| Xmega Application Note | |||||
Data Structures | |
| struct | genclk_config |
| Hardware representation of a set of generic clock parameters. More... | |
Enumerations | |
| enum | genclk_source { GENCLK_SRC_OSC0 = 0, GENCLK_SRC_OSC1 = 1, GENCLK_SRC_PLL0 = 2, GENCLK_SRC_PLL1 = 3 } |
Generic clock source ID. More... | |
Generic clock configuration | |
|
| |
| static void | genclk_config_defaults (struct genclk_config *cfg, unsigned int id) |
| Initialize cfg to the default configuration for the clock identified by id. | |
| static void | genclk_config_read (struct genclk_config *cfg, unsigned int id) |
| Read the currently active configuration of the clock identified by id into cfg. | |
| static void | genclk_config_set_divider (struct genclk_config *cfg, unsigned int divider) |
| Set a new divider in configuration cfg. | |
| static void | genclk_config_set_source (struct genclk_config *cfg, enum genclk_source src) |
| Select a new source clock src in configuration cfg. | |
| static void | genclk_config_write (const struct genclk_config *cfg, unsigned int id) |
| Activate the configuration cfg on the clock identified by id. | |
Enabling and disabling Generic Clocks | |
|
| |
| static void | genclk_disable (unsigned int id) |
| Disable the generic clock identified by id. | |
| static void | genclk_enable (const struct genclk_config *cfg, unsigned int id) |
| Activate the configuration cfg on the clock identified by id and enable it. | |
Chip-specific generic clock definitions | |
|
| |
| #define | GENCLK_DIV_MAX ((1 << AVR32_PM_GCCTRL_DIV_SIZE) * 2) |
| Maximum divider supported by the generic clock implementation. |
Generic clocks are configurable clocks which run outside the system clock domain. They are often connected to peripherals which have an asynchronous component running independently of the bus clock, e.g. USB controllers, low-power timers and RTCs, etc.
Note that not all platforms have support for generic clocks; on such platforms, this API will not be available.
| #define GENCLK_DIV_MAX ((1 << AVR32_PM_GCCTRL_DIV_SIZE) * 2) |
Maximum divider supported by the generic clock implementation.
The max division factor applicable to the source clock of the generic clock.
Definition at line 50 of file genclk.h.
Referenced by genclk_config_set_divider().
| enum genclk_source |
Generic clock source ID.
Each generic clock may be generated from a different clock source. These are the available alternatives provided by the chip.
| GENCLK_SRC_OSC0 |
Oscillator 0. |
| GENCLK_SRC_OSC1 |
Oscillator 1. |
| GENCLK_SRC_PLL0 |
PLL 0. |
| GENCLK_SRC_PLL1 |
PLL 1. |
Definition at line 57 of file genclk.h.
00057 { 00058 GENCLK_SRC_OSC0 = 0, 00059 GENCLK_SRC_OSC1 = 1, 00060 GENCLK_SRC_PLL0 = 2, 00061 GENCLK_SRC_PLL1 = 3, 00062 };
| void genclk_config_defaults | ( | struct genclk_config * | cfg, | |
| unsigned int | id | |||
| ) | [inline, static] |
Initialize cfg to the default configuration for the clock identified by id.
Definition at line 70 of file genclk.h.
References genclk_config::ctrl.
00072 { 00073 cfg->ctrl = 0; 00074 }
| void genclk_config_read | ( | struct genclk_config * | cfg, | |
| unsigned int | id | |||
| ) | [inline, static] |
Read the currently active configuration of the clock identified by id into cfg.
Definition at line 76 of file genclk.h.
References genclk_config::ctrl.
00078 { 00079 cfg->ctrl = AVR32_PM.gcctrl[id]; 00080 }
| void genclk_config_set_divider | ( | struct genclk_config * | cfg, | |
| unsigned int | divider | |||
| ) | [inline, static] |
Set a new divider in configuration cfg.
Definition at line 99 of file genclk.h.
References Assert, genclk_config::ctrl, and GENCLK_DIV_MAX.
00101 { 00102 Assert(divider > 0 && divider <= GENCLK_DIV_MAX); 00103 00104 /* Clear all the bits we're about to modify */ 00105 cfg->ctrl &= ~(AVR32_PM_GCCTRL_DIVEN_MASK | AVR32_PM_GCCTRL_DIV_MASK); 00106 00107 if (divider > 1) { 00108 cfg->ctrl |= 1U << AVR32_PM_GCCTRL_DIVEN; 00109 cfg->ctrl |= ((divider >> 1) - 1) << AVR32_PM_GCCTRL_DIV; 00110 } 00111 }
| void genclk_config_set_source | ( | struct genclk_config * | cfg, | |
| enum genclk_source | src | |||
| ) | [inline, static] |
Select a new source clock src in configuration cfg.
Definition at line 88 of file genclk.h.
References Assert, and genclk_config::ctrl.
| void genclk_config_write | ( | const struct genclk_config * | cfg, | |
| unsigned int | id | |||
| ) | [inline, static] |
Activate the configuration cfg on the clock identified by id.
Definition at line 82 of file genclk.h.
References genclk_config::ctrl.
00084 { 00085 AVR32_PM.gcctrl[id] = cfg->ctrl; 00086 }
| void genclk_disable | ( | unsigned int | id | ) | [inline, static] |
| void genclk_enable | ( | const struct genclk_config * | cfg, | |
| unsigned int | id | |||
| ) | [inline, static] |
Activate the configuration cfg on the clock identified by id and enable it.
Definition at line 113 of file genclk.h.
References genclk_config::ctrl.
00115 { 00116 AVR32_PM.gcctrl[id] = cfg->ctrl | (1U << AVR32_PM_GCCTRL_CEN); 00117 }
| Generated on Fri Oct 22 12:15:26 2010 for AVR1300 Using the Xmega ADC by 1.6.3 |


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I enjoy writing front-end code with this technologies:
You can contact me onGitHub


---
layout: post
title: realworld test
date: '2007-04-26T09:34:00.000+02:00'
author: Steven Van Acker
tags:
modified_time: '2008-01-18T10:47:31.387+01:00'
blogger_id: tag:blogger.com,1999:blog-5513234137363262204.post-4349556583010768300
blogger_orig_url: http://www.singularity.be/2007/04/realworld-test.html
---
2 days ago, I installed Ubuntu Feisty to see if my hardware would bring me any new problems. It seems not.
(My laptop is a Dell Latitude D610 with ATI X300 graphics card).
I did however have problems getting wireless to work. For some reason, my ancient wireless setup using WEP, wouldn't allow my laptop to connect. But now that I've "upgraded" my accesspoint to WPA, problems are gone.
I'm glad to report that "Suspend" works nicely out of the box, and closing the lid will put the laptop in suspend. Exactly what I want.
Just to be on the safe side, I will reinstall ubuntu again and see how much I need to tweak to get things done.
This time, I'll also create a swap partition to try out Hibernate.


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The Lingo Club at Eastmont Computing Center
event_count avoid_fruit cleanup_dishes eat_fruit race race_score
event_count
avoid_fruit
cleanup_dishes
eat_fruit
race
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©2000 John Henry Thompson.


{% csrf_token %}
Delete a track
Warning: Clicking a track below will delete it irrecoverably.
{% if track_list %}
Your Tracks
{% for track in track_list %}
{{track.name}} - {{track.purpose}}
{% endfor %}
{% endif %}


@* * Copyright 2019 HM Revenue & Customs * * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. * You may obtain a copy of the License at * * http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. *@ @import config.AppConfig @import controllers.routes @import domain.Cancel @import uk.gov.hmrc.play.views.html.helpers @(mrn: String, form: Form[Cancel])(implicit request: Request[_], messages: Messages, appConfig: AppConfig) @main_template(title = s"${messages("cancelpage.titleAndHeader")} $mrn") { @helpers.form(routes.DeclarationController.handleCancelForm(mrn)) { @components.back_link() @components.error_summary(form.errors)
} }


| pcoords {pbdBASE} | R Documentation |
Grabs the existing BLACS context grid information.
base.pnum(ICTXT, PROW, PCOL) base.pcoord(ICTXT, PNUM)
ICTXT |
BLACS context number. |
PROW, PCOL |
BLACS grid location row/column |
PNUM |
process rank |
For advanced users only. These functions are simple recreations of the BLACS
routines BLACS_PNUM
and BLACS_PCOORD
. The former gets the
process number associated with the BLACS process grid location
c(MYPROW, MYPCOL)
, while the latter does the reverse.
pnum
returns an integer; pcoord
returns a list
containing elements PROW
and PCOL
.
## Not run: # Save code in a file "demo.r" and run with 2 processors by # > mpiexec -np 2 Rscript demo.r library(pbdBASE, quiet = TRUE) init.grid() blacs_ <- blacs(ICTXT = 0) # get the ICTXT = 0 BLACS coordsinates for process 0 myCoords <- pcoord(ICTXT = 0, PNUM = 0) comm.print(myCoords) finalize() ## End(Not run)


--- title: Logs url: /logs layout: index --- Logs The list of current, usable logs Certificate Transparency logs are "append-only" and publicly-auditable ledgers of certificates being created, updated, and expired. Go to Monitors Go to User Agents {{< logs-grid >}} Add your Log to this list Join the Google Group All Usable Logs Organisation Log name Start End Uptime (%)* Loading logs... *Uptime as measured by Google's network perspectives. For production use, please refer to the published log lists from user agents. Learn how to run a log Read about Let's Encrypt setting up a log Lessons learned running a log Read about Let's Encrypt's operational lessons learned
Certificate Transparency logs are "append-only" and publicly-auditable ledgers of certificates being created, updated, and expired.
Go to Monitors Go to User Agents
Add your Log to this list
Join the Google Group
Read about Let's Encrypt setting up a log
Read about Let's Encrypt's operational lessons learned


✓The game has 2 players, playing in rounds
✓In each turn, a player rolls a dice as many times as he/she whishes. Each result get added to his CURRENT score
✓BUT, if the player rolls a 1, all his/her CURRENT score gets lost. After that, it's the next player's turn
✓The player can choose to 'Hold', which means that his CURRENT score gets added to his/her ROUND score. After that, it's the next player's turn
✓The first player to reach 100 points on ROUND score wins the game
✓To start the game,fill in the fields then click on PLAY button
Current Score:
Round Score:
Current Score:
Round Score:


Pokémon Sword and Shield – How to breed and capture Shiny Pokémon
by Darryn BonthuysSo you want to be a master? Pokémon! So you want to be number one? Pokémon! Sure, you could be the best at battling on the Pokeblock, but nothing says superior training more than Shiny Pokémon. They’re rarer than people who don’t believe that Epstein killed himself, they automatically make you better than the rest of the rabble and sweet Arceus I need one now.
Here’s the trick: Even if you plug a hundred hours into Pokémon Sword and Shield, you may never see a Shiny Pokémon outside of that one bloke who imported all of his rare critters into the game from back in the day when he exploited the QR code functionality of Pokémon X and Y. That guy totally isn’t me. There are ways to get a Shiny Pokémon though, primarily through breeding and wild encounters.  Here how they work.
Shiny charm
The best item to have for increasing your odds of finding a Shiny, the Shiny Charm is awarded to a player…Once they’ve completed their Pokedex. So good luck on that. Once you do have it, your chances of finding a Shiny will skyrocket to 1 in 1365.33. Not bad!
Breeding
Let’s say you want to play the Shiny lottery and breed Pokémon until you get a Shiny. That’ll take forever, but you can increase your odds by breeding with a Pokémon from a game in another language. This will leave you with 1 in 682.7 odds, and with a Shiny Charm equipped that number will drop to 1 in 512. Be prepared to still do a lot of cycling though.
Wild encounters
Let’s say you’re out in the Wild Area, engaging in some Shiny Hunting. The concept is simple: By only encountering the same Pokémon every single time, you increase the rate at which a Shiny might pop up, a trick that has been used since the days of Omega Ruby and Alpha Sapphire. It’s easier than ever to do this now, as you can see which Pokémon are hiding in the long grass in Sword and Shield.
Said odds are still high with the numbers breaking down like this:
50 Pokémon battled
- 1.5% encounter rate
- 1 in 2048 without a Shiny Charm
- 1 in 1024 with Shiny Charm
100 Pokémon battled
- 2% encounter rate
- 1 in 1365.333 without a Shiny Charm
- 1 in 819.2 with Shiny Charm
200 Pokémon battled
- 2.5% encounter rate
- 1 in 1024 without a Shiny Charm
- 1 in 682.6667 with Shiny Charm
300 Pokémon battled
- 3% encounter rate
- 1 in 819.2 without a Shiny Charm
- 1 in 585.1429 with Shiny Charm
500 Pokémon battled
- 3% encounter rate
- 1 in 682.6667 without a Shiny Charm
- 1 in 512 with Shiny Charm
Happy hunting!


Watching the TV: Perdue had 30 thousand votes taken from him. About 13 seconds in you'll see Perdue's count go from 774 thousand to 742 thousand.  The courts said too early, now they say too late. It's a fukin game. But all this means more evidence for us I'm pretty sure Pulitzer is in there right now too.
 How do you arrest people for committing crimes that haven’t happened yet.   No money - no help - no support to ANYONE unless they are like minded freedom lovers. Do not support any organization that does not hold true to constitutional freedoms. Girl Scouts, Netflix, Facebook, Twitter, etc. If you believe in the country, make the sacrifices needed. Only buy what you need. Only spend what you have to. Enough of the talk. Support your fellow believers - all others can fend fir themselves.. If you think this is cold and callous, wait til they dictate how you shall live and what they will take from you.
I realize it will probably never happen, but we truly have taxation without representation right now.
 It’s going to require something like that...some kind of massive pushback. Or else it’s civil war I guess because this can’t be allowed to stand...ever!
Are you and me the only ones seeing this... I’m madd as hell and now they have to cheat more so it won’t be called till tomorrow morning. Sound familiar? I’m 😡
They don't care. Unless charges for Treason are actually done they will keep doing it straight in front of the world, they are laughing at all Americans because they all think they are above any law or person.. Citizens of Georgia should storm their homes and make citizen arrest..

 EVERY ELECTION HAS BEEN LIKE THIS. They report counties so of course the numbers are big. 30,000 people live and vote in counties.
I am counting votes the way you seem to believe is legitimate.. Here we go, say it with me.. 29,996-29,997-29,998-29,999-0 ZERO!! when you are counting up from Zero, what kind of ballot do you open that makes you deduct 30,000 votes from the republican candidate? Are you high?? How come all of these split second vote deductions, where a candidate looses 30 or 40 Thousand votes never happen to a Democrat? This phenomenon only happens to conservatives..

The Jews are stealing the election.
OMG 😤
They knew was going to happen! Same people working and same machines! This is fraud like before period!
 No money - no help - no support to ANYONE unless they are like minded freedom lovers.
Do not support any organization that does not hold true to constitutional freedoms. Girl Scouts, Netflix, Facebook, Twitter, etc.
If you believe in the country, make the sacrifices needed. Only buy what you need. Only spend what you have to.
Enough of the talk. Support your fellow believers - all others can fend fir themselves..
If you think this is cold and callous, wait til they dictate how you shall live and what they will take from you.
They just F***king did it again
I was also watching fox and seen 78% votes counted. And seconds later it showed 76% with lead going to the rats.
Couldn’t have predicted that one.....
Are you and me the only ones seeing this... I’m madd as hell and now they have to cheat more so it won’t be called till tomorrow morning. Sound familiar? I’m 😡
They don't care.
Unless charges for Treason are actually done they will keep doing it straight in front of the world, they are laughing at all Americans because they all think they are above any law or person..
Citizens of Georgia should storm their homes and make citizen arrest..

 EVERY ELECTION HAS BEEN LIKE THIS. They report counties so of course the numbers are big. 30,000 people live and vote in counties.
No kidding, I saw it flip too.
Please stop it
I am sick of this bs 🤬🤬🤬🤬
Civil War 2
There's several videos going around around showing his figures changing at different times....they'll keep changing them till Ossoff wins...
Oh, so you guys watch CNN now?


{% extends 'layout.html' %} {% block title %}: {{ item }}{% endblock %} {% block content %} {# Render item form for deleting item #}
Are you sure you want to delete this item?


ICU care patients Care staff Critical Intensive resources triage hospital Medical Society University Med procedures adequate provide influenza clinical respiratory {border-style critical patient control equipment health px;} ventilation #ffffff;} Center, capacity training criteria medical Emergency essential support Hospital infection needs Triage available ill key management safe supplies surge Force Medicine, Task appropriate based following high-risk personnel points pressure use Christian Control Hospitals Influenza Medicine Table [], admission emergency equipment, representative). representative); severe ICUs Mass SARS benefit critically determine disaster disease expansion high important pharmaceuticals regional safety services Department Emerg Hospital, areas beds care. coordination crisis ensure include including levels number oxygen potential recommendations required used ventilators (HN) Crit Health Health, Infect authority collab: developed grid.. guidelines information mass national non-clinical outside pandemic prioritization protection protocols px; require systems time transmission ventilation, {font-family Executive Group Hebrew Hong Patients Rubinson acute additional availability departments epidemic failure font-size hospitals increasing likely lines mechanical outbreak physical practice preparation requiring review risk roles syndrome treatments units usual working < Chest Clinical Essential Hick Infection Management Pandemic SOP central clinicians collaboration communication confidence double; expanded expanding facilities family framework increase necessary pandemic. personal possible protective recommended resource risks sufficient therapy trained treatment using Care, Charles College Devereaux Dichter Disaster Ensure European Geiling Hadassah Incident Infectious John Kong New Procedures Severe Tel air allocation and/or assessed authorities border-color border-width casualty come, coordinate disaster: events exclusion expert facility filter healthcare identify inclusion individuals infectious intensive interface issues local, manpower meeting mmHg organ patients, perform performed physicians plan planning plans point policies potentially preparedness procedure procedures, pulmonary reduce research respirators scarce served’ solid; suction systematic tool unit ventilatory {font-style {font-weight ‘first > American Ann Class Definitive Dis EECG ICU. INR Israeli JAMA. Jerusalem; Joynt Key MCE Marshall Ministry National Public Robert SOPs Suction Suppl Toronto Unit World Zealand able administration advanced airway altering area arrest available. baseline basis bed blood bold broad cardiac change conditions, consider contamination courier; current data day days death detailed doi:./jama.. duties elective em; environmental exercise experience experienced facility, followed function functions guide include: infrastructure interventions knowledge laboratory level limited literature local maintain maximal mortality new normal occupational operating performance personnel, phased port process provided provides pt;} public remain requirements resources, response rest restricting scope score specialists, staffing summit supervised supplementary surgical trial tube ventilator ward weeks


NSString(HBAdditions) Category Reference
| Declared in | NSString+HBAdditions.h |
|---|
Overview
NSString (HBAdditions) is a class category in Cephei
that provides some convenience methods.
– hb_stringByEncodingQueryPercentEscapes
Returns a string encoded for an HTTP query parameter.
- (NSString *)hb_stringByEncodingQueryPercentEscapes
Return Value
A string encoded for an HTTP query parameter.
Discussion
This method encodes a variety of symbols that can conflict with other portions of a URL, such as
&
and =
, and other similar symbols that could otherwise be misinterpreted by some
implementations.
Declared In
NSString+HBAdditions.h
– hb_stringByDecodingQueryPercentEscapes
Returns a string decoded from an HTTP query parameter.
- (NSString *)hb_stringByDecodingQueryPercentEscapes
Return Value
A string decoded from an HTTP query parameter.
Discussion
This method decodes percent escapes, as well as spaces encoded with a +
.
Declared In
NSString+HBAdditions.h
– hb_queryStringComponents
Returns a dictionary containing the HTTP query parameters in the string.
- (NSDictionary *)hb_queryStringComponents
Return Value
An NSDictionary object containing the keys and values from the query string.
Discussion
The string is expected to be in the format key=value&key=value
, with both keys and values
encoded where necessary.
Declared In
NSString+HBAdditions.h


Overview
LiDAR and UAV-derived SfM model of the tallest tropical tree in the world, a Shorea Faguetiana of the Dipterocarpaceae family coined “Menara”, located in the Danum Valley Conservation Area in North Borneo. See https://www.eci.ox.ac.uk/news/2019/0408.html. Data taken in August 2018. It was measured by climber Unding Jami with a tape measure as being 100.8 meters tall. The tree was discovered by colleagues Chris Chandler and Doreen Boyd of the University of Nottingham. Thank you to many people, including: Yadvinder Malhi, Mat Disney, Phil Wilkes, Andrew Burt, Noreen Majalap, Reuben Nilus, zlin bin Sailim, Azwan bin Tamring, Fredino John, Ahmad bin Jelling and Sabidee Rizan; as well as the logistical team at the Danum Valley Field Centre: Johnny Larenus, Mohd Fadil nin Abd Karim, Rusili Elizabath and Fyen Lyvicy Thomas.


/ Romeo Martinez / is
a Product Designer
/ Currently Working /
@ Capital One
/ About / Coffee Lover
Hobby Enthusiast,
Husband & Father
/ Resume // Contact /


Driver hospitalized after crashing SUV into apartment
Cause of crash not known
by Joey Gill, Digital Content ProducerMURFREESBORO, Tennessee (CNN) - A driver is in the hospital after crashing an SUV into an apartment at The Cove at Center Point Two on Hanover Street, WSMV reported.
Details about the male victim's condition is unknown. It is unclear at this time what caused the crash.
Murfreesboro Police and Murfreesboro Fire are investigating the incident. Building inspectors are also checking to make sure the building is still structurally sound.


Unique Array class For working with unique string array values.
Creates an array with hash/dictionary indexing. Adding or removeing values without using the provided methods can break the functionality of this class. Additional methods exported by requering uniquearray are .set and .makedictionary
local Unique_Array = require('__stdlib__/stdlib/utils/classes/unique_array')
local my_array = Unique_Array('first')
my_array:add('second')
if my_array['second'] then
print('its truthy')
end'
| unique_array:new(...) | Create a new unique array. |
| unique_array:add(other) | Add a string to the array if it doesn't exist in the array. |
| unique_array:remove(other) | Remove the strings from the array if they exist. |
| unique_array:toggle(other) | Toggles the passed name in the array by adding it if not present or removing it if it is. |
| unique_array:diff(other) | Get all items that are NOT in both arrays. |
| unique_array:intersects(other) | Get all items that are in both arrays. |
| unique_array:sort([cmp]) | Sort the unique_array in place. |
| unique_array:concat(other) | Create a new unique_array by concatenating together. |
| unique_array:find(pattern) | Find all members in a unique array that match the pattern. |
| unique_array:clear() | Clear the array returning an empty array object |
| unique_array:all(other) | Does this array have all of the passed strings. |
| unique_array:any(other) | Does this array have any of the passed strings. |
| unique_array:none(other) | Does this array have none of the passed strings. |
| unique_array:same(other) | Do both arrays contain the same items |
| unique_array:disjointed(other) | Do the unique arrays have no items in common |
| unique_array:tostring() | Convert the array to a string. |
| unique_array:make_dictionary([func][, ...]) | Return a dictionary mapping of the array. |
| unique_array.exports | These functions are available when requiring this class. |
Create a new unique array.
Parameters:
Add a string to the array if it doesn't exist in the array.
Parameters:
Remove the strings from the array if they exist.
Parameters:
Toggles the passed name in the array by adding it if not present or removing it if it is.
Parameters:
Get all items that are NOT in both arrays.
Parameters:
Get all items that are in both arrays.
Parameters:
Sort the unique_array in place.
Parameters: Returns:
Create a new unique_array by concatenating together.
Parameters:
Find all members in a unique array that match the pattern.
Parameters: Returns:
Clear the array returning an empty array object
Returns:
Does this array have all of the passed strings.
Parameters:
Does this array have any of the passed strings.
Parameters:
Does this array have none of the passed strings.
Parameters:
Do both arrays contain the same items
Parameters:
Do the unique arrays have no items in common
Parameters:
Convert the array to a string.
Returns:
Return a dictionary mapping of the array.
can be passed a function to set the value of the field.
Parameters:These functions are available when requiring this class.
Fields:


This is a small sample code collection using JSFrame. All of the code presented here can be found on GitHub.
Create a window and the basic use of windows
How to handle window events and the events of the content in the window
Customize the look of your windows
Set the offset position of the entire window of jsFrame, and set the display order (z-index).
Controls make it easier to implement things like maximizing and minimizing windows


Open Problem - Private All-Pairs Distances
Background: Suppose we are interested in computing the distance between two vertices in a graph. Under edge or node differential privacy, this problem is not promising because the removal of a single edge can make distances change from 1 to \(n − 1\) or can even disconnect the graph. However, a different setting that makes sense to consider is that of a weighted graph \((G, w)\) whose topology \(G = (V, E)\) is publicly known but edge weight function \(w : E \to \mathbb{R}^+\) must be kept private. (For instance, consider transit times on a road network. The topology of the road network may be publicly available as a map, but the edge weights corresponding to transit times may be based on private GPS locations of individual cars.)
Suppose that two weight functions \(w\) and \(w’\) of the same graph \(G\) are considered to be neighbors if they differ by at most 1 in \(\ell^1\) norm. Then the length of any fixed path is sensitivity-one, so the distance between any pair of vertices is also sensitivity-one and can be released privately via the Laplace mechanism. But what if we want to release all \(\Theta(n^2)\) distances between all pairs of vertices in \(G\)? We can do this with accuracy roughly \(O(n \log n )/\varepsilon\) by adding noise to each edge, or roughly \(O(n \sqrt{\log(1/\delta)}/\varepsilon)\) using composition theorems. Both of these are roughly \(n/\varepsilon\). But is this linear dependence on \(n\) inherent, or is it possible to release all-pairs distances with error sublinear in \(n\)?
This setting and question were considered in [S16].
Problem 1: Let \(G\) be an arbitrary public graph, and \(w : E \to \mathbb{R}^+\) be an edge weight function. Can we release approximate all-pairs distances in \((G, w)\) with accuracy sublinear in \(n\) while preserving the privacy of the edge weight function, where two weight functions \(w, w’\) are neighbors if \(\|w − w’\|_1 \le 1\)? Or can we show that any private algorithm must have error \(\Omega(n)\)? A weaker (but nontrivial) lower bound would also be nice.
Reward: A bar of chocolate.
Other related work: [S16] provided algorithms with better error for two special cases, trees and graphs of a priori bounded weight. For trees, it is possible to release all-pairs distances with error roughly \(O(\log^{1.5} n)/\varepsilon\), while for arbitrary graphs with edge weights restricted to the interval \([0,M]\), it is possible to release all-pairs distances with error roughly \(O( \sqrt{nM\varepsilon^{-1}\log(1/\delta)})\)
Submitted by Adam Sealfon on April 9, 2019.
[cite this]


{% load widget_tweaks %}
The Machine Folk Session is a community website dedicated to folk music generated by, or co-created with, machines. Give your generated tune a title, hit 'Archive', and it will become part of the collection there. You'll be taken to its page on the machine folk session site, where you can hand-craft settings of the tune and discuss it with others.


Chocolate Pizza
POSTED ON 15 DEC 2013 / DESSERTS
For the fig-swirl: Melt buter over medium heat in a saucepan. Add brown sugar and stir to dissolve. Halve all of the figs and toss in the saucepan with water and lemon juice. Cook over medium heat, stirring frequently, until you have a chunky-jammy mixture. Add salt with one or two stirs, set aside and let cool completely.
Ice cream: In a small pot over medium heat, combine milk, and granulated sugar until sugar is completely dissolved and the milk is just barely lukewar. Whisk in the egg yolks. Set mixture in the fridge and wait until the fig mixture is cooled.
Using an ice cream machine, pour liquids into the frozen basin and process according to the manufacture instructions, i.e., let spin and thicken for 20 minutes before ading mascarpone, fig jam mixture, and the nuts. Continue to process for +/- 10 minutes. Pour semi-frozen mixture into a pyrex dish or glass tupperware. Freeze for at least two hours before serving.


Node Release Tools is a simple NPM package which makes it really easy automate releases of your Node.js libraries
Just install it via NPM:
$ npm install releasetools
The easiest way to use it is by requiring it in your build script. You can see an example of usage in this package Jakefile file.
Additionally, it's possible to customize its behaviour by changing some parameters:
var releaseTools = require('releasetools');
releaseTools.setOptions({
changelogFile: 'History.md',
authorsFile: 'AUTHORS',
siteIndexPath: 'site/index.html',
siteAssetsPaths: [
'site/javascripts',
'site/stylesheets'
],
examplePaths: [
'examples/*'
]
});
You just have to checkout this package from GitHub, install development dependencies and execute the tests:
$ git clone "https://github.com/rafeca/node-releasetools.git"
$ npm install --dev
$ npm test
(The MIT License)
Copyright (c) 2011 Rafael de Oleza<EMAIL_ADDRESS>Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the 'Software'), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED 'AS IS', WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.


One step means a partition size,and {{connector.name}} partition size = {{connector.size}}
After submitted, please go to "Jobs" to check the job status
Cron expression is using browser's time zone: {{timezone}}


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object --+ | AbstractParameter
Abstract parameterization, which can exist independently or be coupled to other parameters upon request. Virtual/coupled/derived parameters cannot be overwritten explicitly, but their values will get recomputed once their corresponding base parameters get updated. This is a lazy process - parameter recalculation happens only when an out of date parameter is requested. This triggers a real-time cascaded update which affects all parameters from the nearest consistent base down to the current inconsistent node.
Implementing subclasses must override AbstractParameter._validate and virtual parameters should additionally override AbstractParameter._compute.
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is_virtual True if this parameter is virtual (computed) |
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name Parameter name |
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value Parameter value (guaranteed to be up to date) |
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| Method Details |
x.__init__(...) initializes x; see help(type(x)) for signature
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Bind the current parameter to a base parameter. This converts the current parameter to a virtual one, whose value will get implicitly updated to be consistent with its base. Note that virtual parameters must have exactly one base; computing a parameter from multiple bases is not allowed. Cycles are also not allowed; the topology must always stay a tree with a non-virtual parameter at the root.
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Find and return the non-virtual base parameter that is the root of the current hierarchy. If self is not virtual, return self.
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Update the value of this parameter. This is not possible for virtual parameters.
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| Property Details |
is_virtualTrue if this parameter is virtual (computed)
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nameParameter name
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valueParameter value (guaranteed to be up to date)
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| Generated by Epydoc 3.0.1 on Tue Jul 4 20:19:06 2017 | http://epydoc.sourceforge.net |


We are a fairly small, flexible design studio that designs for print and web. We work flexibly with clients to fufill their design needs. Whether you need to create a brand from scratch, incluing marketing materials.
We are a fairly small, flexible design studio that designs for print and web. We work flexibly with clients to fufill their design needs. Whether you need to create a brand from scratch, incluing marketing materials.
We are a fairly small, flexible design studio that designs for print and web. We work flexibly with clients to fufill their design needs. Whether you need to create a brand from scratch, incluing marketing materials.
We are a fairly small, flexible design studio that designs for print and web. We work flexibly with clients to fufill their design needs. Whether you need to create a brand from scratch, incluing marketing materials.
Sample stuff here
Tab stuff here


Chapter 6 Analysis with Groups in R
6.0.1 Packages Used in this Chapter
6.0.2 Hypothetical Experimental Data
We will demonstrate some functions using this hypothetical dataset:
| id | race | blood.type | gender | age | study.group | speed |
|---|---|---|---|---|---|---|
| 1 | black | A | female | 31 | treatment | 596.4 |
| 2 | white | B | male | 48 | treatment | 536.7 |
| 3 | white | B | female | 46 | treatment | 698.7 |
| 4 | black | A | female | 52 | treatment | 576 |
| 5 | white | B | male | 35 | treatment | 508.4 |
| 6 | black | B | female | 36 | treatment | 702.5 |
6.1 Group Structure
The two most important skills as you first learn a data programming language are:
- Translating English phrases into computer code using logical statements
- Organizing your data into groups
This lecture focuses on efficienty splitting your data into groups, and then analyzing your data by group.
6.1.1 What Are Groups?
A group represents a set of elements with identical characteristics - mice all belong to one group and elephants belong to another. Easy enough, right?
In data analysis, it is a little more complicated because a group is defined by a set of features. Each group still represents a set of elements with identical characteristics, but when we have multiple features there is a unique group for each combination of features.
The simple way to think about this is that the cross-tab of features generates a grid (table), and each cell represents a unique group:
We might be interested in simple groups (treatment cases versus control cases), or complex groups (does the treatment effect women and men differently?).
In previous lectures you have learned to identify a group with a logical statement, and analyze that group discretely.
In this lecture you will learn to define a group structure, then analyze all of your data using that structure.
| female | male | |
|---|---|---|
| control | 460 | 362.2 |
| treatment | 612.4 | 517.6 |
6.1.2 Main Take-Away
R has been designed to do efficient data analysis by defining a group structure, then quickly applying a function to all unique members.
The base R packages do this with a set of functions in the apply() family. The tapply() function allows you to specify an outcome to analyze and a group, then ask for results from a function.
| female | male | |
|---|---|---|
| control | 460 | 362.2 |
| treatment | 612.4 | 517.6 |
The dplyr package makes this process easier using some simple verbs and the “pipe” operator.
| study.group | gender | ave.speed |
|---|---|---|
| control | male | 362.2 |
| control | female | 460 |
| treatment | male | 517.6 |
| treatment | female | 612.4 |
6.1.3 Example
Let’s think about a study looking at reading speed. The treatment is a workshop that teaches some speed-reading techniques. In this study we have data on:
- gender (male,female)
- race (black,white,asian)
- blood.type (A,B)
- age (from 18 to 93)
Examining descriptive statistics we can see that reading speed varies by gender and the treatment group, but not by race or blood type:
The question is, how many unique groups can we create with these four factors?
Each individual factor contains a small number of levels (only 2 or 3 in this case), which makes the group structure look deceptively simple at first glance. When we start to examine combinations of factors we see that group structure can get complicated pretty quickly.
If we look at gender alone, we have two levels: male and female. So we have two groups. If we look at our study groups alone we have two groups: treatment and control.
If we look at gender and the study groups together, we now have a 2 x 2 grid, or four unique groups.
If the race factor has three levels, how many unique groups will we have considering the study design, gender, and race together?
We can calculate the size of the grid by multiplying number of levels for each factor. We see here we have 12 unique groups:
If we add blood type, a factor with two levels (A and B), we now have 24 unique groups:
What about age? It is a continuous variable, so it’s a little more tricky.
We can certainly analyze the relationship between age and speed using correlation tools.
But we can also incorporate this independent variable into a group structure. We can treat each distinct age as a separate group. The ages in this study range from 18 to 93, so we have 65 distinct ages represented.
If we think about the overall group structure, then, we have unique groups defined by gender, race, blood type, and study design, and another 65 age groups. So in total we now have 24 x 65 = 1,560 groups! That is getting complicated.
This group design is problematic for two reasons. From a pragmatic standpoint, we can’t report results from 1,500 groups in a table. From a more substantive perspective, we although we have 1,500 distinct cells in our grid, many may not include observations that represent the unique combination of all factors. So this group design is not very practical.
A similar problem arises if our data includes time. If our data includes the time of events recorded by hours, days of the week, months, and years, we can have generate complicated group structures if we try to analyze every unique combination.
We can simplify our analysis by thinking about age ranges instead of ages, or in other words by binning our continuous data. If we split it into five-year ranges, for example, we have gone from 65 distinct ages to 12 distinct age groups.
age.group <- cut( age,
breaks=seq(from=20,to=80,by=5),
labels=paste( seq(from=20,to=75,by=5), "to", seq(from=25,to=80,by=5) ) )
group.structure <- formula( speed ~ age.group )
boxplot( group.structure, las=2, frame.plot=F, outline=F, main="Age Group" )
We have now simplified our analysis from 1,560 to 288 possible groups. Combinations of groups will also be easier:
6.2 Analysis by Group
Let’s demonstrate some analysis of groups using the Lahman package and some dplyr verbs. Let’s do some analysis of player salaries (Salaries dataset), and start with a simple group structure - teams in the National League and time.
- Which team has the highest average player salary?
- Which team has the most players paid over $5 million a season?
- Which team has raised it’s pay the most over the past decade?
Let’s start by thinking about group structure. We have teams, and we have seasons. Teams is stored as a factor, and seasons as a numeric value, so we can consider group for each by counting levels and unique values:
So we can potentially calculate 32 x 46 = 1,472 average player salaries.
6.2.1 Highest Ave Player Salary
For our first question, we will select only teams from the National League. Let’s use the most recent year of data to calculate average pay.
Salaries %>% filter( lgID == "NL", yearID == 2016 ) %>%
group_by( teamID) %>%
summarize( Ave_Salary = mean(salary) )
### # A tibble: 15 x 2
### teamID Ave_Salary
### <fct> <dbl>
### 1 ARI 3363041.
### 2 ATL 2362010.
### 3 CHC 5312678.
### 4 CIN 3066899.
### 5 COL 3413487
### 6 LAD 6322525.
### # ... with 9 more rows
Since the salaries are large, they are a little hard to read. Let’s clean up the table a bit.
Salaries %>%
filter( lgID == "NL", yearID == 2016 ) %>%
group_by( teamID ) %>%
summarize( Ave_Salary=dollar( mean(salary,na.rm=T) ) ) %>%
arrange( desc(Ave_Salary) ) %>%
pander()
| teamID | Ave_Salary |
|---|---|
| SFG | $6,890,151 |
| LAD | $6,322,525 |
| WSN | $5,448,179 |
| CHC | $5,312,678 |
| NYM | $4,958,857 |
| STL | $4,614,629 |
| SDP | $3,756,475 |
| PIT | $3,706,387 |
| COL | $3,413,487 |
| ARI | $3,363,041 |
| CIN | $3,066,899 |
| MIA | $2,761,222 |
| ATL | $2,362,010 |
| MIL | $2,292,508 |
| PHI | $2,033,793 |
6.2.2 Most Players Paid Over $5 Million
This question requires you to utilize a logical statement in order to translate from the question to code. We need to inspect each salary, determine whether it is over the $5m threshold, then count all of the cases. The operation will look something like this:
It gets a little trickier when we want to do the operation simultaneously across groups. Our team group structure is already defined, so let’s define our logical vector and count cases that match:
dat.NL <- filter( Salaries, yearID == 2010 & lgID == "NL" ) %>% droplevels()
gt.5m <- dat.NL$salary > 5000000
table( dat.NL$teamID, gt.5m )
### gt.5m
### FALSE TRUE
### ARI 23 3
### ATL 21 6
### CHN 19 8
### CIN 21 5
### COL 23 6
### FLO 23 4
### HOU 24 4
### LAN 20 7
### MIL 25 4
### NYN 19 9
### PHI 18 10
### PIT 27 0
### SDN 25 1
### SFN 21 7
### SLN 19 6
### WAS 26 4
This solution works, but the table provides too much information. We can use dply to simplify and format the table nicely for our report:
Salaries %>%
filter( yearID == 2010 & lgID == "NL" ) %>%
group_by( teamID ) %>%
summarise( gt_five_million = sum( salary > 5000000 ) ) %>%
arrange( desc(gt_five_million) ) %>%
pander
| teamID | gt_five_million |
|---|---|
| PHI | 10 |
| NYN | 9 |
| CHN | 8 |
| LAN | 7 |
| SFN | 7 |
| ATL | 6 |
| COL | 6 |
| SLN | 6 |
| CIN | 5 |
| FLO | 4 |
| HOU | 4 |
| MIL | 4 |
| WAS | 4 |
| ARI | 3 |
| SDN | 1 |
| PIT | 0 |
6.2.3 Fielding Positions
Which fielding position is the highest paid?
merge( Salaries, Fielding ) %>%
filter( yearID == 2016 ) %>%
group_by( POS ) %>%
summarize( Mean_Salary = dollar( mean(salary) ) ) %>%
pander
| POS | Mean_Salary |
|---|---|
| 1B | $5,570,032 |
| 2B | $3,162,075 |
| 3B | $3,579,088 |
| C | $2,521,903 |
| OF | $3,546,115 |
| P | $3,401,676 |
| SS | $2,510,833 |
6.2.4 Country of Birth
Which country has produced the highest paid baseball players?
merge( Salaries, Master ) %>%
filter( yearID == 2016 ) %>%
group_by( birthCountry ) %>%
summarize( Mean_Salary = dollar( mean(salary) ) ) %>%
pander
| birthCountry | Mean_Salary |
|---|---|
| Aruba | $650,000 |
| Australia | $523,400 |
| Brazil | $1,548,792 |
| CAN | $7,854,167 |
| Colombia | $3,125,289 |
| Cuba | $5,532,484 |
| Curacao | $5,724,167 |
| D.R. | $5,102,318 |
| Germany | $511,500 |
| Japan | $8,247,012 |
| Mexico | $4,617,038 |
| Netherlands | $2,425,000 |
| Nicaragua | $2,375,000 |
| P.R. | $3,241,378 |
| Panama | $2,946,550 |
| Saudi Arabia | $522,500 |
| South Korea | $5,326,190 |
| Taiwan | $6,750,000 |
| USA | $4,189,640 |
| V.I. | $507,500 |
| Venezuela | $4,521,051 |
6.2.5 Pay Raises
To examine pay raises, we will now use more than one year of data. Since the question asks about pay raises over the past decade, we will filter the last ten years of data.
And how since we are looking at patterns over teams and over time, we need to define a group structure with two variables:
Salaries %>% filter( yearID > 2006 & lgID == "NL" ) %>%
group_by( teamID, yearID ) %>%
summarize( mean= dollar(mean(salary)) ) %>%
head( 20 ) %>% pander
| teamID | yearID | mean |
|---|---|---|
| ARI | 2007 | $1,859,555 |
| ARI | 2008 | $2,364,383 |
| ARI | 2009 | $2,812,141 |
| ARI | 2010 | $2,335,314 |
| ARI | 2011 | $1,986,660 |
| ARI | 2012 | $2,733,512 |
| ARI | 2013 | $3,004,400 |
| ARI | 2014 | $3,763,904 |
| ARI | 2015 | $2,034,250 |
| ARI | 2016 | $3,363,041 |
| ATL | 2007 | $3,117,530 |
| ATL | 2008 | $3,412,189 |
| ATL | 2009 | $3,335,385 |
| ATL | 2010 | $3,126,802 |
| ATL | 2011 | $3,346,257 |
| ATL | 2012 | $2,856,205 |
| ATL | 2013 | $3,254,501 |
| ATL | 2014 | $4,067,042 |
| ATL | 2015 | $2,990,885 |
| ATL | 2016 | $2,362,010 |
This might seem like an odd format. We might expect something that looks more like our grid structure:
dat.NL <- filter( Salaries, yearID > 2010 & lgID == "NL" ) %>% droplevels()
tapply( dat.NL$salary, INDEX=list(dat.NL$teamID, dat.NL$yearID), FUN=mean, na.rm=T ) %>% pander
| 2011 | 2012 | 2013 | 2014 | 2015 | 2016 | |
|---|---|---|---|---|---|---|
| ARI | 1986660 | 2733512 | 3004400 | 3763904 | 2034250 | 3363041 |
| ATL | 3346257 | 2856205 | 3254501 | 4067042 | 2990885 | 2362010 |
| CHC | NA | NA | NA | NA | NA | 5312678 |
| CHN | 5001893 | 3392194 | 3867989 | 2426759 | 4138547 | NA |
| CIN | 2531571 | 2935843 | 4256178 | 3864911 | 4187862 | 3066899 |
| CLE | NA | NA | NA | 4500000 | NA | NA |
| COL | 3390310 | 2692054 | 2976363 | 3180117 | 3827544 | 3413487 |
| FLO | 2190154 | NA | NA | NA | NA | NA |
| HOU | 2437724 | 2332731 | NA | NA | NA | NA |
| LAD | NA | NA | NA | NA | NA | 6322525 |
| LAN | 3472967 | 3171453 | 6980069 | 6781706 | 7441103 | NA |
| MIA | NA | 4373259 | 1400079 | 1549515 | 2835688 | 2761222 |
| MIL | 2849911 | 3755921 | 3077881 | 3748778 | 3477586 | 2292508 |
| NYM | NA | NA | NA | NA | NA | 4958857 |
| NYN | 4401752 | 3457555 | 1648278 | 3168777 | 3870667 | NA |
| PHI | 5765879 | 5817965 | 6533200 | 5654530 | 4295885 | 2033793 |
| PIT | 1553345 | 2248286 | 2752214 | 2756357 | 3065259 | 3706387 |
| SDN | 1479650 | 1973025 | 2342339 | 2703061 | 4555435 | NA |
| SDP | NA | NA | NA | NA | NA | 3756475 |
| SFG | NA | NA | NA | NA | NA | 6890151 |
| SFN | 4377716 | 3920689 | 5006441 | 5839649 | 6100056 | NA |
| SLN | 3904947 | 3939317 | 3295004 | 4310464 | 4586212 | NA |
| STL | NA | NA | NA | NA | NA | 4614629 |
| WAS | 2201963 | 2695171 | 4548131 | 4399456 | 5365085 | NA |
| WSN | NA | NA | NA | NA | NA | 5448179 |
Later on we will look at the benefits of “tidy data”, but the basic idea is that you can “facet” your analysis easily when your groups are represented as factors instead of arranged as a table. For example, here is a time series graph that is faceted by teams:
Salaries %>% filter( yearID > 2000 & lgID == "AL" ) %>%
group_by( teamID, yearID ) %>%
summarize( Mean_Player_Salary=mean(salary) ) -> t1
qplot( data=t1, x=yearID, y=Mean_Player_Salary, geom=c("point", "smooth") ) + facet_wrap( ~ teamID, ncol=5 )
Now you can quickly see that Detroit is the team that has raised salaries most aggressively.
If we need to, we can easily convert a tidy dataset into something that looks like a table using the spread() function:
Salaries %>% filter( yearID > 2006 & lgID == "NL" ) %>%
group_by( teamID, yearID ) %>%
summarize( mean = dollar(mean(salary)) ) %>%
spread( key=yearID, value=mean, sep="_" ) %>%
select( 1:6 ) %>% na.omit() %>%
pander
| teamID | yearID_2007 | yearID_2008 | yearID_2009 | yearID_2010 | yearID_2011 |
|---|---|---|---|---|---|
| ARI | $1,859,555 | $2,364,383 | $2,812,141 | $2,335,314 | $1,986,660 |
| ATL | $3,117,530 | $3,412,189 | $3,335,385 | $3,126,802 | $3,346,257 |
| CHN | $3,691,494 | $4,383,179 | $5,392,360 | $5,429,963 | $5,001,893 |
| CIN | $2,210,483 | $2,647,061 | $3,198,196 | $2,760,059 | $2,531,571 |
| COL | $2,078,500 | $2,640,596 | $2,785,222 | $2,904,379 | $3,390,310 |
| FLO | $984,097 | $660,955 | $1,315,500 | $2,112,212 | $2,190,154 |
| HOU | $3,250,333 | $3,293,719 | $3,814,682 | $3,298,411 | $2,437,724 |
| LAN | $3,739,811 | $4,089,260 | $4,016,584 | $3,531,778 | $3,472,967 |
| MIL | $2,629,130 | $2,790,948 | $3,083,942 | $2,796,837 | $2,849,911 |
| NYN | $3,841,055 | $4,593,113 | $5,334,785 | $4,800,819 | $4,401,752 |
| PHI | $2,980,940 | $3,495,710 | $4,185,335 | $5,068,871 | $5,765,879 |
| PIT | $1,427,327 | $1,872,684 | $1,872,808 | $1,294,185 | $1,553,345 |
| SDN | $2,235,022 | $2,376,697 | $1,604,952 | $1,453,819 | $1,479,650 |
| SFN | $3,469,964 | $2,641,190 | $2,965,230 | $3,522,905 | $4,377,716 |
| SLN | $3,224,529 | $3,018,923 | $3,278,830 | $3,741,630 | $3,904,947 |
| WAS | $1,319,554 | $1,895,207 | $2,140,286 | $2,046,667 | $2,201,963 |
Salaries %>% filter( yearID > 2006 & lgID == "NL" ) %>%
group_by( teamID, yearID ) %>%
summarize( mean = dollar(mean(salary)) ) %>%
spread( key=teamID, value=mean, sep="_" ) %>%
select( 1:6 ) %>%
pander
| yearID | teamID_ARI | teamID_ATL | teamID_CHC | teamID_CHN | teamID_CIN |
|---|---|---|---|---|---|
| 2007 | $1,859,555 | $3,117,530 | NA | $3,691,494 | $2,210,483 |
| 2008 | $2,364,383 | $3,412,189 | NA | $4,383,179 | $2,647,061 |
| 2009 | $2,812,141 | $3,335,385 | NA | $5,392,360 | $3,198,196 |
| 2010 | $2,335,314 | $3,126,802 | NA | $5,429,963 | $2,760,059 |
| 2011 | $1,986,660 | $3,346,257 | NA | $5,001,893 | $2,531,571 |
| 2012 | $2,733,512 | $2,856,205 | NA | $3,392,194 | $2,935,843 |
| 2013 | $3,004,400 | $3,254,501 | NA | $3,867,989 | $4,256,178 |
| 2014 | $3,763,904 | $4,067,042 | NA | $2,426,759 | $3,864,911 |
| 2015 | $2,034,250 | $2,990,885 | NA | $4,138,547 | $4,187,862 |
| 2016 | $3,363,041 | $2,362,010 | $5,312,678 | NA | $3,066,899 |


Try me!
Blinds
Day
Night
0
Windows
All closed
Door
secure
Power
2
4
Power
All closed
Blinds
Lamps
All off


senate_states_1976_2016.Rd
This dataset contains constituency-level returns for elections to the U.S. Senate.
Each record in the dataset gives the number of votes reported in a constituency for a candidate.The data source is the document "[Statistics of the Congressional Election](http://history.house.gov/Institution/Election-Statistics/Election- Statistics/)," published biennially by the Clerk of the U.S. House of Representatives.
senate_states_1976_2016
A dataframe
with 14 variables:
year
Year of election.
state
State name.
state_postal
State U.S. Postal Service abbreviation, or two-letter ISO 1366 code.
state_fips
Numeric state FIPS code per FIPS PUB 5-2.
state_census
Numeric U.S. Census state code.
state_icpsr
Numeric ICPSR state code.
office
The office for which the candidate
ran.
district
District associated with the office
, where applicable. Not applicable in U.S. Senate elections.
stage
The electoral stage, either gen
for general elections or pri
for primary elections.
special
Whether the election was a special election, either TRUE
for special elections or FALSE
otherwise.
candidate
The name of the candidate.
writein
Whether the record describes a write-in candidate, either TRUE
or FALSE
.
party
Party of the candidate
, where applicable. Candidates may run on multiple party lines, so to compute two-party vote shares or candidate vote totals, aggregate over party
.
votes
Number of votes received.
https://dx.doi.org/10.7910/DVN/PEJ5QU, version 2018-03-02T21:54:16Z.


What might it be like to interface with the world—traversing between personal, local, and global issues—untethered from screens and with a deep capacity to listen to data? Imagine:
You wake up and ask your voice assistant for your daily sound dispatch. First, it plays the sound of someone tossing and turning. You recognize this auditory icon because you’ve heard it before, weeks ago, when your voice assistant explained that it meant you had a restless night of sleep.
Next, instead of telling you your resting heart rate, your voice assistant plays a heartbeat pulse.  So, as you finish brushing your teeth, you ask how many beats per minute. “60 bpms,” it responds. “Would you like to hear last night’s resting heart rate compared with the past month’s average?” You say sure, and hear two overlapping, syncopated rhythms—with different sound qualities—and reflect on how rapid the new heart rate sounds.
Heading to the kitchen, you ask “Can you play my heart rate trends leading up to the last three times I got sick?” Listening as you scramble some eggs, you notice how quickly your heart rate changed in the past, so you ask your voice assistant to zoom in on the two days before the last time you got sick. It does, and plays the tones again. You say, “That doesn’t sound like this time. Note that I don’t have any symptoms at the moment.”
While you eat breakfast, your voice assistant says it has new county and state Covid-19 cases data as part of your daily sound dispatch. You hear a smoothly rising crescendo that gets louder over several seconds, which you’ve learned means that cases have begun increasing again. When you first heard the Covid-19 data, you remember it was presented as discrete pings—but as your voice assistant coached you how to listen, you figured out together that a continuous representation is easier for you to understand. As you put your dishes in the sink, you say, “Can you ask me again this evening if I have any symptoms?” Your voice assistant confirms that it will.


Tourism NZ
Home
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Contact
CONTACT
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Follow US on Social Media
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Country
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Subject


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public interface ClientStart extends RemoteObjectAccess, NetworkInterface
ServerStart
.
It is the main entry-point for any Client-related actions. It further never exposes its implementation. You may initialize a Client the following way:
final String address = "localhost"; // the address of the Server
final int port = 4444; // the port of the Server
final ClientStart clientStart = ClientStart.at(address, port);
Note however, that the Server has to:
clientStart.launch()
The following code starts a Server and a Client, which both successfully connect:
ServerStart serverStart = ServerStart.at(4444);
ClientStart clientStart = ClientStart.at("localhost", 4444);
try {
serverStart.launch();
} catch(Exception ignored) {}
new Thread(() -> {
try {
serverStart.acceptAllNextClients();
} catch(ClientConnectionFailedException ignored) {}
}).start();
try {
clientStart.launch();
} catch(Exception ignored) {}
Once the launch method of the ServerStart is finished, the ClientStart might be launched. If however, the ServerStart is not yet launched, the ClientStart.launch method will fail and throw an StartFailedException.
ServerStart
| Modifier and Type | Method and Description |
|---|---|
void |
addDisconnectedHandler(DisconnectedHandler disconnectedHandler)
Adds a Handler, that will be invoked once the Connection between the Server and the Client is terminated.
|
void |
addFallBackDeSerialization(DeSerializationAdapter<String,Object> deSerializationAdapter)
Adds a
DeSerializationAdapter as a fallback deserialization instance to this ClientStart. |
void |
addFallBackSerialization(SerializationAdapter<Object,String> serializationAdapter)
Adds a
SerializationAdapter as a fallback serialization instance to this ClientStart. |
static ClientStart |
at(String address,
int port)
Creates a new ClientStart.
|
void |
clearCache()
This Method is a shortcut for:
Cache.reset() |
Awaiting |
createNewConnection(Class key)
This Method-Call will create a new Connection, identified by the provided
key . |
RemoteObjectFactory |
getRemoteObjectFactory()
Returns the internally maintained
RemoteObjectFactory . |
Sender |
send()
Used to send Objects to the ServerStart.
|
void |
setDecryptionAdapter(DecryptionAdapter decryptionAdapter)
Sets an Adapter for decryption of received Strings.
|
void |
setEncryptionAdapter(EncryptionAdapter encryptionAdapter)
Sets an Adapter for encryption of Strings that should be send.
|
void |
setMainDeSerializationAdapter(DeSerializationAdapter<String,Object> mainDeSerializationAdapter)
Sets the
DeSerializationAdapter as the main deserialization instance to this ClientStart. |
void |
setMainSerializationAdapter(SerializationAdapter<Object,String> mainSerializationAdapter)
Sets the
SerializationAdapter as the main serialization instance to this ClientStart. |
void |
setSocketFactory(SocketFactory factory)
Sets the Factory, to create the Socket, used by the ClientStart
|
getRemoteObject, updateRemoteInvocationProducer
cache, getCommunicationRegistration, setConnectionFactory
setLogging
static ClientStart at(String address, int port)
The use of this method is recommended to be used! At all times, this method is ensured to never change. Therefore, relying on any implementation details, is not recommended, because they might become subject to change.
address
- the address of the already running ServerStart as a Stringport
- the port, that the ServerStart is bound to.Awaiting createNewConnection(Class key)
key
.
Calling this Method, will lead to a request-response-chain between the Server and the Client.
After this, the Connection will be created an is usable. This whole procedure is asynchronous. To allow synchronization,
you may use the returned Awaiting
instance
key
- the identifying Class for the new ConnectionAbstractConnection
,
Connection
void setSocketFactory(SocketFactory factory)
factory
- the factory, that creates the SocketSocketFactory
Sender send()
void addFallBackSerialization(SerializationAdapter<Object,String> serializationAdapter)
SerializationAdapter
as a fallback serialization instance to this ClientStart.serializationAdapter
- the adapter, that should be used.SerializationAdapter
void addFallBackDeSerialization(DeSerializationAdapter<String,Object> deSerializationAdapter)
DeSerializationAdapter
as a fallback deserialization instance to this ClientStart.deSerializationAdapter
- the adapter, that should be used.DeSerializationAdapter
void setMainSerializationAdapter(SerializationAdapter<Object,String> mainSerializationAdapter)
SerializationAdapter
as the main serialization instance to this ClientStart.
This instance will be asked first, before the fallback instances will be asked
mainSerializationAdapter
- the adapter, that should be used.SerializationAdapter
void setMainDeSerializationAdapter(DeSerializationAdapter<String,Object> mainDeSerializationAdapter)
DeSerializationAdapter
as the main deserialization instance to this ClientStart.
This instance will be asked first, before the fallback instances will be asked
mainDeSerializationAdapter
- the adapter, that should be used.DeSerializationAdapter
void addDisconnectedHandler(DisconnectedHandler disconnectedHandler)
Any Handler set, will be completely invoked if:
Client.disconnect()
.disconnectedHandler
- the Handler, that should be called once the Connection is terminatedvoid setDecryptionAdapter(DecryptionAdapter decryptionAdapter)
decryptionAdapter
- the DecryptionAdapterDecryptionAdapter
void setEncryptionAdapter(EncryptionAdapter encryptionAdapter)
encryptionAdapter
- the EncryptionAdapterEncryptionAdapter
void clearCache()
Cache.reset()
Cache.reset()
RemoteObjectFactory getRemoteObjectFactory()
RemoteObjectFactory
.
This method will never return null.
Copyright © 2018. All rights reserved.


Word Order
Input a block of text and I'll show all the unique words and times they appeared!
Write a paragraph here:
Submit


CHAPTER 382
LABOR DISPUTES; STEVEDORING INDUSTRY
Section
382-1 Definitions
382-2 Proclamation of emergency; preparations for government
operations
382-3 Government operations
382-4 Employees under government operation
382-5 Stevedoring fund; compensation for appropriation and
use
382-6 Termination of government operations
382-7 Interference with government operations; injunctions
382-8 Employee need not work involuntarily
382-9 Rules and regulations
382-10 Powers in addition to other powers


Shinya SUZUMURA.
Yahoo Japan Corporation.
Kioi Tower, Tokyo Garden Terrace Kioicho, 1-3, Kioi-cho, Chiyoda-ku, Tokyo, Japan.
ssuzumur [AT] yahoo-corp.jp
suzumura.mllab.nit [AT] gmail.com
Ph.D. degree in Computer Science from NIT (Nagoya Institute of Technology).
Hobby
I like to play Japanese chess called "Shogi".
My rating score is usually 1950 -- 2000 in 81Dojo;
the rating distribution of the whole users in 81Dojo is available at
https://81dojo.com/announcements/140906-1.jpg


|
|||||||||
| PREV CLASS NEXT CLASS | FRAMES NO FRAMES | ||||||||
| SUMMARY: NESTED | FIELD | CONSTR | METHOD | DETAIL: FIELD | CONSTR | METHOD |
public interface PathMatcher
Strategy interface for String
-based path matching.
Used by PathMatchingResourcePatternResolver
,
AbstractUrlHandlerMapping
,
PropertiesMethodNameResolver
,
and WebContentInterceptor
.
The default implementation is AntPathMatcher
, supporting the
Ant-style pattern syntax.
AntPathMatcher
| Method Summary | |
|---|---|
String |
combine(String pattern1,
String pattern2)
Combines two patterns into a new pattern that is returned. |
String |
extractPathWithinPattern(String pattern,
String path)
Given a pattern and a full path, determine the pattern-mapped part. |
Map<String,String> |
extractUriTemplateVariables(String pattern,
String path)
Given a pattern and a full path, extract the URI template variables. |
Comparator<String> |
getPatternComparator(String path)
Given a full path, returns a Comparator suitable for sorting patterns
in order of explicitness for that path. |
boolean |
isPattern(String path)
Does the given path represent a pattern that can be matched
by an implementation of this interface? |
boolean |
match(String pattern,
String path)
Match the given path against the given pattern ,
according to this PathMatcher's matching strategy. |
boolean |
matchStart(String pattern,
String path)
Match the given path against the corresponding part of the given
pattern , according to this PathMatcher's matching strategy. |
| Method Detail |
|---|
boolean isPattern(String path)
path
represent a pattern that can be matched
by an implementation of this interface?
If the return value is false
, then the match(java.lang.String, java.lang.String)
method does not have to be used because direct equality comparisons
on the static path Strings will lead to the same result.
path
- the path String to check
true
if the given path
represents a patternboolean match(String pattern, String path)
path
against the given pattern
,
according to this PathMatcher's matching strategy.
pattern
- the pattern to match againstpath
- the path String to test
true
if the supplied path
matched,
false
if it didn'tboolean matchStart(String pattern, String path)
path
against the corresponding part of the given
pattern
, according to this PathMatcher's matching strategy.
Determines whether the pattern at least matches as far as the given base path goes, assuming that a full path may then match as well.
pattern
- the pattern to match againstpath
- the path String to test
true
if the supplied path
matched,
false
if it didn'tString extractPathWithinPattern(String pattern, String path)
This method is supposed to find out which part of the path is matched dynamically through an actual pattern, that is, it strips off a statically defined leading path from the given full path, returning only the actually pattern-matched part of the path.
For example: For "myroot/*.html" as pattern and "myroot/myfile.html" as full path, this method should return "myfile.html". The detailed determination rules are specified to this PathMatcher's matching strategy.
A simple implementation may return the given full path as-is in case
of an actual pattern, and the empty String in case of the pattern not
containing any dynamic parts (i.e. the pattern
parameter being
a static path that wouldn't qualify as an actual pattern
).
A sophisticated implementation will differentiate between the static parts
and the dynamic parts of the given path pattern.
pattern
- the path patternpath
- the full path to introspect
path
(never null
)Map<String,String> extractUriTemplateVariables(String pattern, String path)
For example: For pattern "/hotels/{hotel}" and path "/hotels/1", this method will return a map containing "hotel"->"1".
pattern
- the path pattern, possibly containing URI templatespath
- the full path to extract template variables from
Comparator<String> getPatternComparator(String path)
Comparator
suitable for sorting patterns
in order of explicitness for that path.
The full algorithm used depends on the underlying implementation, but generally,
the returned Comparator
will
sort
a list so that more specific patterns come before generic patterns.
path
- the full path to use for comparison
String combine(String pattern1, String pattern2)
The full algorithm used for combining the two pattern depends on the underlying implementation.
pattern1
- the first patternpattern2
- the second pattern
IllegalArgumentException
- when the two patterns cannot be combined
|
|||||||||
| PREV CLASS NEXT CLASS | FRAMES NO FRAMES | ||||||||
| SUMMARY: NESTED | FIELD | CONSTR | METHOD | DETAIL: FIELD | CONSTR | METHOD |


Back
|
Glossary
About LPS Viewer
The LPS Viewer is based on the VTK library. The version of VTK that is currently supported is 5.0.1. The user interface is based on Java JDK 1.5. See the
usage documentation
for more detailed information about the various features of the viewer.
Copyright © 2014 Limit Point Systems, Inc.
Quicktime® is a registered trademark of Apple Computer corporation.
Back
|
Glossary


{% extends "base_staff.html" %} {% load form_errors %} {% block title %}Staff{% endblock %} {% block content %} Add users to the course {{ course.title }} {% form_errors form %} {% csrf_token %} Each user must be on their own line. Usernames must not contain spaces; any characters after a space will be ignored. Any user that is not in the system will be created with their password as their username. To remove a user from the course, remove them from this list. Users: {{ users }} {% endblock %} {% block location %}Add/Remove User{% endblock %}


{% extends "base/base.html" %} {% load member_auth %} {% load staticfiles %} {% block css %} {{block.super}} {% endblock css %} {% block page_title %}Report recipients{% endblock %} {% block content %}
Each day, month or week a development report will be send to these recipients for the associated boards of each one.


An additional search can made in the search engine of your choice. To specify a search engine that is not in the list, select "Other" and enter the URL with "%s" where the search term should be inserted. For example, the URL for Google is https://www.google.com/search?q=%s.
Removes any custom settings for Handy Dictionary stored in your web browser's local storage.


Compounding Interest
31st March, 2017
We will begin with the basic equations of going forward in time (using percentage units):
# compounding.py
from math import exp, log
# FUNCTIONS SHOULD RETURN NUMBERS, FORMAT SEPARATELY
def simple_compounding(rate, times, pv=100):
return "{:.4f}".format(pv * (1 + (rate/100)) ** times)
def continuous_compounding(rate, times, pv=100):
return "{:.4f}".format(pv * exp((rate/100) * times))
A useful conversion is taking a long period, such as a year, and finding the equivalent monthly rate that when compounded simply, will result in the original rate.
def divide_rate(rate, times):
fv = 1 + (rate/100)
r = log(fv) / times
return "{:.6f}%".format((exp(r) - 1) * 100)
Suppose I have an investment yielding 10% in a year. What would be the equivalent monthly rate if the investment were to be compounded monthly?
>>> divide_rate(10, 12)
'0.797414%'
>>> simple_compounding(0.797414, 12)
'110.0000'
>>> (1+0.00797414) ** 12
1.0999999943832588
If an investment compounds yearly at 10%, what is the equivalent continuously compounding rate?
Suppose I will need $100,000 in five years, and I have $50,000 to invest. What rate of return do I need?
def


| Package | org.si.cml |
| Class | public class CMLSequence |
| Inheritance | CMLSequence org.si.cml.core.CMLState |
USAGE
1) CMLSequence(cannonML_String or bulletML_XML); creates a new sequence from certain cannonML/bulletML.
2) CMLObject.execute(CMLSequence); apply sequence to CMLObject.
3) CMLSequence.global; makes it global. You can access the child sequences of global sequence from everywhere.
4) CMLSequence.findSequence(); finds labeled sequence in cannonML/bulletML.
5) CMLSequence.getParameter(); accesses parameters of sequence.
6) CMLSequence.registerUserCommand(); registers the function called from cannonML.
7) CMLSequence.registerUserVariable(); registers the function refered by cannonML.
// Create enemys sequence from "String of cannonML" or "XML of bulletML". var motion:CMLSequence = new CMLSequence(String or XML); ... enemy.create(x, y); // Create enemy on the stage. enemy.execute(motion); // Execute sequence.
See also
| Property | Defined by | ||
|---|---|---|---|
| childSequence : * [read-only] dictionary of child sequence, you can access by label
| CMLSequence | ||
| global : Boolean Flag of global sequence.
| CMLSequence | ||
| isEmpty : Boolean [read-only] Is this sequence empty ?
| CMLSequence | ||
| label : String [read-only] label of this sequence
| CMLSequence |
| Method | Defined by | ||
|---|---|---|---|
|
CMLSequence(data:* = null, globalSequence:Boolean = false)
Construct new sequence by a String of cannonML or an XML of bulletML.
| CMLSequence | ||
|
clear():void
Make this sequence empty.
| CMLSequence | ||
|
findSequence(label_:String):CMLSequence
Find child sequence that has specifyed label.
| CMLSequence | ||
|
getParameter(idx:uint):Number
Get parameter of this sequence.
| CMLSequence | ||
|
parse(data:*):void
Parse CannonML-String or BulletML-XML.
| CMLSequence | ||
|
registerUserCommand(name:String, func:Function, argc:int = 0, requireSequence:Boolean = false):void
[static] Register user defined command "&[a-z_]+".
| CMLSequence | ||
|
registerUserValiable(name:String, func:Function):void
[static] Register user defined variable "$[a-z_]+".
| CMLSequence |
| childSequence | property |
childSequence:*
[read-only]dictionary of child sequence, you can access by label
Implementation public function get childSequence():*
| global | property |
global:Boolean
[read-write]Flag of global sequence.
Child sequences of a global sequence are accessable from other sequences.
public function get global():Boolean
public function set global(value:Boolean):void
var seqG:CMLSequence = new CMLSequence("#LABEL_G{...}"); seqG.global = true; var seqA:CMLSequence = new CMLSequence("&LABEL_G"); // Success; you can refer the LABEL_G. seqG.global = false; var seqB:CMLSequence = new CMLSequence("&LABEL_G"); // Error; you cannot refer the LABEL_G.
| isEmpty | property |
isEmpty:Boolean
[read-only]Is this sequence empty ?
Implementation public function get isEmpty():Boolean
| label | property |
label:String
[read-only]label of this sequence
Implementation public function get label():String
| CMLSequence | () | constructor |
public function CMLSequence(data:* = null, globalSequence:Boolean = false)
Construct new sequence by a String of cannonML or an XML of bulletML.
Parametersdata:* (default = null ) — Sequence data. Intstance of String or XML is available. String data is for CannonML, and XML data is for BulletML.
|
|
globalSequence:Boolean (default = false ) — Flag of global sequence.
|
| clear | () | method |
public override function clear():void
Make this sequence empty.
This function disconnects all statement chains and enable to be caught by GC.
| findSequence | () | method |
public function findSequence(label_:String):CMLSequence
Find child sequence that has specifyed label.
Parameterslabel_:String — to find.
|
CMLSequence —
Found sequence.
|
// You can access the child sequence. var seq:CMLSequence = new CMLSequence("#A{v0,2[w30f3]}"); var seqA:CMLSequence = seq.findSequence("A"); // seqA is "v0,2[w30f]". // You can use the access operator. var seq:CMLSequence = new CMLSequence("#A{ #B{v0,2[w30f4]} #C{v0,4[w10f2]} }"); var seqAB:CMLSequence = seq.findSequence("A.B"); // seqAB is "v0,2[w30f4]". Same as seq.findSequence("A").findSequence("B") var seqAC:CMLSequence = seq.findSequence("A.C"); // seqAB is "v0,4[w10f2]". Same as seq.findSequence("A").findSequence("C")
| getParameter | () | method |
public function getParameter(idx:uint):Number
Get parameter of this sequence.
This function gives the parameters of this sequence.
Parameters of a sequence are shown like "#LABEL{0,1,2 ... }" in cml-string.
idx:uint — Index of argument.
|
Number — Value of argument.
|
var seq:CMLSequence = new CMLSequence("#A{10,20 v0,2[w30f3]}"); var seqA:CMLSequence = seq.findSequence("A"); trace(seqA.getParameter(0), seqA.getParameter(1), seqA.getParameter(2)); // 10, 20, 0
| parse | () | method |
public function parse(data:*):void
Parse CannonML-String or BulletML-XML.
Parametersdata:* — or XML is avairable. String is for CannonML, and XML is for BulletML.
|
| registerUserCommand | () | method |
public static function registerUserCommand(name:String, func:Function, argc:int = 0, requireSequence:Boolean = false):void
Register user defined command "&[a-z_]+".
This function registers the command that can use in CML string.
name:String — The name of command that appears like "&name" in CML string.
|
|
func:Function — The callback function when the command appears in sequence.The type of callback is function(fbr:CMLFiber, args:Array):void .
The 1st argument gives a reference of the fiber that execute the sequence.
And the 2nd argument gives the arguments of the command.
|
|
argc:int (default = 0 ) — The count of argument that this command requires. |
|
requireSequence:Boolean (default = false ) — Specify true if this command require the sequence as the '&', ' |
See also
// In the cml-string, you can use "&sound[sound_index],[volume]" that plays sound. CMLSequence.registerUserCommand("sound", playSound, 2); function playSound(fbr:CMLFiber, args:Array) : void { // function _playSound(index, volume) plays sound. if (args.length >= 2) _playSound(args[0], args[1]); }
| registerUserValiable | () | method |
public static function registerUserValiable(name:String, func:Function):void
Register user defined variable "$[a-z_]+".
This function registers the variables that can use in CML-string.
name:String — The name of variable that appears like "$name" in CML-string.
|
|
func:Function — The callback function when the reference appears in sequence.The type of callback is function(fbr:CMLFiber):Number . The argument gives a fiber that execute the sequence.
|
See also
// In the cml-string, you can use "$life" that returns Enemy's life. CMLSequence.registerUserValiable("life", referLife); function referLife(fbr:CMLFiber) : Number { // Enemy class is your extention of CMLObject. return Enemy(fbr.object).life; }


pip
Python package manager.
Some subcommands such as `pip install` have their own usage documentation.
More information: home page
- Install a package (see `pip install` for more install examples):
pip install package_name
- Install a package to the user's directory instead of the system-wide default location:
pip install --user package
- Upgrade a package:
pip install --upgrade package_name
- Uninstall a package:
pip uninstall package_name
- Save installed packages to file:
pip freeze > requirements.txt
- Show installed package info:
pip show package_name
- Install packages from a file:
pip install --requirement requirements.txt


"Best cookies in Seattle! You have to try them.
They're a Seattle staple. There aren't many better combinations
than cookies and salmon!" - Mr. Limpet
111 Post Alley
Seattle, WA 98102
Monday - Friday: 10AM-6PM
Saturday - Sunday: 12PM-5PM
205-555-7849
Salmookies@Salmookies.com


Imputation of incomplete continuous or categorical datasets; Missing values are imputed with a principal component analysis (PCA), a multiple correspondence analysis (MCA) model or a multiple factor analysis (MFA) model; Perform multiple imputation with and in PCA or MCA.
| Version: | 1.13 |
| Depends: | R (≥ 3.3.0) |
| Imports: | FactoMineR, graphics, grDevices, mice, mvtnorm, stats, utils |
| Suggests: | knitr |
| Published: | 2018-06-25 |
| Author: | Francois Husson, Julie Josse |
| Maintainer: | Francois Husson <husson at agrocampus-ouest.fr> |
| License: | GPL-2 | GPL-3 [expanded from: GPL (≥ 2)] |
| URL: | http://math.agrocampus-ouest.fr/infoglueDeliverLive/membres/Francois.Husson, http://juliejosse.com/ |
| NeedsCompilation: | no |
| Citation: | missMDA citation info |
| In views: | MissingData, OfficialStatistics, Psychometrics |
| CRAN checks: | missMDA results |
| Reference manual: | missMDA.pdf |
| Vignettes: |
MulitpleImputation missMDA |
| Package source: | missMDA_1.13.tar.gz |
| Windows binaries: | r-devel: missMDA_1.13.zip, r-release: missMDA_1.13.zip, r-oldrel: missMDA_1.13.zip |
| OS X binaries: | r-release: missMDA_1.13.tgz, r-oldrel: missMDA_1.13.tgz |
| Old sources: | missMDA archive |
| Reverse imports: | INSPIRE, smartdata |
| Reverse suggests: | CorReg, denoiseR, FactoMineR |
Please use the canonical form https://CRAN.R-project.org/package=missMDA to link to this page.


This example shows how ItsNat can cooperate with client JavaScript libraries like jQuery and black-boxed JS components like jCarousel.


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{% extends "base.html" %} {% load static %} {% block content %}
Predicting relations between biological entities using machine learning is a common and important task in computational biology. We propose a new challenge which aims to continuously evaluate prediction methods as new biological knowledge becomes available. We invite individuals or teams to predict biological relations. New relations between biological entities are retrieved in regular intervals using SPARQL queries and prediction results automatically evaluated against the query results.
We will frequently (at least monthly) update a set of relations with high confidence (usually reflecting experimental evidence) available from several biological knowledge bases. Each updated set of relations will obtain a timestamp. Evaluation will consist of comparing predictions to the set of relations that become available. Each relation type will be evaluated separately.
With respect to submissions, please keep the following in mind:
For each challenge, we provide two types of information: a dataset usually in RDF, and a SPARQL query to an endpoint serving the evaluation data. The SPARQL query is used to retrieve the set of relations considered to be true at the timepoint at which the query is evaluated, and will be executed monthly to generate test data and update the evaluations.
We use two types of evaluation metrics.
Metric One: The first applies to relations in which both the subject and object are considered without any semantics that may arise from specific axioms within the graph. For these, we compute recall at ranks (Hits@k), AUROC, AUPR, and mean reciprocal rank (MRR).
Metric Two: We use a second type of evaluation metrics for relations between biological entities and classes in ontologies; the aim of the second set of metrics is to account for the semantics of relations to classes in ontologies (for example, if R(x,C) and SubClassOf(C,D), then a prediction of R(x,D) would be considered correct but less specific than R(x,C)). For this set of relations we use the evaluation metrics developed by CAFA, in particular F_max and S_min, which take class specificity into account.
All team members need to register at the website using their emails or orcid accounts. In order to form a team, a member needs to register the team and invite all other members to join the team. Any of the team members can submit predictions.
Submissions should be submitted in the format of compressed tab-separated file (tsv.gz, tsv.zip) with four columns:
To submit, first find an open Task and follow the instructions.


{{ if eq .Site.Title .Title }}
Mintcode is a one-man design & development studio,focused on crafting elegant web experiences.


Home
About Us
Contact Us
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Welcome to Salmon Cookies Project
My paragraph only about Salmon Cookies in town. which everyone is dying to eat.


This project provides a set of examples of client and server code for pvAccess.
For more information about EPICS generally, please refer to the home page of the Experimental Physics and Industrial Control System.
This project provides a set of examples of client and server code for pvAccess. Each example can be built separately by building in the example subdirectory. The complete set of examples can also be built by building in exampleJava itself.
Each example can be used as a template for building other applications:
Just type:
mvn package
Just type:
mvn package
Provides a server that has a database of V4 PVRecords.
Provides a set of examples that use pvaClientJava.
In order to run the examples, database must also be built and then started as follows:
mrk> pwd /home/epicsv4/master/exampleJava/database/shell mrk> ./exampleDatabase
Implements a PVRecord that is an example of a PVRecord intended to be accessed via channelPutGet. It is a simple HelloWorld example.
A very simple example of an EPICS V4 RPC service: This implements a "Hello World" example of a service that is accessed via Channel RPC.
This is a starting point for writing an RPC service without using pvDatabaseJava.
But note that example database has a similar example implemented via pvDatabaseJava, which allows clients to monitor the result of each request.
This implements a V3 IOC that has a PVRecord doubleArray and a PVRecord exampleLink that monitors changes to doubleArray. PVRecord exampleLink uses pvAccess client code to monitor for changes in doubleArray. It can use either provider local, pva, or ca to connect to doubleArray.
This is an example of creating a PVRecord that uses a somewhat complicated top level PVStructure. It simulates a power supply.
The example also has an example pvaClient for accessing the PVRecord.
An example of a pvDatabase PVRecord which also supports RPC services.
The record represents a 2-D position, but also provides an RPC service which moves the position through a sequence of points before returning. A channel to the record supports the usual operations (such as get, put and monitor) but also supports Channel RPC, i.e. the RPC service has the same channel name as the position PV.
A client ("move") for calling the service is supplied.
This is an example that shows performance for an array of longs.
arrayPerformanceMain implement a PVRecord that is a long array. It has a process method with code that causes the array to be updated at selectable rates and sizes.!
It also has pvaClient examples that can get, put, and monitor the long array record.
Start with its RDBSERVICE_README.txt.
The rdbService example is a complete functional server for accessing SQL databases such Oracle via an EPICS V4 server, and returning the resulting table ResultSet data back to an EPICS V4 client. rdbService illustrates a number of facets of EPICS V4's, use for writing high performance synchronous servers, plus some other stuff:
The "serviceapi" package in src/org/epics/serviceAPI is composed of a single class server and a single class client, and is intended to illustrate the principles of RPC comms in EPICS V4. It shows how one defines and populates the pvStructure conforming to the NTURI Normative Type, for making an RPC query to a pvAccess server, and it shows how the server defines and populates a table of data conforming to the NTTable Normative Type for encoding the reply.
Start with its RDBSERVICE_README.txt
rdbService is the classic hello world idea, implemented as a server and a client. The client sends an argument "your name" to the server, which replies just saying "hello that name". The server is built on top of the RPCSever framework in pvAccessJava.


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Open Source Icons for the Web
Smaller than similar webfonts and with fewer network requests than other solutions, Geomicons Open is built from the ground up for performance. Designed on a grid for pixel-precision at standard sizes, each icon is hand-coded to avoid the extra bloat from traditional vector drawing applications.
Link to geomicons.min.js
and include the geomicons.svg
file in your project.
Use the following markup with the geomicon
class and a data-id
attribute.
Style with CSS.
For more detailed documentation, view the README.


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