Liver metastases:
Brain metastases:
LDH:
WHO performance status:
Clark score:
Disclaimer: This calculator is for demonstration purposes only and should not be used to guide clinical decisions.


Chapter 3 Design-based Approach
Before discussing design-based approach, let us review some of concepts related to sampling
.
3.1 Sampling
3.1.1 Steps of generalization
Example: Let us consider CCHS.
- Target population: You think about a
target population
in you PICOT.- Canadian population 12 years of age and over
- Sampling Frame: But all of your target population may not belong to a
sampling frame
compiled by a government.- Canadian population 12 years of age and over exluding about 3% population (e.g., aboriginal settlements, canadian Forces, institutionalized, foster care, 2 selected Quebec health regions)
- Active Frame: People that are still reachable
- E.g., not dead or have not moved
- Sampling list
- Prepared from a specific sampling technique (SRS, stratified, cluster, complex)
- Actual sample: people that have responded
- some don’t respond
Note that, results from ‘actual sample’ are generalized to the ‘active frame’. An inference from a sample may not really be generalizable to the target population (strictly speaking).
3.1.2 Types of sampling techniques
- Probability
- Non-probability
3.2 Statistical inference
3.2.1 Model-based
Most of the statistical techniques we have seen in our pre-requisite courses (SPPH 400, 500
) generally assumed that we are dealing with a sample that was obtained from an infinite population. We usually assume that a random process can approximate such data generation process, and the data was collected by a simple random sampling or SRS (everyone has equal opportunity to be selected in the sample). All our conclusions are based on such assumptions. If we are wrong in specifying correct distribution to approximate the data generating process, our subsequent inferences may not be valid anymore.
3.2.2 Design-based
Generally, when wide-scale surveys are designed, simple random sampling or SRS may not be feasible for various practical considerations. May be researchers and policy-makers want that a special but small sub-group subjects should be included in our sample (e.g., people suffering from a rare disease), but it is possible that by a SRS scheme, none of the subject from that small subgroup will be included. For convenience of sampling, and for controlling variance, researchers may have to make desicions regarding how the survey needs to collect sample. Researchers may resort to cluster or stratified sampling; or a mix of both (trade-off between cost and precision). Unfortunately, in these cases, equal probability of being selected in the sample is not there any more. Lumley (2011) discussed the following properties for making design-based inference:
- properties needed to get valid estimates
- non-zero probability (\(P_i>0\) for subject i) of being selected in the sample
- every subject has a known probability (\(P_i\)) of being seleted
- properties needed to acieve accuracy of those estimates
- Every pair of subjects must have a non-zero probability (\(P_{ij}>0\) for subjects i and j) of being selected in the sample and
- that probability (\(P_{ij}\)) must be known as well.
3.3 Complex surveys
3.3.1 Design features
<IP_ADDRESS> Stratification
Considering sub-groups that are sufficiently different from each other with respect to characteristics. Usual examples:
- different geographical location: Manitoba vs. Nunavut
- high income vs. low income
- gender
For each stratum (single unit), sampling is done separately. As we can select sample size from each stratum, we are able to control for variability of the estimates (SE
) from each strata as well.
Source: link
<IP_ADDRESS> Clustering
Clustering is done for convenience of data collection, generally. In a nationwide survey, researchers may choose to collect more samples from selected geographic locations. This is generally the case for cost considerations. In doing so, the surveyers don’t have to travel too far, as they could essentially get many neighboring subjects at a much lower cost. An obvious consequence could be that the neighboring subjects may be more correlated with each other
compared to subjects who are selected by randomness. This may cause the observations not being independent
anymore.
Source: link
<IP_ADDRESS> Weighting
Assume that, in a SRS, a subject is selected in a sample with a probability of \(p_i = 0.04\). This mean, that person is representing \((1/p_i) = (1/0.04) = 25\) subjects in the population. We call this the sampling weight
(\(w_i = 25\)). There are other type of weight:
precision weight
frequency weight
but we are not really interested about those in this course in general.
In a complex survey, where we have stratification and clustering, this weight is not as straight-forward becasue, then, it is coming from an unequal probability sampling. As a consequence, not all subjects in the population will have the same probability \((p_i)\) of being included in the sample, and the sampling weights (\(w_i\)) will vary as well (but the probability or weight is known for each subjects).
3.3.2 Design effect
Compared to a SRS, all of the design features of a complex survey, such as, stratification, cluster sampling, and weighting generally influence the SEs of the estimates. Survey researchers use a ratio called design effect, to account for the difference in SEs between a complex survey versus a SRS:
\(DE^2 = \frac{SE^2_{Complex.Survey}}{SE^2_{SRS}}\).
3.4 Other ideas
3.5 Further readings
Available via UBC library:
3.6 Exercise
- Skim through the first chapter (from the further readings list). Should be easier to read most of it after this lecture.
- If any terminology remains unfamiliar, please discuss on Canvas.


The class for a sample task of one variable. More...
#include <Common.h>
Public Member Functions | |
| void | print () |
Public Attributes | |
| std::vector< int > | pos_of_sample_variable |
| std::vector< int > | auxs |
| std::vector< int > | aux2s |
| std::vector< std::vector < double > * > | weights |
| std::vector< void * > | mbs |
| std::vector< int > | crids |
| std::vector< int > | fids |
| std::vector< int > | funcids |
| int | vid |
| int | vvalue |
| int | vtrain |
| int | vdomain |
| double | stepSize |
| double | log_improve_ratio |
The class for a sample task of one variable.
|
inline |
Pring the content.
| std::vector<int> mia::elly::SampleInput::aux2s |
Each entry corresponds to a factor. Each entry is aux info. of that factor.
| std::vector<int> mia::elly::SampleInput::auxs |
Each entry corresponds to a factor. Each entry is weight index of that factor.
| std::vector<int> mia::elly::SampleInput::crids |
Each entry corresponds to a factor. Each entry is correlation relation ID.
| std::vector<int> mia::elly::SampleInput::fids |
Each entry corresponds to a factor. Each entry is factor state ID.
| std::vector<int> mia::elly::SampleInput::funcids |
Each entry corresponds to a factor. Each entry is factor function ID.
| double mia::elly::SampleInput::log_improve_ratio |
Filled by mia::elly:alg::GibbsSampling for the improvement of new sample assignment compared with orginal assignment.
| std::vector<void*> mia::elly::SampleInput::mbs |
Each entry corresponds to a factor. Each entry is pointer to factor state
| std::vector<int> mia::elly::SampleInput::pos_of_sample_variable |
Each entry corresponds to a factor. Each entry is the position of target variable in that factor.
| double mia::elly::SampleInput::stepSize |
Step size for learning.
| int mia::elly::SampleInput::vdomain |
Domain of target variable. a variable can take value from 0,...,vdomain-1
| int mia::elly::SampleInput::vid |
Target variable ID.
| int mia::elly::SampleInput::vtrain |
Training assignment of target variable. -1 if not unknown.
| int mia::elly::SampleInput::vvalue |
Current assignment of target variable.
| std::vector<std::vector<double>* > mia::elly::SampleInput::weights |
Each entry corresponds to a factor. Each entry is pointer to weights vector of that factor.


If there's a mistake in your mortgage deed, or you are not happy to agree to the terms, inform your conveyancer as soon as possible.
The best way to do this is to get back in touch with the person who asked you to sign the deed.
If you have selected “No” in error, just go back to continue signing your mortgage deed.


Indian butter biscuits recipe
by Monisha BharadwajMy grandmother was an exceptional nankhatai maker and I remember her baking these for us as a teatime snack when we got home from school. They were crisp and crumbly with a melt- in-the-mouth texture that made them completely moreish.
Prep time: 15 minutes, plus chilling time | Cooking time: 20 minutes
MAKES
15
INGREDIENTS
- 130g plain flour
- 60g gram flour
- 30g fine semolina
- 80g icing sugar
- 1 tsp ground cardamom
- 110g ghee or unsalted butter
- 20g mixed pistachios and almonds, crushed or very finely chopped
METHOD
- Preheat the oven to 150C/130C fan/gas mark 2.
- Line a baking tray with greaseproof paper.
- Put the plain flour, gram flour, semolina, icing sugar, cardamom and a large pinch of salt into a large mixing bowl and mix well.
- Rub in the ghee or unsalted butter to make a stiff dough. Cover and refrigerate for 15 minutes.
- Divide the dough into 15 equal portions each about 3cm in diameter. Shape each portion into a flat, round shape and place on the lined baking tray.
- Press an indent in the top of each round with your finger and fill with chopped nuts. Press them down slightly into the dough.
- Bake in the oven for 20 minutes until cracks appear on the top of each biscuit.
- Transfer to a wire rack and leave to cool. The biscuits will be soft as they come out of the oven, but will crisp up as they cool. When completely cool, store in an airtight container and consume within a week.
Recipe from ‘Indian in 7’ by Monisha Bharadwaj (Kyle Books ,£17.99). Order your copy from books.telegraph.co.uk


CatchSprite()
语法
Result = CatchSprite(#Sprite, *MemoryAddress [, Mode])概要
Loads the specified sprite from the given memory area. A screen should be opened with OpenScreen() or OpenWindowedScreen() before loading a sprite. Sprites can be in BMP format or any other format supported by the ImagePlugin library. A catched sprite can be freed by using the FreeSprite() function. The 'CatchSprite' function is useful when using the 'IncludeBinary' PureBasic keyword. The images can be packed inside the executable. Nevertheless, use this option very carefully, as it will take more memory than storing the file in an external file (the file will be in both executable memory and loaded into physical memory).
The following functions can be used to enable automatically more image formats:
UseJPEGImageDecoder()
UseJPEG2000ImageDecoder()
UsePNGImageDecoder()
UseTIFFImageDecoder()
UseTGAImageDecoder()
参数
#Sprite A number to identify the new sprite. #PB_Any 可以用来自动生成这个编码. *MemoryAddress The memory address holding the image used to create the sprite. 模式 （可选） It can be a combination of the following values (with the '|' operator): #PB_Sprite_PixelCollision: Add special information to handle pixel collision trough SpritePixelCollision(). #PB_Sprite_AlphaBlending : Sprite is created with per pixel alpha-channel support, needed for DisplayTransparentSprite(). The image format has to support it (only PNG and TIFF for now).
返回值
Nonzero if the sprite has been created, zero otherwise. If #PB_Any was used for the #Sprite parameter then the generated number is returned on success.
示例
Debug CatchSprite(0, ?Pic) DataSection Pic: IncludeBinary "Sprite.bmp" EndDataSection
备注
The "?" is a pointer to a label. More information about pointers and memory access can be found in the relating chapter here.
参阅
CreateSprite(), LoadSprite()
已支持操作系统
所有


--- layout: post title: Comments return... date: 2004-08-10 00:00:00 category: personal ---
Right, comments are back thanks to an MT-Blacklist install. Selfless bod that I am, I put my own weblog into the firing line before installing it elsewhere (as I need to do for a few people now). Seems to work. So far.
But hey, what do YOU think?


Service Level Authorization Guide
Purpose
This document describes how to configure and manage Service Level Authorization for Hadoop.
Prerequisites
Make sure Hadoop is installed, configured and setup correctly. For more information see:
- Single Node Setup for first-time users.
- Cluster Setup for large, distributed clusters.
Overview
Service Level Authorization is the initial authorization mechanism to ensure clients connecting to a particular Hadoop service have the necessary, pre-configured, permissions and are authorized to access the given service. For example, a MapReduce cluster can use this mechanism to allow a configured list of users/groups to submit jobs.
The ${HADOOP_CONF_DIR}/hadoop-policy.xml configuration file is used to define the access control lists for various Hadoop services.
Service Level Authorization is performed much before to other access control checks such as file-permission checks, access control on job queues etc.
Configuration
This section describes how to configure service-level authorization via the configuration file {HADOOP_CONF_DIR}/hadoop-policy.xml.
Enable Service Level Authorization
By default, service-level authorization is disabled for Hadoop. To enable it set the configuration property hadoop.security.authorization to true in ${HADOOP_CONF_DIR}/core-site.xml.
Hadoop Services and Configuration Properties
This section lists the various Hadoop services and their configuration knobs:
| Property | Service |
|---|---|
| security.client.protocol.acl | ACL for ClientProtocol, which is used by user code via the DistributedFileSystem. |
| security.client.datanode.protocol.acl | ACL for ClientDatanodeProtocol, the client-to-datanode protocol for block recovery. |
| security.datanode.protocol.acl | ACL for DatanodeProtocol, which is used by datanodes to communicate with the namenode. |
| security.inter.datanode.protocol.acl | ACL for InterDatanodeProtocol, the inter-datanode protocol for updating generation timestamp. |
| security.namenode.protocol.acl | ACL for NamenodeProtocol, the protocol used by the secondary namenode to communicate with the namenode. |
| security.inter.tracker.protocol.acl | ACL for InterTrackerProtocol, used by the tasktrackers to communicate with the jobtracker. |
| security.job.submission.protocol.acl | ACL for JobSubmissionProtocol, used by job clients to communciate with the jobtracker for job submission, querying job status etc. |
| security.task.umbilical.protocol.acl | ACL for TaskUmbilicalProtocol, used by the map and reduce tasks to communicate with the parent tasktracker. |
| security.refresh.policy.protocol.acl | ACL for RefreshAuthorizationPolicyProtocol, used by the dfsadmin and mradmin commands to refresh the security policy in-effect. |
Access Control Lists
${HADOOP_CONF_DIR}/hadoop-policy.xml defines an access control list for each Hadoop service. Every access control list has a simple format:
The list of users and groups are both comma separated list of names. The two lists are separated by a space.
Example: user1,user2 group1,group2.
Add a blank at the beginning of the line if only a list of groups is to be provided, equivalently a comman-separated list of users followed by a space or nothing implies only a set of given users.
A special value of * implies that all users are allowed to access the service.
Refreshing Service Level Authorization Configuration
The service-level authorization configuration for the NameNode and JobTracker can be changed without restarting either of the Hadoop master daemons. The cluster administrator can change ${HADOOP_CONF_DIR}/hadoop-policy.xml on the master nodes and instruct the NameNode and JobTracker to reload their respective configurations via the -refreshServiceAcl switch to dfsadmin and mradmin commands respectively.
Refresh the service-level authorization configuration for the NameNode:
$ bin/hadoop dfsadmin -refreshServiceAcl
Refresh the service-level authorization configuration for the JobTracker:
$ bin/hadoop mradmin -refreshServiceAcl
Of course, one can use the security.refresh.policy.protocol.acl property in ${HADOOP_CONF_DIR}/hadoop-policy.xml to restrict access to the ability to refresh the service-level authorization configuration to certain users/groups.
Examples
Allow only users alice, bob and users in the mapreduce group to submit jobs to the MapReduce cluster:
<property> <name>security.job.submission.protocol.acl</name> <value>alice,bob mapreduce</value> </property>
Allow only DataNodes running as the users who belong to the group datanodes to communicate with the NameNode:
<property> <name>security.datanode.protocol.acl</name> <value>datanodes</value> </property>
Allow any user to talk to the HDFS cluster as a DFSClient:
<property> <name>security.client.protocol.acl</name> <value>*</value> </property>


---
title: "PET/ANIMAL PORTRAITS IN COLORED PENCIL"
date: "2000-01-01 19:01:31 -06:00"
categories:
- adult
- eh
subtitle: ""
layout: "events"
event-image: "pet-portrait.jpg"
event-dates: "THURSDAY-SATURDAY, JANUARY 31 - FEBRUARY 2"
event-location: "Red Butte Garden Classroom"
button-type: "register"
tickets-link: "#"
registration-info: "Registration is closed for this class
Email Elaine Hultgren with questions<EMAIL_ADDRESS>instructor-info: "Elaine Hultgren"
member-cost: "$160"
public-cost: "$200"
event-notes: ""
tickets-button: ""
published: false
---
REGISTRATION REQUIRED
This workshop has a focus of fins, feathers and fur. All pets and favorite animals are welcome as a photographic image. Please bring to class a sharp COLOR 9x12 or 8x10 reference photo. Using colored pencils, you will render your portrait using layering techniques and detailed textures to bring life to your subject. Your portrait will be a lasting tribute to a beloved pet, pets or animal of your choosing. Colored pencil experience preferred.
THURSDAY, JANUARY 31, 6-9PM
FRIDAY, FEBRUARY 1, 6-9PM
SATURDAY, FEBRUARY 2, 9AM-4PM
Materials for artist to bring to class:
Lemon Yellow #915
Magenta #930
Ultramarine #902
Questions? Please e-mail Elaine at<EMAIL_ADDRESS>or text at<PHONE_NUMBER>.


My name is Nick Del Toro and I have two dogs named Milo and Athena.
In my spare time I like to take care of my plants, all fourteen of them!
In the evenings I love to cook food. So far pies and pasta have been the main dishes.
Im also a big cosplay player.
In my travels I met Jensen and Jared of Supernatural
example


{% comment %} This snippet adds links to for the views provided by django-userexensions and is designed to be used within a bootstrap navbar. To use this, add the following in your navbar where appropriate: {% include 'snippits/user_theme.htm' %} {% endcomment %} {% load userextension_tags %} Profile Service Accounts Recents Favorites


Transparent Language, Inc. Privacy Policy
Last Updated: 5/24/18
This Privacy Policy sets out how Transparent Language, Inc. ("Transparent") uses and protects any information that you give to Transparent when you use our website (the "Site").
Transparent is committed to protecting your privacy. Should our Site ask you to provide any information by which you can be identified, you may be assured that that information will only be used in accordance with this Privacy Policy.
This Privacy Policy covers any use of the Site. Note, however, that the Site may contain links to other websites of interest. As we have no control over such sites, they are not governed by this Privacy Policy and we cannot be responsible for the protection and privacy of any information which you provide to them.
This Privacy Policy is effective May 1, 2018. We reserve the right to change this Privacy Policy from time to time to ensure that it complies with current legal requirements or to implement changes to our services. Any such changes will be posted to Site.
Information Collection
We do not collect personal identifiable information from visitors who simply visit and browse the Site. However, should you choose to sign up for a service, place an order, request a download, or make an inquiry using forms or other features on the Site, Transparent may collect the following personal information directly or through our order processing agents:
- First and last name
- Email address
- Phone number
- Address information
- Country of origin
- Organization name, if applicable
- IP address
- Languages of interest
- Category of user (personal learner, educator, librarian, company representative, etc.), if you provide such information
- Any answers you provide if you voluntarily choose to participate in customer surveys
- Any additional contact information (such as Skype name, cell phone, fax, etc.) that you voluntarily choose to provide if you interact with Transparent employees, including tutors, instructors, and technical support
In addition, the following technical information may be collected in an anonymized format, not linked directly to your account:
- Browser type and version used to access the Site
- Operating system used to access the Site
- Referrer URL / Previously visited page
Additional information may be requested if you register as a user of any of our applications. See the Privacy Policy for the applicable application for more details.
Information Usage
Transparent requires the information we gather to generally understand your needs and to provide you with better service, and in particular for the following reasons:
- To customize your experience with the Site according to your interests
- To enable internal record keeping and reporting
- To improve our products and services
- To periodically send promotional emails about new products, special offers, or other information which we think you may find interesting
- To contact you from time to time for market research purposes
Information Security
Transparent is committed to ensuring that your information is secure. To prevent unauthorized access or disclosure, we have put in place suitable physical, electronic, and managerial procedures to safeguard and secure the information we collect online.
We also adhere to the principles of purpose limitation and data minimization. Therefore, we only store your personal data for as long as it is necessary to achieve the purposes mentioned here or as long as the storage periods required by law. After discontinuation of the respective purpose or expiry of these deadlines, the corresponding data will be blocked or deleted in accordance with the statutory provisions. Our legal basis for the deletion or blocking of personal data is Art. 5 (1) b) GDPR and Art. 5 (1) c) GDPR.
You have the right to complain about any improper processing of your personal data (such as collection, storage, modification, transfer, deletion, etc.) by us to the proper authorities.
Information Control
You have the right to request a copy of your information, to object to our use of your information (including for marketing purposes), and to request the deletion or restriction of your information. Transparent will respond to requests about these matters within a reasonable timeframe.
- Accessing and updating your information: Should you wish to access or update any of your information, you can contact us through any of the forms on the site, or by email at<EMAIL_ADDRESS>Likewise, you have the right to contact us and request an electronic file with a copy of your information.
- Opting out of communications: You may opt out of receiving communications from us by using the unsubscribe link within each email, by updating your email preferences within the Site, or by contacting Transparent at<EMAIL_ADDRESS>- Accepting or declining cookies: Cookies are small files which can be placed on your computer's hard drive to allow websites to respond to you as an individual. With cookies, web Sites can tailor their operations to your needs, likes, and dislikes by gathering and remembering information about your preferences. Within the Site, we use traffic log cookies to identify which pages are being used. We only use this information for statistical analysis purposes, then remove the data from our system. The cookies in no way gives us access to your computer or any information about you, other than the data you choose to share with us. Most web browsers automatically accept cookies, but you can change your browser setting to decline cookies if you prefer. Please be aware that if you choose to decline cookies, some or all of the Site may be inaccessible or non-functional.
- Requesting that we forget you: If you no longer want Transparent to continue storing information about you, you may contact us and request that your account be forgotten. When we receive such a request, we will retain your data for 30 days. If you change your mind during this period, you can contact us again to stop the process of being forgotten. If you have not contacted us within 30 days, your account will be permanently forgotten.
Information Sharing
Transparent will not sell, distribute, or lease your personal information to third parties unless we have your permission or are required by law to do so. However, we may use the following sales and marketing products for web analytics and social media marketing:
- Google Analytics: We use Google Analytics, a web analytics service provided by Google Inc. ("Google"), on the Site. Google Analytics uses its own cookies to evaluate site usage, compile site activity reports, and provide other services related to website activity and internet usage on behalf of Transparent. Google is a U.S. company that uses servers in the United States.
- Google AdWords: If you reach the Site via an advertisement sent by Google, Google AdWords sets a cookie on your computer. These cookies lose their validity after 30 days and are not used for personal identification. If you visit certain pages on the Site and the cookie has not expired, we and Google may recognize that you clicked on the ad and were redirected to this page. Google is a U.S. company that uses servers in the United States.
- HubSpot: HubSpot tracks, documents, and manages many interactions on the Site to help us assess the effectiveness of our sales and marketing strategy. HubSpot programs access data collected on the Site from services such as Google AdWords and compile reports that provide information about the demand for our products and our clientele. HubSpot also uses cookies to track Site visitors in real time and recognize potential customers among them. Finally, HubSpot email tracking provides a notification when a customer opens an email sent by us and clicks on the link to our offers. We can then respond to the customer with a personal email or an automated answer. HubSpot is a U.S. company that uses servers in the United States, and has signed a data processing agreement with us to keep our data safe.
- HubSpot LiveChat: HubSpot LiveChat is built into the Site to let our users communicate with Transparent employees. HubSpot LiveChat collects access data such as IP address, browser, and operating system type as well as the dial-in location from users of the live chat. In addition, cookies are used to collect further data about user behavior. HubSpot has represented to us that it processes this information solely to improve its own services. HubSpot stores and processes all collected data on servers located in the United States.
- SurveyMonkey: SurveyMonkey lets Transparent create surveys that users of the Site may choose to participate in. We use these surveys to collect user opinions and feedback that helps us improve the Site and our products. Surveys are anonymous but may allow users to submit questions along with their email address. SurveyMonkey attempts to distinguish users by using their IP addresses as identifiers. SurveyMonkey is a U.S. company.
- TypeForm: We uses TypeForm to create data input forms that allow users to submit technical support cases and other customer service requests. TypeForm is a Barcelona-based online Software as a Service (SaaS) company that specializes in online form building and online surveys.
- GrooveHQ: We use GrooveHQ for technical support case management, knowledge base, and LiveChat. GrooveHQ is a U.S. company.
- CloudAMQP: We use CloudAMQP to host an instance of RabbitMQ, which is used to send data to Transparent servers for processing. Information is not stored by CloudAMQP and is encrypted in transit.
- Amazon Web Services (AWS): We use AWS to host content, send emails and notifications, maintain access logs, and provide support within Transparent authoring tools. AWS encrypts data at rest and uses secret keys to control who can access various data points.
- HotJar: HotJar allows Transparent to gather anonymous data about how users interact with the Site. Over time, we analyze that data to improve the Site's usability and determine the most used features. From a single visitor session, HotJar may create a heatmap; visually recreate the session by replaying mouse movements, page scrolls, and click coordinates; evaluate the user's progress through various steps; and note which fields of a form a user filled out before abandoning it. Visual recreations of sessions will not include sensitive or personal information entered by the user. HotJar attempts to distinguish users by using their IP addresses as identifiers. HotJar also uses cookies to distinguish new users from returning ones to avoid redundant behaviors. HotJar is a U.S. company.
- BlueSnap: BlueSnap processes Transparent's payment transactions. Transparent also uses a cart hosted by BlueSnap servers to gather customer payment info at purchase time, which means Transparent never has access to that info. After a transaction has taken place between BlueSnap and a Transparent customer, Transparent may view transaction information such as date, customer email, customer name, customer address, amount, payment method, and product purchased, but not payment info. BlueSnap is a U.S. company.
- FeedBlitz: We use FeedBlitz to send emails to voluntary subscribers to our blogs and Word of the Day service. FeedBlitz is a U.S. company.
- WordPress: We use WordPress to power our blogs. WordPress may collect the name, email address, and IP address of users who voluntarily choose to comment on our blog articles. WordPress is a U.S. company.
Our legal basis for the use of these third-party products is provisions 6 (1) b) and 6 (1) f) of GDPR.


public interface Document extends TypedAccess, IAdaptable
| Modifier and Type | Interface and Description |
|---|---|
static class |
Document.ComputeWithFormAction |
static interface |
Document.ComputeWithFormCallback
Callback to get notified about errors during
computeWithForm(boolean, ComputeWithFormCallback) |
static class |
Document.ComputeWithFormPhase
Possible validation phases for
computeWithForm(boolean, ComputeWithFormCallback) |
static class |
Document.EncryptionMode |
static interface |
Document.IAttachmentProducer
Interface to create document attachments in-memory without
the need to write files to disk.
|
static class |
Document.LockMode
Document locking mode
|
static interface |
Document.SignatureData
Container for note signature data
|
| Modifier and Type | Method and Description |
|---|---|
Stream<Item> |
allItems()
Returns a stream of all document items
|
Document |
appendItemValue(String itemName,
Object value)
Appends an item value to the document, leaving any other items of the same name in place.
|
Document |
appendItemValue(String itemName,
Set<Item.ItemFlag> flags,
Object value)
Appends an item value to the document, leaving any other items of the same name in place.
|
Document |
appendItemValue(String itemName,
Set<Item.ItemFlag> flags,
Object value,
boolean allowDataTypeChanges)
Appends an item value to the document, leaving any other items of the same name in place.
|
void |
appendToTextList(String itemName,
String value,
boolean allowDuplicates)
This function appends an entry to a text list item.
If the text list item does not already exist, one will be created using the name provided and the text provided as the FIRST entry. |
Attachment |
attachFile(String filePathOnDisk,
String uniqueFileNameInDoc,
Attachment.Compression compression)
Attaches a file to the document, not associated with a rich-text item.
|
Attachment |
attachFile(String uniqueFileNameInDoc,
TemporalAccessor fileCreated,
TemporalAccessor fileModified,
Document.IAttachmentProducer producer)
Attaches a file to the document, not associated with a rich-text item.
|
AutoCloseableDocument |
autoClosable()
Returns an instance of this document that implements the
AutoCloseableDocument
interface and can be used within a try-with-resources block for immediate handle
disposal. |
void |
compileLotusScript()
Compiles all LotusScript attached to this note
|
void |
computeWithForm(boolean continueOnError,
Document.ComputeWithFormCallback callback)
For each form field associated with this document, this function will:
Validate fields with Default Value formulas, Validate fields with Translation formulas, Validate fields with Validation formulas, Calculate values for Computed fields. If no error callback routine is supplied and the flag continueOnError is not
set, if one of the validation formulas fails, an error is returned.If the continueOnError flag is set, the error is not returned.If a callback routine is supplied and one of the validation formulas fails, the error callback routine is called. If the continueOnError flag is set, error processing for the field is skipped;if a callback function is supplied, the callback function will be ignored. |
boolean |
convertRichTextItem(String itemName,
Document targetNote,
String targetItemName,
IRichTextConversion... conversions)
Applies one of multiple conversions to a rich text item, writing the new version in the target document and item.
|
boolean |
convertRichTextItem(String itemName,
IRichTextConversion... conversions)
Applies one or multiple conversions to a rich text item, updating the original item in-place.
|
Document |
copyAndEncrypt(UserId id,
Collection<Document.EncryptionMode> encryptionMode)
This function copies and encrypts (seals) the encryption enabled fields in a document
(including the document's file objects), using a
UserId .It can encrypt a document in several ways -- by using the Domino public key of the caller, by using specified secret encryption keys stored in the caller's ID, or by using the Domino public keys of specified users, if the document does not have any mime parts. The method decides which type of encryption to do based upon the setting of the flag passed to it in its encryptionMode argument.If the Document.EncryptionMode.ENCRYPT_WITH_USER_PUBLIC_KEY flag is set, it uses the
caller's public ID to encrypt the document.In this case, only the user who encodes the document can decrypt it. This feature allows an individual to protect information from anyone else. If, instead, the Document.EncryptionMode.ENCRYPT_WITH_USER_PUBLIC_KEY flag is not set,
then the function expects the document to contain a field named "SecretEncryptionKeys"
a field named "PublicEncryptionKeys", or both.Each field is either a ItemDataType.TYPE_TEXT or ItemDataType.TYPE_TEXT_LIST field."SecretEncryptionKeys" contains the name(s) of the secret encryption keys in the calling user's ID to be used to encrypt the document. This feature is intended to allow a group to encrypt some of the documents in a single database in a way that only they can decrypt them -- they must share the secret encryption keys among themselves first for this to work. "PublicEncryptionKeys" contains the name(s) of users, in canonical format. The document will be encrypted with each user's Domino public key. The user can then decrypt the document with the private key in the user's ID. This feature provides a way to encrypt documents, such as mail documents, for another user. The document must contain at least one encryption enabled item (an item with the Item.ItemFlag.ENCRYPTED flag set)
in order to be encrypted.If the note has mime parts and flag Document.EncryptionMode.ENCRYPT_SMIME_IF_MIME_PRESENT
is set, then it is SMIME encrypted.If the document is to be signed as well as encrypted, you must sign the document before using this method. |
Document |
copyToDatabase(Database otherDb)
Copies the document to another database.
|
RichTextWriter |
createRichTextItem(String itemName)
Creates a writer for a new rich-text item on the document.
|
void |
decrypt(UserId id)
Decrypts the contents of the document.
|
void |
delete()
Deletes the document from the database.
|
void |
delete(boolean noStub)
Deletes the document from the database.
|
void |
forEachAttachment(BiConsumer<Attachment,Loop> consumer)
Iterates over each attachment in the document, until all attachments have been
processed or the consumer calls
Loop.stop() . |
void |
forEachItem(BiConsumer<Item,Loop> consumer)
Iterates over all document items
|
void |
forEachItem(String itemName,
BiConsumer<Item,Loop> consumer)
Iterates over all document items with the specified name
|
DominoDateTime |
getAddedToFile()
Retrives the time when the document was added to this NSF, regardless of when it was
originally created.
|
String |
getAsText(String itemName,
char separator)
Retrieves the item value as text.
|
Optional<Attachment> |
getAttachment(String fileName)
Retrieves a document attachment by name.
|
DominoDateTime |
getCreated()
Retrieves the creation date of this document.
|
Set<DocumentClass> |
getDocumentClass()
Returns the document classes that apply to this document
|
Optional<Item> |
getFirstItem(String itemName)
Retrieves the first item of the given name on the document.
|
List<?> |
getItemValue(String itemName)
Retrieves the value of the first item of the given name on the document as its
closest Java-type match.
|
DominoDateTime |
getLastAccessed()
Retrieves the last-accessed time of the document, if that information is being tracked
in the database.
|
DominoDateTime |
getLastModified()
Retrieves the time when the document was last modified.
|
List<String> |
getLockHolders() |
DominoDateTime |
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.
|
int |
getNoteID()
Retrieves the note ID of this document.
|
DominoOriginatorId |
getOID()
Retrieves the originator ID of the document, which contains the UNID and additional
modification information.
|
Database |
getParentDatabase()
Retrieves the parent database of this document.
|
default String |
getParentDocumentUNID()
Retrieves the UNID of the parent document of this document, if present.
|
String |
getPrimaryKeyCategory()
Returns the category part of the note primary key
|
String |
getPrimaryKeyObjectId()
Returns the object id part of the note primary key
|
String |
getProfileName()
For profile documents, this method returns the name of the profile
|
String |
getProfileUserName()
For profile documents, this method returns the username of the profile
owner
|
List<Item> |
getReadersFields()
The function returns all the readers items of the document
|
int |
getResponseCount()
Retrieves the count of response documents to this document.
|
IDTable |
getResponses()
Retrieves the response documents to this document.
|
default RichTextRecordList |
getRichTextItem(String itemName)
Retrieves the named rich-text item as an unmodifiable
List of composite data records. |
RichTextRecordList |
getRichTextItem(String itemName,
RecordType.Area variant)
Retrieves the named rich text item as an unmodifiable
List of composite data records, to be treated
as a specific variant of rich text. |
int |
getSequenceNumber()
Retrieves the modification sequence number of the document.
|
String |
getSigner()
Returns the signer of a note
|
String |
getUNID()
Retrieves the universal ID of the document, which is unique across replicas.
|
boolean |
hasComposite()
Returns TRUE if the given note contains any
ItemDataType.TYPE_COMPOSITE items. |
boolean |
hasItem(String itemName)
The method checks whether an item exists
|
boolean |
hasMIME()
Returns TRUE if the given note contains either
ItemDataType.TYPE_RFC822_TEXT
items or ItemDataType.TYPE_MIME_PART items. |
boolean |
hasMIMEPart()
The function returns TRUE if the given note contains any
ItemDataType.TYPE_MIME_PART items. |
boolean |
hasReadersField()
The function returns TRUE if the given document contains any items with reader access flag
|
boolean |
isEditable()
Returns true if the current user has sufficient access rights to modify the document.
|
boolean |
isEncrypted()
Checks whether this note is sealed
|
boolean |
isHiddenFromViews()
Returns true if this is a ghost document.
|
boolean |
isNew()
Returns true if the document has not been saved yet
|
default boolean |
isResponse()
Checks if this document is a response
|
boolean |
isSaveMessageOnSend()
Indicates whether a document is saved to a database when mailed.
|
boolean |
isSigned()
Checks whether this note is signed
|
boolean |
isSoftDeleted()
Returns true if the document has been deleted in a database that has soft deletions
enabled.
|
boolean |
isTruncated()
Indicates that the note has been abstracted (truncated).
This is the case if the database containing the note has replication settings set to "Truncate large documents and remove attachments". |
boolean |
isUnread()
Checks if the document is unread for the user returned by
DominoClient.getEffectiveUserName() .Uses Database.isDocumentUnread(String, int) internally. |
boolean |
isUnread(String userName)
Checks if the document is unread for the specified user.
Uses Database.isDocumentUnread(String, int) internally. |
boolean |
lock(List<String> lockHolders,
Document.LockMode mode)
This function adds an "$Writers" field to a note which contains a list of "writers"
who will be able to update the note.
Any user will be able to open the note, but only the members contained in the "$Writers" field are allowed to update the note. This function will only succeed if the database option "Allow document locking" is set. Please refer to the Domino documentation for a full description of document locking. |
boolean |
lock(String lockHolder,
Document.LockMode mode)
This function adds an "$Writers" field to a note which contains a list of "writers"
who will be able to update the note.
Any user will be able to open the note, but only the members contained in the "$Writers" field are allowed to update the note. This function will only succeed if the database option "Allow document locking" is set. Please refer to the Domino documentation for a full description of document locking. |
void |
makeResponse(Document doc)
Makes the document a response to another document.
|
void |
makeResponse(String unid)
Makes the document a response to another document identified by UNID.
|
void |
removeAttachment(String uniqueFileNameInDoc)
Removes an attachment by the unique internal file name.
|
void |
removeItem(String itemName)
Removes the first item of the provided name from the document.
|
Document |
replaceItemValue(String itemName,
Object value)
Sets the value of an item value to the document, replacing any other items of the same name.
|
Document |
replaceItemValue(String itemName,
Object value,
boolean allowDataTypeChanges)
Sets the value of an item value to the document, replacing any other items of the same name.
|
Document |
replaceItemValue(String itemName,
Set<Item.ItemFlag> flags,
Object value)
Sets the value of an item value to the document, replacing any other items of the same name.
|
Document |
replaceItemValue(String itemName,
Set<Item.ItemFlag> flags,
Object value,
boolean allowDataTypeChanges)
Sets the value of an item value to the document, replacing any other items of the same name.
|
void |
save()
Writes in-memory changes to a document to the database.
|
void |
save(boolean force)
Writes in-memory changes to a document to the database.
|
void |
send()
Mails the note
Convenience function that calls send(boolean, Collection) . |
void |
send(boolean attachform)
Mails the document
Convenience function that calls send(boolean, Collection) . |
void |
send(boolean attachform,
Collection<String> recipients)
Mails the document
Two kinds of items can affect the mailing of the document when you use send: If the document contains additional recipient items, such as CopyTo or BlindCopyTo, the documents mailed to those recipients. If the document contains items to control the routing of mail, such as DeliveryPriority, DeliveryReport, or ReturnReceipt, they are used when sending the document. The isSaveMessageOnSend() property controls whether the sent document is saved
in the database. |
void |
send(boolean attachform,
String recipient)
Mails the document
Convenience function that calls send(boolean, Collection) . |
void |
send(Collection<String> recipients)
Mails the document
Convenience function that calls send(boolean, Collection) . |
void |
send(String recipient)
Mails the document
Convenience function that calls send(boolean, Collection) . |
Document |
setDocumentClass(DocumentClass docClass)
Sets the class of this document.
|
void |
setPrimaryKey(String category,
String objectId)
Writes a primary key information to the note.
|
void |
setSaveMessageOnSend(boolean b)
Indicates whether a document is saved to a database when mailed.
|
void |
setUNID(String newUNID)
Sets the universal ID for the document.
|
void |
setUnread(String userName,
boolean unread)
Method to change the unread state of the document
|
void |
sign()
Signs the document using the active Notes ID.
|
void |
sign(UserId id,
boolean signNotesIfMimePresent)
Function to sign all items in the document.
It allows you to pass a flag to determine how MIME parts will be signed. If the note has MIME parts then it will be SMIME signed. |
int |
size()
Returns the total size of the document
|
void |
undelete()
Undeletes a soft deleted document
|
void |
unlock(Document.LockMode mode)
This function removes the lock on a note.
Only the members contained in the "writers" list are allowed to remove a lock, with the exception of person(s) designated as capable of removing locks. Please refer to the Domino documentation for a full description of document locking.# |
void |
unsign()
Unsigns the note.
|
Document.SignatureData |
verifySignature()
This function verifies a signature on a note or section(s) within a note.
It returns an error if a signature did not verify. |
get, getAsList, getItemNames
getAdapter
AutoCloseableDocument autoClosable()
AutoCloseableDocument
interface and can be used within a try-with-resources block for immediate handle
disposal. The purpose of this method is to get more control about the platform memory
usage instead of leaving this work up to the JNX garbage collector.Database getParentDatabase()
Database
int getNoteID()
String getUNID()
DominoOriginatorId getOID()
DominoOriginatorId
object, never null
int getSequenceNumber()
DominoDateTime getCreated()
Note: by default, this value is derived from the document's UNID, but can be overridden
by the presence of a $Created
date/time item.
DominoDateTime getLastModified()
DominoDateTime getModifiedInThisFile()
DominoDateTime getLastAccessed()
DominoDateTime getAddedToFile()
Optional<Item> getFirstItem(String itemName)
List<?> getItemValue(String itemName)
itemName
- the name of the item to retrieveList
of item values, never null
Optional<Attachment> getAttachment(String fileName)
fileName
- the name of the attachment stored in the documentOptional
describing the attachment, or an empty one if no such
attachment existsvoid forEachAttachment(BiConsumer<Attachment,Loop> consumer)
Loop.stop()
.consumer
- a BiConsumer
to process the attachmentsvoid forEachItem(BiConsumer<Item,Loop> consumer)
consumer
- consumer to receive the Item
void forEachItem(String itemName, BiConsumer<Item,Loop> consumer)
itemName
- item nameconsumer
- consumer to receive the Item
Document appendItemValue(String itemName, Object value)
itemName
- the name of the item to appendvalue
- the value of the item to storeDocument appendItemValue(String itemName, Set<Item.ItemFlag> flags, Object value)
itemName
- the name of the item to appendflags
- a collection of Item.ItemFlag
to apply to the itemvalue
- the value of the item to storeDocument appendItemValue(String itemName, Set<Item.ItemFlag> flags, Object value, boolean allowDataTypeChanges)
itemName
- the name of the item to appendflags
- a collection of Item.ItemFlag
to apply to the itemvalue
- the value of the item to storeallowDataTypeChanges
- to append the item even if its type doesn't match the existing valuesDocument replaceItemValue(String itemName, Object value)
itemName
- the name of the item to storevalue
- the value of the item to storeDocument replaceItemValue(String itemName, Object value, boolean allowDataTypeChanges)
itemName
- the name of the item to storevalue
- the value of the item to storeallowDataTypeChanges
- to append the item even if its type doesn't match the existing valuesDocument replaceItemValue(String itemName, Set<Item.ItemFlag> flags, Object value)
itemName
- the name of the item to storevalue
- the value of the item to storeflags
- a collection of Item.ItemFlag
to apply to the itemDocument replaceItemValue(String itemName, Set<Item.ItemFlag> flags, Object value, boolean allowDataTypeChanges)
itemName
- the name of the item to storevalue
- the value of the item to storeflags
- a collection of Item.ItemFlag
to apply to the itemallowDataTypeChanges
- to append the item even if its type doesn't match the existing valuesRichTextWriter createRichTextItem(String itemName)
itemName
- the name of the rich-text item to createRichTextWriter
to provide the item contentsdefault RichTextRecordList getRichTextItem(String itemName)
List
of composite data records.
This data may span multiple items of the same name, and all are included in the returned list.
Note: though presented as a List
, some operations (in particular size()
)
are particularly expensive. The most efficient way to traverse the result is via Collection.stream()
or List.listIterator()
.
itemName
- the name of the rich text item to retrieveList
of RichTextRecord
objectsRichTextRecordList getRichTextItem(String itemName, RecordType.Area variant)
List
of composite data records, to be treated
as a specific variant of rich text.
This data may span multiple items of the same name, and all are included in the returned list.
Note: though presented as a List
, some operations (in particular size()
)
are particularly expensive. The most efficient way to traverse the result is via Collection.stream()
or List.listIterator()
.
itemName
- the name of the rich text item to retrievevariant
- the data area variant represented by the itemList
of RichTextRecord
objectsAttachment attachFile(String filePathOnDisk, String uniqueFileNameInDoc, Attachment.Compression compression)
Unlike attachFile(String, TemporalAccessor, TemporalAccessor, IAttachmentProducer)
, this
method reads data from the named file on disk directly.
filePathOnDisk
- a path to the file on diskuniqueFileNameInDoc
- a name for the attachment in the documentcompression
- the compression scheme to use when storing the attachmentAttachment attachFile(String uniqueFileNameInDoc, TemporalAccessor fileCreated, TemporalAccessor fileModified, Document.IAttachmentProducer producer)
Unlike #attachFile(String, String, Compression)
, this method allows for specifying file metadata
and source binary data arbitrarily. It does not use compression. No temp
file is created on disk.
uniqueFileNameInDoc
- a name for the attachment in the documentfileCreated
- the creation date for the stored filefileModified
- the modification date for the stored fileproducer
- a Document.IAttachmentProducer
implementation that provides the attachment datavoid removeItem(String itemName)
itemName
- the case-insensitive name of the item to removevoid removeAttachment(String uniqueFileNameInDoc)
Note: this does not remove references to the document from rich-text items.
uniqueFileNameInDoc
- the unique internal name of the attachment in the documentRemoveAttachmentIconConversion
void makeResponse(Document doc)
doc
- the parent documentvoid makeResponse(String unid)
unid
- the UNID of the parent documentdefault boolean isResponse()
void sign()
void sign(UserId id, boolean signNotesIfMimePresent)
signNotesIfMimePresent
parameter is set
to allow Notes signature on MIME parts, then it will be Notes signed.
If the document to be signed is encrypted, this function will attempt to
decrypt the document in order to generate a valid signature.copyAndEncrypt(UserId, Collection)
.id
- id to sign, null for active IDsignNotesIfMimePresent
- If the note has MIME parts and this flag is true it will be SMIME signed, if not set it will be Notes signed.void unsign()
Document.SignatureData verifySignature()
Document copyAndEncrypt(UserId id, Collection<Document.EncryptionMode> encryptionMode)
UserId
.encryptionMode
argument.Document.EncryptionMode.ENCRYPT_WITH_USER_PUBLIC_KEY
flag is set, it uses the
caller's public ID to encrypt the document.Document.EncryptionMode.ENCRYPT_WITH_USER_PUBLIC_KEY
flag is not set,
then the function expects the document to contain a field named "SecretEncryptionKeys"
a field named "PublicEncryptionKeys", or both.ItemDataType.TYPE_TEXT
or ItemDataType.TYPE_TEXT_LIST
field.Item.ItemFlag.ENCRYPTED
flag set)
in order to be encrypted.Document.EncryptionMode.ENCRYPT_SMIME_IF_MIME_PRESENT
is set, then it is SMIME encrypted.id
- user id to be used for encryption, use null for current idencryptionMode
- encryption modevoid setUNID(String newUNID)
newUNID
- a 32-character hexadecimal stringDocument copyToDatabase(Database otherDb)
otherDb
- the target database to store the copied documentvoid decrypt(UserId id)
id
- the UserId
to use for decryption, or null
to use the active runtime Notes IDvoid delete()
This is equivalent to calling delete(false)
.
void delete(boolean noStub)
noStub
- whether to purge the document without leaving a deletion stubvoid save()
This is equivalent to calling save(false)
.
void save(boolean force)
force
- whether to save the document even if another user has modified or deleted itList<String> getLockHolders()
List
of user names that currently have a lock on the documentSet<DocumentClass> getDocumentClass()
Set
of DocumentClass
valuesDocument setDocumentClass(DocumentClass docClass)
docClass
- the DocumentClass
value to setboolean lock(String lockHolder, Document.LockMode mode)
lockHolder
- new lock holdermode
- lock modeboolean lock(List<String> lockHolders, Document.LockMode mode)
lockHolders
- new lock holdersmode
- lock modevoid unlock(Document.LockMode mode)
mode
- lock modeboolean isEditable()
boolean hasReadersField()
List<Item> getReadersFields()
int getResponseCount()
Note: due to the lower-level API, this is likely to be more efficient than
retrieving the response document collection
and checking its count.
IDTable getResponses()
In order for this method to return the response table, this document must have been opened
with the Database.OpenDocumentMode.LOAD_RESPONSES
flag.
IDTable
of responsesboolean hasMIME()
ItemDataType.TYPE_RFC822_TEXT
items or ItemDataType.TYPE_MIME_PART
items.boolean hasMIMEPart()
ItemDataType.TYPE_MIME_PART
items.boolean hasComposite()
ItemDataType.TYPE_COMPOSITE
items.boolean hasItem(String itemName)
hasItem
in interface TypedAccess
itemName
- item nameString getSigner()
boolean isSigned()
boolean isSoftDeleted()
undelete()
to undelete the document.boolean isNew()
boolean isEncrypted()
String getProfileName()
String getProfileUserName()
String getPrimaryKeyCategory()
String getPrimaryKeyObjectId()
void setPrimaryKey(String category, String objectId)
category
and objectKey
are combined
to a string that is expected to be unique within the database.category
- category part of primary keyobjectId
- object id part of primary keyboolean isTruncated()
boolean isHiddenFromViews()
Database.createDocument(Set)
with Database.CreateFlags.HIDE_FROM_VIEWS
void send()
send(boolean, Collection)
.void send(String recipient)
send(boolean, Collection)
.recipient
- The recipient of the document, may include people, groups, or mail-in databases.void send(Collection<String> recipients)
send(boolean, Collection)
.recipients
- The recipients of the document, may include people, groups, or mail-in databases.void send(boolean attachform)
send(boolean, Collection)
.attachform
- If true, the form is stored and sent along with the document. If false, it isn't. Do not attach a form that uses computed subforms.void send(boolean attachform, String recipient)
send(boolean, Collection)
.attachform
- If true, the form is stored and sent along with the document. If false, it isn't. Do not attach a form that uses computed subforms.recipient
- The recipient of the document, may include people, groups, or mail-in databases.boolean isSaveMessageOnSend()
void setSaveMessageOnSend(boolean b)
b
- true to save on sendvoid send(boolean attachform, Collection<String> recipients)
isSaveMessageOnSend()
property controls whether the sent document is saved
in the database. If isSaveMessageOnSend()
is true and you attach the form to the document,
the form is saved with the document.attachform
- If true, the form is stored and sent along with the document. If false, it isn't. Do not attach a form that uses computed subforms.recipients
- The recipients of the document, may include people, groups, or mail-in databases.void computeWithForm(boolean continueOnError, Document.ComputeWithFormCallback callback)
continueOnError
is not
set, if one of the validation formulas fails, an error is returned.continueOnError
flag is set, the error is not returned.continueOnError
flag is set, error processing for the field is skipped;continueOnError
- Ignore error processing for fieldscallback
- error callback routineboolean isUnread(String userName)
Database.isDocumentUnread(String, int)
internally.userName
- username in abbreviated or canonical format, if null, we use DominoClient.getEffectiveUserName()
boolean isUnread()
DominoClient.getEffectiveUserName()
.Database.isDocumentUnread(String, int)
internally.void setUnread(String userName, boolean unread)
userName
- username in abbreviated or canonical format, if null, we use DominoClient.getEffectiveUserName()
unread
- true to mark unread, false to mark readboolean convertRichTextItem(String itemName, IRichTextConversion... conversions)
itemName
- rich text item nameconversions
- conversions, processed from left to rightIRichTextConversion.isMatch(List)
as falseboolean convertRichTextItem(String itemName, Document targetNote, String targetItemName, IRichTextConversion... conversions)
itemName
- rich text item nametargetNote
- note to copy to conversion result totargetItemName
- item name in target note where we should save the conversion resultconversions
- conversions, processed from left to rightIRichTextConversion.isMatch(List)
as falsevoid undelete()
void compileLotusScript()
LotusScriptCompilationException
- if there is a compilation problemString getAsText(String itemName, char separator)
This differs from calling TypedAccess.get(java.lang.String, java.lang.Class<T>, T)
with `String.class` as the type in that it uses
an internal Domino routine to convert values to text.
itemName
- the name of the item to retrieveseparator
- a separator character to use when the item is multi-valuedefault String getParentDocumentUNID()
This is equivalent to calling get("$REF", String.class, "")
.
int size()
void appendToTextList(String itemName, String value, boolean allowDuplicates)
Item.ItemFlag.SUMMARY
.itemName
- item namevalue
- string containing the text you wish to add as the NEXT entry in the existing text list in the documentallowDuplicates
- A boolean containing a flag indicating whether or not to allow duplicate entries in the text list. true
- If you wish to allow them and false
- If you do not.Copyright © 2019–2021 HCL. All rights reserved.


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Preparation time
overnight
Cooking time
over 2 hours
Serves
Serves 8 as a starter
Lamb breast is slow cooked until it's very tender, then coated in breadcrumbs and deep fried.
For the spring lamb belly fritters, preheat the oven to 150C/300F/Gas 2.
Heat a little oil in a large ovenproof frying pan and brown the lamb breasts. Add the garlic, thyme and the four chopped shallots and lightly colour. Pour in the chicken stock and place in the oven for three hours or until the meat is very tender.
Line a terrine mould with cling film. Remove the meat from the pan. Pull the meat from the bones. Press the meat into the tin. Cover with cling film and place a weight on top to compress the meat. Leave overnight in the fridge.
The next day, make a pickling liquor by boiling two teaspoons of water, the white wine vinegar, sugar and fennel seeds in a saucepan. Pour onto the sliced shallots rings and leave to cool.
For the spinach and anchovy mayonnaise, in a food processor, blend the egg yolks, anchovies, mustard and the pickle liquor from the shallots. Then, with the motor running, slowly add the rapeseed oil in a thin steady stream until it emulsifies into a mayonnaise.
Wash the spinach and pat dry using kitchen paper.
For the spring lamb belly fritters, heat a deep-fat fryer to 180C/350F. (CAUTION: hot oil can be dangerous. Do not leave unattended.)
Chop the cooked lamb breast into 2.5x8cm/1x3in rectangles. Coat the lamb in flour and pat off any excess. Dip into the beaten egg then dredge in the breadcrumbs, taking care to completely coat the lamb.
Deep fry the fritters until crisp and golden-brown. Season with salt and pepper when they come out of the fryer.
To serve, dress the spinach leaves with the pickled shallot rings and a little anchovy mayonnaise. Serve with the fritters and the remaining mayonnaise in a dipping bowl on the side.


Election 2019: Government adviser in altercation as health secretary visits Leeds General Hospital
by Catherine NeilanA government special adviser has been involved in an altercation at Leeds General Hospital, during a visit by health secretary Matt Hancock.
Hancock had been dispatched to the hospital after pictures emerged of a four-year old boy with suspected pneumonia sleeping on the hospital floor with coats for cover.
However, Labour activists then turned up at the hospital in order to protest the Conservatives who are blamed for cutting resources to the NHS as a result of austerity.
Multiple journalists have reported the incident, which appears to have occurred after the two groups met.
Hancock’s intervention comes after Prime Minister Boris Johnson was criticised for his tone-deaf response to the picture of Jack Williment-Barr.
Johnson has repeatedly dodged the matter, instead offering a general apologie to anyone who has had a bad experience in A&E. He has also pledged to invest more in the NHS, and framed it as an issue relating to the Brexit delay.
Johnson even took a journalist’s phone away during an interview, rather than look at the picture.
Main image: Getty


Genesis.
Designer
Links
Home
Portfolio
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Project 1
Indesign Booklet Project.
Project 2
Movie Poster Project.
Project 3
Illustrator Image Trace Project.


GAIL Q2 net profit drops 35% YoY to Rs 1,167.6 cr; stock falls over 4%
Profit before tax (PBT) for the quarter under review came in at Rs 1,706.47 crore against Rs 2,700.84 in the corresponding quarter of the previous fiscal.
by SI ReporterGAIL, the state-owned natural gas processing and distribution company, on Friday posted a consolidated net profit of Rs 1,167.58 crore, down 34.7 per cent year-on-year (YoY) for September quarter of FY19. The company had posted net profit of Rs 1,788.98 crore in the year-ago period.
Profit before tax (PBT) for the quarter under review came in at Rs 1,706.47 crore, down 36.81 per cent YoY against Rs 2,700.84 in the corresponding quarter of the previous fiscal.
Revenue from operations slipped 5.6 per cent YoY to Rs 18,249.90 crore against Rs 19,352.98 crore. Total income stood at Rs 18,485.19 crore against Rs 19,500.64 crore in Q2FY19.
Earnings per share (EPS) dropped to Rs 2.59 against Rs 3.96 in the previous year quarter.
Reacting to the news, shares of GAIL fell as much as 4.65 per cent to Rs 126 apiece on the BSE. At 02:31 am, the stock was trading 4 per cent lower at Rs 126.85 against 0.34 per cent decline in the benchmark S&P BSE Sensex.


I am a software engineer.
I know the following programming languages:
<EMAIL_ADDRESS>https://github.com/ssirinte


--- layout: not_homepage quote: All sorts of things can happen when you’re open to new ideas and playing around with things. author: Stephanie Kwolek --- {% include quote.html quote=page.quote author=page.author %}
This project aims to present a study regarding the several fundamental aspects of complex particulate suspensions. We have been exploring the physics of active suspensions, composed of microorganisms, proteins etc. For this purpose, first we studied the behavior of a nematode suspension. Rheological experiments led us to identify anomalous behaviors and to propose a rheological model. In addition, we also work with Immersed Boundary simulations to identify the curvature characteristics that lead to the best propulsion efficiency.
We are currently working with suspensions of methanotrophic bacteria and algae that consume carbon dioxide. These bacteria are responsible for the production of biopolymers, while algae produce biomass. Although the methodology for extracting these biomaterials is known, these processes still require a lot of time and cost. For this reason, we believe that rheological studies can help improve the performance of the process.
The main objective of the STMI project (Software Technologies for Modeling and Inversion) is to develop a set of software technologies for numerical simulation and solution of inversion problems. These technologies will be able to reduce the time required for the development of simulation and inversion codes from years to days. High-level programming languages are designed and implemented that will enable geophysicists to use their specific knowledge of the area to quickly develop and evaluate new algorithms for obtaining images of geologically challenging regions.
I work specifically with machine learning models to perform FWI (Full Waveform Inversion) a high-resolution seismic imaging technique that is based on using the entire content of seismic traces for extracting physical parameters of the medium sampled by seismic waves.
The construction of public policies to face the global pandemic of COVID-19 brings significant challenges, especially for countries like Brazil, which live with great social and economic inequality. The adoption of quarantine and social distancing practices, which have led to promising results in European and Asian countries, has been implemented in Brazil. However, the result is not very promising due to the country's territorial extension and its severe effects on the individual income of the population and the consequent increase in social problems faced by the country.
This project can provide subsidies to public managers for the elaboration of effective policies to control the pandemic and to reduce its effects, in view of the specificities of the population. The models will be scalable, being able to accommodate macro and micro regions, as well as the interrelation between them. For that, we integrate the following models: Control Models(SIR based), Regional Contingency Optimization Models (topology based), Propagation Models (heat conduction based) and Machine Learning (clusterization based).


Code python ET Case Study¶
VegET Code¶
Code Readability¶
Simple and Separate Param Files¶
run params¶
Customer: Tony
Author: Steffi
# ================ ACTIVE config SECTION ==============================
# === Minimalist Beta Version Params ====
start_year: 2015
end_year: 2015
start_day: 001
end_day: 003
accumulate_mode: [daily] #[yearly, monthly, daily]
geojson_file: a.json
shapefile: C:\WaterSmart\Projects\CloudVegET\shapefiles\out_box.shp
# sample tiff could/should be derived from the geojson clip of the ndvi - or maybe not needed - tbd
sample_tiff: C:\WaterSmart\Projects\CloudVegET\local_testing\ndvi\med001.250_m_NDVI.tif
model params¶
Customer: Gabriel Senay
Author: Steffi
# === Parameters set by Gabriel Senay ====
# defining low and high temperature values (in C°) to determine what is snow, sleet, rain
rf_low_thresh_temp: 0
rf_high_thresh_temp: 6
rf_value: 0.167
melt_factor: 0.06
# drainage coefficient = 1 - runoff coefficient
dc_coeff: 0.65
# 1 - dc_coeff
rf_coeff: 0.35
# constant values to adjust NDVI in the ET calculation
k_factor: 1.25
ndvi_factor: 0.2
# water adjustment
water_factor: 0.7
# reference ET bias correction
bias_corr: 0.85
alfa_factor: 1.25
path params¶
TBD
https://github.com/tonybutzer/cloud-veg-et/blob/master/00-tony/00-notebooks/sample_config/path_param.yml
Clean Code and Code Smells¶
Speculative Generality¶
Can/should we move this flavor of code to the attic - Gabe,Tony ?
Code Refactoring¶
Best Practices¶
Github Repo¶
Author¶
<EMAIL_ADDRESS>This is to introduce you to Dr. Darin Schulte (CCed). Darin has the VegET Python GEE code on Github. He will share the site next week.
For now, please exchange notes and collaborate as you see fit to advance the implementation of VegET on the Pangeo framework. This is an exciting step towards integrating land cover and hydrologic modeling.
Collaboration Obstacles¶
CHS is only available inside the USGS networks of VPN Access
Mitigations¶
Credential every collaborator
Replicate
a smaller set of data and aux data into GA buckets for refining/rewriting code to use xarraysThen deploy this in CHS with the known CHS caveats


Modal body text goes here.
Total # of streets to quiz on: Enter a value of 0 for all of the streets in list
Street Point Value:
Changes to quiz settings WILL reset the quiz!


Baguio City is a highly urbanized city located on the Northern Luzon island. It is called " City of Pines" and " The summer capital of the Philippines", It is popular in summer due to cooler weather.
Folks say the city got its name from"Bag-iw" a mossy flowering plant that grew in these parts the American colonizers pronounced as "Bág-ee-yow"
Festival
Also known as Flower Festival. The origin of the term "panagbenga" came from the kankanaey meaning "season of blooming." This festival is held during the month of February and as a tribute to the city's flowers and as a way to rise up from the devastation of the 1990 earthquake. The festival also includes street dancing, presented by dancers clad in flower-inspired costumes, that is inspired by the Bendian, an Ibaloi dance of celebration that came from the Cordillera region.
History
The area now known as Baguio City was first called Kafagway and occupied by the Kankaney and Ibaloi tribes of the Cordilleras. Spanish Occupation. During their occupation of the Philippines, the Spanish colonizers conducted a series of expeditions, led by Juan Salcedo in 1572 and Don Q. M. Quirante in 1694 to the cool mountains of Benguet.
Facts
Baguio, on the Philippines’ Luzon island, is a mountain town of universities and resorts. Called the “City of Pines,” it’s particularly popular in summer due to unusually cooler weather. At its center is Burnham Park, with gardens and a lake. Nearby, Baguio Cathedral, completed in 1936, has a rose-hued exterior. The main thoroughfare is Session Road, lined with shops, restaurants and entertainment.
Photographs by Jerome.


Training Strategies¶
Variance Loss¶
As defined in the normalizing flows section, our model consists of
a PDF over the latent space
a trainable bijection from the latent space to the target space.
Together they allow us to sample points \(x\) from the model distribution \(q(x)\) which is also known for every sampled point.
Our goal is to maximize the integration speed of our integral estimator, i.e. to find the \(q\) that minimizes
Where \(I\) is our desired integral. Note that, because \(q\) is a normalized PDF, the second term in the integral is independent of it and we can limit ourselves to minimizing the first term only:
As an integral this is not a tractable loss function defined on a sample of points, we must build an estimator for it, and the multiple possibilities yield different ways of training the model
Forward Training¶
The most straightforward way to formulate an estimator for the loss \({\cal L}\) is to take it at face value as an expectation value over \(q\):
We can therefore sample a collection of points \(\left\{x_i\right\}_{i=1\dots N}\) from our model, which will be distributed according to \(q\) and build the estimator
of which we can compute the gradient with respects to the parameters of \(q\) and use a standard optimization technique to attempt reaching a minimum. Note that there are actually two sources of dependence on \(q\): the first is the explicit PDF in the denominator, and the second is in each actual point \(x_i\), which is obtained by sampling in latent space and mapping them with our model.
A more explicit way of formulating this training strategy is therefore that we sample points \(\left\{y_i\right\}_{i=1\dots N}\) in latent space from the latent space PDF \(q_y\) and map them to a set \(\left\{x_i\right\}_{i=1\dots N}\) of points in latent space using our transformation \(Q\) and evaluate
While this method is the most straightforward, it carries several downsides
It is susceptible to the initialization of the model. If \(q\) is poorly sampled, it could avoid exploring relevant regions.
It requires resampling new points and re-evaluate the function at each gradient step.
On the other hand, once a decent model has been learned, this approach ensures that most point being sampled are in the relevant regions where the function is enhanced, thus ensuring good end-time performance.
Backward Training¶
As a solution to the drawbacks of the forward training method, we formulate an alternative approach in which we reinterpret the loss integral. Let us consider a different PDF \(p\) over the target space, then
which we now interpret as a different expectation value:
For which an estimator is constructed by sampling a collection of points \(\left\{x_i\right\}_{i=1\dots N}\) from \(p\) and evaluating
Now the sample of points is independent from \(q\) and we can therefore
ensure both that our distribution \(p\) has a good coverage over the whole space
run multiple gradient descent steps using the same batch of points
Note that another practical advantage of this approach is that it yields a simpler computational graph for the loss function, leading to a reduced memory usage at training time.
From which distribution should we \(p\) sample? In practice, we use two standard choices:
a uniform distribution, which ensures that all corners of the integration domain are covered
a frozen copy of the normalizing flow.
The second option can be thought of a similar to the two version of the state-action value model used in deep-Q learning. When sampling, we freeze the weights of the model and think of it a just any other PDF on target space \(p(x)\) and draw a collection of points from it. We then keep training for a while on this sample, meaning that the sample becomes progressively less representative of the distribution defined by the model. Nevertheless, as long as this distribution does not veer too far off the evolving model, it is likely to provide a good estimate of the ideal loss integral.
Adaptive Backward Training¶
The description of the two possible PDFs used for sampling point datasets for backward training should make it clear that there is a “best of both worlds” options: use uniform sampling at the beginning of training, where the model is random and possibly poorly conditioned to evaluate the integal, and later switch to sampling from the frozen model after it has sufficiently improved.
The strategy that we use to time the switch between the two sampling PDFs is to compare the current loss to the loss that we would obtain replacing our model by a uniform model:
If the actual loss is smaller than this quantity, then our model does a better job than the flat distribution at estimating the integral and we therefore switch sampling mode.
Kullback-Leibler Distance Loss¶
A commonly used loss for normalizing flows is the Kullback-Leiber divergence (\(D_\text{KL}\)), which is an information-theoretic distance measure between probability distribution. For two PDFs \(p\) and \(q\), the \(D_\text{KL}\) is defined as
which has a minimum when \(p=q\) as can be easily shown.
In our case, we do not actually have the target PDF, but we the target function \(f\), which is un-normalized. The target PDF is actually \(p(x) = f(x)/I\), where \(I\) is our desired integral. We do, however not need to know the value of \(I\) to optimize our model for this loss:
While the true minimum of the \(D_\text{KL}\) loss is the same as the variance loss, they do yield different practical results. It should be clear that the variance should be the standard choice for the typical user: it optimizes directly the metric that controls the convergence speed of the integrand estimator. If one compares the variance loss and the \(D_\text{KL}\) loss, it appears that the variance loss gives relatively more weight to points where $f$ is very large - which is sensible due to how these affect the integral estimates. This means that, for practical applications, it is more likely for models trained using the \(D_\text{KL}\) loss to correctly approximate the desired PDF in regions where the function is smaller. This is less-than-optimal for direct integral estimation, but can have useful applications, especially if one wants to re-use models trained on the full domain to compute integrals on limited sub-regions, as can be the case in High-Energy Physics when one considers loose- and tight-cut observables.
The same discussion as for the variance loss can be had for converting the integral loss to an estimator defined on an estimator defined on a sample of point: we can define forward training by sampling points from the model itself or backward training by sampling in target space using an arbitrary PDF. Adaptive backward training can of course also be realized, all the more easier since the switching condition corresponds to testing the sign of the loss: if the model were a flat distribution, it would have unit PDF and therefore 0 loss.
This page is still under construction


‘I don’t know how to dribble’ – Barcelona’s Griezmann speaks at Puma event
by Jaikuran RandhawaAccording to the MailOnline, Barcelona superstar Antoine Griezmann has surprisingly admitted that he doesn’t know how to dribble whilst speaking at a Puma event in Spain.
The World Cup winner professed that his skills lie in other areas of the game, he may have even surprised some of his teammates with his comments.
BarcaTimes understand that Griezmann was at the event alongside; current teammates Luis Suarez and Samuel Umtiti, French legend Thierry Henry and former teammate Jan Oblak.
Here’s what the Frenchman had to say at the Puma event in Spain:
‘I don’t know how to dribble, I like to take one or two touches, at pace,’
‘I like that the ball comes out cleanly and to shoot at goal.’
As per Barcelona’s official website, the La Liga champions paid the forward’s mammoth €120m release clause to sign him from Atletico Madrid this summer.
Griezmann has so far struggled to prove his marquee price-tag, the superstar only has four goals in 16 appearances across all competitions.
Ernesto Valverde will have to conjure up a plan to get the best out of the Frenchman very soon considering the club’s massive investment into his signature.
Griezmann will have to be at his best if Barcelona are serious about re-establishing themselves as the best team in the world. The Blaugrana haven’t won a Champions League title since the 2014/15 season.