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We had a team who has the best experience in their respective fields like Website designing, Developing and Marketing as well. More than ten years, We have been working to create exciting digital projects. Our goal is to enable every large, medium and small scale businesses to have it's digital presence.
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The documentation for this class was generated from the following file:
- code/googleappengine-read-only/python/google/appengine/api/search/search_service_pb.py


{% include head.html %}
{% include nav-home.html %} {% include header.html %} {% include what-is-ICT.html %} {% include learning-program.html %} {% include international-learning.html %} {% include exchange-scholarship.html %} {% include career-opportunity.html %} {% include enroll.html %} {% include contact.html %} {% include scripts.html %} {% include footer.html %}


Photos of woman kissing strangers in front of Eiffel Tower, Colosseum go viral
by Gerren Keith GaynorA woman smooching strangers around the globe in a set of photos has gone viral online, giving a whole new meaning to the phrase "kiss and tell."
 To the untrained eye, the photograph appeared to show a typical couple posing in front of one of the world’s most recognizable symbols of love.
Then Kuqi revealed she didn’t know the man at all.
“I hope this guy I met at the Eiffel Tower and asked for a pic of us kissing so I could pretend I had a romantic time in Paris is doing good,” she captioned the photo, which she shared on Twitter.

“I hope this one from Rome is good too,” she wrote.
 Hope he’s good too.”
 Women reacting to her daring posts hailed Kuqi as a “hero” and a “legend.”
“The goal is not to travel the world with your man but to have a man in every part of the world you travel to,” one Twitter user wrote.
Some women were inspired to share their own experiences of kissing men they met during their travels.
“I feel you. I hope this French man I had a fling with in Bali is doing good,” tweeted woman, who shared a photo of her kissing her mystery paramour.
Kuqi, who resides in Virginia, told Fox News she had “no idea” her tweet would go so viral.
The recent college grad, who is originally from Kosovo, said she always had a “bold personality” and simply wanted to show off her confidence and flair for spontaneity.
“Ever since I was young I imagined myself having a picture with a kiss there,” she said about Paris. "I had planned this kiss weeks before I went to Paris and was studying abroad at the time. I had a friend come with me to Paris and she thought it was crazy and didn’t think I would actually do it.”
CLICK HERE TO SIGN UP FOR OUR LIFESTYLE NEWSLETTER
Kuqi admits she was nervous when it came time to stage her Eiffel Tower kiss, but she eventually worked up the nerve to do it. She approached her eventual kissing partner, asked him if he had a girlfriend – he replied no – and the rest is history.
  We took our picture, said our goodbyes and I never saw him again,” she said.
After the success of her kissing photo in France, Kuqi decided it would be fun to take her stunt across the border to Italy, where she asked an Argentinian tourist for a kiss in front of the Colosseum. The fireman kiss, she said, was another spontaneous moment.
“It wasn’t Paris or Rome but it was such an amazing American image,” she said.

“With confidence, you can really do whatever you want. All you need to do is ask,” she said. “Women shouldn’t deny themselves what they want because they are afraid of judgment.”


{% extends "base.html" %} {% block content %}
See you there, {{ entrant.name }}!
Add to CalendarTwilio, 645 Harrison Street, Third Floor | San Francisco, CA 94107 at 6:15pm on Monday
If you can't make it, please change your RSVP status by clicking here.


Expression object for dense matrix-sparse vector multiplications.The DMatSVecMultExpr class represents the compile time expression for multiplications between row-major dense matrices and sparse vectors.
More...
#include <DMatSVecMultExpr.h>
Inherits blaze::MatVecMultExpr< DenseVector< DMatSVecMultExpr< MT, VT >, false > >, and blaze::Computation.
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| enum | : bool { simdEnabled = false
} |
| | Compilation switch for the expression template evaluation strategy.
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| enum | : bool { smpAssignable
} |
| | Compilation switch for the expression template assignment strategy.
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using | This = DMatSVecMultExpr< MT, VT > |
| | Type of this DMatSVecMultExpr instance.
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using | ResultType = MultTrait_< MRT, VRT > |
| | Result type for expression template evaluations.
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using | TransposeType = TransposeType_< ResultType > |
| | Transpose type for expression template evaluations.
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using | ElementType = ElementType_< ResultType > |
| | Resulting element type.
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using | ReturnType = const ElementType |
| | Return type for expression template evaluations.
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using | CompositeType = IfTrue_< useAssign, const ResultType, const DMatSVecMultExpr &> |
| | Data type for composite expression templates.
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using | LeftOperand = If_< IsExpression< MT >, const MT, const MT &> |
| | Composite type of the left-hand side dense matrix expression.
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using | RightOperand = If_< IsExpression< VT >, const VT, const VT &> |
| | Composite type of the right-hand side dense vector expression.
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using | LT = IfTrue_< evaluateMatrix, const MRT, MCT > |
| | Type for the assignment of the left-hand side dense matrix operand.
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using | RT = IfTrue_< evaluateVector, const VRT, VCT > |
| | Type for the assignment of the right-hand side dense matrix operand.
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using | VectorType = VT |
| | Type of the vector.
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| enum | : bool { evaluateMatrix = RequiresEvaluation<MT>::value
} |
| | Compilation switch for the composite type of the left-hand side dense matrix expression.
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| enum | : bool { evaluateVector = IsComputation<VT>::value || RequiresEvaluation<VT>::value
} |
| | Compilation switch for the composite type of the right-hand side sparse vector expression.
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| enum | : bool { useAssign = evaluateMatrix || evaluateVector
} |
| | Compilation switch for the evaluation strategy of the multiplication expression. More...
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using | MRT = ResultType_< MT > |
| | Result type of the left-hand side dense matrix expression.
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using | VRT = ResultType_< VT > |
| | Result type of the right-hand side sparse vector expression.
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using | MCT = CompositeType_< MT > |
| | Composite type of the left-hand side dense matrix expression.
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using | VCT = CompositeType_< VT > |
| | Composite type of the right-hand side sparse vector expression.
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LeftOperand | mat_ |
| | Left-hand side dense matrix of the multiplication expression.
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RightOperand | vec_ |
| | Right-hand side sparse vector of the multiplication expression.
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template<typename MT, typename VT>
class blaze::DMatSVecMultExpr< MT, VT >
Expression object for dense matrix-sparse vector multiplications.
The DMatSVecMultExpr class represents the compile time expression for multiplications between row-major dense matrices and sparse vectors.
◆ anonymous enum
template<typename MT, typename VT>
Compilation switch for the evaluation strategy of the multiplication expression.
The useAssign compile time constant expression represents a compilation switch for the evaluation strategy of the multiplication expression. In case either the matrix or the vector operand requires an intermediate evaluation or the sparse vector expression is a compound expression, useAssign will be set to true and the multiplication expression will be evaluated via the assign function family. Otherwise useAssign will be set to false and the expression will be evaluated via the subscript operator.
◆ DMatSVecMultExpr()
template<typename MT, typename VT>
Constructor for the DMatSVecMultExpr class.
- Parameters
-
| mat | The left-hand side dense matrix operand of the multiplication expression. |
| vec | The right-hand side sparse vector operand of the multiplication expression. |
◆ at()
template<typename MT, typename VT>
Checked access to the vector elements.
- Parameters
-
| index | Access index. The index has to be in the range . |
- Returns
- The resulting value.
- Exceptions
-
| std::out_of_range | Invalid vector access index. |
◆ canAlias()
template<typename MT, typename VT>
template<typename T >
Returns whether the expression can alias with the given address alias.
- Parameters
-
| alias | The alias to be checked. |
- Returns
- true in case the given alias is contained in this expression, false if not.
◆ canSMPAssign()
template<typename MT, typename VT>
Returns whether the expression can be used in SMP assignments.
- Returns
- true in case the expression can be used in SMP assignments, false if not.
◆ isAliased()
template<typename MT, typename VT>
template<typename T >
Returns whether the expression is aliased with the given address alias.
- Parameters
-
| alias | The alias to be checked. |
- Returns
- true in case the given alias is contained in this expression, false if not.
◆ isAligned()
template<typename MT, typename VT>
Returns whether the operands of the expression are properly aligned in memory.
- Returns
- true in case the operands are aligned, false if not.
◆ leftOperand()
template<typename MT, typename VT>
Returns the left-hand side dense matrix operand.
- Returns
- The left-hand side dense matrix operand.
◆ operator[]()
template<typename MT, typename VT>
Subscript operator for the direct access to the vector elements.
- Parameters
-
| index | Access index. The index has to be in the range . |
- Returns
- The resulting value.
◆ operator~() [1/2]
template<typename VT, bool TF>
Conversion operator for non-constant vectors.
- Returns
- Reference of the actual type of the vector.
◆ operator~() [2/2]
template<typename VT, bool TF>
Conversion operator for constant vectors.
- Returns
- Const reference of the actual type of the vector.
◆ rightOperand()
template<typename MT, typename VT>
Returns the right-hand side sparse vector operand.
- Returns
- The right-hand side sparse vector operand.
◆ size()
template<typename MT, typename VT>
Returns the current size/dimension of the vector.
- Returns
- The size of the vector.
The documentation for this class was generated from the following file:


Here is clever content. that was not very clever, but i need more contents, i need at least one more sentence to make this a paragraph.
My Favorite movies
My Favorite fruit flavors
Pictures Provided by Lorem Picsum
Here how we use the strong Tag. Here is how we use the em Tag. Here is how we use the strike Tag.
| First Name | Last Name | |
|---|---|---|
| John | Doe |<EMAIL_ADDRESS>|
| Jane | Doe |<EMAIL_ADDRESS>|
| Enrique | Doe |<EMAIL_ADDRESS>|


For Students
Aarohan is a one of a kind nationwide school level event , organised by the BITS Pilani , Pilani Campus. The event will consist of a regional level round , conducted online in multiple schools across the country.
Students will get a chance to test their knowledge , logical aptitude and coding skills against other students from their region and compete for top honours.


KNOCK OFF A film review by David N. Butterworth Copyright 1998 David N. Butterworth
no stars (out of ****)
Jean-Claude Van Damme movies tend to be, if nothing else, a lot of mindless fun. His latest film, "Knock Off," skips on the fun part leaving a wildly incoherent exercise that is, well, simply mindless.
You can't really blame Van Damme. No one, I suspect, has ever expected "the muscles from Brussels" to contribute a heartfelt performance brimming with introspection and delicate shades of gray. And even his sidekick co-star, "Saturday Night Live" alumnus Rob "makin' copies" Schneider, does what's expected of him, although neither appear to want the role of straight man.
No, the problem with the film is its direction. The first five minutes of the film are incomprehensible ... and it goes downhill from there! Five hundred characters, it seems, are introduced within the first fifteen minutes. Van Damme doesn't land his first kick until after thirty--too late for most action fans--and by the time the hour mark has rolled around, your jaw will be on your cup holder and you'll be wondering why you elected to spend seven dollars on this mess.
Director Hark Tsui, whose previous film was the Van Damme/Dennis Rodman teamed "Double Team," films at such a frenetic pace that it's hard to know what's going on, who's on whose side, and what the point of all this is.
Faster than you can yell "fruit stand!" we're watching a crazed rickshaw race with Schneider being bounced along by an energetic Van Damme. When Schneider starts whacking Van Damme in the behind with a four-foot eel while enthusing "move your beautiful big ass," the movie takes an unprecedented turn to the bizarre.
Every now and again "Knock Off" will deliver some truly ingenious directorial flourishes--inventive camera shots and angles, wild rides down gun silencers and from the tops of buildings to the sprawling street below, revealing cut-ins within the frame--but they all happen too quickly, and within such a furious frame of reference, that they're wasted. If only Hark could have slowed things down just for a minute.
If you're interested in the plot, you're better off reading a capsule review than trying to extract any meaning from the on-screen shenanigans: "Jean-Claude Van Damme plays a shady bluejeans manufacturer who uncovers a Russian Mafia plot to terrorize the world with "nanobombs" hidden in Cabbage Patch knock-offs. Maybe. With Paul Sorvino." A more accurate summary would be "Jean-Claude Van Damme bums around Hong Kong failing to avoid large, blatant Coca-Cola product placements."
The fact that the film is staged during Hong Kong's last days under British rule gets some lip service but doesn't figure in at all.
Perhaps "Knock Off"'s most intriguing credit is that Ron and Russell Mael composed the frenzied music score. Some of you might remember the Mael brothers as the '80s synth pop duo Sparks; their contributions here are as confused as the accompanying action--"Kimono My House" indeed!
Like the cheap jeans and "Pumma" sneakers manufactured in Hong Kong, Van Damme's latest is a real phony.
-- David N. Butterworth<EMAIL_ADDRESS>The review above was posted to the
rec.arts.movies.reviews newsgroup (de.rec.film.kritiken for German reviews).
The Internet Movie Database accepts no responsibility for the contents of the
review and has no editorial control. Unless stated otherwise, the copyright
belongs to the author.
Please direct comments/criticisms of the review to relevant newsgroups.
Broken URLs inthe reviews are the responsibility of the author.
The formatting of the review is likely to differ from the original due
to ASCII to HTML conversion.
Related links: index of all rec.arts.movies.reviews reviews


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Last updated 29 January 2020
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Search
Library section:
Interactions:
Search and Navigation
Style effects
Focus on text input.
–
Clear button appears after entering text in the field, clears the text on click.
–
Implemented features
Interaction styles are set up for the groups of shapes, keep these groups together to maintain behavior.
When resizing, resize the search input element first, then position the "icon" with the spacer element on the left and the btn close group on the right.
search input
icon
btn close
Usage
Small search
Library section:
Interactions:
Search and Navigation
Style effects
Implemented features
See Search above.
See Search above.
Usage


{% extends 'base.html' %} {% block content %} Fantasy Drafts Just Got Easier Cheat Sheets Formulated Entirely From Your Individual League Settings {% csrf_token %} {% if user.is_authenticated %} {% else %} {% endif %} Teams Teams 4 6 8 10 12 14 16 PPR PPR None Half Point Full Point Passing Touchdown Passing TDs 4 6 Flex Flex None Flex RB/WR Flex RB/WR/TE Flex WR/TE Offensive Player QBs QBs 0 1 2 3 RBs RBs 0 1 2 3 WRs WRs 0 1 2 3 TEs TEs 0 1 2 3 Create FantaSheet Standard Leagues {% for default_sheet in default_sheets|slice:':3' %} {% include 'partials/_listing.html' with listing=default_sheet %} {% endfor %} Save Your League Settings Save all your different league settings Save as many different leagues as you have Create FantaSheets Create immediate cheat sheets based entirely on your saved league settings Win Your Leagues Dominate all your leagues by using cheat sheets fitted to your exact settings {% endblock %} {% block javascript %} {% endblock javascript %}
Cheat Sheets Formulated Entirely From Your Individual League Settings
Save all your different league settings Save as many different leagues as you have
Create immediate cheat sheets based entirely on your saved league settings
Dominate all your leagues by using cheat sheets fitted to your exact settings


They say you know you're experiencing a mid life crisis when you suddenly find yourself driving down the road in a red sports car (even though you hate the colour red and you’re not fond of sports cars at all). And after last month’s book club reading of Eat, Pray, Love, not surprisingly you realised your biggest life regret was not finding yourself through travelling and that’s how you end up backpacking through some caves in Asia.
Luckily thats not me, I’m only experiencing my quarter life crisis so it’s not as expensive. 10 years ago I should’ve taken the path to study code but for whatever reason I didn’t. Thus when that clock struck midnight and the realisation of having been alive for a quarter-of-a-century and regret hit me, I knew what I had to do.
So follow me along these 18 weeks of trial and endless error messages as I transition from novice to master coder.


Choose your market
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|
JavaTM 2 Platform Standard Ed. 5.0 |
|||||||||
| PREV CLASS NEXT CLASS | FRAMES NO FRAMES | |||||||||
| SUMMARY: NESTED | FIELD | CONSTR | METHOD | DETAIL: FIELD | CONSTR | METHOD |
public interface Relation
This interface has to be implemented by any MBean class expected to represent a relation managed using the Relation Service.
Simple relations, i.e. having only roles, no properties or methods, can be created directly by the Relation Service (represented as RelationSupport objects, internally handled by the Relation Service).
If the user wants to represent more complex relations, involving properties and/or methods, he has to provide his own class implementing the Relation interface. This can be achieved either by inheriting from RelationSupport class, or by implementing the interface (fully or delegation to a RelationSupport object member).
Specifying such user relation class is to introduce properties and/or methods. Those have to be exposed for remote management. So this means that any user relation class must be a MBean class.
| Method Summary | |
|---|---|
RoleResult |
getAllRoles()
Returns all roles present in the relation. |
Map |
getReferencedMBeans()
Retrieves MBeans referenced in the various roles of the relation. |
String |
getRelationId()
Returns relation identifier (used to uniquely identify the relation inside the Relation Service). |
ObjectName |
getRelationServiceName()
Returns ObjectName of the Relation Service handling the relation. |
String |
getRelationTypeName()
Returns name of associated relation type. |
List |
getRole(String theRoleName)
Retrieves role value for given role name. |
Integer |
getRoleCardinality(String theRoleName)
Returns the number of MBeans currently referenced in the given role. |
RoleResult |
getRoles(String[] theRoleNameArray)
Retrieves values of roles with given names. |
void |
handleMBeanUnregistration(ObjectName theObjName,
String theRoleName)
Callback used by the Relation Service when a MBean referenced in a role is unregistered. |
RoleList |
retrieveAllRoles()
Returns all roles in the relation without checking read mode. |
void |
setRole(Role theRole)
Sets the given role. |
RoleResult |
setRoles(RoleList theRoleList)
Sets the given roles. |
| Method Detail |
|---|
List getRole(String theRoleName) throws IllegalArgumentException, RoleNotFoundException, RelationServiceNotRegisteredException
Checks if the role exists and is readable according to the relation type.
theRoleName
- name of role
IllegalArgumentException
- if null role name
RoleNotFoundException
- if:
- there is no role with given name
- the role is not readable.
RelationServiceNotRegisteredException
- if the Relation
Service is not registered in the MBean ServersetRole(javax.management.relation.Role)
RoleResult getRoles(String[] theRoleNameArray) throws IllegalArgumentException, RelationServiceNotRegisteredException
Checks for each role if it exists and is readable according to the relation type.
theRoleNameArray
- array of names of roles to be retrieved
IllegalArgumentException
- if null role name
RelationServiceNotRegisteredException
- if the Relation
Service is not registered in the MBean ServersetRoles(javax.management.relation.RoleList)
Integer getRoleCardinality(String theRoleName) throws IllegalArgumentException, RoleNotFoundException
theRoleName
- name of role
IllegalArgumentException
- if null role name
RoleNotFoundException
- if there is no role with given nameRoleResult getAllRoles() throws RelationServiceNotRegisteredException
RelationServiceNotRegisteredException
- if the Relation
Service is not registered in the MBean ServerRoleList retrieveAllRoles()
void setRole(Role theRole) throws IllegalArgumentException, RoleNotFoundException, RelationTypeNotFoundException, InvalidRoleValueException, RelationServiceNotRegisteredException, RelationNotFoundException
Will check the role according to its corresponding role definition provided in relation's relation type
Will send a notification (RelationNotification with type RELATION_BASIC_UPDATE or RELATION_MBEAN_UPDATE, depending if the relation is a MBean or not).
theRole
- role to be set (name and new value)
IllegalArgumentException
- if null role
RoleNotFoundException
- if the role is not writable (no
test on the write access mode performed when initialising the role)
InvalidRoleValueException
- if value provided for
role is not valid, i.e.:
- the number of referenced MBeans in given value is less than expected minimum degree
- the number of referenced MBeans in provided value exceeds expected maximum degree
- one referenced MBean in the value is not an Object of the MBean class expected for that role
- a MBean provided for that role does not exist.
RelationServiceNotRegisteredException
- if the Relation
Service is not registered in the MBean Server
RelationTypeNotFoundException
- if the relation type has not
been declared in the Relation Service.
RelationNotFoundException
- if the relation has not been
added in the Relation Service.getRole(java.lang.String)
RoleResult setRoles(RoleList theRoleList) throws IllegalArgumentException, RelationServiceNotRegisteredException, RelationTypeNotFoundException, RelationNotFoundException
Will check the role according to its corresponding role definition provided in relation's relation type
Will send one notification (RelationNotification with type RELATION_BASIC_UPDATE or RELATION_MBEAN_UPDATE, depending if the relation is a MBean or not) per updated role.
theRoleList
- list of roles to be set
IllegalArgumentException
- if null role name
RelationServiceNotRegisteredException
- if the Relation
Service is not registered in the MBean Server
RelationTypeNotFoundException
- if the relation type has not
been declared in the Relation Service.
RelationNotFoundException
- if the relation MBean has not been
added in the Relation Service.getRoles(java.lang.String[])
void handleMBeanUnregistration(ObjectName theObjName, String theRoleName) throws IllegalArgumentException, RoleNotFoundException, InvalidRoleValueException, RelationServiceNotRegisteredException, RelationTypeNotFoundException, RelationNotFoundException
The Relation Service will call this method to let the relation take action to reflect the impact of such unregistration.
BEWARE. the user is not expected to call this method.
Current implementation is to set the role with its current value (list of ObjectNames of referenced MBeans) without the unregistered one.
theObjName
- ObjectName of unregistered MBeantheRoleName
- name of role where the MBean is referenced
IllegalArgumentException
- if null parameter
RoleNotFoundException
- if role does not exist in the
relation or is not writable
InvalidRoleValueException
- if role value does not conform to
the associated role info (this will never happen when called from the
Relation Service)
RelationServiceNotRegisteredException
- if the Relation
Service is not registered in the MBean Server
RelationTypeNotFoundException
- if the relation type has not
been declared in the Relation Service.
RelationNotFoundException
- if this method is called for a
relation MBean not added in the Relation Service.Map getReferencedMBeans()
ObjectName -> ArrayList of String (role names)
String getRelationTypeName()
ObjectName getRelationServiceName()
String getRelationId()
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class: center, middle # IEMS 5780
Building and Deploying Scalable
Machine Learning Services ### Lecture 4 - Text Classification (2) #### Albert Au Yeung
27th September, 2019 --- class: middle, center # Advanced Topics in Document Representation --- # Limitations * Bag-of-word (BoW) and vector space models are very commonly used in text classification * However, they has certain limitations: 1. Ignoring the **order** of the words - Consider `a tea bag` vs. `a bag of tea` 2. Ignoring the **context** of the words - "... take the earliest **train** tomorrow morning ..." - "... will **train** a new model tomorrow morning ..." 3. Ignoring **compound nouns** - `dress shirt` vs. `dress` 4. Do not handle words with **similar** meanings - `cellphones` vs. `smartphones` 5. Cannot handle **new words** --- # Advanced Topics * To address these problems, we will consider: - Consider **n-grams** - Consider **character n-grams** - Use **dimensionality reduction** techniques - **Word embeddings** --- class: center, middle # N-grams --- # N-grams * In our previous examples, we break a document into tokens, which are **individual words** * We call these tokens of individual words **unigrams** ```python sentence = "London is the capital and most populous city of England and the United Kingdom" sentence.lower().split(" ") # [ # 'london', 'is', 'the', 'capital', 'and', # 'most', 'populous', 'city', 'of', 'England', # 'and', 'the', 'united', 'kingdom' # ] ``` * In this example, it would be desirable to capture the existence of the phrase `United Kingdom` (both `united` and `kingdom` can appear separately and have different meanings) * Such kind of features may be **useful** when performing text classification --- # N-grams * Instead of treating every single token as a feature, we can also treat **every two consecutive pair of tokens** as a feature (i.e. **bi-grams**) ```python sentence = "London is the capital and most populous city of England and the United Kingdom" tokens = sentnce.lower().split(" ") bigrams = [] for i in range(len(tokens)-1): bigrams.append("{} {}".format(tokens[i], tokens[i+1])) # bigrams = [ # 'london is', # 'is the', # ... # 'the united', # 'united kingdom.' # ] ``` --- class: split # N-grams: Number of Features .column-left[ * Somtimes, we may also consider **tri-grams** or more * But be **cautious** when adding n-gram features * Using n-grams **significantly increase** the number of features - **More parameters** to learn in your model - May not give significant improvement but requires longer time to train
* (Right: number of features agains n-grams used for the SMS Spam dataset) ] .column-right[
] --- class: split # N-gram: Feature Sparsity .column-left[ * Another potential problem of using large n-grams is **data sparsity** * Each **n-gram** (other than unigrams) may only be found in very few documents * You are more likely to have **unseen n-grams** in the test data * You also will use up a lot of **memory** and **storage**
* (Right: Number of non-zero elements in all feature vectors for the SMS Spam dataset) ] .column-right[
] --- # N-gram * Given the advantages and disadvantages of n-gram features, **when** shall we use them? * Answering this usually requires **doing experiments**, and depends on the problem(s) at hand * Something to consider: - Do we see any **significant** or **desirable** increase in classification performance? - How much more **computing resources** are required (e.g. RAM) - **Time** required to train the model - Is the amount of data **large enough** to make the n-gram features meaningful? --- # Using N-gram in Scikit-learn * You can easily enable n-gram features when using the `CountVectorizer` or the `TfidfVectorizer` * Set the `ngram_range` parameter using a tuple (e.g. `(1, 3)` means that we want to use unigrams, bi-grams and tri-grams as features) ```python from sklearn.feature_extraction.text import CountVectorizer from sklearn.naive_bayes import MultinomialNB from sklearn.pipeline import Pipeline clf = Pipeline([ ('vec', CountVectorizer(ngram_range=(1, 3))), ('nb', MultinomialNB()) ]) clf.fit(X_train, y_train) ``` --- class: split # Character N-grams .column-left[ * In addition to n-grams of words, we sometimes may even consider **character n-grams** * Using character n-grams allow us to capture **sub-words** information * E.g. `compu` inside `computation` and `computer` may indicate that the input is about the same topic to a certain extent * A more general approach compared to **stemming** ] .column-right[ ```python # some character n-grams # for "an apple" { 'an', 'an ', 'ap', 'app', ... 'ppl', 'pple' } ``` ] --- # Character N-grams in Scikit-learn * You can also easily generate **character-level n-grams** in scikit-learn by setting the `analyzer` parameter * There are two different modes: `char` and `char_wb` - `char` treats the whole document as a string - `char_wb` generates n-grams that does NOT cross word boundaries ```python docs = [ "an apple" ] vectorizer = CountVectorizer(analyzer='char_wb', ngram_range=(2, 4)) vectorizer.fit(docs) vectorizer.vocabulary_ # {' a': 0, ' an': 1, ' an ': 2, ' ap': 3, ' app': 4, ... } # Note that n-grams on the edges of the words are padded with spaces ``` --- class: center, middle # Dimensionality Reduction --- # Dimensionality of Feature Vectors * **Feature vectors** in text classification are usually very long (**high dimensional**) - Commonly used words in the order of thousands - New words, names and abbreviations - Will be longer if we use **n-grams** * **Problems** of high dimensionality - Model is very complex - Models with many features require **a lot of data** to train - Require more **memory** during training/prediction - Require more **storage space** to persist the model * Hence, it is desirable if we can **represent the data with fewer dimensions** --- # Problems with Bag-of-Words Model * In additional to the problem of high dimensionality, the bag-of-words model also suffers from other problems * **Synonyms**: - Words with same or similar meanings occupy different dimensions - They are totally **orthogonal** in the vector space * Consider: $$ car = (0, 0, 0, 0, 0, 0, 1, 0, 0) \\\ vehicle = (0, 1, 0, 0, 0, 0, 0, 0, 0) $$ * Image two documents, one with the word `car`, another with the word `vehicle`, we can never tell that they are somehow related with the above representation. --- # Dimensionality Reduction * There are two major ways of finding dense vectors with fewer dimensions to represent words or documents * **Method 1: Co-occurrence** - Words that appears together tend to be similar - Converting a one-hot vector into a dense vector by finding **common topics** among words - Example: Latent Semantic Analysis * **Method 2: Context** - Words that have similar contexts are similar - Converting a one-hot vector into a dense vector by optimizing values in vector based on **similar contexts** - Example: Word embeddings --- # Distributional Representation * Counting **co-occurrence** between words allow us to understand how similar they are * Example: - "networking" usually appears with **protocols** - "football" usually appears with **goals** * Words having the same **probability distributions** are similar * We can then represent words and documents in terms of **topics** instead of high dimensional vectors
--- class: split # Distributed Representation .column-left[ * Words having **similar context** are considered similar * Context refers to the words that **appear before and after** the target word * Example: - **"deep learning using GPU"**: "deep", "learning", "using" forms the context of "GPU" - **"Beijing is a city in China"** vs. **"Shanghai is a city in China"**: "Beijing" and "Shanghai" have similar contexts * We can optimize vectors of words such that words that have similar contexts have vectors **close** to each other ] .column-right[
] --- # Cosine Similarity * We can calculate **similarity** between two vectors using **cosine similarity** * It measures how **close** the two vectors' **directions** are $$ sim(v_1, v_2) = \cos(\theta) = \frac{v_1 \cdot v_2}{||v_1|| \times ||v_2||} $$
--- # Cosine Similarity in Python * Example: computing similarity between documents ```python from numpy import array, dot from numpy.linalg import norm words = ["chinese", "medicine", "doctor", "food", "restaurants"] d1 = array([0.8, 0.9, 0.0, 0.1, 0.0]) # about chinese medicine d2 = array([0.0, 1.2, 1.5, 0.2, 0.0]) # about doctor & medicine d3 = array([1.3, 0.1, 0.0, 2.0, 1.5]) # about chinese food dot(d1, d2) / (norm(d1) * norm(d2)) # 0.47136989433874532 dot(d1, d3) / (norm(d1) * norm(d3)) # 0.39038367556371473 dot(d2, d3) / (norm(d2) * norm(d3)) # 0.09549164248532343 ``` --- class: middle, center # Latent Semantic Analysis (LSA) --- # Latent Semantic Analysis * One way to obtain a dense vector representation of words and documents is **latent semantic analysis** * Assume that we have a set of training data, in which we have **5 documents**, and our **vocabulary size is 10** * Using the bag-of-word model, we can represent this training data set as a **term-document matrix**
--- # Latent Semantic Analysis * LSA uses a mathematical tool called **singular value decomposition** to factorize the term-document matrix into **three** matrices * In `\(\Sigma\)`, the values on the diagonal are called *singular values**, which indicates how important the topics are in the data
--- # Latent Semantic Analysis * LSI tries to group **documents with similar words**, as well as **words that usually appear together** * Check the animation on [https://en.wikipedia.org/wiki/Latent_semantic_analysis](https://en.wikipedia.org/wiki/Latent_semantic_analysis) * These groups can be considered as some **topics** in the data * Intuitively, the number of **topics** should be fewer than the number of **words** * We can then use **topics** to represent words and documents --- # Latent Semantic Analysis * After the decomposition, the size of `\(\Sigma\)` actually represents **the number of topics** that can be found in the data (it is always smaller or equal to the number of words) * However, some topics may only be noise (words that appear less frequently and are not able to be grouped) * To reduce the dimension of the data, we can choose only to keep the **top `\(k\)`** singular values, and to represent words and documents using the **`\(k\)` dimensions** $$ \text{Document in reduced dimension} = U_k \Sigma_k V^T_k $$ --- # Example * Let's consider running LSA on a simple data set ```python # We have four documents as below docs = [ "chinese medicine doctor", # about chinese medicie "chinese companies stock price", # about stock price of chinese companies "medicine doctor hospital", # about medicine "stock market option price" # about stock market ] ``` --- # Performing SVD in Python * Singular value decomposition can be done using the function `scipy.linalg.svd` in the SciPy package ([documentation](https://docs.scipy.org/doc/scipy/reference/generated/scipy.linalg.svd.html)) * It returns the matrices `\(U\)` and `\(V\)`, as well as the singular values ```python from sklearn.feature_extraction.text import CounterVectorizer from scipy.linalg import svd vectorizer = CountVectorizer() A = vectorizer.fit_transform(docs) A.vocabulary_ # {'chinese': 0, 'companies': 1, 'doctor': 2, 'hospital': 3, 'market': 4, # 'medicine': 5, 'option': 6, 'price': 7, 'stock': 8} U, ss, V = svd(A.todense().transpose(), full_matrices=True) ``` --- # Performing SVD in Python ```python U = array([[-0.41, 0.21, -0.54, -0.41, 0.35, -0.21, 0.35, 0.16, 0.16], [-0.27, -0.08, -0.43, 0.39, 0.05, 0.17, 0.05, -0.52, -0.52], [-0.22, 0.58, 0.15, -0.09, -0.32, -0.55, -0.32, -0.2 , -0.2 ], [-0.08, 0.3 , 0.26, 0.71, 0.35, -0.21, 0.35, 0.16, 0.16], [-0.24, -0.17, 0.45, -0.24, 0.7 , -0.02, -0.3 , -0.18, -0.18], [-0.22, 0.58, 0.15, -0.09, -0.03, 0.76, -0.03, 0.04, 0.04], [-0.24, -0.17, 0.45, -0.24, -0.3 , -0.02, 0.7 , -0.18, -0.18], [-0.52, -0.25, 0.03, 0.15, -0.2 , 0.02, -0.2 , 0.68, -0.32], [-0.52, -0.25, 0.03, 0.15, -0.2 , 0.02, -0.2 , -0.32, 0.68]]) ss = array([2.5 , 2.21, 1.46, 0.86]) V = array([[-0.34, -0.69, -0.21, -0.61], [ 0.62, -0.17, 0.66, -0.38], [-0.17, -0.62, 0.38, 0.66], [-0.69, 0.34, 0.61, -0.21]]) ``` --- # Latent Semantic Analysis * You can also perform latent semantic analysis easily using the `TruncatedSVD` class in scikit-learn ```python from sklearn.feature_extraction.text import CountVectorizer from sklearn.decomposition import TruncatedSVD vectorizer = CountVectorizer() X = vectorizer.fit_transform(docs) # X is a term-document matrix (transposed) svd = TruncatedSVD(n_components=2) # Apply SVD on the matrix, keep 2 dimensions svd.fit(X) ``` --- # Latent Semantic Analysis * Now the `svd` model can be used to transform the documents ```python for doc, (i, j) in enumerate(svd.transform(X)): print("Doc {}: ({:7.3f}, {:7.3f})".format(doc+1, i, j)) # Prints the following: # Doc 1: ( 0.846, 1.370) # Doc 2: ( 1.720, -0.368) # Doc 3: ( 0.519, 1.465) # Doc 4: ( 1.522, -0.845) # The documents for reference # docs = [ # "chinese medicine doctor", # about chinese medicie # "chinese companies stock price", # about stock price of chinese companies # "medicine doctor hospital", # about medicine # "stock market option price" # about stock market # ] ``` --- class: middle, center # Word Embeddings --- # Word Embeddings * Word vectors or embeddings are **dense vector** representation of words * A type of **distributed representation** of words * Word embeddings can be obtained by trainnig a neural network on a large corpus of text data * Training samples are genrated from the corpus usually using a **sliding window** to define contexts * Commonly used algorithms: - Word2Vec [https://code.google.com/archive/p/word2vec/](https://code.google.com/archive/p/word2vec/) - GloVe [https://nlp.stanford.edu/projects/glove/](https://nlp.stanford.edu/projects/glove/) - fastText [https://fasttext.cc/](https://fasttext.cc/) --- # Introduction to Word2Vec * **Basic idea**: A word's meaning is given by the words that frequently appear around it * For a word `\(w\)` in a document, its context is the set of words that appear near it * **"Near"** can be defined by a **sliding window of certain size** * Word2vec ([Mikolov et al. 2013](https://arxiv.org/abs/1310.4546)) is an algorithm for learning word vectors
--- # Word2Vec * Word2vec obtains word embeddings by creating a new way of **supervised training** out of **unlabelled** data * **Basic concept**: - Assume that we have a vocabulary of size `\(N\)` - For each word in this vocabulary, we initialize a random vector of dimension `\(D\)`
(Usually `\(D\)` is about 100 to 300) - For each context window, we compute the **probability** of **each context word** given the **target word** using the **similarity** between their vectors - We keep adjusting the values of the vectors to maximize these probabilities --- # Word2Vec * It turns out that **training** word vectors can be done using a neural network * To train a neural network in a supervised way, we need to have some labelled data * There are two ways to train a word vectors: 1. **Skip-gram (SG)** - For each context window, the **target word** (or **center word**) is the **input** - The **context words** on the left and right are the **output** 2. **Continuous Bag of Words (CBoW)** - For each context window, the **context words** are the **input** - The **target word** is the **output** --- # Skip-gram Mode * In the Skip-gram mode, the neural network is trained by using the **target word as input**, and the **context words as output** * Task: train the model to output most likely context words given a target word
--- # Continuous Bag-of-Words Mode * In the CBoW mode, the neural network is trained by using the **context words as input**, and the **target word as output** * Task: using the context words to **predict** which would be the missing target word
--- # Word Embeddings * The trained word vectors show some interesting characteristics
Ref: [Distributed Representations of Words and Phrases and their Compositionality](https://arxiv.org/abs/1310.4546) (Mikolov et. al 2013) --- # Word2Vec in Python * You can play with word vectors using the [`gensim` Python package](https://radimrehurek.com/gensim/) * Obtain **pre-trained word vectors** from [https://drive.google.com/file/d/0B7XkCwpI5KDYNlNUTTlSS21pQmM/edit?usp=sharing](https://drive.google.com/file/d/0B7XkCwpI5KDYNlNUTTlSS21pQmM/edit?usp=sharing) (Note: the file is 1.5GB compressed) ```python from gensim.models.KeyedVectors import load_word2vec_format model = load_word2vec_format('GoogleNews-vectors-negative300.bin', binary=True) # Get vector similarity model.similarity("big", "huge") # Get the vector of the word "great" (a 300-d vector) model["great"] # Find another word that is most similar to the given words model.most_similar(["apple", "orange"]) ``` --- class: middle, center # fastText --- # fastText * [fastText](https://fasttext.cc/) is a library for **text classification** and **representation learning** * An efficient implementation of a shallow neural network for text classification using word embeddings * Comes with command line programs for training, testing and generating predictions * **Python API** available * Functions - Supervised learning: support **multi-class** classification - Unsupervised learning: learning **word embeddings** from input texts --- # fastText * **Supervised learning**: fastText can be used to train a text classification model for **binary** or **multi-class** classification * The model is a shallow **neural network**, which first converts n-grams into **word embeddings**, takes the **average** of the vectors, and passes that to a **softmax** function to generate predictions * Ref: [Bag of Tricks for Efficient Text Classification](https://arxiv.org/abs/1607.01759)
* *"We can train fastText on more than one billion words in less than ten minutes using a standard multicore-CPU, and classify half a million sentences among 312K classes in less than a minute."* --- # fastText * The **softmax function** is a generalized **logistic function**, it converts a vector of real values into another vector whose entries have values ranges from **0 to 1**, and all the entries **sum to 1** $$ \sigma(\mathbf{z})_j = \frac{e^{z_j}}{\sum{e^{z_i}}} $$
--- # fastText * The **word embedding** layer can be **trained** using the training data, or can be initialized with **pre-trained word vectors** * Example of pre-trained vectors: [https://github.com/facebookresearch/fastText/blob/master/pretrained-vectors.md](https://github.com/facebookresearch/fastText/blob/master/pretrained-vectors.md) ```python 香港 0.45552 -0.15176 -0.1004 0.24804 -0.27378 -0.11551 -0.020922 ... 英國 0.46771 0.047616 -0.28287 0.52437 0.34216 -0.1907 0.28798 ... 足球隊 -0.15141 -0.26691 -0.22635 0.19575 0.32993 -0.45426 -0.24766 ... 足球 -0.37035 -0.27073 -0.033765 0.4996 0.62468 -0.35905 -0.087886 ... ``` ```python soccer -0.27709 -0.42679 0.070863 0.64454 0.17607 -0.27767 0.031067 ... football -0.52819 -0.39955 -0.014546 0.22658 0.13472 -0.48197 0.17063 ... country -0.025361 -0.26752 0.35494 0.081725 -0.022434 -0.030652 -0.15959 ... bicycle 0.063549 0.032542 -0.019717 0.1974 -0.11146 -0.3778 0.059583 ... ``` --- # fastText * To try a text classifier using fastText, you need to preprocess your data into a specific format: - Each line contains a single document - The label (class) of the document should have the prefix `__label__` * Note that all symbols and punctunations will be preserved (texts are tokenized using spaces or tabs) * For example: ```html __label__sports top 100 nba players for 2018-19 __label__finance stock market update: over 150 stocks hit 52-week lows on NSE __label__travel gap year holidays: 11 reasons to take a year off to travel in your 30s ... ``` --- # fastText in Python * Assuming that your training data is stored in a file named `train.txt` ```python from fastText import train_supervised # import the supervised training function model = train_supervised( input="train.txt", # training data file epoch=25, # epoch: number of times going through the data lr=1.0, # learning rate wordNgrams=2, # n-gram features verbose=2, # whether to print out more messages minCount=1 # minimum number of times a token should appear ) model.save_model("model.bin") # save model to a file named "model.bin" ``` * Refer to the [full documentaton](https://github.com/facebookresearch/fastText#full-documentation) for the list of parameters --- # fastText in Python * Predicting using fastText ```python from fastText import load_model # load a trained model named "model.bin" model = load_model("model.bin") # text data text = "Manchester United revenues hit record of £590m" # Ask for the top 2 predicted classes labels, scores = model.predict(text, k=2) # labels is a tuple of labels e.g. (('__label__sports', '__label__finance')) # scores is an array of scores e.g. array([0.9997472, 0.0000234]) ``` --- class: middle, center # Assignment 1 --- # Assignment 1 * [Text classification + Telegram Bot](https://iems5780.github.io/1819t1/assignments/assignment-1.html) * Deadline: 19th October, 2019 (Saturday) * Two tasks: 1. Train a **text classifier** for movie review classification 2. Deploy the text classifier as a **Telegram Bot** --- class: split # Assignment 1 .column-left[ * **Task 1** * Train classifier using 1) **naive Bayes**, 2) **logistic regression**, 3) **fastText** * Submit a **Jupyter notebook** with all the steps and results * **Task 2** * Make your model available to other people via **Telegram** * Write a script that keeps receiving message from users, and use the model to generate predictions ] .column-right[
] --- # References * Application of text classification:
[Globally Scalable Web Document Classification Using Word2Vec](https://www.slideshare.net/kouheinakaji/smart-news-documentclassification)
(From [SmartNews](https://www.smartnews.com/)) --- class: center, middle # End of Lecture 4


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java.lang.Object java.util.Observable com.tabuto.j2dgf.Sprite
public abstract class Sprite
Sprite is an Abstract Class that represents an object able draws himself and move on a playfield of fixed dimension.
It has got an ACTIVE flag and a ThisIsMe(Graphics) method called by drawMe(Graphics)
method when ACTIVE is true;
Every Sprite has a position defined by two variables (xc and yc) that identifies the sprite's center;
Every sprite also has speed and angle variable.
To instance a new Sprite you have to call the constructor method Sprite(Dimension,double, double)
to set the dimension plane where sprite live and his start position.
Every sprite can be part of Group
Group
,
Point
,
Vettore
,
Game2D
,
Serialized Form| Field Summary | |
|---|---|
protected boolean |
ACTIVE
Actual Sprite state: If true sprite draws himself, move and live; |
protected boolean |
collided
True if collided. |
protected int |
CollidedRadius
Max radius of Sprite improve CheckCollision performance; If the distance of two sprite is greater of this value, CheckCollision dosn't check other value. |
protected java.awt.Dimension |
d
The Dimension of the playfield where sprites live is. |
int |
DefaultSpeed
The default Speed of the sprite |
protected int |
EAST
Indicate the pixel above, under, on left and right of Sprite's center. |
int |
h
Sprite height |
private int |
id
The integer variable ID identifies every sprite |
protected java.lang.String |
Name
The name of this Sprite |
protected int |
NORTH
Indicate the pixel above, under, on left and right of Sprite's center. |
private static long |
serialVersionUID
|
protected int |
SOUTH
Indicate the pixel above, under, on left and right of Sprite's center. |
protected int |
speed
The speed of the sprite |
protected Vettore |
vector
Vector represent position, direction and speed of each sprite |
int |
w
Sprite width |
protected int |
WEST
Indicate the pixel above, under, on left and right of Sprite's center. |
| Constructor Summary | |
|---|---|
Sprite(java.awt.Dimension dim,
double posX,
double posY)
Creates new Sprite in the playfiled of Dimension dim, with specific coordinates. |
| Method Summary | |
|---|---|
void |
Activate()
Set true the flag ACTIVE |
void |
Deactivate()
Set The Sprite inactive |
void |
drawMe(java.awt.Graphics g)
This method draw the sprite if it is ACTIVE |
double |
getAngle()
Return the sprite's Angle direction in radians |
int |
getAngleDegrees()
Return the sprite's Angle direction in radians |
java.awt.Dimension |
getDimension()
|
int |
getEast()
Return the pixel's number on the right of the Sprite's center; |
int |
getId()
Return the sprite's number id |
protected double |
getMySpeed()
Returns the speed as a double value between 0 and 2.100 |
java.lang.String |
getName()
|
int |
getNorth()
Return the pixel's number above the Sprite center; |
Point |
getPosition()
Return the Sprite Poisition Point |
int |
getSouth()
Return the pixel's number under the Sprite's center; |
int |
getSpeed()
Return the sprite's speed |
int |
getWest()
Return the pixel's number on the right of the Sprite's center; |
double |
getX()
Return the sprite's center x coordinate |
double |
getY()
Return the sprite's center y coordinate |
boolean |
isActive()
Return the sprite's Active state |
boolean |
isCollide(Sprite other)
Return true if this Sprite collides with other one. |
boolean |
isCollide(Sprite other,
double DIST)
Return true if this Sprite collides with other or if
the distance beetween sprite is minus than DIST |
void |
move()
Calculate the new Sprite position coordinate in terms of speed and angle and actual position |
void |
moveTo(double nx,
double ny)
Set sprite's angle direction in order to reach the nx,ny coordinate's point |
void |
moveTo(Point p)
Set sprite's direction in order to reach the Point p |
void |
moveTo(Sprite s)
Set sprite's direction in order to reach the Sprite s |
void |
setAngleDegree(double angle)
Accepts a degree values and set the sprite angle in radians |
void |
setAngleRadians(double angle)
Accepts a radiant values and set the sprite angle |
private void |
setCollisionRadius()
Set the collision radius as the greater value between NORT,SOUTH,EAST WEST variables; This method is necessary to simplify and improve speed in the CollisionCheck. |
void |
setDim(java.awt.Dimension dim)
Set the new dimension of the playfield |
void |
setId(int ID)
Set the Sprite identify |
protected void |
setMaxOffset(int n,
int s,
int w,
int e)
Set the North, South, West, East pixel offset of this sprite in order to let CollisionDetector to check collisions between sprites |
void |
setName(java.lang.String n)
Set the Name of the Sprite |
void |
setPosition(double x,
double y)
Set the sprite center Position |
void |
setPosition(Point pos)
Set the sprite center Position |
void |
setSpeed(int speed)
Accept an integer between 0 and 100 and set the sprite's speed |
void |
setX(double x)
Set the x Sprite's center coordinate |
void |
setY(double y)
Set the y Sprite's center coordinate |
void |
swichCollided()
Not yet implemented |
protected abstract void |
ThisIsMe(java.awt.Graphics g)
This method must be override to implements how the Sprite must be draw |
| Methods inherited from class java.util.Observable |
|---|
addObserver, clearChanged, countObservers, deleteObserver, deleteObservers, hasChanged, notifyObservers, notifyObservers, setChanged |
| Methods inherited from class java.lang.Object |
|---|
clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait |
| Field Detail |
|---|
private static final long serialVersionUID
protected Vettore vector
Vettore
public int h
public int w
protected boolean ACTIVE
true
sprite draws himself, move and live;
private int id
protected boolean collided
protected int NORTH
protected int SOUTH
protected int EAST
protected int WEST
protected java.awt.Dimension d
Dimension playfield = new Dimension(1024,768); playfiled.width return width (1024); playfield.height return heigth (768);
Dimension
protected int speed
Dimension
public int DefaultSpeed
protected int CollidedRadius
CollisionDetector
protected java.lang.String Name
| Constructor Detail |
|---|
public Sprite(java.awt.Dimension dim, double posX, double posY)
Sprite
in the playfiled of Dimension dim, with specific coordinates.
dim
- Dimension the Playfields dimension where Sprite lives inposX
- double the x Sprite coordinateposY
- double the y Sprite coordinate| Method Detail |
|---|
public void Activate()
Activate
in interface Drawable
public void Deactivate()
Deactivate
in interface Drawable
public void drawMe(java.awt.Graphics g)
drawMe
in interface Drawable
g
- the Sprite Graphics contextpublic double getAngle()
public int getAngleDegrees()
Vettore
public java.awt.Dimension getDimension()
Dimension
public int getEast()
EAST
public int getId()
id
protected double getMySpeed()
public java.lang.String getName()
public int getNorth()
NORTH
public Point getPosition()
Point
Point
public int getSouth()
SOUTH
public int getSpeed()
speed
public int getWest()
WEST
public double getX()
public double getY()
public boolean isActive()
ACTIVE
public boolean isCollide(Sprite other)
Sprite
collides with other one.
This method is used by CollisionDetector class
CollisionDetector
public boolean isCollide(Sprite other, double DIST)
Sprite
collides with other or if
the distance beetween sprite is minus than DIST
other
- Sprite
DIST
- int
distance which behind the Sprites collide;
boolean
public void move()
move
in interface Drawable
moveTo(double,double)
public void moveTo(double nx, double ny)
nx
- double
X direction which Sprite move tony
- double
Y direction which Sprite move tomove()
,
Vettore
public void moveTo(Point p)
p
- Point
p Point which Sprite move tomove()
public void moveTo(Sprite s)
s
- Sprite
which this Sprite move tomove()
public void setAngleDegree(double angle)
angle
- an angle express in degreespublic void setAngleRadians(double angle)
angle
- an angle express in radiantprivate void setCollisionRadius()
public void setDim(java.awt.Dimension dim)
public void setId(int ID)
ID
- an ID number for the Spriteprotected void setMaxOffset(int n, int s, int w, int e)
n
- int
max pixel above the Sprite centers
- int
max pixel under the Sprite centerw
- int
max pixel on the left of the Sprite centere
- int
max pixel on the right of the Sprite centerpublic void setName(java.lang.String n)
n
- String name of the Spritepublic void setSpeed(int speed)
speed
- int Sprite's speedpublic void setPosition(Point pos)
pos
- the new position Point
public void setPosition(double x, double y)
x
- the new position x coordinatey
- the new position y coordinatepublic void setX(double x)
x
- the new position x coordinatepublic void setY(double y)
y
- the new position y coordinatepublic void swichCollided()
protected abstract void ThisIsMe(java.awt.Graphics g)
|
||||||||||
| PREV CLASS NEXT CLASS | FRAMES NO FRAMES | |||||||||
| SUMMARY: NESTED | FIELD | CONSTR | METHOD | DETAIL: FIELD | CONSTR | METHOD |


I talked with folks at Middlebury about their “domains” initiative
I got a massage (It didn’t help)
Reading and Viewing and Listening and Gaming
I started reading Hannah Arendt’s The Origins of Totalitarianism
I listed to a lot of Leonard Cohen (RIP) and to the new A Tribe Called Quest album (RIP Phife). I tried watching movies – anything to keep my mind off the election. I cannot listen to the news, particularly NPR’s normalization of authoritarian racism
Family and Friends
I’ve been in almost constantly contact with my kid about the election results. I’m so worried about him – and he is white and male and het. I am so frightened for all the youth I know who are brown and female and queer and Muslim. I am so frightened for this planet, for global climate change that just had fuel thrown on to the Earth-warming fire. I am so appalled to see so many adults in my midst who do not seem to notice that the people in their care – their own children and grandchildren, their friends, their neighbors – are already under assault by this new administration


The source for {{ .Module }} is found here {{ .Source }}.
See https://pkg.go.dev/{{ .Module }} for documentation.


Welcome to our website! This is the place where you'll find every bit of information about our wedding celebration.
CORNY has 5 letters, so does meeee. Ask for good sense of humor, I give you Junior. Living life to the fullest, that's Ernst.
ANGELIC has 7 letters, so does Deborah. Often described as fun, easy-going, a good listener, someone with strong family values.....a devoted foodie to conclude.
This is how it started, and now we know it will never end.
I met my future husband through my little sister Samy. To tell you the truth I was never interested in meeting any of her friends but somehow I went along with her proposal. We chatted a bit over the phone and met for the first time on 12/29/2011 at a “Fritay Night” in FL. He was looking handsome or whatever (lol) but I was determined to have him as a friend rather than a lover. Since that night though, we had become inseparable. Inseparable as in spending days and nights talking to each other for long hours. Long hours as in 12 straight hours on skype without any interruption. A record for both of us. At this point, he had become my best friend. I love seeing his face, listening to his voice, hugging him, just being with him.  Yes, God changed my life for the better when I met the guy on December 29, 2011.
Our first official date was in TN and we went to watch a movie. I believed it happened three months in after we met. Believe it or not, by that time he was already spoiled by my family. (TO BE CONTINUED)
Hartsfield-Jacskon Atlanta International Airport (ATL).
Homewood Suites by Hilton Atlanta I-85 will honor complimentary shuttle service to and from the event venue (Ashton Gardens)
for a minimun of 10 rooms paid and reserved.
All shuttle services will be scheduled accordingly for the day of the event.


If you didn't know, Google has a really useful search
feature that allows you to
find the definition of any word or phrase when using the define:
prefix on
your search term. This along with the built-in OSX dictionary provides an
incredibly useful set of tools to find any definition as quickly as possible.
 Sometimes you just need a definition at that instant and can't be
bothered to switch to another window to find it. Or perhaps you have a list of
words you'd like to quickly grep
and find definitions. This is where
sdvc comes in.
What is sdcv? And how do I get it?
sdcv is a console version of StarDict, an open source utility used to access dictionary files in multiple languages using a common dictionary file format.
The nature of the utility also allows you to download multiple dictionaries to cull through. These dictionaries can range from English dictionaries to dictionaries of hacker jargon, all searchable from the command line. Sound interesting? To install this utility, assuming you already have homebrew installed, all you need to do is:
brew install sdcv
Unfortunately, the default installation doesn't come with any dictionaries. Go
ahead and choose the necessary
[dictionaries](http://abloz.com/huzheng/stardict- dic/dict.org/) you may need
or find useful. Dictionaries should be placed under /usr/share/stardict/dic
to ensure sdcv can detect it.
Adding an alias for the define command
Now that you have sdcv installed, why use and remember an obscure name to query
your definitions. I added the following to my .bash_profile
to define (hah) a
function that will call sdcv with some good defaults with whatever word or
phrase I pass to it.
define () {
# Check if sdcv is installed
if command -v sdcv > /dev/null; then
# -0 show UTF-8 output
# -c show colorized output
sdcv -0c "$@"
else
echo "You need to install sdcv."
fi
}


Bitrain is a financial investment model, is a clever combination between matrix and maketing mmo. With the best investment security and smart use Bitcoin currency in transactions. Bitrain will make you money effectively and genuinely enriched.
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We work to make your business start effectively working for you. Meet the financial and marketing specialists.
In non-technical language, Bitcoin is a digital currency in which transactions can be performed without the need for a credit card or central bank. It's designed to enable users to send money over the Internet in a very simple and efficient way.
The XXX's long-term development plans is to become one of the largest corporations providing bitcoin services, focusing on three main areas which are: providing service on Bitcoin miner, E-commerce by Bitcoin and Bitcoin exchange floor.
Dedicated servers, secured with AVG antivirus and malware program. All information processed on our website is encrypted with EV SSL SGC and powered by GeoTrust.
Minimum withdrawal amount is 0.3 BTC.
This is very simple. Just click over to the withdrawal section of your personal account to initiate a withdrawal.
You can view all your accrued interest in your personal account. It is applied to your account balance in real time, and you can withdraw it at any time.
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"Our greatest weakness lies in giving up. The most certain way to succeed is always to try just one more time." - Thomas A. Edison
This is the quote what I read this morning and stuck in my head all day. Today was a very eventful Monday morning! It was the start of week 3 of Phase 1. Looks like phase 1 is almost over and soon we are moving on to Phase 2! The really great thing about DBC is that they allow you to repeat each Phase, one time (except for Phase 3), I have been thinking a lot about repeating Phase 1, its not that I didn't learn all the concepts and everything or didn't try, I just think of it as another 3 weeks, that could benefit me solidify some of concepts with OO and Active Directory. I just keep telling myself that this is not a race, and that the only person I am competing against is myself. Luckily, we had our mock assessments and I preformed very well, according to my instructors. I think before I have any conversation about repeating Phase 1 I will wait till our Phase 1 official assessment and only repeat if the instructors recommend it.
Other that some of the things that have been going through my head all day, lets talk about what happen today. Today was a very difficult day, I say difficult but that means just challenging. We started today just like any other Monday, with our morning lectures, todays topic was the introduction of Schema and working with SQlite with parallel of Ruby. We went over how to use access data from a database from Ruby; we also hit on some recaps of what MVC was, with some Meta patterns and the processes of how to implement access to Ruby.
We had a few challenge of using SQlite and writing some code in Ruby that would allow us to CRUD (Create, read, update, delete), a database. I worked with Ryan today and as always we worked well together, I learn a lot from working with him and I enjoy seeing how he debugs errors in his code.
Today was a difficult day and I think a very tedious day, but even thou it wasn't my favorite day it went well, because I can say I walked away learning more that I knew yesterday.
Tomorrow is going to be a very interesting day as well will be the first day that I get to mess around with some active directory! Just got to keep strong and make sure that I don't doubt my abilities and make sure that I give each day 100%


Hi Tony, click
this link
to download the zip of your folder.
Or, you can browse through the folder contents.
Click here


{{form}}
the group: {{group}}
Have you seen a lot of movies lately? Are there any genres you are tired of right now? Add them here!
context: {{usermisery}}


--- layout: post title: 'Ways to Raise a Reader:' date: 2010-07-06 09:05:22.000000000 -07:00 type: post parent_id: '0' published: true pd: '' status: publish categories: - Classroom Talk - Reading Instruction tags: - reading - ways to raise a reader meta: _edit_last: '1' _efficient_related_posts: a:10:{i:0;a:4:{s:2:"ID";s:4:"1332";s:10:"post_title";s:65:"Helping Your Child Transition from Easy Readers to Chapter Books";s:7:"matches";s:1:"1";s:9:"permalink";s:32:"http://classroomtalk.com/?p=1332";}i:1;a:4:{s:2:"ID";s:4:"1109";s:10:"post_title";s:20:"Tales From 1st Grade";s:7:"matches";s:1:"1";s:9:"permalink";s:32:"http://classroomtalk.com/?p=1109";}i:2;a:4:{s:2:"ID";s:3:"990";s:10:"post_title";s:28:"Apps that Help Kids with GSP";s:7:"matches";s:1:"1";s:9:"permalink";s:31:"http://classroomtalk.com/?p=990";}i:3;a:4:{s:2:"ID";s:3:"958";s:10:"post_title";s:51:"4th Graders Review of Secrets of the Crystal Cavern";s:7:"matches";s:1:"1";s:9:"permalink";s:31:"http://classroomtalk.com/?p=958";}i:4;a:4:{s:2:"ID";s:3:"882";s:10:"post_title";s:32:"Three Reasons to Memorize a Poem";s:7:"matches";s:1:"1";s:9:"permalink";s:31:"http://classroomtalk.com/?p=882";}i:5;a:4:{s:2:"ID";s:3:"803";s:10:"post_title";s:29:"The Apothecary by Maile Meloy";s:7:"matches";s:1:"1";s:9:"permalink";s:31:"http://classroomtalk.com/?p=803";}i:6;a:4:{s:2:"ID";s:3:"530";s:10:"post_title";s:35:"Setting the Stage to Raise a Reader";s:7:"matches";s:1:"1";s:9:"permalink";s:31:"http://classroomtalk.com/?p=530";}i:7;a:4:{s:2:"ID";s:3:"486";s:10:"post_title";s:28:"Great Book Review for Tweens";s:7:"matches";s:1:"1";s:9:"permalink";s:31:"http://classroomtalk.com/?p=486";}i:8;a:4:{s:2:"ID";s:3:"472";s:10:"post_title";s:25:"Someday by Allison McGhee";s:7:"matches";s:1:"1";s:9:"permalink";s:31:"http://classroomtalk.com/?p=472";}i:9;a:4:{s:2:"ID";s:3:"423";s:10:"post_title";s:27:"Choosing a Non-Fiction Book";s:7:"matches";s:1:"1";s:9:"permalink";s:31:"http://classroomtalk.com/?p=423";}} _relation_threshold: '1' author: login: k email: k@k display_name: mcary first_name: '' last_name: '' ---
1.Read aloud every day to your child.
2. Be a reader yourself. Children learn from what they see.
3. Make reading a special time; get excited about the new books and illustrators that are out there.
4. Find special author’s to follow whose work you both like.
5. Go to the library, book store or start a book club/swap for children your child’s age.


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There are a lot of resources on the web about dependency injection and using StructureMap, however, I wanted to write something that was an extremely simple example. This is basically the tip of the iceberg but hopefully it will help someone.
| We want to make our application very loosely coupled – to achieve this 'loose coupling' we're going to have several projects in the solution. What this means if we need to change any part of this application later on (we wouldn't want to in this case since its a demo and all), we could do so without impacting everything else. Anyways, we're going to create three class libraries and a WinForms application. | |
Next we want to create our main inteface – this will be under the DisplayMessage Project:
namespace DisplayMessage
{
public interface IDisplayMessage
{
string message();
}
}
The interface defines just one method that, when implemented, will return a string stating what class its coming from. Next, we want to create our two implementation classes (one under Implementation1, the other under Implementation2). Please keep in mind I'm not suggesting to have every class in it's own library – it's just for the sake of example :)
Implementation1:
public class MessageOne : IDisplayMessage
{
public string message()
{
return "This is a message from Implementation1";
}
}
Implementation2:
public class MessageTwo : IDisplayMessage
{
public string message()
{
return "This is a message from Implementation2";
}
}
Okay that was easy enough, now on to the Forms App. We're first going to add a reference to StructureMap and the project DisplayMessage and create a file called StructureMap.config – this config file is going to define all of our assemblies. We want to make sure we edit the properties of this file and set the Copy to Output Directory option to "Copy Always." StructureMap will use this file at runtime to get our object references. The config file looks like this:
<?xml version="1.0" encoding="utf-8" ?>
<StructureMap>
<Assembly Name="DisplayMessage" />
<Assembly Name="Implementation1" />
<Assembly Name="Implementation2" />
<PluginFamily Type="DisplayMessage.IDisplayMessage"
Assembly="DisplayMessage"
DefaultKey="MessageOne">
<Plugin Type="Implementation1.MessageOne"
Assembly="Implementation1"
ConcreteKey="MessageOne" />
<Plugin Type="Implementation2.MessageTwo"
Assembly="Implementation2"
ConcreteKey="MessageTwo" />
</PluginFamily>
</StructureMap>
Notice we define a PluginFamily for the IDisplayMessage interface and set the default implementation to be MessageOne (the DefaultKey of PluginFamily references the ConcreteKey of the Plugin). Other than that, this should be pretty straight-forward but if you have any confusion, please check out the StructureMap documentation. Only a couple more things to do before we can run this…
| Ok, we're going to add 3 buttons to our form – one for the default IDisplayMessage and one for each implementation. |
Now to add the code…
//Default IDisplayMessage
private void btnDefault_Click(object sender, EventArgs e)
{
IDisplayMessage msg = StructureMap.ObjectFactory.GetInstance<IDisplayMessage>();
System.Windows.Forms.MessageBox.Show(msg.message());
}
//Implementation1
private void btnOne_Click(object sender, EventArgs e)
{
IDisplayMessage msg = StructureMap.ObjectFactory.GetNamedInstance<IDisplayMessage>("MessageOne");
System.Windows.Forms.MessageBox.Show(msg.message());
}
//Implementation2
private void btnTwo_Click(object sender, EventArgs e)
{
IDisplayMessage msg = StructureMap.ObjectFactory.GetNamedInstance<IDisplayMessage>("MessageTwo");
System.Windows.Forms.MessageBox.Show(msg.message());
}
Lets parse this up a little bit…
-
This statement gets the default IDisplayMessage object in the StructureMap.config file. Currently, it will get the same object as getting a named instance of "MessageOne"
IDisplayMessage msg = StructureMap.ObjectFactory.GetInstance<IDisplayMessage>();
-
This statement gets the object associated with the ConcreteKey "MessageOne"
IDisplayMessage msg = StructureMap.ObjectFactory.GetNamedInstance<IDisplayMessage>("MessageOne");
-
This statement gets the object associated with the ConcreteKey "MessageTwo"
IDisplayMessage msg = StructureMap.ObjectFactory.GetNamedInstance<IDisplayMessage>("MessageTwo");
Instead of simply hitting F5, we will need to build the application – we want to copy the DLL files from Implementation1 and Implementation2 to the bin directory of the forms app and run the executable there. For testing, however, we can add a reference to both projects (this completely defeats the purpose of dependency injection so be sure to remove the references later on) or adjust the output directory of the implementation class libraries to be the same as the Form application's bin directory. Running the application shows that everything is working as expected.
For more information please check out the following links:


The primary research focus of Babu’s laboratory is to characterize the comprehensive networks of physical (protein-protein) and functional (gene-gene, or genetic) interaction maps that underlie major cellular processes in prokaryotes and eukaryotes.
The resulting physical and functional interaction maps will be integrated to organize protein complexes into higher order pathways that mediate and coordinate major bioprocesses in the cell.
The knowledge gained from these interaction maps will not only enhance the fundamental understanding of the molecular wiring of proteins in the cells, but also provide mechanistic insights that could be potentially exploited for identifying novel therapeutic targets as well as therapies for the treatment of human diseases.
In collaboration with Andrew Emili and Jack Greenblatt laboratories at the University of Boston and Toronto, we have extensively mapped physical interactions with high confidence via large-scale isolation and characterization of endogenous soluble and membrane protein complexes under near-physiological conditions using affinity-purification of chromosomally-tagged proteins in combination with mass spectrometry (AP-MS) in organisms such as the yeast Saccharomyces cerevisiae and eubacterium Escherichia coli.
Currently, Babu’s laboratory is engaged in leveraging this approach to other models, including neuron-like cell lines and induced pluripotent stem cells, to address the critical question of how bioprocesses are integrated with one another in cellular pathways and complexes, with special focus on unique cellular compartments such as the mitochondria. Complementary techniques such as co-fractionation both support and expand these techniques to healthy and diseased neuronal tissues with the goal of understanding how networks change during neurodegeneration.
Proteomics approaches can identify physical interactions between proteins, but they do not necessarily reveal pathway-level relationships. Genetic screens, where the growth of a double mutant is compared to the fitness of the corresponding single mutants, can elucidate the functional relationships between genes.
Inspired by successes in other model organisms, in collaboration with Emili and Greenblatt groups, we recently developed a high-throughput genetic screening approach for prokaryotes, termed eSGA (E. coli Synthetic Genetic Arrays). We used this eSGA approach to generate a high-density functional association map for genes involved in cell envelope maintenance or assembly.
We are currently focusing on generating large-scale genetic interaction maps for essential biological processes using hypomorphic alleles, combining genetics and drug screening data to elucidate novel drug targets, and developing an advanced analogous genetic interaction technology for other prokaryotes and eukaryotes, including CRISPR-based methodologies for pathway analysis in human neuron models for gene variants associated with neurodegenerative diseases.
Finally, in collaboration with researchers in Toronto, we are focusing on establishing computational frameworks and machine learning algorithms for processing, integrating, and interpreting large-scale interaction datasets with the aim of understanding the cell physiology.
As well, we are investigating the evolutionary trajectory of biological pathways and complexes using comparative genomic methods to identify a spectrum of behaviors, including complexes that are of highly modular for drug target discovery.


{% extends "framemaster.html" %} {% import "macros.html" as formmacros with context %} {% block content %}


Sass calculator.
Located in /includes/sass/SassCalculator.interface.php (line 31)
The constructor. Create value
Add $other to self. Don't modify $value
Execute division
Return value
Multiply self $other-times.
Execute substraction.
Documentation generated on Mon, 09 Jul 2007 10:05:08 +0200 by phpDocumentor 1.3.0RC3


Check out Chair Fix on Gizmodo. Nice. You could open up a restaurant anywhere in like 5 minutes.  That’s from my referrer logs by the way. So they found me! Damn ;)
How Microsoft Lost the API War
Everyone and their friends seem to be linking to the following article, and I thought I shouldn’t but now I read it and I can’t make myself not link to it. This is really good. For one, he also says, like several people already did to me, that ASP.NET is a really nice programming language. But the more interesting parts are the argument that Microsoft is loosing its grip on software developers.
Check this: A lot of the bets Microsoft made are the wrong ones. For example, WinFS, advertised as a way to make searching work by making the file system be a relational database, ignores the fact that the real way to make searching work is by making searching work. Don’t make me type metadata for all my files that I can search using a query language. Just do me a favor and search the damned hard drive, quickly, for the string I typed, using full-text indexes and other technologies that were boring in 1973.
And for the power of the web: So the Web user interface is about 80% there, and even without new web browsers we can probably get 95% there. This is Good Enough for most people and it’s certainly good enough for developers, who have voted to develop almost every significant new application as a web application.
Which means, suddenly, Microsoft’s API doesn’t matter so much. Web applications don’t require Windows.
It’s a long article but worth the read. Check it out: Joel on Software - How Microsoft Lost the API War
Update: Great discussion going on over at Jeremy’s Ensight.org. Check it out!
Nokia Still Slow
Russell Beattie has a very good post about Nokia’s latest announcements entitled: Nokia: Innovate On the Inside. I highly suggest you read it as he is a pro in relation to what Nokia does and doesn’t do and I also have to say that I can very much agree to his views.
Nokia has a huge number of different phones, but very few that are really any good. Their feature mix in the phones, trying to fit the features to the target audience, is very nice, but I agree with Russell that in some cases you wonder if an additional MP3 player would have been so bad. I mean they had MP3 players in their phones before the 6600 and would it really have made the phone that much more expensive in production to add one?
I really believe that Nokia is focussing too much on two things: Margin and Platform. They are removing everything that is not needed, to increase margin. They are bringing out lots of different phones which are essentially the same because they can. Still, at this time I would not be sure which phone to recommend to somebody. Most of just something missing. The 6230 is moving in the right direction again. What they still need is a nice entry level phone with a good camera in it, plus MMS and the possibility to get the images off without needing to send them per eMail. Then just make it look like a phone, as Russell says, and you have a winner. We are still in a features war. Yes, phones are also fashion objects, but that doesn’t mean we want round key pads. Function first people. Function first.
I am still holding on to my shares though because I still believe that Nokia is amazingly well placed for the future in wireless.
Visual View of Global Searching
Dan Gillmor has a very nice movie showing rays of light over a globe in relation to the numbers of searches via Google in that country: Silicon Valley - Dan Gillmor’s eJournal - Visual View of Global Searching
Improving PowerPoint
Once again, I have to thank John Porcaro for linking to a great blog. beyond bullets by Cliff Atkinson is a great resource on learning more about how to improve on your PowerPoint skills. I followed the rants of some people how PowerPoints sucks and in general I did agree but I was not sure what the better route really was. Cliff will give you some very good ideas. Liking it.
Let the eMail Wars Begin!
There we have it: John Battelle’s Searchblog: News: Yahoo Says: 2 Gigs to You, Google
Users of the free Yahoo! Mail now have 100MB and pay users have 2 GB! Woohoooo!  Very few people I’d say.
I just want Google to allow me to put photos in my storage too and link to them as well. That would make me even happier about GMail and all things Google.
Nokia Connection 2004
If you want to know what is happening in the mobile space and also what Nokia is doing there, I highly suggest listening to the feeds from Nokia Connection 2004. What strikes me as interesting is that Nokia people sound more edgy in the presentations, more nervous if you want. There is less calm and less excitedness at the same time. You don’t feel any real confidence anymore. I still think they are on the right track, and they are best positioned to reap the benefits of the move to a more mobile world, but hey, what do I know? ;)
Anway. Another exciting part is that in the second bit, that started at 1300, you can hear a speach by Joi who talks a bit about mobility, interesting the user, changes in advertising and content. Very nice indeed. I actually have to admit that it made me understand why he invested and works at Six Apart and Technorati. If all he says is true, which seems at least likely, then these kinds of pages will be at the center of attention for a lot of users. Or rather, blogs will and Technorati will help building the possibility for interaction for marketers of the future. Very interesting indeed.
The Flight of 13/11
This is what it could be remembered as. The flight of 13/11. It seems that some people left the US on that date, even though flying was not really permitted. The full story is here: Tampabay: TIA now verifies flight of Saudis
Mintzberg at It Again
In his article The MBA Menace, Henry Mintzberg is at it again, going on about how bad current fulltime MBA programs really are.
The truth is, no one can become a manager in a classroom. Management is not a profession, nor is it science. It is a practice that depends mostly on craft and significantly on art.
First, I agree. Second, he has a bit of an inside agenda because his newest course isn’t full time and largely about sharing experiences. But still, this made me very happy again that my MBA course is part-time, with full work schedule, which links practice and theory very nicely indeed. This is actually why I agree with him. This mix is very good both for learning and for practice. It gives you the best of both worlds and allows you to keep your feets firmly on your ground.


Pay or challenge a Penalty Charge Notice
Penalty Charge Notice {{ref}}
For {% if (ticked == 'K') %} dental treatment received {% else %} a prescription claimed {% endif %} between {{checkdate}}.
Don’t delay
You have 28 days to start paying or challenge this Penalty Charge Notice (by {{challengeDate}}). Otherwise an extra fine of up to £50 will be added to these charges.
Pay
Dental charges {% else %}
Prescription charges {% endif %} £{{charge}}20.00
Penalty charge £{{penalty}}40.00
Payments made £{{penalty}}10.00
Total left to pay £{{totaldue}}50.00
You can pay so that your debt is cleared straight away, or continue to pay in instalments.
You will need a debit or credit card.
Details on the {% if (ticked == 'K') %} dental charge form {% else %} prescription {% endif %}
{% if (ticked == 'K') %} Dental treatment {% else %} Prescription {% endif %} for {{name}}
Address shown on the prescription 3 Old Street, Old Town NE1 0LD
Date of birth 01 January 1985
Exemption reason ticked {{cat}}


For faster prototyping of backend logic on front end without any use of classes and just using html.
Predefined Themes
<head>
tag
<link rel="stylesheet" href="path_to_attricss_file.css" type="text/css">
Custom Theme
$navtext-color: #fff; <-- This is for navbar text color
$font-family: "Nunito", sans-serif; <-- This is Font Family for the theme
$text-color: #000; <-- This is the text color on the body
$body-bg: white; <-- This is body's background color (choose this color with text-color in mind to make it visible)
$code-bg-color: #eeeeee; <-- This is code blocks background color
$tertiary-color: #bdbdbd; <-- Tertiary color for theme theme
$secondary-color: #212121; <-- Secondary color for the theme
$primary-color: #00c853; <-- Primary color for the theme


 If everything sounds good, we'll go ahead and create a new account for you with this email address. Continue {% endblock %} {% block footer %} {% endblock %}
We've identified you as {{ identity.email }}. If everything sounds good, we'll go ahead and create a new account for you with this email address.
Continue


EASTER FRIDAY
Entrance Antiphon
The Lord led his people in hope, while the sea engulfed their foes, alleluia.
Collect
Almighty ever-living God, who gave us the paschal mystery in the covenant you established for reconciling the human race; so dispose our minds, we pray, that what we celebrate by professing the faith we may express in deeds. Through our Lord...
FIRST READING Acts 4:1-12
A reading from the Acts of the Apostles
In those days: As Peter and John were speaking to
the people, the priests and the captain of the temple and the Sadducees came
upon them, annoyed because they were teaching the people and proclaiming in
Jesus the resurrection from the dead. And they arrested them and put them in
custody until the morrow, for it was already evening. But many of those who
heard the word believed; and the number of the men came to about five thousand.
On the morrow their rulers and elders and scribes were gathered together in
The word of the Lord.
RESPONSORIAL PSALM Psalm 118:1-2 and 4.22-24.25-27ab (R. 22)
R. The stone that the builders rejected has become the cornerstone.
Or: Alleluia!
Give praise to the LORD, for he is good; his mercy endures
for ever. Let the house of
R. The stone that the builders rejected has become the
cornerstone.
The stone that the builders rejected has become the
cornerstone. By the LORD has this been done, a marvel in our eyes. This is the day
the LORD has made; let us rejoice in it and be glad.
R. The stone that the builders rejected has become the
cornerstone.
O LORD, grant salvation; O LORD, grant success.
Blest is he who comes in the name of the LORD. We bless you from the house of
the LORD; the LORD is God, and has given us light.
R. The stone that the builders rejected has become the
cornerstone.
The
sequence is optional on the weekdays of the Octave of Easter.
ALLELUIA Psalm 118:24
R. Alleluia. This is the day the Lord has made; let us rejoice in it and be glad. R. Alleluia.
GOSPEL John 21:1-14
A reading from the holy Gospel according to John
At that time: Jesus revealed himself again to the
disciples by the
The Gospel of the Lord.
Prayer over the Offerings
Perfect, O Lord, within us, we pray, the solemn exchange brought about by these paschal offerings, that we may be drawn from earthly desires to a longing for the things of heaven. Through Christ our Lord.
Communion Antiphon
Jesus said to his disciples: Come and eat. And he took bread and gave it to them, alleluia.
Prayer after Communion
Keep safe, O Lord, we pray, those whom you have saved by your kindness, that, redeemed by the Passion of your Son, they may rejoice in his Resurrection. Who lives and reigns for ever and ever.


Your Orders
| PRODUCT DETAILS | QUENTITY | PRICE | TOTAL PRICE | ADDRESS |
|---|---|---|---|---|
|
Lorem ipsum dolor sit amet. |
2 | $150.00 | $300.00 |
Atanu Jana<EMAIL_ADDRESS>7985263321<PHONE_NUMBER> Bay Square 1, Building 7, Office 401-23 Business kolkata West Bengal 700001 |
|
Lorem ipsum dolor sit amet. |
2 | $150.00 | $300.00 |
| Subtotal | ₹3500.00 |
| Delivery | ₹15.00 |
| Total | ₹3515.00 |
| Pay Now |


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Always striving to learn something new!