In this step you need to choose output file name. This is done by typing it
or simply press button "Browse..." and select path and file name.
In case you
wish to overwrite existing file you will be asked if you wish to do so.
After you done this, press button "Next >>"


Some of my favorites include Halo, Gears of War, FIFA, MADDEN and NBA Live series. I've also grown to love the Battefield franchise. With the new Xbox Series X, I'm able to bring my entire collection to next gen. That has truly been awesome for me.
Well currently I'm doing a lot of different jobs. Working for people that I can make money from on the side. I do a lot of freelance work as I'm currently in a transition of jobs. Working in retail was tough for me and I found it would be better if I went a different route.
Hopefully I can start moving forward on a path that alllows for me to become a front end web developer and eventually move on to better things. Every day seems to be a bit of a struggle and I'm hoping that I will be able to live out my dream soon.


A Sampling of Salmon Events
Current Events
Issaquah Salmon Days!
Snoqualmie Salmon Days!
Orcas Island Salmon Days!
Home
Merchandise
Sales Info
About Us


SUBROUTINE ZLATDF( IJOB, N, Z, LDZ, RHS, RDSUM, RDSCAL, IPIV, $ JPIV ) * * -- LAPACK auxiliary routine (version 3.1) -- * Univ. of Tennessee, Univ. of California Berkeley and NAG Ltd.. * November 2006 * * .. Scalar Arguments .. INTEGER IJOB, LDZ, N DOUBLE PRECISION RDSCAL, RDSUM * .. * .. Array Arguments .. INTEGER IPIV( * ), JPIV( * ) COMPLEX*16 RHS( * ), Z( LDZ, * ) * .. * * Purpose * ======= * * ZLATDF computes the contribution to the reciprocal Dif-estimate * by solving for x in Z * x = b, where b is chosen such that the norm * of x is as large as possible. It is assumed that LU decomposition * of Z has been computed by ZGETC2. On entry RHS = f holds the * contribution from earlier solved sub-systems, and on return RHS = x. * * The factorization of Z returned by ZGETC2 has the form * Z = P * L * U * Q, where P and Q are permutation matrices. L is lower * triangular with unit diagonal elements and U is upper triangular. * * Arguments * ========= * * IJOB (input) INTEGER * IJOB = 2: First compute an approximative null-vector e * of Z using ZGECON, e is normalized and solve for * Zx = +-e - f with the sign giving the greater value of * 2-norm(x). About 5 times as expensive as Default. * IJOB .ne. 2: Local look ahead strategy where * all entries of the r.h.s. b is choosen as either +1 or * -1. Default. * * N (input) INTEGER * The number of columns of the matrix Z. * * Z (input) DOUBLE PRECISION array, dimension (LDZ, N) * On entry, the LU part of the factorization of the n-by-n * matrix Z computed by ZGETC2: Z = P * L * U * Q * * LDZ (input) INTEGER * The leading dimension of the array Z. LDA >= max(1, N). * * RHS (input/output) DOUBLE PRECISION array, dimension (N). * On entry, RHS contains contributions from other subsystems. * On exit, RHS contains the solution of the subsystem with * entries according to the value of IJOB (see above). * * RDSUM (input/output) DOUBLE PRECISION * On entry, the sum of squares of computed contributions to * the Dif-estimate under computation by ZTGSYL, where the * scaling factor RDSCAL (see below) has been factored out. * On exit, the corresponding sum of squares updated with the * contributions from the current sub-system. * If TRANS = 'T' RDSUM is not touched. * NOTE: RDSUM only makes sense when ZTGSY2 is called by CTGSYL. * * RDSCAL (input/output) DOUBLE PRECISION * On entry, scaling factor used to prevent overflow in RDSUM. * On exit, RDSCAL is updated w.r.t. the current contributions * in RDSUM. * If TRANS = 'T', RDSCAL is not touched. * NOTE: RDSCAL only makes sense when ZTGSY2 is called by * ZTGSYL. * * IPIV (input) INTEGER array, dimension (N). * The pivot indices; for 1 <= i <= N, row i of the * matrix has been interchanged with row IPIV(i). * * JPIV (input) INTEGER array, dimension (N). * The pivot indices; for 1 <= j <= N, column j of the * matrix has been interchanged with column JPIV(j). * * Further Details * =============== * * Based on contributions by * Bo Kagstrom and Peter Poromaa, Department of Computing Science, * Umea University, S-901 87 Umea, Sweden. * * This routine is a further developed implementation of algorithm * BSOLVE in [1] using complete pivoting in the LU factorization. * * [1] Bo Kagstrom and Lars Westin, * Generalized Schur Methods with Condition Estimators for * Solving the Generalized Sylvester Equation, IEEE Transactions * on Automatic Control, Vol. 34, No. 7, July 1989, pp 745-751. * * [2] Peter Poromaa, * On Efficient and Robust Estimators for the Separation * between two Regular Matrix Pairs with Applications in * Condition Estimation. Report UMINF-95.05, Department of * Computing Science, Umea University, S-901 87 Umea, Sweden, * 1995. * * ===================================================================== * * .. Parameters .. INTEGER MAXDIM PARAMETER ( MAXDIM = 2 ) DOUBLE PRECISION ZERO, ONE PARAMETER ( ZERO = 0.0D+0, ONE = 1.0D+0 ) COMPLEX*16 CONE PARAMETER ( CONE = ( 1.0D+0, 0.0D+0 ) ) * .. * .. Local Scalars .. INTEGER I, INFO, J, K DOUBLE PRECISION RTEMP, SCALE, SMINU, SPLUS COMPLEX*16 BM, BP, PMONE, TEMP * .. * .. Local Arrays .. DOUBLE PRECISION RWORK( MAXDIM ) COMPLEX*16 WORK( 4*MAXDIM ), XM( MAXDIM ), XP( MAXDIM ) * .. * .. External Subroutines .. EXTERNAL ZAXPY, ZCOPY, ZGECON, ZGESC2, ZLASSQ, ZLASWP, $ ZSCAL * .. * .. External Functions .. DOUBLE PRECISION DZASUM COMPLEX*16 ZDOTC EXTERNAL DZASUM, ZDOTC * .. * .. Intrinsic Functions .. INTRINSIC ABS, DBLE, SQRT * .. * .. Executable Statements .. * IF( IJOB.NE.2 ) THEN * * Apply permutations IPIV to RHS * CALL ZLASWP( 1, RHS, LDZ, 1, N-1, IPIV, 1 ) * * Solve for L-part choosing RHS either to +1 or -1. * PMONE = -CONE DO 10 J = 1, N - 1 BP = RHS( J ) + CONE BM = RHS( J ) - CONE SPLUS = ONE * * Lockahead for L- part RHS(1:N-1) = +-1 * SPLUS and SMIN computed more efficiently than in BSOLVE[1]. * SPLUS = SPLUS + DBLE( ZDOTC( N-J, Z( J+1, J ), 1, Z( J+1, $ J ), 1 ) ) SMINU = DBLE( ZDOTC( N-J, Z( J+1, J ), 1, RHS( J+1 ), 1 ) ) SPLUS = SPLUS*DBLE( RHS( J ) ) IF( SPLUS.GT.SMINU ) THEN RHS( J ) = BP ELSE IF( SMINU.GT.SPLUS ) THEN RHS( J ) = BM ELSE * * In this case the updating sums are equal and we can * choose RHS(J) +1 or -1. The first time this happens we * choose -1, thereafter +1. This is a simple way to get * good estimates of matrices like Byers well-known example * (see [1]). (Not done in BSOLVE.) * RHS( J ) = RHS( J ) + PMONE PMONE = CONE END IF * * Compute the remaining r.h.s. * TEMP = -RHS( J ) CALL ZAXPY( N-J, TEMP, Z( J+1, J ), 1, RHS( J+1 ), 1 ) 10 CONTINUE * * Solve for U- part, lockahead for RHS(N) = +-1. This is not done * In BSOLVE and will hopefully give us a better estimate because * any ill-conditioning of the original matrix is transfered to U * and not to L. U(N, N) is an approximation to sigma_min(LU). * CALL ZCOPY( N-1, RHS, 1, WORK, 1 ) WORK( N ) = RHS( N ) + CONE RHS( N ) = RHS( N ) - CONE SPLUS = ZERO SMINU = ZERO DO 30 I = N, 1, -1 TEMP = CONE / Z( I, I ) WORK( I ) = WORK( I )*TEMP RHS( I ) = RHS( I )*TEMP DO 20 K = I + 1, N WORK( I ) = WORK( I ) - WORK( K )*( Z( I, K )*TEMP ) RHS( I ) = RHS( I ) - RHS( K )*( Z( I, K )*TEMP ) 20 CONTINUE SPLUS = SPLUS + ABS( WORK( I ) ) SMINU = SMINU + ABS( RHS( I ) ) 30 CONTINUE IF( SPLUS.GT.SMINU ) $ CALL ZCOPY( N, WORK, 1, RHS, 1 ) * * Apply the permutations JPIV to the computed solution (RHS) * CALL ZLASWP( 1, RHS, LDZ, 1, N-1, JPIV, -1 ) * * Compute the sum of squares * CALL ZLASSQ( N, RHS, 1, RDSCAL, RDSUM ) RETURN END IF * * ENTRY IJOB = 2 * * Compute approximate nullvector XM of Z * CALL ZGECON( 'I', N, Z, LDZ, ONE, RTEMP, WORK, RWORK, INFO ) CALL ZCOPY( N, WORK( N+1 ), 1, XM, 1 ) * * Compute RHS * CALL ZLASWP( 1, XM, LDZ, 1, N-1, IPIV, -1 ) TEMP = CONE / SQRT( ZDOTC( N, XM, 1, XM, 1 ) ) CALL ZSCAL( N, TEMP, XM, 1 ) CALL ZCOPY( N, XM, 1, XP, 1 ) CALL ZAXPY( N, CONE, RHS, 1, XP, 1 ) CALL ZAXPY( N, -CONE, XM, 1, RHS, 1 ) CALL ZGESC2( N, Z, LDZ, RHS, IPIV, JPIV, SCALE ) CALL ZGESC2( N, Z, LDZ, XP, IPIV, JPIV, SCALE ) IF( DZASUM( N, XP, 1 ).GT.DZASUM( N, RHS, 1 ) ) $ CALL ZCOPY( N, XP, 1, RHS, 1 ) * * Compute the sum of squares * CALL ZLASSQ( N, RHS, 1, RDSCAL, RDSUM ) RETURN * * End of ZLATDF * END


| Table of Contents |
|---|
| 1. Outline |
| 2. Tools |
| 3. GUI Examples |
This is a web application created using the Bootstrap framework with jQuery on the front-end, and a C# server backend running .NET Framework 4.7.2 and using an MSSQL database.
The ADO.NET Entity Data Model is used with Entity Framework 6 for interacting with the database content.
For development Visual Studio 2019 Community is used as the primary IDE.
Layout
View - Home
View - About
View - Contact
View - Data


Converting between file formats¶
- The convert command in the biom-format project can be used to convert between biom and tab-delimited table formats. This is useful for several reasons:
- converting biom format tables to tab-delimited tables for easy viewing in programs such as Excel
- converting between sparse and dense biom formats
Note
The tab-delimited tables are commonly referred to as the classic format tables, while BIOM formatted tables are referred to as biom tables.
General usage examples¶
Convert a tab-delimited table to sparse biom format. Note that you must specify the type of table here:
biom convert -i table.txt -o table.from_txt.biom --table-type="otu table"
Convert a tab-delimited table to dense biom format:
biom convert -i table.txt -o table.dense.biom --table-type="otu table" --matrix-type=dense
Convert biom format to tab-delimited table format:
biom convert -i table.biom -o table.from_biom.txt -b
Convert dense biom format to sparse biom format:
biom convert -i table.dense.biom -o table.sparse.biom --dense-biom-to-sparse-biom
Convert sparse biom format to dense biom format:
biom convert -i table.sparse.biom -o table.dense.biom --sparse-biom-to-dense-biom
Convert biom format to classic format, including the taxonomy observation metadata as the last column of the classic format table. Because the BIOM format can support an arbitrary number of observation (or sample) metadata entries, and the classic format can support only a single observation metadata entry, you must specify which of the observation metadata entries you want to include in the output table:
biom convert -i table.biom -o table.from_biom_w_taxonomy.txt -b --header-key taxonomy
Convert biom format to classic format, including the taxonomy observation metadata as the last column of the classic format table, but renaming that column as ConsensusLineage. This is useful when using legacy tools that require a specific name for the observation metadata column.:
biom convert -i table.biom -o table.from_biom_w_consensuslineage.txt -b --header-key taxonomy --output-metadata-id "ConsensusLineage"
Special case usage examples¶
Converting QIIME 1.4.0 and earlier OTU tables to BIOM format¶
If you are converting a QIIME 1.4.0 or earlier OTU table to BIOM format, there are a few steps to go through. First, for convenience, you might want to rename the ConsensusLineage column taxonomy. You can do this with the following command:
sed 's/Consensus Lineage/ConsensusLineage/' < otu_table.txt | sed 's/ConsensusLineage/taxonomy/' > otu_table.taxonomy.txt
Then, you’ll want to perform the conversion including a step to convert the taxonomy string from the classic OTU table to a taxonomy list, as it’s represented in QIIME 1.4.0-dev and later:
biom convert -i otu_table.taxonomy.txt -o otu_table.from_txt.biom --table-type="otu table" --process-obs-metadata taxonomy


 You can accept or reject that this is the amount owed.
Defendant has requested more time to respond
Mary Richards need to respond by 31 March 2018.
Your claim has been sent
Mary Richards has until before 4pm on 31 March 2018 to respond.
Total claim amount
| Claim amount | £1,000.00 |
|---|---|
| Claim fee | £350.00 |
| Total | £1,350.00 |
Your details (claimant)
| Name | Mrs Jan Clark |
|---|---|
| Address | 101 Regents Road Shiplake Henley on Thames RG9 4BW |
| Telephone |<PHONE_NUMBER>2 |
|<EMAIL_ADDRESS>|
Defendant's details
| Name | Mrs Mary Richards |
|---|---|
| Address | 24 Acacia Drive London SW1 H9A |
|<EMAIL_ADDRESS>|
If you've been paid
Tell us if you want to end the claim because the defendant has paid you.
Stopping the claim
If you don't wish the claim to proceed further, you can stop the claim.


Data Protection
The Data Protection Act 1998 governs the handling of data held about individuals and the rights of individuals to access that data. The University is obliged to comply with the 8 principles of the Data Protection Act for handling personal data as follows:
- Personal data shall be processed fairly and lawfully.
- Personal data shall be processed for one or more specified and lawful purpose.
- Personal data shall be adequate, relevant and not excessive to the purpose of processing.
- Personal data shall be accurate and up-to-date.
- Personal data shall not be kept longer than is necessary.
- Personal data shall be processed in accordance with the individual's rights under the Act.
- Appropriate measures shall be taken against unauthorised or unlawful processing of personal data and against accidental loss, destruction or damage.
- Personal data shall not be transferred to a country outside the EEA without adequate protection for the rights and freedoms of data subjects.
How does data protection apply to my work?
- Individuals have the right to see any personal information the University holds about them. They do this by making a subject access request and we have 40 calendar days to respond. Avoid complications by following the procedures.
- We must tell people what we are doing with information about them, including whom we are disclosing it to.
- We must use appropriate security measures to protect personal data and do not keep it for longer than necessary. We must not transfer personal information without safeguards; this includes publishing it on the internet.
The University’s Data Protection Policy sets out how the University aims to comply with the Data Protection Act 1998. Details are also available to all staff and students as to how personal information is handled – these can also be found in the Staff Handbook and Student Handbook of Regulations.
Further general guidance is available from the Information Commissioner's Office .
Good records management and information security will help the University comply with the Act. The way our staff go about their daily tasks is vital to this. The University Solictor is the University's Data Protection Officer and is responsible for providing help and guidance on applying the Data Protection Act, as well as for co-ordinating subject access requests from individuals.
Some individual areas of the University have specific and additional Data Protection and/or Confidentiality Policies, for example, Student Services or the Podiatry Clinic, because their work is of a very specialist nature or may involve people who are not staff or students of the University.
Making a request for information
Under the Data Protection Act 1998 individuals have the right to ask whether information is held about them and if so what it is. This is called a "subject access request". Please complete the Subject Access Request Form and return it to the University Solicitor (address on the form) with evidence of your identity and the £10 subject access request fee.
Further information about subject access requests can be obtained from the Information Commissioner's Office.
How does the University handle data? -"fair processing notices"
The University describes in general terms how personal data is handled. These descriptions are called "Privacy Notices".


- getAddress() - Method in class DataManipulation.Borrower
-
Gets the students address.
- getAdminName() - Method in class DataManipulation.Librarian
-
Gets the librarian user name.
- getAdminPassword() - Method in class DataManipulation.Librarian
-
Gets the librarian password.
- getAuthor() - Method in class DataManipulation.Books
-
Gets book Author.
- getBookTitle() - Method in class DataManipulation.Books
-
Gets books title
- getCardID() - Method in class DataManipulation.BooksIssued
-
Gets the student id number.
- getCardID() - Method in class DataManipulation.Borrower
-
Gets the students id number.
- getCurrentBooks() - Method in class DataManipulation.BooksIssued
-
Gets all current books check-out by a student.
- getDueDate() - Method in class DataManipulation.BooksIssued
-
Gets the dude date of the book.
- getEmail() - Method in class DataManipulation.Borrower
-
Gets the students email.
- getFirstName() - Method in class DataManipulation.Borrower
-
Gets the students first name.
- getIsbn() - Method in class DataManipulation.Books
-
Gets books ISBN
- getIsbn() - Method in class DataManipulation.BooksIssued
-
Gets the isbn number.
- getIssueDate() - Method in class DataManipulation.BooksIssued
-
Gets the issue date of the book.
- getLastName() - Method in class DataManipulation.Borrower
-
Gets the students last name.
- getNumberOfCopies() - Method in class DataManipulation.Books
-
Gets book avaliable copies
- getPhoneNum() - Method in class DataManipulation.Borrower
-
Gets the students phone number.
- getPreparedStatment() - Method in class DataManipulation.MountieQueries
-
Gets the preparedStament
- getPublicationDate() - Method in class DataManipulation.Books
-
Gets book publication year.
- getResultSet() - Method in class DataManipulation.MountieQueries
-
Gets the Result set.
- getReturnDate() - Method in class DataManipulation.BooksIssued
-
Gets the day the book was returned.
- getSearchString() - Method in class DataManipulation.MountieQueries
-
Gets the search String argument.
- getSQL() - Method in class DataManipulation.MountieQueries
-
Gets the SQL statement.
- getTransID() - Method in class DataManipulation.BooksIssued
-
Gets transaction id number


Rules
- Image must be uploaded in .jpg format
- Maintain enough spacing between elements
- Take photos of images in a well lit environment
- Keep lines as straight as possible
- Draw with a pen or pencil
- Do not smudge or draw over lines repeatedly
- Avoid stray pen marks on the sheet
- Draw on white paper, pen ink can be any color
- Avoid shadows and light variations while taking photos


private class GamePlayController.FieldDrawAnimation extends ExtendedAnimationTimer
x
and y
points). Idea is to wait time (it's
in CELL_DRAW_STEP_DELAY
) between iterating steps. There're any for
loops in this class, but they're implemented with two variables (x
and y
) and if
statements.| Modifier and Type | Field and Description |
|---|---|
(package private) GamePlayController.PointerSpawnState |
animationState
State of GP spawn animation.
|
(package private) static int |
CELL_DRAW_STEP_DELAY
Delay between cell drawings.
|
(package private) int |
x
Current x position.
|
(package private) int |
y
Current y position.
|
| Modifier | Constructor and Description |
|---|---|
private |
FieldDrawAnimation() |
| Modifier and Type | Method and Description |
|---|---|
void |
handle(long now)
Implementation of
ExtendedAnimationTimer.start() . |
(package private) javafx.scene.paint.Color |
invertColor(javafx.scene.paint.Color source)
Inverts color.
|
setEndAction, start, stop
static final int CELL_DRAW_STEP_DELAY
int x
int y
GamePlayController.PointerSpawnState animationState
null
- there's no GPSA (Game Pointer Spawn
Animation), when it isn't null
- animation runs.javafx.scene.paint.Color invertColor(javafx.scene.paint.Color source)
Inverts color. For example, this color:
will be turned to this one:
source
- source colorpublic void handle(long now)
ExtendedAnimationTimer.start()
. Here're all animation logic.handle
in class ExtendedAnimationTimer
now
- time in nanoseconds (same as System.nanoTime()
, but with some additions)


Home
Logout
Help
About
Projects
Create a Project
Create a User
Search a Project
Search a User
×
Create a Project
Project Name
Due Time
Manager ID
Developer 1
Developer 2
Developer 3
Project Status
Active
Pending
Completed
×
Create a User
User Name
User Email
Password
Role
Admin
Manager
Developer
Project Name
Due Time
Manager
Developer1
Developer2
Developer3
Project Status
Modify
×
Help
Welcome to the Project Management Application
Fill the form to
Login
Only admins could click
Create a User
to create an account for employees
Admins could
Create
new users or projects
In addition, admins could click corresponding button to
modify
any attributes of users and projects excepyt their names
Admins also could
search
projects and users based on their names
Managers could
create
and
search
projects
Moreover, managers could
modify
any attributs of projects based on their names
Developers could only
show
and
Modify
their basic information
×
About
Welcome to the Project Management Application
Thank you for using this Project Management Application.
Pleasr feel free to sent us your questions.
Developer: Siying Zhang (30542731) Email: sz3y18@soton.ac.uk


- Review
- R stuff
- Model Assumptions
- Same subjects
- Distribution of the Outcome
- Dependent Samples t-Tests
- Choosing between dependent and independent samples t-tests
- Examples
- Next Class
William R. Buchanan, Ph.D.
Strategic Data Fellow at MDE/Adjunct Prof at JSU
# Load the foreign data package and the ggplot2 package
library("foreign"); library("ggplot2")
# Store the URL where the data can be accessed
url <- "http://www.paces-consulting.org/stata/JSU/Fall2014/"
# Load either your data or the example data (Substitute your name to get your data)
example <- as.data.frame(read.dta(paste0(url, "JohnDoe-task3.dta")))
example$mthscore2
example["rlascore1"]
example[, 14]
example
is the name of your data object, $
is a symbol used to reference a column within that data object, and mthscore2
is the name of the variable you're interested inexample
is still the name of your data object and ["rlascore1"]
is the name of the variable you're interested inexample
is still the name of your data object and [, 14]
is the position - or column number - for the variable you are interested inx <- 5; y <- 3; x - y
(you should see [1] 2
if you do that in R)x <- 5; x <- x - 3
# Create a new variable containing the difference between math scores
example$mthdiff <- example$mthscore2 - example$mthscore1
# Create a new variable containing the difference between reading scores
example$rladiff <- example$rlascore2 - example$rlascore1
# Look at summaries of these variables
summary(example[c("rladiff", "mthdiff")])
## rladiff mthdiff
## Min. :-35 Min. :-3
## 1st Qu.: 0 1st Qu.: 6
## Median : 6 Median : 8
## Mean : 7 Mean : 8
## 3rd Qu.: 13 3rd Qu.:11
## Max. : 50 Max. :22
## NA's :20743 NA's :12808
# Create a graph of the distribution of the difference in the scores
ggplot(data = example, aes(x = mthdiff)) + geom_density() + #
ggtitle("Distribution of the Difference in Math Scores: \nPrior vs Current Year") + #
xlab("Difference Score") + ylab("Density of Observations")
# Create a graph of the distribution of the difference in the scores
ggplot(data = example, aes(x = rladiff)) + geom_density() + #
ggtitle("Distribution of the Difference in Reading Scores: \nPrior vs Current Year") + #
xlab("Difference Score") + ylab("Density of Observations")
# Test first hypothesis example
t.test(example$mthscore2, example$mthscore1, paired = TRUE)
##
## Paired t-test
##
## data: example$mthscore2 and example$mthscore1
## t = 774, df = 120533, p-value < 2.2e-16
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## 8.119 8.160
## sample estimates:
## mean of the differences
## 8.139


| |
The ilutGLTexImage function binds an image to an OpenGL texture, simply calls glTexImage2D with the current bound image's data and attributes.
| | ILboolean ilutGLTexImage( GLuint Level );
|
Parameters| | - Level
- Texture level to place the image at. 0 is the base image level, and anything lower is a mipmap. Use ilActiveMipmap to access OpenIL's mipmaps.
|
Errors| | - ILUT_ILLEGAL_OPERATION
- The operation attempted is not allowable in the current state. The function returns with no ill side effects.
|
See Also
|


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Choose difficulty level(s).
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Choose a problem tag.
Apply
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X


Installed Applications
You can download application modules from
Bitnami.com
and install them on top of this Stack.


Best Doctor
help finding the best doctor for you
Search for a condition:
Search by condition or name
Picture
Name
Practice
Accepts New Patients?


This website is written with a combination of Vim, Sass and textile. Jekyll was the piece of software to clue everything together.
I crafted the website in such a way that you can view it easily on your iPhone – it wasn’t easy but in ones you get it, it’s easy like learing cycling. Hope that this website is accessible through many browser.
This website was build in Germany and changes are welcomed every time.


Table of Contents
The Proximity Database Browser is a browser-based interface that lets you explore database entities, create and execute queries, examine models, and run Python scripts that operate on the database.
Objectives
The text and exercises in this chapter demonstrate how to
start and use the Proximity Database Browser
explore objects, links, and attributes in a Proximity database
visualize portions of the database as a graph
set display preferences for the Proximity Database Browser
create a schema analysis report


Web-Trader
Log out
Dashboard
Add Funds
View Portfolio
View Trade Activity
Get Stock Quote
Buy Stock
Sell Stock
Sell Stock
Share
Export
Sell Stock
Execute Sale
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{{ price }}


Sign a bloodoath and enter a contract with your own shinobi animal partner.
Pick your partner's class
Toad
Snake
Slug
Falcon
Tiger
Enter your name
First Name
Last Name


SEVEN A film review by James Berardinelli Copyright 1995 James Berardinelli
RATING (0 TO 10): 7.3
United States, 1995 U.S. Availability: 9/22/95 (wide) Running Length: 2:03 MPAA Classification: R (Gory crime scenes, violence, profanity) Theatrical Aspect Ratio: 2.35:1
Cast: Brad Pitt, Morgan Freeman, Gwyneth Paltrow, John C. McGinley, Richard Roundtree, Kevin Spacey Director: David Fincher Producers: Arnold Kopelson and Phyllis Carlyle Screenplay: Andrew Kevin Walker Cinematography: Darius Khondji Music: Howard Shore U.S. Distributor: New Line Cinema
Frequently, mystery/thrillers present us with a cast of about six or seven characters, set up a sequence of grizzly murders, then "surprise" us by revealing which of those six or seven characters is the guilty party. It's a time-honored method that's repeated in at least several movies each year. At the outset, SEVEN has all the hallmarks of this kind of motion picture. Fortunately, it turns out somewhat smarter and less predictable. Though not without significant flaws, SEVEN isn't transparent or moronic, and it doesn't insult the average viewer's intelligence.
When all is said and done, the mystery of SEVEN is not who the killer is--there's never any question about the identity--but how he will outsmart the police next, and what he will do as a climax to his killing spree. It's refreshing to find an intelligent maniac who is not undone by a moment of sheer stupidity. From beginning to end, SEVEN's murderer has the situation under control. The police are his pawns, not the other way around. Shades of SILENCE OF THE LAMBS.
The good guys are a pair of detectives at opposite ends of their careers. David Mills (Brad Pitt) is new on the job, full of energy and high ideals, and ready to "make a difference" by catching the crooks. William Somerset (Morgan Freeman) is in his last week on the job. His long years studying crime scenes and following up on clues have left him weary and jaded. To him, being a detective isn't about nabbing criminals--it's about methodically collecting and cataloguing evidence in case a prosecutor ever needs it.
The serial killer pursued by Mills and Somerset is choosing each of his victims based on which of the seven deadly sins (gluttony, greed, sloth, lust, pride, envy, wrath) they have most clearly violated. The deaths form a portion of a decidedly warped sermon.. In their quest to end this bloody, sadistic spree, the two cops appear well-paired, as together they make the perfect detective. Mills is all brawn and little brain. Somerset, on the other hand, spends long hours in the library researching Dante and Chaucer, looking for clues that will enable him to prevent the next killing.
One of the problems with SEVEN is that Pitt's Mills is not an especially likable character. He's cocky and arrogant, with an inflated opinion of himself. Up until the end, he's convinced that his way is always the best. Pitt doesn't turn in one of his most impressive performances here, either. There's no subtlety whatsoever. In this film, the actor has a single mode: overdrive. Somerset, on the other hand, is a subdued and balanced personality perfectly essayed by Morgan Freeman. By emoting less than his co-star, Freeman frequently steals scenes from him. Gwyneth Paltrow (FLESH AND BONE, JEFFERSON IN PARIS), one of today's better young actresses, is woefully underused in the role of Mills' wife, Tracy.
SEVEN is unnecessarily gory and runs for a little too long, but neither of these elements detracts much from the film's enjoyability (unless you have a weak stomach). The same is true of several logical flaws--they're there, but not overly apparent while the film is on- screen (they can be ruminated about after the credits have rolled). While SEVEN lacks the cleverness of the superior UNUSUAL SUSPECTS, it's strong enough to hold its own against most other thrillers. SEVEN may always be grim, dark, and rainy, but at least there's a little substance beneath the atmosphere.
- James Berardinelli<EMAIL_ADDRESS>The review above was posted to the
rec.arts.movies.reviews newsgroup (de.rec.film.kritiken for German reviews).
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public abstract class ControllerState
extends java.lang.Object
| Constructor and Description |
|---|
ControllerState() |
| Modifier and Type | Method and Description |
|---|---|
static void |
addErrorMessage(java.lang.String errorMessage)
Adds an error message to the list of stored error messages (errorMessages).
|
static boolean |
checkPlayerActionAndSaveError(PlayerAction playerAction)
Check if a playerAction can be run by executing the check on it, if it cannot be run add an error
|
abstract ControllerState |
nextState(java.util.List<PlayerAction> playerActions,
Game game,
Player player)
This method contains all the logic of a state.
|
void |
sendErrorMessages(CommandHandler commandHandler)
Sends an error message to the client by combining the currently stored error messages and then flush the currently stored error messages
|
abstract void |
sendSelectionMessage(CommandHandler commandHandler)
This needs to be implemented but sends to the client the action they need to perform
|
public abstract void sendSelectionMessage(CommandHandler commandHandler)
commandHandler
- the player commandhandlerpublic abstract ControllerState nextState(java.util.List<PlayerAction> playerActions, Game game, Player player)
playerActions
- the list of actions received from the playergame
- the game on which to execute the actionsplayer
- the player sending the inputpublic void sendErrorMessages(CommandHandler commandHandler)
commandHandler
- the player commandhandlerpublic static void addErrorMessage(java.lang.String errorMessage)
errorMessage
- the message that will be storedpublic static boolean checkPlayerActionAndSaveError(PlayerAction playerAction)
playerAction
- the action to check


Research: Papers
Last updated: 1 October 2012
Published
2012
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- Chen Z, Purdon PL, Brown EN, Barbieri R. A unified point process probabilistic framework to assess heartbeat dynamics and autonomic cardiovascular control. Front Physiol. 2012;3:4. Epub 2012 Feb 1
- Ching S, Purdon PL, Vijayan S, Kopell NJ, Brown EN. A neurophysiological-metabolic model for burst suppression. Proc Natl Acad Sci U S A. 2012 Feb 21;109(8):3095-100. Epub 2012 Feb 7
- Chow BY, Han X, Boyden ES. Genetically encoded molecular tools for light-driven silencing of targeted neurons. Prog Brain Res. 2012;196:49-61
- Chu CJ, Kramer MA, Pathmanathan J, Bianchi MT, Westover MB, Wizon L, Cash SS. Emergence of stable functional networks in long-term human electroencephalography. J Neurosci. 2012 Feb 22;32(8):2703-13
- Desroches M, Burke J, Kaper TJ, Kramer MA. Canards of mixed type in a neural burster, Phys Rev E Stat Nonlin Soft Matter Phys. 2012 Feb;85(2 Pt 1):021920. Epub 2012 Feb 23
- Gregoriou GG, Gotts SJ, Desimone R. Cell-type-specific synchronization of neural activity in FEF with V4 during attention. Neuron. 2012 Feb 9;73(3):581-94
- Han X. Optogenetics in the nonhuman primate. Prog Brain Res. 2012;196:215-33
- Kim KM, Baratta MV, Yang A, Lee D, Boyden ES, Fiorillo CD. Optogenetic mimicry of the transient activation of dopamine neurons by natural reward is sufficient for operant reinforcement. PLoS One. 2012;7(4):e33612. Epub 2012 Apr 10
- Kittelberger K, Hur EE, Sazegar S, Keshavan V, Kocsis B. Comparison of the effects of acute and chronic administration of ketamine on hippocampal oscillations: relevance for the NMDA receptor hypofunction model of schizophrenia. Brain Struct Funct. 2012 Apr;217(2):395-409. Epub 2011 Oct 7
- Kocsis B. Differential Role of NR2A and NR2B Subunits in N-Methyl-D-Aspartate Receptor Antagonist-Induced Aberrant Cortical Gamma Oscillations. Biol Psychiatry. 2012 Jun 1;71(11):987-95. Epub 2011 Nov 4
- Kocsis B. State dependent increase of cortical gamma activity during REM sleep after selective blockade of NR2B subunit containing NMDA receptors. Sleep
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- Kramer MA, Cash SS. Epilepsy as a Disorder of Cortical Network Organization, Neuroscientist. 2012 Jan 10. [Epub ahead of print]
- Larson E, Maddox RK, Perrone BP, Sen K, Billimoria CP. Neuron-specific stimulus masking reveals interference in spike timing at the cortical level. J Assoc Res Otolaryngol. 2012 Feb;13(1):119-29. Epub 2011 Nov 29
- Lepage KQ, Macdonald CJ, Eichenbaum H, Eden UT. The statistical analysis of partially confounded covariates important to neural spiking. J Neurosci Methods. 2012 Apr 15;205(2):295-304. Epub 2012 Jan 17
- Maddox RK, Billimoria CP, Perrone BP, Shinn-Cunningham BG, Sen K. Competing sound sources reveal spatial effects in cortical processing. PLoS Biol. 2012 May;10(5):e1001319. Epub 2012 May 1
- Madisen L, Mao T, Koch H, Zhuo JM, Berenyi A, Fujisawa S, Hsu YW, Garcia AJ 3rd, Gu X, Zanella S, Kidney J, Gu H, Mao Y, Hooks BM, Boyden ES, BuzsÃ?Â¡ki G, Ramirez JM, Jones AR, Svoboda K, Han X, Turner EE, Zeng H. A toolbox of Cre-dependent optogenetic transgenic mice for light-induced activation and silencing. Nat Neurosci. 2012 Mar 25;15(5):793-802. doi: 10.1038/nn.3078
- Malerba P, Kopell N. Phase resetting reduces theta-gamma rhythmic interaction to a one-dimensional map. J Math Biol. 2012 Apr 21. [Epub ahead of print]
- McCarthy MM, Ching S, Whittington MA, Kopell N, Dynamical changes in neurological diseases and anesthesia, Curr Opin Neurobiol. 2012 Mar 23. [Epub ahead of print]
- Melzer S, Michael M, Caputi A, Eliava M, Fuchs EC, Whittington MA, Monyer H, Long-range-projecting GABAergic neurons modulate inhibition in hippocampus and entorhinal cortex, Science. 2012 Mar 23;335(6075):1506-10
- Munro E, Kopell N, Subthreshold somatic voltage in neocortical pyramidal cells can control whether spikes propagate from the axonal plexus to axon terminals: a model study, J Neurophysiol. 2012 May;107(10):2833-52. Epub 2012 Feb 29
- Peyrache A, Dehghani N, Eskandar EN, Madsen JR, Anderson WS, Donoghue JA, Hochberg LR, Halgren E, Cash SS, Destexhe A. Spatiotemporal dynamics of neocortical excitation and inhibition during human sleep. Proc Natl Acad Sci U S A. 2012 Jan 31;109(5):1731-6. Epub 2012 Jan 17
- Puig MV, Miller EK. The role of prefrontal dopamine d1 receptors in the neural mechanisms of associative learning. Neuron. 2012 Jun 7;74(5):874-86
- Siok CJ, Cogan SM, Shifflett LB, Doran AC, Kocsis B, Hajós M. Comparative analysis of the neurophysiological profile of group II metabotropic glutamate receptor activators and diazepam: effects on hippocampal and cortical EEG patterns in rats. Neuropharmacology. 2012 Jan;62(1):226-36. Epub 2011 Jul 26
- Temereanca S, Hämäläinen MS, Kuperberg GR, Stufflebeam SM, Halgren E, Brown EN, Eye movements modulate the spatiotemporal dynamics of word processing J Neurosci. 2012 Mar 28;32(13):4482-94
- Torii M, Hackett TA, Rakic P, Levitt P, Polley DB. EphA Signaling Impacts Development of Topographic Connectivity in Auditory Corticofugal Systems. Cereb Cortex. 2012 Apr 5. [Epub ahead of print]
2011
- Ahveninen J, Hämäläinen M, Jääskeläinen IP, Ahlfors SP, Huang S, Lin FH, Raij T, Sams M, Vasios CE, Belliveau JW. Attention-driven auditory cortex short-term plasticity helps segregate relevant sounds from noise. Proc Natl Acad Sci U S A. 2011 Mar 8;108(10):4182-7. Epub 2011 Feb 22.
- Ainsworth M, Lee S, Cunningham MO, Roopun AK, Traub RD, Kopell NJ, Whittington MA, Dual γ rhythm generators control interlaminar synchrony in auditory cortex. J Neurosci. 2011 Nov 23;31(47):17040-51. Erratum in: J Neurosci. 2012 Feb 22;32(8):2911
- Antzoulatos EG, Miller EK. Differences between neural activity in prefrontal cortex and striatum during learning of novel abstract categories. Neuron. 2011 Jul 28;71(2):243-9
- Barkat TR, Polley DB, Hensch TK. A critical period for auditory thalamocortical connectivity, Nat Neurosci. 2011 Jul 31;14(9):1189-94. doi: 10.1038/nn.2882
- Barnes TD, Mao JB, Hu D, Kubota Y, Dreyer AA, Stamoulis C, Brown EN, Graybiel AM, Advance cueing produces enhanced action-boundary patterns of spike activity in the sensorimotor striatum . J Neurophysiol. 2011 Apr;105(4):1861-78. Epub 2011 Feb 9
- Brown EN, Purdon PL, and Van Dort CJ (2011) "General Anesthesia and Altered States of Arousal: A Systems Neuroscience Analysis", Annu Rev Neurosci. 2011 Apr 20. [Epub ahead of print]
- Buffalo EA, Fries P, Landman R, Buschman TJ, Desimone R. Laminar differences in gamma and alpha coherence in the ventral stream. Proc Natl Acad Sci U S A. 2011 Jul 5;108(27):11262-7. Epub 2011 Jun 20
- Buschman TJ, Siegel M, Roy JE, Miller EK. Neural substrates of cognitive capacity limitations Proc Natl Acad Sci U S A. 2011 Jul 5;108(27):11252-5. Epub 2011 Jun 20
- Chu-Shore CJ., Kramer MA, Bianchi MT, Caviness VS, Cash SS (2011) "Network Analysis: Applications for the Developing Brain" J. Child Neurology, Apr; 26(2): 488-500
- Cimenser A, Purdon PL, Pierce ET, Walsh JL, Salazar-Gomez AF, Harrell PG, Tavares-Stoeckel C, Habeeb K, and Brown (2011) "Tracking brain states under general anesthesia by using global coherence analysis", EN. Proc Natl Acad Sci U S A. 2011 May 24;108(21):8832-7. Epub 2011 May 9.
- Cromer JA, Roy JE, Buschman TJ, Miller EK. Comparison of primate prefrontal and premotor cortex neuronal activity during visual categorization. Cogn Neurosci. 2011 Nov;23(11):3355-65. Epub 2011 Mar 31
- Cromer JA, Roy JE, Buschman TJ, Miller EK. Rapid association learning in the primate prefrontal cortex in the absence of behavioral reversals. J Cogn Neurosci. 2011 Jul;23(7):1823-8. Epub 2010 Jul 28
- Dehghani N, Cash SS, Halgren E, (2011) "Emergence of synchronous EEG spindles from asynchronous MEG spindles" Hum Brain Mapp. Feb 17 (2011).
- Eden UT, Amirnovin R, Eskandar EN. Using point process models to describe rhythmic spiking in the subthalamic nucleus of Parkinson's patients. Conf Proc IEEE Eng Med Biol Soc. 2011;2011:757-60
- Ghitza, O. (2011). "Linking speech perception and neurophysiology: speech decoding guided by cascaded oscillators locked to the input rhythm." Front. Psychology 2:130. doi: 10.3389/fpsyg.2011.00130.
- Haas JS, Zavala B, Landisman CE. Activity-dependent long-term depression of electrical synapses. Science. 2011 Oct 21;334(6054):389-93
- Hackett TA, Barkat TR, O'Brien BM, Hensch TK, Polley DB. Linking topography to tonotopy in the mouse auditory thalamocortical circuit. J Neurosci. 2011 Feb 23;31(8):2983-95
- Han X, Chow BY, Zhou H, Klapoetke NC, Chuong A, Rajimehr R, Yang A, Baratta MV, Winkle J, Desimone R, Boyden ES. A high-light sensitivity optical neural silencer: development and application to optogenetic control of non-human primate cortex. Front Syst Neurosci. 2011;5:18. Epub 2011 Apr 13
- Halassa MM, Siegle JH, Ritt JT, Ting JT, Feng G, Moore CI. Selective optical drive of thalamic reticular nucleus generates thalamic bursts and cortical spindles. Nat Neurosci. 2011 Jul 24;14(9):1118-20. doi: 10.1038/nn.2880
- Howe MW, Atallah HE, McCool A, Gibson DJ, Graybiel AM. Habit learning is associated with major shifts in frequencies of oscillatory activity and synchronized spike firing in striatum. Proc Natl Acad Sci U S A. 2011 Oct 4;108(40):16801-6. Epub 2011 Sep 26 Kahn I, Desai M, Knoblich U, Bernstein J, Henninger M, Graybiel AM, Boyden ES, Buckner RL, Moore CI. Characterization of the functional MRI response temporal linearity via optical control of neocortical pyramidal neurons. J Neurosci. 2011 Oct 19;31(42):15086-91
- Kajikawa Y, Schroeder CE. How local is the local field potential. Neuron. 2011 Dec 8;72(5):847-58
- Kerr CE, Jones SR, Wan Q, Pritchett DL, Wasserman RH, Wexler A., Villanueva JJ, et al. (2011) "Effects of mindfulness meditation training on anticipatory alpha modulation in primary somatosensory cortex", Brain research bulletin, doi:10.1016/j.brainresbull, 03.026
- Kim T, McKenna JT, Brown RE, Winston S, Chen L, Bolortuya Y, Strecker RE, Kocsis B, Deisseroth K, McCarley RW, and Basheer R (2011) "Expression of channelrhodopsins in parvalbumin-positive basal forebrain neurons", Sleep 2011; 34:A14
- Kramer MA, Eden UT, Lepage KQ, Kolaczyk ED, Bianchi MT, Cash SS. Emergence of persistent networks in long-term intracranial EEG recordings. J Neurosci. 2011 Nov 2;31(44):15757-67
- Kveraga K., Ghuman AS, Kassam KS, Aminoff EA, Hämäläinen MS, Chaumon M, and Bar M (2011) "Early onset of neural synchronization in the contextual associations network", Proceedings of the National Academy of Sciences of the United States of America, 108(8), 3389-3394. doi:10.1073/pnas.1013760108
- Lamus C, Hämäläinen MS, Temereanca S, Brown EN, Purdon PL. A spatiotemporal dynamic distributed solution to the MEG inverse problem Neuroimage. 2011 Nov 30. [Epub ahead of print]
- Lepage KQ, Kramer MA, Eden UT. The dependence of spike field coherence on expected intensity. Neural Comput. 2011 Sep;23(9):2209-41. Epub 2011 Jun 14
- Lindén H, Tetzlaff T, Potjans TC, Pettersen KH, Grün S, Diesmann M, Einevoll GT Modeling the spatial reach of the LFP. Neuron. 2011 Dec 8;72(5):859-72
- Long CJ, Purdon PL, Temereanca S, Desai NU, Hämäläinen MS, Brown EN State-space solutions to the dynamic magnetoencephalography inverse problem using high performance computing. Ann Appl Stat. 2011 Jun 1;5(2B):1207-1228
- MacDonald CJ, Lepage KQ, Eden UT, Eichenbaum H. Hippocampal "time cells" bridge the gap in memory for discontiguous events. Neuron. 2011 Aug 25;71(4):737-49
- McCarthy MM, Moore-Kochlacs C, Gu X, Boyden ES, Han X, Kopell N Striatal origin of the pathologic beta oscillations in Parkinson's disease Proc Natl Acad Sci U S A. 2011 Jul 12;108(28):11620-5. Epub 2011 Jun 22.
- Palva S, Kulashekhar S, Hämäläinen M, and Palva JM (2011) "Localization of Cortical Phase and Amplitude Dynamics during Visual Working Memory Encoding and Retention", The Journal of neuroscience: the official journal of the Society for Neuroscience, 31(13), 5013-5025. doi:10.1523/JNEUROSCI.5592-10.2011
- Siegle JH, Carlen M, Meletis K, Tsai LH, Moore CI, Ritt J. Chronically implanted hyperdrive for cortical recording and optogenetic control in behaving mice. Conf Proc IEEE Eng Med Biol Soc. 2011;2011:7529-32
- Siegle JH, Moore CI. Cortical circuits: finding balance in the brain. Curr Biol. 2011 Dec 6;21(23):R956-7. Epub 2011 Dec 5
- Sorman E, Wang D, Hajos M, and Kocsis B (2011) "Control of hippocampal theta rhythm by serotonin: Role of 5-HT2c receptors", Neuropharmacology, 61:489-494, 2011.
- Sternshein H, Agam Y, Sekuler R. EEG correlates of attentional load during multiple object tracking. PLoS One. 2011;6(7):e22660. Epub 2011 Jul 26
- Tort AB, Komorowski R, Kopell N, Eichenbaum H. A mechanism for the formation of hippocampal neuronal firing patterns that represent what happens where. Learn Mem. 2011 Oct 21;18(11):718-27. doi: 10.1101/lm.2307711. Print 2011
- Truccolo W, Donoghue J, Hochberg LR, Eskandar E, Madsen JR, Anderson WS, Brown EN, Halgren E, Cash SS (2011) "Dynamics of Single Neurons in Human Focal Epilepsy" Nature Neuroscience. May; 14(5):635-41
- Wan Q, Kerr C, Pritchett D, Hämäläinen M, Moore C, Jones S. Dynamics of dynamics within a single data acquisition session: variation in neocortical alpha oscillations in human MEG PLoS One. 2011;6(9):e24941. Epub 2011 Sep 22.
- Whittaker RG, Turnbull DM, Whittington MA, Cunningham MO. Impaired mitochondrial function abolishes gamma oscillations in the hippocampus through an effect on fast-spiking interneurons. Brain. 2011 Jul;134(Pt 7):e180; author reply e181. Epub 2011 Mar 4.
- Whittington MA, Roopun AK, Traub RD, Davies CH. Circuits and brain rhythms in schizophrenia: a wealth of convergent targets. Curr Opin Pharmacol. 2011 Oct;11(5):508-14. Review
- Yoshida T and Katz DB, (2011) "Control of Prestimulus Activity Related to Improved Sensory Coding within a Discrimination Task", J Neuroscience, 31:4101-4112
2010
- Brown EN, Lydic R, and Schiff ND (2010) "General anesthesia, sleep, and coma", N Engl J Med. 2010 Dec 30;363(27):2638-50.
- Cardin J, Carlen M, Meletis K, Knoblich U, Zhang F, Desseroth K, Tsai L-H, Moore CI (2010) "Targeted Optogenetic Stimulation and Recording of Neurons in vivo Using Cell Type-Specific Expression of Channelrhodopsin-2", Nature Protocols. 5:247-254.
- Ching S, Cimenser A, Purdon PL, Brown EN, and Kopell NJ (2010) "Thalamocortical model for a propofol-induced alpha-rhythm associated with loss of consciousness", Proc Natl Acad Sci U S A. 2010 Dec 28;107(52):22665-70. Epub 2010 Dec 13.
- Ciesielski KT, Ahlfors SP, Bedrick EJ, Kerwin AA, Hämäläinen MS (2010) "Top-down control of MEG alpha-band activity in children performing Categorical N-Back Task", 2010 Oct;48(12):3573-9. Epub 2010 Aug 14
- Csercsa R, Dombovári B, Fabó D, Wittner L, Eross L, Entz L, Sólyom A, Rásonyi G, Szucs A, Kelemen A, Jakus R, Juhos V, Grand L, Magony A, Halász P, Freund TF, Maglóczky Z, Cash SS, Papp L, Karmos G, Halgren E, Ulbert I., "Laminar analysis of slow wave activity in humans." Brain. 133(9):2814-2829 (2010).
- Dehghani N, Cash SS, Rossetti A, Chen CC, Halgren E (2010) "Magnetoencephalography Demonstrates Multiple Asynchronous Generators of Human Sleep Spindles", J Neurophysiol., 104(1):179-88
- Dehghani N, Cash SS, Chen CC, Hagler DJ Jr, Huang M, Dale AM, Halgren E., "Divergent cortical generators of MEG and EEG during human sleep spindles suggested by distributed source modeling," PLoS One. 2010 Jul 7;5(7):
- Dehghani N, Bédard C, Cash SS, Halgren E, Destexhe A., (2010) "Comparative power spectral analysis of simultaneous electroencephalographic and magnetoencephalographic recordings in humans suggests non-resistive extracellular media: EEG and MEG power spectra. J Comput Neurosci, epub Jun 17 (2010), ahead of print
- Dehghani N, Cash SS, Halgren E, "Topographical frequency dynamics within EEG and MEG sleep spindles" Clinical Neurophys. 122:229-235
- Huang J and Sekuler R (2010) "Attention protects the fidelity of visual memory: Behavioral and electrophysiological evidence",.Journal of Neuroscience", 30 13461-13471
- Jones S, Kerr C, Wan Q, Pritchett D, Hamalainen M, Moore CI (2010) "Cued Spatial Attention Drives Functionally-Relevant Modulation of the Mu Rhythm in Primary Somatosensory Cortex", Journal of Neuroscience, Oct 13;30(41):13760-5
- Jones SR, Kerr CE, Wan Q Pritchett DL, Hämäläinen MS, and Moore CI (2010) "Cued Spatial Attention Drives Representation-Specific Modulation of The Alpha Rhythm in Primary Somatosensory Cortex", J. Neurosci. 2010 30(41):13760-5.
- Keller K., Truccolo W., Gale J., Eskandar E., Thesen T., Carlson C., Devinsky O., Kuzniecky R., Doyle W., Madsen J., Schomer D., Mehta A., Brown E.N., Halgren E., Cash S.S., "Distinct Neuronal Firing Types During Interictal Epileptiform Discharges in the Human Cortex," Brain. Jun;133(Pt 6):1668-81. (2010).
- Kocsis B, and Kittelberger K (2010) "The effect of NMDA antagonism on hippocampal oscillations in freely moving rats", Relevance to schizophrenia, FENS 2010-01-31
- Kopell N, Kramer MA, Malerba P, and Whittington MA (2010) "Are different rhythms good for different functions?", Front Hum Neurosci, 4:187
- Kramer MA, Eden UT, Kolaczyk ED, Zepeda R, Eskandar EN, and Cash SS, (2010) "Coalescence and Fragmentation of Cortical Networks During Focal Seizures," J. Neurosci. Jul 28; 30(30):10076-85
- Lanre-Amos T and Kocsis B (2010) "Hippocampal oscillations in the rodent model of schizophrenia induced by amygdala GABA receptor blockade", Frontiers in Psychiatry, 1:132, 2010
- Moore CI, Carlen M, Knoblich U, and Cardin J (2010) "Neocortical Interneurons: from Diversity, Strength", Cell, Jul 23;142(2):189 -93
- Raij T, Ahveninen J, Lin FH, Witzel T, Jääskeläinen IP, Letham B, Israeli E, Sahyoun C, Vasios C, Stufflebeam S, Hämäläinen M, Belliveau JW (2010) "", Eur J Neurosci, May;31(10):1772-82
- Tort ABL, Fontanini A, Kramer MA, Jones LM, Kopell N, Katz DB (2010) "Cortical networks produce three distinct 7-12 Hz rhythms during single sensory responses in the awake rat", The Journal of Neuroscience, 24 March 2010, 30:12
- Tort ABL, Komorowsi R. Eichenbaum H, and, Kopell N (2010) "Measuring phase-amplitude coupling between neural oscillations", Journal of Neurophysiology 104:1195-210.
- Vaina LM, Calabro F, Lin,FH, Hamalainen M (2010) "Long-range coupling of prefrontal cortex and visual (MT) or polysensory (STP) cortical areas in motion perception", Biomag 2010, pp. 197-201, Springer Verlag
- Vierling-Claassen D, Cardin JA, Moore CI, Jones SR (2010) "Computational modeling of distinct neocortical oscillations driven by cell-type selective optogenetic drive: Separable resonant circuits controlled by low-threshold spiking and fast-spiking interneurons", Frontiers in Human Neuroscience, 2010 Nov 22, 4:198.
- Welbert P, Nguy L, Kocsis B (2010) "The effect of median raphe stimulation on the frequency and amplitude of hippocampal theta rhythm", FENS 2010-01-31
- Ziegler DA. Pritchett,DL, Hosseini-Varnamkhasti P, Corkin S, Hämäläinen M, Moore CI, and Jones SR (2010) "Transformations in oscillatory activity and evoked responses in primary somatosensory cortex in middle age: a combined computational neural modeling and MEG study", NeuroImage, 52(3), 897-912. doi:10.1016/j.neuroimage.2010.02.004
2009
- Feingold J, DePasquale BD, and Graybiel AM (2009) "Modulation of beta power in the prefrontal cortex and caudate nucleus of monkeys during self-timed sequential arm movements. Program No. 863.4. 2009, Neuroscience Meeting Planner. Chicago, IL: Society for Neuroscience, 2009. Online.
- Ghitza, O. and Greenberg, S. (2009). "On the possible role of brain rhythms in speech perception: Intelligibility of time compressed speech with periodic and aperiodic insertions of silence." Phonetica 66:113–126. doi:10.1159/000208934
- Thorn CA and Graybiel AM (2009) "Projection neurons are entrained to different local field frequencies in dorsomedial and dorsolateral striatum", Program No. 567.9. 2009 Neuroscience Meeting Planner. Chicago, IL: Society for Neuroscience, 2009. Online.
Book Chapters
- Jones SR. "Biophysically Principled Computational Neural Network Modeling of Magneto-/Electro- Encephalography Measured Human Brain Oscillations". In: Springer Neuromethods textbook series (#67) Neuronal Network Analysis; Editors: T. Fellin and M. Hallasa. 2011
- Miller, E.K. and Buschman, T.J. (in press) Neural mechanisms for the executive control of attention. In: Oxford's Handbook of Attention, Kastner S. and Nobre, K. (eds). Oxford University Press.
- Miller, E.K. and Wallis, J.D. (in press) The prefrontal cortex and executive brain functions. In : Fundamental Neuroscience, 4th edition.
- Duncan, J. and Miller, E.K. (in press) Adaptive neural coding in frontal and parietal cortex. In: Stuss, D.T. and Knight, R.T. (Eds). Principles of Frontal Lobe Function: Second Edition.
- Miller,E.K. and Buschman, T.J. (2012) Top-Down Control of Attention by Rhythmic Neural Computations. In: Posner, M.I. (ed) Cognitive Neuroscience of Attention New York: Guilford Press.


ultra.input_layer package¶
Submodules¶
ultra.input_layer.base_input_feed module¶
The basic class that contains all the API needed for the implementation of a input data feed.
-
class
ultra.input_layer.base_input_feed.
BaseInputFeed
(model, batch_size, hparam_str, session)¶ Bases:
abc.ABC
This class implements a input layer for unbiased learning to rank experiments.
-
MAX_SAMPLE_ROUND_NUM
= 100¶
-
abstract
__init__
(model, batch_size, hparam_str, session)¶ Create the model.
- Parameters
model – (BasicModel) The model we are going to train.
batch_size – the size of the batches generated in each iteration.
hparam_str – the hyper-parameters for the input layer.
session – the current tensorflow Session (used for online learning).
-
abstract
get_batch
(data_set, check_validation=False)¶ Get a random batch of data, prepare for step. Typically used for training.
To feed data in step(..) it must be a list of batch-major vectors, while data here contains single length-major cases. So the main logic of this function is to re-index data cases to be in the proper format for feeding.
- Parameters
data_set – (Raw_data) The dataset used to build the input layer.
check_validation – (bool) Set True to ignore data with no positive labels.
- Returns
a feed dictionary for the next step info_map: a dictionary contain some basic information about the batch (for debugging).
- Return type
input_feed
-
abstract
get_data_by_index
(data_set, index, check_validation=False)¶ Get one data from the specified index, prepare for step.
- Parameters
data_set – (Raw_data) The dataset used to build the input layer.
index – the index of the data
check_validation – (bool) Set True to ignore data with no positive labels.
- Returns
The triple (docid_inputs, decoder_inputs, target_weights) for the constructed batch that has the proper format to call step(…) later.
-
abstract
get_next_batch
(index, data_set, check_validation=False)¶ - Get the next batch of data from a specific index, prepare for step.
Typically used for validation.
To feed data in step(..) it must be a list of batch-major vectors, while data here contains single length-major cases. So the main logic of this function is to re-index data cases to be in the proper format for feeding.
- Parameters
index – the index of the data before which we will use to create the data batch.
data_set – (Raw_data) The dataset used to build the input layer.
check_validation – (bool) Set True to ignore data with no positive labels.
- Returns
a feed dictionary for the next step info_map: a dictionary contain some basic information about the batch (for debugging).
- Return type
input_feed
-
static
preprocess_data
(data_set, hparam_str, exp_settings)¶ Preprocess the data for model creation based on the input feed.
- Parameters
data_set – (Raw_data) The dataset used to build the input layer.
hparam_str – the hyper-parameters for the input layer.
exp_settings – (dictionary) The dictionary containing the model settings.
-
ultra.input_layer.click_models module¶
ultra.input_layer.click_simulation_feed module¶
Simulate click data based on human annotations.
See the following paper for more information on the simulation data.
Qingyao Ai, Keping Bi, Cheng Luo, Jiafeng Guo, W. Bruce Croft. 2018. Unbiased Learning to Rank with Unbiased Propensity Estimation. In Proceedings of SIGIR ‘18
-
class
ultra.input_layer.click_simulation_feed.
ClickSimulationFeed
(model, batch_size, hparam_str, session=None)¶ Bases:
ultra.input_layer.base_input_feed.BaseInputFeed
Simulate clicks based on human annotations.
This class implements a input layer for unbiased learning to rank experiments by simulating click data based on both the human relevance annotation of each query-document pair and a predefined click model.
-
__init__
(model, batch_size, hparam_str, session=None)¶ Create the model.
- Parameters
model – (BasicModel) The model we are going to train.
batch_size – the size of the batches generated in each iteration.
hparam_str – the hyper-parameters for the input layer.
-
get_batch
(data_set, check_validation=False)¶ Get a random batch of data, prepare for step. Typically used for training.
To feed data in step(..) it must be a list of batch-major vectors, while data here contains single length-major cases. So the main logic of this function is to re-index data cases to be in the proper format for feeding.
- Parameters
data_set – (Raw_data) The dataset used to build the input layer.
check_validation – (bool) Set True to ignore data with no positive labels.
- Returns
a feed dictionary for the next step info_map: a dictionary contain some basic information about the batch (for debugging).
- Return type
input_feed
-
get_data_by_index
(data_set, index, check_validation=False)¶ Get one data from the specified index, prepare for step.
- Parameters
data_set – (Raw_data) The dataset used to build the input layer.
index – the index of the data
check_validation – (bool) Set True to ignore data with no positive labels.
- Returns
The triple (docid_inputs, decoder_inputs, target_weights) for the constructed batch that has the proper format to call step(…) later.
-
get_next_batch
(index, data_set, check_validation=False)¶ - Get the next batch of data from a specific index, prepare for step.
Typically used for validation.
To feed data in step(..) it must be a list of batch-major vectors, while data here contains single length-major cases. So the main logic of this function is to re-index data cases to be in the proper format for feeding.
- Parameters
index – the index of the data before which we will use to create the data batch.
data_set – (Raw_data) The dataset used to build the input layer.
check_validation – (bool) Set True to ignore data with no positive labels.
- Returns
a feed dictionary for the next step info_map: a dictionary contain some basic information about the batch (for debugging).
- Return type
input_feed
-
prepare_sim_clicks_with_index
(data_set, index, docid_inputs, letor_features, labels, check_validation=True)¶
-
ultra.input_layer.deterministic_online_simulation_feed module¶
Simulate online learning process and click data based on human annotations.
See the following paper for more information on the simulation data.
Qingyao Ai, Keping Bi, Cheng Luo, Jiafeng Guo, W. Bruce Croft. 2018. Unbiased Learning to Rank with Unbiased Propensity Estimation. In Proceedings of SIGIR ‘18
-
class
ultra.input_layer.deterministic_online_simulation_feed.
DeterministicOnlineSimulationFeed
(model, batch_size, hparam_str, session)¶ Bases:
ultra.input_layer.base_input_feed.BaseInputFeed
Simulate online learning to rank and click data based on human annotations.
This class implements a input layer for online learning to rank experiments by simulating click data based on both the human relevance annotation of each query-document pair and a predefined click model.
-
__init__
(model, batch_size, hparam_str, session)¶ Create the model.
- Parameters
model – (BasicModel) The model we are going to train.
batch_size – the size of the batches generated in each iteration.
hparam_str – the hyper-parameters for the input layer.
session – the current tensorflow Session (used for online learning).
-
get_batch
(data_set, check_validation=False)¶ Get a random batch of data, prepare for step. Typically used for training.
To feed data in step(..) it must be a list of batch-major vectors, while data here contains single length-major cases. So the main logic of this function is to re-index data cases to be in the proper format for feeding.
- Parameters
data_set – (Raw_data) The dataset used to build the input layer.
check_validation – (bool) Set True to ignore data with no positive labels.
- Returns
a feed dictionary for the next step info_map: a dictionary contain some basic information about the batch (for debugging).
- Return type
input_feed
-
get_data_by_index
(data_set, index, check_validation=False)¶ Get one data from the specified index, prepare for step.
- Parameters
data_set – (Raw_data) The dataset used to build the input layer.
index – the index of the data
check_validation – (bool) Set True to ignore data with no positive labels.
- Returns
The triple (docid_inputs, decoder_inputs, target_weights) for the constructed batch that has the proper format to call step(…) later.
-
get_next_batch
(index, data_set, check_validation=False)¶ - Get the next batch of data from a specific index, prepare for step.
Typically used for validation.
To feed data in step(..) it must be a list of batch-major vectors, while data here contains single length-major cases. So the main logic of this function is to re-index data cases to be in the proper format for feeding.
- Parameters
index – the index of the data before which we will use to create the data batch.
data_set – (Raw_data) The dataset used to build the input layer.
check_validation – (bool) Set True to ignore data with no positive labels.
- Returns
a feed dictionary for the next step info_map: a dictionary contain some basic information about the batch (for debugging).
- Return type
input_feed
-
prepare_true_labels_with_index
(data_set, index, docid_inputs, letor_features, labels, check_validation=False)¶
-
simulate_clicks_online
(input_feed, check_validation=False)¶ Simulate online environment by reranking documents and collect clicks.
- Parameters
input_feed – (dict) The input_feed data.
check_validation – (bool) Set True to ignore data with no positive labels.
- Returns
a feed dictionary for the next step info_map: a dictionary contain some basic information about the batch (for debugging).
- Return type
input_feed
-
ultra.input_layer.direct_label_feed module¶
Create batch data directly based on labels.
See the following paper for more information on the simulation data.
Qingyao Ai, Keping Bi, Cheng Luo, Jiafeng Guo, W. Bruce Croft. 2018. Unbiased Learning to Rank with Unbiased Propensity Estimation. In Proceedings of SIGIR ‘18
-
class
ultra.input_layer.direct_label_feed.
DirectLabelFeed
(model, batch_size, hparam_str, session=None)¶ Bases:
ultra.input_layer.base_input_feed.BaseInputFeed
Feed data with human annotations.
This class implements a input layer for unbiased learning to rank experiments by directly feeding the model with the true labels of each query-document pair.
-
__init__
(model, batch_size, hparam_str, session=None)¶ Create the model.
- Parameters
model – (BasicModel) The model we are going to train.
batch_size – the size of the batches generated in each iteration.
hparam_str – the hyper-parameters for the input layer.
-
get_batch
(data_set, check_validation=False)¶ Get a random batch of data, prepare for step. Typically used for training.
To feed data in step(..) it must be a list of batch-major vectors, while data here contains single length-major cases. So the main logic of this function is to re-index data cases to be in the proper format for feeding.
- Parameters
data_set – (Raw_data) The dataset used to build the input layer.
check_validation – (bool) Set True to ignore data with no positive labels.
- Returns
a feed dictionary for the next step info_map: a dictionary contain some basic information about the batch (for debugging).
- Return type
input_feed
-
get_data_by_index
(data_set, index, check_validation=False)¶ Get one data from the specified index, prepare for step.
- Parameters
data_set – (Raw_data) The dataset used to build the input layer.
index – the index of the data
check_validation – (bool) Set True to ignore data with no positive labels.
- Returns
The triple (docid_inputs, decoder_inputs, target_weights) for the constructed batch that has the proper format to call step(…) later.
-
get_next_batch
(index, data_set, check_validation=False)¶ - Get the next batch of data from a specific index, prepare for step.
Typically used for validation.
To feed data in step(..) it must be a list of batch-major vectors, while data here contains single length-major cases. So the main logic of this function is to re-index data cases to be in the proper format for feeding.
- Parameters
index – the index of the data before which we will use to create the data batch.
data_set – (Raw_data) The dataset used to build the input layer.
check_validation – (bool) Set True to ignore data with no positive labels.
- Returns
a feed dictionary for the next step info_map: a dictionary contain some basic information about the batch (for debugging).
- Return type
input_feed
-
prepare_true_labels_with_index
(data_set, index, docid_inputs, letor_features, labels, check_validation=True)¶
-
ultra.input_layer.interleaving_deterministic_online_simulation_feed module¶
Simulate online learning process and click data based on human annotations.
See the following paper for more information on the simulation data.
Qingyao Ai, Keping Bi, Cheng Luo, Jiafeng Guo, W. Bruce Croft. 2018. Unbiased Learning to Rank with Unbiased Propensity Estimation. In Proceedings of SIGIR ‘18
-
class
ultra.input_layer.interleaving_deterministic_online_simulation_feed.
InterleavingDeterministicOnlineSimulationFeed
(model, batch_size, hparam_str, session)¶ Bases:
ultra.input_layer.base_input_feed.BaseInputFeed
Simulate online learning to rank and click data based on human annotations.
This class implements a input layer for online learning to rank experiments by simulating click data based on both the human relevance annotation of each query-document pair and a predefined click model.
-
__init__
(model, batch_size, hparam_str, session)¶ Create the model.
- Parameters
model – (BasicModel) The model we are going to train.
batch_size – the size of the batches generated in each iteration.
hparam_str – the hyper-parameters for the input layer.
session – the current tensorflow Session (used for online learning).
-
get_batch
(data_set, check_validation=False)¶ Get a random batch of data, prepare for step. Typically used for training.
To feed data in step(..) it must be a list of batch-major vectors, while data here contains single length-major cases. So the main logic of this function is to re-index data cases to be in the proper format for feeding.
- Parameters
data_set – (Raw_data) The dataset used to build the input layer.
check_validation – (bool) Set True to ignore data with no positive labels.
- Returns
a feed dictionary for the next step info_map: a dictionary contain some basic information about the batch (for debugging).
- Return type
input_feed
-
get_data_by_index
(data_set, index, check_validation=False)¶ Get one data from the specified index, prepare for step.
- Parameters
data_set – (Raw_data) The dataset used to build the input layer.
index – the index of the data
check_validation – (bool) Set True to ignore data with no positive labels.
- Returns
The triple (docid_inputs, decoder_inputs, target_weights) for the constructed batch that has the proper format to call step(…) later.
-
get_next_batch
(index, data_set, check_validation=False)¶ - Get the next batch of data from a specific index, prepare for step.
Typically used for validation.
To feed data in step(..) it must be a list of batch-major vectors, while data here contains single length-major cases. So the main logic of this function is to re-index data cases to be in the proper format for feeding.
- Parameters
index – the index of the data before which we will use to create the data batch.
data_set – (Raw_data) The dataset used to build the input layer.
check_validation – (bool) Set True to ignore data with no positive labels.
- Returns
a feed dictionary for the next step info_map: a dictionary contain some basic information about the batch (for debugging).
- Return type
input_feed
-
prepare_true_labels_with_index
(data_set, index, docid_inputs, letor_features, labels, check_validation=False)¶
-
simulate_clicks_online
(input_feed, check_validation=False)¶ Simulate online environment by reranking documents and collect clicks.
- Parameters
input_feed – (dict) The input_feed data.
check_validation – (bool) Set True to ignore data with no positive labels.
- Returns
a feed dictionary for the next step info_map: a dictionary contain some basic information about the batch (for debugging).
- Return type
input_feed
-
ultra.input_layer.stochastic_online_simulation_feed module¶
Simulate online learning process and click data based on human annotations.
See the following paper for more information on the simulation data.
Qingyao Ai, Keping Bi, Cheng Luo, Jiafeng Guo, W. Bruce Croft. 2018. Unbiased Learning to Rank with Unbiased Propensity Estimation. In Proceedings of SIGIR ‘18
-
class
ultra.input_layer.stochastic_online_simulation_feed.
StochasticOnlineSimulationFeed
(model, batch_size, hparam_str, session)¶ Bases:
ultra.input_layer.base_input_feed.BaseInputFeed
Simulate online learning to rank and click data based on human annotations.
This class implements a input layer for online learning to rank experiments by simulating click data based on both the human relevance annotation of each query-document pair and a predefined click model.
-
__init__
(model, batch_size, hparam_str, session)¶ Create the model.
- Parameters
model – (BasicModel) The model we are going to train.
batch_size – the size of the batches generated in each iteration.
hparam_str – the hyper-parameters for the input layer.
session – the current tensorflow Session (used for online learning).
-
get_batch
(data_set, check_validation=False)¶ Get a random batch of data, prepare for step. Typically used for training.
To feed data in step(..) it must be a list of batch-major vectors, while data here contains single length-major cases. So the main logic of this function is to re-index data cases to be in the proper format for feeding.
- Parameters
data_set – (Raw_data) The dataset used to build the input layer.
check_validation – (bool) Set True to ignore data with no positive labels.
- Returns
a feed dictionary for the next step info_map: a dictionary contain some basic information about the batch (for debugging).
- Return type
input_feed
-
get_data_by_index
(data_set, index, check_validation=False)¶ Get one data from the specified index, prepare for step.
- Parameters
data_set – (Raw_data) The dataset used to build the input layer.
index – the index of the data
check_validation – (bool) Set True to ignore data with no positive labels.
- Returns
The triple (docid_inputs, decoder_inputs, target_weights) for the constructed batch that has the proper format to call step(…) later.
-
get_next_batch
(index, data_set, check_validation=False)¶ - Get the next batch of data from a specific index, prepare for step.
Typically used for validation.
To feed data in step(..) it must be a list of batch-major vectors, while data here contains single length-major cases. So the main logic of this function is to re-index data cases to be in the proper format for feeding.
- Parameters
index – the index of the data before which we will use to create the data batch.
data_set – (Raw_data) The dataset used to build the input layer.
check_validation – (bool) Set True to ignore data with no positive labels.
- Returns
a feed dictionary for the next step info_map: a dictionary contain some basic information about the batch (for debugging).
- Return type
input_feed
-
prepare_true_labels_with_index
(data_set, index, docid_inputs, letor_features, labels, check_validation=False)¶
-
simulate_clicks_online
(input_feed, check_validation=False)¶ Simulate online environment by reranking documents and collect clicks.
- Parameters
input_feed – (dict) The input_feed data.
check_validation – (bool) Set True to ignore data with no positive labels.
- Returns
a feed dictionary for the next step info_map: a dictionary contain some basic information about the batch (for debugging).
- Return type
input_feed
-


Live
{% include "shows/_gig_upcoming.html" with gig_list=gig_list.upcoming %}
Past Shows
{% for gig in gig_list.past %}{{ gig.date|date:"M j" }}
{{ gig.date|date:"Y" }}
{{ gig.venue.name }}
{{ gig.venue.city }}
{{ gig.description|safe }}


LifeGPA helps you grade your life so you can improve it, one habit at a time.
Create habits to track things you want to start doing everyday.
Check habits off the list as you get to them.
Create habits to track things you need to stop doing.
Check off each day where you resist the habit.
GPA calculated daily as well as for the past 30, 60, and 90 days
to help keep you on track.
Create your habits.
Keep track of your habits every day.
See your GPA change over time.


Nuzlog
New Game
Save/Load Game
Title
Game
Trainer Name
All Enabled
Journal
Delete Last
Party
No Pokemon
No Pokemon
No Pokemon
No Pokemon
No Pokemon
No Pokemon
Add Pokemon
Check PC
Record Death
Cemetery
Copyright © 2016 Fatih Ridha (TechForce)
Create New Game
Title*
Game*
Name*
Disable Genders
Disable Natures
Disable Abilities
Close
Here is the current save of your game. Copy-paste this somewhere so you can load it later. To load a game, simply copy-paste the save here and press "Load Game".


form-label
Default
Default
Colors
Primary
Secondary
Success
Danger
Warning
Info
Light
Dark
Muted
White
Background Colors
Primary
Secondary
Success
Danger
Warning
Info
Light
Dark
Muted
White


Google Cloud Next 17, happened on March 8-10, 2017 in San Francisco, California, is a three day immersive event that brings together executives, customers, partners, developers, IT decision makers, and Google Engineers to build the future of the cloud.
GDG Philippines is organizing a Google Next 17 Extended Manila!
|
Globe Telepark - Valero Valero Street, Makati, Metro Manila |
|
|
April 28, 2017 - Friday 1:00PM - 6:00PM |
Schedule may change without notice.
| 12.30PM | Registration |
| 1PM | Opening Remarks / House Rules / About GDG Philippinesby Jomar Tigcal / Jonah Micah Mananghaya / Juvar Abrera |
| 1.10PM | Keynote + Google Cloud Next Experienceby Albert Padin |
| 1.50PM | Google Cloud and Firebase Mobile Ecosystemby Benj Tupas |
| 2.20PM | Career Journey with Google Cloudby Bryle Joseph |
| 2.50PM | Cloud Natural Language APIby Brian Baquiran |
| 3.20PM | Cloud SQLby John Paul Alcala |
| 4PM | Codelabs with Mentors |
| 5.50PM | Closing Remarks |
|
Albert Padin Google Developer Expert in Google Cloud |
|
|
Benj Tupas CEO & Founder of Funbuddies |
|
|
Brian Baquiran Managing Director for Engineering at Pez.AI |
|
|
John Paul Alcala Chief Technology Officer of Ayannah |
|
|
Bryle Joseph Application Developer - Analyst, Accenture Cloud First Team |


This is a simple web application that uses ones Date, Month and Year of birth to determine the day of the week when someone was born.It goes a step further depending on the users gender and the day of the week bord determine their Akan i.e Ghanian Name. Follow the instructions below to get results;
Provide Date of Birth
Provide Genderh
Submit to get your Akan Name
| # | Day of the week | Female Name | Male Name |
|---|---|---|---|
| 1 | Sunday | Akosua | Kwasi |
| 2 | Monday | Adwoa | Kwadwo |
| 3 | Teusday | Abenaa | Kwabena |
| 4 | Wednesday | Akua | Kwaku |
| 5 | Thursday | Yaa | Yaw |
| 6 | Friday | Afua | Kofi |
| 7 | Saturday | Ama | Kwame |


ProcessIR¶
-
class
process.process.
ProcessIR
(flist)¶ Bases:
object
Class for processing IR data
- Parameters
flist (list) – list of filenames to process
Attributes Summary
list of filenames to process
Methods Summary
Run the decomposition of the IMA files into there individual reads
edit_hdr
(hdr)Convenience function for updating the header of each read
make_exts
(fname)Generate a list of tuples corresponding to each read.
write_out
(fname)Write out each read in the IMA file into an individual FITS file
Attributes Documentation
-
flist
¶ list of filenames to process
Methods Documentation
-
decompose
()¶ Run the decomposition of the IMA files into there individual reads
-
edit_hdr
(hdr)¶ Convenience function for updating the header of each read
When we create the new FITS files for each individual read, we must update the EXTVER keyword to be 1. This is because there is only one version of each extension type per read.
- Parameters
hdr (astropy.io.fits.Header) – FITS Header
- Returns
Updated version of the input header
- Return type
-
make_exts
(fname)¶ Generate a list of tuples corresponding to each read.
- Parameters
fname (str) – Filename of image to process
-
write_out
(fname)¶ Write out each read in the IMA file into an individual FITS file
Each read will be saved to a file named read_N.fits, where N corresponds to the read number in they order they occured. This means the 4th read will be saved to the file read_4.fits. Note, this is different than the order they are stored in the IMA file.
- Parameters
fname (str) – filename of IMA


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I am currently employed as a Ruby on Rails application developer and specialize in HTML/CSS and design. I enjoy using SLIM, Sass, Sketch, and other technologies that start with the letter S. I'm particularly passionate about accessibility, object oriented CSS, and user interface design.
I enjoy the process of understanding a user's needs, developing a solution to those needs, testing the solution, and integrating results from user testing to create an even better solution. I can tell the user story eloquently, both in person and through visual media. I love to craft positive user experiences and help users understand and embrace provided solutions.
I am an alumnus of Dev Bootcamp Chicago and the Illinois Institute of Technology. I have a degree in psychology and have taken extensive coursework in physics and mathematics.
In the past, I've shipped library books across the world, designed and printed business cards just in time for your big meeting, fit wigs on children and drag queens, and rapped about poop for a living. I've died in several plays and killed off dozens of characters in roleplaying games.
Wanna chat?


Lyrics File Downloads
All songs in the order in which they appeared on released albums (up to and including
Blood on the Tracks
) (251 KiB)
All unique songs (up to and including
Blood on the Tracks
) (218 KiB)


{% comment %} Creates the HTML code for a select component. Inputs : - panel : "panelId" : Unique Id "displayName" : The title of the panel "jsonName" : The name of the parameter to be used in the REST calls. "content" : An object, with the parameters: "dict" : A dictionnary containing the items to populate the select. Each item has a 'key' and a 'description' {% endcomment %}


July 2016 - Now
W
Frontend Developer
Schibsted Tech Polska
- HTML5 & CSS3
- React & Redux
- ES2015
- Webpack, Gulp
--- layout: page title: Work permalink: /work/ ---
July 2016 - Now
May 2015 - June 2016
August 2013 - April 2015
June 2013 - July 2013
September 2012 - May 2013
June 2012 - September 2012
October 2010 - Februrary 2013
Specialization: Web applications and databases


MathJax uses an object-oriented programming model for its main components, such as the Input jax, Output jax, and Element jax. The model is intended to be light-weight and is based on JavaScript’s prototype inheritance mechanism. Object classes are created by making subclasses of MathJax.Object or one of its subclasses, and are instantiated by calling the object class as you would a function.
For example:
MathJax.Object.Foo = MathJax.Object.Subclass({
Init: function (x) {this.setX(x)},
getX: function () {return this.x},
setX: function (x) {this.x = x}
});
var foo = MathJax.Object.Foo("bar");
foo.getX(); // returns "bar"
foo.setX("foobar");
foo.getX(); // returns "foobar"
Object classes can have static properties and methods, which are accessed via the object class variable. E.g., MathJax.Object.Foo.SUPER or MathJax.Object.Foo.Augment() for the object in the example above. Static values are not inherited by subclasses.
Pointer to the super class for this subclass. (It is a reference to MathJax.Object in the example above.)
Creates a subclass of the given class using the contents of the def object to define new methods and properties of the object class, and the contents of the optional static object to define new static methods and properties.
| Parameters : |
|
|---|---|
| Returns : | the new object class |
Adds new properties and methods to the class prototype. All instances of the object already in existence will receive the new properties and methods automatically.
| Parameters : |
|
|---|---|
| Returns : | the object class itself |
Pointer to the constructor function for this class. E.g., foo.constructor would be a reference to MathJax.Object.Foo in the example above.
An optional function that is called when an instance of the class is created. When called, the this variable is set to the newly instantiated object, and the data is whatever was passed to the object constructor. For instance, in the example above, the variable foo is created by calling MathJax.Object.Foo("bar"), which calls the MathJax.Object.Foo object’s Init() method with data equal to "bar". If desired, the Init() method can create a different object, and return that, in which case this becomes the return value for the object constructor.
| Parameters : |
|
|---|---|
| Returns : | null or the object to be returned by the constructor |
Returns true if the object is an instance of the given class, or of a subclass of the given class, and false otherwise. So using the foo value defined above,
foo.isa(MathJax.Object); // returns true
foo.isa(MathJax.Object.Foo); // returns true
foo.isa(MathJax.InputJax); // returns false
Checks if the object has the given method and returns true if so, otherwise returns false. This allows you to test if an object has a particular function available before trying to call it (i.e., if an object implements a particular feature). For example:
foo.can("getX"); // returns true
foo.can("bar"); // returns false
Checks if the object has the given property and returns true if so, otherwise returns false. This allows you to test if an object has a particular property available before trying to use it. For example:
foo.has("getX"); // returns true
foo.has("x"); // returns true
foo.has("bar"); // returns false
If a subclass overrides a method of its parent class, it may want to call the original function as part of its replacement method. The semantics for this are a bit awkward, but work efficiently. Within a method, the value arguments.callee.SUPER refers to the super class, so you can access any method of the superclass using that. In order to have this refer to the current object when you call the super class, however, you need to use call() or apply() to access the given method.
For example, arguments.callee.SUPER.method.call(this,data) would call the superclass’ method and pass it data as its argument, properly passing the current object as this. Alternatively, you can use this.SUPER(arguments) in place of arguments.callee.SUPER. It is also possible to refer to the super class explicitly rather than through arguments.callee.SUPER, as in the following example:
MathJax.Class1 = MathJax.Object.Subclass({
Init: function(x) {this.x = x},
XandY: function(y) {return "Class1: x and y = " + this.x + " and " + y}
});
MathJax.Class2 = MathJax.Class1.Subclass({
XandY: function (y) {return "Class2: "+arguments.callee.SUPER.XandY.call(this,y)}
});
MathJax.Class3 = MathJax.Class2.Subclass({
XandY: function (y) {return "Class3: "+MathJax.Class2.prototype.XandY.call(this,y)}
});
MathJax.Class4 = MathJax.Class1.Subclass({
XandY: function (y) {return "Class4: "+this.SUPER(arguments).XandY.call(this,y)}
});
var foo = MathJax.Class2("foo");
foo.XandY("bar"); // returns "Class2: Class1: x and y = foo and bar"
var bar = MathJax.Class3("bar");
bar.XandY("foo"); // returns "Class3: Class2: Class1: x and y = bar and foo"
var moo = MathJax.Class4("moo");
moo.XandY("cow"); // returns "Class4: Class1: x and y = moo and cow"
Since both of these mechanisms are rather awkward, MathJax provides an alternative syntax that is easier on the programmer, but at the cost of some inefficiency in creating the subclass and in calling methods that access the super class.
Since most calls to the super class are to the overridden method, not to some other method, the method name and the call() are essentially redundant. You can get a more convenient syntax by wrapping the def for the Subclass() call in a call to MathJax.Object.SimpleSUPER(), as in the following example:
MathJax.Class1 = MathJax.Object.Subclass({
Init: function (x) {this.x = x},
XandY: function (y) {return "Class1: x and y = " + this.x + " and " + y}
});
MathJax.Class2 = MathJax.Class1.Subclass(
MathJax.Object.SimpleSUPER({
XandY: function (y) {return "Class2: "+this.SUPER(y)},
AnotherMethod: function () {return this.x} // it's OK if a method doesn't use SUPER
})
);
var foo = MathJax.Class2("foo");
foo.XandY("bar"); // returns "Class2: Class1: x and y = foo and bar"


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