Collecting data over the internet used to be fancy, some twenty years or so ago. Nowadays it can be considered standard, if not old school (collecting data using mobile apps is where the cool kids go at the moment). However, there’s one noteable exception: Collecting reaction time data over the internet remained a challenge. The reason is simply a technological artefact in that an html response time may vary, vary too much as to invalidate the signal from some behavorial reaction time research study.
Build, share and run your experimental studies online with #labjs, easily and for free: https://t.co/ZTb7vO8xeg
— Felix Henninger (@felixhenninger) September 6, 2017
There are some software packages outthere, such as eprime, probably the most well known software package for reaction time studies, good, but co$stly. Same argument applies for inquisit, another nice software package, that costs quite a bit.
Of course, that good stuff has its price has never been devil’s work, but there are other options available by now. Most noteably, there’s some free and open access tool by the name of lab.js. By virtue of its free nature, I’ve been exciting to getting to know to it. While it’s still not as matured as the commercial counterparts, it readily offers a great variety of reaction time based study.
This. is. awesome. 👇 #science https://t.co/JAHOsdce4e
— Sebastian Sauer 🇺🇦 (@sauer_sebastian) December 17, 2018
One great advantage over the other software packages is that it does not need the participants to download some progrm in order to run the experiment - as is needed by inquisit for instance. Alle the experiment is conducted by your browser. That’s possible because lab.js is built on Java Script, which every modern browsers understands. Just awesome!
Download my demo study here.
lab.js produces some html files along with some JS files; upload these files to a server (you’ll need writing access), and that’s it. A number of hosting options for your study are possible; of course, as said, you can always put the files on your own web server.
To get kickstarted, I suggest you read the gread docs. There’s also a paper on psycarxiv.


Demi Moore’s Daughter, Rumer Willis, Goes Topless & Covers Herself With Hat In Racy Photo
November 21, 2019
Demi Moore’s Daughter, Rumer Willis, Goes Topless & Covers Herself With Hat In Racy Photo
Bruce Willis and Demi Moore’s daughter, Rumer Willis, shared a racy photo on Instagram late Wednesday night in which she went topless, covering herself with just a hat.
The black-and-white picture showed the actress standing against a dark wall while she faced the camera. It captured the upper portion of her body, cutting off just below her hips.
 She also wore a pair of white panties with a wide waistband and high-cut legs. The monochrome photo put all the emphasis on Rumer’s hourglass shape, accentuating her shapely shoulders and smooth skin.
Rumer looked to be wearing a light application of makeup that featured dark brows, a light coat of mascara, and contoured cheeks. Not a strand of her hair could be seen, as it was tucked into the cowboy hat. She opted for minimum accessories, wearing only a dainty nose ring and the hat. A hint of a tattoo should be seen on her left shoulder.  The minimalist nature of the shot emphasized just how much she favors her parents in her facial features.
Rumer kept the caption simple, only referencing the two hats.
Her followers raved over how stunning she looked in the snap, and some told her so.
“You look amazing Rumer!! Much love and blessings!!” wrote one excited follower.
“You are gorgeous Rumer,” a second admirer said.
Some of her followers could not help but mention how much she looked like her mother.
Loading...
“I had to do a double take cause i swore this was Demi, beautiful genetics,” one fan commented.
 Mama’s got some strong genes,” another follower wrote.
Rumer doesn’t appear to be one to flash a lot of skin on social media. A quick scroll through her Instagram page shows that she has shared only a few revealing photos. It seems that she would rather keep it tame, sharing selfies as well as photos of her friends and family.
Speaking of family, the 31-year-old recently made headlines when she opened up about her mother’s alcoholism when she was growing up, which was detailed in Demi’s book, Inside Out. She said it was “jarring,” while her sister, Tallulah, said it was like a “monster had came.”


Download
Here you will find the NATS Server (for simple, fast publish-subscribe) , NATS Streaming Server (for data streaming and persistence), and officially supported clients. Our documentation provides detailed instructions on how to get started.
Also available are a wide variety of community contributed clients, connectors, and utilities. We are always happy to receive contributions to review - send us a PR, or contact us at<EMAIL_ADDRESS>to share what you have built with us!
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Please contact us to add your external client to the download list.


I am still getting a feel for web development and so far I am enjoying it. So if I continue to find it fulfilling or am still passionate about it, I want to create a career out of it. I don't have many (or any) ideas of where this might take me but i want to one day be able to work remotely/from home, and have my own business. I also want to be able to have time for other hobbies/ side-hustles and create something big (what? I don't know yet).
I would say I am a pretty fast learner but it really depends on how I learn. my brain takes snapshots of certain random info sometimes and it comes in handy but other times I cannot understand a concept unless I look at multiple explanations. So in short, i’d need the freedom and a little time to look around the web to understand some concepts and other times I just need to ask someone to clarify.
I learn from videos/tutorials the best but i prefer to have an end task to work towards so i know what i need to pay attention to in that video rather than watching it just to watch it.
My sense of urgency is what motivates me plus my joy for development and learning keeps me going doing the tasks each day and setting challenges for myself. I usually don't want to stop until work is done but i end up forgetting to take breaks. I am working on taking care of my wellbeing by using a timer to tell me when to take a break even when i don't feel like it.
I am terrible at planning and organisation- i don’t like to plan things because i feel time will be wasted, but i end up wasting more time not planning (sometimes). i just like to get into work, but as i go, i will try to do better to organize myself.
I don’t know how capable I am so I kinda freak myself out if I have tasks to do that are new to me, worrying if I'll be able to do it in time.
I do sometimes forget about the learning competencies and end up getting caught up in unnecessary details sometimes. I would rather checklists instead of learning competencies (or i should ask for help in understanding the learning competencies).
I dont always ask for help when I need it and I am learning to do this more often.
Absorbing info then doing a task doesn’t work well for me because i will forget info straight away - i need to learn on the go (learn while doing), unless i have enough time to learn and absorb new info.
i need to manage my time better for sure instead of doing all my work at once and stressing myself out.
Managing my time and work load. I am the driver for my family so im not sure how much extra work ill be able to do outside of bootcamp.
Self awareness is super important because if you arent aware of how you affect others, you may not be able to grow and improve or even collaborate while working in groups. I try to be as self aware as possible but sometimes it reaches a point where i get too critical of myself. the end.
I will be respectful and considerate of others so as not to distract others while they work. I will do the work I say I am going to do and if I cant keep that promise (with good reason) I will let somebody know asap. When i have to work in teams i will pull my weight because if i dont do my work, everyone will suffer the consequences too.
Productivity: In order to work productively I am going to continue to set challenges for myself each day and work towards finishing those by the end of the day. I will use a timer to remind myself to take breaks for my own mental wellbeing and get back to work as soon as the timer runs out for my break. I usually start working as soon as i've taken care of myself in the morning but i understand i'll need to start getting up early when bootcamp comes around. So ill try to make it a goal to start earlier.
Pressure: When pressure starts to build and i’m almost at a breaking point i will take a break immediately and do some breathing exercises, meditation, or have some quiet time outside to process everything and settle my mind back down. Walks and getting out sound like a good option too just to give myself a change of environment and fresh air. I assume the things that will make me feel this way in the first place are not understanding what i'm supposed to do, so if this is the case i’ll need to remind myself to ask for help.
If i need help i am going to ask for it from either the facilitators or other students at dev, or ask on one of the slack channels. I’ll ask as soon as I can ( max 30 minutes after I can't find a solution on my own).
I would like facilitators to just help me as best they can or direct me to someone/someplace that can help me when I ask questions, I would like them to respond as soon as they can.
Just a bit of direction because i have no idea what i am doing sometimes
While i am doing foundations, i do alot of driving around for others - such as going to the store for my parents, and dropping off my neighbour in the mornings, picking up my mum from work. But these are only commitments i have during foundations. As soon as bootcamp starts i will have no commitments including driving anybody anywhere.


Canadian Competition, Day 4
Misadventures on the last day of the Canadian Competition.
Canadian Competition, Day 3
With a pretty good night of sleep behind us, we trekked out to the pits.
Canadian Competition Day 2
Day 2 of our competition.
Canadian Competition After a Lovely Little Dip
So after a very lengthy period of not posting, it is time for Canadian Competition again. Right now we are on our way to Southport, Manitoba, rocking to skipping music tracks and testing electrical components. So why are we testing components, well yesterday we decided to let the plane take a dip at the pond adjacent to our flying field.
Jekyll Incorporated Features
Incorporated provides a great typography, responsive design, author details, semantic markup and more.
AUVSI SUAS Competition part 2
It’s been an eventful, stressful, and exciting few days for us at the AUVSI SUAS competition; we've finally finished with all things competition and are totally ready to get some much-needed sleep. We placed 23rd overall out of a total of 33 teams at the competition. We received a $400 cash prize, and our oral presentation scored fairly high at a position of 15th, beating the first place team who got 18th.
AUVSI SUAS Competition
And here we go again...competition #2: the Association for Unmanned Vehicle Systems International (AUVSI) Student Unmanned Aerial Systems (SUAS) competition. This competition is in Maryland in the US.
Competition Flight 2 - Day 3 - USC UAV Competition
This morning, we were able to get out onto the field right away and perform a range test. Soon afterwards, we began our second competition flight.
Competition Flight 1 - Day 2 - USC UAV Competition
After all of the troubles yesterday, today has been a breath of fresh air so far.


Does Country Size Matter for Trade Facilitation Assistance?
Greater attention is now being devoted to trade facilitation measures. The 159 members of the WTO agreed during their December 2013 meeting in Bali on a Trade Facilitation Agreement. And, the European Commission, the World Bank, and other donors have just launched the implementation of the Trade Facilitation Support Program (TFSP).
The TFSP is to help developing countries reform and modernize their border procedures. Such initiative is important as time delays at customs affect trade. Each additional day that a product is delayed prior to being shipped reduces trade by more than 1%. Put differently, each day is equivalent to a country distancing itself from its trade partners by about 70 km on average1.
That being said, there is currently a lot of variation in trade facilitation across countries. For example, while 2 documents are currently required to export a container from France, it requires 10 documents to do the same in Kazakhstan. Does country size affect trade facilitation? Is some of this cross-country variation in trade facilitation due to differences in country size? Our recent research paper, “Trade Facilitation and Country Size,”2 attempts to answer this question.
We extend the literature beyond the conventional trade openness measures in an important way. In contrast to existing studies, we focus on micro-level trade facilitation or “inside the border” measures to define how liberal the trading regime is. We perform a precise analysis of the relationship between country size and the number of documents required to export and import, a measure of trade facilitation.
Three results follow. First, trade facilitation does improve as country size becomes smaller; that is, small countries perform better than large countries in terms of trade facilitation. Second, the relationship between country size and trade facilitation is non-linear. It is much stronger for the relatively small than the large countries. Third, contrary to what existing studies might suggest, the relationship between country size and trade facilitation does not appear to be driven by the fact that small countries trade more as a proportion of their gross domestic product than large countries.
There is a large body of work3 that shows that small countries are likely to benefit more from international trade or a liberal trading regime than large countries. Smallness of market limits the exploitation of economies of scale, forcing the relatively small countries to expand market size through international trade beyond their political borders. However, most of the evidence on the relationship between trade openness and country size is largely focused on measures of trade openness that include trade volume and border taxes.
Our paper extends these findings by showing that small countries perform better than large countries in terms of trade facilitation, too. That is, the number of documents required for export and import clearance, a measure of trade facilitation, tends to increase sharply with country size, proxied by total population. Will the TFSP start in smaller or larger countries? Time will tell.
-
Simeon Djankov & Caroline Freund & Cong S. Pham, 2010. “Trading on Time,” The Review of Economics and Statistics, MIT Press, vol. 92(1), pages 166-173, February. ↩
-
Amin, Mohammad & Haidar, J.I., 2013. “Trade facilitation and country size, “ Empirical Economics, Springer, vol. 47(4), pages 1441-1466. ↩
-
Alesina, Alberto & Spolaore, Enrico & Wacziarg, Romain, 2005. “Trade, Growth and the Size of Countries,” Handbook of Economic Growth, in: Philippe Aghion & Steven Durlauf (ed.), Handbook of Economic Growth, edition 1, volume 1, chapter 23, pages 1499-1542 Elsevier. ↩
You can find the full paper here: Trade facilitation and country size


I have had the chance to fly to Boston in the last week of January to spend a day at the Broad institute of MIT and Harvard. There I met with parts of the cancer data science team.
Their goal is mainly to understand cancer and find new therapies by using techniques of RNAi screening / CRISPR-CAS9 screen which knocks out genes 🧬 the cell. They can then record the new cellular activity. Similarly with drugs screening. A lot of computational tools 🛠 to be designed to infer the reasons and actions of such screens.
I had the chance to meet with Pr. Vasquez, Pr. Tsherniak, Dr. Dahria, Dr pimkin as well as Joshua Dempster, James McFarland, Guillaume kugener, Mustafa Kocak, Phil Montgomery and Jadwiga Grabarek. All very nice and brilliant.
This day was intense and I only had 28hrs in Boston.  However it was an amazing experience. The Broad Institute is amazing and so was Boston. I only had the chance to walk across the river, glide on the frozen water of the Boston public garden and get a humongous shake shack.
The meeting was part of my ongoing effort to find my next research environment in Computational Biology🧪. Here the position is a job of two years as an associate computational biologist.
**I am now very happy to announce that I have been officially accepted and I am flying to Boston in a couple month to live and work there for the next two years!


Wiki ▸ [[API Reference]] ▸ Geometry
See one of:
| [[Voronoi | Voronoi-Geom]] - compute the Voronoi tesselation or Delaunay triangulation from a set of points. |
| [[Quadtree | Quadtree-Geom]] - compute a quadtree from a set of points. |
| [[Hull | Hull-Geom]] - compute the convex hull of a set of points. |
| [[Polygon | Polygon-Geom]] - perform polygon operations, such as computing area and clipping. |


Use one hash symbol followed by a space for a heading, for example:
# This is a heading
Copy this example to add bullet points:
Introduce bullet points with a lead-in line ending in a colon:
* leave one empty line space after the lead-in line
* use an asterisk or a dash followed by a space to add an item
* start each item with a lowercase letter, do not end with a full stop
* leave one empty line space after the last item
To create sub-items, add an indent of 2 spaces before the asterisk or dash.
Copy this example to add a numbered list:
1. Leave one empty line space before starting your list.
2. Enter a number followed by a full stop and a space to add an item.
3. Start each item with a capital letter and end it with a full stop.
4. Leave one empty line space after the last item.
To create sub-items, add an indent of 2 spaces before the number.
To insert a page break, use 3 asterisks. For example:
Content on page 1
***
Content on page 2


{include file="home/user/addhs"} {include file="home/user/addstock"} {include file="home/user/addvisit"} {include file="home/user/editprofile"} {include file="home/user/createprofile"}


[Zend_Registry] element index
Package indexes
Zend_Version
Zend_Validate
Zend_Loader
Zend_Uri
Zend
Zend_Http
Zend_Gdata
All elements
g
i
o
r
s
z
_
_
top
_unsetInstance
Zend_Registry::_unsetInstance()
in Registry.php
Unset the default registry instance.
__construct
Zend_Registry::__construct()
in Registry.php
Constructs a parent ArrayObject with default ARRAY_AS_PROPS to allow acces as an object
g
top
get
Zend_Registry::get()
in Registry.php
getter method, basically same as offsetGet().
getInstance
Zend_Registry::getInstance()
in Registry.php
Retrieves the default registry instance.
i
top
init
Zend_Registry::init()
in Registry.php
Initialize the default registry instance.
isRegistered
Zend_Registry::isRegistered()
in Registry.php
Returns TRUE if the $index is a named value in the registry, or FALSE if $index was not found in the registry.
o
top
offsetExists
Zend_Registry::offsetExists()
in Registry.php
r
top
Registry.php
Registry.php
in Registry.php
s
top
set
Zend_Registry::set()
in Registry.php
setter method, basically same as offsetSet().
setClassName
Zend_Registry::setClassName()
in Registry.php
Set the class name to use for the default registry instance.
setInstance
Zend_Registry::setInstance()
in Registry.php
Set the default registry instance to a specified instance.
z
top
Zend_Registry
Zend_Registry
in Registry.php
Generic storage class helps to manage global data.
g
i
o
r
s
z
_


--- layout: post title: SDK Searching by record type categories: [] tags: dotnet-sdk status: publish type: post published: true meta: {} ---
Today's question of the day, searching by record type...
Here we use the methods built into the TrimMainObject search to build up the search clauses to search for the record type with Uri 1.
using (Database database = new Database()) { database.Id = "H1"; database.WorkgroupServerName = "local"; database.Connect(); TrimMainObjectSearch search = new TrimMainObjectSearch(database, BaseObjectTypes.Record); TrimSearchClause typeClause = new TrimSearchClause(database, BaseObjectTypes.Record, SearchClauseIds.RecordType); typeClause.SetCriteriaFromObject(new RecordType(database, 1)); search.AddSearchClause(typeClause); foreach (Record record in search) { Console.WriteLine(record.Title); } }
I rarely take the approach demonstrated above, in fact I spent 15 minutes trying (and failing) to work up a more complex example. Instead I use string searching, like this:
using (Database database = new Database()) { database.Id = "H1"; database.WorkgroupServerName = "local"; database.Connect(); TrimMainObjectSearch search = new TrimMainObjectSearch(database, BaseObjectTypes.Record); search.SetSearchString("recType:1"); foreach (Record record in search) { Console.WriteLine(record.Title); } }
Or you can use the square braces to do a sub search, here we search for all records derived from record types with the behaviour of Box or Folder.
using (Database database = new Database()) { database.Id = "H1"; database.WorkgroupServerName = "local"; database.Connect(); TrimMainObjectSearch search = new TrimMainObjectSearch(database, BaseObjectTypes.Record); search.SetSearchString("type:[behaviour:Box,Folder]"); foreach (Record record in search) { Console.WriteLine(record.Title); } }
If I am stuck composing a string search I usually:
I can then copy and paste the string search composed for me by HPRM.


Textarea
This example shows basic Textarea
Textarea with growing
option
This example shows how Textarea behaves when growing
is set.
growingMaxLines
-
the number of visible lines. Height of textarea is according to this number.
height
- if
specified, overrides growingMaxLines
to set the height of textarea.
cols
-
approximately, the number of characters in a single line when the width of textarea is not set.
growing
- when set to true, resizes the textarea to either growingMaxLines
or height
, whichever is
applied. As user types, the textarea overflows beyond the width to the next line and grows in height until the
number of growingMaxLines
is reached, or until the height
is reached; whichever takes
precedence. Beyond this, a scrollbar appears.
Textarea with message counter
This example shows how Textarea behaves when showExceededText is set. It also shows form submission.
Textarea with message counter in template-driven form
This example shows how Textarea behaves with a template-driven form with an initial value set.
Textarea with internationalization support.
This example shows how Textarea works with i18n markers to achieve internationalization support. Translation of the
counter message is available directly from the library and will be added to your application's .xlf files on using
the following commands from version 0.21.0 onwards:
ng add @fundamental-ngx/platform
- if you don't already have ngx/platform installed, use this; or
ng update @fundamental-ngx/platform --migrate-only --from=0.20.0
- if you have ngx/platform installed
and want to get the library translations, use this.
You can modify the counter message translation by looking for the i18n trans-unit id
platformI18nTextareaCounterMessage
in your application's .xlf files.


FALLING DOWN A film review by Mark R. Leeper Copyright 1993 Mark R. Leeper
Capsule review: Michael Douglas plays the kind of angry, hard-hitting role his father would have played. After being pushed and chafed by society, one day he just decides to fight back at all the small annoyances that degrade the quality of life. That may make him a villain but, as with Bernard Goetz, it is hard to condemn him. Rating: high +1 (-4 to +4).
Life is a sort of social contract. You put in your work at school and then in your career. You pay your dues and you expect to be paid back with a reasonable level of happiness. You expect to reap the rewards of your work. You expect to live in a society where the rules seem to make sense and where justice prevails. And, of course, all those expectations are in varying degrees wrong. If you walk places where you have a right to walk, you are very liable to be beaten and robbed. If you go into a convenience store, you very often find prices are just too darn high. The hamburger may look good and juicy in the picture, but when you actually get it, it is flat, dried out, and unsavory.
Michael Douglas plays "D-Fens," a 30-something nerd with a crewcut who by his accounting is just not getting his fair share out of life. His marriage has gone sour, his career has gone sour, and life just seems to have picked him to dump on. On a hot day in Los Angeles with traffic at a total standstill, he abandons his car and sets off on foot to get to his daughter's birthday party and at the same time just let everybody know that he is fed up. Today he is going to make sure he gets satisfaction. Overcharge him, and be prepared to have him take your store apart. Push him and he is going to push back. Cheat him only in peril of your life. Then there is Prendergast (played by Robert Duvall). On Prendergast's last day of police work, he becomes fascinated by a string of incidents that he can plot in a line on a map. Somebody is fighting back against all the indignities of life, the same ones that frustrate Prendergast, and Prendergast has to stop him before he gets in serious trouble.
FALLING DOWN is an angry howl against the deterioration in the quality of life we all face. Joel Schumacher directing from Ebbe Roe Smith's script has created a villain cut from the same cloth as Bernard Goetz. It is hard not to sympathize with Douglas's angry man. It would be easy to identify Douglas's character with one political wing or the other, but as he goes from venting his anger on a Korean grocer, to Latino gang members, to a neo-Nazi, and finally to rich, selfish, over-paid whites, we see Douglas as surprisingly center-of-the-road. He is a Moderate who has run fresh out of moderation. This is a protest film that could have been made in the 1960s. (My wife, I, and Vincent Canby independently thought to compare it to THE SWIMMER.) Only toward the end does FALLING DOWN lose its power and become somewhat prosaic. But the film is angry and takes chances few Hollywood films are anxious to take anymore. I rate it a high +1 on the -4 to +4 scale.
Mark R. Leeper att!mtgzfs3!leeper<EMAIL_ADDRESS>.
The review above was posted to the
rec.arts.movies.reviews newsgroup (de.rec.film.kritiken for German reviews).
The Internet Movie Database accepts no responsibility for the contents of the
review and has no editorial control. Unless stated otherwise, the copyright
belongs to the author.
Please direct comments/criticisms of the review to relevant newsgroups.
Broken URLs inthe reviews are the responsibility of the author.
The formatting of the review is likely to differ from the original due
to ASCII to HTML conversion.
Related links: index of all rec.arts.movies.reviews reviews


Am I online?
Once your app does not depend on an internet connection, this question actually matters a lot less than you might think. In many cases users simply do not care if they're connected or not, as long as the app works.
For the cases where it does matter, hoodie provides a simple API:
You current connection status is: online :)
By default, Hoodie checks the connection every 30 seconds once you
run hoodie.connectionStatus.startChecking()
. You can also
check manually with hoodie.connectionStatus.check()
.
 Watch the status shown above.
And that’s it. If your app requires it, you can simply listen to the
disconnect
& reconnect
events to show
notifications or similar.
With that out of the way, let’s look into the more interesting part of Offline First apps: handling data.


AnyBaseConverter Class
AnyBaseConverter is an object that will convert numbers to and from a positional notation with a custom alphabet and base.
Methods
Attributes
alphabet
String
The string of characters to use as single-digit numbers. The length of this string determines the base of the result. Each character should be unique within the string or else it will be impossible to correctly convert a string back into a number. Currently, non-BMP characters are not supported.
Default: '0123456789ABCDEFGHIJKLMNOPQRSTUVWXYZ_abcdefghijklmnopqrstuvwxyz~'
Fires event alphabetChange
Fires when the value for the configuration attribute alphabet
is
changed. You can listen for the event using the on
method if you
wish to be notified before the attribute's value has changed, or
using the after
method if you wish to be notified after the
attribute's value has changed.
Parameters:
-
e
EventFacadeAn Event Facade object with the following attribute-specific properties added:
lookup
Object
protected
Used as a reverse lookup for a character index in alphabet.
Fires event lookupChange
Fires when the value for the configuration attribute lookup
is
changed. You can listen for the event using the on
method if you
wish to be notified before the attribute's value has changed, or
using the after
method if you wish to be notified after the
attribute's value has changed.
Parameters:
-
e
EventFacadeAn Event Facade object with the following attribute-specific properties added:
minusSign
String
A single character string to prepend to negative values. This character should not be in the alphabet. Currently, non-BMP characters are not supported.
Default: '-'
Fires event minusSignChange
Fires when the value for the configuration attribute minusSign
is
changed. You can listen for the event using the on
method if you
wish to be notified before the attribute's value has changed, or
using the after
method if you wish to be notified after the
attribute's value has changed.
Parameters:
-
e
EventFacadeAn Event Facade object with the following attribute-specific properties added:
radixPoint
String
A single character string to insert between the integer and fractional parts of the number. This character should not be in the alphabet. Currently, non-BMP characters are not supported.
Default: '.'
Fires event radixPointChange
Fires when the value for the configuration attribute radixPoint
is
changed. You can listen for the event using the on
method if you
wish to be notified before the attribute's value has changed, or
using the after
method if you wish to be notified after the
attribute's value has changed.
Parameters:
-
e
EventFacadeAn Event Facade object with the following attribute-specific properties added:


carbon-now
Create beautiful images of code.
More information: home page
- Create an image from a file using default settings:
carbon-now path/to/file
- Create an image from a text in clipboard using default settings:
carbon-now --from-clipboard
- Create an image from standard input using default settings:
input | carbon-now
- Create images interactively for custom settings and optionally save a preset:
carbon-now -i path/to/file
- Create images from previously saved preset:
carbon-now -p preset path/to/file
- Start at a specified line of text:
carbon-now -s line path/to/file
- End at a specific line of text:
carbon-now -e line path/to/file
- Open image in a browser instead of saving:
carbon-now --open path/to/file


This route does not violate any usage control policies
This route violates usage control policies
{{ ce.explanation }} {{ step }}


Florida woman accused of hitting child with iron for not getting out of bed
MANATEE COUNTY, Fla. (WWSB) -- A Florida woman faces child abuse charges after deputies said she hit a child with an iron.
WWSB reported that 43-year-old hit the child when the victim wouldn't get out of bed.
The Manatee County Sheriff's Office said the victim jumped out of bed and ran outside, but she followed him and hit him several times.
Eldridge was taken into custody on charges of child abuse without great bodily harm.


Color Picker.js
color picker uses only javascript
Before using the color picker component referring to add to the project file (colorPicker.js)

very easy to convert any element of a web page in the button to select the color for this you need only add that element class (colorPicker)

select color

attributes:
- value - initializes the color picker color also keeps the color result
- cpWidth - sets the width of the color picker
- cpHeight - sets the height of the color picker
- bgColor - dynamically changes the color of the call button color picker
select color
attributes:
- hsvText - displays the color code in the HSV
select color
attributes:
- cpXOff - X sets the offset relative to the call button color picker
- cpYOff - Y sets the offset relative to the call button color picker
select color
attributes:
- cpX - establishes a fixed position for the X color picker
- cpY - establishes a fixed position for the Y color picker
select color
tested only on chrome and firefox!


If you have encountered a bug in Quesa, or would like to log a feature request, please create a new item in the database.
Please note this section is obsolete - these bugs have not been migrated to the
current SourceForge system, which is now the definitive list.
| Severity | ID | Description |
| Medium | 00050 |
IR doesn't support specular colour/control at the vertex level.
|
| Medium | 00053 |
Make use of kQ3XMethodTypeRendererIsBoundingBoxVisible method for culling and support in IR.
|
| Medium | 00023 |
Geometries which are decomposed to TriMeshes fix the attribute data at the time of creation,
should use the use array to allow renderers to update the attribute data with the current
state to provide for inheritence.
|
| Medium | 00024 |
QD3D viewer, doesn't let you rotate around model if running with Quesa.
|
| Medium | 00033 |
IR, Light Test produces white geometry
|
| Minor | 00001 |
Macintosh window draw contexts do not support clipping masks.
|
| Minor | 00002 |
Pixmap window draw contexts do not support clipping masks.
|
| Minor | 00008 |
Wireframe renderer doesn't support individual triangle colours for TriMeshes.
|
| Minor | 00034 |
Viewer, about box button should display dialog on mouse up not mouse down.
|
| Minor | 00035 |
Import Test, storage object not being disposed of somewhere.
|
| Minor | 00036 |
Queeg, Quesa IR renders darker and flatter than QD3D IR.
|
| Minor | 00037 |
Queeg, changing window size produces different image with Quesa than with QD3D.
|
| Minor | 00039 |
Q3BoundingSphere_SetFromPoints3D and Q3BoundingSphere_SetFromRationalPoints4D return significantly incorrect results for some inputs. They are probably using an incorrect algorithm.
|
| Minor | 00046 |
Pixmap/bitmap markers are culled by OpenGL if they are partially off-screen, should be clipped.
|
| Minor | 00047 |
Wireframe renderer doesn't support pixmap/bitmap markers.
|
| Possible | 00004 |
Q3BoundingSphere_SetFromRationalPoints4D, possible bug in QD3D 1.6 implementation.
|
| Possible | 00006 |
Q3View_GetAttributeState, possible bug in QD3D 1.6 docs - correct behaviour not clear.
|
| Possible | 00010 |
Wire elements, don't support IO. Also don't copy the QTAtomContainer, although the
QD3D implementation might - docs on this element's behaviour are needed.
|
| Possible | 00011 |
Q3View_Sync does not work correctly with QD3D interactive renderer, possible bug
in QD3D 1.6 renderer.
|
| Possible | 00041 |
Q3BoundingSphere_SetFromRationalPoints4D, possible bug in QD3D 1.6 implementation.
|
| Possible | 00042 |
Q3Quaternion_SetRotateAboutAxis might be returning the conjugate of the correct result.
|
| Possible | 00048 |
Verify all QD3D pixmap formats for Pixmap marker object, make sure they're converted correctly.
|


Chocolate Pizza
POSTED ON 17 FEB 2017 / DESSERTS
For the fig swirl: Melt butter 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 lukewarm. 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 manufacturer instructions, i.e., let spin and thicken for 20 minutes before adding mascarpone, fig jam mixture, and the nuts. Continue to process for +/ 10 minutes. Pour semi frozen mixture into a pyrex dish or glass tupperwar. Freeze for at least two hours before serving.
- 1 1/2 cups milk
- 1/2 cup mascarpone
- 1/2 tsp pink salt
- 1 lb Black Mission Figs
- 1/2 cup brown sugar
- 2-4 tbsp water
- 1 1/2 cups heavy cream
- 1/3 cup granulated sugar
- 2 egg yolks
- 1 lemon, juiced
- 2 tbsp butter
- 1 cup honey roasted pecans, roughly chopped


// Eager Base
// ---------------
// The EagerBase provides a scaffold for handling with eager relation
// pairing, by queueing the appropriate related method calls with
// a database specific `eagerFetch` method, which then may utilize
// `pushModels` for pairing the models depending on the database need.
'use strict';
var _ = require('lodash');
var Promise = require('./promise');
function EagerBase(parent, parentResponse, target) {
this.parent = parent;
this.parentResponse = parentResponse;
this.target = target;
}
_.extend(EagerBase.prototype, {
// This helper function is used internally to determine which relations
// are necessary for fetching based on the `model.load` or `withRelated` option.
fetch: Promise.method(function (options) {
var relationName, related;
var target = this.target;
var handled = this.handled = {};
var withRelated = this.prepWithRelated(options.withRelated);
var subRelated = {};
// Internal flag to determine whether to set the ctor(s) on the `Relation` object.
target._isEager = true;
// Eager load each of the `withRelated` relation item, splitting on '.'
// which indicates a nested eager load.
for (var key in withRelated) {
related = key.split('.');
relationName = related[0];
// Add additional eager items to an array, to load at the next level in the query.
if (related.length > 1) {
var relatedObj = {};
subRelated[relationName] = subRelated[relationName] || [];
relatedObj[related.slice(1).join('.')] = withRelated[key];
subRelated[relationName].push(relatedObj);
}
// Only allow one of a certain nested type per-level.
if (handled[relationName]) continue;
if (!_.isFunction(target[relationName])) {
throw new Error(relationName + ' is not defined on the model.');
}
var relation = target[relationName]();
handled[relationName] = relation;
}
// Delete the internal flag from the model.
delete target._isEager;
// Fetch all eager loaded models, loading them onto
// an array of pending deferred objects, which will handle
// all necessary pairing with parent objects, etc.
var pendingDeferred = [];
for (relationName in handled) {
pendingDeferred.push(this.eagerFetch(relationName, handled[relationName], _.extend({}, options, {
isEager: true,
withRelated: subRelated[relationName],
_beforeFn: withRelated[relationName] || noop
})));
}
// Return a deferred handler for all of the nested object sync
// returning the original response when these syncs & pairings are complete.
return Promise.all(pendingDeferred)['return'](this.parentResponse);
}),
// Prep the `withRelated` object, to normalize into an object where each
// has a function that is called when running the query.
prepWithRelated: function prepWithRelated(withRelated) {
if (!_.isArray(withRelated)) withRelated = [withRelated];
var obj = {};
for (var i = 0, l = withRelated.length; i < l; i++) {
var related = withRelated[i];
if (_.isString(related)) {
obj[related] = noop;
} else {
_.extend(obj, related);
}
}
return obj;
},
// Pushes each of the incoming models onto a new `related` array,
// which is used to correcly pair additional nested relations.
pushModels: function pushModels(relationName, handled, resp) {
var models = this.parent;
var relatedData = handled.relatedData;
var related = [];
for (var i = 0, l = resp.length; i < l; i++) {
related.push(relatedData.createModel(resp[i]));
}
return relatedData.eagerPair(relationName, related, models);
}
});
var noop = function noop() {};
module.exports = EagerBase; |


--- layout: page title: About header-img: "img/about-bg.png" ---
I am a citizen of the world. I am a competitor. I am a performer. I will use every technology and coding language to relentlessly make the impossible possible.


Chapter 10 Statistical analysis
The general purposes for metabolomics study are strongly associated with research goal. However, since metabolomics are usually performed in a non-targeted mode, statistical analysis methods are always started with the exploratory analysis. The basic target for an exploratory analysis is:
Find the relationship among variables
Find the relationship among samples/group of samples.
This is basically unsupervised analysis.
However, sometimes we have group information which could be used to find biomarkers or correlation between variables and groups or continuous variables. This type of data need supervised methods to process. A general discussion about statistical analysis in metabolic phenotyping can be found here(Blaise et al. 2021). Before we talk the details of algorithms, let’s cover some basic statistical concepts.
10.1 Basic Statistical Analysis
Statistic is used to describe certain property or variables among the samples. It could be designed for certain purpose to extract signal and remove noise. Statistical models and inference are both based on statistic instead of the data.
\[Statistic = f(sample_1,sample_2,...,sample_n)\]
Null Hypothesis Significance Testing (NHST) is often used to make statistical inference. P value is the probability of certain statistics happens under H0 (pre-defined distribution).
For omics studies, you should realize Multiple Comparison issue when you perform a lot of(more than 20) comparisons or tests at the same time. False Discovery Rate(FDR) control is required for multiple tests to make sure the results are not false positive. You could use Benjamini-Hochberg method to adjust raw p values or directly use Storey Q value to make FDR control.
NHST is famous for the failure of p-value interpretation as well as multiple comparison issues. Bayesian Hypothesis Testing could be an options to cover some drawbacks of NHST. Bayesian Hypothesis Testing use Bayes factor to show the differences between null hypothesis and any other hypothesis.
\[Bayes\ factor = \frac{p(D|Ha)}{p(D|H0)} = \frac{posterior\ odds}{prior\ odds}\]
Statistical model use statistics to make prediction/explanation. Most of the statistical model need to be tuned for parameters to show a better performance. Statistical model is build on real data and could be diagnosed by other general statistics such as \(R^2\), \(ROC curve\). When the models are built or compared, model selection could be preformed.
\[Target = g(Statistic) = g(f(sample_1,sample_2,...,sample_n))\]
Bias-Variance Tradeoff is an important concept regarding statistical models. Certain models could be overfitted(small Bias, large variance) or underfitted(large Bias, small variance) when the parameters of models are not well selected.
\[E[(y - \hat f)^2] = \sigma^2 + Var[\hat f] + Bias[\hat f]\]
Cross validation could be used to find the best model based on training-testing strategy such as Jacknife, bootstraping resampling and n-fold cross validation.
Regularization for models could also be used to find the model with best prediction performance. Rigid regression, LASSO or other general regularization could be employed to build a robust models.
For supervised models, linear model and tree based model are two basic categories. Linear model could be useful to tell the independent or correlated relationship of variables and the influences on the predicted variables. Tree based model, on the other hand, try to build a hierarchical structure for the variables such as bagging, random forest or boosting. Linear model could be treated as special case of tree based model with single layer. Other models like Support Vector Machine (SVM), Artificial Neural Network (ANN) or Deep Learning are also make various assumptions on the data. However, if you final target is prediction, you could try any of those models or even weighted combine their prediction to make meta-prediction.
10.2 Differences analysis
After we get corrected peaks across samples, the next step is to find the differences between two groups. Actually, you could perform ANOVA or Kruskal-Wallis Test for comparison among more than two groups. The basic idea behind statistic analysis is to find the meaningful differences between groups and extract such ions or peak groups.
So how to find the differences? In most metabolomics software, such task is completed by a t-test and report p-value and fold changes. If you only compare two groups on one peaks, that’s OK. However, if you compare two groups on thousands of peaks, statistic textbook would tell you to notice the false positive. For one comparison, the confidence level is 0.05, which means 5% chances to get false positive result. For two comparisons, such chances would be \(1-0.95^2\). For 10 comparisons, such chances would be \(1-0.95^{10} = 0.4012631\). For 100 comparisons, such chances would be \(1-0.95^{100} = 0.9940795\). You would almost certainly to make mistakes for your results.
In statistics, the false discovery rate(FDR) control is always mentioned in omics studies for multiple tests. I suggested using q-values to control FDR. If q-value is less than 0.05, we should expect a lower than 5% chances we make the wrong selections for all of the comparisons showed lower q-values in the whole dataset. Also we could use local false discovery rate, which showed the FDR for certain peaks. However, such values are hard to be estimated accurately.
Karin Ortmayr thought fold change might be better than p-values to find the differences (Ortmayr et al. 2016).
10.2.1 T-test or ANOVA
If one peak show significant differences among two groups or multiple groups, T-test or ANOVA could be used to find such peaks. However, when multiple hypothesis testings are performed, the probability of false positive would increase. In this case, false discovery rate(FDR) control is required. Q value or adjusted p value could be used in this situation. At certain confidence interval, we could find peaks with significant differences after FDR control.
10.2.2 LIMMA
Linear Models for MicroArray Data(LIMMA) model could also be used for high-dimensional data like metabolomics. They use a moderated t-statistic to make estimation of the effects called Empirical Bayes Statistics for Differential Expression. It is a hierarchical model to shrink the t-statistic for each peak to all the peaks. Such estimation is more robust. In LIMMA, we could add the known batch effect variable as a covariance in the model. LIMMA is different from t-test or ANOVA while we could still use p value and FDR control on LIMMA results.
10.2.3 Bayesian mixture model
Another way to make difference analysis is based on Bayesian mixture model without p value. Such model would not use hypothesis testing and directly generate the posterior estimation of parameters. A posterior probability could be used to check whether certain peaks could be related to different condition. If we want to make comparison between classical model like LIMMA and Bayesian mixture model. We need to use simulation to find the cutoff.
10.3 PCA
In most cases, PCA is used as an exploratory data analysis(EDA) method. In most of those most cases, PCA is just served as visualization method. I mean, when I need to visualize some high-dimension data, I would use PCA.
So, the basic idea behind PCA is compression. When you have 100 samples with concentrations of certain compound, you could plot the concentrations with samples’ ID. However, if you have 100 compounds to be analyzed, it would by hard to show the relationship between the samples. Actually, you need to show a matrix with sample and compounds (100 * 100 with the concentrations filled into the matrix) in an informal way.
The PCA would say: OK, guys, I could convert your data into only 100 * 2 matrix with the loss of information minimized. Yeah, that is what the mathematical guys or computer programmer do. You just run the command of PCA. The new two “compounds” might have the cor-relationship between the original 100 compounds and retain the variances between them. After such projection, you would see the compressed relationship between the 100 samples. If some samples’ data are similar, they would be projected together in new two “compounds” plot. That is why PCA could be used for cluster and the new “compounds” could be referred as principal components(PCs).
However, you might ask why only two new compounds could finished such task. I have to say, two PCs are just good for visualization. In most cases, we need to collect PCs standing for more than 80% variances in our data if you want to recovery the data with PCs. If each compound have no relationship between each other, the PCs are still those 100 compounds. So you have found a property of the PCs: PCs are orthogonal between each other.
Another issue is how to find the relationship between the compounds. We could use PCA to find the relationship between samples. However, we could also extract the influences of the compounds on certain PCs. You might find many compounds showed the same loading on the first PC. That means the concentrations pattern between the compounds are looked similar. So PCA could also be used to explore the relationship between the compounds.
OK, next time you might recall PCA when you need it instead of other paper showed them.
Besides, there are some other usage of PCA. Loadings are actually correlation coefficients between peaks and their PC scores. Yamamoto et.al. (Yamamoto et al. 2014) used t-test on this correlation coefficient and thought the peaks with statistically significant correlation to the PC score have biological meanings for further study such as annotation. However, such analysis works better when few PCs could explain most of the variances in the dataset.
10.4 Cluster Analysis
After we got a lot of samples and analyzed the concentrations of many compounds in them, we may ask about the relationship between the samples. You might have the sampling information such as the date and the position and you could use boxplot or violin plot to explore the relationships among those categorical variables. However, you could also use the data to find some potential relationship.
But how? if two samples’ data were almost the same, we might think those samples were from the same potential group. On the other hand, how do we define the “same” in the data?
Cluster analysis told us that just define a “distances” to measure the similarity between samples. Mathematically, such distances would be shown in many different manners such as the sum of the absolute values of the differences between samples.
For example, we analyzed the amounts of compound A, B and C in two samples and get the results:
| Compounds(ng) | A | B | C |
|---|---|---|---|
| Sample 1 | 10 | 13 | 21 |
| Sample 2 | 54 | 23 | 16 |
The distance could be:
\[ distance = |10-54|+|13-23|+|21-16| = 59 \]
Also you could use the sum of squares or other way to stand for the similarity. After you defined a “distance”, you could get the distances between all of pairs for your samples. If two samples’ distance was the smallest, put them together as one group. Then calculate the distances again to combine the small group into big group until all of the samples were include in one group. Then draw a dendrogram for those process.
The following issue is that how to cluster samples? You might set a cut-off and directly get the group from the dendrogram. However, sometimes you were ordered to cluster the samples into certain numbers of groups such as three. In such situation, you need K means cluster analysis.
The basic idea behind the K means is that generate three virtual samples and calculate the distances between those three virtual samples and all of the other samples. There would be three values for each samples. Choose the smallest values and class that sample into this group. Then your samples were classified into three groups. You need to calculate the center of those three groups and get three new virtual samples. Repeat such process until the group members unchanged and you get your samples classified.
OK, the basic idea behind the cluster analysis could be summarized as define the distances, set your cut-off and find the group. By this way, you might show potential relationships among samples.
10.5 PLSDA
PLS-DA, OPLS-DA and HPSO-OPLS-DA (Qin Yang et al. 2017) could be used.
Partial least squares discriminant analysis(PLSDA) was first used in the 1990s. However, Partial least squares(PLS) was proposed in the 1960s by Hermann Wold. Principal components analysis produces the weight matrix reflecting the covariance structure between the variables, while partial least squares produces the weight matrix reflecting the covariance structure between the variables and classes. After rotation by weight matrix, the new variables would contain relationship with classes.
The classification performance of PLSDA is identical to linear discriminant analysis(LDA) if class sizes are balanced, or the columns are adjusted according to the mean of the class mean. If the number of variables exceeds the number of samples, LDA can be performed on the principal components. Quadratic discriminant analysis(QDA) could model nonlinearity relationship between variables while PLSDA is better for collinear variables. However, as a classifier, there is little advantage for PLSDA. The advantages of PLSDA is that this modle could show relationship between variables, which is not the goal of regular classifier.
Different algorithms (Andersson 2009) for PLSDA would show different score, while PCA always show the same score with fixed algorithm. For PCA, both new variables and classes are orthognal. However, for PLS(Wold), only new classes are orthognal. For PLS(Martens), only new variables are orthognal. This paper show the details of using such methods (Brereton and Lloyd 2018).
Sparse PLS discriminant analysis(sPLS-DA) make a L1 penal on the variable selection to remove the influences from unrelated variables, which make sense for high-throughput omics data (Lê Cao, Boitard, and Besse 2011).
For o-PLS-DA, s-plot could be used to find features(Wiklund et al. 2008).
10.6 Network analysis
10.6.1 Vertex and edge
Each node is a vertex and the connection between nodes is a edge in the network. The connection can be directed or undirected depending on the relationship.
10.6.2 Build the network
Adjacency matrices were always used to build the network. It’s a square matrix with n dimensions. Row i and column j is equal to 1 if and only if vertices i and j are connected. In directed network, such values could be 1 for i to j and -1 for j to i.
10.6.3 Network attributes
Vertex/edge attributes could be the group information or metadata about the nodes/connections. The edges could be weighted as attribute.
Path is the way from one node to another node in the network and you could find the shortest path in the path. The largest distance of a graph is called its diameter.
An undirected network is connected if there is a way from any vertex to any other. Connected networks can further classified according to the strength of their connectedness. An undirected network with at least two paths between each pairs of nodes is said to be biconnected.
The transitivity of network is a crude summary of the structure. A high value means that nodes are connected well locally with dense subgraphs. Network data sets typically show high transitivity.
Maximum flows and minimum cuts could be used to check the largest volumns and smallest path flow between two nodes. For example, two hubs is connected by one node and the largest volumn and smallest path flow between two nodes from each hub could be counted at the select node.
Sparse network has similar number of edges and the number of nodes. Dense network has the number of edges as a quadratic function of the nodes.
10.7 Software
caret could employ more than 200 statistical models in a general framework to build/select models. You could also show the variable importance for some of the models.
caretEnsemble Functions for creating ensembles of caret models
pROC Tools for visualizing, smoothing and comparing receiver operating characteristic (ROC curves). (Partial) area under the curve (AUC) can be compared with statistical tests based on U-statistics or bootstrap. Confidence intervals can be computed for (p)AUC or ROC curves.
gWQS Fits Weighted Quantile Sum (WQS) regressions for continuous, binomial, multinomial and count outcomes.
Community ecology tool could be used to analysis metabolomic data(Passos Mansoldo et al. 2022).


"Charlie's Angels" - Girls Just Wanna Have Fun (And You Will Too) by Homer Yen (c) 2000
Incredibly, Hollywood has the courage to create yet again another feature-length film based on a classic TV show. You may remember a recent handful of similar attempts that have made disastrous transitions from the small screen to the silver screen. These forgettable projects included "The Avengers," "The Mod Squad," and "Shaft." Received poorly by audiences and critics alike, all were money-losing debacles.
This time around, the latest attempt is the resurrection of "Charlie's Angels," the jiggly and feathery-haired 70's detective show. Would this update suffer the same fate as those other fiascoes? The project certainly had many studio executives scratching their heads in doubt as the film underwent massive rewrites and last-minute reshoots. Gossip also surfaced that there were severe creative differences among the stars, and in particular, between Lucy Liu (who plays Alex, the techno-whiz Angel) and Bill Murray (who plays Bosley, Charlie's avuncular assistant). Meanwhile, production costs skyrocketed to $90 million while people began to wonder about the viability of a film that was taken from a show that had scripts that were so light. Original Angel, Kate Jackson, once mused that "it would take a week to get to the floor if you dropped it from the ceiling."
Happily, reports of its demise are greatly exaggerated. The new "Charlie's Angels," as it turns out, is an unabashed romp. It's a fine mix of action, camp and self-parody. I'm surprised to say this, but this is the most fun that I've had at the movies in weeks!
If you've seen the television show, then you're familiar with the setup. Three beautiful women (Drew Barrymore, Cameron Diaz, and Lucy Liu) work together as an elite trio of detectives who receive their assignment over speakerphone from the disembodied voice of Charlie (spoken, as in the original show, by Baritone-voiced John Forsythe). Of course, they're still glamorous and great to look at. But these new Angels have powered up. They aren't shy about employing their saucy feminine wiles. Their impressive skill sets range widely from speaking Japanese to Indy Car racing. And, thanks to Matrix-like effects, they can really kick some butt.
Their latest case involves thwarting a scheme that threatens to end privacy. The film's elements borrow heavily from any number of James Bond films. There are high-tech gadgets, a notable henchman (a silent, sword-wielding, thin man played by Crispin Glover), a host of enjoyable action sequences and a final showdown at the villain's stronghold.
While the plot is serviceable, the film is more about flash, style, and good-looking babes. The filmmakers know that this isn't matter to be taken seriously. Consequently, they have great fun with this film. Guest stars include comedic lightweights Luke Wilson, Matthew Perry and Tom Green as the Angels' love interests. Diaz energetically wiggles her booty while dancing on Soul Train as Sir Mix-A-Lot's anthem, "I Like Big Butts," plays in the background. Barrymore enjoys a victory moonwalk after dispatching a group of thugs. Liu dons a dominatrix outfit as she trains a group of gawking, bespectacled engineers. What's not to like? Completely watchable, this is the kind of playful material that attracts people to magazines like Maxim or FHM. For this Maxim-reading guy, "Charlie's Angels" was more entertaining than I ever thought possible.
Grade: B+ (yes, really)
S: 1 out of 3 cleavage shots and body hugging suits L: 0 out of 3 V: 1 out of 3 bloodless Matrix-style fight sequences
The review above was posted to the
rec.arts.movies.reviews newsgroup (de.rec.film.kritiken for German reviews).
The Internet Movie Database accepts no responsibility for the contents of the
review and has no editorial control. Unless stated otherwise, the copyright
belongs to the author.
Please direct comments/criticisms of the review to relevant newsgroups.
Broken URLs inthe reviews are the responsibility of the author.
The formatting of the review is likely to differ from the original due
to ASCII to HTML conversion.
Related links: index of all rec.arts.movies.reviews reviews


|
iRobot Roomba 500 Series SDK
1.0.0-alpha
A C++ wrapper for the iRobot Roomba 500 Series Open Interface (OI) Specification.
|
Constant data used to manage data returned from the iRobot® Roomba. More...
Enumerations | |
| enum | ReturnCode : int8_t { INVALID_CHECKSUM = -101, SERIAL_TRANSFER_FAILURE = -100, NO_DATA_AVAILABLE = -2, FAILURE_TO_SYNC = -1, SUCCESS = 0 } |
| Return codes. | |
| enum | PacketId : uint8_t { PACKETS_7_THRU_26 = 0, PACKETS_7_THRU_16 = 1, PACKETS_17_THRU_20 = 2, PACKETS_21_THRU_26 = 3, PACKETS_27_THRU_34 = 4, PACKETS_35_THRU_42 = 5, PACKETS_7_THRU_42 = 6, BUMPS_AND_WHEEL_DROPS = 7, WALL = 8, CLIFF_LEFT = 9, CLIFF_FRONT_LEFT = 10, CLIFF_FRONT_RIGHT = 11, CLIFF_RIGHT = 12, VIRTUAL_WALL = 13, MOTOR_OVERCURRENTS = 14, DIRT_DETECT = 15, INFRARED_CHARACTER_OMNI = 17, BUTTONS = 18, DISTANCE = 19, ANGLE = 20, CHARGING_STATE = 21, VOLTAGE = 22, CURRENT = 23, TEMPERATURE = 24, BATTERY_CHARGE = 25, BATTERY_CAPACITY = 26, WALL_SIGNAL = 27, CLIFF_LEFT_SIGNAL = 28, CLIFF_FRONT_LEFT_SIGNAL = 29, CLIFF_FRONT_RIGHT_SIGNAL = 30, CLIFF_RIGHT_SIGNAL = 31, CHARGING_SOURCES_AVAILABLE = 34, OI_MODE = 35, SONG_NUMBER = 36, SONG_PLAYING = 37, NUMBER_OF_STREAM_PACKETS = 38, REQUESTED_VELOCITY = 39, REQUESTED_RADIUS = 40, REQUESTED_RIGHT_VELOCITY = 41, REQUESTED_LEFT_VELOCITY = 42, RIGHT_ENCODER_COUNTS = 43, LEFT_ENCODER_COUNTS = 44, LIGHT_BUMPER = 45, LIGHT_BUMP_LEFT_SIGNAL = 46, LIGHT_BUMP_FRONT_LEFT_SIGNAL = 47, LIGHT_BUMP_CENTER_LEFT_SIGNAL = 48, LIGHT_BUMP_CENTER_RIGHT_SIGNAL = 49, LIGHT_BUMP_FRONT_RIGHT_SIGNAL = 50, LIGHT_BUMP_RIGHT_SIGNAL = 51, INFRARED_CHARACTER_LEFT = 52, INFRARED_CHARACTER_RIGHT = 53, LEFT_MOTOR_CURRENT = 54, RIGHT_MOTOR_CURRENT = 55, MAIN_BRUSH_MOTOR_CURRENT = 56, SIDE_BRUSH_MOTOR_CURRENT = 57, STASIS = 58, ALL_SENSOR_DATA = 100, PACKETS_7_THRU_58 = 100, PACKETS_43_THRU_58 = 101, PACKETS_46_THRU_51 = 106, PACKETS_54_THRU_58 = 107 } |
| iRobot® Roomba Open Interface (OI) Sensor Packets | |
Functions | |
| ReturnCode | parseSerialData (void) |
| Function to receive serial data. More... | |
| ReturnCode | setParseKey (uint8_t const *const parse_key_) |
| Function to store parse key. More... | |
| ReturnCode | valueOfSensor (const PacketId packet_id_, uint16_t *const value_, bool *const is_signed_) |
| Provides access to sensor data. More... | |
| bool | sensorsReady (void) |
| Indicates ready state of OISensors internals. More... | |
Constant data used to manage data returned from the iRobot® Roomba.
Data type loaded by sensor methods.
This constant data facilitates inserting and retreiving sensor data from the blob. Some "data" is represented "functionally" to reduce wasted space for sparse data sets. The data functions do not contain error checking, and should only be used internally.
Sensor data is returned as a byte array with checksum. This type reflects the data representation of the returned sensor data, as it strikes the balance between usability, size and speed optimization. see OIEncoder::sensors see OIEncoder::queryList see OIEncoder::stream see OIEncoder::pauseResumeStream
Sensor data is returned as a byte array with checksum. This type reflects the data representation of the returned sensor data, as it strikes the balance between usability, size and speed optimization. see OIEncoder::sensors see OIEncoder::queryList see OIEncoder::stream
|
inline |
Function to receive serial data.
Parses data received from Roomba and stores it in memory accessible by the OIEncoder object.
| bool roomba::series500::oi::sensors::sensorsReady | ( | void | ) |
Indicates ready state of OISensors internals.
|
inline |
Function to store parse key.
A parsing key is generated during the call to queryList() or sensors() to describe the format of the data returned by the iRobot® Roomba.
| [in] | parse_key_ | An array of bytes describing the data requested from the iRobot® Roomba. Index 0 contains the length of the array. The remaining values are the packet ids of the data requested from the iRobot® Roomba. |
|
inline |
Provides access to sensor data.
| [in] | packet_id_ | The packet id of the sensor who's value you want |
| [out] | value_ | A pointer to the sixteen-bit field to store the value of the requested sensor |
| [out] | is_signed_ | A boolean indicating if the value of the requested sensor is signed or unsigned |


This page is an example of how one may use modeljs. It demos the many features of modeljs by using modeljs to create a table widget (show below) and using that widget on this page. You can look directly at the source of how this is done. But I would recommend first going through the tutorial below.
Tutorial
Often when developing an software application of a moderate size an architecture using some varient of MVC is used. Modeljs was created to be the javascript library for the model portion of the MVC pattern for whichever MVC javascript architecture used. This page is an example of how to use Modlejs to create an application model and use that model to create a table widget (AKA the view). The buttons and samplecode below would be the logic found in the controllers. After completing this tutorial hopefully you can see the value of modeljs for whatever purpose brought you here.
Table:
Step 1: The inputs below can be inserted in the table by clicking the "add row" button. While reset will reload the initial state of the table. Play around with the buttons. Notice if you close the page and come back to it your state is saved. This is done using HTML local storage
Month:
Savings:
Step 2: press F12 to bring up firebug or your browsers equivlant debugger. Inspect the implementation of the buttons above. If you are curious about the widget implementation you can look at it now or wait till after the tutorial when you have a better understanding of the api.
Step 3: In the debugger console type the javaScript code below to learn how to use the modeljs api:
model // this is the modeljs model created and used by this page. Inspect it.
model.getValue(); // this returns the value of the model you were just inspecting. This should be easier to inspect, since it's just a JSON object with the model Values only (no metadata). Notice the table property. We bind this object to the table widget. More on this later.
// This is how you would traversing the Model and perform operations
model.table.data;
model.exampleProperty.getValue();
model.table.data[1][1].getValue();
model.table.data[1][1].setValue(999999); // Notice this changed the value on the table.
/* Lets go through some features of modeljs using model and tableWidget found on this page */
/** Feature: Metadata **/
// Every model object has a metadata object associated with it.
model.exampleProperty.getMetadata();
/** Feature: custom metadata **/
// You can easily add any custom metadata values directly to this object.
model.exampleProperty.getMetadata().exampleCustomMetadata = "custom value";
/** Feature: load/save **/
// Model Metadata can be saved and loaded with the model. We've already see how closing and reopening the page keeps your state. This is done by saving to JSON and loading from JSON.
model.toJSON(); // returns the model JSON to persist.
model.toJSON(true); // passing true will include the model metadata as well.
// To restore your model from the JSON just pass it to the constructor to re-create the model.
var tableModelFromJSON = new Model(model.table.toJSON(true));
// This is esentially what clone does for you.
/** Feature: clone() **/
var clonedTableModel = model.table.clone();
/** Feature: do not persist **/
// if you don't want a property to be presisted, just indicate so in the metadata.
model.exampleProperty.getMetadata().doNotPersist = true;
model.toJSON(); // Notice it has been nullified..
// This page uses your browser local storage to save/load the model. read http://diveintohtml5.info/storage.html#halma for more about this.
/** Feature: getName() **/
// get Name returns the fully qualified name of the property.
model.table.data.getName(); // === "/root/table/data"
// but we can also get the shortName
model.table.data.getShortName(); // === "data"
//Notice the difference between the name of the tableModelFromJSON and your clone. If you metadata object passed to the model constructor does not have a 'name' key/value pair it will always be named /root. read the jsdoc to learn more about the Model constructor and what clone does exactly.
tableModelFromJSON.getName(); // === "/root"
clonedTableModel.getName(); // === "/table"
// Let look at other thing stored in the metadata
/** Feature: validators **/
// We can add validation functions to any Property. Lets create a validator that restricts the value to being a string.
model.exampleProperty.getMetadata().validator = function (value) {
return typeof value === 'string'
};
// Now only strings can be assigned to this property. Lets test this.
model.exampleProperty.setValue(100);
model.exampleProperty.getValue(); // setValue didn't do anything. it fails silently.
// We can explicitly test validation before assignment by calling validateValue();
model.exampleProperty.validateValue(100); // returns false;
model.exampleProperty.hasValidator(); // tell you if a validator exists
// In addition to validating input values we can also define how our output values should be formatted by calling getFormattedValue()
/** Feature: getFormattedValue() **/
// By default there is no Formatter defined and getFormattedValue() will return the raw value equal to getValue()
model.table.header[0].getFormattedValue(); // returns "Month"
// To format our model Values we have two options, define a global Formatter or define a local formatter function.
// Defining a global formatter. Which returns all String in upper case.
Model.Formatter = function (value) {
var formattedValue = value;
if (typeof value === 'string'){
formattedValue = value.toUpperCase();
}
return formattedValue;
};
model.table.header[0].getFormattedValue(); // now returns "MONTH"
// We can also define a Formatter on a specific property which will override the global formatter.
model.table.header[0].getMetadata().Formatter = function (value) {
var formattedValue = value;
if (typeof value === 'string'){
formattedValue = "--" + value + "--";
}
return formattedValue;
};
model.table.header[0].getFormattedValue(); // now returns "--Month--"
/** Feature: Events **/
// Next up Events. Events are the primary mechanism that decouples the Model from the view which makes the MVC pattern so powerful. modeljs has the following events: PROPERTY_CHANGE, MODEL_CHANGE, DESTROY, CHILD_CREATED, CHILD_DESTROYED and CHANGE. Read the jsdoc for details on each event type.
// We can listen to change event by registering callbacks.
// The following are example of different ways to registar your callback
model.exampleObject.onChange(function () {
window.console.log("exampleObject PROPERTY_CHANGE event fired");
}); // equavlant to model.exampleObject.on(Model.Event.PROPERTY_CHANGE, callback);
model.exampleObject.onChange(function () {
window.console.log("exampleObject CHANGE (ie. PROPERTY OR MODEL) event fired");
}, true); // equavlant to model.exampleObject.on(Model.Event.CHANGE, callback); which is equavilant to registaring it on both PROPERTY_CHANGE and MODEL_CHANGE
model.exampleObject.on(Model.Event.MODEL_CHANGE, function () {
window.console.log("exampleObject MODEL_CHANGE event fired");
}); // equavlant to model.exampleObject.on(Model.Event.MODEL_CHANGE, callback);
model.exampleObject.on(Model.Event.CHILD_CREATED, function () {
window.console.log("exampleObject CHILD_CREATED event fired");
}); // equavlant to model.exampleObject.on(Model.Event.CHILD_CREATED, callback);
model.exampleObject.on(Model.Event.CHILD_DESTROYED, function () {
window.console.log("exampleObject CHILD_DESTROYED event fired");
});
// Now lets do actions that fire events
model.exampleObject.anotherProperty.setValue("new Value"); // triggers a PROPERTY_CHANGE event on anotherProperty and bubbles up a MODEL_CHANGE event which triggers 2 callbacks.
// We can also trigger events manually
model.exampleObject.anotherProperty.trigger(Model.Event.PROPERTY_CHANGE);
model.exampleObject.createProperty("newProp", "newValue"); // triggers a CHILD_CREATED event
model.exampleObject.newProp.destroy(); // triggers the CHILD_DESTROYED event
/** Feature: custom Events **/
// Finally you can listen and trigger your own custom events.
model.exampleObject.on("Foo", function () {
window.console.log("bar");
});
model.exampleObject.trigger("Foo");
// Note, any method that triggers an event will usually take a SuppressNotifications flag.
/** Feature: suppress Notifications **/
model.exampleObject.anotherProperty.setValue("Change Value but don't notify listeners", true);
/** Feature: Transactions **/
// Read the jsdoc for more information on transactions.
Model.startTransaction();
model.exampleObject.anotherProperty.setValue("set inside transaction");
model.exampleObject.anotherProperty.getValue();
model.exampleObject.trigger("Foo");
model.exampleObject.trigger("Foo");
Model.endTransaction();
// Notice transaction are fired at the end. Read jsdoc for more information about transaction options.
// That ends the modeljs tutorial. Hope that is enough to get you started. Check out the documentation and tests for other feature and examples like remote and thin models.


Mezzo-soprano Kimberly Lauritsen, known for her rich tone and excellent diction,
is an active performer in the Great Lakes music scene, appearing most recently
as Alto soloist with The Blue Water Chamber Orchestra and Cleveland Chamber Choir
in the debut of Sam Guarnaccia's eagerly anticipated Oratorio: Emergent Universe.
Ms. Lauritsen made her Cleveland Orchestra debut in 2006 in Debussy's Le Martyre
de Saint Sébastien under the baton of Matthias Pintscher. Since then she has
appeared at Severance Hall and Blossom several times, and has also performed
solos with Akron Symphony, Cleveland Chamber Choir, Canton Symphony Orchestra,
Ashland Symphony, PAND, Classical Revolution, the Nebraska Symphony Chamber
Orchestra, Musica Antiqua, Dulces Voces, Abendmusik, the Omaha Chamber Singers,
and the Cleveland Art Song Festival. She has performed lead roles with Solon
Center for the Arts, Opera per tutti, Lyric Opera Cleveland, Opera Cleveland on
Tour, CIM Opera Theater, the Janiec Opera Company, and UNL's Opera Theater. A
champion of new music, Ms. Lauritsen debuted roles in Jim Harris and Mark Hayes'
new musical Civil War Voices, Monica Houghton's opera The Big Bonanza, and
Randall Eng's opera Florida. Ms. Lauritsen is Adjunct Professor of Voice at The
University of Akron, Head of the Voice Department at The Music Settlement in
Cleveland, Music Director of St. Peter's Lutheran Church in Shaker Heights,
and founding director of Sistema Orchestra Shaker at Lomond Elementary. She
is the recipient of the Boris Goldovsky Prize in Opera, the Milhaud Society
prize, the Vassos Prize in Art Song, and a Special Encouragement Award
from the Metropolitan Opera Guild.


Find residence options near NYU
There are 71 districst in New York City, but only 59 of them are habitable! (12 of them are green areas), nonetheless you can see here all of them
The districts are classified into Boroughs and also contain several Neighborhoods!
Explore and filter the data, when you feel comfortable click a row in the top districts table and the district will be displayed in the map
All distances are in km
Filters
Score column corresponds to affordability of district!
If you click any column the map will highlight the border of the selected district!
Here you find the list of all the Neighborhoods in NY, along with their name centroid coordinates (geografical center) and the borough that they are part of.
Hover over a district


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--- title: "Download" date: 2021-04-29T18:05:49+02:00 draft: true ---
Download Tailwind Starter Kit to get the compiled CSS and the sample pages on plain HTML, or on React, VueJS and Angular.
html-dashboard-page
):
.html
file from one of the above HTML folders
inside one of your browsers
angular-name-page
)npm install
ng server
react-name-page
)npm install
npm start
vuejs-name-page
)npm install
npm run serve


Blank Password, Shows Error
Ran on 8 devices with 8 passing and 0 failing in an average of 2 seconds
HTC-PG762
Droid
HTC One V
Nexus 7
PG86100
Nexus 4
Nexus S
T-Mobile myTouch 3G


Objective
To obtain an Internship in Computer Science for the Summer of 2022.
Education
Rose-Hulman Institute of Technology
Bachelor of Science, Computer Science
Expected Graduation June 2024, Major GPA of 4.0
Skills
- Java Object-Oriented Programming
- Matlab Programming
- SolidWorks Modeling
- Beginning Skills with
- HTML Programming
- CSS Programming
- JavaScript Programming


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java.lang.Object | +--org.apache.bcel.verifier.Verifier
A Verifier instance is there to verify a class file according to The Java Virtual Machine Specification, 2nd Edition. Pass-3b-verification includes pass-3a-verification; pass-3a-verification includes pass-2-verification; pass-2-verification includes pass-1-verification. A Verifier creates PassVerifier instances to perform the actual verification. Verifier instances are usually generated by the VerifierFactory.
VerifierFactory
,
PassVerifier
| Method Summary | |
VerificationResult |
doPass1()
Returns the VerificationResult for the given pass. |
VerificationResult |
doPass2()
Returns the VerificationResult for the given pass. |
VerificationResult |
doPass3a(int method_no)
Returns the VerificationResult for the given pass. |
VerificationResult |
doPass3b(int method_no)
Returns the VerificationResult for the given pass. |
void |
flush()
Forget everything known about the class file; that means, really start a new verification of a possibly different class file from BCEL's repository. |
java.lang.String |
getClassName()
Returns the name of the class this verifier operates on. |
java.lang.String[] |
getMessages()
This returns all the (warning) messages collected during verification. |
static void |
main(java.lang.String[] args)
Verifies class files. |
| Methods inherited from class java.lang.Object |
clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait |
| Method Detail |
public VerificationResult doPass1()
public VerificationResult doPass2()
public VerificationResult doPass3a(int method_no)
public VerificationResult doPass3b(int method_no)
public final java.lang.String getClassName()
VerifierFactory
public void flush()
public java.lang.String[] getMessages()
public static void main(java.lang.String[] args)
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