Home is where I rest my head
It's been a while since the last post and my 100th day on the road seems so far behind. A new personal record is coming up though: six months and 10.000 kms on the road. Six months is sort of a ...
It's been a while since the last post and my 100th day on the road seems so far behind. A new personal record is coming up though: six months and 10.000 kms on the road. Six months is sort of a ...


| Total net profit | -0.24 | Gross profit | 4.94 | Gross loss | -5.18 |
| Profit factor | 0.95 | Expected payoff | -0.01 | ||
| Absolute drawdown | -3.26 | Maximal drawdown (%) | 3.77 (739.22%) | ||
| Total trades | 41 | Profit trades (% of total) | 24 (58.54%) | Loss trades (% of total) | 17 (41.46%) |
| Long positions (won %) | 27 (59.26%) | Short positions (won %) | 14 (57.14%) | ||
| Largest | profit trade | 1.27 | loss trade | -0.44 | |
| Average | profit trade | 0.21 | loss trade | -0.3 | |
| Minimum | profit trade | 0.01 | loss trade | -0.14 | |
| Maximum | take profit in pips | 157.3 | stop loss in pips | -60.1 | |
| Average | take profit in pips | 34.5 | stop loss in pips | -30.6 | |
| Minimum | take profit in pips | 0.6 | stop loss in pips | -13.6 | |
| Maximum | Reward-Risk | 8.2 | Duration in minutes | 3404 | |
| Average | Reward-Risk | 1.3 | Duration in minutes | 265.7 | |
| Minimum | Reward-Risk | 0.0 | Duration in minutes | 1 | |
| Maximum | consecutive wins (profit in money) | 11.0 (1.92) | consecutive losses (loss in money) | 8.0 (-2.74) | |
| Average | consecutive wins | 2.0 | consecutive losses | 2.0 |
| Total net profit | -1.26 | Gross profit | 2.44 | Gross loss | -3.7 |
| Profit factor | 0.66 | Expected payoff | -0.05 | ||
| Absolute drawdown | -2.74 | Maximal drawdown (%) | 3.25 (637.25%) | ||
| Total trades | 27 | Profit trades (% of total) | 16 (59.26%) | Loss trades (% of total) | 11 (40.74%) |
| Largest | profit trade | 0.36 | loss trade | -0.44 | |
| Average | profit trade | 0.15 | loss trade | -0.34 | |
| Minimum | profit trade | 0.01 | loss trade | -0.24 | |
| Maximum | take profit in pips | 86.1 | stop loss in pips | -60.1 | |
| Average | take profit in pips | 27.1 | stop loss in pips | -33.0 | |
| Minimum | take profit in pips | 0.6 | stop loss in pips | -17.2 | |
| Maximum | Reward-Risk | 2.7 | Duration in minutes | 509.0 | |
| Average | Reward-Risk | 0.8 | Duration in minutes | 167.7 | |
| Minimum | Reward-Risk | 0.0 | Duration in minutes | 1.0 | |
| Maximum | consecutive wins (profit in money) | 10.0 (1.48) | consecutive losses (loss in money) | 8.0 (-2.74) | |
| Average | consecutive wins | 2.0 | consecutive losses | 3.0 |
| Total net profit | 1.02 | Gross profit | 2.5 | Gross loss | -1.48 |
| Profit factor | 1.69 | Expected payoff | 0.07 | ||
| Absolute drawdown | -0.95 | Maximal drawdown (%) | 0.88 (-1257.14%) | ||
| Total trades | 14 | Profit trades (% of total) | 8 (57.14%) | Loss trades (% of total) | 6 (42.86%) |
| Largest | profit trade | 1.27 | loss trade | -0.32 | |
| Average | profit trade | 0.31 | loss trade | -0.25 | |
| Minimum | profit trade | 0.07 | loss trade | -0.14 | |
| Maximum | take profit in pips | 157.3 | stop loss in pips | -31.9 | |
| Average | take profit in pips | 48.6 | stop loss in pips | -26.0 | |
| Minimum | take profit in pips | 6.9 | stop loss in pips | -13.6 | |
| Maximum | Reward-Risk | 8.2 | Duration in minutes | 3404.0 | |
| Average | Reward-Risk | 2.2 | Duration in minutes | 454.6 | |
| Minimum | Reward-Risk | Duration in minutes | 9.0 | ||
| Maximum | consecutive wins (profit in money) | 4.0 (0.87) | consecutive losses (loss in money) | 4.0 (-0.88) | |
| Average | consecutive wins | 2.0 | consecutive losses | 2.0 |


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cabal
Command-line interface to the Haskell package infrastructure (Cabal).
Manage Haskell projects and Cabal packages from the Hackage package repository.
More information: home page
- Search and list packages from Hackage:
cabal list search_string
- Show information about a package:
cabal info package_name
- Download and install a package:
cabal install package_name
- Create a new Haskell project in the current directory:
cabal init
- Build the project in the current directory:
cabal build
- Run tests of the project in the current directory:
cabal test


About our project
My name is Yuran. I’m currently living my 21 year old life. I have 2 years of expereince in design. My main priority right now is work.
Dawn | 6:00:00 A.M.
Here I am now, opening my curtains to see the daybreak in Pittsburgh.
Sunrise | 7:00:00 A.M.
On my way to work, I take in the people, buildings, and scenery around me. I'm thinking about the next place I want to live.
1961 designers who prioritize work are not at all thrilled to be going into work, but they do anyway.
678 designers with 1-4 years of experience are going to work feeling unsure about their jobs.
2026 designers are waking up still feeling a little concerned about their jobs.
Morning | 9:00:00 A.M.
This morning, I've just been assigned to a new project. I'm feeling intimidated being the only designer on the team.
This morning, 3271 designers are thinking about ways to augment their cross-functional and multidisciplinary skills.
6332 designers believe that adaptability is a critical design skill for the future of design.
4397 designers this morning are feeling valued for their role.
4988 designers are thankful that they can exercise autonomy and freedom to make decisions.
Midday | 1:00:00 P.M.
I have a cool idea for my project - but it requires some new skills. During our lunch break, my coworkers and I look for tutorials.
801 designers who have 1-4 years of experience feel they are learning something everyday.
2929 designers might use this time to increase their skill sets.
4200 designers who prioritize work are learning something on the job.
Sunset | 6:00:00 P.M.
 He asks me to join him on a new project. It will pay will but it will require me to work overtime. I get ready to work late.
2836 designers are thinking about utilizing their fitness and wellness benefits this evening.
426 designers are thankful for the childcare benefits and looking forward to get back home.
There are 3565 designers who are enjoying their paid parental leave tonight.
Night | 11:00:00 P.M.
At night, I go to sleep thinking about whats to come. I dream about the impact of my project.
1944 designers are falling asleep feeling they're not contributing to worthwhile problems.
3811 designers are reflecting the growing issue of ethics in design.
3277 designers tonight are reflecting on the environmental impacts of design.


In the beginning, I actually just wanted to draw Doraemon's head. Thus, I set the origin coordinates (0,0)
to align with the center of the head.
The first line that I drew was the outer line of the Doraemon's head. To make it easir for further drawing, I split the outer line into two lines: top and bottom parts.
I create the top part using Eq. \eqref{eq:one} as follows:
$$
\begin{equation}\label{eq:one}
y_1(x) = \sqrt{(12-x^2)}
\end{equation}
$$
As for the bottom part, it is quite tricky since we are not drawing a full half-circle like the top one.
We have to make an empty area for Doraemon's collar. To deal with this, I use piece-wise function in Eq. \eqref{eq:two} to draw two arcs of outer head.
$$
\begin{equation}\label{eq:two}
y_2(x) =
\begin{cases}
- y_1(x) & \text{for } x \leq -2.2 \\
- y_1(x) & \text{for } x \geq 2.2
\end{cases}
\end{equation}
$$
Finally, we got the basic lines of the Doraemon's head.
Using similar technique, we draw more lines.
The most challenging part here is how to colorize the are. For example, the Doramemon's eyeball here.
At first time, it seems complicated, but once you get used to, it is easy.
I use implicit equation as shown in Eq. \eqref{eq:three}. Here, we colorize the area between two functions: $d_1(x)$ and $d_2(x)$.
We also set the limit of $x$ value. The result is shown in the image left.
$$
\begin{equation}\label{eq:three}
d_2(x) \leq y \leq d_1(x) \{0.478 \leq x \leq 1 \}
\end{equation},
$$
where,
$$ d_1(x) = \sqrt{0.125 - (x-0.5)^2} + 2 $$
$$ d_2(x) = - \sqrt{0.125 - (x-0.5)^2} + 2 $$
How do I found those exact function? Using intuition, experience, and a bit trial-error. So, just do it, and feel the excitement. :D


Unified storage IO
November 12, 2016
Why not just use store X?
Durability should be simple and flexible. When I started to design replikativ I investigated many different IO options, including many key-value or document stores like CouchDB, Riak, MongoDB, IndexedDB etc. My trouble was that I wanted to have cross-platform code also targeting ClojureScript while I didn’t need a particular storage access pattern from the start. Also deciding for any particular storage is in most cases premature as IO specific requirements change in the lifecycle of an application. In my experience people tend to use one or two storage solutions they know instead of using something minimilastic and then plugging in a more elaborate storage solution when their application needs it. This then complects what features are really required by the application and what is used just because the storage solution prescribes it.
With ClojureScript in particular a problem arises, as this is not a local decision which can be factored away, but either you introduce callbacks everywhere or you default to core.async, besides other more exotic options. You then need to wrap all your code in go-routines for ClojureScript and hence platform-neutral code becomes in the same way affected as code is by IO in Haskell. Since I wanted to have a reasonable default, we picked the back then new core.async and get a sane sequential programming model. This put me out of luck, as all previous JVM libraries and approaches use synchronous IO. I also wanted to start with the minimum necessary, which was the semantics of a Clojure hash-map for me.
The situation has barely changed as truely platform neutral code for IO operations like storage and network is not yet well established in the Clojure(Script) community. konserve (for storage IO) and kabel (for network IO) are efforts to change that and significantly facilitate the development with core.async.
Flexibility by simplicity
Simple and flexible way to store things reliably. Both the material and the language can be switched to address different tradeoffs:
I designed and we use konserve in production as a simple interface to do common tasks like session storages in backends, file storage for caches of binary data in Clojure(Script) and in general any way to durably store state when Datomic’s query capabilities are not necessary. Furthermore advanced storage concepts should be facilitated by such a building block for persistent datastructure concepts, e.g. the hitchhiker-tree or durable-persistence and particulary for the CRDTs in replikativ. So what is desired?
- simpleIn Rich Hickey’s sense as logically simple. durable IO protocol as a building block
- composable to more sophisticated semantics, e.g. append-log
- threadsafe and safe to use without draining core.async’s threadpool with synchronous IO
- ACID properties on the key level, where consistency and durability depend on the underlying store while atomicity and isolation are provided by konserve
- should be reasonable for 95% of usage: both in performance and function
- minimalism: pluggable different backend stores fullfilling konserve semantics: memory, filestore, IndexedDB, LevelDB, redis, riak, CouchDB
- natural Clojure interface with edn associative key-value semantics
- first-class, pluggable serialization mechanism (through incognito)
- allow raw binary IO if desired
- hosted concept with transparent access to the underlying store
- minimal dependencies by default
By reducing the storage interface to a simple key-value store with a core.async interface and edn serialization one gets a very good tradeoff to not having to reason about the interface:
Opting out
It is still possible to get gradually more direct access to the underlying store possible, e.g. for performance or specific features like transactional safety or batch processing, similar to the way Clojure exposes the underlying JVM primitives and interfaces. Konserve furthermore reuses popular Clojure libraries for backends where possible to not reinvent the wheel and allow comfortable direct access. Konserve provides a reasonable default store without dependencies or setup required for the JVM (filestore) and the Browser (IndexedDB).
Performance is fairly good for small key-value pairs accessed in parallel, which is exploited for instance in replikativ by using Merkle-Tree like structures. In general you pay a neglegible cost for the konserve protocol and the usual cost for edn serialization depending on the serializer. The implementation of more advanced storage features on top is intended, for instance a fast append-log with reduction is already implemented and used in replikativ. A write-through caching scheme could also become attractive as well as a small REST interface exposing konserve operations.
Dealing with errors in core.async is not always easy, so it can be helpful to use superv.async instead of plain core.async. Errors happening are returned as wrapped exceptions and need to be rethrown in case you expect this. I hope you find it a useful storage default and report back in the gitter chat or on the Clojure mailing list. Happy storing! :)


{%- seo -%} {% unless page.image %} {% endunless %} {%- include google-analytics.html -%}
News from the community of users and contributors


{% if exercise_basics["exists"] %} {% if exercise_details["show_answer"] or assignment_details["view_answer_late"] or exercise_details["show_student_submissions"] %} {% if not last_submission and not assignment_details["due_date"] %}
You do not have any submissions for this exercise yet. You must first submit a passing solution.
{% else %} {% if (last_submission and last_submission["passed"]) or (assignment_details["due_date"] and curr_time > assignment_details["due_date"] and assignment_details["view_answer_late"]) %}{{ last_submission["code"] }}{% end %} {% if exercise_details["show_answer"] %} {% if exercise_details["answer_code"] != "" %}
{{ exercise_details["answer_code"]}}{% end %} {% if exercise_details["answer_description"] != "" %}
{{ exercise_details["answer_description"] }}
Submission {{ submission[0] }}:
{{ submission[1] }}{% end %}
No other solutions are available at this time.
{% end %} {% end %} {% else %}Either you have not yet passed this exercise or you passed it previously but your most recent submission is not passing. Please make sure you pass the exercise, and then you will be able to view the instructor's answer.
{% end %} {% end %} {% else %}You do not have permission to view the answer for this exercise.
{% end %} {% else %}This exercise does not exist.
{% end %}


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Book Review application
{% for item in book_list %}
-
Title: {{ item.book.title }}
Publisher: {{ item.book.publisher }}
Publication Date: {{ item.book.publication_date }}
Rating: {{ item.book_rating }}
Number of reviews: {{ item.number_of_reviews }}
{% if item.book_rating is null %}
Provide a rating and write the first review for this book.
{% endif %}
{% endfor %}


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| Niveau | {{ bakery.level }} | {{ bakery.levelPrice }} | |
| Moulins | {{ bakery.mills }} | {{ bakery.millPrice }} | |
| Or | {{ golds }} | ||
| Farine | {{ bakery.flour }} | ||
| Baguettes | {{ bakery.baguettes }} |
| golds Earned | golds spend | Flour production | Baguettes production |
|---|---|---|---|
| {{ totalgoldsEarned }} | {{ totalgoldsSpent }} | {{ bakery.total.flour }} | {{ bakery.total.baguettes }} |
| Niveau | {{ bakery.level }} | {{ bakery.levelPrice }} | |
| Moulins | {{ bakery.mills }} | {{ bakery.millPrice }} | |
| Or | {{ golds }} | ||
| Farine | {{ bakery.flour }} | ||
| Baguettes | {{ bakery.baguettes }} |


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Module provides access to an entity type (class) to handle locations.
Import classes: - LocationEntity
from typing import Dict, List
import attr
from dialogy.types.entity import BaseEntity
Location Entity Type
@attr.s
class LocationEntity(BaseEntity):
Use this type for handling locations available with reference-ids. This is not meant for (latitude, longitude) values, those will be covered in GeoPointEntity.
_meta = attr.ib(type=Dict[str, str], default=attr.Factory(Dict))


Inherits ArucoUnity.Controllers.ObjectTrackers.ArucoObjectTracker.
|
| Dictionary< Aruco.Dictionary, int > [] | DetectedMarkers [get, set] |
| |
| Dictionary< Aruco.Dictionary, Std.VectorVectorPoint2f > [] | MarkerCorners [get, set] |
| | Vector of the detected marker corners on each ArucoCamera.Images. Updated by Detect. More...
|
| |
| Dictionary< Aruco.Dictionary, Std.VectorInt > [] | MarkerIds [get, set] |
| | Vector of identifiers of the detected markers on each ArucoCamera.Images. Updated by Detect. More...
|
| |
| Dictionary< Aruco.Dictionary, Std.VectorVectorPoint2f > [] | RejectedCandidateCorners [get, set] |
| | Vector of the corners with not a correct identification on each ArucoCamera.Images. Updated by Detect. More...
|
| |
| Dictionary< Aruco.Dictionary, Std.VectorVec3d > [] | MarkerRvecs [get, set] |
| | Vector of rotation vectors of the detected markers on each ArucoCamera.Images. More...
|
| |
| Dictionary< Aruco.Dictionary, Std.VectorVec3d > [] | MarkerTvecs [get, set] |
| | Vector of translation vectors of the detected markers on each ArucoCamera.Images. More...
|
| |
| bool | IsActivated [get, protected set] |
| | Is the tracker configured and activated? More...
|
| |
◆ Activate()
| override void ArucoUnity.Controllers.ObjectTrackers.ArucoMarkerTracker.Activate |
( |
ArucoTracker |
arucoTracker | ) |
|
|
inlinevirtual |
◆ ArucoObjectsController_DictionaryAdded()
| override void ArucoUnity.Controllers.ObjectTrackers.ArucoMarkerTracker.ArucoObjectsController_DictionaryAdded |
( |
Aruco.Dictionary |
dictionary | ) |
|
|
inlineprotectedvirtual |
◆ ArucoObjectsController_DictionaryRemoved()
| override void ArucoUnity.Controllers.ObjectTrackers.ArucoMarkerTracker.ArucoObjectsController_DictionaryRemoved |
( |
Aruco.Dictionary |
dictionary | ) |
|
|
inlineprotectedvirtual |
◆ Deactivate()
| override void ArucoUnity.Controllers.ObjectTrackers.ArucoMarkerTracker.Deactivate |
( |
| ) |
|
|
inlinevirtual |
◆ Detect()
| override void ArucoUnity.Controllers.ObjectTrackers.ArucoMarkerTracker.Detect |
( |
int |
cameraId, |
|
|
Aruco.Dictionary |
dictionary, |
|
|
Cv.Mat |
image |
|
) |
| |
|
inlinevirtual |
◆ Draw()
| override void ArucoUnity.Controllers.ObjectTrackers.ArucoMarkerTracker.Draw |
( |
int |
cameraId, |
|
|
Aruco.Dictionary |
dictionary, |
|
|
Cv.Mat |
image |
|
) |
| |
|
inlinevirtual |
◆ EstimateTransforms()
| override void ArucoUnity.Controllers.ObjectTrackers.ArucoMarkerTracker.EstimateTransforms |
( |
int |
cameraId, |
|
|
Aruco.Dictionary |
dictionary |
|
) |
| |
|
inlinevirtual |
◆ Place()
| override void ArucoUnity.Controllers.ObjectTrackers.ArucoMarkerTracker.Place |
( |
int |
cameraId, |
|
|
Aruco.Dictionary |
dictionary |
|
) |
| |
|
inlinevirtual |
◆ EstimatePoseMarkerLength
| const float ArucoUnity.Controllers.ObjectTrackers.ArucoMarkerTracker.EstimatePoseMarkerLength = 1f |
|
protected |
◆ RejectedMarkerCandidatesColor
| readonly Color ArucoUnity.Controllers.ObjectTrackers.ArucoMarkerTracker.RejectedMarkerCandidatesColor = new Color(100, 0, 255) |
|
protected |
◆ DetectedMarkers
| Dictionary<Aruco.Dictionary, int> [] ArucoUnity.Controllers.ObjectTrackers.ArucoMarkerTracker.DetectedMarkers |
|
getset |
◆ MarkerCorners
Vector of the detected marker corners on each ArucoCamera.Images. Updated by Detect.
◆ MarkerIds
Vector of identifiers of the detected markers on each ArucoCamera.Images. Updated by Detect.
◆ MarkerRvecs
Vector of rotation vectors of the detected markers on each ArucoCamera.Images.
◆ MarkerTvecs
Vector of translation vectors of the detected markers on each ArucoCamera.Images.
◆ RejectedCandidateCorners
Vector of the corners with not a correct identification on each ArucoCamera.Images. Updated by Detect.
The documentation for this class was generated from the following file:
- C:/Users/an15400/Documents/Maitrise/aruco-unity/aruco-unity/src/aruco_unity_package/Assets/ArucoUnity/Scripts/Controllers/ObjectTrackers/ArucoMarkerTracker.cs


Beefy Ford Ranger Lariat FX4 Will Try To Be A Raptor At SEMA 2019
PaxPower is back with another hefty high-speed off-roader.
by Christopher SmithFord pickup fans in the United States aren’t exactly hurting for aggressive off-road options. The F-150 Raptor is a full-size dune-jumping machine, but that’s not to say folks wouldn’t want a smaller version to play with. A Ranger Raptor already exists in other parts of the world, but for reasons we don’t fully understand, it’s yet to make the transition to the biggest pickup truck market of them all. PaxPower is tired of waiting, so the Texas truck tuner built its own version of a Ranger Raptor with a combination of OEM and aftermarket upgrades.
Other PaxPower Projects:
The 2019 Ranger featured here started life as a Lariat FX4 model. The body panels are actual Ranger Raptor components, sourced from Ford’s overseas operations. As such, the bed and the entire front clip are upgraded over stock, with aftermarket bumpers adding an extra-aggressive touch. The custom bumpers are actually a necessity, since the Raptor’s bodywork and undercarriage add a full five inches to the truck’s width.
Gallery: PaxPower Ranger Raptor
Speaking of the undercarriage, PaxPower decided not to opt for OEM Ranger Raptor components. This midsizer rids on a custom prerunner-type suspension setup from BajaKits that includes ungraded control arms and 2.5-inch racing shocks with ride adjustment knobs. Meaty off-road tires are mounted to 17-inch wheels, and they are powered by a tweaked version of the Ranger’s 2.3-liter turbocharged four-cylinder making “about” 350 horsepower (261 kilowatts). That’s a significant bump over the actual Ranger Raptor’s 210-hp (157 kW) diesel, and according to PaxPower, it should be enough to outperform the full-size Raptor in pretty much every situation.
See You At SEMA:
Such performance does come at a price. Specifically, that price is $65,000, which is about what you’d pay for a new F-150 Raptor. Given the rebirth of the midsize truck segment in the U.S., not to mention the thirst for a legitimate high-flying off-road Ranger, we suspect this is a price many buyers would be willing to pay.
PaxPower will display its Ranger Raptor this week at the 2019 SEMA Show.
Source: PaxPower
Show press release
PaxPower Ranger Raptor Conversion to Debut at SEMA 2019
Texas Truck Builder Creates Ranger Raptor from American Ranger Lariat Model
PaxPower has made its company goal to “build the trucks the OEM won’t.” What started with Supercharged V8 and PowerStroke Diesel Raptor builds from F150s has recently grown into their own Silverado-based Raptor-competitor, the Jackal. PaxPower’s next creation, which is debuting at this week’s SEMA show in Law Vegas, is another truck demanded by the market but denied by Ford.
PaxPower began with a 2019 North American Ford Ranger Lariat FX4 and converted the body to Ranger Raptor spec using OEM panels from the other side of the globe! While the cab on both trucks are the same, the rest of the body had to be changed out, including the entire front clip and a rebuild of bed. Literally, the only parts of the front or rear sections to survive the transformation were the stock Lariat LED headlights. Further, because SEMA builds need to be over the top, the entire truck was repainted in Blue Jean Metallic, a hue not available on Ranger models, foreign or domestic.
With the body addressed, attention then turned to the suspension. The two trucks differ greatly in this regard, so an OEM suspension swap didn’t seem feasible. PaxPower worked with performance suspension builder BajaKits to come up with a prerunner style setup that uses longer and stronger upper and lower control arms. This setup widens the track width 5 inches and greatly improves suspension travel. The truck rides on King 2.5” racing shocks with reservoirs and ride adjustment knobs. The PaxPower Ranger was finished off with Addictive Desert Designs bumpers that were custom fabricated to match the wider Ranger Raptor body, Rigid pro-level lighting, Toyo R/T tires, and custom-painted Icon 17-inch wheels.
PaxPower will be releasing additional details in the coming weeks, but here’s a preview: Power is increased to about 350hp from an engine calibration, making this Ranger to Raptor conversion much faster than the diesel-powered Ranger Raptor from Ford. The PaxPower Ranger should also have no problem leaving full-size Raptors in the dust both on-road and off, thanks to its power to weight and size advantages. Pricing, however, is very similar to the F150 Raptor in that complete trucks will start around $65,000. This high price tag is largely tied to acquiring a truck worth of OEM Ranger Raptor body panels on the other side of the planet!
The PaxPower Ranger will be on display at BajaKits booth at the SEMA Show. The booth is number 37233 in the Upper South Hall. On site media is encouraged to contact PaxPower for interviews and truck inspections. PaxPower can be reached at SEMA via Owner/Builder Ben Norrell at<EMAIL_ADDRESS>or call/text<PHONE_NUMBER>.


Player 1 chooses to contribute: {{player.contribution1}}.
Player 2 chooses to contribute: {{player.contribution2}}.
Player 3 chooses to contribute: {{player.contribution3}}.
Player 4 chooses to contribute: {{player.contribution4}}.
The simulation results are:
The total contribution to POOL is {{result_sum}}.
The return from the POOL is {{result_return}}.
Player 1's payoff: {{result_sim1}}.
Player 2's payoff: {{result_sim2}}.
Player 3's payoff: {{result_sim3}}.
Player 4's payoff: {{result_sim4}}.


{% extends 'base.html' %} {% block content %} {%if home%}
Admin : {{home.admin.name}}
: {{home.location}}
Emergency Service contacts
Police : {{home.police_contact}}
Health : {{home.health_contact}}
View Business


Releases Archive
- HDFS HA for NameNode (manual failover)
- YARN aka NextGen MapReduce
- HDFS Federation
- Performance
- Wire-compatibility for both HDFS and YARN/MapReduce (using protobufs)
- 4 patches in support of non-Oracle JDKs
- several patches to clean up error handling and log messages
- various production issue fixes
- Snappy compressor/decompressor is available
- Occassional deadlock in metrics serving thread fixed
- 64-bit secure datanodes failed to start, now fixed
- Changed package names for 64-bit rpm/debs to use “.x86_64.” instead of “.amd64.”
- Added hadoop-client and hadoop-minicluster artifacts for ease of client install and testing
- Support run-as-user in non-secure mode
- Better compatibility with Ganglia, HBase, and Sqoop
- Lots of bug fixes and improvements in both HDFS and MapReduce
- Major performance work to make this release either match or exceed performance of Hadoop-1 in most aspects of both HDFS and MapReduce.
- Several downstream projects like HBase, Pig, Oozie, Hive etc. are better integrated with this release
- security
- HBase (append/hsynch/hflush, and security)
- webhdfs (with full support for security)
- performance enhanced access to local files for HBase
- other performance enhancements, bug fixes, and features
- Security.
- Latest optimizations of the MapReduce framework introduced in the Hadoop 0.20.security line of releases.
- Disk-fail-in-place.
- JMX-based metrics v2.
- HBase support with
hflush
andhsync
. - New implementation of file append.
- Symbolic links.
- BackupNode and CheckpointNode.
- Hierarchical job queues.
- Job limits per queue/pool.
- Dynamically stop/start job queues.
- Andvances in new mapreduce API: Input/Output formats, ChainMapper/Reducer.
- TaskTracker blacklisting.
- DistributedCache sharing.
- HDFS Federation
- NextGen MapReduce (YARN)
- This release includes a merge of append/hsynch/hflush capabilities from 0.20-append branch, to support HBase in secure mode.
- This release includes the new webhdfs file system, but webhdfs write calls currently fail in secure mode.
Release 2.0.1-alpha available
2012 Jul 26
This release contains important security fixes over hadoop-2.0.0-alpha.
Please see the Hadoop 2.0.1-alpha Release Notes for details.
Release 2.0.0-alpha available
2012 May 23
This is the first (alpha) version in the hadoop-2.x series.
This delivers significant major features over the currently stable hadoop-1.x series including:
Please see the Hadoop 2.0.0-alpha Release Notes for details.
Release 1.0.3 available
2012 May 16
This is a bug fix release for version 1.0.
Bug fixes and feature enhancements in this minor release include:
Please see the Hadoop 1.0.3 Release Notes for details.
Release 1.0.2 available
2012 Apr 3
This is a bug fix release for version 1.0.
Bug fixes and feature enhancements in this minor release include:
Please see the Hadoop 1.0.2 Release Notes for details.
Release 1.0.1 available
2012 Mar 10
This is a bug fix release for version 1.0. This release is now considered stable, replacing the long-standing 0.20.203.
Bug fixes in this minor release include:
Please see the Hadoop 1.0.1 Release Notes for details.
release 0.23.1 available
2012 Feb 27
This is the second alpha version of the hadoop-0.23 major release after the first alpha 0.23.0. This release has significant improvements compared to 0.23.0 but should still be considered as alpha-quality and not for production use.
hadoop-0.23.1 contains several major advances from 0.23.0:
See the Hadoop 0.23.1 Release Notes for details.
release 1.0.0 available
2011 Dec 27
After six years of gestation, Hadoop reaches 1.0.0! This release is from the 0.20-security code line, and includes support for:
Please see the complete Hadoop 1.0.0 Release Notes for details.
release 0.22.0 available
2011 Dec 10
This release contains many bug fixes and optimizations compared to its predecessor 0.21.0. See the Hadoop 0.22.0 Release Notes for details. Alternatively, you can look at the complete change log for this release
Notes:
The following features are not supported in Hadoop 0.22.0.
Hadoop 0.22.0 features
release 0.23.0 available
2011 Nov 11
This is the alpha version of the hadoop-0.23 major release. This is the first release we’ve made off Apache Hadoop trunk in a long while. This release is alpha-quality and not yet ready for serious use.
hadoop-0.23 contains several major advances:
It also has several major performance improvements to both HDFS and MapReduce.
See the Hadoop 0.23.0 Release Notes for details.
release <IP_ADDRESS> available
2011 Oct 17
This release contains improvements, new features, bug fixes and optimizations. This release includes rpms and debs, all duly checksummed and securely signed.
See the Hadoop <IP_ADDRESS> Release Notes for details. Alternatively, you can look at the complete change log for this release.
Notes:


Preparation time
less than 30 mins
Cooking time
10 to 30 mins
Serves
Serves 6-8
Dietary
Originally, the traditional Scottish cranachan celebrated the harvest, but now it is enjoyed year-round. There are many variations, but in all of them the trick is the slow toasting of the oatmeal; this is then mixed into the dish at the last minute so that its texture is retained. The quantities given here for the cardamom honey make a full jar, much more than you need for this amount of cranachan, but it will keep indefinitely; it is great over granola and in coffee.
To make the cardamom honey, crush the cardamom pods in a pestle and mortar to separate the dry pods from the seeds. Discard the husks. Toast the seeds in a dry pan to release their oils so they become aromatic.
Pour half of the honey into a pan and add the crushed and toasted cardamom seeds. Gently heat through until the honey has become more liquid and starts to bubble at the edges; if you have a probe thermometer, the temperature should be 75C/165F. Remove from the heat and pour the flavoured honey back into the jar with the remaining honey. Stir gently, and leave to cool, uncovered. When cool, put on the lid and ideally store for 24 hours before using.
To make the cranachan, heat a heavy-bottomed pan and toast the oatmeal over a lowish heat, stirring occasionally, until it smells warm and nutty and has changed colour. This will take 10-15 minutes, sometimes as long as 20 minutes. Leave to cool.
In a bowl, crush half of the raspberries to make a rough mash. In a separate bowl, whisk the cream with the honey, whisky and rosewater until soft peaks form when the whisk is removed, then fold in the crushed raspberries.
Fold in the toasted oatmeal and two-thirds of the remaining whole raspberries. Adjust the honey and rose water to taste. Spoon the cranachan into small glasses, and garnish with the remaining raspberries and some rose petals, if using. Drizzle with a wee bit more of the cardamom honey and serve.


Murnau
A Bot for the freieslabor.org infrastructure.
Installation
If available in Hex, the package can be installed as:
Add murnau to your list of dependencies in
mix.exs
:def deps do [{:murnau, "~> 0.0.1"}] end
Ensure murnau is started before your application:
def application do [applications: [:murnau]] end


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- IdFactory - Class in cmput301f13t10.model
-
Factory class that generates Id numbers to be used as identifiers for objects
- IdFactoryTest - Class in cmput301f13t10.tests
-
Test the IdFactory
- IdFactoryTest() - Constructor for class cmput301f13t10.tests.IdFactoryTest
-
- IdList - Class in cmput301f13t10.model
-
A list of ids.
- IdList(String[]) - Constructor for class cmput301f13t10.model.IdList
-
Constructor
- IdManager - Class in cmput301f13t10.model
-
Manages the creation and deletion of ids
- IdManager() - Constructor for class cmput301f13t10.model.IdManager
-
Constructor
- IdManagerTest - Class in cmput301f13t10.tests
-
Test the IdManager
- IdManagerTest() - Constructor for class cmput301f13t10.tests.IdManagerTest
-
- ImageCreator - Class in cmput301f13t10.presenter
-
Static class for storing new images as well as resizing an image.
- ImageMedia - Class in cmput301f13t10.presenter
-
Media for pictures that can be added and viewed in a section of an adventure.
- ImageMedia() - Constructor for class cmput301f13t10.presenter.ImageMedia
-
Constructor
- indexOf(SectionModel) - Method in class cmput301f13t10.model.AdventureModel
-
Find the index of a section in the adventure, as determined by the
section's id.
- indexOf(int) - Method in class cmput301f13t10.model.SectionArray
-
Get the index of a given section in the adventures list of sections
- indexOf(SectionModel) - Method in class cmput301f13t10.model.SectionArray
-
Find the index of a section in the adventure, as determined by the
section's id.
- initialize(Context) - Method in class cmput301f13t10.model.AdventureCache
-
- insertTextAction() - Method in class cmput301f13t10.view.AnnotationEditView
-
Create a new text media and update the display.
- InvalidSearchTypeException - Exception in cmput301f13t10.model
-
Thrown when the searchBy method of the searcher is called with an
unrecognized search type
- InvalidSearchTypeException(String) - Constructor for exception cmput301f13t10.model.InvalidSearchTypeException
-
Constructor
- isAdventureOnline() - Method in class cmput301f13t10.presenter.AdventurePresenter
-
- isChildSelectable(int, int) - Method in class cmput301f13t10.view.ExpandableHelpListAdapter
-
- isRandomEnabled() - Method in class cmput301f13t10.presenter.AdventurePresenter
-
- isRandomSet() - Method in class cmput301f13t10.presenter.SectionPresenter
-
- isRandomSet() - Method in class cmput301f13t10.view.SectionReadView
-
- isStartSection() - Method in class cmput301f13t10.presenter.SectionTitle
-


TECS TV Displayer
Customize tabs for TV Displays - Created by Brett Hayes
Enable
Zoom
Insert CSS
Save Changes


If you prefer to browse Amazon by means of the official app, then follow this tutorial on how to use the Bobzon share extension to add products.
1.- Open the Amazon app and search for whatever product your want to watch.
2.- Once in the product page, scroll down until it appears the “SHARE” button and tap it. Here you will find the “Add Product to Bobzon” button. Sometimes you will need to scroll to the right in order to see it.
If the “Add Product to Bobzon” option is not available, then follow the instructions of How to enable the Share button?.
3.- Tap the “Add Product to Bobzon” button and it will automatically open Bobzon app and search for the product information.
4.- Once in the product view, just choose the desired price and tap TRACK product. After that the product will be added to the tracking list.


| DevIL Documentation | Home | Previous | Next |
These functions enable or disable an OpenIL state.
ILboolean ilEnable(
ILenum Mode);
ILboolean ilDisable(
ILenum Mode);
ilEnable enables a mode specified by Mode. ilDisable disables a mode specified by Mode. These two functions are identical to their OpenGL counterparts - glEnable and glDisable.
A list of available modes and their meaning:
IL_INVALID_ENUM - Mode was of an unsupported value.
ilIsEnabled, ilIsDisabled, ilutEnable, ilutDisable, ilOriginFunc


Methods of Class ConnectorSocket
- ConnectorSocket
- ConnectorSocket(
-
| virtual |
abstract |
const |
volatile |
template |
static |
inline |
C-linkage |
| NO |
NO |
NO |
NO |
NO |
NO |
NO |
NO |
- Summary
- Creates a socket that can connect to a (remote) host.
- Parameters
| Type |
For some protocols it might be desirable to
use a different type than sock_stream osl_Socket_TypeSeqPacket
(like osl_Socket_TypeSeqPacket ).
Therefore we do not hide this parameter here.
|
- connect
- oslSocketResult connect(
-
| virtual |
abstract |
const |
volatile |
template |
static |
inline |
C-linkage |
| NO |
NO |
NO |
NO |
NO |
NO |
NO |
NO |
- Summary
- Connects the socket to a (remote) host.
- Parameters
| TargetHost |
The address of the target.
|
| pTimeOut |
The timeout for blocking. If 0, a default system dependent timeout
us used.
|
- Return
osl_Socket_Ok
if connected successfully,
osl_Socket_TimedOut
on timeout,
osl_Socket_Interrupted
if unblocked forcefully (by osl::Socket::close()),
osl_Socket_Error
if connect failed.
Top of Page
Copyright © 2012, The Apache Software Foundation, Licensed under the Apache License, Version 2.0. Apache, the Apache feather logo, Apache OpenOffice and OpenOffice.org are trademarks of The Apache Software Foundation. Other names may be trademarks of their respective owners.


😀
~This is a very easy and no fail recipe for meatloaf. It won't take long to make at all, and it's quite good!
~1 egg
~1 onion,chopped
~ 1 cup of milk
~1 cup of dried bread crumbs
~1 cup of dried bread crumbs
~Salt and Pepper to taste
~2 tablespoons of brown sugar
~2 tablespoons of Prepared Mustard
~Half a cup of Ketchup/Tomato Sauce
~Preheat oven to 350 degrees F (175 degrees C)
~In a large bowl, combine the beef, egg, onion, milk and bread OR cracker crumbs. Season with salt and pepper to taste and place in a lightly greased 5x9 inch loaf pan, OR form into a loaf and place in a lightly greased 9x13 inch baking dish.
~In a large bowl, combine the beef, egg, onion, milk and bread OR cracker crumbs. Season with salt and pepper to taste and place in a lightly greased 5x9 inch loaf pan, OR form into a loaf and place in a lightly greased 9x13 inch baking dish.
~In a separate small bowl, combine the brown sugar, mustard and ketchup. Mix well and pour over the meatloaf.
~Bake at 350 degrees F (175 degrees C) for 1 hour.
~ Enjoy the meaty delicacy
~Your tastebuds will tingle after eating this delicacy to the max!!
~This recipe is for classic mashed potatoes so there are no additions of cheeses or exotic spices. Of course you can add those if you’d like. ~This dish can not be resisted by evn a meatlover. Very Yummy
~Mashed potato or mashed potatoes, colloquially known as mash, is a dish prepared by mashing boiled potatoes. Milk, butter, salt and pepper are frequently used in preparation. The dish is usually a side dish to meat or vegetables. A similar dish not mashed to total smoothness is known as smashed potatoes. Other Ingredients
1. Butter: This is one place where there really are no substitutes. Add real butter to your potatoes and plenty of it. I prefer salted if it have it but unsalted works and the potatoes can be salted to taste.
2. Cream/Milk: I use regular whole milk in this recipe, you can also use cream if you have it on hand. Remember to heat the dairy for the best potatoes.
3. Garlic/Seasonings: Again, keeping this recipe simple, I simply add salt & pepper. If you’d like a little bit of garlic, chop a few cloves and let them boil with the potatoes.
~You can make them in the Crock Pot or Instant Pot, but I really do think the best mashed potatoes are a simple classic.
1. Drain well: I usually let them sit about 5 minutes or so to completely drain or even drain well and put them back in the warm pot for a few minutes to make sure all liquid is evaporated.
2. Mash by Hand: Use a hand masher or a potato ricer. Using a hand mixer, stand mixer or food processor can break down the starches in the potatoes and cause a very gluey or gummy texture. You can still get buttery smooth creamy mashed potatoes mashing by hand.
3. Butter is Your Friend:. There are places you can skimp on butter and this is not one of them. I like to use salted butter and lots of it (but you can use unsalted season yourself). Butter helps your mashed potatoes get a creamy… well, buttery texture.
4. Heat Your Cream: Heat your milk/cream before adding. This keeps the potatoes hot and absorbs better. Add cream/milk a little time to get the right consistency.
~I have four words for you. Loaded Mashed Potato Cakes.
~Loaded Mashed Potato Egg Rolls
~Loaded Mashed Potato Egg Rolls
~Colcannon Recipe (Cabbage and Potatoes)
~Loaded Twice Baked Potato Casserole – Great side dish
~AUTHOR: Chef Mark Lee
Cuisine American
What Is Your Favourite Cake? Tell Us Down In the Section Below.


The Office of the President is the systemwide headquarters of the University of California, managing its fiscal and business operations, and supporting the academic and research missions across its campuses, labs and medical centers.
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CS330 Learnings | Introduction and Overview
This course (officially CS330: Multi-Task and Meta-Learning) has been taken by Prof. Chelsea Finn at Stanford. It deals with an in-depth analysis of multi-task and meta-learning, along with discussions on the practical applications of these concepts in various fields (RL, etc). The course is free to audit online with lectures available on YouTube.
Why do I have blog entries on a course that already has an amazing instructor, free online lecture recordings, and slides? Well, I aim to do two things with this series: Firstly, I believe that these articles can serve as a good “recap” for the material taught in the course (so you can refer to these as notes). These are also fairly elaborate for those that do not (maybe) want to watch hour-long lectures and still understand the concepts to a decent extent. Secondly, writing these helps me evaluate my understanding of these topics (and so I learn better!). I hope you enjoy this and take away something from it :)
Motivation
Let’s begin by understanding the need for Multi-Task and Meta-learning. Do we really need these seemingly niche concepts? What problems are these trying to solve? Don’t existing approaches solve these problems?
Why is this important?
We wish to understand the working of the human brain by building agents that can learn different skills in real-life settings.
Don’t we already have Deep Learning models for this?
Yes. We already have extremely expressive models that can solve certain tasks efficiently, matching, and even surpassing human-level performance.
What is the problem then?
These agents are “specialists” as they can perform extremely well on single-tasks. However, Single-Task learning is not scalable. When a robot is taught how to use a spatula to place an object inside a bowl, it very specifically learns to use “that spatula” to put “that object” into “that bowl”.
Can’t we just give it more data?
We can. We can actually give it a lot of data, with different kinds of spatulas, different objects to pick, etc. However, this either requires a lot of effort (and time) or is just not possible in certain domains (for example, you cannot collect enough data to predict the occurrence of a rare disease).
So, this is only a problem with small datasets right?
Not really. There are several other problems that exist. Some of them are as follows:
- Building a general-purpose AI system: This system would definitely need to solve more than one task (else it is not general-purpose)
- Long-tail data distributions: Maybe you have a lot of data, but this data is from the “very common instances” lot. There may be several other instances that are not very common. As such your data is said to have a long tail (this is actually more common than you’d think) in contrast to the more popular “Gaussian data picture”.
- Building agents that learn new tasks quickly: And quickly is the keyword here. You cannot “quickly” learn a new task from scratch. As such, you’d need to use (and hence have) some prior experience to learn quickly.
So, how do we solve these problems? This is where the concepts of multi-task learning and meta-learning come into the picture.
Understanding learning
In order to build generalist agents discussed above, we can at best draw inspiration from the way humans learn. Imagine that you need to learn how to drive. How would you go about acquiring this new skill? There will definitely be things that you will have to learn afresh, but you can adapt most of the things from similar experiences in your life before.  Most certainly not. You would know these things from your experience as a human so far!
When we train neural networks to learn a new task, we are basically starting with a clean slate. It is analogous to teaching a toddler how to drive! We hence need mechanisms that can instill experience within neural networks to make them better generalists.
What is Multi-task learning?
Given a set of tasks, learn all tasks more quickly and more proficiently than learning them independently. Can this be any set of tasks? Well, no. The simplest constraint for this is that the tasks need to be somewhat similar. If you want to learn how to drive and how to chop onions, learning them together is no better than learning them independently because the tasks are not related at all. If you however want to learn how to ride a bike and how to drive a car, learning one definitely benefits the other.
What is Meta-Learning?
Given experience in some previous tasks, learn a new task more quickly or more efficiently. This follows the same constraint as before requiring the new task to share some similarities with the previous tasks.
What is the difference between the two?
If you have some experience of riding a bike and you are asked to use that experience to learn how to drive a car, that is meta-learning. If instead you are required to simultaneously learn how to ride a bike and drive a car (benefiting from both the experiences), that is multi-task learning.
Lastly, if we wish to compare tasks, how do we define a notion of similarity? In fact, how do we define a task in this respect?
What is a task?
Informally, a task does the following (a formal definition is given in the next lecture):
Given a dataset \(D\) and a loss function \(L\), it optimizes on the loss function \(L\) and outputs a model \(f_{\theta}\) for mapping inputs \(x_{i}\) to their corresponding outputs \(y_{i}\) (where \((x_{i}, y_{i}) \in D\)).
Hence, two tasks would be considered different if they differ in either the dataset \(D\) or the loss function \(L\) or both.
Multi-task and Meta-Learning require tasks to share some structure in the sense that even if they do not appear similar on the surface, they do have certain underlying similarities. For example, opening a can and uncapping a water bottle are different tasks on the surface but use the same principle in practice.
Doesn’t Multi-Task Learning reduce to Single-Task Learning?
You could aggregate data from all the tasks to create a huge dataset (\(D = \bigcup D_{i}\)), sum all the individual losses to optimize for an aggregate loss (\(L = \sum L_{i}\)) and then create a single model for all the tasks, right? Yes, you can (and that is one approach to Multi-Task Learning). However, we can often perform better with the knowledge that the data is coming from different tasks.
As neural networks are universal function approximators, won’t they eventually figure out the differences between the tasks?
They would. But that would probably take a lot of time as the function would then become too complex to optimize for.
Why study this now?
- These algorithms are playing a fundamental, and increasing role in machine learning research. This is evident from the number of search queries and citations of associated papers.
- Their success will be critical for the democratization of deep learning. Essentially, deep learning so far has only been able to (mostly) excel at tasks with huge amounts of data, which is not a very practical component. We need models that can work with much lesser amounts of data and still excel at these tasks and many more!
That concludes this article (and lecture 1 from the course). Please let me know if there are other things that should have been addressed and also potential errors that could have crept in. Thank you for taking the time to read this article (I sincerely hope it helped you in some way).
References
[1] Video for Lecture 1 (Introduction & Overview)
[2] Slides for Lecture 1 (Introduction & Overview)


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TOWARDS A CULTURALLY IDENTIFIABLE ARCHITECTURE by Chian-Yeun Chang
Dissertation submitted to the Faculty of the Virginia Polytechnic Institute and State University in partial fulfillment of the requirements for the degree of Doctor of Philosophy in
Environmental Design and Planning (Architecture)
Joseph Wang, Chairm
Bliznakov
Cetnard Singer
December, 1990
Blacksburg, Virginia


|
ActiveTcl User Guide
|
||
Tk_AllocCursorFromObj, Tk_GetCursor, Tk_GetCursorFromObj, Tk_GetCursorFromData, Tk_NameOfCursor, Tk_FreeCursorFromObj, Tk_FreeCursor - maintain database of cursors
#include <tk.h>
Tk_Cursor
Tk_AllocCursorFromObj(interp, tkwin,
objPtr)
Tk_Cursor
Tk_GetCursor(interp, tkwin, name)
Tk_Cursor
Tk_GetCursorFromObj(tkwin, objPtr)
Tk_Cursor
Tk_GetCursorFromData(interp, tkwin, source, mask, width,
height, xHot, yHot, fg, bg)
CONST char *
Tk_NameOfCursor(display, cursor)
Tk_FreeCursorFromObj(tkwin, objPtr)
Tk_FreeCursor(display, cursor)
These procedures manage a collection of cursors being used by an application. The procedures allow cursors to be re-used efficiently, thereby avoiding server overhead, and also allow cursors to be named with character strings.
Tk_AllocCursorFromObj takes as argument an object describing a cursor, and returns an opaque Tk identifier for a cursor corresponding to the description. It re-uses an existing cursor if possible and creates a new one otherwise. Tk_AllocCursorFromObj caches information about the return value in objPtr, which speeds up future calls to procedures such as Tk_AllocCursorFromObj and Tk_GetCursorFromObj. If an error occurs in creating the cursor, such as when objPtr refers to a non-existent file, then None is returned and an error message will be stored in interp's result if interp isn't NULL. ObjPtr must contain a standard Tcl list with one of the following forms:
The Macintosh version of Tk supports all of the X cursors and will also accept any of the standard Mac cursors including ibeam, crosshair, watch, plus, and arrow. In addition, Tk will load Macintosh cursor resources of the types crsr (color) and CURS (black and white) by the name of the of the resource. The application and all its open dynamic library's resource files will be searched for the named cursor. If there are conflicts color cursors will always be loaded in preference to black and white cursors.
Tk_GetCursor is identical to Tk_AllocCursorFromObj except that the description of the cursor is specified with a string instead of an object. This prevents Tk_GetCursor from caching the return value, so Tk_GetCursor is less efficient than Tk_AllocCursorFromObj.
Tk_GetCursorFromObj returns the token for an existing cursor, given the window and description used to create the cursor. Tk_GetCursorFromObj doesn't actually create the cursor; the cursor must already have been created with a previous call to Tk_AllocCursorFromObj or Tk_GetCursor. The return value is cached in objPtr, which speeds up future calls to Tk_GetCursorFromObj with the same objPtr and tkwin.
Tk_GetCursorFromData allows cursors to be created from in-memory descriptions of their source and mask cursors. Source points to standard cursor data for the cursor's source bits, and mask points to standard cursor data describing which pixels of source are to be drawn and which are to be considered transparent. Width and height give the dimensions of the cursor, xHot and yHot indicate the location of the cursor's hot-spot (the point that is reported when an event occurs), and fg and bg describe the cursor's foreground and background colors textually (any of the forms suitable for Tk_GetColor may be used). Typically, the arguments to Tk_GetCursorFromData are created by including a cursor file directly into the source code for a program, as in the following example:
Tk_Cursor cursor; #include "source.cursor" #include "mask.cursor" cursor = Tk_GetCursorFromData(interp, tkwin, source_bits, mask_bits, source_width, source_height, source_x_hot, source_y_hot, Tk_GetUid("red"), Tk_GetUid("blue"));
Under normal conditions Tk_GetCursorFromData will return an identifier for the requested cursor. If an error occurs in creating the cursor then None is returned and an error message will be stored in interp's result.
Tk_AllocCursorFromObj, Tk_GetCursor, and Tk_GetCursorFromData maintain a database of all the cursors they have created. Whenever possible, a call to Tk_AllocCursorFromObj, Tk_GetCursor, or Tk_GetCursorFromData will return an existing cursor rather than creating a new one. This approach can substantially reduce server overhead, so the Tk procedures should generally be used in preference to Xlib procedures like XCreateFontCursor or XCreatePixmapCursor, which create a new cursor on each call. The Tk procedures are also more portable than the lower-level X procedures.
The procedure Tk_NameOfCursor is roughly the inverse of Tk_GetCursor. If its cursor argument was created by Tk_GetCursor, then the return value is the name argument that was passed to Tk_GetCursor to create the cursor. If cursor was created by a call to Tk_GetCursorFromData, or by any other mechanism, then the return value is a hexadecimal string giving the X identifier for the cursor. Note: the string returned by Tk_NameOfCursor is only guaranteed to persist until the next call to Tk_NameOfCursor. Also, this call is not portable except for cursors returned by Tk_GetCursor.
When a cursor returned by Tk_AllocCursorFromObj, Tk_GetCursor, or Tk_GetCursorFromData is no longer needed, Tk_FreeCursorFromObj or Tk_FreeCursor should be called to release it. For Tk_FreeCursorFromObj the cursor to release is specified with the same information used to create it; for Tk_FreeCursor the cursor to release is specified with its Tk_Cursor token. There should be exactly one call to Tk_FreeCursor for each call to Tk_AllocCursorFromObj, Tk_GetCursor, or Tk_GetCursorFromData.
In determining whether an existing cursor can be used to satisfy a new request, Tk_AllocCursorFromObj, Tk_GetCursor, and Tk_GetCursorFromData consider only the immediate values of their arguments. For example, when a file name is passed to Tk_GetCursor, Tk_GetCursor will assume it is safe to re-use an existing cursor created from the same file name: it will not check to see whether the file itself has changed, or whether the current directory has changed, thereby causing the name to refer to a different file. Similarly, Tk_GetCursorFromData assumes that if the same source pointer is used in two different calls, then the pointers refer to the same data; it does not check to see if the actual data values have changed.
Copyright © 1990 The Regents of the University of California. Copyright © 1994-1998 Sun Microsystems, Inc. Copyright © 1995-1997 Roger E. Critchlow Jr.


Android
Note: At this moment, our publication is being reviewed by Maven. Once published, source code dependency will be changed to AAR dependency.
- Add bridge specification dependency
- Download gcanvas source, add below line to your build.gradle:
compile project (':android:bridge\_spec')
- Implement
IGBridgeModule
interface. The interface should build bridge between GCanvas C++ rendering engine and your Javascript runtime environment. Check comments of the interface for more details. You can extend from an abstract class named 'AbsGBridgeModule'. We strongly recommend implements your bridge by composite implementation. - Implement interfaces with prefix
IJSCallbackXXX
. These interfaces are used for data communicating between Javascript and native. - Inherit from
GTextureView
. This stage is optional, depending on how your runtime communicates between native and Javascript, but an OpenGL-renderable view is necessary.
iOS
- Add Cocoapods dependency Add this pod to your Podfile and run
pod install
to install:pod 'GCanvas'
- Export module and methods of GCanvas
- Implement protocol
GCanvasModuleProtocol
in your bridge which you used, such as Cordova - Implement protocol
GCanvasViewPortocol
in your bridge as a component which contains aGLKView
as display view of OpenGL ES. - Export module to Javascript in your custom bridge For export module,
Weex
andReactNative
supply the methodWX\_PlUGIN\_EXPORT\_MODULE
andRCT\_EXPORT\_MODULE()
- Export method to Javascript in your custom bridge For export method,
Weex
andReactNative
supply the method.- Weex
WX\_EXPORT\_METHOD\_SYNC
, synchronous methodWX\_EXPORT\_METHOD
, asynchornous method - ReactNative
RCT\_EXPORT\_BLOCKING\_SYNCHRONOUS\_METHOD
, synchronous methodRCT\_EXPORT\_METHOD
, asynchornous method
- Weex
- Implement protocol
There may be different ways in bridges. But your bridge must support export asynchornous and synchronous method both for javascript call native method. And you must implement Export method list to ensure GCanvas
works well with your bridge.
Native APIs List
| Method | Async/Sync | Android/iOS | Comments |
|---|---|---|---|
| enable(args) | Sync | Both | Build relationships between GCanvas.js and native view |
| render(cmd, componentId) | Async | Both | Execute render command |
| preLoadImage(data, callback) | Async | Both | Preload image with callback |
| bindImageTexture(data, callback) | Async | Both | Bind image to an OpenGL Texture with callback |
| setContextType | Async | Both | Set GCanvas Context Type, 0-2d, 1-WebGL |
| setLogLevel | Async | Both | Set Native Log Level |
| resetComponent | Async | iOS only | call the method while view disappear |
| extendCallNative | Sync | iOS only | method |
| texImage2D | Sync | Android only | convert image to texture, used for WebGL |
| texSubImage2D | Sync | Android only | convert image to sub-texture, used for WebGL |


Formatted book: Content
Content of this book type is built from HTML or RTF files. We can add
new resources clicking on .
In every resources, we have several options:
-
Type: asset type we want to
add.
-
Local resource: HTML or
RTF file which we add from a local file. We can also create a file
using the <e-Adventure> Editor
clicking on Create and
then on Edit. This action
leads to a simple HTML editor in which the content can be
generated.
-
URL: select a remote file
through its URL.
-
Image: resource is a local
image file.
-
Asset: resource directory.
-
Margin: clicking on Edit
Margins we open a dialog in which using sliders we will be able
to place the content in relation to the background.


A blockchain is a shared database that is managed by a global network of computers. Information held in the database is distributed and continually reconciled by the computers in the network. The computers are often referred to as nodes, miners, or peers. Whatever they’re called, the computers are creating and maintaining their blockchain by validating and transmitting entries. And entries are the data that is published by the users of the network. Often this data represents the movement of cryptocurrency from one user of the network to another user of the network.Most blockchains are not controlled by any single entity and do not have a single point of failure. All the entries are viewable by the whole network. When data is entered in a blockchain, it can’t be removed. It’s there forever. The innovation of blockchains that makes them different from a normal database is that they achieve agreement on the history by sharing and restricting the entries without a central server or authority. Learn More about Blockchain here.
Autonomous things, abbreviated AuT, or the Internet of autonomous things, abbreviated as IoAT, is an emerging term for the technological developments that are expected to bring computers into the physical environment as autonomous entities without human direction, freely moving and interacting with humans and other objects.Self-navigating drones are the first AuT technology in (limited) deployment. It is expected that the first mass-deployment of AuT technologies will be the autonomous car, generally expected to be available around 2020.Other currently expected AuT technologies include home robotics (e.g., machines that provide care for the elderly, infirm or young), and military robots (air, land or sea autonomous machines with information-collection or target-attack capabilities).AuT technologies share many common traits, which justify the common notation. They are all based on recent breakthroughs in the domains of (deep) machine learning and artificial intelligence.They all require extensive and prompt regulatory developments to specify the requirements from them and to license and manage their deployment (see the further reading below). And they all require unprecedented levels of safety (e.g., automobile safety) and security, to overcome concerns about the potential negative impact of the new technology. Learn More about Autonomous things here.
An immersive experience is an illusory environment that completely surrounds you such that you feel that you are inside it and part of it. The term is associated with technology environments that command the senses such as virtual reality and mixed reality.The Future of Content programme was designed to inspire and develop innovative digital experiences for BBC online around specific themes that suit how our audiences are behaving; considering what audiences will be seeing, feeling and doing. We’ve been exploring the technologies we can add to our creative armoury and examine what they can add to the content we create. The programme has been providing the complimentary expertise needed to design these experiences and the platform to try it out in front of our audiences. Learn More about Immersive experiences here


I like to write code that makes lives better.
And I like to draw. And laugh. And talk a lot/ I will most likely interrupt you.
LinkedInWebsite that uses a free API to generate a random quote. Very good for when you post a new image on social media and you ran out of ideas.
Lorem ipsum dolor sit, amet consectetur adipisicing elit. Excepturi neque, ipsa animi maiores repellendus distinctio aperiam earum dolor voluptatum consequatur blanditiis inventore debitis fuga numquam voluptate ex architecto itaque molestiae.
If you want a great dev on your team don't hesitate.
Email me


public interface IImageLineSet<T extends IImageLine>
IImageLine
elements.
This is actually a "virtual" set, it can be implemented in several ways; for example
PngReader.readRows()
method.
| Modifier and Type | Method and Description |
|---|---|
IImageLine |
getImageLine(int n)
Asks for imageline corresponding to row n in the original image (zero based).
|
IImageLine |
getImageLineRawNum(int n)
Like
getImageLine(int) but uses the raw numbering inside the LineSet This makes little sense for a cursor |
boolean |
hasImageLine(int n)
Returns true if the set contain row n (in the original image,zero based) currently allocated.
|
int |
size()
Internal size of allocated rows This is informational, it should rarely be important for the caller.
|
IImageLine getImageLine(int n)
Throws exception if not available. The caller is supposed to know what he/she is doing
IImageLine getImageLineRawNum(int n)
getImageLine(int)
but uses the raw numbering inside the LineSet This makes little sense for a cursorn
- Should normally go from 0 to size()
boolean hasImageLine(int n)
If it's a single-cursor, this should return true only if it's positioned there. (notice that hasImageLine(n) can return false, but getImageLine(n) can be ok)
int size()
Copyright © 2014. All rights reserved.


layout: true class: center, middle --- # MRI analysis in Python # using Nipype, Nilearn and more
## Cambridge (zoom), November 2020 ##### License: CC0 | Image credit: Arno Klein --- layout: false .left-column[ ## Objectives ] .right-column[ ### Gain Skills - use Python for diverse neuroscientific analyses - Empower yourself with tools and technologies to do reproducible, scalable, and efficient research ### Get Involved - Familiarize yourself with a great Python ecosystem for scientific work - Become an active part of a fun & fast growing community ### Be Social - Interact with each other, help each other and learn from each other - Get to know other participants and their projects - Share these new tools and resources with others ] --- .left-column[ ## Objectives ## Your Role ] .right-column[ **Ask questions and give feedback** (via [sli.do](https://app.sli.do/event/cevy3wif/live/questions) and in the zoom chat) Help each other out Tell us about cool things that you are working on or you are aware of If possible interact with everyone and be active Give us feedback and help improve the lectures ] --- .left-column[ ## Objectives ## Your Role ] .right-column[ ### Be aware of 2 important points **First**, this is a 2 day workshop, intended to show you the absolute basics of an unbelievably broad ecosystem and it's possibilities: - a lot of the things will be completly new and/or different from what you did/how you worked so far - don't expect to understand and know everything about everything we'll talk about after the 2 days - it's about knowing what's out there, how it can be done and why it's useful - if you want to get the most out of it, show up, be committed and start using what you'll see and hear here in your own research - things take time, but it's definitely worth the effort and one of the best research communities has your back ] --- .left-column[ ## Objectives ## Your Role ] .right-column[ ### Be aware of 2 important points **Second**, it's your work, take responsibility: - no one can know/be an expert in everything and that's okay - however, you need to have a basic understanding of what you're doing and why you're doing it - a lot of the tools out there let you do a lot of stuff by "just clicking buttons", which is dangerous and (very often) prevents a deeper understanding - the tools presented here usually require you to think way more about your data and the analyses you want to apply, but are yet very easy to use - don't do things just because they're "fancy" or "this is how it was always done" ] --- .left-column[ ## Objectives ## Your Role ## Us ] .right-column[ ### Who are we? **Michael Notter** (EPFL, Switzerland) Instructor | [GitHub](https://github.com/miykael) | [Twitter](https://twitter.com/miyka_el) | [homepage](https://miykael.github.io/) **Peer Herholz** (McGill University, Canada) Instructor | [GitHub](https://github.com/peerherholz) | [Twitter](https://twitter.com/peerherholz) | [homepage](https://peerherholz.github.io/) **Johan Carlin** (University of Cambridge, UK) Organizer | [GitHub](https://github.com/jooh) | [Twitter](https://twitter.com/johancarlin) | [homepage](https://www.neuroscience.cam.ac.uk/directory/profile.php?jdc55) **Olaf Hauk** (University of Cambridge, UK) Organizer | [GitHub](https://github.com/olafhauk) | [Twitter](https://twitter.com/olafhauk) | [homepage](https://www.neuroscience.cam.ac.uk/directory/profile.php?olafhauk) ] --- .left-column[ ## Objectives ## Your Role ## Us ## Schedule* *optimistic ] .right-column[ **9th November** - `09:30-10:00` - Workshop overview - `10:00-10:45` - Quick recap on Jupyter, Python & more - `10:45-12:00` - Explore MRI data with Nibabel & Nilearn - `13:00-13:30` - Setup your system with Conda & Docker - `13:30-15:00` - Functional connectivity and machine learning - `15:00-15:30` - Innovations in neuroimaging tools (I) **10th November** - `09:30-10:00` - Introduction to Nipype - `10:00-11:00` - Exploration of Nipype's building blocks - `11:00-12:00` - Creating a Nipype Pipeline from A-Z - `13:00-14:00` - PyBIDS and Nistats - `14:00-15:00` - Multivariate pattern analysis using Searchlight and Deep Learning - `15:00-15:30` - Innovations in neuroimaging tools (II) **Both days** - Program from `09:30-12:00` and `13:00-15:30` - Lunch from `12:00-13:00` - After `15:30`, open ended for questions and discussions ] --- .left-column[ ## Objectives ## Your Role ## Us ## Schedule ## Logistics ] .right-column[ #### Workshop material - All materials and notes are available online on [github.com/miykael/workshop_pybrain](https://github.com/miykael/workshop_pybrain) #### Use communication channels - Ask questions on [sli.do](https://app.sli.do/event/cevy3wif/live/questions) (prefered) or zoom - Ask us and other participants for help - Provide feedback whenever you have any #### Docker setup for workshop - You'll need docker installed and running, test setup with `docker run hello-world` - Pull/Update docker image for workshop with `docker pull miykael/workshop_pybrain` - The docker image might take up to 10GB of space - Come talk to us if you don't have that much, think you will run out or have other questions ] --- name: inverse layout: true class: center, middle, inverse --- # Questions?


|
fop 0.94 | ||||||||||
| PREV CLASS NEXT CLASS | FRAMES NO FRAMES | ||||||||||
| SUMMARY: NESTED | FIELD | CONSTR | METHOD | DETAIL: FIELD | CONSTR | METHOD |
java.lang.Object org.apache.fop.hyphenation.TernaryTree
A ternary search tree is a hibrid between a binary tree and a digital search tree (trie). Keys are limited to strings. A data value of type char is stored in each leaf node. It can be used as an index (or pointer) to the data. Branches that only contain one key are compressed to one node by storing a pointer to the trailer substring of the key. This class is intended to serve as base class or helper class to implement Dictionary collections or the like. Ternary trees have some nice properties as the following: the tree can be traversed in sorted order, partial matches (wildcard) can be implemented, retrieval of all keys within a given distance from the target, etc. The storage requirements are higher than a binary tree but a lot less than a trie. Performance is comparable with a hash table, sometimes it outperforms a hash function (most of the time can determine a miss faster than a hash).
The main purpose of this java port is to serve as a base for implementing TeX's hyphenation algorithm (see The TeXBook, appendix H). Each language requires from 5000 to 15000 hyphenation patterns which will be keys in this tree. The strings patterns are usually small (from 2 to 5 characters), but each char in the tree is stored in a node. Thus memory usage is the main concern. We will sacrify 'elegance' to keep memory requirenments to the minimum. Using java's char type as pointer (yes, I know pointer it is a forbidden word in java) we can keep the size of the node to be just 8 bytes (3 pointers and the data char). This gives room for about 65000 nodes. In my tests the english patterns took 7694 nodes and the german patterns 10055 nodes, so I think we are safe.
All said, this is a map with strings as keys and char as value. Pretty limited!. It can be extended to a general map by using the string representation of an object and using the char value as an index to an array that contains the object values.
| Nested Class Summary | |
class |
TernaryTree.Iterator
|
| Field Summary | |
protected static int |
BLOCK_SIZE
|
protected char[] |
eq
Pointer to equal branch and to data when this node is a string terminator. |
protected char |
freenode
|
protected char[] |
hi
Pointer to high branch. |
protected CharVector |
kv
This vector holds the trailing of the keys when the branch is compressed. |
protected int |
length
|
protected char[] |
lo
Pointer to low branch and to rest of the key when it is stored directly in this node, we don't have unions in java! |
protected char |
root
|
protected char[] |
sc
The character stored in this node: splitchar. |
| Method Summary | |
void |
balance()
Balance the tree for best search performance |
java.lang.Object |
clone()
|
int |
find(char[] key,
int start)
|
int |
find(java.lang.String key)
|
protected void |
init()
|
void |
insert(char[] key,
int start,
char val)
|
void |
insert(java.lang.String key,
char val)
Branches are initially compressed, needing one node per key plus the size of the string key. |
protected void |
insertBalanced(java.lang.String[] k,
char[] v,
int offset,
int n)
Recursively insert the median first and then the median of the lower and upper halves, and so on in order to get a balanced tree. |
java.util.Enumeration |
keys()
|
boolean |
knows(java.lang.String key)
|
static void |
main(java.lang.String[] args)
|
void |
printStats()
|
int |
size()
|
static int |
strcmp(char[] a,
int startA,
char[] b,
int startB)
Compares 2 null terminated char arrays |
static int |
strcmp(java.lang.String str,
char[] a,
int start)
Compares a string with null terminated char array |
static void |
strcpy(char[] dst,
int di,
char[] src,
int si)
|
static int |
strlen(char[] a)
|
static int |
strlen(char[] a,
int start)
|
void |
trimToSize()
Each node stores a character (splitchar) which is part of some key(s). |
| Methods inherited from class java.lang.Object |
equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait |
| Field Detail |
protected char[] lo
protected char[] hi
protected char[] eq
protected char[] sc
The character stored in this node: splitchar. Two special values are reserved:
This shouldn't be a problem if we give the usual semantics to strings since 0xFFFF is garanteed not to be an Unicode character.
protected CharVector kv
protected char root
protected char freenode
protected int length
protected static final int BLOCK_SIZE
| Method Detail |
protected void init()
public void insert(java.lang.String key, char val)
public void insert(char[] key, int start, char val)
public static int strcmp(char[] a, int startA, char[] b, int startB)
public static int strcmp(java.lang.String str, char[] a, int start)
public static void strcpy(char[] dst, int di, char[] src, int si)
public static int strlen(char[] a, int start)
public static int strlen(char[] a)
public int find(java.lang.String key)
public int find(char[] key, int start)
public boolean knows(java.lang.String key)
public int size()
public java.lang.Object clone()
protected void insertBalanced(java.lang.String[] k, char[] v, int offset, int n)
public void balance()
public void trimToSize()
public java.util.Enumeration keys()
public void printStats()
public static void main(java.lang.String[] args) throws java.lang.Exception
java.lang.Exception
|
fop 0.94 | ||||||||||
| PREV CLASS NEXT CLASS | FRAMES NO FRAMES | ||||||||||
| SUMMARY: NESTED | FIELD | CONSTR | METHOD | DETAIL: FIELD | CONSTR | METHOD |


"ANNA "grace, an aged widow, the daughter of Phanuel. She was a“prophetess,” like Miriam, Deborah, and Huldah (2 Chronicles 34:22). After seven years of married life her husband died, and during her long widowhood she daily attended the temple services. When she was eighty-four years old, she entered the temple at the moment when the aged Simeon uttered his memorable words of praise and thanks to God that he had fulfilled his ancient promise in sending his Son into the world (Luke 2:36, 37).
"ANNA "grace, an aged widow, the daughter of Phanuel. She was a“prophetess,” like Miriam, Deborah, and Huldah (2 Chronicles 34:22).
After seven years of married life her husband died, and during her long widowhood she daily attended the temple services. When she was eighty-four years old, she entered the temple at the moment when the aged Simeon uttered his memorable words of praise and thanks to God that he had fulfilled his ancient promise in sending his Son into the world (Luke 2:36, 37).


Functions to analyze neuronal spike trains from a single neuron or from several neurons recorded simultaneously.
| Version: | 0.3-7 |
| Depends: | R (≥ 2.10), survival, mgcv, R2HTML, gss, codetools |
| Suggests: | lattice, HiddenMarkov, snow, rstream |
| Published: | 2012-11-09 |
| Author: | Christophe Pouzat |
| Maintainer: | Christophe Pouzat <christophe.pouzat at gmail.com> |
| License: | GPL-2 | GPL-3 [expanded from: GPL (≥ 2)] |
| URL: | http://sites.google.com/site/spiketrainanalysiswithr |
| NeedsCompilation: | yes |
| Materials: | ChangeLog |
| In views: | Distributions, HighPerformanceComputing |
| CRAN checks: | STAR results |
| Reference manual: | STAR.pdf |
| Package source: | STAR_0.3-7.tar.gz |
| Windows binaries: | r-devel: STAR_0.3-7.zip, r-release: STAR_0.3-7.zip, r-oldrel: STAR_0.3-7.zip |
| OS X binaries: | r-release: STAR_0.3-7.tgz, r-oldrel: STAR_0.3-7.tgz |
| Old sources: | STAR archive |
| Reverse imports: | conf |
| Reverse suggests: | fitteR |
Please use the canonical form https://CRAN.R-project.org/package=STAR to link to this page.


Using calculus for programming; tweening
I needed a function that would interpolate from A to B over time in a smooth manner. So if the value was initially (or before t=0) 0, then it changed to X at time T, it should behave something like this:
We know from basic trigonometry that if we resolve the easier problem where T=1 and X=1, then we can interpolate the points in the graph and translate them, so let's resolve the easier graph instead:
To find out the equation needed, I knew it had to be at least a 3rd grade equation, since it has two possible points that can become a peak or a valley. So we start with a general equation of:
$$f(t) = A t^3 + B t^2 + C t + D$$
However we not only want that, but we also want to solve it for any initial slope, that is if the graph was already transitioning it should smoothly interpolate to another graph. That's what most interpolating libraries in Javascript get wrong BTW, so that's why I wanted to make this.
We have a few variables there to resolve, so what do we know? We know:
- The initial value is 0,1
- The final value is 1,0
- The initial slope value is S
- The final slope is 0
The final value slope is calculated with the derivative for t=1, so that seems promising:
$$f'(t) = 3A t^2 + 2 B t + C$$
$$f'(t = 0) = 0 + 0 + C = S \rightarrow C = S ⚡$$
$$f'(t = 1) = 3A t^2 + 2 B t + C = 0 \rightarrow$$
$$3A + 2B + S = 0$$
Let's combine that with the initial value:
$$f(t=0) = 0 + 0 + 0 + D = 1 \rightarrow D = 1 ⚡$$
$$f(t=1) = A + B + S + 1 = 0 \rightarrow$$
So we end up with two equations:
$$A + B + S + 1 = 0$$
$$3A + 2B + S = 0$$
Let's resolve for $A = - 1 - S - B$:
$$3(- 1 - S - B) + 2B + S + 1 = 0$$
$$- 3 - 3S - 3B + 2B + S + 1 = 0$$
$$- 2 - 2S - B = 0$$
$$B = - 2 - 2S$$


- edit_query - Static variable in class ca.ualberta.cs.drivr.R.id
-
- editTextBackground - Static variable in class ca.ualberta.cs.drivr.R.attr
-
Must be a reference to another resource, in the form "@[+][package:]type:name
"
or to a theme attribute in the form "?[package:][type:]name
".
- editTextColor - Static variable in class ca.ualberta.cs.drivr.R.attr
-
May be a reference to another resource, in the form "@[+][package:]type:name
"
or to a theme attribute in the form "?[package:][type:]name
".
- editTextStyle - Static variable in class ca.ualberta.cs.drivr.R.attr
-
Must be a reference to another resource, in the form "@[+][package:]type:name
"
or to a theme attribute in the form "?[package:][type:]name
".
- ElasticSearch - Class in ca.ualberta.cs.drivr
-
In ElasticSearch, we store two classes: Requests and Users.
- ElasticSearch(ConnectivityManager) - Constructor for class ca.ualberta.cs.drivr.ElasticSearch
-
- ElasticSearchController - Class in ca.ualberta.cs.drivr
-
In ElasticSearchController, the actual calls to ElasticSearch are done.
- ElasticSearchController() - Constructor for class ca.ualberta.cs.drivr.ElasticSearchController
-
- ElasticSearchController.AddRequest - Class in ca.ualberta.cs.drivr
-
Here, a request is added to ElasticSearch by first building the add query for the document
to be stored in ElasticSearch, then adding it into ElasticSearch, then immediately afterwards
getting the ID where it's stored, appending it to the end of the add query and then replacing
the query we just added with the new query including the ID.
- ElasticSearchController.AddUser - Class in ca.ualberta.cs.drivr
-
Here, the user is either added or updated (since this class handles both) in ElasticSearch
by building it then adding it in the ID which is the same as the user's unique username.
- ElasticSearchController.DeleteRequest - Class in ca.ualberta.cs.drivr
-
Here, ElasticSearch will delete a request with the specified ID.
- ElasticSearchController.DeleteUser - Class in ca.ualberta.cs.drivr
-
Here, ElasticSearch will delete a user in the specified username.
- ElasticSearchController.GetRequest - Class in ca.ualberta.cs.drivr
-
Here, we get a specified request by searching with the request ID and returning it when we
find it.
- ElasticSearchController.GetUser - Class in ca.ualberta.cs.drivr
-
Here, we get a user profile by searching for the unique username and returning it when we
find it.
- ElasticSearchController.SearchForGeolocationRequests - Class in ca.ualberta.cs.drivr
-
Here, the program will get take in the specified geolocation from the user, search within a
5 km radius for open requests where the start and destination locations are within that range
and return the gotten requests.
- ElasticSearchController.SearchForOpenRequests - Class in ca.ualberta.cs.drivr
-
Here, all open requests will be gotten from ElasticSearch by searching for those requests
where the status is either pending or accepted.
- ElasticSearchController.SearchForUserRequests - Class in ca.ualberta.cs.drivr
-
Here, all requests associated to a user are gotten by checking all rider and driver usernames
to see if they contain the user's username, adding them to the ArrayList and then returning
that ArrayList.
- ElasticSearchController.UpdateRequest - Class in ca.ualberta.cs.drivr
-
Here, a request given by the user is updated in ElasticSearch by overwriting the document
it is contained in.
- ElasticSearchControllerTest - Class in ca.ualberta.cs.drivr
-
Tests for the ElasticSearch controller to make sure everything works accordingly.
- ElasticSearchControllerTest() - Constructor for class ca.ualberta.cs.drivr.ElasticSearchControllerTest
-
- ElasticSearchRequest - Class in ca.ualberta.cs.drivr
-
This class is used to hold the document gotten for requests in ElasticSearch before converting
it to a proper request.
- ElasticSearchRequest() - Constructor for class ca.ualberta.cs.drivr.ElasticSearchRequest
-
Constructor for ElasticSearchRequest.
- ElasticSearchTest - Class in ca.ualberta.cs.drivr
-
Tests for the methods in the ElasticSearch class.
- ElasticSearchTest() - Constructor for class ca.ualberta.cs.drivr.ElasticSearchTest
-
- elevation - Static variable in class ca.ualberta.cs.drivr.R.attr
-
Must be a dimension value, which is a floating point number appended with a unit such as "14.5sp
".
- email_login_form - Static variable in class ca.ualberta.cs.drivr.R.id
-
- email_switcher - Static variable in class ca.ualberta.cs.drivr.R.id
-
- emailDriverImageView - Variable in class ca.ualberta.cs.drivr.DriverListAdapter.ViewHolder
-
- emailImageView - Variable in class ca.ualberta.cs.drivr.RequestsListAdapter.ViewHolder
-
- enableGPS() - Method in class ca.ualberta.cs.drivr.GPSTracker
-
Opens settings Activity to allow user to turn on GPS.
- end - Static variable in class ca.ualberta.cs.drivr.R.id
-
- end_padder - Static variable in class ca.ualberta.cs.drivr.R.id
-
- enterAlways - Static variable in class ca.ualberta.cs.drivr.R.id
-
- enterAlwaysCollapsed - Static variable in class ca.ualberta.cs.drivr.R.id
-
- enterDriverMode() - Method in class ca.ualberta.cs.drivr.MainActivityTest
-
- environment - Static variable in class ca.ualberta.cs.drivr.R.attr
-
Must be one of the following constant values.
- equals(DriversList) - Method in class ca.ualberta.cs.drivr.DriversList
-
Compares two ArrayLists of Drivers to see if they're equal.
- error_field_required - Static variable in class ca.ualberta.cs.drivr.R.string
-
- error_incorrect_password - Static variable in class ca.ualberta.cs.drivr.R.string
-
- error_invalid_email - Static variable in class ca.ualberta.cs.drivr.R.string
-
- error_invalid_password - Static variable in class ca.ualberta.cs.drivr.R.string
-
- errorEnabled - Static variable in class ca.ualberta.cs.drivr.R.attr
-
Must be a boolean value, either "true
" or "false
".
- errorTextAppearance - Static variable in class ca.ualberta.cs.drivr.R.attr
-
Must be a reference to another resource, in the form "@[+][package:]type:name
"
or to a theme attribute in the form "?[package:][type:]name
".
- EstimatedPriceCalculator - Class in ca.ualberta.cs.drivr
-
Created by justin on 28/11/16.
- EstimatedPriceCalculator() - Constructor for class ca.ualberta.cs.drivr.EstimatedPriceCalculator
-
- estimateFare(Place, Place) - Method in class ca.ualberta.cs.drivr.EstimatedPriceCalculator
-
- EstimatePrice() - Method in class ca.ualberta.cs.drivr.EstimatePriceTest
-
UC 6 EstimateFare
- EstimatePriceTest - Class in ca.ualberta.cs.drivr
-
Created by justin on 28/11/16.
- EstimatePriceTest() - Constructor for class ca.ualberta.cs.drivr.EstimatePriceTest
-
- exitUntilCollapsed - Static variable in class ca.ualberta.cs.drivr.R.id
-
- expand_activities_button - Static variable in class ca.ualberta.cs.drivr.R.id
-
- expandActivityOverflowButtonDrawable - Static variable in class ca.ualberta.cs.drivr.R.attr
-
Must be a reference to another resource, in the form "@[+][package:]type:name
"
or to a theme attribute in the form "?[package:][type:]name
".
- expanded - Static variable in class ca.ualberta.cs.drivr.R.attr
-
Must be a boolean value, either "true
" or "false
".
- expanded_menu - Static variable in class ca.ualberta.cs.drivr.R.id
-
- expandedTitleGravity - Static variable in class ca.ualberta.cs.drivr.R.attr
-
Must be one or more (separated by '|') of the following constant values.
- expandedTitleMargin - Static variable in class ca.ualberta.cs.drivr.R.attr
-
Must be a dimension value, which is a floating point number appended with a unit such as "14.5sp
".
- expandedTitleMarginBottom - Static variable in class ca.ualberta.cs.drivr.R.attr
-
Must be a dimension value, which is a floating point number appended with a unit such as "14.5sp
".
- expandedTitleMarginEnd - Static variable in class ca.ualberta.cs.drivr.R.attr
-
Must be a dimension value, which is a floating point number appended with a unit such as "14.5sp
".
- expandedTitleMarginStart - Static variable in class ca.ualberta.cs.drivr.R.attr
-
Must be a dimension value, which is a floating point number appended with a unit such as "14.5sp
".
- expandedTitleMarginTop - Static variable in class ca.ualberta.cs.drivr.R.attr
-
Must be a dimension value, which is a floating point number appended with a unit such as "14.5sp
".
- expandedTitleTextAppearance - Static variable in class ca.ualberta.cs.drivr.R.attr
-
Must be a reference to another resource, in the form "@[+][package:]type:name
"
or to a theme attribute in the form "?[package:][type:]name
".
- EXTRA_KEYWORD - Static variable in class ca.ualberta.cs.drivr.SearchRequestActivity
-
- EXTRA_PLACE - Static variable in class ca.ualberta.cs.drivr.NewRequestActivity
-
The key used for passing a Place to this activity as an extra to an intent.
- EXTRA_PLACE - Static variable in class ca.ualberta.cs.drivr.SearchRequestActivity
-
The key used for passing a Place to this activity as an extra to an intent.
- EXTRA_REQUEST - Static variable in class ca.ualberta.cs.drivr.RequestActivity
-
Used as a key for passing a request into this activity as an extra to an intent.
- EXTRA_SEARCH_REQUEST - Static variable in class ca.ualberta.cs.drivr.SearchRequestsListActivity
-


In her waters, deep and true,
lie the answers and a path for you.
Answers may turn in any way after starting in the given direction.
No cell is part of three or more answers.
[See original puzzle for images]
| 1 | SE | American capital to rebuild, not die (10) |
| 2 | E | Setup of Facebook-owned app a bit less than halfway done (7) |
| 3 | SE | Hat material for those afraid of cycles in nothing (7) |
| 4 | E | Nice money for the desperate? (7) |
| 5 | NW | Ill-gotten c-notes possess someone like a mind controller (9) |
| 6 | NW | Sitwell and Wharton’s unstarted corrections (6) |
| 7 | W | “Despacito” singer misses a note, ends up with “Hydrangea” (9) |
| 8 | SW | Many schoolkids’ first pet (7) |
| 9 | E | Some merchandise the end of H. sapiens (3-3) |
| 10 | E | Bad signs about rightwing leader: embezzles and rigs votes, e.g. (10) |
| 11 | SW | Ran halfway from ghost (6) |
| 12 | E | Dirt on outside of gear (4) |
| 13 | NW | Square on street (6) |
| 14 | NW | A deteriorating situation: out of energy, extraterrestrial finds species (8) |
| 15 | NW | Every other bard exile is a love song writer (9) |
| 16 | E | Keener, without Henry, is about to say it (in other words) (8) |
| 17 | NE | Rag found in drab lodgings (7) |
| 18 | E | Positioning a pure substance with no electrons (9) |
| 19 | NE | Minnesota to Louisiana without a kind of cigarette (7) |
| 20 | SW | First woman beheld death at twilight (8) |
| 21 | NW | Nine, containing forty-first and one, becomes endless (8) |
| 22 | SW | The alchemists embrace medicine (10) |
| 23 | W | The Good Place character becomes new after epiphany (6) |
| 24 | W | Refrain from holding pouring water (8) |
| 25 | E | Lava erupts around an hour into the afterlife (8) |
| 26 | E | Leaders of ecumenical inquisition council lambaste the heterodox (9) |
| 27 | E | Consume hollow hallucinogen, becoming pagan (7) |
| 28 | W | Drains soils (7) |
| 29 | NW | Recitation instructor mixed up red pyramids (10) |
| 30 | NE | Judi turned around and, missing cents, lowered the price (9) |


% include com/stdhead.html %>
<%- include('com/nav.html', {navClass: ''}) %>
<% if (reset) { %>
Your password has been updated.
<% } %>
Sign in to <%= appInfo.brandname %>
Username
Password
Forgot password »
Sign in
<% include com/footer.html %> <% include com/stdjs.html %>


Meals
- {{ meal.meal_plans.meal1.recipe_name }}
- {{ meal.meal_plans.meal2.recipe_name }}
- {{ meal.meal_plans.meal3.recipe_name }}
Meal Calories: {{calories}}
Calorie Goal: {{calGoalDelta}}
{% extends "theme/dashboard.html" %} {% block content %}
Meal Calories: {{calories}}
Calorie Goal: {{calGoalDelta}}


Working for us
Every day, we work to change people’s lives for the better. We are here to make a real difference to a diverse range of people who are often the most vulnerable in society.
We put the user at the heart of everything we do. We offer rewarding and challenging roles and will commit to supporting your development.
Our culture
Our values are the foundation of our culture and we demonstrate this through what our people do. We are continuously developing in line with our digital transformation. This is reflected in the inclusive way we work, the decisions we take and the changes we make.
We believe that people benefit from working in an organisation where every contribution is valued and they can be themselves. We invest in their development and encourage them to focus their efforts on the things that really matter.
Competitive Civil Service pension scheme
Generous, flexible working packages and a minimum of 23 days' annual leave
Continuous investment and support to develop in your role
{% include "includes/call_to_action.html" %}


Node Reward Control Panel
TODO: filter by IP/account
Node Counts
?
Daily Summary
Hide zero balances
Hide non-eligibles
10-minute-period Summary
Hide zero balances