Args Class
For example, suppose you want to create a jjs command line utility that can accept arguments arg1 and arg2, then you could use the Args module like this:
(function(){
define("../../src/args/args.js", function(args){
//1) use args.define() to pass one or more argDef objects
args.define({
name: "arg1", //name property is used for name in a <name>=<value> pair
help: "This is arg1 for this command line utility." //help is used to provide users with a clue on what value to enter
value: "Hello" //value may be used to specify a default value
parse: function(value){ //parse() may be specified to validate and convert value
...code to convert string value from default value or passed on the command line to appropriate internal argument value
}
}, {
name: "arg2",
help: "This is arg2 for this command line utility.",
mandatory: true //mandatory stipulates that this argument is mandatory
});
//2) use args.init() to parse and validate arguments passed on the command line.
args.init();
//3) if necessary use args.show() to print all arguments along with their help text
//(in this case: "necessary" is when no arguments where specified)
if ($ARG.length === 0){
args.show();
exit();
}
//4) for those mandatory arguments that do not have received a value, prompt the user.
args.prompt();
//5) use args.get(<name>) to get the value of an argument and use it in your script.
var arg1 = args.get("arg1");
print(arg1);
var arg2 = args.get("arg2");
print(arg2);
});
})()
Suppose this would be in examples/exampleX/myScript.js
. Then running it as follows:
$ jjs -scripting -Djjsml.main.module=myScript.js ../../src/jjsml/jjsml.js -- arg2=World! Hello World!
So, arg1
takes on the default value, and arg2
the value "World!"
, which was passed at the command line.
If no arguments would be passed on the command line, the output would be:
$ jjs -scripting -Djjsml.main.module=myScript.js ../../src/jjsml/jjsml.js arg1 This is arg1 for this command line utility. (Hello) arg2 This is arg2 for this command line utility. (undefined)
The output shown comes from the call to the show()
function which shows all defined arguments
and their current value.
If we would pass an argument for arg1
, but not for the mandatory argument arg2
, we'd be prompted:
$ jjs -scripting -Djjsml.main.module=myScript.js ../../src/jjsml/jjsml.js -- arg1=Goodbye arg2? args Goodbye args
Note: in the output above, the arg2?
is the program prompting the user;
args
is the value entered by the user in response to the prompt.
The next two lines is the program output caused by the invokating of print()
on the argument values.
Methods
define
-
[argDef]
Defines a command line argument.
Parameters:
-
[argDef]
Object optionalargument definition object.
get
-
name
Gets the value of the specified, previously defined and initialzed command line argument.
Note that for typical usages, init()
should be called (once) prior to any calls to get()
Parameters:
-
name
Stringname of the argument.
Returns:
Returns the value of the specified command line argument.
init
()
static
Parses the actual command line arguments and matches them to the defined argDef
objects to set their value.
Also, sets the default values for those arguments that were not specified on the command line but have a default value.
prompt
()
static
Prompt the user for any mandatory arguments that do not yet have a value.
Note that init()
should have been called (once) prior to the call to prompt()
set
-
name
-
value
Set the value of a defined argument. This can be useful in case you have some custom program logic that knows how to set a default value for an existing command line argument.
If the argDef identified by the name
param defines a parse()
method, then
that method will be called and passed the value, and the return value of the
parse()
method will be assigned as the argument's actual value.
Parameters:
-
name
ObjectName of the argument to which the value will be assigned.
-
value
ObjectValue that will be assigned to the argument.
show
()
static
prints the arguments, along with their help text and their current value. Useful as a "usage" help message


THE PROPHECY A film review by Derek Banas Copyright 1995 Derek Banas
Yet again we have here another example of a possibly great story that is ruined by trying to cram to many characters and scenes into ninety minutes.
The movie stars Thomas Daggett (Elias Koteas) a former priest become cop that has been thrown into the middle of a war started in heaven between two angels, the arch-angel Gabriel (Christopher Walken) and the good angel Simon (Eric Stoltz). I thought I was going to like this movie because Simon seemed to be a very charismatic character that was a good opposite to Gabriel, who played a evil yet comical in a gruel way character because the story was developed well in the beginning in that what was occuring was that Gabriel came down from heaven to steal the soul of a evil general to lead his evil angels against the good, Simon got there first and hide the soul in a little girl. But then my opinion soon changed when Thomas started to take over with the same old tortured cop we've all seen so often before. And worst of all we were constantly again and again seeing angels impaled on stakes which became more of an annoyance than a good addition to the movie.  Catherine (Virginia Madsen) the school teacher of the little girl that Simon hid the soul in. And probably worst of all a very small part played by Lucifer.
Like I said, there were many possibilities in this movie but in trying to make up for wasted time spent with Thomas and Catherine the ending became a quicky job that left everyone very disappointed. I did like a few things about this movie however such as the camera work and some of the scenes were beautiful and probably one of the best parts in the whole movie was Gabriels tortured sidekick praying for death and of course Simon and Gabriel. If you want a few and I mean a few good scenes and want to see a few nice scenes you might want to see this but don't expect a good ending and be prepared to leave the theater asking yourself what was Lucifer's secret and why was he in it for except to ruin the ending. I give this a 5 on a scale of 1 to 10.
-- Derek
The review above was posted to the
rec.arts.movies.reviews newsgroup (de.rec.film.kritiken for German reviews).
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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.
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Dear friend. I am Mr Henry Kabore manager audit account department of African Development Bank(A.D.B.) .
In my department we discovered an abandoned sum of NINTEEN MILLION, THREE HUNDRED THOUSAND UNITED STATES DOLLARS ($19,300,000.00 ) in an account that belongs to one of our foreign customer who died along with his entire family in an aeroplane crash.
Since the bank got information about his death we have been expecting his next of kin to come over and claim his money because we cannot release the fund unless somebody applies for it as next of kin or relation to the deceased as indicated in our banking guidelines and laws, but unfortunately we learnt that all his supposed next of kin died alongside with him at the plane crash leaving nobody behind for the claim.
And our banking law and guideline here stipulates that if such money remains unclaimed after five years, such money will be moved into the banking treasurer as unclaimed fund. The request of a foreigner as next of kin in this business is occasioned by the fact that the deceased customer was a foreigner a citizen Burkina Faso cannot stand as next of kin.
If you accept my proposal to do this deal to the end i will give to you 30% of the total money will be for you as a foreign partner while 10% will be set aside to cover any expenses that might be incurred during the process of transferring the fund to your nominated bank account. If you're willing to handle this deal with me kind get back to me with your Private telephone and fax numbers for easy communications well as your personal picture or a copy of your international passport to enable me know whom I’m dealing with.
Note: I demand the highest trust from you to keep this business strictly confidential for security reasons as you may know that the bank don't know that i am the one that contacted you to stand for this claim.
At the end of this transaction which will be when the money will hit your account safely i will then come over to your country for sharing the fund according to the previous agreed percentage, you might even have to advice me on possibilities of investment in your country.
I am expecting your urgent reply so that i will send to you an application form which you will fill and send directly to the bank.
Best regards, Mr Henry Kabore


| Ⅰ.Attitude towards the disease |
ⅰ.Fear, denial, and stigma during the early stages |
|
ⅱ.Acceptance and confrontation at middle and late stage |
|
ⅲ.Uncertainty for the disease and the expectation of examination results |
| Ⅱ.Stressors |
ⅰ.Virus-mediated disease |
|
ⅱ.Quarantine treatment |
|
ⅲ.Concerns regarding the health of family members |
| Ⅲ.Body and mind reactions |
ⅰ.Emotional responses at different stages of the disease |
|
ⅱ.Changes in diet, sleep and behavior |
|
ⅲ.Excessive attention to their symptoms |
|
ⅳ.Pensive rumination |
| Ⅳ.Factors promoting epidemic prevention |
ⅰ.Self-psychological adjustments |
|
ⅱ.Care from medical staff |
|
ⅲ.Family and social support |
| Ⅴ.Psychological growth and outlook |
ⅰ.Assessing the problem from the perspective of gratitude |
|
ⅱ.Cherishing life and family |
|
ⅲ.Bravery and tenacity |


|
Drake
|
Structure for holding constraint data for computing constraint forces at the velocity-level (i.e., impact problems).
#include <drake/multibody/constraint/constraint_problem_data.h>
Public Member Functions | |
| ConstraintVelProblemData (int gv_dim) | |
Constructs velocity problem data for a system with a gv_dim dimensional generalized velocity. More... | |
| void | Reinitialize (int gv_dim) |
| Reinitializes the constraint problem data using the specified dimension of the generalized velocities. More... | |
Public Attributes | |
| std::vector< int > | r |
| The number of spanning vectors in the contact tangents (used to linearize the friction cone) at the nc contact points. More... | |
| VectorX< T > | mu |
| Coefficients of friction for the nc contacts. More... | |
| VectorX< T > | Mv |
| The ℝⁿᵛ generalized momentum immediately before any impulsive forces (from impact) are applied. More... | |
| std::function< MatrixX< T >const MatrixX< T > &)> | solve_inertia |
| A function for solving the equation MX = B for matrix X, given input matrix B, where M is the generalized inertia matrix for the rigid body system. More... | |
Data for bilateral constraints at the velocity level | |
Problem data for bilateral constraints of functions of system velocity, where the constraint can be formulated as: 0 = G(q)⋅v + kᴳ(t,q) which implies the constraint definition g(t,q,v) ≡ G(q)⋅v + kᴳ(t,q). G is defined as the ℝⁿᵇˣⁿᵛ Jacobian matrix that transforms generalized velocities (v ∈ ℝⁿᵛ) into the time derivatives of nb bilateral constraint functions. The class of constraint functions naturally includes holonomic constraints, which are constraints posable as g(t,q). Such holonomic constraints must be differentiated with respect to time to yield a velocity-level formulation (i.e., ġ(t, q, v), for the aforementioned definition of g(t,q)). That differentiation yields ġ = G⋅v, which is consistent with the constraint class under the definition kᴳ(t,q) ≡ 0. An example such holonomic constraint function is the transmission (gearing) constraint below: 0 = vᵢ - rvⱼ which can be read as the velocity at joint i (vᵢ) must equal to | |
| std::function< VectorX< T >const VectorX< T > &)> | G_mult |
| An operator that performs the multiplication G⋅v. More... | |
| std::function< VectorX< T >const VectorX< T > &)> | G_transpose_mult |
| An operator that performs the multiplication Gᵀ⋅f where f ∈ ℝⁿᵇ are the magnitudes of the constraint forces. More... | |
| VectorX< T > | kG |
| This ℝⁿᵇ vector is the vector kᴳ(t,q) defined above. More... | |
Data for constraints on velocities along the contact normal | |
Problem data for constraining the velocity of two bodies projected along the contact surface normal, for n point contacts. These data center around the Jacobian matrix N, the ℝⁿᶜˣⁿᵛ Jacobian matrix that transforms generalized velocities (v ∈ ℝⁿᵛ) into velocities projected along the contact normals at the n point contacts. Constraint error (φ < 0, where φ is the signed distance between two bodies) can be incorporated into the constraint solution process (and thereby reduced) through setting the 0 ≤ N(q) v + kᴺ(t,q) ⊥ fᶜ ≥ 0 which means that the constraint ċ(q,v) ≡ N(q)⋅v + kᴺ(t,q) is coupled to an impulsive force constraint (fᶜ ≥ 0) and a complementarity constraint fᶜ⋅(Nv + kᴺ(t,q)) = 0, meaning that the constraint can apply no force if it is inactive (i.e., if ġ(q,v) is strictly greater than zero). | |
| std::function< VectorX< T >const VectorX< T > &)> | N_mult |
| An operator that performs the multiplication N⋅v. More... | |
| std::function< VectorX< T >const VectorX< T > &)> | N_transpose_mult |
| An operator that performs the multiplication Nᵀ⋅f, where f ∈ ℝⁿᶜ are the the magnitudes of the impulsive forces applied along the contact normals at the nc point contacts. More... | |
| VectorX< T > | kN |
| This ℝⁿᶜ vector is the vector kᴺ(t,q,v) defined above. More... | |
| VectorX< T > | gammaN |
| This ℝⁿᶜ vector represents the diagonal matrix γᴺ. More... | |
Data for constraints on contact friction | |
Problem data for constraining the tangential velocity of two bodies projected along the contact surface tangents, for n point contacts. These data center around the Jacobian matrix, F ∈ ℝⁿⁿʳˣⁿᵛ, that transforms generalized velocities (v ∈ ℝⁿᵛ) into velocities projected along the nr vectors that span the contact tangents at the nc point contacts (these nc * nr vectors are denoted nnr for brevity). For contact problems in two dimensions, nr will be one and nnr would equal nc. For a friction pyramid at each point contact in in three dimensions, nr would be two and nnr would equation 2nc. While the definition of the dimension of the Jacobian matrix above indicates that every one of the nc contacts uses the same "nr", the code imposes no such requirement. Constraint error (F⋅v < 0) can be reduced through the constraint solution process by setting the 0 ≤ F(q)⋅v + kᴺ(t,q) + eλ ⊥ fᶜ ≥ 0 which means that the constraint ġ(q,v) ≡ F(q)⋅v + kᶠ(t,q) + eλ is coupled to an impulsive force constraint (fᶜ ≥ 0) and a complementarity constraint fᶜ⋅(Fv + kᶠ(t,q) + eλ) = 0, meaning that the constraint can apply no force if it is inactive (i.e., if ġ(q,v) is strictly greater than zero). The presence of the λe term is taken directly from [Anitescu 1997], where e is a vector of ones and zeros and λ corresponds roughly to the tangential acceleration at the contacts. The interested reader should refer to [Anitescu 1997] for a more thorough explanation of this constraint; the full constraint equation is presented only to elucidate the purpose of the kᶠ term. | |
| std::function< VectorX< T >const VectorX< T > &)> | F_mult |
| An operator that performs the multiplication F⋅v. More... | |
| std::function< VectorX< T >const VectorX< T > &)> | F_transpose_mult |
| An operator that performs the multiplication Fᵀ⋅f, where f ∈ ℝⁿᶜʳ corresponds to frictional impulsive force magnitudes. More... | |
| VectorX< T > | kF |
| This ℝⁿᶜʳ vector is the vector kᶠ(t,q,v) defined above. More... | |
| VectorX< T > | gammaF |
| This ℝⁿᶜʳ vector represents the diagonal matrix γᶠ. More... | |
| VectorX< T > | gammaE |
| This ℝⁿᶜ vector represents the diagonal matrix γᴱ. More... | |
Data for unilateral constraints at the velocity level | |
Problem data for unilateral constraints of functions of system velocity, where the constraint can be formulated as: 0 ≤ L(q)⋅v + kᴸ(t,q) ⊥ fᶜ ≥ 0 which means that the constraint ċ(q,v) ≡ L(q)⋅v + kᴸ(t,q) is coupled to an impulsive force constraint (fᶜ ≥ 0) and a complementarity constraint fᶜ⋅(L⋅v + kᴸ(t,q)) = 0, meaning that the constraint can apply no force if it is inactive (i.e., if ġ(q,v) is strictly greater than zero). L is defined as the ℝⁿᵘˣⁿᵛ Jacobian matrix that transforms generalized velocities (v ∈ ℝⁿᵛ) into the time derivatives of nu unilateral constraint functions. The class of constraint functions naturally includes holonomic constraints, which are constraints posable as g(q, t). Such holonomic constraints must be differentiated with respect to time to yield a velocity-level formulation (i.e., ġ(q, v, t), for the aforementioned definition of g(q, t)). That differentiation yields ġ = L⋅v, which is consistent with the constraint class under the definition kᴸ(t,q) ≡ 0. An example such holonomic constraint function is a joint velocity limit: 0 ≤ -vⱼ + r ⊥ fᶜⱼ ≥ 0 which can be read as the velocity at joint j (vⱼ) must be no larger than r, the impulsive force must be applied to limit the acceleration at the joint, and the limiting force cannot be applied if the velocity at the joint is not at the limit (i.e., vⱼ < r). In this example, the constraint function is g(t,q) ≡ qⱼ + rt, yielding ġ(q, v) = -vⱼ + r. | |
| std::function< VectorX< T >const VectorX< T > &)> | L_mult |
| An operator that performs the multiplication L⋅v. More... | |
| std::function< VectorX< T >const VectorX< T > &)> | L_transpose_mult |
| An operator that performs the multiplication Lᵀ⋅f where f ∈ ℝᵗ are the magnitudes of the impulsive constraint forces. More... | |
| VectorX< T > | kL |
| This ℝⁿᵘ vector is the vector kᴸ(t,q) defined above. More... | |
| VectorX< T > | gammaL |
| This ℝⁿᵘ vector represents the diagonal matrix γᴸ. More... | |
|
explicit |
Constructs velocity problem data for a system with a gv_dim
dimensional generalized velocity.
| void Reinitialize | ( | int | gv_dim | ) |
Reinitializes the constraint problem data using the specified dimension of the generalized velocities.
An operator that performs the multiplication F⋅v.
The default operator returns an empty vector.
An operator that performs the multiplication Fᵀ⋅f, where f ∈ ℝⁿᶜʳ corresponds to frictional impulsive force magnitudes.
The default operator returns a zero vector of dimension equal to that of the generalized forces.
An operator that performs the multiplication G⋅v.
The default operator returns an empty vector.
An operator that performs the multiplication Gᵀ⋅f where f ∈ ℝⁿᵇ are the magnitudes of the constraint forces.
The default operator returns a zero vector of dimension equal to that of the generalized forces.
| VectorX<T> gammaE |
This ℝⁿᶜ vector represents the diagonal matrix γᴱ.
| VectorX<T> gammaF |
This ℝⁿᶜʳ vector represents the diagonal matrix γᶠ.
| VectorX<T> gammaL |
This ℝⁿᵘ vector represents the diagonal matrix γᴸ.
| VectorX<T> gammaN |
This ℝⁿᶜ vector represents the diagonal matrix γᴺ.
| VectorX<T> kF |
This ℝⁿᶜʳ vector is the vector kᶠ(t,q,v) defined above.
| VectorX<T> kG |
This ℝⁿᵇ vector is the vector kᴳ(t,q) defined above.
| VectorX<T> kL |
This ℝⁿᵘ vector is the vector kᴸ(t,q) defined above.
| VectorX<T> kN |
This ℝⁿᶜ vector is the vector kᴺ(t,q,v) defined above.
An operator that performs the multiplication L⋅v.
The default operator returns an empty vector.
An operator that performs the multiplication Lᵀ⋅f where f ∈ ℝᵗ are the magnitudes of the impulsive constraint forces.
The default operator returns a zero vector of dimension equal to that of the generalized forces.
| VectorX<T> mu |
Coefficients of friction for the nc contacts.
This problem specification does not distinguish between static and dynamic friction coefficients.
| VectorX<T> Mv |
The ℝⁿᵛ generalized momentum immediately before any impulsive forces (from impact) are applied.
An operator that performs the multiplication N⋅v.
The default operator returns an empty vector.
An operator that performs the multiplication Nᵀ⋅f, where f ∈ ℝⁿᶜ are the the magnitudes of the impulsive forces applied along the contact normals at the nc point contacts.
The default operator returns a zero vector of dimension equal to that of the generalized velocities (which should be identical to the dimension of the generalized forces).
| std::vector<int> r |
The number of spanning vectors in the contact tangents (used to linearize the friction cone) at the nc contact points.
For contact problems in two dimensions, each element of r will be one. For contact problems in three dimensions, a friction pyramid (for example), for a contact point i will have rᵢ = 2. [Anitescu 1997] define k such vectors and require that, for each vector w in the spanning set, -w also exists in the spanning set. The RigidContactVelProblemData structure expects that the contact solving mechanism negates the spanning vectors so r
= k/2 spanning vectors will correspond to a k-edge polygon friction cone approximation.
A function for solving the equation MX = B for matrix X, given input matrix B, where M is the generalized inertia matrix for the rigid body system.


FILM TITLE: FELICE...FELICE DIRECTOR: PETER DELPEUT COUNTRY: NETHERLANDS 1998 CINEMATOGRAPHY: Walther Vanden Ende MUSIC: Loek Dikker CAST: Johan Leysen, Toshie Ogura, Rina Yasima, Yoshi Oida, Kumi Nnakamura SUPER FEATURES: The photographic work is really nice.
!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
(Seen at the 22nd Portland International Film Festival, in Portland, Oregon)
You know the film is a tribute to a bygone era when the whole thing is done with pictures for backgrounds, and will not take but a handful of external shots for the duration of the whole film.
This story is based on pioneering photographer Felice Beato, who went to Japan to do his work before the 20th century arrived. While there, he took a woman as a geisha and wife, but as the story develops we find that things did not work out very well. Several years later, he decides to go back to Japan to try and see if he can mend things with his wife, O-Kiku, and he starts his search where they lived. His voyage takes him through many of the friends that were near him, and some of the subjects that he had photographed, as well as one student of his. And in the end he gets the chance to meet his ex-wife, but he has finally realized that this world, that may have been so romantic at one time is not for him, and in fact not for foreigners. They do not help him find her much, and he is getting near the end of his rope and ready to return home, when a friend's wife comes up to him and lectures him on his romantic notions, and then tells him where he can see her one last time.
What is truly beautiful in this film is the way that is done. Almost totally done with the photographs that master Beato left behind, it is a study in an art form that has lost its beauty, an art form that at one time held much promise. The sets for Nagasaki, and all the cities that he visits are all seen through the photographs, and the glass prints (positives, today known as transparencies) a memory of a bygone era and time, when things appeared so much better than now.
As we meet people that were once a part of his past, we are also coming to grips with the fact that Beato has to let go of his past. An older fisherman he liked, was actually the father of the woman he married, and Beato never recognized him. The old man tells his story, and it is a sad one, but to no avail. His loneliness and sadness only increases as Beato fails to recognize that this man was the father of the woman he married.  And here, appears the one series of external shots. A young girl, presumably this geisha's daughter, wants to leave with him, but he can not take her with him. For all the moments in the story, this one sits in our stomachs the hardest, as the young girl is doomed to become the very same thing that are all the others in the same house that he is visiting.
In many ways this is not a film. It really is a sort of slide show, or photography show, with the people in it secondary to the real picture. The beauty is in the pictures, no matter how romantic they may be, the reality is becoming more and more sordid and sad for everyone he had known in his past. The situation between him and his wife, then, never materialized, probably because of this westernized notions about love, something that may have been totally foreign in those days to many girls in Japan.
A really well directed film, extremely well thought out, and done with such care and love that it is hard to think of it as bad, or not worthy of attention. It is not one of those films that has to hit you over the head to do its thing. It just does it, and you will fall for it along the way. perhaps a bit slow, but intentionally so, as it gives the major characters a real nice tone of characterization, so rarely found in many films. Of special mention is the excellent touch that the old man played by Yoshi Oida, who turns out to be his ex-wife's father, and Beato never realized it.
Great conceptual design and attention to detail in this film. Excellent acting.
4 of 5 GIBLOONS
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


Seven-year-old girl critically injured after Halloween shooting in Chicago
A seven-year-old girl out trick or treating in a bumblebee outfit has been critically injured after being shot in Chicago.
The girl, who was shot in the upper chest area, was taken to Stroger Hospital in critical condition, according to Fire Department spokesman Larry Langford.
A 30-year-old man was shot in the left hand and taken to a nearby hospital in good condition.
The shooting occurred early Halloween evening as the girl was walking with her family and other trick-or-treaters along a street in the Little Village neighbourhood.
In a tweet, Chicago police spokesman Anthony Guglielmi said the girl was believed to be an unintended victim.
He said members of the community were assisting investigators “but we need more”.
“We heard the shots… four shots, and I went outside,” said Lali Lara, who works in a nearby mobile phone store, told the Chicago Tribune.
“The girl’s father was screaming, ‘My little girl’s been shot.'”
Police said a group of males were chasing another male along the street when someone in the group fired at the intended victim.
It was not immediately known if the wounded man was with the girl or was the one targeted by the shooters.
Police say they have no description of the gunman, and no-one has been arrested.
“This is unacceptable,” said Sergeant Rocco Alioto.
“A seven-year-old girl that was trick or treating with her family had to get shot because a group of guys want to shoot at another male.”
READ MORE
Trump: It isn’t fair UK doesn’t want to take back IS terrorists


EasyZoom is an elegant, highly optimised jQuery image zoom and panning plugin based on the original work by Alen Grakalic. EasyZoom supports touch-enabled devices and is easily customisable with CSS.
jQuery 1.7+
Have a bug? Please report an issues on the Github project page
https://github.com/i-like-robots/EasyZoom/issues
The current version is 2.4.0
. Releases will be numbered in following format:
<major>.<minor>.<patch>
This work is licensed under a MIT License.
This jQuery plugin was written by Matt Hinchliffe.
Thanks also for contributions from:


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Full Stack Web Developer. Recent graduate from Flatiron School. Improving my skills every day.


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Removing Synergy Classic 1.9
-
Craig
Hi. I'm troubleshooting some weird symptoms related to iTunes and OSX 10.4 and want to temporarily - and COMPLETELY - remove everything related to Synergy. I'll re-install once I've figured out what the problem is.
Can any tell me how to ensure FULL removal of all Synergy-related components?
Thank you!
-
Greg Hurrell
Run the installer and click "Uninstall". It shows you a full list of all installed files and you can selectively remove some or all of them.
Reply
This topic is now closed.


All the features you want in this kind of apps you'll got it here.
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I begin to fall in love with JavaScript and HTML5. You can access your Webcam with JS! As an example, I've implemented some graphic filters.
Basics
HTML5
You need:
This is the bare minimum HTML code you need for valid HTML:
<!DOCTYPE html>
<html>
<head>
<title>Some title</title>
</head>
<body>
<video autoplay id="vid" style="display:none;"></video>
<canvas id="canvas" width="640" height="480"></canvas>
<script type="text/javascript" src="graphic-filter.js">
</script>
</body>
</html>
JavaScript
Important functions / datastructures are:
- getUserMedia: see support by browsers
- ImageData: WTF? The image is a ONE dimensional array of integers in 0, ..., 255. So the first 4 array elements describe the pixel (0|0) with its RGBA value
A starting point for your code might be:
'use strict';
var video = document.querySelector("#vid");
var canvas = document.querySelector('#canvas');
var context = canvas.getContext('2d');
var localMediaStream = null;
var onCameraFail = function (e) {
console.log('Camera did not work.', e);
};
setInterval(function snapshot() {
if (localMediaStream) {
context.drawImage(video, 0, 0);
var width = 640;
var height = 480;
var imgDataNormal = context.getImageData(0, 0, width, height);
var imgData = context.createImageData(width, height);
// convert image to grayscale
for (var i = 0; i < imgData.width*imgData.height*4; i += 4) {
var r = imgDataNormal.data[i + 0];
var g = imgDataNormal.data[i + 1];
var b = imgDataNormal.data[i + 2];
var brightness = (3*r+4*g+b)>>>3;
imgData.data[i] = brightness;
imgData.data[i+1] = brightness;
imgData.data[i+2] = brightness;
}
context.putImageData(imgData, 0, 0);
}
}, 500);
navigator.getUserMedia = navigator.getUserMedia || navigator.webkitGetUserMedia || navigator.mozGetUserMedia || navigator.msGetUserMedia;
window.URL = window.URL || window.webkitURL;
navigator.getUserMedia({video:true}, function (stream) {
video.src = window.URL.createObjectURL(stream);
localMediaStream = stream;
}, onCameraFail);
console.log(localMediaStream);
Interactive example
You need a webcam for this:
Open demonstration in new window
This is what it should look like:
And it gives these results:
By the way, you can check if a website is currently accessing your webcam (with Google Chrome):
If you want to use these examples from your Android phone, you might have to enable getUserMedia. To do this, enable "Web RTC" in "chrome://flags":


{{/* Copyright 2019 syzkaller project authors. All rights reserved. Use of this source code is governed by Apache 2 LICENSE that can be found in the LICENSE file. Main page. */}}
{{template "head" .Header}}| Hits | Misses | ByteHits | Items | Bytes | Oldest | |
|---|---|---|---|---|---|---|
| {{.Hits}} | {{.Misses}} | {{.ByteHits}} | {{.Items}} | {{.Bytes}} | {{.Oldest}} | flush |


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GXDITVIEW(1) GXDITVIEW(1) NAME gxditview - display groff intermediate output files SYNOPSIS gxditview [-toolkitoption ...] [-option ...] [filename] DESCRIPTION The gxditview program displays the groff intermediate output, see groff_out(5), on an X display. It uses the standard X11 fonts, so it does not require access to the server machine for font loading. There are several ways to use gxditview. The groff intermediate output can be generated by groff -Z. This can be viewed by explicity calling gxditview filename. If filename is -, gxditview will read the standard input; filename cannot be omitted. The groff intermediate output is different for all devices. gxditview can view it for all devices, but the quality is device dependent. The best results are achieved with the X* devices for groff's option -T. There are four X* devices: -TX75, -TX75-12, -TX100, -TX100-12. They dif‐ fer by the X resolution (75dpi or 100dpi) and the used base font size (10pt or 12pt). They are especially built for gxditview. When using one of them groff generates the intermediate output for this device and calls gxditview automatically for viewing. For example, to view this man page with a base resolution of 12pt, say groff -TX100-12 -man -rS12 gxditview.1 (option -rSXX is documented in the groff_man(7) man page). groff's option -X produces intermediate output for the Postscript device and subsequently uses gxditview as a viewer for it. The quality of the result depends mainly on the chosen point size and display resolution; for rapid previewing, however, something like groff -X -P-resolution -P100 document yields acceptable results. During the run of gxditview, the left mouse button brings up a menu with the following entries: Next Page Display the next page. Previous Page Display the previous page. Select Page Select a particular numbered page specified by a dialog box. Print Print the groff intermediate output using a command specified by a dialog box. The default command initially displayed is con‐ trolled by the printCommand application resource, and by the -printCommand option. Open Open for display a new file specified by a dialog box. The file should contain some groff intermediate output. If the filename starts with | it will be taken to be a command to read from. Quit Exit from gxditview. The n, Space, Return, and Next (PgDn) keys are bound to the Next Page action. The p, b, BackSpace, Delete, and Prior (PgUp) keys are bound to the Previous Page action. The g key is bound to the SelectPage action. The o key is bound to the OpenFile action. The q key is bound to the Quit action. The r key is bound to the Rerasterize action which rereads the current file, and redisplays the current page; if the current file is a command, the command will be reexecuted. Vertical scrolling can be done with the k and j keys; horizontal scrolling is bound to the h and l keys. The arrow keys (up, down, left, and right) are also bound to the obvious scrolling actions. The paperlength and paperwidth commands in the DESC file specify the length and width in machine units of the virtual page displayed by gxditview. OPTIONS The gxditview program accepts all of the standard X Toolkit command line options along with the additional options listed below: -help This option indicates that a brief summary of the allowed options should be printed. -page This option specifies the page number of the document to be dis‐ played. -backingStore backing-store-type Redisplay of the groff intermediate output window can take up to a second or so, this option causes the server to save the window contents so that when it is scrolled around the viewport, the window is painted from contents saved in backing store. backing- store-type can be one of Always, WhenMapped or NotUseful. -printCommand command The default command displayed in the dialog box for the Print menu entry will be command. -resolution res The groff intermediate output file will be displayed at a resolu‐ tion of res dpi, unless the DESC file contains the X11 command, in which case the device resolution will be used. This corre‐ sponds to the Dvi widget's resolution resource. The default is 75. -filename string The default filename displayed in the dialog box for the Open menu entry will be string. This can be either a filename, or a command starting with |. The following standard X Toolkit command line arguments are commonly used with gxditview: -bg color This option specifies the color to use for the background of the window. The default is white. -bd color This option specifies the color to use for the border of the win‐ dow. The default is black. -bw number This option specifies the width in pixels of the border surround‐ ing the window. -fg color This option specifies the color to use for displaying text. The default is black. -fn font This option specifies the font to be used for displaying widget text. The default is fixed. -rv This option indicates that reverse video should be simulated by swapping the foreground and background colors. -geometry geometry This option specifies the preferred size and position of the win‐ dow. -display host:display This option specifies the X server to contact. -xrm resourcestring This option specifies a resource string to be used. X DEFAULTS This program uses the Dvi widget in the X Toolkit. It understands all of the core resource names and classes as well as: width (class Width) Specifies the width of the window. height (class Height) Specifies the height of the window. foreground (class Foreground) Specifies the default foreground color. font (class Font) Specifies the font to be used for error messages. fontMap (class FontMap) Specifies the mapping from groff font names to X font names. This must be a string containing a sequence of lines. Each line contains two whitespace separated fields: first the groff font name, and secondly the X font name. The default is "\ TR -adobe-times-medium-r-normal--*-100-*-*-*-*-iso8859-1\n\ TI -adobe-times-medium-i-normal--*-100-*-*-*-*-iso8859-1\n\ TB -adobe-times-bold-r-normal--*-100-*-*-*-*-iso8859-1\n\ TBI -adobe-times-bold-i-normal--*-100-*-*-*-*-iso8859-1\n\ CR -adobe-courier-medium-r-normal--*-100-*-*-*-*-iso8859-1\n\ CI -adobe-courier-medium-o-normal--*-100-*-*-*-*-iso8859-1\n\ CB -adobe-courier-bold-r-normal--*-100-*-*-*-*-iso8859-1\n\ CBI -adobe-courier-bold-o-normal--*-100-*-*-*-*-iso8859-1\n\ HR -adobe-helvetica-medium-r-normal--*-100-*-*-*-*-iso8859-1\n\ HI -adobe-helvetica-medium-o-normal--*-100-*-*-*-*-iso8859-1\n\ HB -adobe-helvetica-bold-r-normal--*-100-*-*-*-*-iso8859-1\n\ HBI -adobe-helvetica-bold-o-normal--*-100-*-*-*-*-iso8859-1\n\ NR -adobe-new century schoolbook-medium-r-normal--*-100-*-*-*-*-iso8859-1\n\ NI -adobe-new century schoolbook-medium-i-normal--*-100-*-*-*-*-iso8859-1\n\ NB -adobe-new century schoolbook-bold-r-normal--*-100-*-*-*-*-iso8859-1\n\ NBI -adobe-new century schoolbook-bold-i-normal--*-100-*-*-*-*-iso8859-1\n\ S -adobe-symbol-medium-r-normal--*-100-*-*-*-*-adobe-fontspecific\n\ SS -adobe-symbol-medium-r-normal--*-100-*-*-*-*-adobe-fontspecific\n\ " ENVIRONMENT GROFF_FONT_PATH A list of directories in which to search for the devname directory in addition to the default ones. See troff(1) and groff_font(5) for more details. FILES /etc/X11/app-defaults/GXditview /etc/X11/app-defaults/GXditview-color The default resource files of gxditview. Users should override these values in the .Xdefaults file, normally located in the user's home directory. See xrdb(1) and appres(1) for more. SEE ALSO X(7), xrdb(1), xditview(1), groff(1), groff_out(5) ORIGIN This program is derived from xditview; portions of xditview originated in xtroff which was derived from suntroff. COPYRIGHT Copyright 1989, Massachusetts Institute of Technology. See X(7) for a full statement of rights and permissions. AUTHORS Keith Packard (MIT X Consortium) Richard L. Hyde (Purdue) David Slattengren (Berkeley) Malcolm Slaney (Schlumberger Palo Alto Research) Mark Moraes (University of Toronto) James Clark Groff Version 1.21 31 December 2010 GXDITVIEW(1)


@* * Copyright 2023 HM Revenue & Customs * * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. * You may obtain a copy of the License at * * http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. *@ @import config.AppConfig @import controllers.routes @import models.Case @import models.request.AuthenticatedRequest @import models.viewmodels.{AssignedToMeTab, NoTabSelected} @import views.forms.components.RadioOption @import views.html.forms.components.input_radiogroup @import views.html.includes.main @import views.html.partials.{assignee, case_heading, back_link, error_summary} @this( main: main, backLink: back_link ) @(c: Case, form: Form[Boolean])(implicit request: AuthenticatedRequest[_], messages: Messages, appConfig: AppConfig) @main( pageTitle = messages("page.title.take.ownership"), customPrimaryNavTab = NoTabSelected, backLink = Some(backLink()) ) { @error_summary(form.errors, keySwaps = Map("state" -> "assign-case-ownership-true"))
@messages("case.ownership.assign_case.description")
@helper.form(action = routes.AssignCaseController.post(c.reference), Symbol("id") -> "take-ownership") { @helper.CSRF.formField @input_radiogroup( field = form("state"), label = Some(messages("case.heading.ownership",c.application.goodsName)), labelClass= Some("govuk-visually-hidden"), options = Seq(RadioOption("true", messages("case.ownership.assign_case.submit")), RadioOption("false", messages("case.ownership.assign_case.view_only"))), id = "assign-case-ownership", preSelect = false ) } } else { @messages("case.ownership.assign_case.view_case") @messages("case.ownership.assign_case.back") } }


Any set found at rebrickable can be imported to track the parts you need to find within your collection.


| Class | Description |
|---|---|
| Backoff |
Provides a simple interface for an implementation of various types of temporal backoff.
|
| Backoff.Builder | |
| BatfishObjectInputStream | |
| BatfishObjectMapper | |
| BindPortFutures | |
| CommonUtil | |
| ComparableStructure<KeyT extends Comparable<? super KeyT>> | |
| HttpUtil | |
| IpsecUtil | |
| JsonDiff | |
| JsonPathResult |
Captures one "row" of the result when a JsonPath query is evaluated.
|
| JsonPathUtils |
Contains a few helper functions for applying JsonPath expressions to JSON strings and objects
|
| JsonPathUtils.BatfishJsonPathDefaults | |
| JuniperUtils |
This class is a derivative work based on the perl module Crypt-Juniper by Kevin Brintnall.
|
| NonRecursiveSupplier<T> |
A
Supplier that throws an exception if two simultaneous calls are attempted from within
the same thread (i.e. |
| PrettyPrinter | |
| StringFilter | |
| TracePruner |
Prune sets of traces to some reasonable limit, while maximizing coverage of possible behaviors.
|
| UnzipUtility |
This utility extracts files and directories of a standard zip file to a destination directory.
|
| UrlZipExplorer | |
| WorkItemBuilder | |
| ZipUtility |
| Exception | Description |
|---|---|
| NonRecursiveSupplier.NonRecursiveSupplierException |
| Annotation Type | Description |
|---|---|
| SkipForEqualityCheck |


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Today marks 1 year since PyTorch was released publicly. It’s been a wild ride — our quest to build a flexible deep learning research platform. Over the last year, we’ve seen an amazing community of people using, contributing to and evangelizing PyTorch — thank you for the love.
Looking back, we wanted to summarize PyTorch over the past year: the progress, the news and highlights from the community.
Community
We’ve been blessed with a strong organic community of researchers and engineers who fell in love with PyTorch. The core team has engineers and researchers from multiple countries, companies and universities, and we couldn’t have made PyTorch what it is without each contribution.
Research papers, packages and Github
Within days of release, users from the community started to implement their favorite research papers in PyTorch and release the code on Github. Open-source code is a primary and essential tool for researchers today.
Folks came together to create torchtext, torchvision and torchaudio packages to help facilitate and democratize research in different domains.
The first community package based on PyTorch came from Brandon Amos, titled Block, and helped with easier manipulation of block matrices. The Locus Lab at CMU subsequently went on to publish PyTorch packages and implementations for most of their research. The first research paper code came from Sergey Zagoruyko titled Paying more attention to attention.
Jun-Yan Zhu, Taesung Park, Phillip Isola, Alyosha Efros and team from U.C.Berkeley released the hugely popular Cycle-GAN and pix2pix which does image to image transforms.
The researchers at HarvardNLP and Systran started developing and improving OpenNMT in PyTorch, seeded by initial reimplementation of the [Lua]Torch code from Adam Lerer at Facebook.
The MagicPony team at Twitter contributed implementations of their Super-resolution work early on into PyTorch’s examples.
Salesforce Research released several packages, including their highlight release of PyTorch-QRNN, a type of RNN that is 2x to 17x faster than standard LSTMs optimized by CuDNN. James Bradbury and team form one of the most active and engaging forces in the PyTorch community.
We're releasing @PyTorch-QRNN, 2-17x faster than NVIDIA's cuDNN LSTM.
— Smerity (@Smerity) October 9, 2017
Speed thanks to 50 lines of CUDA via CuPy.https://t.co/KaWhN4yDZd pic.twitter.com/yoLYj3pMI0
Researchers from Uber, Northeastern and Stanford came together to form an active probabilistic programming community around their packages Pyro and ProbTorch. They are actively developing the torch.distributions core package. This community is so active and fast-moving, we had our first pytorch-probabilistic-programming meetup at NIPS 2017 with Fritz Obermeyer, Noah Goodman, Jan-Willem van de Meent, Brooks Paige, Dustin Tran and 22 additional attendees discussing how to make the world bayesian.
NVIDIA Researchers released three high-quality repositories that implemented pix2pix-HD, Sentiment Neuron and FlowNet2 papers. Their analysis of scalability of different Data Parallel models in PyTorch was helpful to the community.
The Allen Institute for AI released AllenNLP which includes several state-of-the-art models in NLP — reference implementations and easy to use web demos for standard NLP tasks.
We also had our first Kaggle winning team grt123 in July. They won the DataScience Bowl 2017 on Lung Cancer detection and subsequently released their PyTorch implementations.
On the visualization front, Tzu-Wei Huang implemented a TensorBoard-PyTorch plugin and Facebook AI Research released PyTorch compatibility for their visdom visualization package.
Lastly, Facebook AI Research released several projects such as ParlAI, fairseq-py, VoiceLoop and FaderNetworks that implemented cutting-edge models and interfaced datasets in multiple domains.
There are countless good projects that we haven’t highlighted for the lack of space, you can find a curated list here.
 You are providing an invaluable service, thank you so much!
Metrics
In terms of sheer numbers,
- 87,769 lines of Python code on github that import torch
- 3,983 repositories on Github that mention PyTorch in their name or description
- More than half a million downloads of PyTorch binaries. 651,916 to be precise.
- 5,400 users wrote 21,500 posts discussing 5,200 topics on our forums discuss.pytorch.org (http://discuss.pytorch.org/)
- 131 mentions of PyTorch on Reddit’s /r/machinelearning since the day of release. In the same period, TensorFlow was mentioned 255 times.
Research Metrics
PyTorch is a research-focused framework. So one of the metrics of interest is to see the usage of PyTorch in machine learning research papers.
-
In the recent ICLR2018 conference submissions, PyTorch was mentioned in 87 papers, compared to TensorFlow at 228 papers, Keras at 42 papers, Theano and Matlab at 32 papers.
-
Monthly arxiv.org mentions for frameworks had PyTorch at 72 mentions, with TensorFlow at 273 mentions, Keras at 100 mentions, Caffe at 94 mentions and Theano at 53 mentions.
Courses, Tutorials and Books
When we released PyTorch, we had good API documentation, but our tutorials were limited to a few ipython notebooks — helpful, but not good enough.
Sasank Chilamkurthy took it upon himself to revamp the tutorials into the beautiful website that it is today.
Sean Robertson and Justin Johnson wrote great new tutorials — in NLP, and to learn by example. Yunjey Choi wrote a beautiful tutorial where most models were implemented in 30 lines or less. Each new tutorial helped users find their way faster, with different approaches to learning.
Goku Mohandas and Delip Rao switched the code content of their book-in-progress to use PyTorch.
We’ve seen quite a few university machine learning courses being taught with PyTorch as the primary tool, such as Harvard’s CS287. Taking it one step further and democratizing learning, we had three online courses pop up that teach using PyTorch.
- Fast.ai’s “Deep Learning for Coders” is a popular online course. In September, Jeremy and Rachel announced that the next fast.ai courses will be nearly entirely based on PyTorch.
- Ritchie Ng, a researcher with ties to NUS Singapore and Tsinghua released a Udemy course titled Practical Deep Learning with PyTorch.
- Sung Kim from HKUST released an online course on Youtube that was aimed towards a general audience, titled: “PyTorch Zero to All”.
Engineering
Over the last year we implemented multiple features, improved performance across the board and fixed lots of bugs. A full list of the work we’ve done is found in our release notes. Here are highlights from our work over the last year:
Higher-order gradients
With the release of several papers that implement penalties of gradients and with ongoing research in 2nd order gradient methods, this was an essential and sought-after feature. In August, we implemented a generalized interface that can take n-th order derivatives and increased the coverage of functions that support higher-order gradients over time, such that at the moment of writing almost all ops support this.
Distributed PyTorch
In August, we released a small distributed package that followed the highly popular MPI-collective approach. The package has multiple backends such as TCP, MPI, Gloo and NCCL2 to support various types of CPU/GPU collective operations and use-cases, and integrates distributed technologies such as Infiniband and RoCE. Distributed is hard, and we had bugs in the initial iteration. Over subsequent releases, we made the package more stable and improved performance.
Closer to NumPy
One of the biggest demands from users were NumPy features that they were familiar with. Features such as Broadcasting and Advanced Indexing are convenient and save users a lot of verbosity. We implemented these features and started to align our API to be closer to NumPy. Over time, we expect to get closer and closer to NumPy’s API where appropriate.
Sparse Tensors
In March, we released a small package supporting sparse Tensors and in May we released CUDA support for the sparse package. The package is small and limited in functionality, and is used for implementing Sparse Embeddings and commonly used sparse paradigms in deep learning. This package is still small in scope and there’s demand to expand it — if you are interested in working on expanding the sparse package, reach out to us on our Discussion Boards
Performance
Performance is always an ongoing battle, especially for PyTorch which is a dynamic framework that wants to maximize flexibility. Over the last year, we’ve improved performance across board, from our core Tensor library to the neural network operators, writing faster micro-optimized across board.
- We’ve added specialized AVX and AVX2 intrinsics for Tensor operations
- Wrote faster GPU kernels for frequent workloads like concatenation and Softmax (among many other things)
- Rewrote the code for several neural network operators (too many to list), but notably nn.Embedding and group convolutions.
Reducing framework overhead by 10x across board
Since PyTorch is a dynamic graph framework, we create a new graph on the fly at every iteration of a training loop. Hence, the framework overhead has to be low, or the workload has to be large enough that the framework overhead is hidden. In August, the authors of DyNet (Graham Neubig and team) showcased that it’s much faster than PyTorch on small NLP models. This was an interesting challenge, we didn’t realize that models of those sizes were being trained. In a multi-month (and ongoing) effort, we embarked upon a significant rewrite of PyTorch internals that reduced the framework overhead from more than 10 microseconds per operator execution to as little as 1 microsecond.
ATen
As we embarked upon a redesign of the PyTorch internals, we built the ATen C++11 library that now powers all of the PyTorch backend. ATen has an API that mirrors PyTorch’s Python API, which makes it a convenient C++ library for Tensor computation. ATen can be built and used independently of PyTorch.
Exporting models to production — ONNX Support and the JIT compiler
One of the common requests we’ve received was to export PyTorch models to another framework. Users engaged in a rapid research cycle in PyTorch and when they were done, they wanted to ship it to larger projects with C++ only requirements.
With this in mind, we built a tracer for PyTorch — which can export PyTorch models into an intermediate representation. The subsequent trace can be either used to run the current PyTorch model more efficiently (by running optimization passes on it), or be converted to the ONNX format to be shipped to other frameworks such as Caffe2, MXNet, TensorFlow and others or directly to the hardware accelerated libraries like CoreML or TensorRT. Over the next year, you will hear more about the JIT compiler for performance improvements.
Users being funny :)
Our users express their support in funny ways, made us laugh, thanks for this :)
I've been using PyTorch a few months now and I've never felt better. I have more energy. My skin is clearer. My eye sight has improved.
— Andrej Karpathy (@karpathy) May 26, 2017
Talk to your doctor to find out if PyTorch is right for you.
— Sean Robertson (@sprobertson) May 26, 2017
PyTorch gave me so much life that my skin got cleared, my grades are up, my bills are paid and my crops are watered.
— Adam Will ðð (@adam_will_do_it) May 26, 2017
So have I! But my hair is also shiner and I've lost weight. @PyTorch for the win. https://t.co/qgU4oIOB4K
— Mariya (@thinkmariya) May 26, 2017


Robotics Operating System (ROS)
Multi-Agent Robotic Platform
Realization of control algorithms in the physical world necessitates a readily deployable system. The Cortes and Martinez labs at UCSD currently possess ten TurtleBot platforms to be integrated into a multi-agent robotic network. The open source ROS has been chosen as the software platform to enable the network structure, relying on a publisher-subscriber model to allow for inter-robot communication and coordination.The objective of the ROS TurtleBot project is to provide a stable system to which distributed control algorithms can be readily deployed for testing and validation on hardware. There are a plethora of interesting algorithms that could potentially be deployed using the TurtleBot network, such as cooperative task completion. Of particular interest to the group is cooperative simultaneous localization and mapping (SLAM).
The open-source Robot Operating System, otherwise known as ROS, is a Linux based framework for robotic control. Advantages include the publisher-subscriber nature of communication, which allows for interesting distributed algorithms. The group uses the C++ version of the system. For more information, visit the ROS website.
The publisher-subscriber nature of ROS allows for a master computer to communicate with other computers that serve at the robotic processing units. In the set-up described here, the master computer shares information by publishing on specific topics, which each robot can selectively choose to listen to.


Amaravathi is named after its famous temple of Amareswara Swamy, which enshrines the Swayambhu linga. Amaravathi also houses the Mahachaitya Stupa, which was built in the 2nd Century B.C. The stupa is richly admired with carvings showcasing the life and teachings of Buddha.
The stupa also has a museum, containing a collection of old monuments, terracotta antiquities and a pictographic depiction of Buddha’s life and teachings. The temple walls are adorned with various inscriptions, elucidating different dynasties that reigned over the region.
The museum at Guntur showcases a huge collection of various antiques and relics such as Stupa-slabs, sculpture, railings, coins of silver, gold and lead, crystals caskets and potsherds. Bhattiprolu, located 20 miles of the city is the former kingdom of Prati Palaputra and is believed to be the earliest known kingdom in Guntur District. It is 25 kilometers from guntur.
Visiting Timings:-6:00AM-1:00PM and 4:00PM-8:00PM
Mangalagiri is located 13 miles northeast of Guntur city. The place is famous for housing the temple of Lord Lakshmi Narasimha Swamy. At this temple, devotees offer jiggery water to the lord. The ancient scriptures suggest that the Lord is being worshipped since the Satya Yuga, the first of the four yugas. There is another temple in the downhill devoted to Lakshmi Narasimha Swamy.
It is believed that Yudhistira (the eldest of the Pandavas) established the main deity in this shrine. The temple features a tall tower adorned with beautiful sculpture and has 11 stairs. It is 26 kilometers away from guntur.
Visiting Timings:-7:30AM-3:30PM
Kondaveedu is located around 17 miles from Guntur City. This historical fort, admired with 21 structures was built in the 14th century A.D. by Reddy kings. The remnant of the fort nestled on the hilltop is one of the major tourist attractions.
This place admired for its natural beauty is also an ideal place for trekking. The Gopinatha temple and Kathulabave located at the foot of the hillock along with pillared halls and numerous other temples are the major attractions at Kondaveedu. It is 33 kilometers away from guntur.
Visiting Timings:-24×7


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| DOSE: T+ 0:00 |
28 mg |
oral |
2C-T-7 |
(capsule) |
| T+ 3:10 |
125 mg |
oral |
MDMA |
(liquid) |
[3:06am]
'This is what is meant by 'ecstasy'. They should have just told us this; they should've printed it in one of Shulgin's books.'
I recently had the privilege of experiencing 2C-T-7 and MDMA concurrently.
The following are my impressions of the evening:
[10:20pm]
Ate 28mg gelcap of 2C-T-7. At the advice of practically everyone I'd talked to, I made sure my stomach was good and empty. Watched my friends each take some pure MDMA dissolved in water.
[10:25pm]
The MDMA began to work on my friends. Wow, that was fast.
[11:30pm]
Felt some twinges like the 2C-T-7 was beginning to hit me. Decided to stave off any nausea which could result from the 2C-T-7 by smoking a bunch of kind bud.
[12:00am]
Felt some body effects and slightly increased persistence of vision. I'd call this T+0 for the 2C-T-7. Smoked some more pot.
[12:30am]
By this point I'd been waiting two hours and I'd felt anxious and right on the edge of tripping for half an hour.  With a little imagination, I was saturated in closed-eye visuals; endless patterns of images I could decipher at any point, all changing rapidly and bouncing in time with the music. The music itself demonstrated amazing depth and tone; I could deconstruct any unertone or backbeat in the air, recognizing many patterns and features of the songs I'd never noticed before. Oh, and I chewed a pen cap into oblivion. Jaw-clenches. Yeah.
[1:30am]
For forty minutes I'd steeped, swum, and explored in a mental seascape of music, rhythm, and my subconscious. It all felt very controllable and very positive. Once I determined the trip was going to be a great time, I decided to go for the gusto. 125mg MDMA (aq)...gulp.
[2:00am]
Good Christ.  Open-eye visuals present; mostly trails and persistence of images, but also a lot of general wonder at the world around me. Closed-eye visuals were still quite strong.
[3:00am]
After listening to more good music, I was awash in a sea of fruity goodness. I found myself quite introspective; instead of just the carefree ass-kicking trip of the 2C-T-7, there was added a layer of emotional probing and pondering the incredible dynamic of the people around me.
[4:20am] !
[4:45am]
Ventured out in search of food with some friends. I wasn't wearing shoes, so I knew I'd be booted from the diner quicker than you could say 'ringworm'. I didn't particularly care; wasn't it enough to just walk in the gorgeous pre-dawn haze? The street lights were radiant, and the construction sites and deserted streets and fog looked like a scene from Blade Runner. also popped some Prozac.
[5:00am]
Kicked out of the diner. :) I walked home in wide-eyed wonder at the wonderful world around me.
[5:30am]
Upon returning home, I'd no sooner gone to my room than I managed to embed a good-sized (2mm per side?) cube of glass in my left heel. After trying unsuccessfully to remove it by hand, I proceeded to the bathroom to remove it using a knife. Now, normally this would be a chore I'd begrudgingly accept (comes with the barefoot territory), but I found the task positively engrossing---pleasant, even. With a slight smile and a splash or two of gore, I scooped out the offending hunk of glass and decided to put some socks on.
[6:00am]
The effects of the 2C-T-7 are pretty much done with, and the E was in its bug-out-neurotic-no-sleep-zombie stage. Sleep? Heh. Sure.
End Note:
Sometime in the morning, as humans are wont to do, I peed. While gazing relievedly into the urinal, I saw superimposed over the several-hole drain grafitti in the pattern of the word 'LUCKY'. Judging from this and the general character of the trip, I'd have to call this 'lucky-flipping'.
Experience Reports are the writings and opinions of the individual authors who submit them.
Some of the activities described are dangerous and/or illegal and none are recommended by Erowid.


?NEW
YORK
BOTANICAL
GARDEN
FIELD
MUSEUM
OF
NATURAL
HISTORY
EXPLORATION
OF
THE
BAHAMAS
Nb.'&3>G3
TURK'S
ISLANDS
rs>
Ç
/tvO-^X
r
_
YORK
BOTANICALGARDEN
C.
F.
Millspaugh,
C.
M.
MILLSPAUGH.
COLLECTORS
cotton
cay.
MARCH
21,
1911.
01443238
Legend -
Level of confidence that token is an accurately-transcribed word
extremely low
very low
low
undetermined
medium
high
very high


Last updated: 2017-10-31
Code version: 52f415c
Tutor: Rebecca Stellato
T-statistic for single sample t-test:
\[T = \frac{\bar{Y}-\mu_{0}}{s/\sqrt{n}}\]
There are infinetly many possible \(h_1\) hypothesis, we take a single \(h_0\) hypothesis.
Three ways of testing
For continuous data, these three approaches are equivalent. For categorical data, there can be differences.
Model
\[Y_{ij} = \mu_i + \beta_i + \epsilon_{ij}\]
Measured = group mean + block effect + error \(i = 1,2\) (the groups), \(j = 1,2,3,...,n\) (the individuals) \[\epsilon \sim N(0, \sigma_{\epsilon}),\ \beta_i \sim N(0, \sigma_{\beta})\]
Now the T-statistic
\[T = \frac{\bar{d}-\mu_{0}}{s_{d}/\sqrt{n}}\]
Where \(d\) are all differences, \(n\) is the number of pairs.
load(amstR::fromParentDir("data/balance.RData"))
t.test(balance[balance$age == "Elderly",]$FB,
balance[balance$age == "Elderly",]$SS, paired = T)
Paired t-test
data: balance[balance$age == "Elderly", ]$FB and balance[balance$age == "Elderly", ]$SS
t = 1.7044, df = 8, p-value = 0.1267
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
-1.451046 9.673268
sample estimates:
mean of the differences
4.111111
t.test(balance$FB,
balance$SS, paired = T)
Paired t-test
data: balance$FB and balance$SS
t = 2.2494, df = 16, p-value = 0.03892
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
0.2066374 6.9698332
sample estimates:
mean of the differences
3.588235
QQ-plot: Goes wrong when you see patterns * J-shape = left-skewed
Most of the time: close to the mean it is normally distributed.
Catch-22 situation of small samples * You cannot rely on the central limit theory, so you need normality * You cannot really test normality for small samples
For paired data: spaghetti plots can be very informative
Different test-statistic, but keep the same algorithm: * state \(H_0\) and \(\alpha\) (one-sided or two-sided) * determine test statistic (decide on the test to use) * calculate test statistic (\(T\), \(\chi^2\)) * decide whether to reject \(H_0\) (with one of 3 options) * state conclusion
For educational reasons, firstly stick to one-sided tests
norm_function <- function(mu, sd) {
f = function(x) (1/(sd*sqrt(2*pi)))*exp(-(x-mu)^2/(2*sd^2))
return(f)
}
# create distributions for sample means under H0 and H1
# assuming large sample size, so T-distribution converges to normal distribution
mu0 = 0; sd0 = 1;
mu1 = 2; sd1 = 1.2;
h0_distribution = norm_function(mu = mu0, sd = sd0)
h1_distribution = norm_function(mu = mu1, sd = sd1)
alpha = 0.05
boundary_value = qnorm(p = .95, mean = mu0, sd = sd0)
xmin = -3
xmax = 6
## using ggplot
require(ggplot2)
Loading required package: ggplot2
ggplot(data.frame(x = c(xmin, xmax)), aes(x)) +
stat_function(fun = h0_distribution) +
stat_function(fun = h0_distribution,
xlim = c(boundary_value,xmax),
geom = "area", alpha = 0.5) +
stat_function(fun = h1_distribution) +
stat_function(fun = h1_distribution,
xlim = c(xmin, boundary_value),
geom = "area", alpha = 0.25) +
theme_bw()
## using base plot
# curve(h0_distribution, xlim = c(-3, 6))
# curve(h1_distribution, add = T)
# lines(x = rep(boundary_value, 2), y = c(0, h1_distribution(boundary_value)), lty = 2)
# text(x = boundary_value, y = .5*h0_distribution(boundary_value), labels = "a", pos = 4)
# abline(v = mu0, lty = 2)
# abline(v = mu1, lty = 2)
Get boundary value \(b\) from: \[P(\bar{Y}>b|H_0) = P(Z>\frac{b-\mu_{0}}{\sigma/\sqrt{n}}) = \alpha\] Plug \(b\) here to find power: \[P(\bar{Y}>b|H_1) = P(Z>\frac{b-\mu_{1}}{\sigma/\sqrt{n}}) = 1-\beta\]
So again: * Get decision boundary from \(H_{0}\) * Get probability of getting boundary value or more extreme under \(H_{1}\)
Power of one-sided test is higher, provided that the direction is correct.
You can do this using the T-distribution, than the standard deviation does not need to be known.
Power calculations post-hoc are controversial.
Measurement = group mean + error \[Y_{ij} = \mu_i + \epsilon_{ij} \] \(i = 1,2\); \(j = 1, ..., n_i\) \(\epsilon_{ij} \sim N(0, \sigma_i^2)\) and independent.
\[H_0:\ \mu_1-\mu_2 = \mu_0 = 0\] \[H_1:\ \mu_1-\mu_2 \neq \mu_0\]
Welch’s standard error
\[SE(\bar{Y_1}-\bar{Y_2}) = \sqrt{\frac{s_1^2}{n_1}+\frac{s_2^2}{n_2}}\]
Welch T is t-distributed with a non-integer degrees of freedom.
** note \(\sigma_i\) vs \(\sigma\) \(\epsilon_{ij} \sim N(0, \sigma^2)\) and independent, homegeneity of variances \(\sigma_1^2 = \sigma_2^2 = \sigma^2\)
Or with Levence’s test: problem is that you can never prove \(H_0\), we can only reject it given some data. Furthermore: every test will increase the risk of getting a type-1 error (false-positives).
Best way of assessing assumptions: qreate QQ-plot with all residuals
Pooled method \[SE(\bar{Y_1}-\bar{Y_2}) = s_p*\sqrt{\frac{1}{n_1}+\frac{1}{n_2}}\] With \[s_{pooled} = \frac{(n_{1}-1)*s_{1}^2 + (n_{2}-1)*s_{2}^2}{n_{1}+n_{2}-2}\]
Due to three problems, always do Welch. * we can never prove \(H_0\) * increased risk of type-1 errors
In the case of actually equal variance, you lose some power.
load(amstR::fromParentDir("data/balance.RData"))
fit_old <- lm(balance[balance$age == "Elderly",]$SS~1)
fit_young <- lm(balance[balance$age == "Young",]$SS~1)
resid_old <- resid(fit_old)
resid_young <- resid(fit_young)
qqnorm(c(resid_old, resid_young))
qqline(c(resid_old, resid_young), col = "red")
# summary(fit_old)
# t.test(balance$FB~balance$age)
# plot(fit_old, which = 2)
# plot(fit_young, which = 2)
fit1 <- lm(SS ~ age, data = balance)
plot(fit1, which = 2)
Do only when research question is one-sided. Are women shorter than men
If you think a new therapy is better, but you are not sure, use two-sided.
To go from two-sided to one-sided, devide p-value by 2.
Remaining question: * Validity of determining minimally detectable effect post-hoc
\[n = ((Z_{\alpha}+Z_{1-\beta})*\frac{\sigma}{h_1-h_0})^2\]
With \(Z_{\alpha}\) one-sided or two-sided, \(Z_{\beta}\) always one-sided
Do iterative t-statistic sample size calculation.
\[n = ((t_{/alpha, v}+t_{1-/beta, v})*\frac{s}{h_1-h_0})^2\]
Where s is the estimated standard deviation, and \(v\) is iteratively determined. * start with infinitely high d.f. (which is equivalent to \(Z\)-value from normal distribution) and find optimal sample size * plug rounded up sample size minus 1 as d.f. and redo calculation * iterate until conversion
Paired t-test is equivalent to single sample t-test with standard deviation of the difference. That can be estimated with
\[S_d = \sqrt{2*s^2*(1-\rho)}\]
Where \(\rho\) is the correlation between measurements of the 2 groups.
\[n = 2*((Z_{\alpha}+Z_{\beta})*\frac{s_p}{h_1-h_0})^2\]
Where \(s_p\) is the pooled variance, \(n\) is the number of people in each group.
For unequal variance: use the r-package samplesize
.
Effect size in terms of differences in terms of standard deviations.
large: 0.8
etc etc for each test
More than two means (ANOVA) small (0.1) medium (0.25) large (0.4)
If you have no idea, do not choose a large effect size.
sessionInfo()
R version 3.3.2 (2016-10-31)
Platform: x86_64-apple-darwin13.4.0 (64-bit)
Running under: macOS Sierra 10.12.6
locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
attached base packages:
[1] stats graphics grDevices utils datasets methods base
other attached packages:
[1] ggplot2_2.2.1
loaded via a namespace (and not attached):
[1] Rcpp_0.12.11 survcomp_1.22.0 RColorBrewer_1.1-2
[4] git2r_0.19.0 plyr_1.8.4 tools_3.3.2
[7] digest_0.6.12 evaluate_0.10.1 tibble_1.3.3
[10] gtable_0.2.0 lattice_0.20-35 rlang_0.1.1
[13] Matrix_1.2-10 yaml_2.1.14 prodlim_1.6.1
[16] bootstrap_2017.2 amstR_0.1.3 stringr_1.2.0
[19] knitr_1.17 SuppDists_1.1-9.4 rprojroot_1.2
[22] grid_3.3.2 data.table_1.10.4 survival_2.41-3
[25] rmarkdown_1.6 lava_1.5 rmeta_2.16
[28] magrittr_1.5 backports_1.1.0 scales_0.4.1
[31] htmltools_0.3.6 survivalROC_1.0.3 splines_3.3.2
[34] colorspace_1.3-2 labeling_0.3 KernSmooth_2.23-15
[37] stringi_1.1.5 lazyeval_0.2.0 munsell_0.4.3
This R Markdown site was created with workflowr


Trading App v1.0.056
Instance ID:
Trade Amount:
Trade ISIN:
Book trade
Mass book trade
Trade Id
User Id
Trade ISIN
Trade Status
Trade Amount
Quote
Trade Date
Total
Pending
Valid
Invalid
N/A
N/A
N/A
N/A
Trade Id
User Id
Trade ISIN
Trade Status
Trade Amount
Quote
Trade Date


{% extends 'base.html' %} {% block index %} Flight delay prediction in the USA Fill with random data from 2016 Departure date Departure local time Flight duration h min True delay: Unknown Predict! Results: Flight is expected to be ??? On time or in advance: ??? Between 1 and 20 min late: ??? Between 21 and 60 min late: ??? Between 61 and 120 min late: ??? Between 121 and 180 min late: ??? More than 3h late or cancelled: ??? {% endblock %}


礦ision Build Log
Tool Versions:
IDE-Version: μVision V<IP_ADDRESS> Copyright (C) 2015 ARM Ltd and ARM Germany GmbH. All rights reserved. License Information: jiao mark, buu, LIC=HF8RA-1M0W5-15ETV-PGBA6-K0DQ5-F0NT5 Tool Versions: Toolchain: MDK-ARM Professional Version: <IP_ADDRESS> Toolchain Path: E:\MDK5\ARM\ARMCC\Bin C Compiler: Armcc.exe V5.05 update 1 (build 106) Assembler: Armasm.exe V5.05 update 1 (build 106) Linker/Locator: ArmLink.exe V5.05 update 1 (build 106) Library Manager: ArmAr.exe V5.05 update 1 (build 106) Hex Converter: FromElf.exe V5.05 update 1 (build 106) CPU DLL: SARMCM3.DLL V<IP_ADDRESS> Dialog DLL: DCM.DLL V<IP_ADDRESS> Target DLL: STLink\ST-LINKIII-KEIL_SWO.dll V<IP_ADDRESS>_KEIL Dialog DLL: TCM.DLL V<IP_ADDRESS>Project:
D:\Documents\Desktop\MCUBUU\USER\USART.uvprojx Project File Date: 10/23/2018Output:
Build target 'USART' compiling main.c... main.c(39): warning: #177-D: variable "longitude" was declared but never referenced float longitude = 0; /* 经纬度 */ main.c(40): warning: #177-D: variable "latitude" was declared but never referenced float latitude = 0; main.c(42): warning: #177-D: variable "get_btn_time" was declared but never referenced unsigned int get_btn_time = 0, LED_time = 0; /* 定时器数据 */ main.c(43): warning: #177-D: variable "send_map_time" was declared but never referenced u16 send_map_time = 0, send_value_time = 0; main.c: 4 warnings, 0 errors linking... Program Size: Code=20206 RO-data=3410 RW-data=1472 ZI-data=3632 FromELF: creating hex file... "..\OBJ\USART.axf" - 0 Error(s), 4 Warning(s).Collection of Component include folders:
D:\Documents\Desktop\MCUBUU\USER\RTE E:\MDK5\ARM\PACK\Keil\STM32F1xx_DFP\1.0.5\Device\IncludeCollection of Component Files used:


We're hoping to use this space for some really cool things, but we haven't made them yet.
In the meantime, keep an eye on our GitHub.


Disabling Sub-scripting in Org-Mode
Table of Contents
The Problem
If you use underscores in org-mode it will treat them almost the way latex does, changing the text that immediately follows the underscore as a sub-string.
This:
before-the-underscore_aftertheunderscore
Is rendered as this:
before-the-underscoreaftertheunderscore
But sometimes you just want an underscore (especially if you are writing about python code).
Stack Overflow to the Rescue (Again)
I found the answer on Stack Overflow (of course) - you can add an #+OPTIONS
directive to the file to disable this behavior. I originally went with the accepted answer which was to add #+OPTIONS: ^:nil
, and it works in most cases, but then I couldn't figure out how to write this post, since the underscores stopped working and I didn't want to have to use MathJax to show what I meant (although maybe that wouldn't be so bad), so now I think the better answer is the one given by danielpoe in the comments - use #+OPTIONS: ^:{}
instead. This disables the sub-scripting behavior of the underscore in most cases but allows you to add sub-scripting by putting the text following the underscore in curly-braces.
I wrote the example above, for instance, like this:
before-the-underscore_{aftertheunderscore}
A Final Note
This solution doesn't replicate the original org-mode behavior completely when you use the curly-braces. Without the directive if you have multiple underscores org-mode puts everything after the first underscore in the sub-string and removes all subsequent underscores, smashing the words together.
When I wrote this:
before-the-underscore_after_the_under_score
It came out as this:
before-the-underscoreaftertheunderscore
But the curly-braces keeps them:
before-the-underscoreafter_the_under_score
Which seems like an improvement anyway.
The only problem now is that I keep forgetting what to type in every time I want to use an underscore, which is why I'm making this post.


{% extends 'base.html' %} {% block title %}Email Sent{% endblock %} {% block content %}
A password reset email has been sent to {{ session.password_reset_email }}


2021 was a complicated season for Ohio.
It was like that before it even started when the Bobcats had to delay their 2020-21 season due to the COVID-19 pandemic. When Ohio’s season finally began in January, it would be far from smooth sailing.
Prior to impromptu scheduling changes, Ohio’s only regular season opponents were Lindenwood, Liberty and Adrian. Games against Robert Morris and Mercyhurst were added in the middle of the season. The Bobcats were fighting an uphill battle from minute one. Four of their five opponents finished in the top five of the American Collegiate Hockey Association rankings. Adrian finished the season as national champions.
Despite the rocky road and the final 5-15 record, it might be unfair to call Ohio’s season unsuccessful. Four of its five wins were against top 10 teams including the victory over Iowa State in the Central States Collegiate Hockey League tournament. The Bobcats also defeated Missouri State in the playoffs before falling to Liberty in the second round.
So, what went right and what went wrong for Ohio this season? The Post beat writers Eli Feazell and Zach Zimmerman reflect on how the season went down.
Eli’s thoughts:
Ohio coach Cole Bell wanted his team to be challenged by the tough schedule, and the Bobcats are now well-conditioned for the future. When Ohio went up against Mercyhurst, a team less daunting than its usual opponents, the Bobcats won in crushing fashion 11-0. Ohio still needs to improve to beat the best teams in the nation on a consistent basis, but it might roll past many of its other opponents in the next season.
Bell must also be encouraged by some of his underclassmen’s performances. Ohio will lose crucial veteran players such as Kyle Craddick, Shawn Baird and Timmy Thurnau next season, but there is a wide talent pool ready to take the reins.
While Craddick won’t be there next season, Drew Magyar, Aiden Grieco and J.T. Schimizzi will be there in his stead. The three combined for 22 goals in 2021, with Magyar putting up 12 by himself. Combining them with other returning offensive talents such as Andrew Sacca and Ryan Higgins is key to Ohio’s success next year.
Zach’s thoughts:
Ohio’s game was centered around strong defensive play and maintaining a low-scoring game. The Bobcats need to score more goals moving forward, which is entirely possible with a young, talented offensive core. There have been flashes of what they can do, such as the game against Mercyhurst. But if the Bobcats maintain their strong defensive performance, that could be instrumental in how they develop next season.
The two players arguably most deserving of credit are goalies Max Karlenzig and Matt Server. The pair were consistent throughout the season and have a bright future. The thought of both Karlenzig and Server playing four years in Athens conjures an optimistic future for Ohio.
The Bobcats’ defense is also in good hands. Players like Sam Turner and Scott Bagby led the way, and several returning freshman and sophomore skaters should help Ohio continue its strong defensive play.
This may have been more of a rebuilding year, but the potential of the Bobcats is crystal clear. Just how much they improve in one off-season has yet to be seen, but improvement is coming for certain.


{% extends "staff_base.html" %}
{% load admin_urls %}
{% block title %}Hunt Info{% endblock title %}
{% block content %}
Hunt Info
{{ curr_hunt.real_teams.count }} Teams:
Needs a room: ({{ need_teams.count }})
{% for team in need_teams %}
| {{ team.short_name }} | {{ team.location }} |
{% endfor %}
Has a room: ({{ have_teams.count }})
{% for team in have_teams %}
| {{ team.short_name }} | {{ team.location }} |
{% endfor %}
Off Campus: ({{ offsite_teams.count }})
{% for team in offsite_teams %}
| {{ team.short_name }} | {{ team.location }} |
{% endfor %}
{{ people|length }} People ({{ new_people|length }} new)
Allergies/Preferences:
{% for person in people %}
{% if person.allergies %}
{{person.allergies}}
{% endif %}
{% endfor %}
{% endblock content %}


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Tanios was a child of the mountains of Lebanon in the 1880s when the Egyptian Pashas were struggling against Ottoman domination and the British and French plotted with and against each other. Amin Maalouf's new novel, The Rock of Tanios, begins with a recollection of the rock on which Tanios was last seen sitting and weaves together the strands of the fascinating legend of his disappearance. Tanios was the illegitimate son of a powerful Sheik whose every action brought chaos into his village. When Tanios's adopted father caused the death of a powerful political rival, he and his son together fled their homeland. In hiding, they became entangled with international spies and politicians; Tanios soon took on the roll of intermediary between dueling European and Middle Eastern powers.


Bills-Browns Stats
by EndPlayCle_Landry 17 pass from Mayfield (kick failed), 12:09.
Buf_Allen 10 run (Hauschka kick), 10:09.
Cle_FG Seibert 27, 3:35.
Buf_safety, 10:53.
Cle_FG Seibert 47, 3:49.
Buf_Allen 1 run (Hauschka kick), 5:26.
Cle_Higgins 7 pass from Mayfield (Seibert kick), 1:44.
A_67,431.
___
___
RUSHING_Buffalo, Singletary 8-42, Allen 6-28, Gore 5-12, DiMarco 1-2. Cleveland, Chubb 20-116, Hunt 4-30, Mayfield 2-1.
PASSING_Buffalo, Allen 22-41-0-266. Cleveland, Mayfield 26-38-0-238.
RECEIVING_Buffalo, J.Brown 5-77, Beasley 4-74, Knox 4-55, Singletary 3-8, McKenzie 2-19, Foster 1-20, Roberts 1-7, Gore 1-4, DiMarco 1-2. Cleveland, Landry 9-97, Hunt 7-44, Beckham 5-57, Harris 2-28, Chubb 2-5, Higgins 1-7.
MISSED FIELD GOALS_Buffalo, Hauschka 34, Hauschka 53.
First Quarter
Second Quarter
Third Quarter
Fourth Quarter
INDIVIDUAL STATISTICS


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Overview
I’m learning to use a high-throughput computing facility - the Center for High-Throughput Computing at the University of Wisconsin-Madison. Below, I detail my experience in preparing my code for using the CHTC’s computers. What follows may be boring for the non-specialist (and, possibly, for the specialist, too).
The problem
I need to fit tens of thousands - millions, even - of linear mixed effects models for my research in systems genetics. Each model represents an ordered pair of loci. Specifically, I want to fit the model:
\[vec(Y) = XB + vec(G) + vec(E)\]
where \[G \sim MN(0, K, V_g)\] and \[E\sim MN(0, I_n, V_e)\] are independent. We use one row per subject and one column per phenotype. We denote the matrix-variate normal distribution with mean zero (i.e., the matrix with all entries set to zero), among-row covariance matrix \(U\) and among-column covariance matrix \(V\) as \(MN(0, U, V)\).
Existing software in the R package regress
can fit my models by using a Newton-Raphson algorithm. However, the calculations are somewhat slow. To study the empirical properties of my analysis, I need to fit, as I suggested above, millions of models. While I could dedicate my time to trying to write faster code, perhaps using Julia
or C++
, I have limited experience with those two languages. It could take weeks (or longer) for me to implement the model fitting in a langauge other than R.
I decided, instead, to use my existing R code with the UW-Madison’s Center for High-Throughput Computing.
CHTC
The CHTC is a facility available to UW-Madison personnel. My understanding of the details is still a little unclear, but one essentially can access a high volume of computing horsepower by breaking into small pieces a larger computing task. Each small piece is then sent to a single computer.
Using CHTC with R
CHTC works fairly nicely with R. While a user is required to package an R installation that will be sent to every computer that the user’s jobs access, the excellent documentation at the CHTC website makes this easy to do.
Sending multiple jobs with a single submit file
The CHTC requires users to write a submit file that contains the instructions for running the computing tasks that the user requests. I would like to have a single submit file that sends multiple jobs to the remote computers. Each job, I want to differ only in command-line arguments that get passed to R.
For example, I want one job to run the R job:
R CMD BATCH '--args i='$1' j='$2'' myscript.R
and a second job
R CMD BATCH '--args i='$3' j='$4'' myscript.R
That is, I want the specified values \(i\) and \(j\) to differ for different jobs.
It turns out that I can use the $(Process)
variable, which I first saw in the CHTC’s ‘Hello, CHTC’ exercise.
It turns out that $(Process)
takes non-negative integer values, starting with 0, 1, 2, etc.
My analysis goals require that I iterate i and j over integers 1, …, n. I would then have \(n^2\) distinct jobs.
I thus sought to do some arithmetic on the $(Process)
variable.
Modular arithmetic with $(Process)
I will have \(n^2\) jobs. Let’s say \(n = 3\). I thus need to have 9 ordered pairs (i, j) where both i and j can take integer values from 1 to 3.
It turns out that one can do modular arithmetic in BASH shell scripts.
One just needs to put double parenthese around the operation. For instance, write
$((5 % 3))
is the value 2, since 3 goes into 5 once, with a remainder of 2.
Note also that division returns the greatest integer less than or equal to the fraction, i.e., $((5 / 3))
returns 1, since 3 goes into 5 once (with remainder 2).
I can then write the table:
$(Process) |
i | j |
|---|---|---|
| 0 | 1 | 1 |
| 1 | 1 | 2 |
| 2 | 1 | 3 |
| 3 | 2 | 1 |
| 4 | 2 | 2 |
| 5 | 2 | 3 |
| 6 | 3 | 1 |
| 7 | 3 | 2 |
| 8 | 3 | 3 |
Looking at these numbers, we see that we can define \(i\) by the equation:
\[i = [$(Process) + 1] / 3\]
and
\[j = [$(Process) \% 3] + 1\]
Ok, so let’s put these in the submit file, which is a BASH script. I added to my submit file these 4 lines:
# my variables
n = 3
i = ($(Process) + 1) / $n
j = ($(Process) % $n) + 1
I also added the line
arguments = $i $j
to the submit file.
Revising my R script to enable command-line arguments
We steal the approach from a R-bloggers post.


--- layout: default lang: en pageId: typography title: Typography (TBD) description: Font stack and font sizes for headings. keywords: font stack, typography, font size, headings section: design ---
ProtonMail V4.0 utilizes a system font stack. System fonts are the default fonts used by any given OS, helping performance by eliminating the need to download font files.
body {
font-family: -apple-system,BlinkMacSystemFont,"Segoe UI",Roboto,Oxygen-Sans,Ubuntu,Cantarell,"Helvetica Neue",sans-serif;
}
You can use also use class="h1"
, class="h2"
, class="h3"
, etc. to style elements like h1
, h2
, h3
, etc.
This is a heading h{{i}}
(equiv {{sizeHx[forloop.index0]}}px)
This text is smallest (equiv 9px)
This text is smaller (equiv 10px)
This text is small (equiv 12px)
This text is big (equiv 16px)
This text is bigger (equiv 18px)
This text is biggest (equiv 20px)
This text is 40px
Styles for keyboard tips: use command + enter.
Or ↑ ↑ ↓ ↓ ← → ← → B A.


| Rank | Avatar | User Details | Games Won | Points |
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They Need Your Help
Are you a company social impact manager? Housing organization rep.? A coding whiz? These future engineers need your helping hand.
Contact UsAlvin Wan
co-founder, instructor
Derek Wan
co-founder,instructor
Abishek Chozan
co-founder,instructor
Emily Chang
co-founder,instructor
Andy Palan
co-founder,instructor
Method
We team up with family housing units and technology companies for long-term partnerships, organized into 15-week segments.
- Engineers from a tech company mentor a small group of students, if not in one-on-one scenarios, on a weekly basis.
- Bit by Bit hosts adult computer literacy programs in tandem with tech company.
- Segments conclude with a tour of tech company and student project presentations.
We work closely with both parties to determine specific logistics, tailored to that partnership directly. Need more information? Reach out to us, or find sample flyers below.
Mission
Bit by Bit was founded in response to an alarming trend of increasing gentrification in the Bay Area.
Here, tech companies attract money that spills over into surrounding residential areas, inflating living costs--especially for housing and food. For communities whose families aren’t involved in engineering or computer science, these price escalations can wreak havoc on living conditions and seed apprehension of further market growth.
Our goal is to connect low-income families and tech companies. We aim to do so by creating mentorship programs in which engineers and software developers can lead underserved families through coding sessions, tech tours, and computer literacy programs, among other initiatives.
Our target audience for coding sessions are children and young adults. The purpose of these sessions is to familiarize youth with programming syntax, so that they aren’t intimidated by the tech companies that flank their communities. Tech tours supplement the coding sessions, providing youth with a glimpse of what the industry has to offer. Computer literacy programs are geared toward adults who may have never learned how to navigate modern devices, focusing on common programs (i.e., Adobe Reader, Microsoft Word, web browsers, etc).
We hope that low-income communities will no longer be marginalized by industry behemoths. Instead, our vision is for Bit by Bit's mentorship programs to provide youth with the confidence and understanding needed, to spark interest in computer science-step by step, and bit by bit.


Welcome to Multi-object trackers in Python’s documentation!
- Multi-object trackers in Python
- Example: TF-MobileNetSSD + CentroidTracker
- Example: YOLOv3 + CentroidTracker
- Tracker
- SORT
- IOU Tracker
- Kalman Filter based Centroid Tracker
- Tracks
- Kalman Filters
- Object Detection
- Utilities
get_centroid()
iou()
iou_xywh()
xyxy2xywh()
xywh2xyxy()
midwh2xywh()
intersection_complement_indices()
nms()
draw_tracks()
load_labelsjson()
dict2jsonfile()
create_filechooser()
select_caffemodel_prototxt()
select_caffemodel_weights()
select_caffemodel()
select_videofile()
select_yolo_weights()
select_coco_labels()
select_yolo_config()
select_yolo_model()
select_pbtxt()
select_tfmobilenet_weights()
select_tfmobilenet()
- Download pretrained neural-network weights.
- References and Credits
- Contributor Covenant Code of Conduct


@(game: pa.FootballMatch)(implicit request: RequestHeader) @import implicits.Football._
@game.homeTeam.name @if(game.isFixture){ v } else {<EMAIL_ADDRESS>} @game.awayTeam.name
@if(game.isFixture){ Kick-off: @game.date.toString("HH:mm") } else { @MatchStatus(game.matchStatus) }
@if(game.homeTeam.aggregateScore){(agg<EMAIL_ADDRESS>} @game.comments.map{ comment =>@comment
}


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Scenario CV.SC.05 : activity diagram
Created:
15/05/2018 11:11:58
Modified:
15/05/2018 11:22:06
Project:
Author:
alfiva
Version:
1.0
Advanced:
ID:
{C0464ABD-7A5F-4e5a-8E1F-01D7C001D3A1}


Locate about 50nm north east of Hobart, Triabunna is a quiet fishing village in the shelter of Spring Bay. It is surrounded by eucalyptus forests and has great beaches.
Position 42°30′42"S,147°54′51"E.
There are med-style berths along the waterfront.
Last updated April 2016.


Ducati Streetfighter V4 voted as "Most Beautiful Bike of the Show" at EICMA 2019
Over 14,500 enthusiasts voted for the bike, helping Streetfighter V4 garner over 36 percent of the votes.
Italian sportbike manufacturer Ducati's Streetfighter V4 has been bestowed with the honour of 'Most Beautiful Bike of the Show' at EICMA 2019.
Over 14,500 enthusiasts voted for the bike, helping Streetfighter V4 garner over 36 percent of the votes.
"We are particularly proud to receive this award in a competition where all the manufacturers participate with their flagship models and the general public of EICMA, the most important motorcycle fair in the world, has elected the Streetfighter V4 as the most beautiful," said Andrea Ferraresi, Director, Ducati Design Center.
The Ducati Streetfighter V4 is powered by a 1,103cc liquid-cooled V4 Desmosedici Stradale, churning out a mind-numbing 211 PS of maximum power and 124 Nm of peak torque.
It has a 6-speed gearbox with up and down quickshifter and also comes with bi-plane wings and a latest-generation electronics package.Get access to India's fastest growing financial subscriptions service Moneycontrol Pro for as little as Rs 599 for first year. Use the code "GETPRO". Moneycontrol Pro offers you all the information you need for wealth creation including actionable investment ideas, independent research and insights & analysis For more information, check out the Moneycontrol website or mobile app.


| Name | Energy(Kcals) | Moisture(g) | Proteins(g) | Fats(g) | Minerals(g) | Fibre(g) | Carbos(g) | Calcium(mg) | Phosphorous(mg) | Iron(mg) | |
|---|---|---|---|---|---|---|---|---|---|---|---|
| {{food.Name}} | {{food.Energy}} | {{food.Moisture}} | {{food.Proteins}} | {{food.Fat}} | {{food.Minerals}} | {{food.Fibre}} | {{food.Carbos}} | {{food.Calcium}} | {{food.Phosphorous}} | {{food.Iron}} |