HackDFW is the design-thinking hackathon of Dallas.
It is a place where you can be part of the nebulous, wondrous, awe-inspiring, frustrating-yet-glorious process of creating. A place where ideas aren't limited by fear or doubt, just 24 hours.
What is design thinking?
A way of problem solving that allows you to create innovative solutions rooted in people's needs through collaboration, empathy and iteration.
We're currently finalizing details for our Fall 2016 hackathon. Stay tuned on our social media pages!
All creators are welcome! This includes high schoolers, undergraduate students, graduate students, and professionals!
Teams are composed of 2-5 people. You can do it alone, but it'll be less fun!
We'll have a scheduled time where people can form teams after the opening ceremony. Before then, join us at the HackDFW Attendees Page!
Laptops, chargers, cell phones, and of course passion are helpful. Other useful items include: toiletries, a change of clothes, a pillow, some earplugs, an eye mask, a jacket, and...deodorant.
Knives, firearms, and other dangerous things are not permitted at the event.
HackDFW is 100% free for students! This includes WiFi, food, drinks, and miscellaneous swag for the entire hackathon!
A limited number of professionals will be welcomed to participate at a cost. Professional registrants are first come first serve until tickets are sold out. Stay tuned for our early-bird tickets!
A hackathon is an experience to build something new within a set amount of time. Therefore, you cannot work on past projects. However, using your own open-source or third party libraries is fine, so long as the project is new.
We will have a variety of hardware available for use. A full hardware list is available here.
We will be providing reimbursements to a select percentage of out-of-state students only.
Yes! We are currently finalizing our bussed regions, but will be initially dependent on historic attendance record and refined by registration numbers.
We'll be announcing our location soon after registrations open. Hint: It'll be in Dallas! If you're flying, the two options are DFW International Airport (DFW) or Dallas Love Field (DAL). Generally speaking, DFW has cheaper flights, but DAL is closer to our potential venue.
We'd love to hear from you! We are a friendly bunch and open to discussing new ideas, improvements or partnerships.
<EMAIL_ADDRESS>us a visit or send us snail mail.
1201 International Pkwy. STE 200A
Richardson, TX 75081


Posted by: Tosca Wilson
Date: 28.09.2019
The principles of neuroplasticity are essentially the science behind what is called growth mindset. Neuroplasticity can be described as the ability of the brain to restructure itself and grow. Just like a muscle, the brain can get stronger the more you use it.
 The more that this connection is used the stronger it becomes, and it will get both easier and faster for the brain to use the pathway. This makes practice and repetition some of the best ways to increase neuroplasticity.
When people understand neuroplasticity, their perception of their own abilities can also change and can understand that their intelligence and abilities are not actually fixed traits. This shift perception is towards what is called a Growth Mindset. This shift in mindset can also make it easier for people to embrace mistakes, obstacles, and challenges, as these are all important aspects of the learning process.


Some recent github projects I have worked on:
Contact me at<EMAIL_ADDRESS>I've done many things, and worn many different hats over the years. I've always focus on projects and jobs where I can learn new skills and implement old ones too. I think the following sums up my background best:
I'm a Aviation graduate with a passion for technology and longboarding and have spent sa considerable amount of time after graduation learning about SEO, SEM and pursuing my passion for IT. I completed a DBIT program at Strathmore University, and I'm polishing my developer skills now at the Moringa school intro course. Finally, I also work as a content writer and have my own niche blog - downhill254.com. I am extremely committed to pursuing my passions and overcoming any challenges that present themselves.


Polling is a system that allows you to check regularly if there is any changes in the some data. When there is a change in the data you can execute a expression in a storage item.
To configure polling you need first configure the event PollingEvent in DrapoMiddlewareOptions.
In this event you will receive 3 paremeters.
domain: the domain uses in this context
connectionId: the connectionId of the client that sent the request
key: the d-pollingKey of the storage item in the client side
Your function will be like this one below
In the storage item you need to configure 2 new atributes.
d-pollingKey will be the key for the polling. Each storage must have an unique value for it.
d-pollingTimespan is the timespan between each check. This is an optional attribute. If you miss him the default value will be 10000ms
Bellow an sample using it


Please reach out to me, I would love to help anyway I can!
Call/Text<PHONE_NUMBER>
Email<EMAIL_ADDRESS>Facebook:
https://www.facebook.com/donnasdanceclass/My address:
2163 Shadow Wood Dr. Lehi Utah
Please reach out to me, I would love to help anyway I can!
Call/Text<PHONE_NUMBER>
Email<EMAIL_ADDRESS>Facebook:
https://www.facebook.com/donnasdanceclass/My address:
2163 Shadow Wood Dr. Lehi Utah


Home
Rankings
Browsing all rankings
Top Time Travel Anime
Best time travel anime
Top Action Anime
Best action anime
Top Animation in Anime
Best animation anime


Cub Blog | 28 April 2020
Words: Cubs Name (first name only is fine, or write as "Anonymous Cubs")
I am a paragraph
I am a new pargraph
I am a paragraph
I am bold
I am italics
I'm some bigger text
I'm some red text
I'm the Hulk
I am a paragraph
I am a paragraph
I am a paragraph
I am a paragraph
These are emojis: 😀 😄 😍 💗
These are bigger emojis: 😀 😄 😍 💗


Lightweight
Skaffold is client-side only. With no on-cluster component, there is no overhead or
maintenance burden to your cluster.
Works Everywhere
Skaffold is the easiest way to share your project with the world: 'git clone', then
'skaffold run'.
Additionally, you can use profiles, local user config, environment variables, and flags to
easily incorporate differences across environments.
Feature Rich
Skaffold has many essential features for container & Kubernetes development, including
policy-based image tagging, resource port-forwarding and logging, file syncing, and much more.
Optimized Development
Skaffold's inner loop is tight and highly optimized, giving you instant feedback while
developing.
{{< /blocks/values-section >}}
{{< blocks/testimonial-section >}}
"Our customers love [Kubernetes], but consistently gave us feedback that
developing on Kubernetes was cumbersome. Skaffold hit the mark in addressing this problem. Changes to a docker
image or a configuration that previously took several minutes to deploy now take seconds. Skaffold's plugin
architecture gives us the ability to deploy to Helm or Kustomize, and to use various docker build plugins such
as Kaniko. Skaffold replaced our bespoke collection of utilities and scripts with a streamlined tool that is
easy to use."
Warren Strange, Engineering Director, ForgeRock
"When we evaluated what workflows we could use with Kubernetes, Skaffold stood out
as a tool we'd want for both development and deployment. It gives us a common entry point across applications
that we can also reuse for CI/CD. Right now, all of our CI/CD pipelines for Kubernetes applications use Skaffold
when building and deploying."
Taylor Barrella, Software Engineer, Quora
"Skaffold is an amazing tool that simplified development and delivery for us.
Skaffold hit our sweet spot by covering two dimensions: First, the entire development cycle from local
development, integration testing to delivery. Second, Skaffold enabled us to develop independently of the platform
on Linux, OSX, and Windows, with no platform specific logic required."
Martin Höfling, Principal Consultant, TNG Technology Consulting
GmbH
{{< /blocks/testimonial-section >}}


Online and offline archiving of COCO data.
create
and get
are the main functions to create and retrieve online and
local offline archives. Local archives can be listed via ArchivesLocal
(experimental/beta), already used online archives are listed in ArchivesKnown
.
An online archive class defines, and is defined by, a source URL containing an archive definition file and the archived data.
get('all') returns all "officially" archived data as given in a folder
hierarchy (this may be abondoned in future). Derived classes "point" to
subfolders in the folder tree and "contain" all archived data from a single
test suites. For example, get('bbob') returns the archived data list
for the bbob
testbed.
First, we prepare the datasets. A dataset is a (tar-)zipped file containing a full experiment from a single algorithm. The first ten-or-so characters of the filename should be readible and informative. Datasets can reside in an arbitrary subfolder structure, but the folders should contain no further (ambiguous) files in order to create an archive from the archive root folder.
Second, we create the new archive with create
,
>>> import cocopp >>> from cocopp import archiving >>> local_path = './my-archive-root-folder' >>> archiving.create(local_path) # doctest:+SKIP
thereby creating an archive definition file in the given folder. The
created archive can be re-instantiated with cocopp.archiving.get
and
all data can be processed with cocopp.main
, like
>>> my_archive = archiving.get(local_path) # doctest:+SKIP >>> cocopp.main(my_archive.get_all('')) # doctest:+SKIP
We may want to check beforehand the archive size like
>>> len(my_archive) # doctest:+SKIP
as an archive may contain hundreds of data sets. In case, we can choose a
subset to process (see help of main
and/or of the archive instance).
Third, we put a mirror of the archive online, like:
rsync -zauv my-archives/unique-name/ http://my-coco-online-archives/a-name
Now, everyone can use the archive on the fly like
>>> remote_def = 'http://my-coco-online-archives/a-name' >>> remote_archive = cocopp.archiving.get(remote_def) # doctest:+SKIP
just as a local archive. Archive data are downloaded only on demand. All data can be made available offline (which might take long) with:
>>> remote_archive.get_all('') # doctest:+SKIP
Remote archives that have been used once can be listed via ArchivesKnown
(experimental/beta).
Details: a definition file contains a list of all contained datasets by path/filename, a sha256 hash and optionally their approximate size. Datasets are (tar-)zipped files containing a full experiment from a single algorithm.
| Class | ArchivesKnown |
Known (and already used) remote COCO data archives. |
| Class | ArchivesLocal |
COCO data archives somewhere local on this machine. |
| Class | COCOBBOBBiobjDataArchive |
This class "contains" archived data for the 'bbob-biobj' suite. |
| Class | COCOBBOBDataArchive |
list of archived data for the 'bbob' test suite. |
| Class | COCOBBOBNoisyDataArchive |
This class "contains" archived data for the 'bbob-noisy' suite. |
| Class | COCODataArchive |
Data archive based on an archive definition file. |
| Class | ListOfArchives |
List of URLs or path names to COCO data archives available to this user. |
| Class | OfficialArchives |
overdesigned class to connect URLs, names, and classes of "official" archives. |
| Class | RemoteListOfArchives |
Elements of this list can be used directly with cocopp.archiving.get . |
| Function | create |
create a definition file for an existing local "archive" of data. |
| Function | get |
return a data archive COCODataArchive . |
| Function | read_definition_file |
return definition triple list |
| Variable | __author__ |
Undocumented |
| Variable | backup_last_filename |
Undocumented |
| Variable | coco_url |
Undocumented |
| Variable | coco_urls |
Undocumented |
| Variable | cocopp_home |
Undocumented |
| Variable | default_archive_location |
Undocumented |
| Variable | default_definition_filename |
Undocumented |
| Variable | listing_file_extension |
Undocumented |
| Variable | listing_file_start |
Undocumented |
| Variable | official_archives |
Undocumented |
| Class | _old_ArchivesOfficial |
superseded by OfficialArchives |
| Function | _abs_path |
return a (OS-dependent) user-expanded path. |
| Function | _definition_file_to_read |
return absolute path for sound definition file name. |
| Function | _definition_file_to_write |
return absolute path to a possibly non-exisiting definition file name. |
| Function | _download_definitions |
download definition file and sync url into it |
| Function | _get_remote |
return remote data archive as COCODataArchive instance. |
| Function | _hash |
compute hash of file file_name |
| Function | _is_url |
Undocumented |
| Function | _make_backup |
backup file with added time stamp if it exists, otherwise do nothing. |
| Function | _makedirs |
Undocumented |
| Function | _old_move_official_local_data |
move "official" archives folder to the generic standardized location once and for all |
| Function | _repr_definitions |
Undocumented |
| Function | _str_to_list |
try to return a non-string iterable in either case |
| Function | _url_add |
add ('_url_', url), to the definition file in folder . |
| Function | _url_to_folder_name |
return a path within the default archive location |
create a definition file for an existing local "archive" of data.
The archive in local_path
must have been prepared such that it
contains only (tar-g-)zipped data set files, one file for each data
set / algorithm, within an otherwise arbitrary folder structure (it is
possible and for large archives often desirable to create and maintain
sub-archives within folders of an archive). Choose the name of the zip
files carefully as they become the displayed algorithm names.
If a definition file already exists it is backed up and replaced.
The "created" archive is registered with ArchivesLocal
serving as a
user-owned machine-wide memory. cocopp.archiving.ArchivesLocal()
shows the list.
>>> from cocopp import archiving >>> # folder containing the data we want to become known in the archive: >>> local_path = 'my-archives/my-first-archive' >>> >>> my_archive = archiving.create(local_path) # doctest:+SKIP >>> same_archive = archiving.get(local_path) # doctest:+SKIP
An archive definition file is a list of (relative file name, hash and (optionally) filesize) triplets.
Assumes that local_path
points to a complete and sane archive or
a definition file to be generated at the root of this archive.
In itself this is not particularly useful, because we can also directly load or use the zip files instead of archiving them first and accessing the data then from the archive class within Python.
However, if the data are put online together with the definition file,
everyone can locally re-create this archive via get
and use the
returned COCODataArchive
without downloading any data
immediately, but only "on demand".
return a data archive COCODataArchive
.
url_or_folder
must be an URL or a folder, any of which must contain
an archive definition file of name coco_archive_definition.txt
. Use
create
to create this file if necessary.
When an URL is given the archive may already exist locally from
previous calls of get
. Then, get(url).update() updates the
definition file and returns the updated archive. Only the definition
file is updated, no data are downloaded before they are requested. The
updated class instance re-downloads requested data when the saved hash
disagrees with the computed hash. With new instances of the archive, if
COCODataArchive.update
is not called on them, an error message
may be shown when they try to use outdated local data and the data can
be deleted manually as specified in the shown message.
Remotely retrieved archive definitions are registered with ArchivesKnown
and cocopp.archiving.ArchivesKnown() will show a list.
>>> import cocopp >>> url = 'https://cma-es.github.io/lq-cma/data-archives/lq-gecco2019' >>> arch = cocopp.archiving.get(url).update() # downloads a 0.4KB definition file >>> len(arch) 4 >>> assert arch.remote_data_path.split('//', 1)[1] == url.split('//', 1)[1], (arch.remote_data_path, url)
See cocopp.archives
for "officially" available archives.
return a (OS-dependent) user-expanded path.
os.path.abspath
takes care of using the right os.path.sep
.
return absolute path for sound definition file name.
The file or path may or may not exist.
return absolute path to a possibly non-exisiting definition file name.
Creates a backup if the file exists. Does not create the file or folders when they do not exist.
Details: if filename is None, tries to guess whether the first
argument already includes the filename. If it seems necessary,
default_definition_filename
is appended.
return remote data archive as COCODataArchive
instance.
If necessary, the archive is "created" by downloading the definition file
from url
to target_folder
which doesn't need to exist.
Details: The target folder name is by default derived from the url
and
created within default_archive_location == ~/.cocopp/data-archives.


| PostgreSQL 9.0.10 Documentation | ||||
|---|---|---|---|---|
| Prev | Up | Chapter 13. Concurrency Control | Next |
Because readers in PostgreSQL do not lock data, regardless of transaction isolation level, data read by one transaction can be overwritten by another concurrent transaction. In other words, if a row is returned by SELECT it doesn't mean that the row is still current at the instant it is returned (i.e., sometime after the current query began). The row might have been modified or deleted by an already-committed transaction that committed after the SELECT started. Even if the row is still valid "now", it could be changed or deleted before the current transaction does a commit or rollback.
Another way to think about it is that each transaction sees a snapshot of the database contents, and concurrently executing transactions might very well see different snapshots. So the whole concept of "now" is somewhat ill-defined anyway. This is not normally a big problem if the client applications are isolated from each other, but if the clients can communicate via channels outside the database then serious confusion might ensue.
To ensure the current validity of a row and protect it against concurrent updates one must use SELECT FOR UPDATE, SELECT FOR SHARE, or an appropriate LOCK TABLE statement. (SELECT FOR UPDATE and SELECT FOR SHARE lock just the returned rows against concurrent updates, while LOCK TABLE locks the whole table.) This should be taken into account when porting applications to PostgreSQL from other environments.
Global validity checks require extra thought under MVCC. For example, a banking application might wish to check that the sum of all credits in one table equals the sum of debits in another table, when both tables are being actively updated. Comparing the results of two successive SELECT sum(...) commands will not work reliably in Read Committed mode, since the second query will likely include the results of transactions not counted by the first. Doing the two sums in a single serializable transaction will give an accurate picture of only the effects of transactions that committed before the serializable transaction started — but one might legitimately wonder whether the answer is still relevant by the time it is delivered. If the serializable transaction itself applied some changes before trying to make the consistency check, the usefulness of the check becomes even more debatable, since now it includes some but not all post-transaction-start changes. In such cases a careful person might wish to lock all tables needed for the check, in order to get an indisputable picture of current reality. A SHARE mode (or higher) lock guarantees that there are no uncommitted changes in the locked table, other than those of the current transaction.
Note also that if one is relying on explicit locking to prevent concurrent changes, one should either use Read Committed mode, or in Serializable mode be careful to obtain locks before performing queries. A lock obtained by a serializable transaction guarantees that no other transactions modifying the table are still running, but if the snapshot seen by the transaction predates obtaining the lock, it might predate some now-committed changes in the table. A serializable transaction's snapshot is actually frozen at the start of its first query or data-modification command (SELECT, INSERT, UPDATE, or DELETE), so it is possible to obtain locks explicitly before the snapshot is frozen.


Hi, I’m Csongor, CEO of Mosaik, and I want to give you a warm welcome to our Mosaik community.
Above all, we’re so happy you’re here. To us, community is everything, and the fact that you’ve
chosen to participate in ours couldn’t make us happier.
In my former life, pre-Mosaik, I was always obsessed with creating new and better ways to help
startups in Budapest flourish. Years of building relationships, organizing meetups, getting awesome
speakers to meet us and our city, and getting our awesome startups to see the world, this has
defined my career, and I’ve brought this enthusiasm and drive to its own home in Mosaik.
If you’re here for the same reasons, we’re already helping each other. This place is for you,
and we would love to see you use the tools and platforms at Mosaik to help you grow, and thereby
help Mosaik grow. We are a young organization, and we expect to develop and change a lot, quickly.
One thing we know will be constant -- Our culture of community. No matter
how many members we have, how many events we throw, or how many pots of coffee we brew, we will
always be the friendliest and most open, collaborative community in Budapest.
Thanks for taking part.
When you’re looking through our guide, we’d like you to keep one thing in mind. We created this
book as a compass, a guide, a north star if you will, to help give you some context and information
about how we operate. But keeping an open mind, collaborating, and continuously learning are all
values that we take extremely seriously, and this applies to our guide as well!
You’ll notice we haven’t printed our book, and that’s because we’re constantly adding to it,
developing it, and changing it accordingly. If you have any thoughts or suggestions, let us know.
Or better yet…
Our handbook will also be available as an opensource project on GitHub. We’ve engineered it so
that it’s constantly ready for improvement and new development -- to submit a change simply make
a pull request and add your contribution!
If you already know how to make edits and submit pull requests via Github, you know how this works.
If you don't, but want a lesson in using GitHub, post a message on Slack!
Look for a TODO on the bottom of pages where further development is required if you need help
getting started on contributing.


About
Thank you for visiting Banjo Speedruns! This website was created for the Banjo-Kazooie speedrunning community
to be used as a central hub for all things Banjo-Kazooie speedrunning: tricks, tutorials, glitches, routes, social links, and resources.
Our hope is that it serves as a reliable source of information for established runners, as well as a convenient first stop for new runners
getting started with running these games.
The Team
- Dechrissen – Creator, designer, site upkeep
- Azmi – Banjo-Kazooie category, movement, & bitclips pages
- Brandino – Banjo-Kazooie level pages
- dingoslayer – Banjo-Tooie level pages
- FalconXFalcon1 – Banjo-Tooie version differences page
- Kongkrete – Conception and initial planning
- oxylen – Banjo-Kazooie version differences page
Special Thanks To
- Those who have donated to help keep this website running
- The Banjo-Kazooie speedrunning community
- unlink2, for webserver setup assistance
- The creators of all the tutorials and videos found on this website


Neal Ford Presentation Patterns
https://presentationpatterns.com
Example: Displaying a List
Do not show all items in a list when the slide is first shown because the audience will read ahead and not listen to what you are saying about the first bullet.
Also when showing subsequent items either remove or gray out the previous items.
● Example: fade-in-then-out
● Example: fade-in-then-semi-out
● Nice 😃
Why reveal?
● Alternative to Google Drive, Power Point, Keynote, ...
● Has some nice features (themes, transitions, speaker notes, remote control, ...)
● Write HTML (everybody knows HTML)
● Available via url (e.g. deploy to Heroku, Surge.io)
● Source control
● Export as PDF
Live coding HTML Tags
Security
Running in browser locally no different than visiting a website (right?)
BUT ...
Must run the code as a node server to be able to generate a PDF. This is most assuredly a no no because running a server locally has disk access.


“Joey, there’s no living with it. The killing. There’s no going back from it. Right or wrong is a brand. A brand sticks. There’s no going back.  There aren’t any more guns in the valley.”
I can't think of a more fitting ending for Hugh Jackman's Wolverine, or indeed Patrick Stewart's Charles Xavier than Logan. A pitch-perfect movie in almost every regard, and gut-wrenching in every sense of the word. It's been 17 great years with Hugh playing the mutton-chopped, cigar-smoking, claw-wielding badass and now it's time to say goodbye.


Name
Email (Will not spam)
Breif Project description
Platforms?
iOS
Web
Android
Print
Has this project already been started?
Yes
No
What's your budget?


Contact Us
If you need technical support, you can contact us through the following channels :
- melonJS developer forum
- gitter web chat
Donation
melonJS is a open source project and free for everybody to use, and all development and support is mainly done during our sparetime. While we do not ask anyone to donate in exchange for anything, potential donations by grateful users are however welcome and will be used to cover the cost of maintaining this website, related resources, and maybe some useful hardware for testing.
Thank you !


*Source: ^emma™ conducted by Ipsos MediaCT, people 14+ for the 12 months ending September 2014, Nielsen Online Ratings September 2014, people 14+. Based on visited domain.com.au on tablet with app or website or domain.com.au on mobile with app or m-site / website or visited domain.com.au on desktop or allhomes.com.au last 4 weeks or read Domain in The Canberra Times (Sat L4W Net), Domain in the Newcastle Herald (Sat L4W Net), Domain in the Sunshine Coast Daily (Sat L4W Net), Domain in The Sydney Morning Herald (Sat L4W Net), Domain in The Age (Sat L4W Net), Domain in The Chronicle (Sat L4W Net), Domain in the Weekend Bulletin (L4W Net).
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--- layout: base title: "Page not found" description: "Whatever you were looking for, it is not here." permalink: 404.html plausible: 404 eleventyExcludeFromCollections: true ---
Nothing here, apparently. Maybe there never was? If you are sure there should be something you can contact us here: > Contact < Head back home >


When I look back at my life and reflect on my frustrations, I cringe, both for the situation itself, and my reaction as well. We've all been there. The grocery store clerk taking too long to help the customer in front of you ( only it's mostly just shooting the shit?) Or what about The driver you let in, only to go 5 MPH under the speed limit? Pretty understandable frustrations right? That co-worker who isn't pulling their weight. That neighbor who continually negates their overgrown tree growing over into your yard. Life is full of frustrations. Coding, in my opinion, will be no different.
Rock climbing, for me, is one of the most therapeutic and relaxing gifts I can give to myself. I plan to spend as much time as I can, Sundays, working out any and all frustrations the week may have thrown my way. It also allows for a clear and ready mind to conquer the new week on Monday.
Walking the 2 little ankle biters momentarily relieves my stress and frustrations and allow me to do a mind reset. After all, this life change is, to an extent, to better their lives.


The <javaFinalClass> element provides a flag within the ClassModifiersopt for classes that cannot be subclassed.
A class can be declared final if its definition is complete and no subclasses are required. A class cannot be declared as both abstract and final. The <javaFinalClass> element should not be used with the <javaAbstractClass> element. This element is the specialization of the apiQualifier element from the generic topic types.
<javaClass id="com.ibm.xtools.viz.ui.UMLDiagramResourceUtil"> <apiName>UMLDiagramResourceUtil</apiName> <shortdesc>Represents an utility class that exposes UML diagram resource lifecycle operations and provides access to the modeling platform.</shortdesc> <javaClassDetail> <javaClassDef> <javaFinalClass/> <javaClassAccess value="public"/> <javaBaseClass href="http://java.sun.com/../Object.html" format=”html” scope=”external”>java.lang.Object</javaBaseClass> </javaClassDef> <apiDesc>API clients should not instantiate this class.</apiDesc> </javaClassDetail> </javaClass>


Topic 1 Introduction
- Subjects
- Overview of statistical learning.
- Getting started with R, RStudio, and RMarkdown
- Reading: Chapter 2 of ISL
- Programming basics 1: Names, classes, and objects
1.1 Statistical and Machine Learning
The two terms, “statistical learning” and “machine learning,” reflect mainly the artificialities of academic disciplines.
- Statisticians focus on the statistical aspects of the problems
- Computer scientists are interested in “machines”, including hardware and software.
“Data Science” is a new term that reflects the reality that both statistical and machine learning are about data. Techniques and concepts from both statistics and computer science are essential.
1.1.1 Example 1: Machine translation of natural languages
Computer scientists took this on.
- Identification of grammatical structures and tagging text.
- Dictionaries of single-word equivalents, common phrases.
Story from early days of machine translation:
- Start with English: “The spirit is willing, but the flesh is weak.”
- Translate into Russian
- Translate back into English. Result: “The vodka is good, but the meat is rotten.”
Statistical approach:
- Take a large sample of short phrases in language A and their human translation into language B: the dictionary
- Find simple measures of similarity between phrases in language A (e.g. de-stemmed words in common)
- Take new phrase in language A, look up it’s closest match in the dictionary phrases in language A. Translation is the corresponding dictionary entry in language B
Where did the sample of phrases come from?
- European Union documents routinely translated into all the member languages. Humans mark correspondence.
- “Mechanical Turk” dispersal of small work tasks.
Result: Google translate.
1.1.2 Example 2: From library catalogs to latent semantic indexing
Early days: computer systems with key words and search systems (as in library catalogs)
Now: dimension reduction (e.g. singular value decomposition), angle between specific documents and what might be called “eigen-documents”
Result: Google search
1.1.3 Computing technique
Each student in the class as a personal repository on GitHub. The instructor is also a contributor to this repository and can see anything in it. Complete instructions for doing this are in the appendix.
- Set up some communications and security systems (e.g. an RSA key)
- Clone your repository from GitHub. It is at an address like
github.com/dtkaplan/math253-bobama
.
1.2 Review of Day 1
We discussed what “machine learning” means and saw some examples of situations where machine-learning techniques have been used successfully to solve problems that had at best clumbsy solutions before. (Natural language translation, catalogs of large collections of documents.)
We worked through the process of connecting RStudio to GitHub, so that you can use your personal repository for organizing, backing up, and handing in your work.
The Day-1 programming activity introduced some basic components of R: assignments, strings, vectors, etc.
1.3 Theoretical concepts ISL §2.1
“Data science” lies at the intersection of statistics and computer science.
1.3.1 Statistics concepts
- Sampling variability
- Bias and variance
- Characterization of precision
- Function estimation frameworks, e.g. generalized linear models
- Assumed probability models
- Prior and posterior probabilities (Bayesian framework)
1.3.2 Computing concepts
- Algorithms
- Iteration
- Simulation
- Function estimation frameworks, e.g. classification trees, support vector machines, artificial intelligence techniques
- Kalman filters
1.3.3 Cross fertilization
- Assumed probability models supplanted by simulation
- Randomization and iteration
- Cross validation
- Bootstrapping
- Model interpretibility Rather than an emphasis on the output of a function, interest in what the function has to say about how the world works.
1.4 Many techniques
“Learning” is an attractive word and suggests that “machine learning” is an equivalent for what humans do. Perhaps it is to some extent …
But “modeling” is a more precise term. We will be building models of various aspects of the world based on data.
- Model: A representation for a purpose. Blueprints, dolls, model airplanes.
- Mathematical model: A model built of mathematical stuff
- polynomials: Math 155
- functions more generally: e.g. splines, smoothers, …
- trees
- geometry of distance: e.g. which exemplar are the inputs closest to?
- projection onto subspaces
- Statistical model: A mathematical model founded on data.
1.4.1 Unsupervised learning
Wait until the end of the semester.
We will be doing only supervised learning until late in the course.
1.4.2 Supervised learning:
- We have a set of cases, \(i = 1, 2, 3, \ldots, n\), called the training data.
- For each case, we have an input \({\mathbf X_i}\) consisting potentially of several variables measured on that case.
- The subscript \(i\) in \({\mathbf X_i}\) means that we have one \({\mathbf X}\) for each case. The boldface \({\bf X}\) means that the input can be multi-dimensional, that is, consisting of multiple variables.
- The subscript \(i\) in \({\mathbf X_i}\) means that we have one \({\mathbf X}\) for each case. The boldface \({\bf X}\) means that the input can be multi-dimensional, that is, consisting of multiple variables.
- For each case, we have an output \(Y_i\).
- We want to learn the overall pattern of the relationship between the inputs \({\mathbf X}\) and the outputs \(Y\), not just for our \(n\) training cases, but for potential cases that we have not encountered yet. These as yet unencountered cases are thought of as the testing data.
- We are going to represent the pattern with a function \(\hat{f} : {\bf X} \rightarrow Y\).
- Sometimes I’ll use the word model instead of function. A model is a representation for a purpose. A function is a kind of representation. So some models involve functions. That’s the kind we’ll focus on in this course. I say “model” out of habit, but it’s a good habit that reminds us that the purpose of the function is important and we should know what that purpose is when we undertake learning.
1.5 Basic dicotomies in machine learning
There are fundamental trade-offs that describe the structure of learning from data. There are also trade-offs that arise between different methods of learning. Finally, there are dicotomies that stem from the different purposes for learning.
These dicotomies provide a kind of road map to tell you where are are and identify where you might want to go.
And, as always, it’s important to know why you are doing what you’re doing: your purpose.
1.5.1 Purposes for learning:
- Make predictions. Given new inputs, e.g. data about an event, predict what the result of the event will be. e.g. weather forecasting, credit card fraud, success in college, …. In statistics, this is sometimes thought of as “analyzing data from an observational study.”
- Anticipate the effects of an intervention that we impose, e.g., giving a patient a drug, changing funding for schools, … Traditionally in statistics, this has been tied exclusively to data from experiments. There is now greater acceptance that experiments are not always possible, and it’s important to be able to make reasonable inferences about causation from observational studies.
- Find structure in a mass of data.
1.5.2 Dicotomies
- make predictions vs capture causal mechanism (in this course: common sense. There are also formal techniques to guide causal reasoning.)
- flexibility vs variance (need some tools for this)
- black box vs interpretable models (comparing model architectures)
- reducible vs irreducible error (“bias” vs “residuals”)
- regression vs classification (easy!)
- supervised vs unsupervised learning (easy!)
1.5.3 Prediction versus mechanism
Example: Malignancy of cancer from appearance of cells. Works for guiding treatment. Does it matter why malignant cells have the appearance they do?
Story: Mid-1980s. Heart rate variability spectral analysis and holter monitors. (Holters were cassette tape recorders set to record ECG very, very slowly. Spectral analysis breaks down the overal signal into periodic components.) Very large spike at 0.03 Hz seen in people who will soon die.
Could use for prediction, but researchers were also interested in the underlying physiological mechanism. Causal influences. We want to use observations to inform our understanding of what influences what.
Story continued: The very large spike was the “wow and flutter” in the cassette tape mechanism. This had an exact periodicity: a spike in the spectrum. If the person was sick, their heart rate was steady: they had no capacity to vary it as other conditions in the body (arterial compliance, venus tone) called for. Understanding what happens in cardiac illness is, in part, about understanding how the various control systems interact.
1.5.4 Flexibility versus variance
In traditional statistics, this is often tied up with the concept of “degrees of freedom.”
Not flexible:
Flexible:
And in multiple variables:
Not flexible:
Flexible:
1.5.5 Black box vs interpretable models
Many learning techniques produce models that are not easily interpreted in terms of the working of the system. Examples: neural networks, random forests, etc. The role of input variables is implicit. Characterizing it requires experimenting on the model. In other learning techniques, the role of the various inputs and their interactions is explicit (e.g. model coefficients).
The reason to use a black-box model is that it can be flexible. So this tradeoff might be called “flexibility vs interpretability.”
A quick characterization of several model architectures (which they call “statistical learning methods”)
1.5.6 Reducible versus irreducible error
How good can we make a model? How do we describe how good it is?
What does this mean? (from p. 19)
\[\begin{array}{rcl} E(Y - \hat{Y})^2 & = & E[f(X) + \epsilon - \hat{f}(X)]^2\\ & = & \underbrace{[f(X) - \hat{f}(X)]^2}_{Reducible} + \underbrace{Var(\epsilon)}_{Irreducible}\\ \end{array}\]
Notation:
- \(X\) — the inputs that determine the output \(Y\).
- \(Y\) — the output, that is, the quantity we want to predict
- \(\hat{Y}\) — our prediction
- hat means estimated, no hat means “real” (whatever that might mean)
- \(E(Y - \hat{Y})^2\) — the mean of the square difference between our prediction and the “real” value. \(E\) means “expectation value.”
- \(f(X)\) — what \(Y\) would be, ideally, for a given \(X\)
- \(\hat{f}(X)\) — our estimate of \(f(X)\)
- \(\epsilon\) — but \(Y\) is subject to other, “random” influences. \(\epsilon\) represents these. \(\epsilon\) is a misleading notation because it may not be at all small in practice. But \(\epsilon\) is alway centered on zero (by definition).
- \(|f(X) - \hat{f}(X)|\) — the magnitude of the difference between the “real” \(f()\) and our estimate. This can be made small by
- collecting more data
- using a more flexible model
- expanding the set of inputs considered
- \(Var(\epsilon)\) — the “variance” of \(\epsilon\). This is the mean square of \(\epsilon\), that is, \(E(\epsilon^2)\).
1.5.7 Regression versus classification
Regression: quantitative response (value, probability, count, …)
Classification: categorical response with more than two categories. (When there are just two categories, regression (e.g. logistic regression) does the job.)
1.5.8 Supervised versus unsupervised
- Demographics groups in marketing.
- Poverty vs middle-class
- Political beliefs … left vs right?
1.6 Programming Activity 1
1.7 Review of Day 2
1.7.1 Trade-offs/Dicotomies
- Regression vs classification
- Different kinds of functions. A classifier has output as a categorical level. A regression has output as a number.
- Many classifiers are arranged to produce as output a set of numbers: the probability of each of the possible levels of the categorical output. When there are just two such levels, only one probability is needed. (The other is simply \((1-p)\).) So for two-level classifiers, there’s not necessarily a distinction between regression and classification. Thus, “logistic regression.”
- Supervised vs unsupervised learning
- In supervised learning, with have an output (response variable) \(Y\) which we want to generate from input \(\mathbf{X}\). We train a function \(\hat{f}: \mathbf{X} \rightarrow Y\)
- In unsupervised learning, there is no identified response variable. Instead of modeling the response as a function of \(\mathbf{X}\), we look for patterns within \(\mathbf{X}\).
- In supervised learning, with have an output (response variable) \(Y\) which we want to generate from input \(\mathbf{X}\). We train a function \(\hat{f}: \mathbf{X} \rightarrow Y\)
- Prediction vs causal mechanism
- Two different kinds of purpose. There may well be different kinds of functions best suited to each purpose.
Accuracy (flexibility) vs interpretability
We always want models to be accurate. Whether we need to be able to interpret the model depends on our overall purpose.Reducible error vs irreducible error
It’s good to know how accurate our models can get. That gives a goal for trying out different types of models to know when we don’t need to keep searching.
1.8 A Classifier example
A classification setting: Blood cell counts.
Build a machine which takes a small blood sample and examines and classifies individual white blood cells.
The classification is to be based on two measured inputs, shown on the x- and y-axes.
Training data has been developed where the cell was classified “by hand.” In medicine, this is sometimes called the gold standard. The gold standard is sometimes not very accurate. Here, each cell is one dot. The color is the type of the cell: granulocytes, lymphocytes, monocytes, …
1.9 Programming Activity 2
1.10 Day 3 theory: accuracy, precision, and bias
1.10.1 Figure 2.10
In constructing a theory, it’s good to have a system you can play with where you know exactly what is going on: e.g. a simulation.
The dark blue line in the left panel is a function the authors created for use in a simulation:
The dots are data the textbook authors generated from evaluating the function at a few dozen values of \(x\) and adding noise to each result.
The difference between the dots’ vertical position and the function value is the residual, which they are calling the error. The mean square error MSE is
\[\mbox{MSE} = \frac{1}{n} \sum_{i=1}^n (y_i - f(x_i))^2\]
- Take this notation apart. What’s \(n\)? What’s \(i\)?
- Suppose that \(f(x)\) were constant. In that situation, what kind of statistical quantity does this resemble?
- In actual practice, we don’t know \(f(x)\). (Indeed, it’s a matter of philosophy whether this is an \(f(x)\) — it’s a kind of Platonic form.) Here we know \(f(x)\) because we are playing a game: running a simulation.
Looking again at the left panel in Figure 2.9, you can see three different functions that they have fitted to the data. It’s not important right now, but you might as well know what these model architectures are:
- Linear regression line (orange)
- Smoothing splines (green and light blue). A smoothing spline is a functional form with a parameter: the smoothness. The green function is less smooth than the light blue function.
- That smoothness measure can also be applied to the linear regression form
Each of these three functions were fitted to the data. Another word for fitted is trained. As such, we use the term training error for the difference between the data points and the fitted functions. Also, because the functions are not the Platonic \(f(x)\), they are written \(\hat{f}(x)\).
For each of the functions, the training MSE is
\[\mbox{Training MSE} = \frac{1}{n} \sum_{i=1}^n (y_i - \hat{f}(x_i))^2\]
Right panel of the graph is something completely different: both the axes are different than in the left panel.
- x-axis: the smoothness of the functions. This is labelled flexibility.
- The three x positions correspond to the smoothness of the three models. This is measured as the effective number of parameters of the function.
Why does the straight-line function have a smoothness of 2? - y-axis: the MSE
- The dots connected by the gray curve show the training MSE of the three models.
- The dots connected by the orange curve show the testing MSE of the three models.
- The continuous curves were constructed by calculating the MSE for many more values of smoothness than shown in the left panel.
- How did they measure the training MSE?
1.10.2 Another example: A smoother simulated \(f(x)\).
- What’s different between the right panel of 2.9 and that of 2.10?
1.10.3 What’s the “best” of these models?
When examining training MSE, the more flexible model has the smaller MSE. This answer is pre-ordained, regardless of the actual shape of the Platonic \(f(x)\).
In traditional regression, we use ANOVA or adjusted$ \(R^2\) to help avoid this inevitability that more complicated models will be closer to the training data. Both of those traditional methods inflate* the estimate of the MSE by taking into account the “degrees of freedom,” df, in the model and how that compares to the number of cases \(n\) in the training dataset. The inflation looks like
\[ \frac{n}{n - \mbox{df}} \]
So when \(\mbox{df} \rightarrow n\), we inflate the MSE quite a lot.
Another approach to this is to use testing MSE rather than training MSE. So pick the model with flexibility at the bottom of the U-shaped testing MSE curve.
1.10.4 Why is testing MSE U-shaped?
- Bias: how far \(\hat{f}(x)\) is from \(f(x)\)
- Variance: how much \(\hat{f}\) would vary among different randomly selected possible training samples.
In traditional regression, we get at the variance by using confidence intervals on parameters. The broader the confidence interval, the higher the variation from random sample to random sample. These confidence intervals come from normal theory or from bootstrapping. Bootstrapping is a simulation of the variation in model fit due to training data.
Bias decreases with higher flexibility.
Variance tends to increase with higher flexibility.
Irreducible error is constant.
1.10.5 Measuring the variance of independent sources of variation
Simulation: Make and edit a file Day-03.Rmd
.
<IP_ADDRESS> Explore
Add three different sources of variation. The width of the individual sources is measured by the standard deviation sd=
.
n <- 1000
sd( rnorm(1000, sd=3) + rnorm(1000, sd=1) + rnorm(1000, sd=2) )
## [1] 3.951577
- Divide into small groups and
- construct a theory about how the variation in the individual components relates to the variation in the whole.
- test whether your theory works for other random distributions, e.g.
rexp()
<IP_ADDRESS> Result (Don’t read until you’ve drawn your own conclusions!)
The variance of the sum of independent random variables is the sum of the variances of the individual random variables.
1.10.6 Equation 2.7
\[E( y - \hat{f}(x) )^2 = \mbox{Var}(\hat{f}(x)) + [\mbox{Bias}(\hat{f}(x))]^2 + \mbox{Var}(\epsilon)\]
Breaks down the total “error” into three independent sources of variation:
- How \(y_i\) differs from \(f(x_i)\). This is the irreducible noise: \(\epsilon\)
- How \(\hat{f}(x_i)\) (if fitted to the testing data) differs from \(f(x_i)\). This is the bias.
- How the particular \(\hat{f}(x_i)\) fitted to the training data differs from the \(\hat{f}(x_i)\) that would be the best fit to the testing data.
\[\underbrace{E( y - \hat{f}(x) )^2}_{\mbox{Total error}} = \underbrace{\mbox{Var}(\hat{f}(x))}_{\mbox{source 3.}} + \underbrace{[\mbox{Bias}(\hat{f}(x))]^2}_{\mbox{source 2.}} + \underbrace{\mbox{Var}(\epsilon)}_{\mbox{source 1.}}\]
1.11 Programming Activity 3
1.12 Review of Day 3
- \(f({\mathbf X})\) versus \(\hat{f}({\mbox{X}})\): Platonic idea versus what we get out of the training data. Quip: “The hat means there’s a person underneath the model.”
- Mean Square Error — like the standard deviation of residuals
- Training vs testing data
- Smoothness, a.k.a. flexibility, model degrees of freedom
- More flexibility \(\rightarrow\) better training MSE
- Components of MSE
- Irreducible random noise: \(\epsilon\)
- Bias: \(f({\mathbf X}) - \hat{f}({\mathbf X})\)
- Caused by too much smoothness
- Caused by omitting a relevant variable
- Caused by including an irrelevant variable
- \(Var(\hat{f}({\mathbf X}))\) — how much \(\hat{f}\) varies from one possible training set to another.
- Increased by too many degrees of freedom: overfitting
- Increased by collinearity and multi-collinearity.
- Increased by large \(\epsilon\)
- Decreased by large \(n\)
1.14 Day 4 Preview
- The linear model (e.g. what
lm()
does) - A variety of questions relevant to different purposes, e.g.
- how good will a prediction be?
- what’s the strength of an effect?
- is there synergy between different factors?
ISL book’s statement on why to study linear regression
“Though it may seem somewhat dull compared to some of the more modern statistical learning approaches described … later …, linear regression is still a useful and widely used statistical learning method. Moreover, it serves as a good jumping-off point for newer approaches…. Consequently, the importance of having a good understanding of linear regression before studying more complex learning methods cannot be overstated.”
Concepts from linear regression:
- Compact representation of model form: polynomial coefficients.
- Much of inference (confidence intervals, hypothesis tests) can be expressed in terms of a polynomial coefficient.
- “Size” of model quantifiable as an integer: number of coefficients: degrees of freedom.
- Highly efficient estimation (when doing least squares)
1.15 Small data
The regression techniques were developed in an era of small data, such as that that might be written in a lab notebook or field journal. As a result:
- Emphasis on very simple descriptions, such as means, differences between means, simple regression.
- Theoretical concern with details of distributions, such as the t-distribution.
- the difference between z- and t-distributions are of no consequence for moderate DF and higher.
- No division into training and testing data. Data are too valuable to test! (Ironic, given the importance of replicability in the theory of the scientific method.)
As a consequence of (3), there’s a great deal of concern about assumptions, e.g.
- linearity of \(f({\mathbf X})\)
- structure of \(\epsilon\): IID — Independent and Identically Distributed
- uncorrelated between cases
- each is a draw from the same distribution.


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Ever since we arrived for vacation at my parents home August 17, I had the bug to go for a froggy hike in the Adirondacks. We did not bring any of the backpacking gear, or I would have figured out a way to do a short trip. We also didnt bring any decent hiking boots, but decided to go for it anyway.
Before college, when I lived out east, I did some hiking and backpacking in the Adirondacks, but I was not nearly as into it as I have been recently. So I decided to go fairly big.
After some research online, I decided to hike Gothics, the tenth highest Adirondack peak, with a couple more of the 46ers -- peaks over 4000 feet -- thrown in. After further research I found out we could add two more of the 46ers if we did two more 1/2 mile sidetracks one way. We were looking at a dead minimum of 14.5 miles without the side trips, so it was looking to be a pretty epic day.
I decided to go in at the Keene Valley entrance, known as the Garden, and do an all day trip.
Link to google terrain map: http://goo.gl/maps/Ex5od
Link to the ADK list of peaks:
http://www.adk46r.org/peaks
So here is the intended itinerary. I wisely concluded that we would not be able to do the second optional side hike to Lower Wolf Jaw, but that turns out to be fine because there is no individual 46er patch for the wolf jaws individually.
We got the Garden around 7:30 and started the hike.
Registering at the Garden.
A bridge just before John's Brooke Lodge (JBL)
JBL
This next stretch was one of the dicey bits of the trail, as far as staying on the right track. Most of the trails were quite well marked and easy to follow, but this bit needs a little help from the trail crew.
Crossing a stream just south of JBL.
We eventually found the trail marker for the orebed trail we intended to take up to the high peaks.
A good example of the trails we were on early and late in the day -- as we used to say in Connecticut, "roots, rock, reggae".
Lush dense lowland forest in the background.
In this picture and the next, you can now start to see some of the damage caused by Hurricane Irene from almost exactly a year ago in August 2011.
Robert scrambling up one of the many rock ledges we crossed.
Part of the trail along a water bed, the "Orebed" trail, whose name motivation will become obvious.
Lots of iron from the brook stains the rocks along this section of Orebed.
The next three pictures show more of the terrain we crossed on the Orebed trail.
About to hit one of the most interesting parts of Orebed.
At 3500 feet elevation.
Here are around 350 steps built and maintained by the Adirondack Mountain Club, presumably after Hurricane Irene.
Looking down on Robert and more of the destruction caused by Irene.
Finally at 4000 and close to the saddle between Gothics and Saddleback mountain.
A picture of flowers for the flower picture likers out there.
We arrived at the the trail junction for Gothics, where we had to make a call on adding the 1 mile to bag Saddleback.
At the saddle between Saddleback and Gothics.
We decided to add Saddleback with the optional hiking. It turned out to be fairly steep, but nothing like Gothics which was waiting ahead.
Panoramic view from close to Saddleback. The mountain with the scars in the foreground is Basin, with Haystack just peaking over the shoulder. Upper Ausable lake in the distance, which apparently and unfortunately is not available as a camping spot for a backpacking trip.
After lunch rest and foot recuperation. Sneakers are not the best footwear for such an endeavor.
The trail around the horn to get Saddleback.
After saddleback and retracing our steps, this is on the way to Gothics. I dont know where he got the energy, but Robert sprinted up this little face, maybe just because that little face was there.
We were liking the lichen.
A panorama from the approach to Gothics. Far left is Haystack, then the familiar Basin, and finally the clear saddle of Saddleback.
Robert on the Gothics approach.
Robert's favorite part of the day. We were on this cabled route for something like 1/3 a mile, and he was grinning from ear to ear the entire time.
We saw two backpackers going
down
the steep face pitch to Gothics. We think they did not get the good advice we did to go counter-clockwise from above and go up Orebed.
Close to the geological marker for Gothics, Marcy is just peeking out behind Basin.
My favorite picture form the hike, on Gothics with Marcy (1st highest), Haystack (3rd highest), and Basin (9th highest, scars) showing.
Looking south from Gothics towards Upper Ausable lake and some ponds.
Robert positioned to scare his Grandmother. She might even gasp seeing this on her computer.
Proof of Gothics
The trail from Gothics towards Armstrong is not as open as the previous trail, narrow through dense scrub pines and cedar.
Panoramic from Armstrong (I am pretty sure, although we did not see the survey marker), with Gothics, Saddleback, Basin, Marcy visible, if you care. All those other ones we need to get next time.
On the way down. You can start to see some evidence of the mile of delta z vertical we have covered.
Even more evidence.
Some mushrooms that Larissa assures us are edible. There were lots of these in the woods along the trail at the higher elevations.
The way down was hard. Harder than the way up, no question. Tons of ledges with roll your own path.
A ledge.
Another ledge.
Yet another ledge.
A ledge with a ladder (1). Just check out that smile!
A ledge with a ladder (2).
A ledge with a ladder (3).
Kid is still smiling, and this is something like mile 11 or 12.
At mile 14, still smiling! Maybe because we just refilled the water bottles with the MSR filter at Wolfjaw brook. I like mechanical filters because you can drink right away and enjoy clear, cold, clean water without any wacky taste ruination.
We chose an unfortunate route back, the south trail along John's Brook, and had to do quite a bit of bushwhacking and some of the route along the boulders of John's brook.
We even had advice from a well experienced local (Thanks Anne!) who told us not to take the southside trail, but it looked so good on the map ...
Final stretch of the Southside trail, maybe 1.5 miles from the car (1).
Final stretch of the Southside trail (2).
Last picture on the trail. That kid is fairly toast. When I said I left a snickers bar on Gothics, he said we can get another one.
What a great trip! We spent about 10.5 hours on the trail covering 17.5 miles and 6000 feet of delta Z. We bagged 4 of the Adirondack 46ers and really enjoyed the day. If we could change one thing, we agreed we would have loved to have had our real hiking boots from back in California. Next time ...
I treated Robert and myself to some Adirondack peak patches from the Mountaineer outfitter in Keene.
Route map, courtesy of my Dad's GPS.
Route profile, courtesy of my Dad's GPS. Not sure why the GPS said 17.5 distance but the profile map says 16, but who cares.
-


You’re the last thing I think of when I go to bed
Enn you are the first thing i think of when am a wake.


For each metadata class, there is an XML type with the same name than in the abstract specification
(for example, mdb:MD_Metadata
and cit:CI_Citation
).
All of these types may be used as the root of an XML document.
It is therefore possible to write a document representing a complete MD_Metadata
object,
or to write a document representing only a CI_Citation
object.
ISO 19115-3 standard arranges the content of these objects in an unusual way:
for each property whose value type is itself another class of ISO 19115,
the value is wrapped in an element that represents its type, rather than being written directly.
For example, in an object of the CI_Citation
type,
the value of the citedResponsibleParty
property is incorporated
into a CI_Responsibility
element.
This practice doubles the depth of the hierarchy, and introduces duplication at all levels for each value,
as in the following example:
<MD_Metadata> <identificationInfo> <MD_DataIdentification> <citation> <CI_Citation> <citedResponsibleParty> <CI_Responsibility> <party> <CI_Party> <contactInfo> <CI_Contact> <onlineResource> <CI_OnlineResource> <linkage> <URL>https://www.ogc.org</URL> </linkage> </CI_OnlineResource> </onlineResource> </CI_Contact> </contactInfo> </CI_Party> </party> </CI_Responsibility> </citedResponsibleParty> </CI_Citation> </citation> </MD_DataIdentification> </identificationInfo> </MD_Metadata>
The preceding example, like all documents that conform to ISO 19115-3, consists of a systematic alternation of two types of XML elements:
It begins with the name of the property, which always begins with a lower-case letter (ignoring prefixes).
In Java APIs, each property corresponds to a method in its enclosing class.
In the example above, mdb:identificationInfo
(a property of MD_Metadata
class)
corresponds to the Metadata.getIdentificationInfo()
method.
The value type is included under each property, unless it has been replaced with a reference
(the following sub-section will elaborate on this subject).
The value type is an XML element whose name always begins with an upper-case letter,
ignoring prefixes.
In the example above we had MD_DataIdentification
,
which corresponds to the DataIdentification
Java interface.
It is this XML element that contains the child properties.
In order to reduce the complexity of the libraries, GeoAPI and Apache SIS only expose publicly a single unified view of these two types of elements. The public API basically corresponds to the second group. However, when writing an XML document, elements of the first group must be temporarily recreated. The corresponding classes are defined in internal SIS packages. These classes may be ignored, unless the developer wishes to implement his or her own classes whose instances must be written in JAXB.
The parent element may contain an id
or uuid
attribute.
If one of these attributes is present, the parent attribute may be completely omitted;
it will be replaced at the time of reading by the element referenced by the attribute.
In the following example, the part on the left defines an element associated with the identifier “my_id
,”
while the part on the right references this element:
<MD_MetaData> <identificationInfo> <MD_DataIdentification id="my_id"> <!-- insert child properties here --> </MD_DataIdentification> </identificationInfo> </MD_MetaData>
<MD_MetaData> <identificationInfo xlink:href="#my_id"/> </MD_MetaData>
The decision of which attribute to use depends on the scope of the referenced item:
id
is only valid in the same XML document that defines the object it references.
uuid
may be valid outside the XML document,
but someone must maintain a database providing the objects for each given UUID.
xlink:href
may reference another XML document accessible on the Internet.
In the SIS library, all objects that can be identified in an XML document
implements the org.apache.sis.xml.IdentifiedObject
interface.
Each instance of this interface provides a view of its identifiers in the form of a Map<Citation,String>
,
in which the Citation
key indicates the type of identifier and the value is the identifier itself.
Some constants representing different types of identifiers are listed in IdentifierSpace
,
including ID
, UUID
and HREF
.
Each of these keys may be associated with a different type of value (usually String
,
UUID
or URI
) depending on the key.
For example, the following code defines a value for the uuid
attribute:
import org.apache.sis.metadata.iso.DefaultMetadata;
import org.apache.sis.xml.IdentifierSpace;
import java.util.UUID;
public class MyClass {
public void myMethod() {
UUID identifier = UUID.randomUUID();
DefaultMetadata
metadata = new DefaultMetadata
();
metadata.getIdentifierMap().putSpecialized
(IdentifierSpace.UUID, identifier);
}
}
Although this mechanism has been defined in order to better support the representation of XML attributes
of the gco:ObjectIdentification
group,
it also conveniently allows other types of identifiers to be manipulated.
For example, the ISBN
and ISSN
attributes of
Citation
may be manipulated in this way.
The methods of the IdentifiedObject
interface therefore provides a specific location
where all types of identifiers (not only XML) associated with an object may be manipulated.
When a property is not defined, the corresponding GeoAPI method usually returns null
.
However, things become complicated when the missing property is a value considered mandatory by ISO 19115 standard.
ISO 19115-3 allows for the omission of mandatory properties so long as the reason for the missing value is indicated.
The reason may be that the property is not applicable (inapplicable
),
that the value probably exists but is not known (unknown
),
that the value cannot exist (missing
),
or that the value cannot be revealed (withheld
), etc.
The transmission of this information requires the use of a non-nul object, even when the value is missing.
SIS will then return an object that, besides implementing the desired GeoAPI interface,
also implements the org.apache.sis.xml.NilObject
interface.
This interface flags the instances where all methods return an empty collection, an empty table, null
,
NaN
, 0
or false
, in this preference order, as permitted by the return types of the methods.
Each instance that implements NilObject
provides a getNilReason()
method
indicating why the object is nil.
In the following example, the left side shows a CI_Citation
element containing a
CI_Series
element, while on the right side the series is unknown.
If the CI_Series
element had been completely omitted,
then the Citation.getSeries()
method would return null
in Java.
But when a nilReason
is present, the SIS implementation of
getSeries()
returns instead an object that implements both the
Series
and NilReason
interfaces, and in which the
getNilReason()
method returns the constant UNKNOWN
.
<CI_Citation> <series> <CI_Series> <!-- insert child properties here --> </CI_Series> </series> </CI_Citation>
<CI_Citation> <series nilReason="unknown"/> </CI_Citation>


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I've been working in healthcare technology and software for the past 10+ years. In addition writing code and supporting customers for a large Electronic Medical Record (EMR) vendor, I've also built software connecting EMR software to a prescription refill software system. Outside of building software, I've also worked as a Product Manager, built websites, managed Customer Relationship Management (CRM) and email marketing software, and consulted for a variety of healthcare technology startups around the country.
I enjoy learning more about the healthcare technology space, helping health systems and startups innovate and use technology to improve the lives of both patients and clinical staff. I'm also excited to be learning more software development and using the Ionic framework to build mobile apps or using Javascript for either front-end or server-side applications.


The Perl interface to Berkeley DB version 1.85 is called DB_File. The Perl interface to Berkeley DB version 2 is called BerkeleyDB.
DB_File is distributed with the standard Perl source distribution (look in the directory "ext/DB_File"). You can find both DB_File and BerkeleyDB on CPAN, the Comprehensive Perl Archive Network of mirrored FTP sites. The master CPAN site is ftp://ftp.funet.fi/.
Questions about the DB_File and BerkeleyDB modules are best asked in the Usenet newsgroup comp.lang.perl.modules.
The Tcl and Python interfaces to Berkeley DB are less well-developed than Perl. Please contact Sleepycat Software for the latest information on Berkeley DB development using them.


Before Change
A representation of output features as a dict mapping keys (logical column
names) to values.
try:
element = element.element
except AttributeError:
pass
if saved_model_dir != self._saved_model_dir:
self._initialize_graph(saved_model_dir)
feed_dict = impl_helper.make_feed_dict(self._inputs, self._input_schema,
element)
After Change
A representation of output features as a dict mapping keys (logical column
names) to values.
if (not hasattr(self._thread_local, "graph_state") or
self._thread_local.graph_state.saved_model_dir != saved_model_dir):
self._num_graph_loads.inc(1)
self._thread_local.graph_state = self._GraphState(
saved_model_dir, self._input_schema, self._output_schema)
feed_dict = impl_helper.make_feed_dict(
self._thread_local.graph_state.inputs, self._input_schema, element)
fetched_dict = self._thread_local.graph_state.session.run(
self._thread_local.graph_state.outputs, feed_dict=feed_dict)


I enjoy watching Rugby and Boxing. I always try to watch Ireland's rugby games.
My favourite player is Keith Earls.
My favourite Boxer is Anthony Joshua, the heavyweight champion of multiple federations
I myself am part of the boxing club in UCC, and I row at home during the summers.


Forward and inverse kinematics for arbitrary Finger robots.

Provides forward and inverse kinematics functions for a Finger robot with arbitrarily many independent fingers.
◆ __init__()
| def trifinger_simulation.pinocchio_utils.Kinematics.__init__ |
( |
|
self, |
|
|
|
finger_urdf_path |
|
) |
| |
Initializes the robot model.
- Parameters
-
| finger_urdf_path | Path to the URDF file describing the robot. |
| tip_link_names | Names of the finger tip frames, one per finger. |
◆ forward_kinematics()
| def trifinger_simulation.pinocchio_utils.Kinematics.forward_kinematics |
( |
|
self, |
|
|
|
joint_positions, |
|
|
|
typing, |
|
|
|
List, |
|
|
|
np, |
|
|
|
ndarray |
|
) |
| |
Compute end-effector positions for the given joint configuration.
- Parameters
-
| joint_positions | Flat list of angular joint positions. |
- Returns
- List of end-effector positions. Each position is given as an np.array with x,y,z positions.
◆ inverse_kinematics()
| def trifinger_simulation.pinocchio_utils.Kinematics.inverse_kinematics |
( |
|
self, |
|
|
|
tip_target_positions |
|
) |
| |
Inverse kinematics for the whole manipulator.
- Parameters
-
| tip_target_positions | List of finger tip target positions, one for each finger. |
| joint_angles_guess | See :meth:inverse_kinematics_one_finger . |
| tolerance | See :meth:inverse_kinematics_one_finger . |
| max_iterations | See :meth:inverse_kinematics_one_finger . |
- Returns
-
tuple First element is the joint configuration, second element is a list of (x,y,z)-errors of the tip positions.
◆ inverse_kinematics_one_finger()
| def trifinger_simulation.pinocchio_utils.Kinematics.inverse_kinematics_one_finger |
( |
|
self, |
|
|
|
finger_idx |
|
) |
| |
Inverse kinematics for a single finger.
- Parameters
-
| finger_idx | Index of the finger. |
| tip_target_positions | Target position for the finger tip in world |
| frame. | |
| joint_angles_guess | Initial guess for the joint angles. |
| tolerance | Position error tolerance. Stop if the error is less then that. |
| max_iterations | Max. number of iterations. |
- Returns
-
tuple First element is the joint configuration (for joints that are not part of the specified finger, the values from the initial guess are kept). Second element is (x,y,z)-error of the tip position.
The documentation for this class was generated from the following file:
- python/trifinger_simulation/pinocchio_utils.py


A nebula is a huge cloud of combined gas and dust in space. It can contain hydrogen, helium, and/or plasma. They are sometimes considered nurseries, because oftentimes they are the location where stars are created and born.
 Each blast is the extremely bright, super-powerful explosion of a star. As the star runs out of nuclear fuel, some of its mass flows into its core. Eventually, the core is so heavy that it cannot withstand its own gravitational force. The core collapses, which results in the giant explosion of a supernova.
 The gravity is so strong because matter has been squeezed into a tiny space. This can happen when a star is dying - AKA a nebula - .


Submit a post
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- Host this file anywhere on internet
- Replace [TARGET] by the vulnerable Mgento domain
- Adjust the MAGMI path if you have a custom path
- Open the file in a browser as a victim while being authenticated to MAGMI
- A new executable file "info.php" will be created on the server


Trump impeachment highlights | Farage slams Johnson: WATCH the top world news videos for today
'I couldn't believe what I was hearing': Highlights from Day 3 of impeachment hearings
Four witnesses testified on Tuesday at the third public impeachment hearing before the US House of Representatives Intelligence Committee.
Nigel Farage slams Boris Johnson after election debate
Nigel Farage has accused Boris Johnson of showing no leadership.
Hong Kong protesters attempt escape via sewage system
Some of the anti-government protesters occupying Hong Kong Polytechnic University are attempting risky escape routes as the siege enters a fourth day.
Over 100 killed in Iran unrest - Amnesty International
At least 106 protestors have been killed in 21 cities in Iran during unrest that broke out over fuel price rises last week, Amnesty International said on Tuesday.
91% of Americans support marijuana legalisation
According to a new poll from the Pew Research Center, two-thirds of Americans support the legalisation of marijuana.


Sample Page
Sample site
This is a sample page to demonstrate the functionality of servepy!
Submit
{{names}}


AP govt launches toll-free number to curb sand mafia
by JBS UmanadhIn an effort to curb sand mafia from taking advantage of the lopsided demand and supply of sand Andhra Pradesh government decided to introduce a toll-free number.
Chief Minister YS Jagan Mohan Reddy has launched ‘toll-free number 14500. After the launch here on Monday, the Chief Minister dialled the number and gave his suggestions to the employees of the call centre.
Complaints can be made to the number if any irregularities are found like hoarding, smuggling, and selling sand at a higher price and such irregularities would attract penal action.
The action would be taken against those who hoard, resell or transport sand illegally and such violations would attract a fine of Rs 2 lakh and imprisonment up to two years.
The severe shortage of sand has crippled construction work in the state affecting the lives of over 35 lakh workers. Opposition parties say that 42 workers either died or committed suicide due to unemployment. TDP chief undertook a day-long Sand dharna and the government responded to the same with the sand weekend.


White powder prompts hazmat response to Palmdale office of former Rep. Katie Hill
by ABC7.com staffPALMDALE, Calif. (KABC) -- Hazardous materials crews responded to the Palmdale congressional office formerly belonging to ex-Rep. Katie Hill to examine an unknown powder delivered to the building.
Los Angeles County Fire Department units were called to the office on West Avenue M14 just after noon on a report of an envelope with an unknown white powder.
Several people were exposed to the powder but none are showing symptoms at this time. They will go through a decontamination process and be taken to a medical facility.
Investigators are working to determine what the white powder is.
Hill resigned last week amid allegations of an inappropriate relationship with a staffer.


|
No-Knead Bread
Adapted from Jim Lahey, Sullivan Street Bakery
Time: About 1 1/2 hours plus 14 to 20 hours' rising
Yield: One 1 1/2-pound loaf.
|
| 3 cups all-purpose or bread flour | mix in large bowl | cover,rest for 12-18 hours | dust with flour and fold over 2x, cover loosely, let rise 15min | shape dough into ball, use dusted towels to cover for 2-3hrs | bake 450°F in preheated cast iron pot covered 30min, then uncovered 15min |
| 1/4 tsp instant yeast |
| 1 1/4 tsp salt |
| olives, caraway seeds |
| 1 5/8 cups water | add |
| Cornmeal or wheat bran as needed. | |
|
Mackerel Fillets
Simmered in Soy Sauce
Time: 20 minutes
Yield: 3 to 4 servings.
|
| 1/2 cup soy sauce | mix in 12-in skillet | bring to boil and simmer at med. for 5mins,uncovered | add skin-side down cook for 7-10 mins. | plate |
| 1/3 cup sake |
| 1 tablespoon sugar |
| 2 tablespoons rice vinegar or white wine vinegar |
| 5 or 6 thin slices peeled fresh ginger |
| Finely grated zest of 1 lemon |
| 3 or 4 crushed garlic cloves |
| 1/2 cup water | add |
| 4 mackerel fillets, about 1 pound total, skin on | |
| Cooked white rice for serving | |
| Chopped scallions for garnish. |
|
Broiled Boston Mackerel
Time: 30 minutes
Yield: 4 servings.
|
| 2 tablespoons extra virgin olive oil | whisk | season and place skin-side down on baking sheet,broil 6mins |
| Grated zest of 1 lemon |
| 4 1-pound Boston mackerel or black sea bass fillets, rinsed and patted dry | |
| Kosher salt and Freshly ground black pepper |
|
Spring Herb Salad and Sugar Snap Peas
Time: 30 minutes
Yield: 4 servings.
|
| 1 1/2 packed cups fresh mixed herbs (parsley,basil,mint,celery leaves,lovage,tarragon) | combine | dress and season, arrange salad on top of fillets |
| 6 ounces sugar snap peas, trimmed and thinly sliced lengthwise |
| 5 small radishes, thin slice |
| 3 scallions, white and green parts, thin slice lengthwise |
| 2 tbsp EVOO | |
| 1 tbsp freshly squeezed lemon juice |
| Coarse sea salt or kosher salt |
| Freshly ground black pepper. |
|
Roasted Sardines With Smoked Paprika Oil
Time: 10 minutes
Yield: 4 servings
Preheat: 450°F
|
| 2 tsp smoked hot paprika | whisk | coat with oil, roast 8min |
| 1/4 cup extra virgin olive oil |
| 8 2-ounce fresh sardines,gutted,rinsed and patted dry. | |
|
Aioli Potatoes
Time: 40 minutes
|
| 1 large egg yolk | whisk together | add oil, whisk constantly | whisk in, season | spoon over potatoes |
| 2 tsp fresh lemon juice |
| 1/4 cup EVOO | combine |
| 3 tbsp canola oil |
| 1 large garlic clove, peeled | salt and mash to paste | |
| 4 Yukon Gold potatoes | boil 20mins until tender | |
|
Rainbow Trout Stuffed with Spinach, Pine Nuts and Dill
Time: 30 minutes
Yield: 4 servings
|
| 2 tablespoons unsalted butter |
melt in large pan |
add,saute 1min |
add, wilt 2-3min |
add, cook 1min |
stuff with filling, secure with skewers or string, cook over medium heat 5min 1st side, 3min 2nd side |
| 1/4 cup pine nuts | |
| 1 garlic clove, minced |
| 10 oz spinach, cleaned | |
| 1/4 cup golden raisins, chopped | |
| 3 tbsp fresh chopped dill |
| 4 8-ounce rainbow trout, filleted, butterflied, rinsed and patted dry | |
|
Pan-Fried Pacific Halibut
Time: 15 minutes
Yield: 4 servings
|
| 4 tbsp EVOO |
heat until very hot,lower to med-low |
add skin-side down,cook 4min/side |
add,remove from heat |
top fillets w/ onions + excess vinegar |
plate, garnish |
| 4 Pacific halibut fillets, rinsed, patted dry |
|
| 4 tbsp balsamic vinegar |
|
|
Balsamic-Glazed Spring Onions and Thyme
Time: 15min
|
| 3 bunches spring onions | trim,leave root intact,remove outer layer,quarter | add to pan, cover, 3min, uncover, 3min | add,cook 10sec,transfer to bowl |
| 2 tbsp EVOO | heat in large saute pan |
| 6 sprigs fresh thyme | |
| 2 tbsp balsamic | |
| chopped chives | |
|
Miso Carrot Sauce with Ginger
Time: 10 minutes
Yield: About 1 1/4 cup.
|
| 1/4 cup peanut or neutral oil, like grapeseed or corn |
pulse in food processor to mince carrots |
to taste |
| 1/4 cup rice vinegar |
| 3 tablespoons white miso, sold at Asian markets and specialty stores |
| 1 tablespoon dark sesame oil |
| 2 medium carrots, peeled and cut into big pieces |
| 1 inch-long piece fresh ginger, peeled and cut into coins |
| Salt and freshly ground black pepper. |
|


Calculate and interpret the Mean-Median Difference for each set of election results. Do the elections show a significant partisan bias to the Republicans under each district plan? (Again, do not include 100-0 districts in the calculations.) Use the calculator below to compute standard deviation and find \(t\text{.}\)
in-context


Player 1: Input any number of your choice from [0 - 10]
Player 2: Guess the number inputed by player 1
Check Reset


{% extends "Base.html" %} {% block content %}
You are about to delete the restaurant {{restaurant.name}}. Are you sure you want to do this? Cancel


Dear reader,
As of today, due to trademark reasons, the Kindlebox service has been renamed to BookDrop. From now on, kindlebox.me is obsolete. If you need to stop syncing your Dropbox folder or update your active Kindle devices, please visit the new site, getbookdrop.com.
What does that mean for you? Not much. You can continue using BookDrop by adding books to Dropbox/Apps/kindle-box. Users who register after today will have the folder Dropbox/apps/book-drop.
Thanks for your continued support, and happy reading!
BookDrop


--- title: About me layout: default ---
Hello, my name is Jonathan and I'm a fullstack javascript developer.
Curious and autodidact, I like to think outside of the box and challenge myself.
Continuous learning is what makes me like the web most, with its abundance of creativity and innovations, it keeps me interested and motivated to stay up to the lastest standards.
I have a good technical culture, especially open source softwares, which helps me identify the right opportunities when evaluating new technologies.
folder_open See my projects
I am currently looking for a full stack javascript developer job, if you are interested in my profile feel free to contact me.


Your Donation Counts
Participating in Team Member Giving creates immediate impact in our local communities and among each other. Last year, our contributions not only supported life-saving technological advancements and facility improvements across all WellStar facilities, but they also provided scholarships for continued education and aided team members during crisis situations.
For more information, contact one of the hundreds of Foundation Ambassadors across the System. You can also contact Amy Thomas with the Foundation at<EMAIL_ADDRESS>or call<PHONE_NUMBER>.


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| < Day Day Up > |
10.2 Why (Not) Use a Hierarchy?A neighbor cache improves performance by providing some extra fraction of requests as cache hits. In other words, some of the requests that are misses in your cache may be hits in the neighbor cache. If your cache can download these neighbor hits faster than from the origin server, the hierarchy should improve performance overall. The downside is that neighbor caches usually provide only a small percentage of requests as hits. About 5%, or maybe 10% if you're lucky, of your requests that are cache misses will be hits in a neighbor. In some cases, this small benefit doesn't justify the hassle of joining a hierarchy. In other cases, such as networks with poor or overutilized connectivity, hierarchies definitely improve performance for end users. If you use Squid inside a firewalled network, you may need to configure the firewall proxy as a parent. In this case, Squid forwards every request to the firewall because it can't connect directly to outside origin servers. If you have some origin servers inside the firewall, you can instruct Squid to connect to them directly. You can also use a hierarchy to send web traffic in different directions. This is sometimes called application-layer routing, or more recently, content routing. Consider, for example, a large organization with two Internet connections. Perhaps the second connection costs less, or has higher latency, than the other. This organization may want to use the second connection for low-priority traffic, such as downloading binaries, audio and video files, or other kinds of large transfers. Or, perhaps they want to send all HTTP traffic over one link, and non-HTTP traffic over the other. Or, perhaps certain users' traffic should go through the low-priority connection, while premium customers get to use the more expensive link. You can accomplish any of these scenarios with a hierarchy of caching proxies. Trust is one of the most important issues for the members of a cache hierarchy. You must trust your neighbors to serve correct, unmodified responses. You must trust them with sensitive information, such as the URIs requested by your users. You must trust that they maintain secure and up-to-date systems to minimize the chances of unauthorized access and denials of service. Another problem with hierarchies is the way that they normally propagate errors. When a neighbor cache experiences an error, such as an unreachable server, it generates an HTML page that explains the error and its origin. Your users may become confused if they get errors from neighbor caches outside the immediate organization. If the problem persists, they'll have a hard time finding an administrator who can help them. Sibling relationships are subject to special problem, known as false hits. This occurs when Squid sends a request to a sibling, believing it will be a cache hit, but the sibling is unable to satisfy the request without contacting the origin server. False hits happen in a number of circumstances, but usually with a low probability. Furthermore, Squid and other HTTP proxies have features for automatically retrying such requests so that the user isn't even aware of the problem. A forwarding loop is another problem sometimes seen in cache hierarchies. It occurs when Squid forwards a request somewhere, but that request comes back to Squid again, as shown in Figure 10-1. Figure 10-1. A forwarding loopForwarding loops typically happen when two caches consider each other parents. If you have such an arrangement, make sure that you use the cache_peer_access directive to prevent loops. For example, if the neighbor's IP address is <IP_ADDRESS>, the following lines ensure Squid won't cause a forwarding loop: acl FromNeighbor src <IP_ADDRESS> cache_peer_access the.neighbor.name deny FromNeighbor Forwarding loops can also occur with HTTP interception, especially if the interception device is on the path between Squid and an origin server. Squid detects forwarding loops by looking for its own hostname in the Via header. You may actually get false forwarding loops if two cooperating caches have the same hostname. The unique_hostname directive is useful in this situation. Note that if the Via header is filtered out (e.g., with headers_access), Squid can't detect forwarding loops. |
| < Day Day Up > |


Answer: {{answer.answer}}
Objective Evidence: {{answer.objectiveEvidence}}
Assumptions: {{answer.assumptionsYes}}
Notes: {{answer.notesYes}}
Answer: {{answer.answer}}
Who: {{answer.who}}
When: {{answer.when}}
Risk: {{answer.risk}}
What: {{answer.what}}
Reason: {{answer.reason}}
Assumptions: {{answer.assumptionsNo}}
Notes: {{answer.notesNo}}
Answer: {{answer.answer}}
Documentation: {{answer.documentation}}
Assumptions: {{answer.assumptionsNA}}
Notes: {{answer.notesNA}}
User Who Answered: {{answer.userId}}
Answered At: {{answer.updatedAt | date:'medium'}}


Home
Predict
About
{% if 'username' not in session %}
Login/Signup
{% else %}
Hi, {{session['username']}}
{% endif %}
Breathing Problem
Fever
Dry Cough
Sore Throat
Running Nose
Asthma
Chronic Lung
Headache
Heart Disease
Diabetes
Hyper Tension
Fatigue
Gastro Problem
Abroad Travelled
Contacted with anyone having COVID-19
Atended Large Gathering
Visited highly exposed Area
Family working in Public Exposed Places


VB.Net Interface 2
The method is explained with a sample program that converts a midi type 1 file (c:\temp\playtest.mid) to a midi type 0 file (c:\temp\playtestnew.mid).
The Visual Basic program consists of one form that has one command button called Convert MIDI Type. The file HP_midifile.dll should be located in the same folder as the basic program.
The method used here is to declare the names of dll functions that will be needed in the basic program using the internal identifications of the library. These "decorated names" are listed in the map-file HP_midifile.map. Use the string starting with the letter "?" in HP_Midifile.map for the "EntryPoint" in Visual Basic declare statement.
Care must be taken with the data type used. E.g. If the C++ dll is expecting:
UINT, Visual Basic should use ByVal Uint32
int, use Int32
reference *handle, use IntPtr
reference *string, use StringBuilder
reference *char, use StringBuilder
For more information on data type conversion between C++ and VB, refer to proper conversion tables in the web. For example
https://msdn.microsoft.com/de-de/library/47zceaw7.aspx
http://www.marvintest.com/KnowledgeBase/KBArticle.aspx?ID=210
For further information on declaring functions of HP_Midifile.dll, refer to P-Invoke on MSDN or http://www.pinvoke.net/The functions often refer to arguments such as HP_SMF0. The values expected by the dll are defined in the headerfile HP_midifile.h. For example the line
#define HP_SMF0 0 /* Standard MIDI file-format 0 */
means passing a "0" signifies a type 0 midi file.
The basic code in Form1
The VB project is compiled with VB.Net 2010 (and later) using "Platform target: Any CPU"
======================================================== Imports System.Text Imports System.Runtime.InteropServices Public Class Form1 <DllImport("HP_midifile.dll", EntryPoint:="?HP_Init@@YAPAVMIDIFile@@XZ", CharSet:=CharSet.Unicode, ExactSpelling:=True, CallingConvention:=CallingConvention.Cdecl)> Public Shared Function HP_init() As IntPtr End Function <DllImport("HP_midifile.dll", EntryPoint:="?HP_Load@@YAIPAVMIDIFile@@PBD@Z", CharSet:=CharSet.Ansi, ExactSpelling:=True, CallingConvention:=CallingConvention.Cdecl)> Public Shared Function HP_Load( ByVal mf As IntPtr, ByVal Name As StringBuilder) As UInt32 End Function <DllImport("HP_midifile.dll", EntryPoint:="?HP_Free@@YAIPAVMIDIFile@@@Z", CharSet:=CharSet.Unicode, ExactSpelling:=True, CallingConvention:=CallingConvention.Cdecl)> Public Shared Function HP_Free(ByVal mf As IntPtr) As UInt32 End Function <DllImport("HP_midifile.dll", EntryPoint:="?HP_Save@@YAIPAVMIDIFile@@PBDH@Z", CharSet:=CharSet.Ansi, ExactSpelling:=True, CallingConvention:=CallingConvention.Cdecl)> Public Shared Function HP_Save(ByVal mf As IntPtr, ByVal Name As StringBuilder, ByVal SMFType As Int32) As UInt32 End Function <DllImport("HP_midifile.dll", EntryPoint:="?HP_Play@@YAIPAVMIDIFile@@H_N@Z", CharSet:=CharSet.Unicode, ExactSpelling:=True, CallingConvention:=CallingConvention.Cdecl)> Public Shared Function HP_Play(ByVal mf As IntPtr, ByVal Selection As Int32, ByVal EventBefore As Boolean) As UInt32 End Function 'Const Const HP_ALL = 1 'MIDI File Formats Const HP_SMF0 = 0 ' Standard MIDI file-format 0 Const HP_SMF1 = 1 ' Standard MIDI file-format 1 Const HP_Error_None = 0 'Variables Private FileName As New StringBuilder Private NewFileName As New StringBuilder Private HmF As IntPtr 'Handle for Midi Private Sub Form1_Load(sender As Object, e As EventArgs) Handles MyBase.Load FileName.Clear() FileName.Append("c:\temp\playtest.mid") NewFileName.Clear() NewFileName.Append("c:\temp\playtestNew.mid") End Sub Private Sub btnConvertMidiType_Click(sender As Object, e As EventArgs) Handles btnConvertMidiType.Click Dim RC As UInt32 'Initialize Handle for Midi session HmF = HP_init() If HmF = 0 Then 'do some error handling here MsgBox("Error initializing Midi") End If 'Load Midi file RC = HP_Load(HmF, FileName) If RC <> HP_Error_None Then 'Do some error handling here MsgBox("Error opening Midi file") End If 'Play Midi file RC = HP_Play(HmF, 0, False) If RC <> HP_Error_None Then 'Do some error handling here MsgBox("Error playing Midi file") End If 'Save file as type 0 RC = HP_Save(HmF, NewFileName, HP_SMF0) If RC <> HP_Error_None Then 'Do some error handling here MsgBox("Error saving Midi file") End If 'Free handle RC = HP_Free(HmF) If RC <> HP_Error_None Then 'do some error handling here MsgBox("Error in free Midi") End If End Sub Private Sub Form1_FormClosing(sender As Object, e As FormClosingEventArgs) Handles MyBase.FormClosing FileName = Nothing NewFileName = Nothing End Sub End Class ========================================================


--- title: Recent Posts extends: listing_main.j2 default_block: test listable: false --- {% block navi %}


Power application - evaluate empirical power to detect association
Version 0.01, 12.27.2017
Christopher DeBoever and Manuel A. Rivas
C.D. led the research project and developed the web application. M.A.R designed and supervised the study.
Heads up!
This app shows the power to detect genetic associations for collapsed protein-truncating variants (PTVs) in different populations from gnomAD and the UK Biobank (UKB). We estimated collapsed PTV allele frequencies and 95% confidence intervals for each gene that contains PTVs and calculate power using the GeneticsDesign R package. More information on the methods to come.
Created by Chris DeBoever (https://cdeboever3.github.io/)
Video Tutorial for Power App
Christopher explains the Power app and teaches viewers how to interpret the plot and table using examples.


ArgumentInterpreter¶
-
class
support.arguments.
ArgumentInterpreter
¶ Bases:
object
Interprets the argument provided as the specied type of iterable
If a list is expected, a single value is embedded in a list. A tuple is converted to a list. An nparray is flattened and converted to a list:
a = ArgumentInterpreter() l = a.as_list(thing)
Note
As needed, we will as ‘as_nparray’, ‘as_tuple’
Methods Summary
as_list
(argument)Methods Documentation
-
as_list
(argument)¶
-


THIS IS THE UNIVERSAL LAYOUT:
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{% block content %}{% endblock %}
© 2020 The awesome DataDiVR Team


Project Description
D CRM Editable Grid is a zero code (Html5 JavaScript) add on for Dynamics CRM 2013, 2015, and 2016 for easily implementing custom editable grids
Features
- In-line editing
- Editing multiple records at the same time
- Allows multiple entity relationships to be represented in a single CRM form
- Allows multiple grids on a single form in different sections
- One configuration to display multiple entities on one form. Add field default value, condition, re-arrange fields display order, and set other options
- Aggregates
- Header and cell colors and custom CSS. For cells, a condition can be specified
- Filter
- Add continuous new records without opening any sub-windows or forms
- JavaScript call back mechanism for Validation, Delete, Create, load option set (to enable/disable options) and Save operations
- Ability to query any record, whether related to the current record or not
- Export to Excel, PDF, and CSV
- User settings (currency symbol, numeric separator, decimal symbol, date and time formats) are used for formatting currency, numeric, and date time values.
- Built in validation for Required, MaxLength, MinValue, and MaxValue field attributes
- Set record status
- Optional auto save and auto refresh on save
- Sorting
- Paging
- Navigation via keyboard arrows or tab keys
- Unlimited undo - Place focus on a cell that has gone through one/multiple changes, use undo button to traverse back through the changes until reaching the original value.
- Undo all changes - Resets all changed cells to their original value.
- Open record from the grid (in the same window or in a new window)
Installation
Configuration
First Configuration Set Up - Display One Entity
Display Two Unrelated Entities
Display Two related Entities
Display Entities on Different Sections of the Same Form
Documentation
Configuration
Translation
Display on a dashboard


| Name | Title | Phone |
|---|---|---|
| Antonio Barboni | UX Director |<PHONE_NUMBER> |
| Camelot Rinkus | Assistant to the UX Director |<PHONE_NUMBER> |
| Christian Pledger | Secret assistant to the UX Director |<PHONE_NUMBER> |
The ias-sort directive allows the user to specify a property to store the sort expression of the table.
The ias-sort-on allows the user to specify a sort expression for individual columns in the table. This expression is stored in the property designated in ias-sort.
| Name | Title |
|---|---|
|
|
{{person.title}} |
| Name | Title |
|---|---|
|
|
UX Director |
|
|
Assistant to the UX Director |
|
|
Secret assistant to the UX Director |
| Name | Action | Title |
|---|---|---|
| Antonio Barboni |
|
UX Director |
| Camelot Rinkus |
|
Assistant to the UX Director |
| Christian Pledger |
|
Secret assistant to the UX Director |


gov.nih.ncats.chemkit.api.Chemical
objects to different formats.See: Description
| Interface | Description |
|---|---|
| ChemFormat | |
| ChemFormat.AromaticAwareChemFormatWriterSpecification | |
| ChemFormat.ChemFormatWriterSpecification | |
| ChemFormat.HydrogenAwareChemFormatWriterSpecification | |
| ChemicalReader |
Read
Chemical s from some kind of datasource,
often a file. |
| ChemicalWriter |
Interface for writing (multiple) Chemicals out.
|
| WriterOptions |
| Class | Description |
|---|---|
| ChemFormat.MolFormatSpecification | |
| ChemFormat.SdfFormatSpecification | |
| ChemFormat.SmartsFormatSpecification | |
| ChemFormat.SmilesFormatWriterSpecification | |
| ChemicalReaderFactory |
Factory class that can create
ChemicalReader s
from various input sources. |
| ChemicalWriterFactory | |
| CtTableCleaner |
Utility class to fix or "clean up" v2000 mol and sdfile formatted
data by fixing common problems that happen from
copy and paste errors or known tools and produce invalid results.
|
| PushbackBufferedReader |
Wraps a BufferedReader so that it can "unread" lines.
|
| StandardChemFormats | |
| WriterOptionsBuilder |
gov.nih.ncats.chemkit.api.Chemical
objects to different formats.Copyright © 2021 NIH/NCATS. All rights reserved.