The Top Five... High Fidelity
By Ross Anthony
Better title: "The Top Five." Of course you'd ask ... "The top five what?" And that's just where the advertisers would love to have you. Well, the top five most humiliating break ups for John Cusack, as a matter of fact.
Cusack, mug to camera, provides self-depreciating commentary to his dumpy little life as a used record store owner/operator on the more urban streets of Chicago. Fun flashbacks provide entertaining history of the top four heartbreakers as Cusack tragically lives through break up number five in real time.
 "I can't fire these guys, I hired them for three days a week and they started showing up everyday ... that was three years ago."  Black is ever on the attack aggressively defending his musical edicts.
But it's Cusack's cynical rambling that gives "HI-FI" it's biting irony. Here are just a few juicy quotes. The ever so diplomatic... "You f***ing bitch, let's work it out." And then on the human attraction phenomena... "It's what you like, not what you are like." Then there's the crude, yet clever ... "My guts have sh** for brains." Lastly, the nagging film-opening question ... "Which came first the music or the misery?" (referring to the depression itself vs. the depressing songs meant to ease/enable). Cusack spends much of the film trying to understand why he's such an "F***ing a**hole."
A little slow getting going, but fills out nicely with rough-toothed dialogue. Eclectic music snobs are sure to have a good time.
Danish actress Iben Hjejle (who plays the lead female role) sums the film well, "It's the most direct script about men's struggles with relationships I've ever read. It really surprised me that it has this honesty and I think it's very unusual for an American film to deal with subjects like this."
High Fidelity. Copyright © 2000. CrimeRated R. Starring John Cusack, Iben Hjejle, Todd Louiso, Jack Black, Lisa Bonet, Catherine Zeta-Jones, Joan Cusack, Tim Robbins, Lili Taylor, Natasha Gregson Wagner. Directed by Stephen Frears. Written by D.V. Devincetis & Steve Pink & John Cusack and Scott Rosenberg based on the book by Nick Hornby. Produced by Time Bevan, Rudd Simmons at Touchstone/Working Title/Dogstar/New Crime
Grade..........................A-
-- Copyright © 2000. Ross Anthony, currently based in Los Angeles, has scripted and shot documentaries, music videos, and shorts in 35 countries across North America, Europe, Africa and Asia. For more reviews visit: http://RossAnthony.com
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
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Related links: index of all rec.arts.movies.reviews reviews


In the Ruby Programming Language, the code is represented by two separate yet equally important collections: the Hash, which pair Keys with Values; and the Array, which orders items from zero. These are their stories.
Dun Dun DUN!!
This week we got into Ruby; a programming language that we will primarily be using during Dev Bootcamp. It's quite a feat to get into, but a lot of the features of the language make sense when you spend time playing around with it.
Two key components we learned were called Hashes and Arrays. These are two very similar, but distinct ways of collecting information using Ruby.This post will go over each, and attempt to clarify why you would use them.
The Array is an ordered index of any type of items (be it an integer, some text, or a number with a period, which in ruby is called a float). It is typically denoted using [brackets] and each item is separated by commas.
i.e. cool_people = ["aneliesse", "karla", "celso", "kristina", "jessica"]
The Hash is another kind of collection of items that stresses the connection between a key, and it's value. Hashes can also take on any type of item, but it's paired inside {curly brackets} with another item using "=>"
i.e. people_i_know = { "aneliesse" => "family", "celso" => "bff", "karla" => "family" , "kristina" => "boo"}
Even though we are collecting information in both, there are instances when an array is better to use than a hash, and vice versa.
Let's say you're a Night club owner, and you were fixin' to be the hottest night spot this side of the Mississippi, so you wanted to build a program to help you out. Perhaps you wanted to make some saturday night specials and you need to collect and use information about your patrons to do so.
You decide you'd like to give the first 50 people in the door on a saturday night a free shot of whiskey! As each person comes through the door, you take their name, and you give them a shot (Don't worry officers, they're all legal drinking age). In Ruby, you'd want to use an ARRAY to collect this information. Why? because:
- An Array prioritizes the order in which you enter information. so as you add each person's name to your array, it is counting towards 50. NOTE: in ruby, and in coding in general. when you're counting items in an array, you start with ZERO and go up from there. so the first person that comes in the door is person ZERO, not person ONE.
 Whenever they show up at the club they get their own table, and a dedicated server! Mira Que Fancy!
In ruby, you'd want to use a HASH to collect this information. Why? because:
- A Hash doesn't worry about the order your information is put in, only the value you give it. So, your regular "Crl Crrll" comes in, and he's the 9424th person to come in? Doesn't matter, HE'S a VIP and you'll treat him as such! Hash will also let you call the values and give you a list, regardless of where the items are in the Hash.
Starting to make sense?
To summarize, Hashes and Arrays are wonderful ways to collect and manipulate information within ruby, but knowing what's most important to you, between value and order, will help you decide which path to take.


PageRank implementation in JavaScript
JavaScript implementation of the PageRank Algorithm that was originally invented by Sergej Brin and Larry Page, the founders of Google, and built the original basis for their world famous search engine.
This implementation uses the power method to determine the rankings.
You can read more about PageRank in the original paper by Brin and Page,
Brin, S., Page, L., 1998. The Anatomy of a Large-Scale Hypertextual Web Search Engine. The Seventh International World Wide Web Conference, 14.-18. April 1998, S. 107-117
or the brilliant and extensive book by Langville and Meyer,
Langville, Amy N., and Carl D. Meyer. Google's PageRank and Beyond: The Science of Search Engine Rankings. Princeton University Press, 2006.


{{ SITE_NAME }} resources should be reusable by the greater scientific
community. To facilitate the reuse of your content, please adhere to the following reusability guidelines.
Files are provided in an open format such as text, csv, or WFDB, rather than a proprietary format.
Code must be provided in source format.
Data files must be machine readable.
Large regularly-sampled time-series data, especially waveforms, are provided in WFDB format.
Filenames must only contain letters, numbers, dashes, underscores, and dots.
If appropriate, provide a csv file named subject-info.csv containing information on all subjects, such as age and gender.
WFDB Files
Provide a list of all WFDB format records, named RECORDS. Example file. If you upload this file, you will see a link to view your project's waveforms in LightWAVE.
Provide a tab delimited list of all WFDB format annotation files, named ANNOTATORS. Example file.


{{> header}}
@eliendrae
You may download alternative versions of my resume on this site. The files are hosted on Dropbox and may have been modified since your last visit!
{{name}} {{description}}
{{.}}


A MonoBehaviour that interpolates a transform's position, rotation or scale.
More...
|
| void | Reset () |
| | Stops the transform in place and terminates any animations. More...
|
| |
| void | SetTargetPosition (Vector3 target) |
| | Sets the target position for the transform and if position wasn't already animating, fires the InterpolationStarted event. More...
|
| |
| void | SetTargetRotation (Quaternion target) |
| | Sets the target rotation for the transform and if rotation wasn't already animating, fires the InterpolationStarted event. More...
|
| |
| void | SetTargetLocalRotation (Quaternion target) |
| | Sets the target local rotation for the transform and if rotation wasn't already animating, fires the InterpolationStarted event. More...
|
| |
| void | SetTargetLocalScale (Vector3 target) |
| | Sets the target local scale for the transform and if scale wasn't already animating, fires the InterpolationStarted event. More...
|
| |
| void | SnapToTarget () |
| | Snaps to the final target and stops interpolating More...
|
| |
| void | StopInterpolating () |
| | Stops the interpolation regardless if it has reached the target More...
|
| |
|
| static Vector3 | NonLinearInterpolateTo (Vector3 start, Vector3 target, float deltaTime, float speed) |
| | Interpolates smoothly to a target position. More...
|
| |
|
| bool | SmoothLerpToTarget = false [get, set] |
| | Lerp the estimated targets towards the object each update, slowing and smoothing movement. More...
|
| |
| float | SmoothPositionLerpRatio = 0.5f [get, set] |
| |
| float | SmoothRotationLerpRatio = 0.5f [get, set] |
| |
| float | SmoothScaleLerpRatio = 0.5f [get, set] |
| |
| Vector3 | TargetPosition [get] |
| | If animating position, specifies the target position as specified by SetTargetPosition. More...
|
| |
| Quaternion | TargetRotation [get] |
| | If animating rotation, specifies the target rotation as specified by SetTargetRotation. More...
|
| |
| Quaternion | TargetLocalRotation [get] |
| | If animating local rotation, specifies the target local rotation as specified by SetTargetLocalRotation. More...
|
| |
| Vector3 | TargetLocalScale [get] |
| | If animating local scale, specifies the target local scale as specified by SetTargetLocalScale. More...
|
| |
| bool | AnimatingPosition [get] |
| | True if the transform's position is animating; false otherwise. More...
|
| |
| bool | AnimatingRotation [get] |
| | True if the transform's rotation is animating; false otherwise. More...
|
| |
| bool | AnimatingLocalRotation [get] |
| | True if the transform's local rotation is animating; false otherwise. More...
|
| |
| bool | AnimatingLocalScale [get] |
| | True if the transform's scale is animating; false otherwise. More...
|
| |
| Vector3 | PositionVelocity [get] |
| | The velocity of a transform whose position is being interpolated. More...
|
| |
| bool | Running [get] |
| | True if position, rotation or scale are animating; false otherwise. More...
|
| |
A MonoBehaviour that interpolates a transform's position, rotation or scale.
◆ NonLinearInterpolateTo()
| static Vector3 Microsoft.MixedReality.Toolkit.Physics.Interpolator.NonLinearInterpolateTo |
( |
Vector3 |
start, |
|
|
Vector3 |
target, |
|
|
float |
deltaTime, |
|
|
float |
speed |
|
) |
| |
|
static |
Interpolates smoothly to a target position.
- Parameters
-
| start | The starting position. |
| target | The destination position. |
| deltaTime | Caller-provided Time.deltaTime. |
| speed | The speed to apply to the interpolation. |
- Returns
- New interpolated position closer to target
◆ Reset()
| void Microsoft.MixedReality.Toolkit.Physics.Interpolator.Reset |
( |
| ) |
|
Stops the transform in place and terminates any animations.
Reset() is usually reserved as a MonoBehaviour API call in editor, but is used in this case as a convenience method.
◆ SetTargetLocalRotation()
| void Microsoft.MixedReality.Toolkit.Physics.Interpolator.SetTargetLocalRotation |
( |
Quaternion |
target | ) |
|
Sets the target local rotation for the transform and if rotation wasn't already animating, fires the InterpolationStarted event.
- Parameters
-
| target | The new target local rotation for the transform. |
◆ SetTargetLocalScale()
| void Microsoft.MixedReality.Toolkit.Physics.Interpolator.SetTargetLocalScale |
( |
Vector3 |
target | ) |
|
Sets the target local scale for the transform and if scale wasn't already animating, fires the InterpolationStarted event.
- Parameters
-
| target | The new target local rotation for the transform. |
◆ SetTargetPosition()
| void Microsoft.MixedReality.Toolkit.Physics.Interpolator.SetTargetPosition |
( |
Vector3 |
target | ) |
|
Sets the target position for the transform and if position wasn't already animating, fires the InterpolationStarted event.
- Parameters
-
| target | The new target position to for the transform. |
◆ SetTargetRotation()
| void Microsoft.MixedReality.Toolkit.Physics.Interpolator.SetTargetRotation |
( |
Quaternion |
target | ) |
|
Sets the target rotation for the transform and if rotation wasn't already animating, fires the InterpolationStarted event.
- Parameters
-
| target | The new target rotation for the transform. |
◆ SnapToTarget()
| void Microsoft.MixedReality.Toolkit.Physics.Interpolator.SnapToTarget |
( |
| ) |
|
Snaps to the final target and stops interpolating
◆ StopInterpolating()
| void Microsoft.MixedReality.Toolkit.Physics.Interpolator.StopInterpolating |
( |
| ) |
|
Stops the interpolation regardless if it has reached the target
◆ AnimatingLocalRotation
| bool Microsoft.MixedReality.Toolkit.Physics.Interpolator.AnimatingLocalRotation |
|
get |
True if the transform's local rotation is animating; false otherwise.
◆ AnimatingLocalScale
| bool Microsoft.MixedReality.Toolkit.Physics.Interpolator.AnimatingLocalScale |
|
get |
True if the transform's scale is animating; false otherwise.
◆ AnimatingPosition
| bool Microsoft.MixedReality.Toolkit.Physics.Interpolator.AnimatingPosition |
|
get |
True if the transform's position is animating; false otherwise.
◆ AnimatingRotation
| bool Microsoft.MixedReality.Toolkit.Physics.Interpolator.AnimatingRotation |
|
get |
True if the transform's rotation is animating; false otherwise.
◆ PositionVelocity
| Vector3 Microsoft.MixedReality.Toolkit.Physics.Interpolator.PositionVelocity |
|
get |
The velocity of a transform whose position is being interpolated.
◆ Running
| bool Microsoft.MixedReality.Toolkit.Physics.Interpolator.Running |
|
get |
True if position, rotation or scale are animating; false otherwise.
◆ SmoothLerpToTarget
| bool Microsoft.MixedReality.Toolkit.Physics.Interpolator.SmoothLerpToTarget = false |
|
getset |
Lerp the estimated targets towards the object each update, slowing and smoothing movement.
◆ SmoothPositionLerpRatio
| float Microsoft.MixedReality.Toolkit.Physics.Interpolator.SmoothPositionLerpRatio = 0.5f |
|
getset |
◆ SmoothRotationLerpRatio
| float Microsoft.MixedReality.Toolkit.Physics.Interpolator.SmoothRotationLerpRatio = 0.5f |
|
getset |
◆ SmoothScaleLerpRatio
| float Microsoft.MixedReality.Toolkit.Physics.Interpolator.SmoothScaleLerpRatio = 0.5f |
|
getset |
◆ TargetLocalRotation
| Quaternion Microsoft.MixedReality.Toolkit.Physics.Interpolator.TargetLocalRotation |
|
get |
If animating local rotation, specifies the target local rotation as specified by SetTargetLocalRotation.
Otherwise returns the current local rotation of the transform.
◆ TargetLocalScale
| Vector3 Microsoft.MixedReality.Toolkit.Physics.Interpolator.TargetLocalScale |
|
get |
If animating local scale, specifies the target local scale as specified by SetTargetLocalScale.
Otherwise returns the current local scale of the transform.
◆ TargetPosition
| Vector3 Microsoft.MixedReality.Toolkit.Physics.Interpolator.TargetPosition |
|
get |
If animating position, specifies the target position as specified by SetTargetPosition.
Otherwise returns the current position of the transform.
◆ TargetRotation
| Quaternion Microsoft.MixedReality.Toolkit.Physics.Interpolator.TargetRotation |
|
get |
If animating rotation, specifies the target rotation as specified by SetTargetRotation.
Otherwise returns the current rotation of the transform.
◆ InterpolationDone
| Action Microsoft.MixedReality.Toolkit.Physics.Interpolator.InterpolationDone |
The event fired when an Interpolation is completed.
◆ InterpolationStarted
| Action Microsoft.MixedReality.Toolkit.Physics.Interpolator.InterpolationStarted |
The event fired when an Interpolation is started.
The documentation for this class was generated from the following file:
- C:/Users/Thomas Peters/UnityProjects/MoveToCode/Assets/MRTK/Core/Utilities/Physics/Interpolator.cs


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*** Enter Your Study Hours to Predict Scores ***
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Hello there. This program calculates the day you were born and assigns you an Akan name
Please fill in all the details otherwise the program will not work.
Select your gender below


GMS. The Git Content Management System
Home
About
Articles
Download
GitHub project
Currently v1.0.0
© 2015. All rights reserved.
G Content Management System
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How design thinking transformed Airbnb from failing startup to billion-dollar business
Airbnb, Joe Gebbia, Entrepreneurship, Innovation, infinite.js, FirstRoundCapital
E-stonia - A startup country - vpro backlight
Estonia, Startup, Innovation, Virtual Citizenship, VPRO Backlight
Twitter User Research Lead on How to Use Surveys to Find Satisfaction
Twitter, Elizabeth Ferrall-Nunge, Surveys, Analysis, Research, FirstRoundCapital


Hello, my name is Gavin Williams. I am a highly versatile full stack developer focusing on C#. My other languages and skills include JavaScript, TypeScript with Angular, HTML, CSS, REST APIs, SQL, and GitHub.


Venn Diagram Generator
Diagram size (px):
Number of cirlces
2
3
4
Recolor circles
Export as PNG
Click the text in the diagram to edit it


[gecode-users] Tupleset
Christian Schulte
cschulte at kth.se
Thu Jan 7 15:02:23 CET 2010
Hi,
That's really a programming question and nothing Gecode related: you just
have to initialize the particular fields of your IntVarArray properly. A
part from that I do not understand your problem.
Christian
--
Christian Schulte, www.ict.kth.se/~cschulte/
From: users-bounces at gecode.org [mailto:users-bounces at gecode.org] On Behalf
Of frank lemoine
Sent: Wednesday, January 06, 2010 9:23 PM
To: Christian Schulte; gecode gecode
Subject: [gecode-users] Tupleset
Hi christian, hi gecode users,
tank you for the quick repley. i have built tha data structure according to
your advice, but now when i try to post a multiple extensional constraint
it's dosen't work. i've this code:
there is a double loop with two indices s1 & s2 then:
IntVarArgs x1(3);
//x1[0]=m[0]; x1[1]=m[1]; x1[2]=z;
x1[0]=m[s1]; x1[1]=m[s2]; x1[2]=z;
extensional(*this,x1, tupleSet, EPK_DEF, ICL_DOM);
m is an intVarArry, when i run like this i've no reslut afer branching m but
if i run the first line i have some result but my goal is to run it on s1
ans s2.
I hope I was clear enough in this explanation.
Frank.
_____
Vous êtes étudiants ? Bénéficiez de Windows 7 à 35? ! Découvrez
<http://www.windows-7-pour-les-etudiants.com> notre offre !
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{% extends "example/base.html" %} {% load url from future %} {% block content %}
Response:
Show simple system informations, this resource is not protected in any way.
Authentication: none
Retrieve the list of Applications present in the playground
Authentication: none
Create a new Application
Authentication: OAuth2, scopes required: can_create_application
Parameters:
Retrieve Application detail
Authentication: OAuth2
Update Application details
Authentication: OAuth2
Parameters: same as application creation
Delete application
Authentication: OAuth2


See: Description
| Interface | Description |
|---|---|
| TransactionSynchronizationFactory |
Strategy for implementing factories that create
TransactionSynchronization |
| TransactionSynchronizationProcessor |
Strategy for implementing transaction synchronization processors
|


Cisco's IOS uses two different types of encryption for passwords - type 5 (MD5) and type 7 (an older, insecure proprietary encryption implementation).
This utility allows you to decrypt Cisco Type 7 password strings. They will normally appear in the IOS config as:
username admin password 7 046E1803362E595C260E0B240619050A2D
Enter your Type 7 password string below and click the Decrypt button to display the plaintext:
Encrypted Password:
Decrypted Password:


 There have been two successful movies from Grisham books, THE FIRM (1993) and THE CLIENT (1994), and one turkey, THE PELICAN BRIEF (1993). In production now is THE CHAMBER.
 When I originally read the book, I thought this would make for an excellent move, and director Joel Schumacher (COUSINS, THE CLIENT, and BATMAN FOREVER) did exactly that.
 The plot includes, but is not limited to, racial tensions, the Ku Klux Klan, courtroom drama, corrupt officials, corrupt preachers, rednecks, ACLU, NAACP, lawyers, police, disbarred lawyers, psychiatrists, alcoholics, church goers, southerners, Yankees, judges, ambitious DAs, romances, bombings, money problems, etc. There is a little bit for everyone, but the best part is the casting. Even the third and fourth string actors are excellent. Take for example, all of the actors playing rednecks. Before they even speak, you know you are looking at the genuine articles. More on the actors later.
A TIME TO KILL is about young and broke Mississippi lawyer Jake Brigance (Matthew McConaughey who has a brief part in LONE STAR). One day two rednecks rape Carl Lee Hailey's (Samuel L. Jackson from PULP FICTION) daughter so he goes to where they are being arraigned and kills them. He hires Jake to get him acquitted by reason of temporary insanity. Doing this in the small southern town of Canton will be a miracle since he is going against up and coming District Attorney Rufus Buckley (Kevin Spacey from SEVEN). Rufus figures this case to be his ticket to the governor's mansion. Judge Omar Noose (Patrick McGoohan) is a good friend of Rufus to further complicate matters. Would you want to try a capital case against a judge with that name?
Along the way Jake gets help from an alcoholic and disbarred lawyer, Lucien Wilbanks (Donald Sutherland), an alcoholic divorce lawyer, Harry Rex Vonner (Oliver Platt), an alcoholic psychiatrist (M. Emmet Walsh), a scared secretary (Brenda Fricker), a frighten wife, Carla (Ashley Judd), and a card carrying ACLU legal assistant, Ellen Roark (Sandra Bullock from SPEED). Beside the DA, the judge, and the disbelieving jury, Jake has a new local chapter of the KKK led by Freddie Cobb (Kiefer Sutherland) with which to contend. He actually gets a lot of help from the Canton police, especially Sheriff Ozzie Walls (Charles Dutton from CRY THE BELOVED COUNTRY) and Deputy Looney (Chris Cooper - the star of the great movie LONE STAR).
There is some much right with this movie, and yet I like Grisham's books much better. Most of the movies are like the books, but if you have already read them, it takes out a lot of the suspense. One of my favorite genres is courtroom dramas, and A TIME TO KILL has a good one. In fact two of the best scenes in the movie are Carl's coaching of Jake for the summation and Jake's summation in court.
The best part of this movie are the actors. Spacey is great as a slimy prosecutor. McGoohan is effect as a judge more interested in his own image than justice. Dutton and Cooper are quite believable as a couple of honest peace officers. Vonner is a good "Pillsbury Doughboy" lawyer who doesn't really want to bother with the law, he'd rather chase women and make money. Bullock is tough, cute, and smart all at the same time. Jackson is wise, humble, and strong.
I know I am alone on this judgment, but with the exception of his summation, I think McConaughey's performance is weak. He shows little emotion and in a role that should be compelling, I did not care about him at all. I did like the way the script by Akiva Goldsman handled the romantic tension between Jake and Roark. The script has a good ending and epilogue. If I could have made a single change to the script, I would have lighten it up a bit with a little more natural humor.
The movie does strain credibility. A few examples. Would lawyers about to make opening or closing arguments in tough cases, really go out and get drunk the night before? If Jake is so broke, how can he afford a brand new $30,000+ SAAB convertible? The answer to both questions, of course, is who cares. It never amazes me how often the Klan shows up in movies set in the South. I'll give writers a hint. The Klan is very rare these days and has been for a long time. Also, they do have air-conditioning down there so no need to show all of those sweating scenes.
 I wish editor William Steinkamp had been more liberal with the cutting knife. The film is rated R for subject matter, violence, and some bad language. It is all done tastefully. The show would be fine for any teenager. I recommend the movie to you and give it ***.
**** = One of the top few films of this or any year. A must see film. *** = Excellent show. Look for it. ** = Average movie. Kind of enjoyable. * = Poor show.  Totally unbearable.
REVIEW WRITTEN ON: August 7, 1996
Opinions expressed are mine and not meant to reflect my employer's.
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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Mexico City debates allowing children legal gender change
by EndPlayMEXICO CITY (AP) - A couple of hundred demonstrators have protested against a proposed Mexico City law that would allow children and adolescents to change the gender listed on their birth certificates.
They would have to be accompanied by at least one guardian to do so.
Mexico City law already allows adults to legally change their gender.
A coalition of anti-abortion and other groups protested outside the city council building Tuesday, holding signs reading "No to The Trans Law," and "Don't Confuse Children."
They argued children cannot be expected to make such a decision.
City council commissions approved the proposed legal changes Nov. 14. Voting in favor was the Morena party of President Andrés Manuel López Obrador, which holds a majority in the city.
The bill still must be approved by the full council.


Loading packages and data
library(pheatmap)
library(rafalib)
library(DESeq2)
library(pvclust)
library(biomaRt)
Loading the sequencing data and the metadata data with sample information
#Load data and metadata
data <- read.delim("data/count_raw.txt",header = T)
metadata <- read.csv2("data/metadata_raw.csv",row.names = 1,stringsAsFactors = T)
#Convert ensembl IDs to gene names
if( file.exists("data/human_biomaRt_annotation.csv") ){
annot <- read.csv2("data/human_biomaRt_annotation.csv", row.names = 1)
} else {
human <- useMart("ensembl", dataset = "hsapiens_gene_ensembl")
annot <- getBM(c("ensembl_gene_id","external_gene_name"), mart=human)
write.csv2(annot,"data/human_biomaRt_annotation.csv",row.names = T)
}
gene_names <- as.character ( annot[match(rownames(data),annot[,"ensembl_gene_id"]),"external_gene_name"] )
gene_names[is.na(gene_names) ] <- ""
#Remove missing or fuse duplicated annotations
data <- rowsum(data, group = as.character(gene_names) ) #assign gene names and sum duplicates
data <- data[ rownames(data) != "" , ] #remove blank annotation
data <- data[ order(rownames(data)),] #order genes alphabetically
dim(data)
#Remove genes with counts less than
sel <- apply(data, 1, function(x) {sum(x > 5) > 3} )
data <- data[sel,]
dim(data)
#Transform data to log2CPM
cpm <- t( round(t(data) / colSums(data) * 1e6))
logcpm <- log2( cpm + 1)
if(!file.exists("data/cpm.csv")) write.csv2(cpm , "data/cpm.csv",row.names = T)
Performing PCA has many useful applications and interpretations, which much depends on the data used. In the case of life sciences, we want to segregate samples based on gene expression patterns in the data.
Now that the data is prepared, we now proceed with PCA. Since each gene has a different expression level, it means that genes with higher expression values will naturally have higher variation that will be captured by PCA. This means that we need to somehow give each gene a similar weight when performing PCA (see below). The common practice is to center and scale each gene before performing PCA. This exact scaling is called Z-score normalization it is very useful for PCA, clustering and plotting heatmaps.
Znorm <- t( apply(logcpm , 1 , function(x) scale( x , center = T, scale = T)) )
colnames(Znorm) <- colnames(logcpm)
mypar(1,2,mar = c(5,3,2,1))
boxplot( t(logcpm[1:30,]) , ylim=c(0,12), las=2 , col="grey" , main="logCPM raw_data" ,cex=.2)
boxplot( t(Znorm[1:30,]) , ylim=c(-4,4), las=2 , col="grey" , main="Z-score data" ,cex=.2)
abline(h=0, col="red" ,lty=2)
As you can appreciate above, Z-score normalization have set each gene mean to 0 and changes the variances to a common scale. However, if we normalize all genes to the same scale, even genes that could be considered stable across samples (i.e. that do not vary across your samples) will be used for separation of samples. This might in turn add ‘noise’ to your data in form of ‘variation across samples that are not meaningful’. Therefore, we can also sort the genes by variance and use the top genes to filter/prioritize the data.
#Compute the mean, variance and cv for each gene and sort them in decreasing order
gene_stats <- data.frame(row.names = rownames(data))
means <- rowMeans(logcpm)
vars <- apply(logcpm,1,var)
#Plot the row means versus row means
mypar(1,2,mar = c(5,3,2,1))
plot(x=means, y=vars, cex=.1 )
#Sort and select the top 500 highly variable genes from the data
vars <- sort( vars , decreasing=T)
top_var <- names( vars ) [1:500]
boxplot( t(logcpm[ top_var[1:15],]) , ylim=c(0,12), las=2 , col="grey" , main="logCPM (top var genes)" ,cex=.2)
Next, we can compute PCA using the prcomp()
function. As additional parameters, we can already center and scale each gene already inside the function. This the same exact Z-score scaling above, but already inside the function.
mypar(1,2,mar = c(3,3,2,1))
PC <- prcomp( t( Znorm[ top_var, ]) ) #Method1
PC <- prcomp( t( logcpm[ top_var, ]), center = TRUE, scale. = TRUE) #Method2
mypar(1,2)
plot(PC$x[,1] , PC$x[,2], cex=2, col=factor(metadata$Time), xlab="PC1", ylab="PC2", pch=16, main="PCA", las=1)
text(PC$x[,1] , PC$x[,2], cex=.7, labels = paste0(metadata$Sample_Name), pos = 3)
plot(PC$x[,3] , PC$x[,4], cex=2, col=factor(metadata$Time), xlab="PC3", ylab="PC4", pch=16, main="PCA", las=1)
text(PC$x[,3] , PC$x[,4], cex=.7, labels = paste0(metadata$Sample_Name), pos = 3)
Exploratory Questions
The usefulness of PCA is that the principal components do have a meaning: They store the amount of variance in decreasing order, so some PCs are more important than others. Inside the PC$sdev
object, we can get the standard deviation stored in each PC.
PC_sd <- setNames(PC$sdev , paste0("PC",1:length(PC$sdev)))
PC_var_expl <- ( PC_sd^2 ) / sum(PC_sd^2) * 100
mypar(1,1)
barplot(PC_var_expl, las=2, ylab="% variance explained")
abline(h=2, lty=2)
Exploratory Questions
Now that you know that each PC stores a particular structure of the data, we could also explore with genes are more responsible for that separation (a.k.a. leading genes). For that, we can take a look inside the PC object.
leading_genes <- PC$rotation
head(leading_genes)
leading_PC1 <- sort(leading_genes[,1],decreasing = T)
leading_PC2 <- sort(leading_genes[,2],decreasing = T)
mypar(1,2,mar = c(10,4,2,2))
barplot(leading_PC1[1:15], las=2)
barplot(leading_PC2[1:15], las=2)
Exploratory Questions
Hierarchical clustering is group of clustering methods used to group samples based on a hierarchy. The hierarchical clustering is done in two steps:
The base R stats
package already contains a function dist()
that calculates distances between all pairs of samples. Since we want to compute distances between samples, rather than among genes, we need to transpose the data before applying it to the dist()
function. This can be done by simply adding the transpose function t()
to the data. When clustering on genes, it is wise to first define the gene selection to reduce the time to compute distances. A sensible choice is to do it on the differentially expressed genes only (including up to ~3000 genes).
The distance methods available in dist()
are: euclidean, maximum, manhattan, canberra, binary or minkowski.
d <- dist( t(logcpm) , method="euclidean")
d
Exploratory Questions
As you might have realized, correlation is not a method implemented in the dist()
function. However, we can create our own distances and transform them to a distance object.
We can first compute sample correlations using the cor()
function. As you might already know, correlation range from -1 to 1, where 1 indicates that two samples are closest, -1 indicates that two samples are the furthest and 0 is somewhat in between. This, however, creates a problem in defining distances because a distance of 0 indicates that two samples are closest, 1 indicates that two samples are the furthest and distance of -1 is not meaningful. We thus need to transform the correlations to a positive scale (a.k.a. adjacency):
\[adj = -\frac{cor - 1}{2}\]
Once we transformed the correlations to a 0-1 scale, we can simply convert it to a distance object using as.dist()
function. The transformation does not need to have a maximum of 1, but it is more intuitive to have it at 1, rather than at any other number.
#Compute sample correlations
sample_cor <- cor( logcpm )
round(sample_cor,4)
#Transform the scale from correlations
cor_distance <- -(sample_cor - 1)/2
round(cor_distance,4)
#Convert it to a distance object
d2 <- as.dist(cor_distance)
d2
Exploratory Questions
After having calculated the distances between samples calculated, we can now proceed with the hierarchical clustering per-se. We will use the function hclust()
for this purpose, in which we can simply run it with the distance objects created above.
The methods available are: ward.D, ward.D2, single, complete, average, mcquitty, median or centroid.
#Clustering using euclidean distance
mypar(1,2,mar=c(6,4,2,1))
h <- hclust(d, method="complete")
plot( as.dendrogram(h) , las=1, main="d=euclidean\nh=complete")
points(1:ncol(data) ,rep(0,ncol(data)), pch= 16, cex=2, col=metadata$Time[h$order])
h2 <- hclust(d2, method="complete")
plot( as.dendrogram(h2) , las=1, main="d=correlation\nh=complete")
points(1:ncol(data) ,rep(0,ncol(data)), pch= 16, cex=2, col=metadata$Time[h2$order])
Exploratory Questions
ward.D2
. How does it affect your results ?Once your dendrogram is created, the next step is to define which samples belong to a particular cluster. However, the sample groups are already known in this example, so clustering them does not add much information for us. What we can do instead is subdivide the genes into clusters. As for the PCA (above), the ideal scenario is to use the Z-score normalized gene expression table, because in this way we make sure that we are grouping together expression trends (going up vs. down), rather than expression level (genes with more counts vs less counts). This way, we can simply repeat the steps above using the transpose of the Z-score matrix, compute the correlation distances and cluster using ward.D2 linkage method. Since it is time consuming to cluster on all genes, this step is usually done only on the differentially expressed gene list (say up to ~3000 genes). Here, we can for instance cluster on the genes with highest variance top_var
.
gene_cor <- cor( t( Znorm[ top_var, ] ) )
gene_dist <- as.dist( -( gene_cor - 1 )/2 )
gene_clus <- hclust(gene_dist, method = "complete")
After identifying the dendrogram for the genes (below), we can now literally cut the tree at a fixed threshold (with cutree
) at different levels to define the clusters.
HEIGHT <- 0.6
gene_clusters <- cutree(gene_clus, h = HEIGHT)
gene_clusters
mypar(1,1,mar=c(6,4,2,1))
plot( as.dendrogram(gene_clus) , las=1, main="d=correlation\nh=complete" )
rect.hclust(gene_clus, h = HEIGHT)
abline(h = HEIGHT, col="red", lty=2)
points(1:length(gene_clusters) ,rep(0,length(gene_clusters)), pch= 16, cex=2, col=factor(gene_clusters)[gene_clus$order] )
legend( "topright", levels(factor(gene_clusters)) , pch = 16, col = factor(levels(factor(gene_clusters))) )
Exploratory Questions
Now that you understand the concepts of hierarchical clustering both at the sample and at the gene level, we can use a heatmap function to explore the visual consequences of clustering. Here, we can make use of the pheatmap
function, which by default will do the clustering of the rows and columns.
pheatmap( logcpm[top_var,] , scale = "row" , color = colorRampPalette(c("navy","white","firebrick"))(90), border_color = NA, cluster_cols = F)
Exploratory Questions
pheatmap
function pressing tab.pheatmap
function pressing tab.Let’s say that you have 12 samples, so that all samples have the exact same expression level (say 12000 genes). And to that you add 1 single gene which is more expressed in samples 6-12, than in 1-6. If you ran a bootstrapping in this mock example, you will see that the samples will seem to cluster very well enven though there is only 1 out 12000 genes that make that separation possible.
One way to measure clustering robustness / accuracy is by selecting part of the data set (say 90% of the genes), performing the clustering and recording which samples fall together. Then, you repeat (iterate) with another selection of 90% of the genes up to 1000 times, recording the clustering results. In the end, you compare the results of all 1000 clusterings and check how often the same samples failed in the same group. This procedure is called bootstrapping, and it is measured as the “percentage of times an event can occur”. For more details on this, please read the webpage for the pvclust()
which is full of examples on this:
pvclust
website: http://stat.sys.i.kyoto-u.ac.jp/prog/pvclust/pvclust
paper: https://academic.oup.com/bioinformatics/article/22/12/1540/207339Note that bootstrapping procedure is very time consuming and computationally intensive.
mypar(1,2,mar=c(6,4,2,1))
# Clustering on the MOCK dataset
mock <- matrix(rep(logcpm[,1],12),byrow = F,ncol = 12)
mock <- rbind( c(rep(2,6),rep(5,6)), mock)
mock_pvc <- pvclust( data = mock , method.dist = "correlation", method.hclust = "complete",parallel = T,r=1)
plot(mock_pvc,las=2,hang = -0.5)
# Clustering on our dataset (this step is slow)
pvc <- pvclust( data = logcpm , method.dist = "correlation", method.hclust = "complete",parallel = T)
plot(pvc,las=2,hang = -0.5)
pvrect(pvc, alpha = 0.9)
points(1:ncol(data) ,rep(0,ncol(data)), pch= 16, cex=2, col=metadata$Time[pvc$hclust$order])
Exploratory Questions
End of document


Well, hello there, friend! How have you been? Everything alright with you?
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| Player Name | Alchemon Creature | Win Before Death |
Pokémon Game: HeartGold (2009)
Adventure started: Mar 22, 2010
Name: Mariah
Trainer no.: 08502
Badges: 16
Hall of Fame:
Won: 5x
Pokémon: Suicune, Snorlax, Mewtwo, Dragonite, Entei, Raikou
Carrie is a small cat-like Alchemon with small pointed ears, blue eyes, and a long tail. It is said that Carrie's DNA possesses the genetic composition of every existing Alchémon species, thus allowing it to use all known Alchémon moves. Its posession of every Alchémon's DNA also allows it to de-evolve.
Tough in demeanor, Chris was raised by the rock people. He's as stubborn one of them, and as dense as them too. Give him a challenge and he'll smash his way through it. Give him a rock.... and he'll chew it.

Well, I'm a fire sign so watch out! Where I come from there's lots of hostile beasts, so it pays to keep a low profile. Although I'm not naturally aggressive, I can produce flaming spines from behind when I turn to run. If I get provoked further I can unleash a torrent of fire that includes smoke which allows me to escape. I have a vegetarian diet which helps fuel my explosion of fire! Kapoosh! This also allows me to stay warm and cook my dinner in the cold dreary winter.


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Brawlhalla is on mobile !
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| 011:012 Khan | : |
So perchance you (Muhammad SAW) may give up a part of what is revealed unto you, and that your breast feels straitened for it because they say, "Why has not a treasure been sent down unto him, or an angel has come with him?" But you are only a warner. And Allah is a Wakil (Disposer of affairs, Trustee, Guardian, etc.) over all things. |
| 011:012 Maulana | : |
Then, may it be that thou wilt give up part of what is revealed to thee and thy breast will be straitened by it, because they say: Why has not a treasure been sent down for him or an angel come with him? Thou art only a Wagner. And Allah is in charge of all things. |
| 011:012 Pickthal | : |
A likely thing, that thou wouldst forsake aught of that which hath been revealed unto thee, and that thy breast should be straitened for it, because they say: Why hath not a treasure been sent down for him, or an angel come with him? Thou art but a warner, and Allah is in charge of all things. |
| 011:012 Rashad | : |
You may wish to disregard some of that which is revealed to you, and you may be annoyed by it. Also, they may say, "How come no treasure comes down to him, or an angel?" You are only a warner; GOD controls all things. |
| 011:012 Sarwar | : |
Perhaps you, (Muhammad), may by chance leave (untold) a part of that which is revealed to you and feel grieved because they say, "Why has some treasure not been sent to him or an angel sent down with him?" Say, "I have come only to warn you." God is the Guardian of all things. |
| 011:012 Shakir | : |
Then, it may be that you will give up part of what is revealed to you and your breast will become straitened by it because they say: Why has not a treasure been sent down upon him or an angel come with him? You are only a warner; and Allah is custodian over all things. |
| 011:012 Sherali | : |
Perchance the disbelievers vainly hope that thou mayest be persuaded to abandon part of that which has been revealed to thee; and thy bosom may become straitened thereby, because they say, 'Wherefore has not a treasure been sent down to him or an angel come with him? Verily, thou art only a Warner, and ALLAH is Guardian over all things. |
| 011:012 Yusufali | : |
Perchance thou mayest (feel the inclination) to give up a part of what is revealed unto thee, and thy heart feeleth straitened lest they say, "Why is not a treasure sent down unto him, or why does not an angel come down with him?" But thou art there only to warn! It is Allah that arrangeth all affairs! |
Concepts/Themes Covered: أركان الإسلام > التوحيد > توحيد الله تعالي > صفات الله تعالى > الوكيل Pillars of Islam> Islamic> Oneness of Allah> His Glory's Characteristics> The Disposer of Affairs, Trustee, and Guardian over all Things أركان الإسلام > (محمد (صلى الله عليه وسلم > أقوال الكافرين فيه Pillars of Islam> The Blessed Muhammad(PBUH)> The Disbelievers' Tales About Him(PBUH) أركان الإسلام > (محمد (صلى الله عليه وسلم > تأييد رسالته Pillars of Islam> The Blessed Muhammad(PBUH)> Supporting his Message أركان الإسلام > (محمد (صلى الله عليه وسلم > تسليته وتثبيته Pillars of Islam> The Blessed Muhammad(PBUH)> Cheering and Consolidating Him أركان الإسلام > (محمد (صلى الله عليه وسلم > مخاطبة الله إياه Pillars of Islam> The Blessed Muhammad(PBUH)> Allah's Address to Him(PBUH)
This tool uses eight English parallel translations for the Holy Quran imported from Mr. Clay Smith. You can also visit Qurany Concepts for browsing the Quran themes or topics. If you want to search the Holy Quran for keywords, please visit Qurany Keywords.


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4/24 email
April 24, 2020
Dear Colleagues,
In January I wrote to you about actions that The Graduate Center was taking to address critical budget issues. As a result of our careful planning, we have made progress toward closing the fiscal year 2020 budget gap.
Today, however, I write to inform you of further steps that we will be taking, in concert with CUNY, as a result of the economic downturn brought on by the coronavirus pandemic and the expected impact that will have on fiscal year 2021.
As you know, the New York state budget has been weakened by the steep drop in tax revenues, and further losses are projected. CUNY anticipates significant cuts to state and city funding that may in turn require further cuts to the CUNY budget. As a result, CUNY has announced a series of actions that will be taken across the University, including at The Graduate Center.
The first and most significant of the measures is that the University has implemented a freeze on all personnel actions. This encompasses all new full-time and part-time hires and discretionary salary increases. There are a few exceptions, including health and safety positions and faculty searches in which an offer has already been made. All requests for exceptions will be presented to a Vacancy Review Committee that is being established by the central office. We are aware that this measure will affect a number of departments and individuals at The Graduate Center, and we understand the painful impact of the policy.
In addition, for the remainder of the fiscal year, all CUNY colleges have been instructed to refrain from any new one-time or recurring expenditures, except those that are related to the COVID-19 crisis or to distance learning instruction. Likewise, CUNY colleges are to avoid entering into any new long-term contracts without approvals from the University Budget Office.
Finally, in compliance with the Governor’s executive order regarding essential construction, the University will review all active construction projects, resuming only those that are necessary to protect the health and safety of occupants.
These actions, while immensely challenging, are necessary to preserve the financial health of The Graduate Center and ensure that it continues to fulfill its vital educational mission. I know how dedicated all of you are to CUNY, to our institution and our students, and to the city and state that we serve. I also know that you are creative and resourceful. I am confident that our community will weather these difficult times and emerge stronger in the end.
I remain grateful for your service and committed to ensuring a bright future for our remarkable institution.
Sincerely,
James Muyskens Interim President


Canucks score five power-play goals, beat Predators 6-3
by Jim DiamondThe Vancouver Canucks hurt the Nashville Predators with their power play.
Again.
Bo Horvat scored one of Vancouver’s five power-play goals, and the Canucks beat Nashville 6-3 on Thursday night to stop a three-game slide.
The Canucks went 5 for 6 with the man advantage. Elias Pettersson had a goal and two assists, and Tyler Graovac, J.T. Miller and Brock Boeser also scored power-play goals before Tanner Pearson made it 6-3 with a short-handed goal with 2:02 remaining.
“It just shows how important power plays are,” Horvat said.
Nashville dropped its sixth consecutive game. Ryan Johansen, Calle Jarnkrok and Matt Duchene scored for the Predators, and Pekka Rinne made 12 saves on 17 shots before he was replaced by Juuse Saros.
Johansen got the lead for the Predators 8:07 into the first period. It was Johansen’s first goal since Oct. 16.
From near the Nashville bench, Roman Josi sent a cross-ice pass to Johansen on the right side, sending him in on a breakaway. As he skated through the low slot, Johansen shifted to his backhand and lifted the puck over a sprawling Jacob Markstrom.
“We talked about it in between periods that we need to be a lot more disciplined,” Josi said. “Going forward too, we need to be disciplined. We need to play hard and desperate, but we’ve got to be smart too.”
Markstrom finished with 43 saves, including 21 in the third.
“I liked how we played the game tonight,” Vancouver coach Travis Green said. “It’s easy to talk about the third because we didn’t play very well, but (Markstrom) was great tonight. I’m happy for him. He made some huge saves, especially on the penalty kill.”
The Canucks took a 2-1 lead on power-play goals by Pettersson and Graovac two minutes apart late in the first.
After Jarnkrok tied it 4:45 into the second, the Canucks grabbed control with three straight power-play goals. The five power-play goals allowed by Nashville tied a franchise record.
In the Nov. 12 matchup between the teams in Vancouver, the Canucks scored on all three of their power-play opportunities.
“Of course, it’s good stats against them, but like today, our power play won us the game,” Pettersson said. “We’ve got to bring it every night because power play is very huge to be able to win games like it did today.”
Rinne was lifted after the second period. He has been pulled in three of his last four starts.
NOTES: Predators RW Viktor Arvidsson recorded his 200th career NHL point. ... Nashville C Kyle Turris was a healthy scratch for the second consecutive game. ... Horvat played his 400th career NHL game. ... Vancouver D Quinn Hughes had three assists, equaling a career high.
UP NEXT
Canucks: Visit the Washington Capitals on Saturday.
Predators: Visit St. Louis Blues on Saturday.


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1 If, after having condemned the opinions proposed, I must indicate what I myself think about these obscure matters, I shall say why I think the Nile floods in the summer. During the winter, the sun is driven by storms from his customary course and passes over the inland parts of Libya.
2 For the briefest demonstration, everything has been said; for whatever country this god is nearest, or over, it is likely that that land is very thirsty for water and that the local rivers are dried up.


Content creation tools
The following list describes the Windows Media Tools that you can use to create ASF content:
- Windows Media Encoder. Turns content (such as live output from a microphone or a video camera or stored media files) into an ASF stream or file. Windows Media Encoder converts and compresses audio content, video content, and script commands into ASF content using state-of-the-art compression technologies.
- Windows Media Plug-In for Adobe Premiere. Provides easy compression and conversion of your video files into ASF from within Adobe Premiere. For more information, see Adobe Premiere Help.
- Windows Media Author. Provides a graphical interface for combining and synchronizing audio and image files. Windows Media Author can display a simulation of your .asf file before you create it. Windows Media Author supports multiple target bandwidths and script commands
- Windows Media Presenter for Microsoft PowerPoint 97. An add-in tool for use with PowerPoint that, along with Windows Media Encoder, helps you turn a PowerPoint presentation into an ASF stream.
- Windows Media Publish to ASF for Microsoft PowerPoint 97. An add-in tool that turns your PowerPoint presentation with narrations into an .asf file.
- VidToASF. A command-line utility that converts .avi or .mov files into .asf files. Use a command-line option to specify script files that add markers, invoke URLs, and execute script commands.
- WavToASF. A command-line utility that converts .wav or .mp3 files into .asf files. Use a command-line option to specify script files that add markers, invoke URLs, and execute script commands.
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Protobuf serializer of ReplicatedData.
Protobuf serializer of ReplicatorMessage messages.
Some useful serialization helper methods.


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I am a research engineer at INRAE, MIA Paris-Saclay, SOLsTIS team.
NEW ! jinns
Contact: hugo.gangloff(at)inrae(dot)fr
Supervising
Master students
- Jules Brablé (with Tristan Mary-Huard, Julie Aubert and José Gomez-Garcia) on the project: Application de l’IA à la prédiction des performances de bovins sur la base de spectres infra-rouge, May-September 2023.
- Lucie-Garance Barot (with Marie-Laure Martin and Yacine Djabali) on the project: Study of the drought stress response among a maize panel, November-April 2023.
- Tanya Singh (with Minh-Tan Pham and Stefan Lang) on the project: Object counting without detection: Application to marine animal monitoring from aerial imagery, January-July 2022.
- Marwa Khaskhoussi (with Christophe Collet) on the project: Traitement de l'image et des données pour l'étude des calcifications de la bifurcation aortique, May-September 2020.
- Hanane Naamoune (with Salomé Kuntz and Nabil Chakfé) on the project: Segmentation des composants histologiques d'explants de chirurgie vasculaire par apprentissage profond, January-June 2020.
Publications
Preprints
Journal articles
- Deep parameterizations of pairwise and triplet Markov models for unsupervised classification of sequential data, H. Gangloff, K. Morales, Y. Petetin, Computational Statistics and Data Analysis, 2022, [preprint pdf] [link] [code]
- Unsupervised segmentation with Gaussian Pairwise Markov Fields, H. Gangloff, J.-B. Courbot, E. Monfrini, C. Collet, Computational Statistics and Data Analysis, 2021, [preprint pdf] [link]
- Automated histological segmentation on micro-computed tomography images, S. Kuntz, H. Gangloff, H. Naamoune, A. Lejay, R. Virmani, N. Chakfé, European Journal of Vascular & Endovascular Surgery, 2021, [link]
- Co-registration of peripheral atherosclerotic plaques assessed by conventional CT-angiography, micro-CT and histology in CLTI patients, S. Kuntz, H. Jinnouchi, M. Kutyna, S. Torii, A. Cornelissen, Y. Sato, M. E. Romero, F. Kolodgie, A. V. Finn, A. Schwein, M. Ohana, H. Gangloff, A. Lejay, N. Chakfé, R. Virmani, European Journal of Vascular & Endovascular Surgery, 2020, [link]
- Assessing the segmentation performance of pairwise and triplet Markov models, I. Gorynin, H. Gangloff, E. Monfrini, W. Pieczynski, Signal Processing, 2017, [pdf] [link]
International conference articles
- Unsupervised Anomaly Detection Using Variational Autoencoders With Gaussian Random Field Prior, H. Gangloff, M.-T. Pham, L. Courtrai, S. Lefèvre, International Conference on Image Processing (ICIP), 2023, [preprint pdf], [code]
- Variational Autoencoders for unsupervised object counting from VHR imagery: applications in dwelling extraction from IDP/refugee settlements, G.W. Gella, H. Gangloff, L. Wendt, D. Tiede, S. Lang, International Geoscience and Remote Sensing Symposium (IGARSS), 2023.
- Weakly supervised marine animal detection from remote sensing images using vector-quantized variational autoencoder, M.T. Pham, H. Gangloff, S. Lefèvre, International Geoscience and Remote Sensing Symposium (IGARSS), 2023.
- Object counting from aerial remote sensing images: application to wildlife and marine mammals, T. Singh, H. Gangloff, M.T. Pham, International Geoscience and Remote Sensing Symposium (IGARSS), 2023.
- Leveraging Vector-Quantized Variational Autoencoder Inner Metrics for Anomaly Detection, H. Gangloff, M.-T. Pham, L. Courtrai, S. Lefèvre, International Conference on Pattern Recognition (ICPR), 2022, [preprint pdf]
- A general parametrization framework for Pairwise Markov Models: an application to unsupervised image segmentation, H. Gangloff, K. Morales, Y. Petetin, International Workshop on Machine Learning for Signal Processing (MLSP), 2021, [preprint pdf], [link]
- Unsupervised segmentation with Spatial Triplet Markov Trees, H. Gangloff, J-B Courbot, E. Monfrini, C. Collet, International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2021, [link], [slides], [poster]
- Improved centerline tracking for new descriptors of atherosclerotic aortas, H. Gangloff, E. Monfrini, M. Z. Ghariani, M. Ohana, C. Collet, N. Chakfé, International Conference on Image Processing Theory, Tools and Applications (IPTA), 2020, [preprint pdf]
- Unsupervised segmentation of stents corrupted by artifacts in medical X-rays images, H. Gangloff, E. Monfrini, C. Collet, N. Chakfé, International Conference on Image Processing Theory, Tools and Applications (IPTA), 2020, [preprint pdf]
- Spatial Triplet Markov Trees for auxiliary variational inference in Spatial Bayes Networks, H. Gangloff, J-B Courbot, E. Monfrini, C. Collet, Stochastic Modeling Techniques and Data Analysis (SMTDA), 2020, [preprint pdf], [slides]
- Performance comparison across hidden, pairwise and triplet Markov models' estimators, I. Gorynin, L. Crelier, H. Gangloff, E. Monfrini, W. Pieczynski, International Conference on Applied and Computational Mathematics (ICACM) , 2016
French conference articles
- Study of the drought stress response among a maize panel, L.-G. Barot, H. Gangloff, Y. Djabali, M. Blein-Nicolas, M.-L. Martin, Congrès Junior Pluridisciplinaire de l'Université Paris-Saclay, 2023
- Chaînes de Markov cachées à bruit généralisé, H. Gangloff, K. Morales, Y. Petetin, GRETSI, 2022, [preprint pdf] (une présentation alternative des chaînes de Markov triplet généralisées)
- Autoencodeurs variationnels à registre de vecteurs pour la détection d’anomalies, H. Gangloff, M.-T. Pham, L. Courtrai, S. Lefèvre, Reconnaissance des Formes, Image, Apprentissage et Perception (RFIAP), 2022, [pdf] (adaptation française de l'article ICPR 2022)
- Segmentation non-supervisée dans les champs de Markov couples gaussiens, H. Gangloff, J-B Courbot, E. Monfrini, C. Collet, GRETSI, 2019
- Segmentation de stents dans des données médicales à rayons-X corrompues par les artéfacts, H. Gangloff, E. Monfrini, C. Collet, N. Chakfe, GRETSI, 2019
See also abstract selected publications.
PhD Thesis
- Probabilistic models for image processing: applications in vascular surgery, H. Gangloff, defended on December 15, 2020, [pdf] [slides]
Awards
- Regional award André Blanc-Lapierre from the Electrity, Electronics and Information Scommunication Technology Society (SEE), in 2017, for the research project Stent segmentation and classification in superficial femoral artery in microCT images.
- Second Prize for the best research internship from Mines-Télécom Foundation, in 2017, for the research project Stent segmentation and classification in superficial femoral artery in microCT images.
Code
Physics Informed Neural Networks
- Physics Informed Neural Networks with JAX: jinns Python package
Variational Autoencoders
- VAE and VAE-GRF for anomaly detection [code]
- Tutorial session on Variational Autoencoders for scRNA-seq data for DIGIT-BIO seminar [code]
Deep Hidden Markov Models
- Unsupervised signal processing with deep extensions of Hidden Markov Chains: Python project
- Deep parameterizations of pairwise and triplet Markov models for unsupervised classification of sequential data [code]
Some scripts and algorithm implementations
- Experimentally show the equivalence between a gradient ascent over the EM quantity Q and a gradient ascent over the model likelihood, in the case of the training of a Hidden Markov Chain with Gaussian Independent Noise: Python script, my pdf note and see section 2.1 of this paper
- Gaussian Markov Random Field simulation using Fourier properties and base matrices for efficiency: Python script
- Rescaled Forward-Backward algorithm for Hidden Markov Chains with Jax using lax.scan: Python script
- Extremely fast rescaled Forward-Backward algorithm for Hidden Markov Chains with Python calling C using ctypes and a shared library: Python script and C files
- Chromatic Gibbs sampler for a binary Ising Markov Random Field with Jax using jit, vmap and lax.scan: Python script
- Sequential Importance Resampling Particle Filter with Jax using jit and lax.scan: Python script
Teaching
See previous teaching experiences here.
About me
My research interests are:
- Hidden Markov models and Bayesian inference
- Statistical signal processing
- Machine learning, deep learning
- Solving problems in medical and environmental sciences
Between 2020 and 2022, I was a postdoctoral researcher at Télécom SudParis and IRISA. I worked on unsupervised statistical image segmentation approaches and unsupervised anomaly detection. Between 2017 and 2020, I was a PhD student working at Geprovas and ICube. I worked on developping new tools for the processing of images from vascular surgery. Before that, I graduated from the French engineering school Télécom SudParis in 2017.


Many feared dead as herdsmen invade Adamawa village
by Hindi LivinusHindi Livinus
Many residents of Shuwa-Kalaa village in the Hong Local Government Area of Adamawa State have been feared dead following an attack on the community by suspected herdsmen.
A resident and eyewitness, Emmanuel Nicholas, told our correspondent on Saturday that heavily armed herdsmen invaded their village, shooting indiscriminately.
He said many residents fled their homes after sighting the herdsmen while some were unlucky to escape the gunfire.
Another resident, Sani Ahmed, said since the commencement of the harvest season, herdsmen had been terrorising the area.
He said, “This morning (Saturday), they (herdsmen) attacked and robbed a man who went to the farm to bring home his harvest. He returned and had barely informed the community of what happened when we saw the armed herdsmen coming from all areas.
“Our local vigilante men quickly mobilised and put up resistance and were able to repel them. But there were casualties on both sides. I cannot ascertain the number of dead persons.”
Ahmed said the military had also intervened in chasing away the herdsmen from the community.
The Adamawa Police Command spokesperson, DSP Suleiman Nguroje, however, could not confirm the incident, saying he needed time to get the report from the Divisional Police Officer in charge of the Hong Local Government Area.
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