 newsgroups website, extracts the archive contents
in the ~/scikit_learn_data/20news_home
folder and calls the
sklearn.datasets.load_files
on either the training or
testing set folder, or both of them:
>>> from sklearn.datasets import fetch_20newsgroups
>>> newsgroups_train = fetch_20newsgroups(subset='train')
>>> from pprint import pprint
>>> pprint(list(newsgroups_train.target_names))
['alt.atheism',
'comp.graphics',
'comp.os.ms-windows.misc',
'comp.sys.ibm.pc.hardware',
'comp.sys.mac.hardware',
'comp.windows.x',
'misc.forsale',
'rec.autos',
'rec.motorcycles',
'rec.sport.baseball',
'rec.sport.hockey',
'sci.crypt',
'sci.electronics',
'sci.med',
'sci.space',
'soc.religion.christian',
'talk.politics.guns',
'talk.politics.mideast',
'talk.politics.misc',
'talk.religion.misc']
The real data lies in the filenames
and target
attributes. The target
attribute is the integer index of the category:
>>> newsgroups_train.filenames.shape
(11314,)
>>> newsgroups_train.target.shape
(11314,)
>>> newsgroups_train.target[:10]
array([ 7, 4, 4, 1, 14, 16, 13, 3, 2, 4])
It is possible to load only a sub-selection of the categories by passing the
list of the categories to load to the
sklearn.datasets.fetch_20newsgroups
function:
>>> cats = ['alt.atheism', 'sci.space']
>>> newsgroups_train = fetch_20newsgroups(subset='train', categories=cats)
>>> list(newsgroups_train.target_names)
['alt.atheism', 'sci.space']
>>> newsgroups_train.filenames.shape
(1073,)
>>> newsgroups_train.target.shape
(1073,)
>>> newsgroups_train.target[:10]
array([0, 1, 1, 1, 0, 1, 1, 0, 0, 0])
<IP_ADDRESS>. Converting text to vectors¶
In order to feed predictive or clustering models with the text data,
one first need to turn the text into vectors of numerical values suitable
for statistical analysis. This can be achieved with the utilities of the
sklearn.feature_extraction.text
as demonstrated in the following
example that extract TF-IDF vectors of unigram tokens
from a subset of 20news:
>>> from sklearn.feature_extraction.text import TfidfVectorizer
>>> categories = ['alt.atheism', 'talk.religion.misc',
... 'comp.graphics', 'sci.space']
>>> newsgroups_train = fetch_20newsgroups(subset='train',
... categories=categories)
>>> vectorizer = TfidfVectorizer()
>>> vectors = vectorizer.fit_transform(newsgroups_train.data)
>>> vectors.shape
(2034, 34118)
The extracted TF-IDF vectors are very sparse, with an average of 159 non-zero components by sample in a more than 30000-dimensional space (less than .5% non-zero features):
>>> vectors.nnz / float(vectors.shape[0])
159.01327...
sklearn.datasets.fetch_20newsgroups_vectorized
is a function which
returns ready-to-use token counts features instead of file names.
<IP_ADDRESS>. Filtering text for more realistic training¶
It is easy for a classifier to overfit on particular things that appear in the 20 Newsgroups data, such as newsgroup headers. Many classifiers achieve very high F-scores, but their results would not generalize to other documents that aren’t from this window of time.
For example, let’s look at the results of a multinomial Naive Bayes classifier, which is fast to train and achieves a decent F-score:
>>> from sklearn.naive_bayes import MultinomialNB
>>> from sklearn import metrics
>>> newsgroups_test = fetch_20newsgroups(subset='test',
... categories=categories)
>>> vectors_test = vectorizer.transform(newsgroups_test.data)
>>> clf = MultinomialNB(alpha=.01)
>>> clf.fit(vectors, newsgroups_train.target)
MultinomialNB(alpha=0.01, class_prior=None, fit_prior=True)
>>> pred = clf.predict(vectors_test)
>>> metrics.f1_score(newsgroups_test.target, pred, average='macro')
0.88213...
(The example Classification of text documents using sparse features shuffles the training and test data, instead of segmenting by time, and in that case multinomial Naive Bayes gets a much higher F-score of 0.88. Are you suspicious yet of what’s going on inside this classifier?)
Let’s take a look at what the most informative features are:
>>> import numpy as np
>>> def show_top10(classifier, vectorizer, categories):
... feature_names = vectorizer.get_feature_names_out()
... for i, category in enumerate(categories):
... top10 = np.argsort(classifier.coef_[i])[-10:]
... print("%s: %s" % (category, " ".join(feature_names[top10])))
...
>>> show_top10(clf, vectorizer, newsgroups_train.target_names)
alt.atheism: edu it and in you that is of to the
comp.graphics: edu in graphics it is for and of to the
sci.space: edu it that is in and space to of the
talk.religion.misc: not it you in is that and to of the
You can now see many things that these features have overfit to:
Almost every group is distinguished by whether headers such as
NNTP-Posting-Host:
andDistribution:
appear more or less often.Another significant feature involves whether the sender is affiliated with a university, as indicated either by their headers or their signature.
The word “article” is a significant feature, based on how often people quote previous posts like this: “In article [article ID], [name] <[e-mail address]> wrote:”
Other features match the names and e-mail addresses of particular people who were posting at the time.
With such an abundance of clues that distinguish newsgroups, the classifiers barely have to identify topics from text at all, and they all perform at the same high level.
For this reason, the functions that load 20 Newsgroups data provide a
parameter called remove, telling it what kinds of information to strip out
of each file. remove should be a tuple containing any subset of
('headers', 'footers', 'quotes')
, telling it to remove headers, signature
blocks, and quotation blocks respectively.
>>> newsgroups_test = fetch_20newsgroups(subset='test',
... remove=('headers', 'footers', 'quotes'),
... categories=categories)
>>> vectors_test = vectorizer.transform(newsgroups_test.data)
>>> pred = clf.predict(vectors_test)
>>> metrics.f1_score(pred, newsgroups_test.target, average='macro')
0.77310...
This classifier lost over a lot of its F-score, just because we removed metadata that has little to do with topic classification. It loses even more if we also strip this metadata from the training data:
>>> newsgroups_train = fetch_20newsgroups(subset='train',
... remove=('headers', 'footers', 'quotes'),
... categories=categories)
>>> vectors = vectorizer.fit_transform(newsgroups_train.data)
>>> clf = MultinomialNB(alpha=.01)
>>> clf.fit(vectors, newsgroups_train.target)
MultinomialNB(alpha=0.01, class_prior=None, fit_prior=True)
>>> vectors_test = vectorizer.transform(newsgroups_test.data)
>>> pred = clf.predict(vectors_test)
>>> metrics.f1_score(newsgroups_test.target, pred, average='macro')
0.76995...
Some other classifiers cope better with this harder version of the task. Try
running Sample pipeline for text feature extraction and evaluation with and without
the --filter
option to compare the results.
Data Considerations
The Cleveland Indians is a major league baseball team based in Cleveland, Ohio, USA. In December 2020, it was reported that “After several months of discussion sparked by the death of George Floyd and a national reckoning over race and colonialism, the Cleveland Indians have decided to change their name.” Team owner Paul Dolan “did make it clear that the team will not make its informal nickname – the Tribe – its new team name.” “It’s not going to be a half-step away from the Indians,” Dolan said.”We will not have a Native American-themed name.”
Recommendation
When evaluating text classifiers on the 20 Newsgroups data, you should strip newsgroup-related metadata. In scikit-learn, you can do this by setting
remove=('headers', 'footers', 'quotes')
. The F-score will be lower because it is more realistic.This text dataset contains data which may be inappropriate for certain NLP applications. An example is listed in the “Data Considerations” section above. The challenge with using current text datasets in NLP for tasks such as sentence completion, clustering, and other applications is that text that is culturally biased and inflammatory will propagate biases. This should be taken into consideration when using the dataset, reviewing the output, and the bias should be documented.
7.2.3. The Labeled Faces in the Wild face recognition dataset¶
This dataset is a collection of JPEG pictures of famous people collected over the internet, all details are available on the official website:
Each picture is centered on a single face. The typical task is called Face Verification: given a pair of two pictures, a binary classifier must predict whether the two images are from the same person.
An alternative task, Face Recognition or Face Identification is: given the picture of the face of an unknown person, identify the name of the person by referring to a gallery of previously seen pictures of identified persons.
Both Face Verification and Face Recognition are tasks that are typically performed on the output of a model trained to perform Face Detection. The most popular model for Face Detection is called Viola-Jones and is implemented in the OpenCV library. The LFW faces were extracted by this face detector from various online websites.
Data Set Characteristics:
Classes
5749
Samples total
13233
Dimensionality
5828
Features
real, between 0 and 255
<IP_ADDRESS>. Usage¶
scikit-learn
provides two loaders that will automatically download,
cache, parse the metadata files, decode the jpeg and convert the
interesting slices into memmapped numpy arrays. This dataset size is more
than 200 MB. The first load typically takes more than a couple of minutes
to fully decode the relevant part of the JPEG files into numpy arrays. If
the dataset has been loaded once, the following times the loading times
less than 200ms by using a memmapped version memoized on the disk in the
~/scikit_learn_data/lfw_home/
folder using joblib
.
The first loader is used for the Face Identification task: a multi-class classification task (hence supervised learning):
>>> from sklearn.datasets import fetch_lfw_people
>>> lfw_people = fetch_lfw_people(min_faces_per_person=70, resize=0.4)
>>> for name in lfw_people.target_names:
... print(name)
...
Ariel Sharon
Colin Powell
Donald Rumsfeld
George W Bush
Gerhard Schroeder
Hugo Chavez
Tony Blair
The default slice is a rectangular shape around the face, removing most of the background:
>>> lfw_people.data.dtype
dtype('float32')
>>> lfw_people.data.shape
(1288, 1850)
>>> lfw_people.images.shape
(1288, 50, 37)
Each of the 1140
faces is assigned to a single person id in the target
array:
>>> lfw_people.target.shape
(1288,)
>>> list(lfw_people.target[:10])
[5, 6, 3, 1, 0, 1, 3, 4, 3, 0]
The second loader is typically used for the face verification task: each sample is a pair of two picture belonging or not to the same person:
>>> from sklearn.datasets import fetch_lfw_pairs
>>> lfw_pairs_train = fetch_lfw_pairs(subset='train')
>>> list(lfw_pairs_train.target_names)
['Different persons', 'Same person']
>>> lfw_pairs_train.pairs.shape
(2200, 2, 62, 47)
>>> lfw_pairs_train.data.shape
(2200, 5828)
>>> lfw_pairs_train.target.shape
(2200,)
Both for the sklearn.datasets.fetch_lfw_people
and
sklearn.datasets.fetch_lfw_pairs
function it is
possible to get an additional dimension with the RGB color channels by
passing color=True
, in that case the shape will be
(2200, 2, 62, 47, 3)
.
The sklearn.datasets.fetch_lfw_pairs
datasets is subdivided into
3 subsets: the development train
set, the development test
set and
an evaluation 10_folds
set meant to compute performance metrics using a
10-folds cross validation scheme.
References:
Labeled Faces in the Wild: A Database for Studying Face Recognition in Unconstrained Environments. Gary B. Huang, Manu Ramesh, Tamara Berg, and Erik Learned-Miller. University of Massachusetts, Amherst, Technical Report 07-49, October, 2007.
<IP_ADDRESS>. Examples¶
7.2.4. Forest covertypes¶
The samples in this dataset correspond to 30×30m patches of forest in the US, collected for the task of predicting each patch’s cover type, i.e. the dominant species of tree. There are seven covertypes, making this a multiclass classification problem. Each sample has 54 features, described on the dataset’s homepage. Some of the features are boolean indicators, while others are discrete or continuous measurements.
Data Set Characteristics:
Classes
7
Samples total
581012
Dimensionality
54
Features
int
sklearn.datasets.fetch_covtype
will load the covertype dataset;
it returns a dictionary-like ‘Bunch’ object
with the feature matrix in the data
member
and the target values in target
. If optional argument ‘as_frame’ is
set to ‘True’, it will return data
and target
as pandas
data frame, and there will be an additional member frame
as well.
The dataset will be downloaded from the web if necessary.
7.2.5. RCV1 dataset¶
Reuters Corpus Volume I (RCV1) is an archive of over 800,000 manually categorized newswire stories made available by Reuters, Ltd. for research purposes. The dataset is extensively described in 1.
Data Set Characteristics:
Classes
103
Samples total
804414
Dimensionality
47236
Features
real, between 0 and 1
sklearn.datasets.fetch_rcv1
will load the following
version: RCV1-v2, vectors, full sets, topics multilabels:
>>> from sklearn.datasets import fetch_rcv1
>>> rcv1 = fetch_rcv1()
It returns a dictionary-like object, with the following attributes:
data
:
The feature matrix is a scipy CSR sparse matrix, with 804414 samples and
47236 features. Non-zero values contains cosine-normalized, log TF-IDF vectors.
A nearly chronological split is proposed in 1: The first 23149 samples are
the training set. The last 781265 samples are the testing set. This follows
the official LYRL2004 chronological split. The array has 0.16% of non zero
values:
>>> rcv1.data.shape
(804414, 47236)
target
:
The target values are stored in a scipy CSR sparse matrix, with 804414 samples
and 103 categories. Each sample has a value of 1 in its categories, and 0 in
others. The array has 3.15% of non zero values:
>>> rcv1.target.shape
(804414, 103)
sample_id
:
Each sample can be identified by its ID, ranging (with gaps) from 2286
to 810596:
>>> rcv1.sample_id[:3]
array([2286, 2287, 2288], dtype=uint32)
target_names
:
The target values are the topics of each sample. Each sample belongs to at
least one topic, and to up to 17 topics. There are 103 topics, each
represented by a string. Their corpus frequencies span five orders of
magnitude, from 5 occurrences for ‘GMIL’, to 381327 for ‘CCAT’:
>>> rcv1.target_names[:3].tolist()
['E11', 'ECAT', 'M11']
The dataset will be downloaded from the rcv1 homepage if necessary. The compressed size is about 656 MB.
7.2.6. Kddcup 99 dataset¶
The KDD Cup ‘99 dataset was created by processing the tcpdump portions of the 1998 DARPA Intrusion Detection System (IDS) Evaluation dataset, created by MIT Lincoln Lab 2. The artificial data (described on the dataset’s homepage) was generated using a closed network and hand-injected attacks to produce a large number of different types of attack with normal activity in the background. As the initial goal was to produce a large training set for supervised learning algorithms, there is a large proportion (80.1%) of abnormal data which is unrealistic in real world, and inappropriate for unsupervised anomaly detection which aims at detecting ‘abnormal’ data, i.e.:
qualitatively different from normal data
in large minority among the observations.
We thus transform the KDD Data set into two different data sets: SA and SF.
SA is obtained by simply selecting all the normal data, and a small proportion of abnormal data to gives an anomaly proportion of 1%.
SF is obtained as in 3 by simply picking up the data whose attribute logged_in is positive, thus focusing on the intrusion attack, which gives a proportion of 0.3% of attack.
http and smtp are two subsets of SF corresponding with third feature equal to ‘http’ (resp. to ‘smtp’).
General KDD structure :
Samples total
4898431
Dimensionality
41
Features
discrete (int) or continuous (float)
Targets
str, ‘normal.’ or name of the anomaly type
SA structure :
Samples total
976158
Dimensionality
41
Features
discrete (int) or continuous (float)
Targets
str, ‘normal.’ or name of the anomaly type
SF structure :
Samples total
699691
Dimensionality
4
Features
discrete (int) or continuous (float)
Targets
str, ‘normal.’ or name of the anomaly type
http structure :
Samples total
619052
Dimensionality
3
Features
discrete (int) or continuous (float)
Targets
str, ‘normal.’ or name of the anomaly type
smtp structure :
Samples total
95373
Dimensionality
3
Features
discrete (int) or continuous (float)
Targets
str, ‘normal.’ or name of the anomaly type
sklearn.datasets.fetch_kddcup99
will load the kddcup99 dataset; it
returns a dictionary-like object with the feature matrix in the data
member
and the target values in target
. The “as_frame” optional argument converts
data
into a pandas DataFrame and target
into a pandas Series. The
dataset will be downloaded from the web if necessary.
References
- 2
Analysis and Results of the 1999 DARPA Off-Line Intrusion Detection Evaluation, Richard Lippmann, Joshua W. Haines, David J. Fried, Jonathan Korba, Kumar Das.
- 3
K. Yamanishi, J.-I. Takeuchi, G. Williams, and P. Milne. Online unsupervised outlier detection using finite mixtures with discounting learning algorithms. In Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining, pages 320-324. ACM Press, 2000.
7.2.7. California Housing dataset¶
Data Set Characteristics:
- Number of Instances
20640
- Number of Attributes
8 numeric, predictive attributes and the target
- Attribute Information
MedInc median income in block group
HouseAge median house age in block group
AveRooms average number of rooms per household
AveBedrms average number of bedrooms per household
Population block group population
AveOccup average number of household members
Latitude block group latitude
Longitude block group longitude
- Missing Attribute Values
None
This dataset was obtained from the StatLib repository. https://www.dcc.fc.up.pt/~ltorgo/Regression/cal_housing.html
The target variable is the median house value for California districts, expressed in hundreds of thousands of dollars ($100,000).
This dataset was derived from the 1990 U.S. census, using one row per census block group. A block group is the smallest geographical unit for which the U.S. Census Bureau publishes sample data (a block group typically has a population of 600 to 3,000 people).
An household is a group of people residing within a home. Since the average number of rooms and bedrooms in this dataset are provided per household, these columns may take surpinsingly large values for block groups with few households and many empty houses, such as vacation resorts.
It can be downloaded/loaded using the
sklearn.datasets.fetch_california_housing
function.
References
Pace, R. Kelley and Ronald Barry, Sparse Spatial Autoregressions, Statistics and Probability Letters, 33 (1997) 291-297


Quick Solr Setup
Environment
- OS X 10.8 or newer versions
- Java SE Runtime 1.7
- Apache Solr 4.10.0
Installation
The easiest way to install Solr is by using homebrew.
Continue reading...
The easiest way to install Solr is by using homebrew.
I have been working on Apache Solr for last few months, and have been recieving requirements to speed up query process. As part of the investigation, i found out as retrieved documents’ unique id generation contributes query processing.And hence i have decided to add this post.
Our sample data structure (field section from schema.xml) looks like specified below:
<fields>
<field name="id" type="string" indexed="true" stored="true" required="true" multiValued="false" />
<field name="name" type="text_general" indexed="true" stored="true" />
<field name="_version_" type="long" indexed="true" stored="true" />
</fields>
In addition to this, I’ve added the information about which field is the one that should contain unique identifiers. This was also done in schema.xml file:
<uniqueKey>id</uniqueKey>
In addition to changes in the schema.xml file, i need to modify the solrconfig.xml file and introduce a proper UpdateRequestProcessorChain like specified below:
<updateRequestProcessorChain>
<processor class="solr.UUIDUpdateProcessorFactory">
<str name="fieldName">id</str>
</processor>
<processor class="solr.LogUpdateProcessoryFactory" />
<processor class="solr.RunUpdateProcessorFactory" />
</updateRequestProcessorChain>
Above informs Solr that id field contents are to be generated automatically.
Enough with the configuration, time to test the configuration. Run below command from terminal to update document before querying indexed documents.
curl -XPOST 'localhost:8993/solr/update?commit=true' --data-binary '<add><doc><field name="name">Test</field></doc></add>' -H 'Content-type:application/xml'
If above command runs successfully without any errors, document will get indexed. After then, in order to query below command can be used:
curl -XGET 'localhost:8993/solr/select?q=*:*&indent=true'
Above will return queried documents specified below:
<?xml version="1.0" encoding="UTF-8"?>
<response>
<lst name="responseHeader">
<int name="status">0</int>
<int name="QTime">0</int>
<lst name="params">
<str name="indent">true</str>
<str name="q">*:*</str>
</lst>
</lst>
<result name="response" numFound="1" start="0">
<doc>
<str name="name">Test</str>
<str name="id">1cdee8b4-c42d-4101-8301-4dc350a4d522</str>
<long name="_version_">1439726523307261952</long>
</doc>
</result>
</response>
If you analyze response, you can see the unique identifier was automatically generated. Now if you run same commands ( addition of document & query ) then result would looks like this:
<?xml version="1.0" encoding="UTF-8"?>
<response>
<lst name="responseHeader">
<int name="status">0</int>
<int name="QTime">1</int>
<lst name="params">
<str name="indent">true</str>
<str name="q">*:*</str>
</lst>
</lst>
<result name="response" numFound="2" start="0">
<doc>
<str name="name">Test</str>
<str name="id">1cdee8b4-c42d-4101-8301-4dc350a4d522</str>
<long name="_version_">1439726523307261952</long>
</doc>
<doc>
<str name="name">Test</str>
<str name="id">9bedcb5f-1b71-4ab7-80a9-9882a6bf319e</str>
<long name="_version_">1439726693819351040</long>
</doc>
</result>
</response>
As you can see both documents show two different unique identifier generated by solr.
This might be one of the old topic - specially when lot of developers are working with RESTful Services, but i would like to point out couple of pros and cons.
As most of the developers know, XML schema is very powerful, but they lack object-orientation, and are intended to capture a data model rather than an object model. In last one and half decades, there has been few tools available to architects and developers to be able to make use off some of the object orientation principles - like polymorphism for flexibility. But ultimately the whole point of the services must be general enough that it can communicate across languages. Hence i have attempted to put together some information around some of the very well known Design patterns.
Some of the XML Schema design patterns include Russian Doll, Venetian Blind & Salami Slice.
Let us have a look at Russian Doll Design pattern. As name indicates - this design pattern permits single global root element which contains constituent types. All element declarations are nested inside the root element, and can therefore only be used once in within the context.
Some of the characteristics of this pattern are:
One of the ceavet while considering this patter, do not use this pattern when you would want to reuse types. Enough with the theory, let’s have a look at the example.
Equivalent Xml Schema Document can be found below:
<?xml version="1.0"?>
<?xml-stylesheet href="../2008/09/xsd.xsl" type="text/xsl"?>
<xs:schema targetNamespace="http://www.w3.org/XML/1998/namespace" xml:lang="en" xmlns="http://www.w3.org/1999/xhtml" xmlns:xs="http://www.w3.org/2001/XMLSchema">
<xs:annotation>
<xs:documentation>Schema using Russian Doll Pattern</xs:documentation>
</xs:annotation>
<xs:element name="Production">
<xs:complexType>
<xs:sequence>
<xs:element name="Name" type="xs:string" />
<xs:element name="Type">
<xs:simpleType>
<xs:restriction base="xs:string">
<xs:enumeration value="TheatrePlay" />
<xs:enumeration value="Movie" />
<xs:enumeration value="MusicAlbum" />
</xs:restriction>
</xs:simpleType>
</xs:element>
<xs:element name="Producers" minOccurs="1" maxOccurs="unbounded" >
<xs:complexType>
<xs:sequence>
<xs:element name="Name" type="xs:string" />
<xs:element name="ExecutiveProducer" type="xs:string" minOccurs="1" maxOccurs="unbounded" />
</xs:sequence>
</xs:complexType>
</xs:element>
<xs:element name="Writers" minOccurs="1" maxOccurs="unbounded" >
<xs:complexType>
<xs:sequence>
<xs:element name="Name" type="xs:string" />
<xs:element name="Scene" type="xs:string" minOccurs="1" maxOccurs="unbounded" />
</xs:sequence>
</xs:complexType>
</xs:element>
<xs:element name="Nominations" minOccurs="1" maxOccurs="unbounded">
<xs:complexType>
<xs:sequence>
<xs:element name="Category" type="xs:string" minOccurs="1" maxOccurs="unbounded" />
<xs:element name="Year" type="xs:int" />
<xs:element name="Actor" minOccurs="1" maxOccurs="unbounded">
<xs:complexType>
<xs:sequence>
<xs:element name="Name" type="xs:string" />
<xs:element name="Role" type="xs:string" minOccurs="1" maxOccurs="unbounded" />
</xs:sequence>
</xs:complexType>
</xs:element>
</xs:sequence>
</xs:complexType>
</xs:element>
<xs:element name="Cast" minOccurs="1" maxOccurs="unbounded">
<xs:complexType>
<xs:sequence>
<xs:element name="ActorName" type="xs:string" />
<xs:element name="CastingRole" type="xs:string" minOccurs="1" maxOccurs="unbounded" />
<xs:element name="CastingName" type="xs:string" />
</xs:sequence>
</xs:complexType>
</xs:element>
</xs:sequence>
</xs:complexType>
</xs:element>
</xs:schema>
As you can see, above schema defines a single global element
One of the biggest disadvantages of this pattern with respect to straight XML is that the types you carefully defined cannot be reused elsewhere, hence causing very large schema.
This design pattern is perhaps an appropriate design to use when wrapping legacy data from a source that is fairly static.
This pattern represents opposite end of the design spectrum from Russian Doll.
By using this pattern, use can declare all the elements as global, but declare all types locally. Pattern is named as Salami Slice because of the ability to set all the elements in global namespace and to make them reusable - which acts as a single definition slice. These slices can be used to combined with others for better reusablility.
With this pattern, schema can have more components, all defined individually - which are then brought together into global elements. This pattern is entirely open, allowing a wide variety of possible combinations compared to its counter part pattern Russian Doll - which is absolutely rigid and inflexible. Physical structure of the Russian Doll allows only one way to put the constituents toghether, while slices offer no guidance for how they might be arranged in schema.
Some of the characteristics of this pattern are:
Example from Russian Doll has been redesigned and represented differently with salami slice:
Equivalent Xml Schema Document can be found below:
<?xml version="1.0"?>
<?xml-stylesheet href="../2008/09/xsd.xsl" type="text/xsl"?>
<xs:schema targetNamespace="http://www.w3.org/XML/1998/namespace" xml:lang="en" xmlns="http://www.w3.org/1999/xhtml" xmlns:xs="http://www.w3.org/2001/XMLSchema" xmlns:tns="http://design.pattern.org/salami" elementFormDefault="qualified">
<xs:annotation>
<xs:documentation>Schema using Salami Slice Design Pattern</xs:documentation>
</xs:annotation>
<xs:complexType name="ProducerType">
<xs:sequence>
<xs:element name="Name" type="xs:string" />
<xs:element name="ExecutiveProducer" type="xs:string" minOccurs="1" maxOccurs="unbounded" />
</xs:sequence>
</xs:complexType>
<xs:complexType name="CastType">
<xs:sequence>
<xs:element name="ActorName" type="xs:string" />
<xs:element name="CastingRole" type="xs:string" minOccurs="1" maxOccurs="unbounded" />
<xs:element name="CastingName" type="xs:string" />
</xs:sequence>
</xs:complexType>
<xs:complexType name="NominationType">
<xs:sequence>
<xs:element name="Category" type="xs:string" minOccurs="1" maxOccurs="unbounded" />
<xs:element name="Year" type="xs:int" />
<xs:element name="Actor" minOccurs="1" maxOccurs="unbounded">
<xs:complexType>
<xs:sequence>
<xs:element name="Name" type="xs:string" />
<xs:element name="Role" type="xs:string" minOccurs="1" maxOccurs="unbounded" />
</xs:sequence>
</xs:complexType>
</xs:element>
</xs:sequence>
</xs:complexType>
<xs:complexType name="WriterType">
<xs:sequence>
<xs:element name="Name" type="xs:string" />
<xs:element name="Scene" type="xs:string" minOccurs="1" maxOccurs="unbounded" />
</xs:sequence>
</xs:complexType>
<xs:element name="Name" type="xs:string" />
<xs:element name="Producers" type="xml:ProducerType" />
<xs:element name="Writers" type="xml:WriterType" />
<xs:element name="Nominations" type="xml:NominationType" />
<xs:element name="Performer" type="xml:CastType" />
<xs:element name="Director" type="xs:string" />
<xs:element name="Production">
<xs:complexType>
<xs:sequence>
<xs:element ref="xml:Name" />
<xs:element name="Type">
<xs:simpleType>
<xs:restriction base="xs:string">
<xs:enumeration value="TheatrePlay" />
<xs:enumeration value="Movie" />
<xs:enumeration value="MusicAlbum" />
</xs:restriction>
</xs:simpleType>
</xs:element>
<xs:element ref="xml:Producers" />
<xs:element ref="xml:Writers" />
<xs:element ref="xml:Nominations" />
<xs:element ref="xml:Director" />
<xs:element ref="xml:Performer" />
</xs:sequence>
</xs:complexType>
</xs:element>
</xs:schema>
The main advantage of this pattern is that becuase the elements are declared globally, schema is reusable. But because changing an element affects the composing elements, this pattern is considered tightly coupled.
This schema design pattern are verbose and are clearly arranged and flat.
This pattern is an extension of Russian Doll Pattern. It contains only one signle global root element. It departs from Russian Doll in that it allows for reuse of all the types as well asd the global root element.
Use of this pattern means that schema designer wants to define a single global root element for instanciantion and compose it with externally defined types. This has the benefit of miximiizing reuse.
Some of the characteristics of this pattern are:
Original Example from Russian Doll has been redesigned and represented differently with salami slice:
Equivalent Xml Schema Document can be found below:
<?xml version="1.0"?>
<?xml-stylesheet href="../2008/09/xsd.xsl" type="text/xsl"?>
<xs:schema targetNamespace="http://www.w3.org/XML/1998/namespace" xml:lang="en" xmlns="http://www.w3.org/1999/xhtml" xmlns:xs="http://www.w3.org/2001/XMLSchema" xmlns:tns="http://design.pattern.org/salami" elementFormDefault="qualified">
<xs:annotation>
<xs:documentation>Schema using Salami Slice Design Pattern</xs:documentation>
</xs:annotation>
<xs:complexType name="CommonName">
<xs:sequence>
<xs:element name="Name" type="xs:string" />
</xs:sequence>
</xs:complexType>
<xs:group name="ContributionGroup">
<xs:sequence>
<xs:element name="Name" type="xml:CommonName" />
<xs:element name="Contribution" type="xs:string" minOccurs="1" maxOccurs="unbounded" />
</xs:sequence>
</xs:group>
<xs:complexType name="ProducerType">
<xs:sequence>
<xs:group ref="xml:ContributionGroup" minOccurs="1" maxOccurs="unbounded"/>
</xs:sequence>
</xs:complexType>
<xs:complexType name="CastType">
<xs:sequence>
<xs:element name="ActorName" type="xml:CommonName" />
<xs:element name="CastingRole" type="xs:string" minOccurs="1" maxOccurs="unbounded" />
<xs:element name="CastingName" type="xs:string" />
</xs:sequence>
</xs:complexType>
<xs:complexType name="NominationType">
<xs:sequence>
<xs:element name="Category" type="xs:string" minOccurs="1" maxOccurs="unbounded" />
<xs:element name="Year" type="xs:int" />
<xs:group ref="xml:ContributionGroup" minOccurs="1" maxOccurs="unbounded"/>
</xs:sequence>
</xs:complexType>
<xs:complexType name="WriterType">
<xs:sequence>
<xs:group ref="xml:ContributionGroup" minOccurs="1" maxOccurs="unbounded"/>
</xs:sequence>
</xs:complexType>
<xs:element name="Name" type="xs:string" />
<xs:element name="Producers" type="xml:ProducerType" />
<xs:element name="Writers" type="xml:WriterType" />
<xs:element name="Nominations" type="xml:NominationType" />
<xs:element name="Performer" type="xml:CastType" />
<xs:element name="Director" type="xs:string" />
<xs:element name="Production">
<xs:complexType>
<xs:sequence>
<xs:element ref="xml:Name" />
<xs:element name="Type" >
<xs:simpleType>
<xs:restriction base="xs:string">
<xs:enumeration value="TheatrePlay" />
<xs:enumeration value="Movie" />
<xs:enumeration value="MusicAlbum" />
</xs:restriction>
</xs:simpleType>
</xs:element>
<xs:element ref="xml:Producers" />
<xs:element ref="xml:Writers" />
<xs:element ref="xml:Nominations" />
<xs:element ref="xml:Director" />
<xs:element ref="xml:Performer" />
</xs:sequence>
</xs:complexType>
</xs:element>
</xs:schema>
Because this pattern has more flexibiliy, it is de-facto design pattern during XML Design pattern. There are several other patterns - like Garden of Eden and Chameleon - make uses of these basic patterns to solve other issues. I will try and attend rest of the patterns in next part.


{{ _('To add a title to your message, use the number sign (#) followed by a space. Try to make your title an answer rather than a question. For example:') }}
# {{ _('This is a title') }}
{{ _('This is plain paragraph text') }}
{{ _('This is plain paragraph text') }}
{{ _('To separate the message into sections, use three underscores') }} (___) {{ _('to create a horizontal line. This can help recipients take in information at a glance. For example:') }}
{{ _('Section 1') }}
___
{{ _('Section 2') }}
{{ _('Section 1') }}
{{ _('Section 2') }}
{{ _('To add bullet points, use asterisks (*) followed by a space. Bullet points help recipients identify important information quickly. For example:') }}
* {{ _('point 1') }}
* {{ _('point 2') }}
* {{ _('point 3') }}
{{ _('To emphasize part of your message like a block quote, use a caret (^) followed by a space in front of the text to add a vertical grey bar beside the text. For example:To emphasize text, use a caret. There will be a grey bar beside the text you want to emphasize:') }}
^ {{ _('Text to emphasize') }}
{{ _('Text to emphasize') }}


@Meyse since you asked, if you claim using native, the interest goes to new address, not same seed, need to keep wallet.dat safe
Meyse08-Nov-18 03:35 PM
i just use a seed, im probably one of many
grewalsatinder08-Nov-18 03:35 PM
I once heard a phrase in Andreas's talk that, cryptocurrency will teach people how to manage passwords and be sensible enough to not create passwords like 123456 or password
Meyse08-Nov-18 03:35 PM
i care about usability, not extreme safety
snowdamn08-Nov-18 03:35 PM
yes, seed with SPV, sends to same address/seed
Nabob08-Nov-18 03:36 PM
If you use seed on Spv, Rewards and amount move to same address
Meyse08-Nov-18 03:36 PM
so @ComputerGenie 👴👴👴 and @snowdamn you are very extreme in this point of view i suppose?
grewalsatinder08-Nov-18 03:36 PM
being a bank of your own, you'll learn about safety by experiencing.
snowdamn08-Nov-18 03:36 PM
extreme paranoia, yes!
ComputerGenie 👴👴👴08-Nov-18 03:36 PM
i care about usability, not extreme safety
Said everyone until they get hacked the 1st time
grewalsatinder08-Nov-18 03:37 PM
those of us who have lost data for X reasons, we learned it the hard way.
Apparently, Money is data now.(edited)
Meyse08-Nov-18 03:37 PM
but if you say these things, then why are there options for seeds? this makes no sense to me you are very extremely cautious. why not make everything mandatory native?
ComputerGenie 👴👴👴08-Nov-18 03:37 PM
@Meyse I lost 29k KMD last week to a worm infection; so, there's that....
Meyse08-Nov-18 03:37 PM
this wont work will it?
ComputerGenie 👴👴👴08-Nov-18 03:39 PM
why not make everything mandatory native?
Because SPV has valid usecases, just not really good for "everything I own"
Meyse08-Nov-18 03:39 PM
ah check, yes that might be a good point
snowdamn08-Nov-18 03:39 PM
@Meyse options are good and so is our chat (edited)
SHossain08-Nov-18 03:40 PM
animated won't work unless we have discord nitro
1
grewalsatinder08-Nov-18 03:40 PM
:smh:
wolfgang08-Nov-18 03:40 PM
is there an issue with the chain? I fired up desktop agama wallet after not opening it for 8weeks.
keep getting RCP failure
ComputerGenie 👴👴👴08-Nov-18 03:40 PM
@wolfgang do you have the latest agama?
wolfgang08-Nov-18 03:40 PM
how recent is the latest?
ComputerGenie 👴👴👴08-Nov-18 03:41 PM
less than 8 weeks
snowdamn08-Nov-18 03:41 PM
@wolfgang is komodo.conf still there?
grewalsatinder08-Nov-18 03:42 PM
blockchain databases are kinda touchy feely. they can get corrupted for X reasons, based on the previous actions or new actions required to perform.
Try the help from #support and if nothing else works, may be try re-scan.
Meyse08-Nov-18 03:44 PM
the regular "investor" probably aint a bagholder like you guys are. but if you are saying don't make using a seed as accessible and easy to use as possible i understand. but then again, imo this is neccessary for low entry barriers, to get users onboard.. difficult
I haven't follow the convo. using "seed" is difficult?
what issue we are talking here?
Meyse08-Nov-18 03:47 PM
no, but i want to make claiming rewards on mobile easy. and the counterpoint to that is that its unsafe
or im trying to figure out if people would want such an app
snowdamn08-Nov-18 03:47 PM
no, that part was a good idea, it would basically be the same thing as SPV claim
Meyse08-Nov-18 03:48 PM
@snowdamn oh, then what were we talking about?
grewalsatinder08-Nov-18 03:48 PM
rewards is the MOST easiest feature I have ever seen in a cryptocurrency.
doesn't require me to keep open my wallet to either PoW miing or PoS staking.

Meyse08-Nov-18 03:48 PM
it would be exactly the same as spv claim, just an app with 2 buttons -> claim rewards & set notification when best time is to claim
grewalsatinder08-Nov-18 03:50 PM
best time starts after 1 hour.
Meyse08-Nov-18 03:50 PM
depends on how much you have no?
grewalsatinder08-Nov-18 03:50 PM
it takes a 10 KMD UTXO to start accruing in abotu 60 blocks of confirmation.
minimum UTXO size is 10 KMD.
Meyse08-Nov-18 03:50 PM
i know
but you lose the txcosts
grewalsatinder08-Nov-18 03:51 PM
for that you do the maths......
Meyse08-Nov-18 03:52 PM
or let me rephrase that: button to set notification to claim in 1 month or whatever, so you dont miss the max amount of 5.1%
i was hoping other people would react to this idea instead of you guys haha
like "noobs"
grewalsatinder08-Nov-18 03:54 PM
notifications.... good idea....
Nabob08-Nov-18 03:54 PM
We have that feature already on Agama, whenever you have rewards to claim, There is a notification " ! " on Claim button. It notifies user to claim(edited)
grewalsatinder08-Nov-18 03:54 PM
I'd love that too....
I didn't claim rewards for 2 months
can feel ya mate
ComputerGenie 👴👴👴08-Nov-18 03:54 PM
https://www.thecalculatorsite.com/finance/calculators/compoundinterestcalculator.php
on 500 KMD you'd make 0.06 KMD more compounding daily rather than monthly(edited)
grewalsatinder08-Nov-18 03:54 PM
@Nabob I guess he wants a system notification
like wallet is closed but you still want notification
Meyse08-Nov-18 03:55 PM
notification on mobile
grewalsatinder08-Nov-18 03:55 PM
like an alarm
Meyse08-Nov-18 03:55 PM
with just this purpose, holders of some coins that dont want to hastle with other things
grewalsatinder08-Nov-18 03:55 PM
I should actually just setup a monthly calandar for it....
Nabob08-Nov-18 03:55 PM
Too many requests
wolfgang08-Nov-18 03:56 PM
@ComputerGenie 👴👴👴 damnm.... only 0,06kmd... not that much extra really
snowdamn08-Nov-18 03:56 PM
better idea: find hooker to remind you, claim, pay her with interest :mindblown:
ComputerGenie 👴👴👴08-Nov-18 03:56 PM
holders of some coins that dont want to hastle with other things
ffs, if sending 1 tx a month is a "hassle", then they should stick with fiat
Meyse08-Nov-18 03:56 PM
you need to get your mind more to the average user instead of what you think of things
grewalsatinder08-Nov-18 03:57 PM
OMG!! 0.06 * 7777 = 466.62 USD!
wolfgang08-Nov-18 03:57 PM
...?
why multiplied by 7777?
grewalsatinder08-Nov-18 03:57 PM
for fun (edited)
1
Meyse08-Nov-18 03:57 PM
value in 1yr
ComputerGenie 👴👴👴08-Nov-18 03:57 PM
3888500
?
wolfgang08-Nov-18 03:58 PM
?
ComputerGenie 👴👴👴08-Nov-18 03:58 PM
goes back to networking
snowdamn08-Nov-18 03:59 PM
i'd rather get shot than claim daily
grewalsatinder08-Nov-18 04:01 PM
the nerd ones I know have made a cronjob of such thing too


Stargazers
Sky Map
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Sun Distance 152094773 km
Moon Distance 394050 km
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To switch the widget to a right-to-left representation, just enable a single property - "rtl". When this property is on, the horizontal slider is mirrored.


This tab displays the available file templates.
You can edit the existing templates, or create new ones.
(The templates shown in bold font cannot be deleted; their names and extensions cannot be edited.)


Demo: jquery-tabs
jQuery plugin that creates the basic interactivity for an ARIA tabs widget.
Don't pay too much attention to the style here, it's only for demonstration purposes ;-)
Delivery Services
USPS
The United States Postal Service (originally called the U.S. Post Office Department, when it was completely managed by the U.S. government before 1971) also known as the Post Office, U.S. Mail, or Postal Service, often abbreviated as USPS is an independent agency of the United States federal government responsible for providing postal service in the United States. It is one of the few government agencies explicitly authorized by the United States Constitution.
The USPS traces its roots to 1775 during the Second Continental Congress, where Benjamin Franklin was appointed the first postmaster general. The cabinet-level Post Office Department was created in 1792 from Franklin's operation and transformed into its current form in 1971 under the Postal Reorganization Act.
UPS
United Parcel Service of North America, Inc., typically referred to and branded as UPS (stylized as ups), is one of the largest shipment and logistics companies in the world. The American global package delivery company is headquartered in Sandy Springs, Georgia, which is part of the Greater Atlanta metropolitan area. UPS delivers more than 15 million packages a day to more than 6.1 million customers in more than 220 countries and territories around the world.[4][5][6]. UPS is known for its brown delivery trucks and uniforms, hence the company nickname "Brown". UPS also operates its own airline and air cargo delivery service (IATA: 5X, ICAO: UPS, Call sign: UPS) based in Louisville, Kentucky.
FedEx
FedEx Ground is a shipping company that is a subsidiary of the FedEx Corporation. It is headquartered in Moon Township, Pennsylvania, a suburb of Pittsburgh. Originally conceived as a lower cost competitor to UPS, Roadway Package System (RPS) was created to take advantage of new barcode, material handling and computer technologies.After beginning service on March 11, 1985, the company grew, expanding service from its initial coverage of the Mid-Atlantic states, so much so that it eventually became the largest subsidiary of its parent company, Akron-based Roadway Services. By 1996, RPS had achieved 100% coverage of the United States and Canada. In addition, Roadway Services had been reformed as a new holding company called Caliber System, Inc.
Delivery Services (Progressive Enhancement)
USPS
The United States Postal Service (originally called the U.S. Post Office Department, when it was completely managed by the U.S. government before 1971) also known as the Post Office, U.S. Mail, or Postal Service, often abbreviated as USPS is an independent agency of the United States federal government responsible for providing postal service in the United States. It is one of the few government agencies explicitly authorized by the United States Constitution.
The USPS traces its roots to 1775 during the Second Continental Congress, where Benjamin Franklin was appointed the first postmaster general. The cabinet-level Post Office Department was created in 1792 from Franklin's operation and transformed into its current form in 1971 under the Postal Reorganization Act.
UPS
United Parcel Service of North America, Inc., typically referred to and branded as UPS (stylized as ups), is one of the largest shipment and logistics companies in the world. The American global package delivery company is headquartered in Sandy Springs, Georgia, which is part of the Greater Atlanta metropolitan area. UPS delivers more than 15 million packages a day to more than 6.1 million customers in more than 220 countries and territories around the world.[4][5][6]. UPS is known for its brown delivery trucks and uniforms, hence the company nickname "Brown". UPS also operates its own airline and air cargo delivery service (IATA: 5X, ICAO: UPS, Call sign: UPS) based in Louisville, Kentucky.
FedEx
FedEx Ground is a shipping company that is a subsidiary of the FedEx Corporation. It is headquartered in Moon Township, Pennsylvania, a suburb of Pittsburgh. Originally conceived as a lower cost competitor to UPS, Roadway Package System (RPS) was created to take advantage of new barcode, material handling and computer technologies.After beginning service on March 11, 1985, the company grew, expanding service from its initial coverage of the Mid-Atlantic states, so much so that it eventually became the largest subsidiary of its parent company, Akron-based Roadway Services. By 1996, RPS had achieved 100% coverage of the United States and Canada. In addition, Roadway Services had been reformed as a new holding company called Caliber System, Inc.
Delivery Services (Selected Tab)
USPS
The United States Postal Service (originally called the U.S. Post Office Department, when it was completely managed by the U.S. government before 1971) also known as the Post Office, U.S. Mail, or Postal Service, often abbreviated as USPS is an independent agency of the United States federal government responsible for providing postal service in the United States. It is one of the few government agencies explicitly authorized by the United States Constitution.
The USPS traces its roots to 1775 during the Second Continental Congress, where Benjamin Franklin was appointed the first postmaster general. The cabinet-level Post Office Department was created in 1792 from Franklin's operation and transformed into its current form in 1971 under the Postal Reorganization Act.
UPS
United Parcel Service of North America, Inc., typically referred to and branded as UPS (stylized as ups), is one of the largest shipment and logistics companies in the world. The American global package delivery company is headquartered in Sandy Springs, Georgia, which is part of the Greater Atlanta metropolitan area. UPS delivers more than 15 million packages a day to more than 6.1 million customers in more than 220 countries and territories around the world.[4][5][6]. UPS is known for its brown delivery trucks and uniforms, hence the company nickname "Brown". UPS also operates its own airline and air cargo delivery service (IATA: 5X, ICAO: UPS, Call sign: UPS) based in Louisville, Kentucky.
FedEx
FedEx Ground is a shipping company that is a subsidiary of the FedEx Corporation. It is headquartered in Moon Township, Pennsylvania, a suburb of Pittsburgh. Originally conceived as a lower cost competitor to UPS, Roadway Package System (RPS) was created to take advantage of new barcode, material handling and computer technologies.After beginning service on March 11, 1985, the company grew, expanding service from its initial coverage of the Mid-Atlantic states, so much so that it eventually became the largest subsidiary of its parent company, Akron-based Roadway Services. By 1996, RPS had achieved 100% coverage of the United States and Canada. In addition, Roadway Services had been reformed as a new holding company called Caliber System, Inc.


By Developers, for Developers
Simplifying life. No more hunting for downloads, extracting archives, or tinkering with
_HOME
and PATH
environment variables.
@()(implicit request: Request[AnyContent]) @main(title = "Home", request) { @includes.advertisement()
Go on then, paste and run the following in a terminal:
curl -s "https://get.sdkman.io" | bash
Meet SDKMAN! – your reliable companion for effortlessly managing multiple Software Development Kits on Unix systems. Imagine having different versions of SDKs and needing a stress-free way to switch between them. SDKMAN! steps in with its easy-to-use Command Line Interface (CLI) and API. Formerly known as GVM, the Groovy enVironment Manager, SDKMAN! draws inspiration from familiar tools like apt, pip, RVM, and rbenv and even Git. Think of it as your helpful toolkit friend, ready to streamline SDK management for you. 🛠
Simplifying life. No more hunting for downloads, extracting archives, or tinkering with
_HOME
and PATH
environment variables.
Install Software Development Kits for the JVM such as Java, Scala, Kotlin and Groovy. Ant, Gradle, Grails, Maven, SBT, Spark, Spring Boot, Vert.x and many others also supported.
Written in Rust and bash and only requires curl, zip, and unzip dependencies to be present on your system. Even works with ZSH too.
Operates seamlessly across various UNIX-based platforms, including macOS, Linux, and Windows Subsystem for Linux (WSL) on Windows.
New Clients can easily be written by consuming our open Broker API. Vendors can publish and announce their own releases through a secure Vendor API.
Backed by a global community of developers.
Licensed under Apache 2.0


About The Website
- A basic website consisting of :
- Covid Tracker
- Precautions
- What is Covid?
- The website contains various features and information which has been used to make this informative.
- Wikepedia
- World Health Organization
- Geoplugin
- Hope this helps and up for sugesstion.


Overview
Before I could develop the tools to help create better maps on geoportals, I had to decide how to model the different elements of a map. Imagine, before you can bake an amazing pie (yummy!), you need to identify the ingredients, their roles and in which order to add them to the batter.
Well, first I look into the ingredient for a map and how they relate to each other. Then, I look into the sequence of tasks and decisions that a cartographer would go though in the map-making process. Many of these aspects have already been researched by previous scholars, but I had to bring them into a model compatible with the context of geoportals.
The model I developped and the rules associates to it are called CartoWiz. Which is what came out of mixing [ Cartography + Wizard + Vis(ualization) ].
Contextual Map Model
The Contextual Map Model provides the basic elements onto which I could formalize cartographic principles into strict rules, that could then be implemented as functions into a geoportal. It includes among other element a map object, layer object, feature objects, style and rule object. In the image below, you find a simplified representation of the contextual map model. In the section further down, some of the parameters of the model are explained more in detailed
Map Types
If you like maps, you probably already realized that maps can look very different from each other as well as serve different purposes. This is what we call map types. Because of the variety of maps and of their content, the map making and design process is such a broad topic. So broad that it would be a unending task. Which is why, we decided to focus on a few map types, the ones that are the most used with the data usually available on geoportals.
General-purpose maps
General-purpose maps focus on displaying the main feature of an area, both physical and man-made. They helps with orientation and understanding the spatial organization of a territory.
Physical maps
Physical maps focus on displaying the topographic features of the landscape, such as mountains, rivers and lakes. They might also show a few cultural information to help with the orientation.
Political maps
Political maps focus on showing administrative boundaries (national, regional, and/or local) and labelling places (cities, regions, countries, etc). They rarely show details of the topography, but might display rivers and water bodies to help with the orientation.
Thematic maps
Thematic maps focus on a specific topic and display thematic data, such as statistics for administrative units or pollution values for a stream. They show some geographic and cultural features for orientation purposes but focus on spatial data with z (or attribute values).
Layer Semantics
As a cartographer, so many decisions I take while I design a map are based on the content of the map and not just on the geometry or data type of the information. A computer can know whether the spatial data have polygon or line geometries or whether the values of the attributes are text or numbers, but it cannot guess the meaning of the content: is this line a river or an administrative border? Is this forest or a lake?
Well, it cannot, as long as you don't tell it.
This means that we need a way to tell the computer what is the semantic (= related to meaning) of the data. After a survey of more than 20 national or regional geoportals, we were able to have an a suitable overview of the content of such geoportals. It allows us to define categories and themes of the most often encountered spatial data.
Layer Categories
Layer categories broadly describe the kind of content of the layers. Major distinctions are made between geographical objects (such as a road or a river) and thematic dat (such as population density or air pollution levels). Among geographic objects another difference is made between “natural” objects and “man-made” objects.
1. Map image
Layers that are complete maps, with several themes combined and symbolized as a whole. Thus, they must be treated differently than other layer. Examples are scanned maps, topographic maps offered as one layer
2. Thematic information
Such layers usually do not represent features on the ground but values associated with administrative areas, a delimited perimeter, or points of measurement. Examples: population density, pollution levels.
Geographical features
These layers represent concrete objects in the real world, meaning that one can go to the location shown on the map and see them. They are divided in the two following categories:
3. Natural
Geographical features that are natural (not man-made). Examples: a mountain, a river, a forest.
4. Cultural
Geographical features that have been made or shape by man. Examples: an airport, a road, a building.
Layer Themes
Layer themes allow a finer differentiation among the topics covered by the layers and usually group features that are similar in terms of semantic and symbolization.
Main Layers
The main layers on the map are the ones that are central to the meaning and theme of the map. They are the ones that should be displayed prominently. Other layers are either secondarily important to the map or they are 'only' on the map as supporting information in the background.


Move your Pinboard bookmarks into Standard Notes.
Visit your Pinboard backup screen and export your bookmarks as JSON.
Upload the exported bookmarks file below.
Loading...
Once that's done, download a file to be imported into Standard Notes.
Press "Account" on the bottom-right of the Standard Notes desktop app and select the "Import Data from Archive".
Made by Evan Hahn. Enjoy!


The core-progress percentage attribute is used for showing different percentages as its apparent on the actual element.
The core-progress features a status attribute where you can change the type of progress between "success", "warning", and "exception".
The core-progress features a color attribute where you can change the color of the displayed line or circle. The colors follow the coloring naming style of vanilla HTML.
The core-progress features a show-text where it actually makes the percentage visisible in numerical form outside the line or circle.
The core-progress features the attribute stroke-width, which allows you to change the width of the line or circle.
The core-progress features the text-inside attribute, which depends on the show-text attribute. This makes the numerical percentage show inside the actual line or circle.
The core-progress features the type attribute where you can change the shape of the progression including "line" or "circle". By default, it will be a line when this attribute is not included.
type="line"
type="circle"


Reading chapter 6 of Search Inside Yourself prompted us to conduct an introspection exercise to examime what drives us as people, and the way in which we can align our passions with what we do.
It got me thinking (as the author was probably hoping that it would) about where I am in life today, and specifically how I can see EDA contributing to the big picture of what it is that I want to achieve. The following is a quick overview of this.
One of the big things that I would like to promote is financial literacy in NZ, and specifically knowledge of a comfort with the financial markets. While there are any number of programmes and resources focused on getting people to budget, save, etc., there is realtively less focused on getting people to invest in the stock market. There are groups like Snowball Effect promoting investment in businesses not yet listed on the NZX, which is a great thing, however not so much is being done on getting people to invest in companies already listed on the NZX. There are any number of educational products out there (www.invested.co.nz, etc.), however the process of getting set up with an equties trading account is no simplier.
Overseas, companies like Robin Hood (in the US and Australia), making investing simple. You do everything that you need to from your phone, and there are no commisions to pay (in NZ the minimum amount charged in commision is 30 NZD; a complete rort).
I'm starting to see how the tech skills that I'm gaining through EDA will help me to do something about an issue that I have wanted to address for a long time. I'm not quite sure exactly how this will work, but I'm a lot closer to making a difference than I was even a few short weeks ago.


XSite supports language localisation of content HTML files, skins and even resources.
By convention, given the root source directory the localised content can be specified by a language code, which identifies a language-specific source directory. Eg:
/content/sitemap.xml - main sitemap /it/content/sitemap.xml - italian sitemap /it/templates/skin.html - italian skin /fr/content/sitemap.xml - french sitemap: uses main skin /pt/content/sitemap.xml - portuguese sitemap /pt/templates/skin.html - portuguese skin /pt/resources - portuguese resources
An additional (non-mandatory) localisations option will specific the language localisation codes as CSV. For each a sitemap file is expected (using the same relative path as the main content). The skin and resource files will default to main content ones if not found in language-specific path.


{% extends "global/Page.html" %} {% load otree static %} {% block title %} Instruction {% endblock %} {% block content %}
Before we begin, we want to thank you all for accepting this invitation and participatingin this exercise. The objective of this exercise is to understand how people make decisionsrelated to the use of a shared natural resource. All the decisions you make, as well as all the other information you will provide us, will remain confidential.Welcome. This experiment allows you to earn money. Following the instructions and making rational decisions will allow you to earn the maximum possible amount. The money you earn will be paid in cash at the end of the experiment.This exercise attempts to create a situation where a group of individuals must make decisions on how to use a shared resource — e.g., a forest, a water source, or a fishery. In the case of Xi’an, an example would be the use of (). You have been selected to participate ina group of five people among those signed up for playing. You will play for several rounds.At the end of the game, you will be able to earn cash. The cash prizes will depend on the quantity of tokens that you accumulate over the rounds. At the beginning of each round, your group consists of five people will share a fishery of200 fish. In each round, each one of the group members must decide how many fish between0 and 40 you decide to catch. This means you can catch 0, or 1, or 2, .., or 40 fish. Each fishcaught equals 1 token. These decisions will be made at the same time and no communicationis allowed. At the end of the round, the number of fish doubles, up to the lake’s capacity of200 fish and is divided equally among the group members.
{% next_button %} {% endblock %}


Dev_Max
(Web)
Pro
Home
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HTML & CSS
JavaScript
FireBase DB
Copyright © 2021 MobileWeb-Specialist . All Rights Reserved


@* * Copyright 2021 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 config.AppConfig @import models.request.AuthenticatedRequest @import controllers.routes @import models.AppealType.AppealType @import models.{AppealType, Case} @import views.forms.components.RadioOption @import views.html.forms.components.input_radiogroup @import views.html.includes.main @import views.html.partials.{case_heading, error_summary, back_link} @import models.viewmodels.NoTabSelected @(c: Case, form: Form[AppealType])(implicit request: AuthenticatedRequest[_], messages: Messages, appConfig: AppConfig) @main(bodyClasses = None, customTitle = Some(messages("page.title.choose.appeal.type")), customPrimaryNavTab = NoTabSelected) { @back_link() @error_summary(form.errors) @case_heading(c)


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--- title: OCHA Basic theme page-title: Colours options: - include_ocha_basic ---
This styleguide no longer accurately reflects the Common Design.
Color scheme based on the OCHA palette, see OCHA Graphics Style Book (pdf)
Use the contextual colours (text, background, border) rather than the palette colours directly.
Use a colour contrast checker to confirm that your colour combinations are accessible - http://webaim.org/resources/contrastchecker/.
Sass file: variables/_colours.scss


ddl.omf.OMFReader
class OMFReader: ddl.DDLReader.DDLReader;
Reader implementation to ease OMF Parsing.
The OMFReader helps with some OMF specific behaviors, such as specalized string formats
and special index fields. The reader also contains the current 'type' for the current
record, which has certain implications for the getVWord and getVByte methods.
The class is abstract and is implemented directly via WordOMFREader and DWordOMFReader.
- ubyte getType();
- Returns:
the type of the reader as provided in the construtor.
class WordOMFReader: ddl.omf.OMFReader.OMFReader;
Subclass of OMFReader that provides the getVWord and getVByte methods as appropriate
for word-oriented (even) OMF records.
- this(void[] data
, ubyte type
);
- Constructor
- this(IBuffer buffer
, ubyte type
);
- Constructor
- this(IConduit conduit
, ubyte type
);
- Constructor
- OMFReader get(inout VWord x
);
- Returns:
the next word in the buffer/conduit
- OMFReader get(inout VByte x
);
- Returns:
the next byte in the buffer/conduit
class DWordOMFReader: ddl.omf.OMFReader.OMFReader;
Subclass of OMFReader that provides the getVWord and getVByte methods as appropriate
for dword-oriented (odd) OMF records.
- this(IBuffer buffer
, ubyte type
);
- Constructor
- this(IConduit conduit
, ubyte type
);
- Constructor
- this(ubyte[] data
, ubyte type
);
- Constructor
- OMFReader get(inout VWord x
);
- Returns:
the next dword in the buffer/conduit
- OMFReader get(inout VByte x
);
- Returns:
the next word in the buffer/conduit


| Class | Description |
|---|---|
| AddAffiliation |
Task that adds an affiliation for a room in the other cluster nodes.
|
| AddMember |
Task that adds a new member to the room in the other cluster nodes.
|
| BroadcastMessageRequest |
Task that broadcasts a message to local room occupants.
|
| BroadcastPresenceRequest |
Task that broadcasts the presence of a room occupant to the occupants of the room
being hosted by the cluster node.
|
| ChangeNickname |
Task that changes the nickname of an existing room occupant in the cluster node.
|
| DestroyRoomRequest |
Task that destroys the local room in the cluster node.
|
| GetNewMemberRoomsRequest |
Task requested by each cluster node when a new node joins the cluster.
|
| GetNumberConnectedUsers |
Task to be executed in each cluster node to obtain the total number of
users using the multi user chat service.
|
| MUCRoomTask<V> |
Task related to a room to be executed in a cluster node.
|
| MUCServicePropertyClusterEventTask |
This task updates or deletes a property in a cluster node's muc service property map.
|
| OccupantAddedEvent |
Task that will remove a room occupant from the list of occupants in the room.
|
| OccupantLeftEvent |
Task that removes a room occupant from the list of occupants in the room.
|
| QueuedTasksManager |
Queue tasks while this JVM is joining the cluster and the requested room was still not loaded.
|
| RoomAvailableEvent |
Task that adds a new local room to the cluster node.
|
| RoomInfo |
Representation of a room configuration and its occupants.
|
| RoomRemovedEvent |
Task that will remove a local room from the cluster node.
|
| RoomUpdatedEvent |
Task that updates the configuration of a local room.
|
| SeniorMemberServicesRequest |
Task to be requested by a node that joins a cluster and be executed in the senior cluster member to get
the services with rooms with occupants.
|
| ServiceAddedEvent |
Task that will add a service to the cluster node.
|
| ServiceInfo |
Representation of a service configuration.
|
| ServiceRemovedEvent |
Task that will remove a service from the cluster node.
|
| ServiceUpdatedEvent |
Task that will update a service configuring in the cluster node.
|
| UpdateHistoryStrategy |
Cluster task that will update the history strategy used by a MultiUserChatService
service.
|
| UpdateOccupant |
Task that updates all information regarding a room occupant.
|
| UpdateOccupantRequest |
Task that requests the cluster node hosting a room occupant to change his
role and/or affiliation.
|
| UpdatePresence |
Task that updates the presence of an occupant in a room.
|
Copyright © 2003-2008 Jive Software.


CAPTAIN CORELLI'S MANDOLIN
Reviewed by Harvey Karten Universal Pictures/ StudioCanal/Miramax Director: John Madden Writer: Shawn Slovo, novel by Louis De Bernieres Cast: Nicolas Cage, Penelope Cruz, John Hurt, Christian Bale, David Morrissey, Irene Papas Screened at: Lincoln Sq. Thea NY 8/13/01
As I was taking in "Captain Corelli's Mandolin," I couldn't help thinking of two bits of advice that I got early on; one from my high school social studies teacher, the other from an older friend and mentor. The first and less important is that political alliances are temporary. According to Mr. Pompa back in 10th grade, difficult as it is to believe given our great relationship with England today, we went to war with the UK twice and were it not for the fact that both our countries have McDonald's outlets, there's no telling when we'd be battling it out again. The second, and one which served me well over and over and over is: forget flowers and chocolate. The way to a woman's heart is with music. What does all this have to do with a picture about a romance that occurs during World War 2? Absolutely everything.
John Madden's movie, based on Louis De Bernieres' novel and set amid John Toll's picture-postcard photography on the smashing Greek island of Cephallonia, opens in 1940 as the Italians, far better suited for wine, women and song than for war, are suffering setbacks while advancing in southeastern Europe under Mussolini's orders. A smaller Greek army has pushed them back back into Albania but a force has nonetheless landed with the help of Hitler's Germans on the island of Cephallonia, and for a time even the Nazis under Captain Gunther Weber (David Morrissey), are chilling out as they watch Italian Captain Antonio Corelli (Nicolas Cage) and his men singing Puccini and Verdi, enjoying the sun, the sand, and the Greek women. While Corelli sets his eyes on the daughter of Dr. Iannis (John Hurt), Pelagia (Penelope Cruz)--who is engaged to Greek resistance fighter Mandras (Christian Bale)--outside forces are ominous. Italy, which had been a reluctant ally of Germany, has surrendered to the Allies, and Germany--none too pleased by this while rightly convinced that the Italian troops on Cephallonia are about to turn their weapons over to the Greek resistance--are about to reverse their affiliation with their confederates to the south with extreme prejudice.
"Captain Corelli's Mandolin," then, is, like "Pearl Harbor," both a war story and a romance, more realistic than Michael Bay and Jerry Bruckheimer's summer blockbuster in the courtship department but quite a bit less flashy in scenes of combat. Shawn Slovo's screenplay gives the best lines to John Hurt in the role of Pelagia's astute ol' dad, a veritable fortune cookie of wisdom about love, life and medicine. His learning must come strictly from experience since he couldn't possibly raise the money to acquire books--in one scene he's shown getting paid for medical treatment with a live chicken. (Sounds like more than most of our HMO docs deserve, doesn't it?) Iannis tells his little girl not to marry Mandras not because he's an American psycho- -he's not, he's Greek and he was born on Cephallonia--but because he doesn't know how to read and he's of a lower social class than the ambitious young woman. Iannis also knows from experience that there's a difference between love and being in love--the latter being what happens in the beginning when the earth movies while the former occurs during the next forty years when you're shopping for Pampers and washing the dishes.
I haven't the foggiest idea how these different groups communicated with one another in real life, because Mr. Pompa, my 10th grade social studies teacher, never told us about Cephallonia, but all the folks in John Madden's picture have a dandy time speaking in English while maintaining their special European accents throughout. Nic Cage has no problem with his Italian inflection and really looks as though he's playing that mandolin that he carries around on his back. Remember what I said in the beginning about how serenading women is better than giving them chocolate and flowers? In the movie's best scene, Pelagia is about to stomp away from Corelli--after all, he's the enemy--when the Italian whips out his instrument and strums away. She's transfixed.
"Captain Corelli's Mandolin" is not a great romance and not a great war movie, though it tries to combine to two genres in the way that major combat fare ("The Bridge on the River Kwai," "The Longest Day," "Saving Private Ryan") wouldn't think of doing. As summer entertainment, though, it provides some spectacular photography which you can take in from your air- conditioned location without suffering Cephallonia's August heat.
Rated R. Running time: 127 minutes. (C) 2001 by Harvey Karten<EMAIL_ADDRESS>========== X-RAMR-ID: 29201 X-Language: en X-RT-ReviewID: 243911 X-RT-TitleID: 1109530 X-RT-SourceID: 570 X-RT-AuthorID: 1123
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{{unref|date=August 2011}} {{Contains Chinese text}} {{Infobox prepared food | name = Misua | image = | caption = Fried misua noodles | alternate_name = | country = China | region = Fujian | creator = | course = | type = Chinese noodles | served = | main_ingredient = Wheat flour | variations = | calories = | other = }} {{Chinese|t=麵線|s=面线|p=miànxiàn|l=noodle threads|poj=mī-sòaⁿ}}
Misua (also spelled mee sua or miswa) is a very thin variety of salted Chinese noodles made from wheat flour. It originated in Fujian, China. The noodles differ from mifen (rice vermicelli) and cellophane noodles in that the latter two are made from rice and mung beans, respectively, and are typically a lot thinner than those two varieties. {{Citation needed|date=June 2014}}
Misua is made from wheat flour. Cooking misua usually takes less than 2 minutes in boiling water, and sometimes significantly less.{{Citation needed|date=June 2014}}
Misua is cooked during important festivities, and eaten in mainland China as well in Taiwan, Malaysia, Indonesia, Singapore, Vietnam, Brunei, Thailand, and the Philippines. Misua signifies long life in Chinese culture, and as such is a traditional birthday food.{{Citation needed|date=June 2014}}
It is usually served topped with ingredients such as eggs, oysters, pig's large intestine, shiitake mushroom, beef, shallots, or scallions, and roasted nuts.<!--peanuts or cashews?-->
In Taiwan, there are two forms of misua. The first is plain, while the second has been steamed at high heat, caramelizing it to a light brown colour. For birthdays, plain misua is usually served plain with pork hocks ({{lang|zh-Hant|猪腳麵線}}) in stewed broth as a Taiwanese birthday tradition. Brown misua can be cooked for prolonged periods without disintegrating in the cooking broth and is used in oyster vermicelli ({{lang|zh-Hant|蚵仔麵線}}), a dish popular in Taiwan.{{Citation needed|date=June 2014}}
{{Fujian cuisine}} {{Taiwanese cuisine}} {{Noodle}}
{{China-cuisine-stub}}