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This form will compute the difference between ship heading and true wind direction and true wind speed.
Difference between ship heading and apparent wind direction and apparent wind speed must be provided. Note that
if apparent wind speed is supplied in knots, it is necessary to supply the ship speed in knots.
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A Common Goal
03 May 2014Having someone with which to strive toward a common goal will exponentially increase your chances of success. This is exactly why the cohort model at The Iron Yard is so awesome. When sailing out into unchartered territory and attempting to achieve something big, it is vital to have someone you can be accountable to—someone who is able to celebrate each success, yet encourage with every setback.
An Equal Challenge
When my wife and I finally decided I would attend The Iron Yard, she also wanted to challenge herself. She knew I was facing the most difficult challenges of my life so; she also wanted conquer something equally as challenging—run a marathon. As we have both had our ups and downs in striving to reach our goals, we are able to keep each other motivated to stay on track to reach our respective goals.
Motivated By Others
This weekend, I was able to fly across the country for just a day and a half to see my wife complete the Western Pacific Marathon. It was incredibly motivating to see her cross that finish line. I keep a picture of her wearing the finisher's medal on my desktop and often look at it during the day. It motivates me to press forward becuase I so often get discouraged.
There have been numerous times along the way that I've really wanted to throw in the towel. It says intensive on The Iron Yard website for good reason! But, I try to remember the people pulling for me, and I don't want to let them down. I would encourage you in setting out to achieve something big, take someone along for the ride.


FUNNY GAMES
Austria 1997. Director/Screenplay - Michael Haneke, Producer - Veit Heiduschka, Photography - Jurgen Jurges, Music - Pietro Mascagni & John Zorn, Special Effects - Danny Bellens, Willi Neuner & Mac Steinmaier, Production Design - Christoph Kanter. Production Company - Wega-Film.  They are visited by two polite teenage boys, friends of their lakeside neighbours, initially come to borrow some eggs, but whose initial friendliness suddenly explodes into torture and sadistic games.
>From the film's opening credits where a family of three, on a placid drive to their holiday home change tapes on the car stereo and the gentle sounds of Mozart are suddenly replaced by a loud and raucous blast of death metal, director Michael Haneke signals his intent. And sure to his word `Funny Games' quickly shapes to be a film in the mold of `Straw Dogs', `The Last House on the Left' and `The Texas Chainsaw Massacre' wherein the idyll of a middle-class family is savagely overturned by outsiders and descends into a gruelling assault of sadism and torture.
It all begins innocently enough, with the introduction of the two boys, both dressed in white jerseys, shorts and plimsolls as though they were preppies returned from a day at the beach. An incident, which is comical in its gaucheness, where Frank Giering comes to borrow some eggs but keeps dropping them suddenly, unexpectedly turns violent. And from that point on `Funny Games' becomes a harrowing, psychologically gripping work of terror. (And one that not unfavourably compares to the aforementioned works above). The scenes of violence come with quite disturbingly casual regard - the dog and the child are blown away without any of the taboo reticence that affects Hollywood models. The casualness of the violence never comes more coolly understated than the shot focused on tormentor Arno Frisch who hears a gunshot ringing out from the next room while he is out in the kitchen making a sandwich and then calmly completes his sandwich before going to investigate. But director Haneke also propels home the full effect of the assault with emotionally gruelling regard - in one shot the camera remains in a single wide-angle for several minutes focused on a room with a body in one corner shot through the head, its blood splattering the tv, while wife Lothar sobs in the middle of the room and at the same time tries to drag herself past her grief to make an escape before the boys return. The prolonged effect of the shot is remarkably harrowing. The film is all the more effective for the performances of the two boys, especially Arno Frisch who manages quite unnervingly to swing between a perfect mannered politeness and casual sadism without the slightest change in expression - one moment insisting his tormentees stop screaming and start referring to everybody by their first name, the next breaking people's legs with golf clubs.
Haneke also manages to be surprisingly playful. At several points he starts to engage in meta-fictional (or should one say meta-filmic ?) play and has Frisch directly address the audience - turning to the camera to ask if the audience really thinks him as bad as his victims do; or offering a bet to his tormentees to see if they can stay alive until morning and then turning to ask the audience who they would bet on. At another point he decides that he will prolong the torture in order to pad the film out to feature length. And in the most amusing moment Lothar manages to get ahold of the gun and blow Giering away whereupon Frisch decides that things can't be allowed to happen that way, scrabbles for a remote control and rewinds the film to just before she got ahold of the gun so he can disarm her. Juggling between raw, disturbing violence and this sort of playful humour is extremely difficult a balancing act to get right without allowing the film to descend to bad taste or campiness, but it is to Haneke's credit that he succeeds.
Reviewed at the 1998 Wellington International Film Festival Copyright Richard Scheib 1998
The review above was posted to the
rec.arts.movies.reviews newsgroup (de.rec.film.kritiken for German reviews).
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belongs to the author.
Please direct comments/criticisms of the review to relevant newsgroups.
Broken URLs inthe reviews are the responsibility of the author.
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Related links: index of all rec.arts.movies.reviews reviews


US to increase educational opportunities and academic exchanges for Pakistani students | Pakistan - Geo.tv
by APPThe United States wants more students from Pakistan to study in universities across America, according to a statement made by US Embassy Minister Counselor for Public Affairs Lisa Heller last week.
A 5 per cent increase been recorded in Pakistani students’ enrollments this year compared to last year, Heller said at a ceremony, adding that Pakistan is among the fastest emerging market places of origin for international students in America.
According to an official statement made by the embassy, the US government is committed to supporting educational opportunities and academic exchanges for Pakistani students from all backgrounds.
The US Embassy, Education USA and the US Educational Foundation in Pakistan (USEFP) last week celebrated International Education Week (IEW). During the week they encouraged potential Pakistani leaders to study in the United States.
“Over 7,900 Pakistanis are currently studying at universities across the US, and the US government will like to increase that number,” said US Embassy Minister Counselor for Public Affairs Lisa Heller during the annual High Achievers’ and Counselors’ Recognition Ceremony in Islamabad.
“IEW activities are planned and organised by Education USA advisers who have firsthand experience studying in the US and are well-positioned to guide students through their applications,” said USEFP Executive Director Rita Akhtar, speaking during the ceremony.


Tablet of Vision:
Countless thoughts beyond comprehension sear painfully into your brain. You hear the faint whisper of a woman's voice.
A vision appears of a man, smiling down upon the Stone of Commune! It appears he's in a vast library.
It's Valdis, Grand Magister of Rune Castle Town Library!


Nothing is ture, Everything is permitted
Determination, Passion, Respect, Intergrity, Honesty
Old ways of commerce has been overturned by new ways, and the new way is being refreshed each day. We are contibuting to change the way our world use to create and deploy apps. Using the power of technology to help companies facing a new-era challange. Are you ready?
InteractivePlus is focusing on cross-platform app programming, we have made several libraries including EasyCrossPlatform, BoostPHP that makes programming easier.
Full Stack Website Developing, C++/C#, Big Data Operating, High Concurrency
Organize with determination, act with passion, treat others with respect, intergrity and honesty.
InteractivePlus is mainly a student-operated organization. We do not want to be famous this time, but to strengthen our own operating and programming technique.
"We eagerly want the power of innovation, but we have never forgot our initial dreams" This is what windy discover on May 11, 2016.
The best achievement, the most beautiful sceneries and the most suffering things is only a tiny little part of this universe. So we decide, wherever we achieve in the future, our final goal is to fuel the development of the human beings. We have never been lack of the power of innovation, but we may give up our amibitions and stop fighting for it. How many people does not fully understand one programming language and goes to another? We can never lose our passion, and this article is not only for you who are supporting us, but also for ourselves to always get power and fuel ourselves, and finally we will achieve our greatest height.
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Well, where do I start? So many things running through my mind after this week, and I know that there is muuuuch more to come... First of all, I'm not a "blogger" kind of person. I'm not used to share my thoughts with people, so I'm making a big effort to getting better on this. It has been tough and sometimes takes me too much time trying to pick right words, but I'm loving the experience. It is something that I want and I need to develop, so being out of my comfort zone will for sure make me learn a lot. I think the experience in the DBC is all about that. As Shereef mentioned in the video, you can have two different approaches when attending to the DBC. The first one is to treat it as package that you bought and just expect to receive the service, as a consumer. The second one is about making your own experience, making it to be awesome, learn as much as you can, put a lot of dedication and doing all of that engaged with the team. Of course everybody expects to have good teachers, good lectures, good material, but the success of the program depends much more on ourselves than anything else. I'm total aligned with the second approach and I'm really excited to make this bootcamp an amazing experience together with my class mates and teachers. I'm pretty sure we have a lot to learn from each other!


../ 215-image-inpainting-with-output_12_0.png 12-Jul-2023 00:11 16222 215-image-inpainting-with-output_14_0.png 12-Jul-2023 00:11 544222 215-image-inpainting-with-output_16_0.png 12-Jul-2023 00:11 508083 215-image-inpainting-with-output_20_0.png 12-Jul-2023 00:11 592799


Protests lined up against Citizenship Bill in Assam
Bharatiya Janata Party is likely to reintroduce the Bill during the winter session of Parliament
by Special CorrespondentMore than 100 civil society and students’ organisations and political parties have lined up protests against the BJP’s move to reintroduce the Citizenship (Amendment) Bill during the winter session of Parliament.
The Bill seeks to fast-track the process of granting citizenship to non-Muslims who have allegedly fled religious persecution from Afghanistan, Bangladesh and Pakistan till December 31, 2014.
The Left Democratic Front, Assam, a coalition of nine political parties including the Communist Party of India (Marxist), Janata Dal (Secular) and Nationalist Congress Party, has called for a State-wide protest on Monday.
‘Anti-Assam’
“The Bill is communal and anti-Assam, as it would render the expensive exercise of updating the National Register of Citizens meaningless,” a spokesperson of the Front said on Sunday.
About 30 organisations aligned with the All Assam Students’ Union have chalked out a series of programmes for drumming up support against the push for the “jatidhwangshi” (race-destroying) Bill.
According to the Union, the six-year Assam Agitation for ejecting illegal migrants would go waste if the Bill is passed by Parliament.
The Assam Accord of August 1985 that put an end to the agitation had prescribed March 24, 1971, as the cut-off date for detecting and deporting illegal migrants from the State.
1966 base year
The NRC is being updated on the basis of this cut-off date, although officials have allegedly considered 1966 as the base year for ascertaining the citizenship of people belonging to the non-indigenous communities.
Another 70 organisations, led by the Krishak Mukti Sangram Samiti, have also announced a “door-to-door” campaign against the Bill from Monday.
A Samiti spokesperson criticised Assam Health and Finance Minister Himanta Biswa Sarma for insisting that the Bill would be in the State’s interest.


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{% extends "transport_goods/layout.html" %} {% block page_title %} Upload financial evidence {% endblock %} {% block content %} {% include "includes/phase_banner_beta.html" %} {{formData | safe}} Upload financial evidence Upload financial evidence Upload a file Upload another document {% endblock %}


Make sure you tune in to Resonance today, between noon and midnight, for the second half of the Fundraiser Weekend Marathon. Mr Key will be on air from 1.30 to 2.00 PM announcing the auction of Derek The Dust Particle, Bring Me The Head Of Derek The Dust Particle!, and a complete set of Massacre, issues one to five of the anthologies published in the 1990s. I think bidding for items, by telephone, email, or metal tapping machine, continues until midnight, but all will become clear if you listen carefully.


| HEITOR CHANG | |
|<EMAIL_ADDRESS>● +55-11-99907-4867 ● http://heitorchang.github.io/ |
| EDUCATION | |
| New York University, Courant Institute of Mathematical Sciences, New York, NY | |
| M.S. in Mathematics in Finance, Cumulative GPA 3.4/4.0 | Sept. 2007 — Jan. 2009 |
| Stanford University, Stanford, CA | |
| M.S. in Materials Science & Engineering, Cumulative GPA 3.6/4.0 | Sept. 2005 — June 2007 |
| B.S. in Mathematical & Computational Science , Cumulative GPA 3.7/4.0 | Sept. 2001 — June 2005 |
| WORK EXPERIENCE | |
| Pontual Exportação e Importação, Ltda. | |
| Sales Analyst and Webmaster | Nov. 2011 — Present |
|


Win an intelligent robot vacuum from Roborock & Android Central!
Source: Nick Sutrich/Android Central
These days, having a robot vacuum isn't so much a luxury as it is a necessity. We're all busy people, and having the ability to automize some of your home tasks can be life changing! Roborock vacuums are exceptionally good robot vacuums that perform just as well as other brands on the market, but without the hefty pricetag that some other brands bring with them. They offer powerful suction, offer a range of smart abilities, and seriously just make your life more convenient.
We're big fans of these powerful little machines, and we know you will be too, so we've teamed up with Roborock to give away six of these powerful units to our readers! Scroll down for details and to enter!
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Six winners will take home a Roborock S6 or S4 robot vacuum from Roborock (3 winners per model)!
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[gecode-users] Subclassing JavaBrancingDesc
Malcolm Ryan
malcolmr at cse.unsw.edu.au
Tue Apr 8 04:30:34 CEST 2008
Is there any reason why JavaBranchingDesc is final in Gecode/J? My
problem naturally has a number of different kinds of branch
descriptions depending on what variable they apply to. It is annoying
to have to encode this into some arbitrary integer and then decode it
again when I commit. It would be much nice to have a collection of
different BranchingDesc classes each with their own commit method.
Malcolm
More information about the gecode-users
mailing list


--- layout: default ---
I am Debarsho Sannyasi. Currently, a final year undergraduate student at CSE Dept, IIT Kanpur.
My main research interests are Deep learning on Graphs and theoretical computer science (mainly graph algorithms). Yes I love graphs :)
Take a look at my first paper (also a single author paper): Improved Approximation Algorithms for Weighted Edge Coloring of Graphs here
Take a look at my past and current research activities here (will be up soon).
Take a look at my past and current projects here (will be up soon).


i like to find gaps in our world and try to fill them. that's why i'm a developer by day & explorer by night. i’m also a runner, but you’ll only see me chasing things i’m passionate about.
i received my bba in marketing from ut austin's mccombs school of business in 2017. in my time at ut, i was heavily involved with camp kesem, and served on the board of the texas chapter as the volunteer coordinator. (if you're familiar with kesem, my camp name is mushu!)
after graduating, i attended dev bootcamp where i gained experience as a fullstack developer. my love for code stems from my persistent curiosity with art, design, and functionality in a variety of contexts. i hope to marry my love for wellness or music with my love for tech. when i'm not playing on my computer, you can find me rock climbing, running, cooking, reading about lyrics on genius, or simply learning something new.
cv | resume


Accepts a) the "/search" path, a search term as a path parameter, and the optional query parameter "offset=n" (where n is a number between 1 and 10) or b) the "/recent" path. Returns a) image URLs, alt text, and source URLs (page n of results) or b) the last 10 search terms. Ex. 1. Ex. 2. Ex. 3.


In the fields below, search for either a product ID or customer ID and receive 6 recommended products to suggest to our customers.


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Borno Governor Engages 150 Cameroonian Vigilantes To Fight Insurgents
The Cameroonian vigilante group known as Kesh-Kesh, were presented with four surveillance vehicles and other fighting equipment on Saturday at a brief ceremony in Damasak, headquarters of Mobbar Local Government Area on the fringes of the Lake Chad Basin.
by SaharaReporters, New YorkBorno State Governor, Babagana Zulum, has engaged and equipped 150 Cameroonian vigilantes expected to join Nigerian hunters and vigilantes in fighting against Boko haram insurgents in the state.
Zulum, represented by Commissioner for Local Government and Emirate Affairs, Sugun Mai Mele, presented vehicles and other items needed to carry out the task.
The Cameroonian vigilante group known as Kesh-Kesh, were presented with four surveillance vehicles and other fighting equipment on Saturday at a brief ceremony in Damasak, headquarters of Mobbar Local Government Area on the fringes of the Lake Chad Basin.
Zulum has since August accelerated the recruitment of thousands of hunters and vigilantes from different parts of Northern Nigeria to combat Boko Haram insurgents.


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Class ScreenComponent
Manage the state of the faces on the robot.
Implements
Inherited Members
Namespace: Anki.Vector
Assembly: Anki.Vector.dll
Syntax
public class ScreenComponent : Component, INotifyPropertyChanged
Fields
| Improve this Doc View SourceScreenHeight
The screen height in pixels
public const int ScreenHeight = 96
Field Type
| Int32 |
ScreenWidth
The screen width in pixels
public const int ScreenWidth = 184
Field Type
| Int32 |
TotalPixels
The total number of pixels in the display
public const int TotalPixels = 17664
Field Type
| Int32 |
Methods
| Improve this Doc View SourceDisplayImageRgb24(Byte[], UInt32, Boolean)
Display an image on Vector's Screen (his "face").
public Task<StatusCode> DisplayImageRgb24(byte[] imageData, uint durationMs, bool interruptRunning = true)
Parameters
| Type | Name | Description |
|---|---|---|
| Byte[] | imageData | A byte array representing all of the pixels (24bit color in RGB format) |
| UInt32 | durationMs | The number of milliseconds the image should remain on Vector's face. |
| Boolean | interruptRunning | Set to true so any currently-streaming animation will be aborted in favor of this. |
Returns
| Type | Description |
|---|---|
| Task<StatusCode> | A task that represents the asynchronous operation. The task result contains the result of the operation. |
DisplayImageRgb565(Byte[], UInt32, Boolean)
Display an image on Vector's Screen (his "face").
public Task<StatusCode> DisplayImageRgb565(byte[] imageData, uint durationMs, bool interruptRunning = true)
Parameters
| Type | Name | Description |
|---|---|---|
| Byte[] | imageData | A byte array representing all of the pixels (16bit color in rgb565 format) |
| UInt32 | durationMs | The number of milliseconds the image should remain on Vector's face. |
| Boolean | interruptRunning | Set to true so any currently-streaming animation will be aborted in favor of this. |
Returns
| Type | Description |
|---|---|
| Task<StatusCode> | A task that represents the asynchronous operation. The task result contains the result of the operation. |
DisplayImageRgba32(Byte[], UInt32, Boolean)
Display an image on Vector's Screen (his "face").
public Task<StatusCode> DisplayImageRgba32(byte[] imageData, uint durationMs, bool interruptRunning = true)
Parameters
| Type | Name | Description |
|---|---|---|
| Byte[] | imageData | A byte array representing all of the pixels (32bit color in RGBA format) |
| UInt32 | durationMs | The number of milliseconds the image should remain on Vector's face. |
| Boolean | interruptRunning | Set to true so any currently-streaming animation will be aborted in favor of this. |
Returns
| Type | Description |
|---|---|
| Task<StatusCode> | A task that represents the asynchronous operation. The task result contains the result of the operation. |
DisplaySolidColor(Color, UInt32, Boolean)
Set Vector's Screen (his "face"). to a solid color.
public Task<StatusCode> DisplaySolidColor(Color color, uint durationMs, bool interruptRunning = true)
Parameters
| Type | Name | Description |
|---|---|---|
| Color | color | Desired color to set Vector's Screen. |
| UInt32 | durationMs | The number of milliseconds the image should remain on Vector's face. |
| Boolean | interruptRunning | Set to true so any currently-streaming animation will be aborted in favor of this. |
Returns
| Type | Description |
|---|---|
| Task<StatusCode> | A task that represents the asynchronous operation. The task result contains the result of the operation. |


TAXI DRIVER A film review by James Berardinelli Copyright 1996 James Berardinelli
RATING (0 TO 10): 9.2 Alternative Scale: **** out of ****
United States, 1976 Running Length: 1:53 MPAA Classification: R (Violence, profanity, mature themes) Theatrical Aspect Ratio: 1.85:1
Cast: Robert De Niro, Jodie Foster, Albert Brooks, Harvey Keitel, Leonard Harris, Cybill Shepherd Director: Martin Scorsese Producers: Julia Phillips and Michael Phillips Screenplay: Paul Schrader Cinematography: Michael Chapman Music: Bernard Herrmann U.S. Distributor: Columbia Pictures
In March 1977, the Academy of Motion Pictures Arts and Sciences committed another in their seemingly-endless series of injustices, awarding the Best Picture Oscar to John Avildsen's ROCKY, thereby snubbing one of the great modern American classics, Martin Scorsese's TAXI DRIVER. While ROCKY represents solid entertainment, it lacks the psychological depth of Scorsese's picture, replacing daring character development with a feel-good sports formula. TAXI DRIVER is on a level that ROCKY neither aspires to nor attains.
Like RAGING BULL, TAXI DRIVER features Robert De Niro in top form. As good as the actor has been elsewhere, these two pictures mark the apex of his superlative career. From his first scene in TAXI DRIVER, De Niro is Travis Bickle, a 26-year old ex-Marine searching for work that will keep him up all night as a means of combating insomnia. At the outset, Travis is a lonely, disillusioned man who can still function within the "normal" constraints of society. As time passes, however, Travis becomes increasingly alienated from the world around him, spiraling into a state of dissociated delusion. He sees New York City as a place of urban decay populated by "animals" and "scum" that need to be swept away. And who better than him to initiate the process? De Niro's performance is so perfectly-tuned that we in the audience don't have a moment's doubt or disbelief about what's taking place in Travis' troubled mind.
Initially, Travis is attracted to the cool-but-beautiful Betsy (Cybill Shepherd), a volunteer for Senator Palantine's Presidential campaign. Although Travis' stares initially make Betsy uneasy, his persistence pays off when she agrees to accompany him to the movies.  Following that gaffe, Betsy dumps him, accelerating Travis' descent into isolation.
The next woman to enter Travis' life is a twelve-year old prostitute named Iris. Played by a fresh-faced Jodie Foster with an enthralling mix of youth and world-weary sophistication, Iris' apparent innocence is belied by her profession. Travis decides to save her, although his motivation results less from a concern for her well-being than from a need to be seen as a savior. Iris really isn't a person to him; she's a symbol. But redeeming one girl is only an aspect of his plan -- he also intends to assassinate Senator Palantine. Travis is tired of sitting back and taking what life dishes out. He wants to act, even if the action has no basis in logic, because, by this time, he is beyond rational considerations.
Scorsese and writer Paul Schrader append the perfect conclusion to TAXI DRIVER. Steeped in irony, the five-minute epilogue underscores the vagaries of fate. The media builds Travis into a hero, when, had he been a little quicker drawing his gun against Senator Palantine, he would have been reviled as an assassin. As the film closes, the misanthrope has been embraced as the model citizen -- someone who takes on pimps, drug dealers, and mobsters to save one little girl.
There's no doubt that TAXI DRIVER paints an extremely disturbing portrait -- we find ourselves understanding Travis' mindset. This is expert film making from Scorsese, cinematographer Michael Chapman, and the actors. Schrader's script, which was inspired by such diverse works as Dostoevsky's NOTES FROM UNDERGROUND and Harry Chapin's song, "Taxi", is a masterful psychological study, the depth of which can only fully be appreciated on repeat viewings.
Twenty years after its initial release, TAXI DRIVER has reached screens in a new, pristine print featuring a remastered stereo soundtrack. And, despite the passage of two decades, the only thing dated about this film are the fashions. TAXI DRIVER's message still rings as true as ever, and the characters are as shockingly believable as in the mid-seventies. This re-release offers movie-goers another opportunity to see one of Scorsese's most influential and disturbing films on the big screen.
- James Berardinelli e-mail<EMAIL_ADDRESS>web: http://www.cybernex.net/~berardin
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Related links: index of all rec.arts.movies.reviews reviews


class: title-slide, left, middle background-position: 85% 50% background-size: 30% background-color: #F9F8F3 .pull-left[ # An introduction to tidymodels ### Max Kuhn ] --- # Modeling in R .pull-left[ .font90[ * R has always had a rich set of modeling tools that it inherited from S. For example, the formula interface has made it simple to specify potentially complex model structures. * _R has cutting edge models_. Many researchers in various domains use R as their primary computing environment and their work often results in R packages. * _It is easy to port or link to other applications_. R doesn't try to be everything to everyone. ] ] .pull-right[ However, there is a huge _consistency problem_. For example: * There are two primary methods for specifying what terms are in a model. Not all models have both. * 99% of model functions automatically generate dummy variables. * Many package developers don't know much about the language and omit OOP and other core R components. Two examples follow... ] --- # Between-Package Inconsistency Syntax for computing predicted class probabilities: |Function |Package |Code | |:-------------|:------------|:------------------------------------------| |`lda` |`MASS` |`predict(obj)` | |`glm` |`stats` |`predict(obj, type = "response")` | |`gbm` |`gbm` |`predict(obj, type = "response", n.trees)` | |`mda` |`mda` |`predict(obj, type = "posterior")` | |`rpart` |`rpart` |`predict(obj, type = "prob")` | |`Weka` |`RWeka` |`predict(obj, type = "probability")` | |`logitboost` |`LogitBoost` |`predict(obj, type = "raw", nIter)` | |`pamr.train` |`pamr` |`pamr.predict(obj, type = "posterior")` | --- # Within-Package Inconsistency: `glmnet` Predictions The `glmnet` model can be used to fit regularized generalized linear models with a mixture of L1 and L2 penalties. We'll look at what happens when we get predictions for a regression model (i.e. numeric _Y_) as well as classification models where _Y_ has two or three categorical values. The models shown below contain solutions for three regularization values ( `\(\lambda\)` ). The predict method gives the results for all three at once (👍). --- # Numeric `glmnet` Predictions Predicting a numeric outcome for two new data points: ```r new_x ``` ``` ## x1 x2 x3 x4 ## sample_1 1.649 -0.483 -0.294 -0.815 ## sample_2 0.656 -0.420 0.880 0.109 ``` ```r predict(reg_mod, newx = new_x) ``` ``` ## s0 s1 s2 ## sample_1 9.95 9.95 10 ## sample_2 9.95 9.95 10 ``` A matrix result and we will assume that the `\(\lambda\)` values are in the same order as what we gave to the model fit function. --- # `glmnet` predictions formats **Numeric model, numeric prediction** - numeric sample x penalty array -- **Binary model, class prediction** - _character_ sample x penalty array (Note: outcome input is required to be a factor) -- **Binary model, probability prediction** - _numeric_ sample x penalty array (values are 2nd factor level) -- **Multinomial model, probability prediction** - _numeric_ class x sample x penalty array --- # `glmnet` predictions formats 😳 Most people have at least four different scripts for the same model > _Am I working for `glmnet` or is it is working for me?_ Maybe a structure like this would work better: ``` ## # A tibble: 6 × 4 ## a b c lambda ## <dbl> <dbl> <dbl> <dbl> ## 1 0.333 0.333 0.333 1 ## 2 0.333 0.333 0.333 1 ## 3 0.333 0.333 0.333 0.1 ## 4 0.333 0.333 0.333 0.1 ## 5 0.373 0.244 0.383 0.01 ## 6 0.327 0.339 0.334 0.01 ``` --- # What We Need Unless you are doing a simple one-off data analysis, the lack of consistency between, and sometimes within, R packages can be very frustrating. If we could agree on a set of common conventions for interfaces, return values, and other components, everyone's life would be easier. Once we agree on conventions, **two challenges** are: * As of Sep 2021, there are over 18K R packages on CRAN. How do we "harmonize" these without breaking everything? * How can we guide new R users (or people unfamiliar with R) in making good choices in their modeling packages? These prospective and retrospective problems will be addressed in a minute. --- class: middle background-image: url(images/tm_hexes.png) background-position: right background-size: contain .pull-left[ ## Our job is to make modeling data with R ~~suck less~~ better1. ## "Modeling" includes everything from classical statistical methods to machine learning. .footnote[1 Narrator: _It's actually pretty good_] ] --- # The Tidyverse .pull-left[ The [tidyverse](http://www.tidyverse.org/) is an opinionated collection of R packages designed for data science. All packages share an underlying design philosophy, grammar, and data structures. The principles of the tidyverse: 1. Reuse existing data structures. 1. Compose simple functions with the pipe. 1. Embrace functional programming. 1. Design for humans. This results in more specific conventions around interfaces, function naming, etc. ]s .pull-right[ For example, we try to use common prefixes for auto-complete: `grid_latin_hypercube()`, `grid_max_entropy()`, ... There is also the notion of [tidy data](http://vita.had.co.nz/papers/tidy-data.pdf): 1. Each variable forms a column. 1. Each observation forms a row. 1. Each type of observational unit forms a table. Based on these ideas, we can create modeling packages that have predictable results and are a pleasure to use. ] --- # Tidymodels `tidymodels` is a collection of modeling packages that live in the tidyverse and are designed in the same way. My goals for tidymodels are: 1. Encourage empirical validation and good methodology. 1. Smooth out diverse interfaces. 1. Build highly reusable infrastructure. 1. Enable a wider variety of methodologies. The `tidymodels` packages address the *retrospective* and *prospective* issues. We are also developing a set of principles and templates to make *prospective* (new R packages) easy to create. --- background-image: url(images/hex_wall.png) background-size: contain --- layout: false class: inverse, middle, center # [`tidymodels.org`](https://www.tidymodels.org/) # _Tidy Modeling with R_ ([`tmwr.org`](https://www.tmwr.org/)) --- # Selected Modeling Packages * [`broom`](https://broom.tidymodels.org/) takes the messy output of built-in functions in R, such as `lm`, `nls`, or `t.test`, and turns them into tidy data frames. * [`recipes`](https://recipes.tidymodels.org/) is a general data preprocessor with a modern interface. It can create model matrices that incorporate feature engineering, imputation, and other tools. * [`rsample`](https://rsample.tidymodels.org/) has infrastructure for _resampling_ data so that models can be assessed and empirically validated. * [`parsnip`](https://parsnip.tidymodels.org/) gives us a unified modeling interface. * [`tune`](https://tune.tidymodels.org/) has functions for grid search and sequential optimization of model parameters. --- # Loading the Meta-Package  .pull-left-a-lot[ .code60[ ```r library(tidymodels) ``` ``` ## Registered S3 method overwritten by 'tune': ## method from ## required_pkgs.model_spec parsnip ``` ``` ## ── Attaching packages ────────────────────────────────────── tidymodels 0.1.3 ── ``` ``` ## ✓ broom 0.7.9 ✓ recipes <IP_ADDRESS>00 ## ✓ dials <IP_ADDRESS>00 ✓ rsample 0.1.0 ## ✓ dplyr 1.0.7 ✓ tibble 3.1.4 ## ✓ ggplot2 3.3.5 ✓ tidyr 1.1.3 ## ✓ infer 1.0.0 ✓ tune 0.1.6 ## ✓ modeldata 0.1.1 ✓ workflows 0.2.3 ## ✓ parsnip <IP_ADDRESS>0 ✓ workflowsets 0.1.0 ## ✓ purrr 0.3.4 ✓ yardstick 0.0.8 ``` ``` ## ── Conflicts ───────────────────────────────────────── tidymodels_conflicts() ── ## x dplyr::collapse() masks glue::collapse() ## x purrr::discard() masks scales::discard() ## • Use tidymodels_prefer() to resolve common conflicts. ``` ```r tidymodels_prefer(quiet = FALSE) ``` ``` ## [conflicted] Will prefer dplyr::filter over any other package ``` ``` ## [conflicted] Will prefer dplyr::select over any other package ``` ``` ## [conflicted] Will prefer dplyr::slice over any other package ``` ``` ## [conflicted] Will prefer dplyr::rename over any other package ``` ``` ## [conflicted] Will prefer dials::neighbors over any other package ``` ``` ## [conflicted] Will prefer parsnip::fit over any other package ``` ``` ## [conflicted] Will prefer plsmod::pls over any other package ``` ``` ## [conflicted] Will prefer purrr::map over any other package ``` ``` ## [conflicted] Will prefer recipes::step over any other package ``` ``` ## [conflicted] Will prefer themis::step_downsample over any other package ``` ``` ## [conflicted] Will prefer themis::step_upsample over any other package ``` ``` ## [conflicted] Will prefer tune::tune over any other package ``` ``` ## [conflicted] Will prefer yardstick::precision over any other package ``` ``` ## [conflicted] Will prefer yardstick::recall over any other package ``` ``` ## ── Conflicts ──────────────────────────────────────────── tidymodels_prefer() ── ``` ``` ## ``` ] ] .pull-right-a-little[ .font90[ Let's start by predicting the [ridership of the Chicago "L" trains](https://bookdown.org/max/FES/chicago-intro.html). We have data over 5,698 days between 2001 and 2016 in `data(Chicago, package = "modeldata")`. What are our predictors? Date, weather data, home game schedules, 14-day lags at other stations. ] ] --- # What are our _features_?  ```r chicago_rec <- recipe(ridership ~ ., data = Chicago) ``` --- # What are our _features_?  ```r chicago_rec <- recipe(ridership ~ ., data = Chicago) %>% * step_date(date, features = c("dow", "month", "year")) ``` --- # What are our _features_?  ```r chicago_rec <- recipe(ridership ~ ., data = Chicago) %>% step_date(date, features = c("dow", "month", "year")) %>% * step_holiday(date) ``` --- # What are our _features_?  ```r chicago_rec <- recipe(ridership ~ ., data = Chicago) %>% step_date(date, features = c("dow", "month", "year")) %>% step_holiday(date) %>% * update_role(date, new_role = "id") ``` --- # What are our _features_?  ```r chicago_rec <- recipe(ridership ~ ., data = Chicago) %>% step_date(date, features = c("dow", "month", "year")) %>% step_holiday(date) %>% update_role(date, new_role = "id") %>% * step_dummy(all_nominal_predictors()) ``` Other selectors are: * `all_nominal()`, `all_numeric()`, and `has_type()` * `all_predictors()`, `all_outcomes()`, and `has_role()` * Standard `dplyr` selectors like `starts_with()` and so on. --- # What are our _features_?  ```r chicago_rec <- recipe(ridership ~ ., data = Chicago) %>% step_date(date, features = c("dow", "month", "year")) %>% step_holiday(date) %>% update_role(date, new_role = "id") %>% step_dummy(all_nominal_predictors()) %>% * step_normalize(all_numeric_predictors()) ``` --- # What are our _features_?  ```r chicago_rec <- recipe(ridership ~ ., data = Chicago) %>% step_date(date, features = c("dow", "month", "year")) %>% step_holiday(date) %>% update_role(date, new_role = "id") %>% step_dummy(all_nominal_predictors()) %>% step_normalize(all_numeric_predictors()) *#? step_pca(one_of(stations), num_comp = 10) ``` --- # What are our _features_?  ```r chicago_rec <- recipe(ridership ~ ., data = Chicago) %>% step_date(date, features = c("dow", "month", "year")) %>% step_holiday(date) %>% update_role(date, new_role = "id") %>% step_dummy(all_nominal_predictors()) %>% step_normalize(all_numeric_predictors()) *#? step_umap(one_of(stations), outcome = vars(ridership), num_comp = 10) ``` --- # What are our _features_?  ```r chicago_rec <- recipe(ridership ~ ., data = Chicago) %>% step_date(date, features = c("dow", "month", "year")) %>% step_holiday(date) %>% update_role(date, new_role = "id") %>% step_dummy(all_nominal_predictors()) %>% step_normalize(all_numeric_predictors()) *#? step_ns(Harlem, deg_free = 5) ``` --- # What are our _features_?  ```r chicago_rec <- recipe(ridership ~ ., data = Chicago) %>% step_date(date, features = c("dow", "month", "year")) %>% step_holiday(date) %>% update_role(date, new_role = "id") %>% step_dummy(all_nominal_predictors()) %>% step_normalize(all_numeric_predictors()) *#? step_mutate(temp = (32 * temp − 32) * 5 / 9 ) ```

 ***Let's fit a linear regression model!*** With `parsnip`, we first create an object that specifies the _type_ of model and then the software _engine_ to do the fit. --- # Linear regression specification  .pull-left-a-lot[ ```r linear_mod <- linear_reg() ``` This says "Let's fit a model with a numeric outcome, and intercept, and slopes for each predictor." * Other model types include `nearest_neighbors()`, `decision_tree()`, `rand_forest()`, `arima_reg()`, and so on. The `set_engine()` function gives the details on _how_ it should be fit. ] --- # Let's fit it with...  .pull-left-a-lot[ ```r linear_mod <- linear_reg() %>% set_engine("lm") ``` ] .pull-right-a-little[  ] --- # Let's fit it with...  .pull-left-a-lot[ ```r linear_mod <- linear_reg() %>% set_engine("keras") ``` ] .pull-right-a-little[  ] --- # Let's fit it with...  .pull-left-a-lot[ ```r linear_mod <- linear_reg() %>% set_engine("spark") ``` ] .pull-right-a-little[  ] --- # Let's fit it with...  .pull-left-a-lot[ ```r linear_mod <- linear_reg() %>% set_engine("stan") ``` ] .pull-right-a-little[  ] --- # Let's fit it with...  .pull-left-a-lot[ ```r linear_mod <- linear_reg() %>% set_engine("glmnet") ``` ] .pull-right-a-little[  ] --- # Let's fit it with...  .pull-left-a-lot[ ```r linear_mod <- linear_reg(penalty = 0.1, mixture = 0.5) %>% set_engine("glmnet") ``` ] .pull-right-a-little[  ] --- # A modeling _workflow_  We can _optionally_ bundle the recipe and model together into a pipeline _workflow_: ```r glmnet_wflow <- workflow() %>% add_model(linear_mod) %>% add_recipe(chicago_rec) # or add_formula() or add_variables() ``` Fitting and prediction are very easy: ```r glmnet_fit <- fit(glmnet_wflow, data = Chicago) predict(glmnet_fit, Chicago %>% slice(1:7)) ``` ``` ## # A tibble: 7 × 1 ## .pred ## <dbl> ## 1 13.8 ## 2 15.0 ## 3 14.7 ## 4 14.6 ## 5 14.1 ## 6 2.36 ## 7 1.73 ``` --- # Model tuning  We probably don't have a good idea for what `penalty` and `mixture` should be? We can _mark them for tuning_ : ```r linear_mod <- linear_reg(penalty = tune(), mixture = tune()) %>% set_engine("glmnet") glmnet_wflow <- glmnet_wflow %>% update_model(linear_mod) ``` Recipe arguments can also be simultaneously tuned (e.g. `num_comp` in `step_pca()`). --- # Resampling and grid search  We'll use time series resampling and grid search to optimize the model: .code80[ .pull-left[ ```r chicago_rs <- sliding_period( Chicago, date, period = "month", lookback = 14 * 12, assess_stop = 1 ) chicago_rs ``` ``` ## # Sliding period resampling ## # A tibble: 19 × 2 ## splits id ## <list> <chr> ## 1 <split [5123/28]> Slice01 ## 2 <split [5141/31]> Slice02 ## 3 <split [5144/30]> Slice03 ## 4 <split [5143/31]> Slice04 ## 5 <split [5144/30]> Slice05 ## 6 <split [5143/31]> Slice06 ## 7 <split [5144/31]> Slice07 ## 8 <split [5144/30]> Slice08 ## 9 <split [5143/31]> Slice09 ## 10 <split [5144/30]> Slice10 ## 11 <split [5143/31]> Slice11 ## 12 <split [5144/31]> Slice12 ## 13 <split [5144/29]> Slice13 ## 14 <split [5142/31]> Slice14 ## 15 <split [5145/30]> Slice15 ## 16 <split [5144/31]> Slice16 ## 17 <split [5145/30]> Slice17 ## 18 <split [5144/31]> Slice18 ## 19 <split [5145/28]> Slice19 ``` ] .pull-right[ ```r library(doMC) registerDoMC(cores = parallel::detectCores()) set.seed(1) glmnet_tune <- glmnet_wflow %>% tune_grid(chicago_rs, grid = 25) show_best(glmnet_tune, metric = "rmse") ``` ``` ## # A tibble: 5 × 8 ## penalty mixture .metric .estimator mean n std_err .config ## <dbl> <dbl> <chr> <chr> <dbl> <int> <dbl> <chr> ## 1 3.02e- 3 0.505 rmse standard 2.05 19 0.198 Preprocessor1_Model12 ## 2 1.15e- 3 0.539 rmse standard 2.05 19 0.196 Preprocessor1_Model13 ## 3 1.57e-10 0.461 rmse standard 2.05 19 0.196 Preprocessor1_Model11 ## 4 4.89e- 9 0.794 rmse standard 2.05 19 0.196 Preprocessor1_Model20 ## 5 4.81e- 5 0.987 rmse standard 2.05 19 0.196 Preprocessor1_Model25 ``` ] ] --- # Grid search results  .pull-left[ Grid points using the default space-filling design:  ] .pull-right[ ```r autoplot(glmnet_tune) ```  ] --- # Next steps There are functions to plot the results, substitute the best parameters for the `tune()` placeholders, fit the final model, measure the test set performance, etc etc. These API's focus on harmonizing _Existing_ packages. (If we still have time) Let's talk about designing better packages. --- # Principles of Modeling Packages .font90[ We have [a set of _guidelines_](https://tidymodels.github.io/model-implementation-principles/) for making good modeling packages. For example: * Separate the interface that the **modeler** uses from the code to do the computations. They serve two very different purposes. * Have multiple interfaces (e.g. formula, x/y, etc). * The _user-facing interface_ should use the most appropriate data structures for the data (as opposed to the computations). For example, factor outcomes versus 0/1 indicators and data frames versus matrices. * `type = "prob"` for class probabilities 😄. * Use S3 methods. * The `predict` method should give standardized, predictable results. Rather than try to make methodologists into software developers, have tools to **help them create high quality modeling packages**. ] --- # Making better packages  We have methods for creating all of the S3 scaffolding for modeling packages. You have some functions for creating a model fit; `hardhat` provides a package directory using best practices: ```r library(hardhat) create_modeling_package("~/tmp/lantern", model = "lantern_mlp") ``` There is a [video demo](https://canal.uned.es/video/5dd25b9f5578f275e407dd88) that shows how to create a package in 9 steps. --- # What's coming up ### MLOps Model Operations tools for deploying, monitoring, and updating models. ### censored data models (a.k.a survival analysis) These models are used to predict time-to-event data (in medicine, sports, HR, etc). ### Case weights Not sexy. Really tedious. Often requested. This will impact many of the tidymodels packages. ### Supervise feature selection Via recipe steps and other functions (e.g., `rfe()`). --- # Thanks Thanks for the invitation to speak today! The tidymodels team: Davis Vaughan, Julia Silge, and Hanna Frick. Special thanks for the other folks who contributed so much to tidymodels: Edgar Ruiz, Emil Hvitfeldt, Alison Hill, Desirée De Leon, and the tidyverse team. These slides were made with the [`xaringan`](https://bookdown.org/yihui/rmarkdown/xaringan.html) package and styled by Alison Hill. Sources are located at [`github.com/topepo/an-introduction-to-tidymodels`](https://github.com/topepo/an-introduction-to-tidymodels).


Eastern Teacher
Abstraction is often one floor above you.
Boris Valencia
Wesley Choi
Mickey Jordan
Layer 1


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For external use only
Do not apply to wounds or to damaged or irritated skin
Directions:For Alternating Therapy
For persons under 18 years of age, ask a doctor before using. Adults and persons 18 years of age an older: Start by applying the Cold Therapy to the affected area, then switch to the Hot Therapy and then back to the Cold Therapy and then to the Hot Therapy, using each therapy no more than 3 to 4 times per day. Best results typically occur after 2 to 4 weeks of continuous use. Wash hands thoroughly with soap and water immediately after use. See package insert for more information.
Benzyl Alcohol, Cetyl Alcohol, Glyceryl Stearate, Isopropyl Myristate, PEG-100 Stearate, Purified Water, Sorbitol Solution, White Petrolatum
Benzyl Alcohol, Ceteareth-20, Cetearyl Alcohol, Cetyl Alcohol, Glyceryl Stearate, Isopropyl Myristate, PEG-100 Stearate, Petrolatum, Sorbitol, Water
Call: 1-800-899-3116, Mon. - Thurs. 9:00 am - 5:00 pm EST, Fri. 9:00 am - 2:30 pm EST. Serious side effects associated with the use of this product may be reported to this number.
Rev #499:01 7/12
NEW!
NDC 61787-499-02
ZOSTRIX®
HOT & COLD
THERAPY SYSTEM
Breakthrough Pain Relief!
The Power of 2 Alternating Therapies Relieves Pain Like No Other!
Back Aches • Arthritis • Bursitis
Body Aches • Sore Muscles • Sprains
Capsaicin 0.025% Hot Therapy Cream Net Wt. 1 oz. (28.3 g)
Menthol 4.0% Cold Therapy Cream Net. Wt. 1 oz. (28.3 g)
Topical Analgesic
|
ZOSTRIX HOT AND COLD THERAPY SYSTEM
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| Labeler - Health Care Products (101196749) |
| Registrant - Hi-Tech Pharmacal Co., Inc. (101196749) |
| Establishment | |||
| Name | Address | ID/FEI | Business Operations |
|---|---|---|---|
| San-Mar Laboratories, Inc. | 078718053 | MANUFACTURE(61787-499) |


| tidy.gmm {broom} | R Documentation |
Tidy summarizes information about the components of a model. A model component might be a single term in a regression, a single hypothesis, a cluster, or a class. Exactly what tidy considers to be a model component varies across models but is usually self-evident. If a model has several distinct types of components, you will need to specify which components to return.
## S3 method for class 'gmm' tidy(x, conf.int = FALSE, conf.level = 0.95, exponentiate = FALSE, ...)
x |
A |
conf.int |
Logical indicating whether or not to include a confidence
interval in the tidied output. Defaults to |
conf.level |
The confidence level to use for the confidence interval
if |
exponentiate |
Logical indicating whether or not to exponentiate the
the coefficient estimates. This is typical for logistic and multinomial
regressions, but a bad idea if there is no log or logit link. Defaults
to |
... |
Additional arguments. Not used. Needed to match generic
signature only. Cautionary note: Misspelled arguments will be
absorbed in |
A tibble::tibble()
with columns:
conf.high |
Upper bound on the confidence interval for the estimate. |
conf.low |
Lower bound on the confidence interval for the estimate. |
estimate |
The estimated value of the regression term. |
p.value |
The two-sided p-value associated with the observed statistic. |
statistic |
The value of a T-statistic to use in a hypothesis that the regression term is non-zero. |
std.error |
The standard error of the regression term. |
term |
The name of the regression term. |
Other gmm tidiers:
glance.gmm()
library(gmm) # examples come from the "gmm" package ## CAPM test with GMM data(Finance) r <- Finance[1:300, 1:10] rm <- Finance[1:300, "rm"] rf <- Finance[1:300, "rf"] z <- as.matrix(r - rf) t <- nrow(z) zm <- rm - rf h <- matrix(zm, t, 1) res <- gmm(z ~ zm, x = h) # tidy result tidy(res) tidy(res, conf.int = TRUE) tidy(res, conf.int = TRUE, conf.level = .99) # coefficient plot library(ggplot2) library(dplyr) tidy(res, conf.int = TRUE) %>% mutate(variable = reorder(term, estimate)) %>% ggplot(aes(estimate, variable)) + geom_point() + geom_errorbarh(aes(xmin = conf.low, xmax = conf.high)) + geom_vline(xintercept = 0, color = "red", lty = 2) # from a function instead of a matrix g <- function(theta, x) { e <- x[, 2:11] - theta[1] - (x[, 1] - theta[1]) %*% matrix(theta[2:11], 1, 10) gmat <- cbind(e, e * c(x[, 1])) return(gmat) } x <- as.matrix(cbind(rm, r)) res_black <- gmm(g, x = x, t0 = rep(0, 11)) tidy(res_black) tidy(res_black, conf.int = TRUE) ## APT test with Fama-French factors and GMM f1 <- zm f2 <- Finance[1:300, "hml"] - rf f3 <- Finance[1:300, "smb"] - rf h <- cbind(f1, f2, f3) res2 <- gmm(z ~ f1 + f2 + f3, x = h) td2 <- tidy(res2, conf.int = TRUE) td2 # coefficient plot td2 %>% mutate(variable = reorder(term, estimate)) %>% ggplot(aes(estimate, variable)) + geom_point() + geom_errorbarh(aes(xmin = conf.low, xmax = conf.high)) + geom_vline(xintercept = 0, color = "red", lty = 2)


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