EMPIRE OF PASSION (Ai no borei)
Japan/France. 1978. Director/Screenplay - Nagisa Oshima, Based on the Novel `Three Generations to Make the Land Fertile: The Earth' by Itoko Namura, Photography - Yoshio Miyajima, Music - Toru Takemitsu. Production Company - Argos Films/Oshima Productions. Kazuko Yoshiyuki (Seki), Tatsuya Fuji (Toyoji), Takahiro Tamura (Gisaburo Desa), Takuzo Kawatani (Inspector Hotta)
Plot: In a small Japanese village in 1895, a wife Seki has an affair with a younger man Toyoji.  Seki gets Gisaburo drunk, they kill him and then dump his body down a well and pretend that he has gone away to find work. But three years later Gisaburo returns as a ghost, confused and lost. The guilt of his return drives Seki and Toyoji to desperation.
This venture into the kaidan eiga (or Japanese ghost story) was highly acclaimed on its release in 1978 but was little seen out of limited arthouse release and has been entirely forgotten since. The director Nagisa Oshima is highly respected in Japanese cinema, having made some forty films, of which the best known in the West is probably `Merry Christmas, Mr Lawrence' (1983).
Far more so than Western ghost stories ever do, most kaidan eiga centre around themes of guilt and retribution. Oshima's twist on the ghost story here is that his ghost isn't so much an avenging spirit as it is a sad and confused lost soul whose vague stumblings around drive his murderers to guilt and anguish. Oshima's presages the ghost's appearances with a genuine eeriness - three years after the body has been dumped down the well, a friend dreaming of Gisaburo telling her to bring him some clean clothes, he is tired of sleeping in the same ones; or his daughter dreaming of him telling her he will be home by New Year. Locating of the story in a 19th Century rural peasant setting, Oshima employs the style of Japanese post-War neo-realist cinema to considerable effect. At times the camera little more than sits impassively observing scenes of extraordinarily raw effect transpiring before it. Throughout the love scenes and the shaving scene Oshima keeps focused on Yoshiyuki's face, watching not the passion or the act but the pain on her face and the effect has cuttingly real emotional power. The actual murder plot occurs with astonishing casualness - Fuji shaves Yoshiyuki's pubic hair and then suddenly announces "We must kill him. How can we let him see you shaved ?"
Copyright 1998 Richard Scheib
The review above was posted to the
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|
|
Welcome to payments!
| LIST all payments: |
/payments |
[GET] |
| CREATE a payment: |
/payments |
[POST] |
| RETRIEVE a payment: |
/payments/{id} |
[GET] |
| UPDATE a payment: |
/payments/{id} |
[PUT] |
| DELETE a payment: |
/payments/{id} |
[DELETE] |
| QUERY payments: |
/payments?{key}={value} |
[GET] |
| SET default payment: |
/payments/{id}/set-default |
[PUT] |
| CHARGE default payment: |
/payments/charge |
[PUT] |
| UPDATE a payment: |
/payments/{id} |
[PATCH] |
Example Data:
{
"charge-history": 0,
"default": false,
"detail": {
"expires": "01/2020",
"name": "John Johnson",
"number": "1234567898765432",
"type": "American Express"
},
"id": 1,
"nickname": "my-credit",
"type": "credit"
}
|


@GuardedBy
annotations in which the guarding field
is not final
.
Guarding on a non-final field may result in unexpected race conditions, as locks will be held on the value of the field (which may change), rather than the field itself.
Example:
private ReadWriteLock lock = new ReentrantReadWriteLock(); //not final guarding field
private Object state;
@GuardedBy("lock")
public void bar() {
state = new Object();
}
Supported @GuardedBy
annotations are:
net.jcip.annotations.GuardedBy
javax.annotation.concurrent.GuardedBy
org.apache.http.annotation.GuardedBy
com.android.annotations.concurrency.GuardedBy
androidx.annotation.GuardedBy
com.google.errorprone.annotations.concurrent.GuardedBy


Located on the River Derwent on Tasmaina's south eastern coast, Hobart is the state capital. Its port is the home of both the Australian and French Antarctic and Southern Ocean support services.
Position 42°52′50″S,147°19′30″E.
On arrival, call the Port Authority on VHF channel 16 or 12.
Last updated April 2016.
If approaching Hobart from the east and using the Denison Canal to avoid the long sail south round the Forestier Peninsula, it is necessary to call the Canal Authority a day before planned transit. (See details below).
Call Hobart Port Control on VHF channel 16 for advice.
Three of the yacht clubs have visitors' berths.
Constitution Dock offers a safe, well protected berth in the centre of the town. If staying for more than a night, the weekly charge is good value (there is no daily rate). It has water, power, showers and a laundry.
Elizabeth Pier is quieter and close to the restaurants but is more expensive.
There are several other berthing options around Hobart.
Margate is approximately 12nm south of Hobart (about 8 by road).
See also Kettering, about 20nm south of Hobart.
Anchorages
There is an anchorage off Battery Point, outside the moorings in 15m over mud, another off Sandy Point (Nutgrove Beach) in 11m over sand. The latter being quite a further from the city.
The anchorage in Kingston Bay(11m over sand) is just a dinghy ride away from supermarkets, fuel and restaurants.
More anchorages can be found further up the River Derwent.
Last updated April 2016.


{% load static %} {% include 'bases/head_base.html' %}
{% if not derail %}
Collabo
Dev
has been reset to factory settings. Please restart your server, then click
here
{% else %}
Whoops! you shouldn't be here right now. if you were taken here by a link, please open an issue over
here
{% endif %} {% include 'bases/footer_base.html' %}


I used to be really big on drug use, especially hallucinogens. I did skittles (DXM), LSD, psilocybin mushrooms, and shitloads of deliriants, i.e. diphenhydramine (Benadryl). and obviously, weed.
I did DXM once. Then I went on a diphen spree because it was cheaper than weed. I was on it for maybe a month or two or three (that stuff FUCKS with your memory). I did it maybe twenty times in all. Of course I got mental problems from this. Since being on diphen is basically putting yourself into a psychotic state, I pretty much bought the ticket and took the ride. And I got what I deserved. I got really bad HPPD from this, for those of you who don't know what this is, it's basically like your whole mindset is one flashback to whatever hallucinogenic drug you got it from. (However this is usually associated with LSD.)
 I realized that I not only had HPPD but I was also hearing the voices that I would hear on diphen trips, only at night when I went to sleep. I found that I only heard them at night when I tried to sleep. Also I got extreme depression, to the point of suicidal thoughts the majority of the week. Well marijuana basically solved the problems of depression and hearing voices for me, so for pretty much the whole summer I sat around with my friends and toked. My HPPD didn't get worse in the long run but it definitely turned weed into a strangely pseudo-psychedelic experience that I had never experienced before. It was weird.
Eventually my parents caught me for smoking pot. They decided that in order to keep me from making this 'mistake' again, they would drug test me. Well the voices came back.  I basically lost it, not because of the voices but because my mind was constantly in this weird psychotic state with my thoughts branching off like a tree, over-analyzing everything from a building to a speck of dust. Everything was a weird metaphor for something or some kind of strange symbolism.
A few months later I was offered LSD at my work.  I dropped acid by myself once at night (1 hit) and then once during the day at school, however it was something like 5 or 6 hits this time. I felt afterwards like my mind had returned to complete normal, except the HPPD was still the same, no better or worse. The best part of the experience of all was that it completely killed the voices in my head and constant thoughts of suicide.
Then I made the dumbass mistake of doing mushrooms. Not that LSD was the greatest idea in the world, but the little good that LSD had done (by accident) was undone by shrooms. I did them once, I ate about a half eighth. It was cool I guess but the voices came back and so did the depression.
After a few months I just couldn't handle it anymore and finally explained to my parents what I had done and what my mental and psychological issues were. We went through multiple doctors, some good, some straight-up retards.  Well when I looked at the MRI I said 'that's not exactly a small amount of tissue loss' but since the area didn't have anything to do with my problems (?... I dont know how they 'knew' this)
I was sent back to a psychiatrist. I told him about what I had read on the internet about HPPD patients and that my symptoms were very similar, even though I got it from diphenhydramine instead of LSD. He originally suggested Risperidal and I shot him down immediately because I've read MULTIPLE stories of risperidone making HPPD much worse. I suggested Klonopin since I had read great things about it helping HPPD patients. He agreed to try it, I started on .5 mg at bedtime.
WHAT A DIFFERENCE. My mind completely shifted back to its normal state. I felt calm and not always on edge. My HPPD reduced by maybe 65%. I still had some static but not much else. He agreed to increase the dosage to .5mg at night and .5mg in the morning. Since then my HPPD has improved by 85% and the only effects I still have are some minor static. I would say that clonazepam is the closest thing to a miracle drug there is for HPPD sufferers. Unfortunately, Klonopin did not solve the issue of hearing voices at night, nor did it solve my depression.
I don't want to take antipsychotics because they usually make HPPD worse (like I said before) and also because having the diagnosis of schizophrenia is a very bad thing, it makes it hard to get a drivers license, and since I want to be a politician someday (I'm still in high school now) it would be bad if someone found out that I was 'schizophrenic' which I'm not, I know the voices aren't real and I don't really have any other symptoms in common with schizophrenia.

In short, Klonopin has changed my life for the better. I am a much more stable and sane person because of it, not to mention I can make the long process of HPPD recovery much mroe tolerable and maybe even shorter because of it.
I hope to all you who have HPPD learned something from my mistakes or maybe even your own. Klonopin works miracles.
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.


| < Day Day Up > |
Hack 11 Use an Interactive Shell
Save and share an entire login session. How many times have you either struggled with or tried to troubleshoot another user through a thorny problem? Didn't you wish you had another set of eyes behind you so you could simply type your command set, point at the troublesome output, and say, "That's the problem." Well, if you can't bring another user to your output, you can still share that real-time output using an interactive shell. 1.12.1 Recording All Shell Input and OutputThere are actually several ways to share what is happening on your screen. Let's start by recording all of your input and output to a file. Then we'll see how we can also allow another user to view that output from another terminal. Your BSD system comes with the script command which, not surprisingly, allows you to script your session. This command is extremely simple to use. Simply type script: % script Script started, output file is typescript By default, script will create an output file named typescript in your current directory. If you prefer, you can specify a more descriptive name for your script file: % script configure.firewall.nov.11.2003 Script started, output file is configure.firewall.nov.11.2003 Regardless of how you invoke the command, a new shell will be created. This means that you will see the MOTD and possibly a fortune, and your .cshrc will be reread. You can now carry on as usual and all input and output will be written to your script file. When you are finished, simply press Ctrl-d. You will see this message: Script done, output file is configure.firewall.nov.11.2003 If you've ended a script and decide later to append some more work to a previous session, remember the -a (append) switch: % script -a configure.firewall.nov.11.2003 This will append your current scripting session to the named file. I find script extremely useful, especially when I'm learning how to configure something for the first time. I can easily create a written record of which commands I used, which commands were successful, and which commands caused which error messages. It also comes in handy when I need to send an error message to a mailing list or a program's maintainer. I can simply copy or attach my script file into an email. 1.12.2 Cleaning Up script FilesThe script utility is a very quick and dirty way to record a session, and it does have its limitations. One of its biggest is that it records everything, including escape characters. For example, here is the first line from one of my script files: [1mdru@~ [m: cd /s [K/ysr/ [K [K [K [K [Kusr/ports/security/sn o rt It's a bit hard to tell, but this is what script was recording: cd /usr/ports/security/snort This isn't really script's fault; it's ugly for several reasons. One, my customized prompt contains control characters. Those display as [1m and [m around my username. Second, I had problems typing that day. Instead of /usr, I typed /s and had to backspace a character. Then I typed /ysr and had to backspace three characters. Finally, I used tab completion. You can see that I tried to tab at sn but received a beep; I then tried to tab at sno and had my input completed to snort. Granted, if I had first used the file utility on my script file, I would have received a warning about this behavior: % file configure.firewall.nov.11.2003 configure.firewall.nov.11.2003: ASCII English text, with CRLF, CR, LF line terminators, with escape sequences All is not lost, though. This command will get rid of most of the garbage characters: % more configure.firewall.nov.11.2003 | \ col -b > configure.firewall.nov.11.2003.clean col is an interesting little utility. It silently filters out what it doesn't understand. Here's an example where this actually works to our advantage. col doesn't understand control characters and escape sequences, which is exactly what we wish to get rid of. Including -b also asks col to remove backspaces. The result is much more readable: 1mdlavigne6@~m: cd /usr/ports/security/snort % file configure.firewall.nov.11.2003.clean configure.firewall.nov.11.2003.clean: ASCII English text I've found that using an editor during a script session also produces very messy output into my script file. The preceding col -b command will clean up most of the mess, but I still won't have a very good idea of exactly what I typed while I was in that editor. For this reason, I use the echo command to send little comments to myself: % echo # once you open up /etc/rc.conf % echo # change this line: linux_enable="NO" % echo # to this: linux_enable="YES" % echo # and add this line: sshd_enable="YES"
1.12.3 Recording an Interactive Shell SessionLet's look at an alternate way of recording a session. This time I'll use the -i (or interactive) switch of my shell: % csh -i | & tee test_session.nov.12.2003
In that command, I used -i to start an interactive tcsh shell. I then piped (|) both stdout and stderr (&) to the tee command. If you've ever looked at physical pipe plumbing, you'll recognize the job of a "tee" in a pipe: whatever is flowing will start going in both directions when it hits the "tee." In my case, all stdout and stderr generated by my shell will flow to both my monitor and to the test_session.nov.12.2003 file. When I'm finished recording my session, I can type Ctrl-c, Ctrl-d, or exit to quit. Like the previous script command, an interactive csh shell will present me with a new shell. However, this method does not record escape characters, meaning I won't need to use the col -b command to clean up the resulting file. But if I try to use vi during my session, the shell will refuse to open the editor and will instead present me with this interesting error message: ex/vi: Vi's standard input and output must be a terminal. If I try to use ee, it will open, but none of the commands will work. pico works nicely but still throws garbage into the session file. So, if I need to use an editor during my session, I'll still echo some comments to myself so I can remember what I did while I was in there. Appending works almost exactly like it does for script, again with the -a (append) switch: % csh -i | & tee -a test_session.nov.12.2003 1.12.4 Letting Other People Watch Your Live Shell SessionsRegardless of which method you choose to record a session, another user can watch your session as it occurs. In order for this to work, that user must:
For example, I've created a test account on my system and configured sshd. I'll now see if I can ssh into my system as the user test and watch the results of dru's test_session.nov.12.2003. % ssh -l test <IP_ADDRESS> Password: % Once I successfully log in, my customized prompt indicates I'm the test user. I can now use the tail command to watch what is happening in dru's session: % tail -f ~dru/test_session.nov.12.2003 My prompt will appear to change to indicate I am the user dru. However, I'm not. I'm simply viewing dru's session. In fact, I can see everything that the user dru is seeing on her terminal. This includes all of her input, output, and any error messages she is receiving. While tail is running, I won't be able to use my prompt. If I try typing anything, nothing will happen. I also can't interact with the user or change what is happening on her terminal. However, I do have a bird's eye view of what that user is experiencing on her terminal. When I'm ready to return to my own prompt, which will also end my view of the session, I simply need to press Ctrl-c. 1.12.5 See Also
|
| < Day Day Up > |


Inscription C009
Index
Area Plan
MI Plan C
< Prev.
Next >
Cross on 2 plinths, inscription on east face only.
Upper plinth:
EMILY JANE PAYNE
WHO DIED AT EALING
AUG 6
TH
1893
AGED 70 YEARS
Lower plinth:
ALSO
EMILY LOUISA SUCH
HER DAUGHTER
WHO FELL ASLEEP ON JULY 21
ST
1933
IN HER 82
ND
YEAR
Burial Register: B224, B688
The Parish Church of St. Mary, Cogges, Oxfordshire.
Copyright © St. Mary's Church Cogges Parochial Church Council, 2005
Updated 10th February 2005
Feedback


Dashboard
Generate ReportRain fall Average (Daily)
{{ daily_avg }}cm
Rain fall Average (Weekly)
{{ weekly_avg }} cm
Current Rain fall
{{ current_rainfall }}cm
{% if next_state == 'drought' %}
Next Hour State
{% elif next_state == 'almost_drought' %}
Next Hour State
{% else %}
Next Hour State
{% endif %}
{{ next_state|title }}


Instructions
Search the target(s) specified by your instructor for flags and submit them to this scoring server. Flags
generally look like hexadecimal hashes unless otherwise specified (e.g. "327a6c4304ad5938eaf0efb6cc3e53dc").
They are not case sensitive.
This server is considered out-of-scope for the CTF
exercise. Messing with it will earn disqualification.


{% extends 'layout.html' %} {% block content %}
In life, you only have 60 seconds to impress someone. 1 minute can make or break you. How do we make sure that you use your 1 minute to actually say something meaningful?
This is an application that allows users to use that one minute wisely. The users will submit their one minute pitches and other users will leave comments to give their feedback on them.
{% endblock %}


East View, 3 Beds, 3 Baths, 1600 Sq Ft Area.
Subscribe to Our Newsletter to Get Our Latest News About Real Estate, New Projects, Properties, Agents and ect...


Most
Harmless
Econometrics,
Economics
]
There are four questions that we need to consider before we start a research agenda.
The most credible and influential research designs use random assignment. There are three problems we want to emphasize about experiments in uncovering causal effect.
First, we define a binary random variable D = 0 or 1. The outcome is denoted by Y.
In other word, Y(0i) is the health status of an individual had he not gone to the hospital and Y(1i) is the individual’s health status if he goes.
We can rewrite the observed outcome Y(i) = Y(0i) + (Y(1i) - Y(0i)) * D(i). And we denote Y(1i) - Y(0i) as the causal effect of hospitalization for an individual.
The comparison of average health conditional on hospitalization status is formally linked to the average causal effect as the following explanation.
The first term (average treatment effect on treated) is the average causal effect of hospitalization on those who were hospitalized while the second term is the selection bias, which is the difference in average between those who were and were not hospitalized.
Random assignment of D(i) solves the selection problem because random assignment makes D(i) independent of potential outcomes.
The quasi-experimental study of class size by Angrist and Lavy (1999) illustrates the manner in which non-experimental data can be analyzed in an experimental spirit.
Regression is a useful tool for the study of causal questions, including the analysis of data from experiments. Suppose (for now) that the treatment effect is the same for everyone, say Y(1i) - Y(0i) , a constant. Selection bias amounts to correlation between the regression error term and the regressor, D(i). Regression plays an exceptionally important role in empirical economic research. Some regressions are simply descriptive tools, as in much of the research on earnings inequality.


Success! The tags have been generated and are in the output below.
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Oh snap! There was an error during the copy to the clipboard. You can still do it yourself with Ctrl+C or Cmd+C.
Oh snap! There is nothing to copy to the clipboard.


Some convenience functions for pls regression, including backward variable selection and validation procedures, image based predictions and plotting.
| Version: | 1.3 |
| Depends: | pls |
| Suggests: | rgdal, raster |
| Published: | 2015-02-24 |
| Author: | Sebastian Schmidtlein, with contributions from C. Oldenburg and H. Feilhauer and with a code snipped borrowed from Bjorn-Helge Mevik |
| Maintainer: | Sebastian Schmidtlein <schmidtlein at kit.edu> |
| License: | GPL-2 |
| NeedsCompilation: | no |
| Citation: | autopls citation info |
| CRAN checks: | autopls results |
| Reference manual: | autopls.pdf |
| Package source: | autopls_1.3.tar.gz |
| Windows binaries: | r-devel: autopls_1.3.zip, r-release: autopls_1.3.zip, r-oldrel: autopls_1.3.zip |
| OS X binaries: | r-release: autopls_1.3.tgz, r-oldrel: autopls_1.3.tgz |
| Old sources: | autopls archive |
Please use the canonical form https://CRAN.R-project.org/package=autopls to link to this page.


3 items, $399.49
Track Your Order
Please enter your Order ID in the box below and press Enter. This was given to you on your receipt and in the confirmation email you should have received.
You can track the status of your order 24 hours after it is placed.
If you cannot find your order number or for status of an international order, please contact us at 01 123 456 789.
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The Digital Chroma uses the VOLUME slider as a performance control, rather than as a master volume control on the final output. Similarly, the tone controls are actual parameter controls that are stored as part of the program. You should use the volume and tone controls of your amplifier or mixer to compensate for the overall level or the sound of the speakers, and use the Chroma's controls for relative adjustments.
There is, however, a master volume control.  It is set to maximum whenever the Digital Chroma is started.
Finally, there is an Analog Master {_global_parameter} which is described {Global_analog_master here}. It reduces the analog output relative to the digital output, to avoid clipping when driving low-impedance headphones.


Preparation time
less than 30 mins
Cooking time
10 to 30 mins
Serves
Serves 4
Mediterranean flavours of fennel, black olives and capers are cooked in a sweet and sour sauce that works brilliantly with salmon fillets, garlicky mussels and clams.
Blanch the fennel, pepper and carrots in a large pan of boiling water for three minutes. Remove, drain and set aside.
In a frying pan, heat some olive oil and gently fry the garlic and chilli. Add the olives, capers and the blanched vegetables and fry for a further couple of minutes. Add the sugar and vinegar and cook until slightly caramelised. Remove from heat and set aside.
Season the salmon fillet on the skin side, place a few thyme sprigs onto the skin and drizzle with a little olive. In a hot non-stick frying pan, seal the salmon on each side for about 2-3 minutes leaving the middle slightly raw, or cooked to your liking.
For the mussels and clams, discard any mussels or clams that have broken shells and discard any open ones that do not close when given a sharp tap.
Heat a large saucepan and add the oil. Once hot, add the garlic and chilli and cook until softened.
Add the mussels, clams, parsley and wine and season with salt and pepper. Place a lid on top and cook until the mussels and clams have opened up. Discard any that do not open. Drain the shellfish reserving the cooking liquid. Set the shellfish aside and keep warm.
Bring the shellfish cooking liquid to a boil then simmer until reduced to a sauce consistency.
To serve, arrange the vegetables on serving plates with the salmon on top skin-side up. Place the shellfish around and pour over the reduced cooking liquid. Finish with a drizzle of olive oil.


A lightning talk by Andrew Armbruster
For fun outside of the day-to-day
For the determined - DIY single-purpose apps
iOS generally very locked down - why not?
Free
Some magical backdoor
Drag-and-drop building of 'workflows'
Lots of good integrations: Apps, snippets, and UI
Curated, pre-built workflows ("Gallery")
Lightweight scripting/programming capabilities
Currently, can invoke:
get/set/name/concat vars, program flow (if, repeat, wait)
'stdlib': Basic math, b64 enc, get hash, some regex, basic input
also: "Content graph" conversion system
Including: syntax highlighting in editor, auto-completion, REPL terminal
Standard library, and then some
A number of modules specifically for Pythonista on iOS
Benefits of a 'normal' programming language (relative to iOS)
Classic functionality: things like running a server, small app
Can invoke via Workflow, also directly from share extension, grab image/text/URLs
Access to services/data: location, motion, photos, reminders, notifications, bluetooth peripherals, contacts, text-to-speech, and more
Eek out more(?): Obj-C bridge
Use case:
Pro tip: Using Pythonista directly? Will want a bluetooth keyboard...


Instant HOTPOT!!!
It’s one of the most popular Ramen stores in Japan. Ths shops is famous for the signature “Tanmen” noodles which comes with spicy soup with meat and vegetable stocks. It’s not only about the spiciness, but has got rich and aromatic flavours of meat and vegetable blended with special Miso paste.
The noodles are about double the width of a standard instant noodle and flat. Very floppy but very nice. The broth is indeed chicken flavored, and has a nice mushroom taste at that. The included vegetables hydrated perfectly – all the way down to the peas.
Samyang Spicy Fried Chicken Ramen.Buldalk Bokkeummyeon. Sapghetti Style Noodle in Unique Really Spicy Sauce (4404 SHU).Ready in 4-5 minutes, Boil the noodle first and Mix with sauce. 0g Trans Fat.
Chinese style instant noodles instant noodles, childhood taste.Pork and chicken stock blend, high quality wheat development.Fish and shrimp balls and eggs lead to a more delicious taste, rich in nutrients
Samyang Bulldark Spicy Chicken Roasted Noodles .You can enjoy the spicy taste of Korean ramen.Spicy flavor of roasted chicken and softness of Carbo cream is harmonious taste.Composition: Noodle, liquid soup, vegetable flakes
The most classic chinese instant noodle, hundreds of millions of bags are sold every year. Private stewed beef secret formula, make soups from a mix of vegetables, low-fat soups.Delicious and nutritiouss


Our trombones use the shiniest brass which is sourced locally. This will increase the longevity of your purchase.
We make sure you recieve your trombone as soon as we have finished making it. We also provide free returns if you are not satisfied.
For every purchase you make, we will ensure there are no damages or faults and we will check and test the pitch of your instrument.
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--- layout: archive title: Zulip Chat Archive permalink: /stream/113488-general/topic/how.20to.20use.20.60solve_by_elim.60.html ---
What is the syntax for giving solve_by_elim
a discharger. I tried solve_by_elim `[cc]
like the docs suggest, but I couldn't get it to work.
That would be a case of outdated documentation. Try solve_by_elim { discharger := `[cc] }
I just updated it. https://github.com/leanprover/mathlib/pull/266
Hi @Simon Hudon, I proposed a further modification to solve_by_elim
at https://github.com/leanprover/mathlib/pull/324. I'd be interested in your opinion sometime.
It's essentially just an extra option to make it convenient to add congr_fun
and congr_arg
to the assumptions.
Cool idea!
Thanks, @Simon Hudon and @Reid Barton for looking at my proposed changes to solve_by_elim
. I've just pushed another change that make it quite a bit more usable --- it's now quite like the syntax for adding and removing lemmas from simp
.
https://github.com/leanprover/mathlib/pull/324
What's the attribute name?
Oh -- there's no attribute that is automatically hooked into solve_by_elim
, although we could do that!
You can write solve_by_elim [f, a]
, where f
is a lemma, and a
is an attribute, it will will look up all lemmas tagged with a
, and then use the local context, f
, and those a
-lemmas.
You can also write solve_by_elim only [f, a]
to not include the local context.
Actually, now I see what you did and it might be better to refrain from having a default solve_by_elim
attribute, at least for now. We're battling the huge size of the simp
list at the moment, maybe we can avoid or at least postpone the same situation for solve_by_elim
and other attribute-based tactics.
Hi @Simon Hudon, I noticed what I think is a bug, or at least suboptimal behaviour in solve_by_elim, preventing successful backtracking when an apply
generates multiple subgoals:
example {α : Type} (r : α → α → Prop) (f : α → α → α) (l : ∀ a b c : α, r a b → r a (f b c) → r a c) (a b c : α) (h₁ : r a b) (h₂ : r a (f b c)) : r a c := begin -- solve_by_elim ought to work here: have w : r a c, { apply l, apply h₁, apply h₂ }, clear w, -- sadly, it doesn't, because of the way we recurse to subgoals. solve_by_elim, -- (Once solve_by_elim successfully uses h₂ on the first goal, it can't -- backtrack after realising that was a bad idea.) end
There's an easy fix, but I thought I'd check if you agreed it warrants fixing before I PR it.
It looks like your proof of w
is the only path that solve_by_elim
can take. Can you show me the mistaken approach it tries?
It can trying applying h2 first. That succeeds, but then it has no where to go, and it fails to backtrack.
(Because with multiple sub goals solve_by_elim currently uses ;
to recurse into them, it can’t backtrack across multiple sub goals.
That is, it tries:
apply l, apply h2
And then is faced with the goal r a (f (f b c) ?m)
and can’t proceed.
Ah! I see! How do you suggest to solve this?
It's pretty easy: see https://github.com/leanprover/mathlib/pull/393/commits/3fab845ccdfb081febf09821c4c1e43dbb82f75b
{% endraw %}Last updated: Apr 24 2020 at 19:02 UTC


Save
Share
vim:
Delete
logout
tabs
{{script}}
{{nextrow}}
Run
<
Fast
Medium
Slow
Snail
Molasses
Loading...
>
Play
Show Visualization Errors:
{% if share %}
Author: {{author}}
{% endif %}
Help
{{viz}}
{{nextrow}}
Output:
{% if share %} {% include "social.html" %} {% endif %}
{% if share %}
Comments and Feedback
{% include "comments.html" %}
+
{% endif %}
{% include "all.html" %}


{% extends base.html %} {% block header %}
An open, human-curated classification of neuroimaging literature.
Have a list of articles to insert? Provide us with a JSON file in the following format and we'll add it to our database. Note that file sizes are capped at 2 MB. The year
, journal
, experiments
, doi
, and meshHeadings
fields are all optional.
[ { "pmid": "22521477", "title": "Hundreds of brain maps in one atlas...", "authors": ["Bezgin G", "Vakorin VA", "van Opstal AJ"], "abstract": "Non-invasive measuring methods such as EEG/MEG, fMRI and DTI are increasingly...", "year": 1997, "journal": "The Journal of neuroscience : the official journal of the Society for Neuroscience", "experiments": [ { "id": "11069", "title": "Areas of brain activation during...", "caption": "Notes on..", "locations": ["-42.0,54.0,-4.0", "-62.0,-14.0,-24.0", "-42.0,-62.0,50.0"] }, { "id": "12000", "title": "Areas of brain activation during...", "caption": "Notes on..", "locations": ["-42.0,54.0,-4.0", "-62.0,-14.0,-24.0", "-42.0,-62.0,50.0"] } ], "doi": "10.1016/j.neuroimage.2012.04.013", "meshHeadings": [ { "name": "Animals", "code": "B01.050", "majorTopic": "N" }, ... , { "name": "Computer Simulation", "code": "L01.224.160", "majorTopic": "N" } ] }, { ... } ]


Martin Bontrager
Genetics PhD, Teacher, and Evolutionary Biologist
Currently
I’m a fifth-year Genetics PhD student at the University of Wisconsin-Madison, due to finish around September 2017. During my time at UW-Madison I have been studying the microbiome of an invasive copepod species across independent saltwater to freshwater invasions. I am currently working on a project examining parallel evolution in the copepod using pooled population genomics data.
Education
2011-15
University of Wisconsin - Madison Genetics PhD (supervisor: Carol Lee)
2009-11
SUNY Brockport BSc Biology (Summa Cum Laude)
2001-03
Finger Lakes Community College AS General Studies
Teaching
TA
2013
GEN 466 Genetics
2015
Bio 100 Exploring Biology
Lecturer
2015
Bio 100 Exploring Biology
Undergraduate Mentorship
2012-15
Joe Connolly and Khamsai Vang Culture-based Isolation of Microbial Symbionts from Copepod Hosts
Sonya Loberger Detecting Signatures of Selection using Pooled Population Genomics
HHMI Teaching Fellowship
2015
I received training in pedagogical methods and active-learning strategies, then developed a team-taught introductory Biology course with my cohort which we taught to Freshmen students. Part of the aim of this fellowship was to aid in the retention of minority and underpriveledged students.
Teaching Evolution online course
2014-15
I developed modules for an online teacher-training course for middle and high school teachers. The goal of the online modules was to provide teachers with access to good materials and lesson plans about Evolutionary Biology.
Presentations
Talks
2015
Along for the Ride: A Copepod Microbiome across Habitat Invasions (UW Genetics Summer Colloquium Series, Madison, WI)
Copepods as Reservoirs for Flavobacterium and other Fish Pathogens (NOAA SeaGrant, Madison, WI)
2014
Statistical Tests of Primate Common Ancestry (Evolution Seminar Series, Madison, WI)
Taxonomic Diversity in the Microbiome of an Invasive Copepod (UW Genetics Summer Colloquium Series, Madison, WI)
2011
Density and Seagrass Preference of the Marine Gastropod Cerithium litteratum in Relation to Current and Wave Action (SUNY Brockport Scholar’s Day, Brockport, NY)
Identification of Rad52 Protein Isoforms via Site-Directed Mutagenesis (SUNY Brockport Scholar’s Day, Brockport, NY)
Posters
2011
The Role of RAD52 in Mitochondrial Genome Stability (SUNY Brockport Summer Research Session, Brockport, NY)
The Role of RAD52 in Mitochondrial Genome Stability (Rochester Academy of Sciences Meeting, Ithaca, NY)
Publications
2015
Bontrager, Martin B. Larget, C. Ane, and D. Baum. Statistical Evidence for Common Ancestry: Testing for Signal in Silent Sites. (Submitted)
Larget, Bret, C. Ane, M. Bontrager, S. Hunter, N. Stenz, and D. Baum. Statistical evidence for common ancestry: New tests of universal ancestry. (Submitted)
Baum, Davide, C. Ane, B. Larget, C. Solis-Lemus, L.S.T. Ho, P. Boone, C. Drummond, M. Bontrager, S. Hunter, and D. Baum. S atistical evidence for common ancestry: Application to Primates. (Submitted)
2014
Almeida, Pedro, C. Goncalves, S. Teixeira, D. Libkind, M. Bontrager, I. Masneuf-Pomarede, W. Albertin, P. Durrens, D. Sherman, P. Marullo, C.T. Hittinger, P. Goncalves, J.P. Sampaio. A Gondwanan Imprint on the Global Diversity and Domestication of the Wine and Cider Yeast Saccharomyces uvarum. Nature Communications Vol 5. June 2014
Media
2015
Morgridge Institute for Research: Microbial communities may inform understanding of rapid evolution
Technical skills
- R
- Statistics
- Markdown
- Git
- UNIX / Linux
- Python
- Perl
- Bash scripting
- Emacs
- Inkscape
- Data visualisation
- Cluster computing
- HTML and CSS
- Javascript
Activities
Evolution Coordinating Committee
2013-15
I am a member of the Evolution Coordinating Committee for the JF Crow Institute for the study of Evolution at UW-Madision. I am involved in the development of a graduate program in Evolution, as well as in many outreach and teaching activities.
Evolution Seminar Series
2013-15
I organize and run the UW-Madison Evolution Seminar Series by inviting all associated speakers and writing grants for funding visiting speakers.
MoMiEE
2014-15
I co-founded a group meeting of microbial ecologists and computational biologists on campus which meets monthly to talk about methods in microbial ecology and evolution.
Evolution Journal Club
2014-15
I founded and help run a weekly evolution journal club for graduate students and post-docs.
Awards
NSF Graduate Research Fellowhip Program (3 years PhD fees + enhanced stipend)
Genetics Department NIH training grant (2 years PhD fees + stipend)
HHMI Teaching Fellowship (Teacher training and course design)
UW-Madison Zoology research grant ($2500 research funding)
Paul and Agnes Bower Biology summer reserach scholarship ($2000 research funding)
Links
References
Available on request.


diane szczepaniak
drawing
painting
sculpture
about
{% if section == 'about' %}
Statement
Events
Press
Resume
Contact
{% endif %}
{% block body -%}{%- endblock body %}


Welcome to Gecko!
Gecko is here to help you find the most relevant papers to your research
and give you a more complete sense of the research landscape.
Simply provide several 'seed papers' that define an area you are interested
in and Gecko will search out all the papers that they cite and all the papers that cite them.
It then builds a network from all of these papers allowing you to quickly
identify important papers you may have missed.
For example, papers that are cited by all of your seed papers are likely to be important
foundational papers in the field, whilst papers that cite a lot of the seed
papers are likely to be more recent papers in the same area that might be worth reading.
The more seed papers you pick the more you can narrow in on a specific topic.
Getting Started
To get started add some seed papers by clicking 'add more seed papers' in the left-hand panel.
This will give you three options:
- Add by DOI
- Search by Title
- Upload BibteX
For a demo of what Gecko can do, click Upload BibTex and then click DEMO.
The papers are displayed in a list in the left-hand panel and as a graph in the right hand panel.
List View
In the list view you can switch between a list of your seed papers and the list of papers connected to them
by clicking the 'Seeds' or 'Connected' tabs respectively.
Seeds and be removed by clicking the red cross in the top left-hand corner of the box
Connected papers can be sorted by either the number of "Seeds Cited" or "Seeds Cited-By" in order to find the highly connected papers.
If one of the connected papers seems highly relevant you can add it as a
seed paper by clicking the green plus in the top left corner of the box. This expands the network in order to uncover more papers.
Network View
In this view you can see how each paper connects to the different seed papers graphically.
You can switch between viewing a graph showing only references of the seed papers and a graph
showing only the papers that cite the seed papers by clicking the grey node in the key to toggle between the two.
In order to hide less well connected papers from the Network View use the slider to select
the minimum number of connections a paper requires to be displayed.
Click on a node to highlight the papers it is connected to. You can turn a connected paper into a seed in the network view by clicking the 'Make Seed' button in the info box that pops up.
Feedback
Thank you for using this demo version of Gecko. If you have any feedback i.e. bugs or feature requests drop me an email at<EMAIL_ADDRESS>or start an issue thread on Github here.
Enjoy!


Letters fall from the top of the screen.
Type a word from the letters, and press space to clear those letters.
Prevent letters from reaching the bottom of the screen.
Some letters are special, use them in a word to unleash the power!
| Word | Pts |
|---|---|
| {{word.word}} | {{word.points}} |
| Total | {{points}} |


Test page
First table
First column - First row
Second column - First row
First column - Second row
Second column - Second row
First column - third row
Second column - third row
Lorem Ipsum test 2 23
Test column
Third column
Second section


tolerance: Statistical Tolerance Intervals and Regions
Statistical tolerance limits provide the limits between which we can expect to find a specified proportion of a sampled population with a given level of confidence. This package provides functions for estimating tolerance limits (intervals) for various univariate distributions (binomial, Cauchy, discrete Pareto, exponential, two-parameter exponential, extreme value, hypergeometric, Laplace, logistic, negative binomial, negative hypergeometric, normal, Pareto, Poisson-Lindley, Poisson, uniform, and Zipf-Mandelbrot), Bayesian normal tolerance limits, multivariate normal tolerance regions, nonparametric tolerance intervals, tolerance bands for regression settings (linear regression, nonlinear regression, nonparametric regression, and multivariate regression), and analysis of variance tolerance intervals. Visualizations are also available for most of these settings.
Downloads:
Reverse dependencies:
Linking:
Please use the canonical form
https://CRAN.R-project.org/package=tolerance
to link to this page.


--- layout: post authors: ["Greg Wilson"] title: Our First Hackathon date: 2012-12-05 time: "10:00:00" tags: ["Community"] ---
We'd like to invite our friends to a week-long hackathon at Mozilla's office in Toronto in June to:
We will have a small budget to help with travel and accommodation, but unfortunately most people will have to fund themselves. On the other hand, Toronto's beautiful that time of year, and there are lots of interesting places a visiting scientist could give a talk. If you're interested, please stay tuned for dates (which we should have in January).
We hope to see you there!


This server runs:
git-Bukkit-0.0.0-624-g69e600f-b627jnks (MC: 1.4)
This server uses the following plugins:
PluginsManager v11.04.02, SomeOtherPlugin v1.2.3, SomeOtherPlugin1 v1.2.3, SomeOtherPlugin2 v4.2.3
This page was generated on: 2011/04/03 @ 17:53:35 using PluginsManager v11.04.02


--- layout: articles article_header: type: cover image: src: https://github.com/yuanl1u/yuanl1u.github.io/blob/master/docs/assets/images/project_banner.png?raw=true ---
Still work in progress...


Description
MATH 110 is an entry-level course in mathematics that introduces the skills
associated with the application of calculus techniques to business and social
science applications. We will review and deepen our understanding of
polynomial and exponential functions as well as introduce the concepts of
derivatives and integration. Most topics will be explored analytically and
graphically as they are applied to real life examples and problems. Students
are expected to study the concepts and examples and be able to extend the
skills and techniques to the solution of similar problems.
Prerequisite
MATH 022 or satisfactory performance on the mathematics proficiency
examination.
Objectives
Math 110, Techniques of Calculus I focuses on learning and practicing the
fundamental rules of calculus and their applications to business and the
social sciences. You will get some exposure to mathematical proofs in the
readings and lectures. I will take many opportunities to remind you of the
underlying concepts during lecture, and you should attain a broad conceptual
understanding of the theory by semester's end. You will be pushed to consider
concepts more deeply during activities and group discussion. However, the
assessments and exams are problem centered.
Upon successful completion of this course, the student should be able to:
-
Identify polynomial, rational, power, exponential, and logarithmic
functions.
-
Calculate the domains of polynomial, rational, power, exponential, and
logarithmic functions.
-
Calculate the sums, differences, products, quotients, and compositions of
functions.
- Model cost, revenue, profit, supply, and demand business functions.
-
Calculate equilibrium points within supply/demand markets and interpret the
results.
-
Calculate or estimate finite/infinite limits of functions given by formulas,
graphs, or tables.
- Calculate one-sided limits of functions.
-
Determine whether a function given by a graph or formula is continuous at a
given point or on a given interval.
-
Determine whether a function given by a graph or formula is differentiable
at a given point or on a given interval.
-
Distinguish between average and instantaneous rate of change and interpret
the definition of the derivative graphically.
-
Determine derivatives of some functions using the definition of derivative
of a function.
-
Calculate derivatives of polynomial, rational, power, exponential, and
logarithmic functions, and combinations of these functions.
- Calculate derivatives of implicitly defined functions.
-
Apply the ideas and techniques of derivatives to related rate problems to
include basic algebraic/geometric models and cost/average cost,
revenue/average revenue, profit/average profit, supply, and demand models
-
Apply the ideas and techniques of derivatives to perform marginal analysis
of basic economics models.
-
Apply the ideas and techniques of derivatives to calculate elasticity of
basic economics models.
- Apply the ideas and techniques of derivatives to finding extrema.
- Apply the ideas and techniques of derivatives to graphing functions.
-
Apply the ideas and techniques of derivatives to optimization problems to
include basic algebraic/geometric models and cost, revenue, profit, supply,
and demand models.
-
Apply the ideas and techniques of derivatives to solve compound interest,
continuous interest, effective interest rate, and present value business
models.
-
Calculate the Riemann sum for a given function, partition and collection of
evaluation points.
- Describe a definite integral as the limit of a Riemann sum.
- Determine anti-derivatives of basic algebraic functions.
-
Calculate values of definite integrals using anti-derivatives and areas.
- Apply substitution techniques to integrate basic functions.
- Apply the ideas of definite integrals to solve problems of areas.
-
Calculate the average value of business models using the definite integral.
-
Apply the ideas and techniques of the definite integral to evaluate
consumer/producer surplus, future/present value of income streams, and
annuity business models.
Textbook
Applied Calculus for the Managerial, Life, and Social Sciences A Brief
Approach, 8th Edition by S. T. Tan (Brooks/Cole, 2009)
Lessons
Each lesson will include a set of prerecorded lectures covering topics for the
week. These lectures are typically 10-15 minutes in length (60-90 mins total).
Each lecture covers one or two particular topics or concepts, along with
examples. This allows you to focus on one topic at a time, at your own pace,
though the pace must be fast enough to keep up with the overall course.
Activity
Each week there will be an activity containing additional suggested problems
and conceptual questions with solutions. This activity is not graded, but
working through the problems and questions should be helpful in preparing you
for quizzes and exams.
Office Hours / Recitation
Each week (time to be determined and announced) there will be a live
recitation. This will be an opportunity for you to get more in depth feedback
about particular problems, review more involved examples, and be reminded of
important concepts in the week's lesson. These sessions will be recorded for
those who are not able to attend the live lecture. However, the content of the
recitation will depend on your questions and/or the points of difficulty in
the lesson. If you are not able to attend the live lecture, you may still
submit questions via email or the discussion forum on Piazza.
Problem Quiz (30 pts)
Each lesson will have a problem quiz worth 30 points. You can take each
problem quiz up to 3 times; you will have immediate feedback on the solutions
after taking each quiz. Your quiz grade will be based on the highest score of
the 3 attempts. Your overall quiz score for the course will be based on the
ten best lessons.
Concept Quiz (10 pts)
Each lesson will have a concept quiz worth 10 points. You are only allowed one
attempt on the concept quiz. Make sure you read through the lesson activity
and try the practice questions there before taking the concept quiz.
Grading
| Assignments |
Points |
| 2 Midterm Exams (Proctored, 150pts each) |
300 |
| 11 Weekly Lessons, (40pts each) |
400 |
| 11 Weekly Lessons, (40pts each) |
400 |
| Concept Quizzes (Best 10) |
100 |
| Problem Quizzes (Best 10) |
300 |
| Final Exam (Proctored) |
300 |
| Total |
1000 |
Grading Scale
| Letter Grade |
% Score |
Total Points |
| A |
93-100% |
926-1000 |
| A- |
90-92% |
896-925 |
| B+ |
87-89% |
866-895 |
| B |
83-86% |
826-865 |
| B- |
80-82% |
796-825 |
| C+ |
77-79% |
766-795 |
| C |
70-76% |
696-765 |
| D |
60-69% |
596-695 |
| F |
0-59% |
0-595 |
Arranging a Proctor
You will need to secure a proctor in order to take exams in this course. A
proctor will not automatically be assigned to you; rather, you must make the
necessary contacts to secure a professional who will serve in this capacity.
-
Contact a person who meets the qualifications and ask him or her to proctor
your exam.
-
Student Services must approve your proctor before any exams can be taken.
Please see instructions for securing a
suitable proctor
. While many proctors will serve on a voluntary basis, you are responsible
for paying any expenses incurred in retaining a proctor.
-
You must complete a Proctor Information Form and submit the completed form
with the proctor verification documentation. Note: If your proctor has been
previously approved by the World Campus during a prior course within two
years, you do not need to obtain verification. World Campus retains proctor
information on file for two years.
-
If your proctor does not meet the required specifications, Student Services
will notify you within 5 to 7 business days.
-
You will need to complete an Exam Request Form for each exam. Contact your
proctor to confirm the date, time, and location of your exam(s). Complete
the form 3 weeks prior to your scheduled exam to allow for processing the
request and mailing exams to proctors. If you are located outside the United
States, in order to allow adequate time for mailing, please plan to request
your exam earlier than 3 weeks prior to the exam week.
- Contact Student Services if you cannot take a scheduled exam.


# Weekly Programmer - Week 16 ## Lists From a Single Direction From [Weekly Programmer Challenges](./index.html) ## Description Using only the language primitives (and standard library where applicable) of your chosen language, implement a singly linked list that implements the following operations: * Prepend element to List * Append element to List * Add element at arbitrary index * Remove first element in list * Remove last element in list * Remove element at arbitrary index * Retrieve element at index The singly linked list should be typesafe to the extent your chosen language allows. ## Bonus challenges 1. Create a unit test suite that proves the functionality of your list 1. Make your list thread safe 1. Add additional operations to your list * For example: Add an iterator


ECOLAB- chloroxylenol solution
Ecolab Inc.
Disclaimer: Most OTC drugs are not reviewed and approved by FDA, however they may be marketed if they comply with applicable regulations and policies. FDA has not evaluated whether this product complies.
Active ingredient
Chloroxylenol 0.5%
Purpose
Antiseptic handwash
Uses
- For handwashing to decrease bacteria on the skin
Warnings
When using this product
- If in eyes, rinse promptly and thoroughly with water
- Discontinue use if irritation and redness develop
Stop use and ask a doctor if
- Skin irritation or redness occurs for more than 72 hours
Keep out of reach of children. If swallowed, get medical help or contact a Poison Control Center right away.
Directions
- Wet skin and spread a small amount on hands and forearms
- Discontinue use if irritation and redness develop
Other information
- Read Material Safety Data Sheet (MSDS) before using this product
- Emergency Healthcare Information: 1<PHONE_NUMBER>. If located outside the United States and Canada, call collect 1<PHONE_NUMBER> (number is in the US)
Inactive ingredients water (aqua), potassium cocoate, hexylene glycol, sodium sulfate, tetrasodium EDTA, sodium lauryl sulfate, hydroxyethylcellulose, glycerin, cocoglucoside, glyceryl oleate, citric acid, frangrance methylchloroisothiazolinone, methylisothiazolinone, FDC red 40, FDC yellow 5, DC red 33
Questions? call 1<PHONE_NUMBER>
Principal Display Panel and Representative Label
ECOLAB
NDC 47593-501-31
MEDI-STAT™
Anitmicrobial Hand Soap
Active Ingredient: 0.5% Chloroxylenol
6000033
Net Contents: 540 mL (18 fl oz)
Ecolab Inc.


Curtiss
Hewie
<EMAIL_ADDRESS>7.2.b evaluation context for the non-outermost binding elements
You must see a value of "Curtiss" for First Name, a value of "Hewie" for Last
Name, and a value of<EMAIL_ADDRESS>for Email Address.
First Name :
Last Name :
Email Address :


Hernias are enlarged natural openings in the body or separations of muscle, that allow underlying tissue to protrude through them.
Melanoma is a cancer of the pigment producing cells of the skin called melanocytes. The incidence of melanoma is increasing faster than any other malignancy.
The most common causes of varicose veins are pregnancy, pelvic or leg injuries, hip or knee surgery, heredity, and occupations where you stand or sit for prolonged periods of time.
Breast and lung cancer are the most common causes of cancer deaths in American women. About 12% of women will develop breast cancer during their lifetime.
Gallbladder symptoms range from mild, upper abdominal discomfort, indigestion, and nausea after eating, to severe upper abdominal pain that will penetrate into the back near the right shoulder blade.
Colon cancer strikes male and females equally. Around the world it is more frequent in industrialized countries, and this suggests genetic and environmental influences.
Salem Surgical is a general surgery group practice dedicated to providing our patients with the highest quality surgical care available.
Our doctors and professional staff at Salem Surgical strive for a "patient first" approach by designing a care plan that suits the patients needs and desires. The patient's safety and care come first. Our physicians and staff attend yearly conferences to stay abreast of the latest research and information on venous disease. Our facilities include an accredited vascular lab which allows patient's testing, diagnosis, and treatment to be done comfortably and conveniently, on site.
Follow us on social media:
Copyright © 2016 Salem Surgical Associates. All Rights Reserved. Site by Brett Ripley


Blog
- Rails 4.0 Sneak Peek: Asynchronous ActionMailer
How to send your emails using the new Rails 4.0 Queue
- Rails 4.0 Sneak Peek: Queueing
A look at the new Queueing API
- Lessons Learned: The First Six Months of Running a Software Consultancy
Brian talks about what has worked, what has not worked, and the changes that have been made at DockYard during its first six months
Events
- Boston Ruby Group
Boston Ruby is a local community of Rubyists and enthusiasts of open source software. The main event is the second Tuesday of every month.
- RailsCamp New England
- Startup Sailing
Open Source
- ClientSideValidations
Client Side Validations made easy for your Rails applications
- ValidAttribute
ValidAttribute is a minimalist matcher for validation BDD
- BetterAR
BetterAr replaces the .all, .first and .count methods for ActiveRecord
- RemarkableMongoid
RSpec Matchers for Mongoid
NOTE: This gem is based upon the 2.0 Release Candidate of Mongoid and the 4.0 alpha of Remarkable - Capybara-email
Test you ActionMailer and Mailer messages with Capybara
- Postgres_Ext
Adds support for missing PostgreSQL data types to ActiveRecord


This will send a request to reindex all data from your primary datasource into Elasticsearch. This will fix missing data and mapping issues, however you will lose any ES data and mapping that was defined outside of your application. This request will return immediately and the reindexing will run in the background. You will see "Elasticsearch: Successfully performed reindexing" in your logs when this is complete.
This can take a long time and is susceptible to concurrency issues so it should be performed when the number of users is low.


Managing Comments on Your Portfolio
When you view a portfolio you have created, the middle section of the display contains comments that have been left for you
to see. (You cannot see comments that were left by someone else and marked as private.) The Comments section looks something
like this:
Each comment has a header in boldface type. The header includes the following:
- The word "public," "shared," or "private," indicating whether everyone, you and the person who left the comment, or only you
can view the comment.
- The name of the person who left this comment
- The date and time the comment was left
- One or more links with which you can manage this comment. Using these links you may delete the comment or change the audience
that is permitted to view it.
The next line or lines contain the comment itself.
To manage a comment, take any of these steps:
- Delete it by clicking the Delete link in the header. Sakai deletes the comment immediately.
Caution: Be sure you want to delete a comment before clicking Delete. Sakai does not display a delete verification window before deleting
the comment.
- Change the audience to include only yourself and the person who left the comment by clicking the make shared link.
- Change the audience to include everyone by clicking the make public link.
- For a comment you have left, change the audience to private by clicking the make private link.


Simple Selection Component (like a drop down) with Image in addition. It supports "selection" event, which can be used for some other actions
Link to blog: Design Studio SDK: Image Carousel Component


Moore had to step into his new role quickly. There was only a month left before Ohio’s season opener, but he was ready to lead the Bobcats to victory.
Being at the helm of a program isn’t unfamiliar territory to Moore. He previously was the head coach at Western Texas College from 2006-2010, where he led the Westerners to their first-ever regionals appearance. Before then, Moore was an assistant and recruiting coordinator at Tennessee Tech. He also had a two-year stint at Creighton before coming to Ohio in 2013.
Moore’s experiences with different levels of play throughout his career have helped him mature as a coach and understand what to prioritize. He knows there is a hierarchy of needs for the team both on and off the field.
“When I first was a head coach, I was 29-years old,” Moore said. “My personality has calmed down a lot since I’ve gotten older.”
Moore understands that as a leader he needs to be there for his players through good and bad. Keeping an even keel in the dugout and being a positive light no matter the situation on the field has been his go-to way of showing his players that he is there for them. Moore wants to see his team succeed.
The Bobcat coach also understands that having a struggle sometimes is inevitable for his team. As a former Division I player himself, he is able to relate to the roller-coaster of experiences that his athletes face. He’s familiar with the upsetting feeling of batting slumps or the feeling of having to wake up early to go to lifting before class from his four letterman years at Nebraska.
His past experiences as a player have also helped create an open-air with his athletes. Moore encourages them to talk to him about what they’re experiencing and even shares stories of his own time on the field. This has helped Moore deeply connect with the team and establish a mutual respect between the two.
“My philosophy as a coach is just to be open with everybody and try to have as much fun while we are competing at a very high level,” Moore said.
Naturally, it took time for the team to adjust to Moore’s coaching style and methodology. Once the team bought into it, Ohio began the 2021 season with the idea of success on its mind.
The Bobcats have had a lot of it, too. Ohio’s winning record is its first in four years and has come off the backs of breakout offensive performances and dominant pitching. Moore and the Bobcats have also been having fun, most of which comes when the team’s theme song plays in the last inning of home games and the team dances along.
But the success hasn’t been without its rocky moments. There have been a few losses along the way for Ohio, and the team had to cancel a series in March due to positive COVID-19 tests and subsequent contact tracing on its roster.
“We’ve had success on some weekends, and we’ve been humbled,” Moore said.
The score isn’t the thing that matters most to Moore, though. He wants his players to enjoy the game. He wants them to be excited to come to practice, and he knows that engaging the players in practice will lead to better outcomes.
Competition is Moore’s favorite part of coaching, and with this in mind he gives the team some ownership of the program to build their competitive drive. He likes to let players weigh in on some topics, but at the end of the day, Moore still makes the final call.
There’s a lot of work that goes into what Moore does, too. Like the athletes, he’s there for practices, reads scouting reports and watches film, but those are things he enjoys doing. By putting in this work with his team, Moore is able to show his players that he truly cares for them and only wants the best for them.
Craig Moore has gracefully stepped into the role of leader for the Bobcats. Moore leads his team fearlessly and the team appreciates all he does for them.
“We all love coach Moore,” pitcher Brett Manis said. “He’s done a great job taking over.”


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Detecting SQL Injection Vulnerabilities from HTTP Errors
Understanding HTTP errors generated by SQL injection attacks
While testing for SQL injection vulnerabilities or attempting to take over a query, the attacker may face different HTTP status codes. Those responses may indicate that the SQL injection partially worked and therefore give precious hints to the tester. This section explains in which cases an HTTP error indicate a test is worth further investigation.
Server Error (HTTP 500)
Instead of providing a wealth of information through a detailed error page, the application may simply return an HTTP 500 Server Error. When this kind of error occur, it is better testing only one parameter at the time. You should also try different techniques to inject the parameter. This way, you will increase your chances to generate a crafted input that gets integrated in the original query without making it invalid. In addition, you should use blind SQL injection in order to extract more information about the system. HTTP 500 Server Errors are habitually pretty straight forward and do not contain details about the error, but with the help of blind SQL injection attacks you could get access to precious information.
Not Found (HTTP 404)
Generally speaking, SQL injections do not generate 404 errors. In fact, 404 and alike (400, 401, 403) are generated because the server could not find the specified resource (web page or file) for diverse reasons. Simply put, the error was thrown before the query was executed. You may face this kind of situation if you think parameters are hidden in script path (because of URL rewriting). For example you may think that the requested URL is rewritten as shown below.
URL loaded by the user.
http://www.victim.com/page/paramA/
URL rewriting loads the following page.
http://www.victim.com/page.php?t=paramA
If URL rewriting is not used, trying to craft SQL injection strings in paramA will likely create 404 errors.
Request Timeout (HTTP 408)
Given you are using time-based blind SQL injection technique; an HTTP 408 error could indicate that you successfully achieved an attack. You might face this situation if you are using a long time delay and the web server hosting the pages you are testing has a short execution timeout.
Resource Found or Moved (HTTP 302 and similar)
The visitor will often be redirected to another page when the application code handles the error. This is a common practice to avoid presenting an error page to the end user and it also helps to get unnoticed since the user is transparently transferred to a valid page.
Tools like web proxy will be necessary to monitor the actual server response when this kind of redirection is used. Below is a simple example of a 302 redirection. Note that 301, 303 and 307 errors have similar results but the server response is a little bit different.
Client request.
GET /index.html HTTP/1.1
Host: www.example.com
Server response.
HTTP/1.1 302 Found
Location: http://www.iana.org/domains/example/
You need to know that this is far from an absolute indicator that the parameter tested is vulnerable. In fact, the application could simply detect that an SQL injection attempt was made and redirect the attacker to a default page without executing any query.
HTTP Error Overriding
You also need to be aware that it is possible for developers to return a custom error message or a custom error code whatever the HTTP error generated by the web server is. In this situation, any error code (500, 404, 400, etc.) could end up returning, for example, an HTTP 302 error redirecting to the homepage.
This technique is used to make the detection of SQL injection more difficult. It is not a common practice but it is possible that you face it while testing different systems.
References
For more information about HTTP errors you can consult the list on Wikipedia or the section dedicated to HTTP status code in the official RFC.


Map-Based Planning For BepiColombo: Farewell to ASCII Inputs
Jonathan McAuliffe & Santa Martinez
BepiColombo Science Ground Segment
The Workshop So Far...
We have data!
How do we
munge
it?
How do we
process
it?
How do we
represent
it?
How do we
share
it?
How do we
use
it?
We want
more
data!
What data?
When can we get it?
When best can we get it?
What can I expect from it?
Who do I tell that I want it?
... and how do I tell them?
ESA's Science Ground Segments
Great! So where's the GIS?
credit: mario d'amore, dlr
johnnycakes79.cartodb.com
How do we do 3D?
johnnycakes79


1999 Archive Index
Montana Cycling.org
The Official Website For Competitive Cycling in Montana
1998 Montana Cup Final Standings
1999 Blue Mountain Mtb Results
1999 Dick Blee Memorial MTB Results
1999 Fairfield Road Race Results
1999 Marcus Daly Criterium Results
1999 Montana District Championships Results
1999 Rocky Mountain Roubaix Results
1999 Trek Barr Stage Race Results
1999 Ecology Center Classic Stage Race Results


"SHEEP-FOLD "a strong fenced enclosure for the protection of the sheep gathered within it (Numbers 32:24; 1 Chronicles 17:7; Psalm 50:9; 78:70). In John 10:16 the Authorized Version renders by “fold” two distinct Greek words, aule and poimne, the latter of which properly means a“flock,” and is so rendered in the Revised Version. (See also Matthew 26:31; Luke 2:8; 1 Corinthians 9:7.) (See FOLD.)
"SHEEP-FOLD "a strong fenced enclosure for the protection of the sheep gathered within it (Numbers 32:24; 1 Chronicles 17:7; Psalm 50:9; 78:70).
In John 10:16 the Authorized Version renders by “fold” two distinct Greek words, aule and poimne, the latter of which properly means a“flock,” and is so rendered in the Revised Version. (See also Matthew 26:31; Luke 2:8; 1 Corinthians 9:7.) (See FOLD.)


nips nips2003 nips2003-151 nips2003-151-reference knowledge-graph by maker-knowledge-mining
Source: pdf
Author: Jean-yves Audibert, Olivier Bousquet
Abstract: There exist many different generalization error bounds for classiﬁcation. Each of these bounds contains an improvement over the others for certain situations. Our goal is to combine these different improvements into a single bound. In particular we combine the PAC-Bayes approach introduced by McAllester [1], which is interesting for averaging classiﬁers, with the optimal union bound provided by the generic chaining technique developed by Fernique and Talagrand [2]. This combination is quite natural since the generic chaining is based on the notion of majorizing measures, which can be considered as priors on the set of classiﬁers, and such priors also arise in the PAC-bayesian setting. 1
[1] D. A. McAllester. Some PAC-Bayesian theorems. In Proceedings of the 11th Annual Conference on Computational Learning Theory, pages 230–234. ACM Press, 1998.
[2] M. Talagrand. Majorizing measures: The generic chaining. Annals of Probability, 24(3):1049– 1103, 1996.
[3] S. Boucheron, G. Lugosi, and S. Massart. A sharp concentration inequality with applications. Random Structures and Algorithms, 16:277–292, 2000.
[4] D. A. McAllester. PAC-Bayesian model averaging. In Proceedings of the 12th Annual Conference on Computational Learning Theory. ACM Press, 1999.
[5] V. Vapnik and A. Chervonenkis. Theory of Pattern Recognition [in Russian]. Nauka, Moscow, 1974. (German Translation: W. Wapnik & A. Tscherwonenkis, Theorie der Zeichenerkennung, Akademie–Verlag, Berlin, 1979).
[6] R. M. Dudley. A course on empirical processes. Lecture Notes in Mathematics, 1097:2–142, 1984.
[7] L. Devroye and G. Lugosi. Combinatorial Methods in Density Estimation. Springer Series in Statistics. Springer Verlag, New York, 2001.
[8] P. Bartlett, O. Bousquet, and S. Mendelson. Local rademacher complexities. Preprint, 2003.
[9] D. A. McAllester. Simpliﬁed pac-bayesian margin bounds. In Proceedings of Computational Learning Theory (COLT), 2003.
[10] M. Ledoux and M. Talagrand. Probability in Banach spaces. Springer-Verlag, Berlin, 1991.
[11] M. Talagrand. The Glivenko-Cantelli problem. Annals of Probability, 6:837–870, 1987.
[12] O. Catoni. Localized empirical complexity bounds and randomized estimators, 2003. Preprint.
[13] J.-Y. Audibert. Data-dependent generalization error bounds for (noisy) classiﬁcation: a PACbayesian approach. 2003. Work in progress.


Entropy consists of two players, order and chaos. The aim of order is to score as many points as possible, and the aim of chaos is to stop order from scoring points. Ideally play two games one as chaos and one as order and the highest order score wins.
Components
There are 49 peices in total, 7 lots of 7 different colours.
Game Play
The two players chaos and order take it in turns to add pieces to the board and move them around until the board is full. Each turn consists of the following:
Chaos places a piece onto the board in an empty position
Order can then move any piece on the board, horizontally or vertically without hoping over any pieces
Repeat the two steps above until the board is full
Scoring
Order scores points for each line of symmetry in each row and column, based on the length of the symmetry. For example:


WITNESS LIST
Veteran Affairs & Military Installations
February 20, 2013 1:30 PM - or upon adjournment
Update on Military Issues
ON:
Bennett, Robert Captain (also providing written testimony) (Texas Commanders Council),
Fort Worth, TX
Coburn, Rufus Director, Veterans Education (Texas Veterans Commission), Austin, TX
Graf, Keith Director (also providing written testimony) (Texas Military Preparedness
Commission), Austin, TX
Moore, Paul Deputy Commissioner (also providing written testimony) (Texas Veterans
Land Board), Austin, TX
Nichols, John Major General (also providing written testimony) (Texas Military Forces),
Austin, TX
Palladino, Thomas Executive Director (also providing written testimony) (Texas Veterans
Commission), Austin, TX
Richman, Jim Director of Claims, Representation and Counseling (Texas Veterans
Commission), Austin, TX
Warren, Kevin Deputy Commissioner, Veteran Homes and Cemeteries (Texas Veterans
Land Board), Austin, TX
Registering, but not testifying:
ON:
Johnson, Danny Colonel, Deputy Commander, 902nd Mission Support Group, USAF, Fort
Sam Houston (Joint Base San Antonio), San Antonio, TX
Lovelace, Joe Mental Health Consultant (Texas Council of Community Centers), Austin,
TX
Wood, Kathy Director, Fund for Veterans Assistance (Texas Veterans Commission),
Austin, TX


| gam.models {mgcv} | R Documentation |
This page is intended to provide some more information on how to specify GAMs. A GAM is a GLM in which the linear predictor depends, in part, on a sum of smooth functions of predictors and (possibly) linear functionals of smooth functions of (possibly dummy) predictors.
Specifically let y_i denote an independent random variable with mean mu_i and an exponential family distribution, or failing that a known mean variance relationship suitable for use of quasi-likelihood methods. Then the the linear predictor of a GAM has a structure something like
g(mu_i)=X_i b + f_1(x_1i,x_2i) + f_2(x_3i) + L_i f_3(x_4) + ...
where g is a known smooth monotonic ‘link’ function, X_i b is the parametric part of the linear predictor, the x_j are predictor variables, the f_j are smooth functions and L_i is some linear functional of f_3. There may of course be multiple linear functional terms, or none.
The key idea here is that the dependence of the response on the predictors can be represented as a parametric sub-model plus the sum of some (functionals of) smooth functions of one or more of the predictor variables. Thus the model is quite flexible relative to strictly parametric linear or generalized linear models, but still has much more structure than the completely general model that says that the response is just some smooth function of all the covariates.
Note one important point. In order for the model to be identifiable
the smooth functions usually have to be constrained to have zero mean (usually
taken over the set of covariate values). The constraint is needed if the term involving the
smooth includes a constant function in its span. gam
always applies such constraints
unless there is a by
variable present, in which case an assessment is made of whether
the constraint is needed or not (see below).
The following sections discuss specifying model structures for gam
.
Specification of the distribution and link function is done using the family
argument to gam
and works in the same way as for glm
.
This page therefore concentrates on the model formula for gam
.
Consider the example model.
g(mu_i) = b_0 + b_1 x_1i + b_2 x_2i + f1(x_3i) + f2(x_4i,x_5i)
where the response variables y_i has expectation mu_i and g is a link function.
The gam
formula for this would be
y ~ x1 + x2 + s(x3) + s(x4,x5)
.
This would use the default basis for the smooths (a thin plate
regression spline basis for each), with automatic selection of the
effective degrees of freedom for both smooths. The dimension of the
smoothing basis is given a default value as well (the dimension of the
basis sets an upper limit on the maximum possible degrees of
freedom for the basis - the limit is typically one less than basis
dimension). Full details of how to control smooths are given in
s
and te
, and further discussion of basis
dimension choice can be found in choose.k
.
For the moment suppose that we would like to change
the basis of the first smooth to a cubic regression spline basis with
a dimension of 20, while fixing the second term at 25 degrees of
freedom. The appropriate formula would be:
y ~ x1 + x2 + s(x3,bs="cr",k=20) + s(x4,x5,k=26,fx=TRUE)
.
The above assumes that x_4 and x_5 are naturally on
similar scales (e.g. they might be co-ordinates), so that isotropic smoothing
is appropriate. If this assumption is false then tensor product smoothing might be
better (see te
).
y ~ x1 + x2 + s(x3) + te(x4,x5)
would generate a tensor product smooth of x_4 and x_5.
By default this smooth would have basis dimension 25 and use cubic regression spline marginals.
Varying the defaults is easy. For example
y ~ x1 + x2 + s(x3) + te(x4,x5,bs=c("cr","ps"),k=c(6,7))
specifies that the tensor product should use a rank 6 cubic regression spline marginal
and a rank 7 P-spline marginal to create a smooth with basis dimension 42.
Sometimes it is interesting to specify smooth models with a main effects + interaction structure such as
E(y) = f1 (x) + f2(z) + f3(x,z)
or
E(y) = f1(x) + f2(z) + f3(v) + f4(x,z) + f5(z,v) + f6(z,v) + f7(x,z,v)
for example. Such models should be set up using ti
terms in the model formula. For example:
y ~ ti(x) + ti(z) + ti(x,z)
, or
y ~ ti(x) + ti(z) + ti(v) + ti(x,z) + ti(x,v) + ti(z,v)+ti(x,z,v)
.
The ti
terms produce interactions with the component main effects excluded appropriately. (There is in fact no need to use ti
terms for the main effects here, s
terms could also be used.)
gam
allows nesting (or ‘overlap’) of te
and s
smooths, and automatically generates side conditions to
make such models identifiable, but the resulting models are much less stable and interpretable than those constructed using ti
terms.
by
variables are the means for constructing ‘varying-coefficient models’ (geographic regression models) and
for letting smooths ‘interact’ with factors or parametric terms. They are also the key to specifying general linear
functionals of smooths.
The s
and te
terms used to specify smooths accept an argument by
,
which is a numeric or factor variable of the same dimension as the covariates of the smooth.
If a by
variable is numeric, then its ith element multiples the ith
row of the model matrix corresponding to the smooth term concerned.
Factor smooth interactions (see also factor.smooth.interaction
).
If a by
variable is a factor
then it
generates an indicator vector for each level
of the factor, unless it is an ordered
factor.
In the non-ordered case, the model matrix for the smooth term is then replicated for each factor level,
and each copy has its rows multiplied by the corresponding rows of its
indicator variable. The smoothness penalties are also duplicated for each
factor level. In short a different smooth is generated
for each factor level (the id
argument to s
and te
can be used to force all
such smooths to have the same smoothing parameter). ordered
by
variables are handled in the same
way, except that no smooth is generated for the first level of the ordered factor (see b3
example below).
This is useful for setting up
identifiable models when the same smooth occurs more than once in a model, with different factor by
variables.
As an example, consider the model
E(y_i) = b_0 + f(x_i)z_i
where f is a smooth function, and z_i is a numeric variable.
The appropriate formula is:
y ~ s(x,by=z)
- the by
argument ensures that the smooth function gets multiplied by
covariate z
. Note that when using factor by variables, centering constraints are applied to the smooths,
which usually means that the by variable should be included as a parametric term, as well.
The example code below also illustrates the use of factor by
variables.
by
variables may be supplied as numeric matrices as part of specifying general linear functional terms.
If a by
variable is present and numeric (rather than a factor) then the corresponding smooth is only subjected
to an identifiability constraint if (i) the by
variable is a constant vector, or, (ii) for a matrix
by
variable, L
, if L%*%rep(1,ncol(L))
is constant or (iii) if a user defined smooth constructor
supplies an identifiability constraint explicitly, and that constraint has an attibute "always.apply"
.
It is sometimes desirable to insist that different smooth terms have the same degree of smoothness.
This can be done by using the id
argument to s
or te
terms. Smooths
which share an id
will have the same smoothing parameter. Really this only makes sense if the
smooths use the same basis functions, and the default behaviour is to force this to happen: all smooths
sharing an id
have the same basis functions as the first smooth occurring with that id
. Note
that if you want exactly the same function for each smooth, then this is best achieved by making use of the
summation convention covered under ‘linear functional terms’.
As an example suppose that E(y_i)=mu_i and
g(mu_i) = f1(x_1i) + f2(x_2i,x_3i) + f3(x_4i)
but that f1 and f3 should have the same smoothing parameters (and x_2
and x_3 are on different scales). Then
the gam
formula
y ~ s(x1,id=1) + te(x_2,x3) + s(x4,id=1)
would achieve the desired result. id
can be numbers or character strings. Giving an id
to a
term with a factor by
variable causes the smooths at each level of the factor to have the same smoothing
parameter.
Smooth term id
s are not supported by gamm
.
General linear functional terms have a long history in the spline literature including in the penalized GLM context (see e.g. Wahba 1990). Such terms encompass varying coefficient models/ geographic regression, functional GLMs (i.e. GLMs with functional predictors), GLASS models, etc, and allow smoothing with respect to aggregated covariate values, for example.
Such terms are implemented in mgcv
using a simple ‘summation convention’ for smooth terms: If the covariates of a
smooth are supplied as matrices, then summation of the evaluated smooth over the columns of the matrices is implied. Each
covariate matrix and any by
variable matrix must be of the same dimension. Consider, for example the term
s(X,Z,by=L)
where X
, Z
and L
are n by p matrices. Let f denote the thin plate regression
spline specified. The resulting contibution to the ith
element of the linear predictor is
sum_j^p L_ij f(X_ij,Z_ij)
If no L
is supplied then all its elements are taken as 1. In R code terms, let F
denote the n by p
matrix obtained by evaluating the smooth at the values in X
and Z
. Then the contribution of the term to the
linear predictor is rowSums(L*F)
(note that it's element by element multiplication here!).
The summation convention applies to te
terms as well as s
terms. More details and examples
are provided in
linear.functional.terms
.
Random effects can be added to gam
models using s(...,bs="re")
terms (see
smooth.construct.re.smooth.spec
),
or the paraPen
argument to gam
covered below. See gam.vcomp
, random.effects
and
smooth.construct.re.smooth.spec
for further details. An alternative is to use the approach of gamm
.
In case the ability to add smooth classes, smooth identities, by
variables and the summation convention are
still not sufficient to implement exactly the penalized GLM that you require, gam
also allows you to penalize the
parametric terms in the model formula. This is mostly useful in
allowing one or more matrix terms to be included in the formula, along with a
sequence of quadratic penalty matrices for each.
Suppose that you have set up a model matrix X, and want to penalize the corresponding coefficients, b
with two penalties b'S1 b and b'S2 b.
Then something like the
following would be appropriate:
gam(y ~ X - 1,paraPen=list(X=list(S1,S2)))
The paraPen
argument should be a list with elements having names corresponding to the terms being penalized.
Each element of paraPen
is itself a list, with optional elements L
, rank
and sp
: all other elements
must be penalty matrices. If present, rank
is a vector giving the rank of each penalty matrix
(if absent this is determined numerically). L
is a matrix that maps underlying log smoothing parameters to the
log smoothing parameters that actually multiply the individual quadratic penalties: taken as the identity if not supplied.
sp
is a vector of (underlying) smoothing parameter values: positive values are taken as fixed, negative to signal that
the smoothing parameter should be estimated. Taken as all negative if not supplied.
An obvious application of paraPen
is to incorporate random effects, and an example of this is provided below. In this
case the supplied penalty matrices will be (generalized) inverse covariance matrices for the random effects — i.e. precision
matrices. The final estimate of the covariance matrix corresponding to one of these penalties is given by the (generalized)
inverse of the penalty matrix multiplied by the estimated scale parameter and divided by the estimated
smoothing parameter for the penalty. For example, if you use an identity matrix to penalize some coefficients that are to be viewed as i.i.d.
Gaussian random effects, then their estimated variance will be the estimated scale parameter divided by the estimate of the
smoothing parameter, for this penalty. See the ‘rail’ example below.
P-values for penalized parametric terms should be treated with caution. If you must have them, then use the option freq=TRUE
in
anova.gam
and summary.gam
, which will tend to give reasonable results for random effects implemented this way,
but not for terms with a rank defficient penalty (or penalties with a wide eigen-spectrum).
Simon N. Wood<EMAIL_ADDRESS>Wahba (1990) Spline Models of Observational Data SIAM.
Wood S.N. (2017) Generalized Additive Models: An Introduction with R (2nd edition). Chapman and Hall/CRC Press.
require(mgcv) set.seed(10) ## simulate date from y = f(x2)*x1 + error dat <- gamSim(3,n=400) b<-gam(y ~ s(x2,by=x1),data=dat) plot(b,pages=1) summary(b) ## Factor `by' variable example (with a spurious covariate x0) ## simulate data... dat <- gamSim(4) ## fit model... b <- gam(y ~ fac+s(x2,by=fac)+s(x0),data=dat) plot(b,pages=1) summary(b) ## note that the preceding fit is the same as.... b1<-gam(y ~ s(x2,by=as.numeric(fac==1))+s(x2,by=as.numeric(fac==2))+ s(x2,by=as.numeric(fac==3))+s(x0)-1,data=dat) ## ... the `-1' is because the intercept is confounded with the ## *uncentred* smooths here. plot(b1,pages=1) summary(b1) ## repeat forcing all s(x2) terms to have the same smoothing param ## (not a very good idea for these data!) b2 <- gam(y ~ fac+s(x2,by=fac,id=1)+s(x0),data=dat) plot(b2,pages=1) summary(b2) ## now repeat with a single reference level smooth, and ## two `difference' smooths... dat$fac <- ordered(dat$fac) b3 <- gam(y ~ fac+s(x2)+s(x2,by=fac)+s(x0),data=dat,method="REML") plot(b3,pages=1) summary(b3) rm(dat) ## An example of a simple random effects term implemented via ## penalization of the parametric part of the model... dat <- gamSim(1,n=400,scale=2) ## simulate 4 term additive truth ## Now add some random effects to the simulation. Response is ## grouped into one of 20 groups by `fac' and each groups has a ## random effect added.... fac <- as.factor(sample(1:20,400,replace=TRUE)) dat$X <- model.matrix(~fac-1) b <- rnorm(20)*.5 dat$y <- dat$y + dat$X%*%b ## now fit appropriate random effect model... PP <- list(X=list(rank=20,diag(20))) rm <- gam(y~ X+s(x0)+s(x1)+s(x2)+s(x3),data=dat,paraPen=PP) plot(rm,pages=1) ## Get estimated random effects standard deviation... sig.b <- sqrt(rm$sig2/rm$sp[1]);sig.b ## a much simpler approach uses "re" terms... rm1 <- gam(y ~ s(fac,bs="re")+s(x0)+s(x1)+s(x2)+s(x3),data=dat,method="ML") gam.vcomp(rm1) ## Simple comparison with lme, using Rail data. ## See ?random.effects for a simpler method require(nlme) b0 <- lme(travel~1,data=Rail,~1|Rail,method="ML") Z <- model.matrix(~Rail-1,data=Rail, contrasts.arg=list(Rail="contr.treatment")) b <- gam(travel~Z,data=Rail,paraPen=list(Z=list(diag(6))),method="ML") b0 (b$reml.scale/b$sp)^.5 ## `gam' ML estimate of Rail sd b$reml.scale^.5 ## `gam' ML estimate of residual sd b0 <- lme(travel~1,data=Rail,~1|Rail,method="REML") Z <- model.matrix(~Rail-1,data=Rail, contrasts.arg=list(Rail="contr.treatment")) b <- gam(travel~Z,data=Rail,paraPen=list(Z=list(diag(6))),method="REML") b0 (b$reml.scale/b$sp)^.5 ## `gam' REML estimate of Rail sd b$reml.scale^.5 ## `gam' REML estimate of residual sd ################################################################ ## Approximate large dataset logistic regression for rare events ## based on subsampling the zeroes, and adding an offset to ## approximately allow for this. ## Doing the same thing, but upweighting the sampled zeroes ## leads to problems with smoothness selection, and CIs. ################################################################ n <- 50000 ## simulate n data dat <- gamSim(1,n=n,dist="binary",scale=.33) p <- binomial()$linkinv(dat$f-6) ## make 1's rare dat$y <- rbinom(p,1,p) ## re-simulate rare response ## Now sample all the 1's but only proportion S of the 0's S <- 0.02 ## sampling fraction of zeroes dat <- dat[dat$y==1 | runif(n) < S,] ## sampling ## Create offset based on total sampling fraction dat$s <- rep(log(nrow(dat)/n),nrow(dat)) lr.fit <- gam(y~s(x0,bs="cr")+s(x1,bs="cr")+s(x2,bs="cr")+s(x3,bs="cr")+ offset(s),family=binomial,data=dat,method="REML") ## plot model components with truth overlaid in red op <- par(mfrow=c(2,2)) fn <- c("f0","f1","f2","f3");xn <- c("x0","x1","x2","x3") for (k in 1:4) { plot(lr.fit,select=k,scale=0) ff <- dat[[fn[k]]];xx <- dat[[xn[k]]] ind <- sort.int(xx,index.return=TRUE)$ix lines(xx[ind],(ff-mean(ff))[ind]*.33,col=2) } par(op) rm(dat) ## A Gamma example, by modify `gamSim' output... dat <- gamSim(1,n=400,dist="normal",scale=1) dat$f <- dat$f/4 ## true linear predictor Ey <- exp(dat$f);scale <- .5 ## mean and GLM scale parameter ## Note that `shape' and `scale' in `rgamma' are almost ## opposite terminology to that used with GLM/GAM... dat$y <- rgamma(Ey*0,shape=1/scale,scale=Ey*scale) bg <- gam(y~ s(x0)+ s(x1)+s(x2)+s(x3),family=Gamma(link=log), data=dat,method="REML") plot(bg,pages=1,scheme=1)


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This is basically the select element that has a list attribute, which accepts a 1D array
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{{> rowHeader title=' with value/index'}}
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{{> midRowHeader title=' with ID'}}
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{{> midRowHeader title=' with Class'}}
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{{>MRPDropDown_events}}
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Code
<mrp-drop-down list='{list}' ></mrp-drop-down>
Code
<mrp-drop-down list='["one","2","3"]' id='sample'></mrp-drop-down>
Code
<mrp-drop-down list='["1","two","three"]' class='sampleClass'></mrp-drop-down>
Code
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{{> property name='id' type='String' description='The ID of the element.'}}
{{> property name='class' type='String' description='The class of the element.'}}
{{> property name='list' type='Array' description='The array that holds all the list options.'}}
{{> property name='value' type='String' description='The current value, can be set to a value (case sensitive) to change the default selected option.'}}
{{> property name='index' type='Number' description='The current index, can be set to an index (starts at 0) to change the default selected option.'}}


Excellent Habits To Form In Order To Succeed As A Journalist
Number of Good Habits: 5
Awesome rating: 86% awesome (because let's face it, this job is not easy!)
Are you a confused, restless student that wants some direction into what habits will help you succeed in your future career? Following these five footnotes will set you on the right track to being a successful writer or reporter!
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Flexibility.
In the case of reporting, you’ll have to work with a multitude of clients/interviewees with varied schedules and means for giving their interview rather than them work with you. The world of journalism and news media changes overnight, whether it be how the public finds their news or the subjects that are rolling down the tickertape. The job can sometimes be hard to predict – even if it’s something as simple as remembering a cue. Be prepared to make mistakes and roll with those punches (like these anchors had to!)
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An Open Mind
Staying unbiased is a staple for being a good news writer/reporter. Your personal beliefs and interests will often have to take the back seat in place of tolerance when dealing with your clients and stories, because being a good journalist involves being able to report in a well-rounded and neutral position in order for your audience to get the full story.
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Charisma
Learning to make your clients and your coworkers feel comfortable and able to speak in your presence It isn’t a set in stone task. Reading body language and adjusting your own disposition to fit your interviewee is significant (knowing when to nod/affirm someone, knowing when to add on and speak with your interviewee. People skills in general helps, even after you’ve completed the interview. You’ll have to work with a lot of people before you can tell your story, such as producers and editors.
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Technology Proficiency
To put it simply, you'll be emailing. A lot. Besides that, this is 2020 and you will need to know how to navigate and customize multiple web pages, social media profiles, and programs used for producing palatable media for an audience. Knowing your way around a computer mouse will be a dire need for any journalist to be able to utilize.
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Interest
Being intrigued by what you’re covering isn’t required, but it sometimes helps. You will find yourself in a rewarding line of work if the subject you’re interviewing/reporting for is something that interests you.


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