Before Change
self.name]
stdout, _, _ = vm_util.IssueCommand(show_cmd, suppress_warning=True)
try:
json.loads(stdout)
except ValueError:
return False
return True
After Change
// "set" trick doesn"t work like it does for other resources
// because "vm set" doesn"t allow no-ops, so we are forced to
// actually parse the results of "show".
stdout, _, retcode = vm_util.IssueCommand(
[azure.AZURE_PATH, "vm", "show",
"--json",
self.name] + self.resource_group.args)
return retcode == 0 and stdout != "{}\n"
@vm_util.Retry()


Zinnia Guild Mercenary:
Will you survive?  Better get some rest, and maybe a bath friend.
Quest


It's in Bits
As I write this I’m flicking between 6 different things: this website, learning Octopress, learning ruby, university project A, university project B and Season 1 of Game of Thrones. It is 01:46 in the morning and I have a Doctors appointment at 09:00: this is my life…
… and it’s in bits.
This site is (hopefully) going to turn into a fair representation of who I am, offering potential employers/ anyone a deeper look into my life then can be fairly represented on a C.V. Part of this however means juggling several very different things at once; this site represents the face of my first foray into Cloud PaaS’ as well as my first time publicly promoting my work which until now I’ve always been contented to keeping to myself.
I’ve linked my Github account which documents a fair chunk of the things I do, and as this site pulls together, I’ll include some of the other pieces of work I’ve enjoyed completing; it’s quite an eclectic mix, not least because since my college days my general attitude has been ‘do everything.’ As much as the idea of specialising into an area of computing sounds attractive, I can’t help but feel a sense of unease at the thought that by doing so I’ll limit my ability to complete a fully polished piece of work.
Such is the point of teams though is it not? I have my own thoughts on this that I’ll almost definitely talk about in a later post, but for now, this will suffice for a first post.
It’s something that I can look and smile at. A starting point for what I hope to be a very satisfying project.
Projects…. Game of Thrones….. Bed…..


index
module components.gutime.wrapper
GUTimeWrapper
Contains the GUTime wrapper.
The wrapper takes all sentences in the TarsqiDocument and creates the input
needed by TimeTag.pl, which is the wrapper around TempEx.pm.
The input required by TimeTag.pl looks as follows:
<DOC>
<DATE>20120912</DATE>
<s>
<lex id=1 pos="NNP">Monday</lex>
<lex id=2 pos="NN">morning</lex>
<lex id=3>and</lex>
<lex id=4>today</lex>
</s>
</DOC>
The DOC root is required and so is the DATE tag. Otherwise, only s and lex tags
are allowed. Any kind of spacing between the tags is allowed.
Note that the directory that this wrapper is in has two unused files: gutime.pl
and postTempEx.pl. Much of the functionality in those files is either in this
wrapper or obsolete. They are kept around for reference, mostly for the language
on temporal functions in postTempEx.pl.
class GUTimeWrapper
Inherits from: object
Wrapper for GUTime.
Public Functions
__init__(self, document)
process(self)
process_using_files(self)
Create the input required by TimeTag.pl, call the Perl script and
collect the TIMEX3 tags.
process_using_string(self)
Create the input required by TimeTag.pl, call the Perl script and
collect the TIMEX3 tags.
module functions


This is an annual event organized by Nvidia. Here, I took the course titled "Building Transformer Based Natural Language Applications." Covered NLP Embeddings (Word2Vec, RNN, and Transformers), Self-Attention, Self-supervision (BERT, Megatron, etc), NLP Models (text classification, NER, Question Answering), and Deployment & Inferencing with Nvidia NeMo and Triton
PrivateThis project involves a series of thorough analysis of Machine Learning Algorithms. Supervised Algorithms covered include: NNs, DTrees, K-NNs, SVMs, Boosted-DTrees. Unsupervised include: PCA, ICA, ISOMap, Randomized Projections, K-Means, GMMs. Randomized Optimization include: Randomized Hill Climbing, Simulated Annealing, Genetic Algorithms, and MIMIC. Reinforcement Learning Algorithms include: Policy Iteration, Value Iteration, and Q-Learning.
PrivateThis is an annual event organized by Nvidia. Here, I participated in four topics at the institute's DLI workshop: (1) CUDA python with Numba, (2) 3D Segmentation with VNet, (3) Anomaly Detection with Variational AutoEncoders, and (4) Data Augmentation and Segmentation with GANs.
PrivateAn Online Ensemble Forecasting algorithm was developed by combining ARIMA, Prophet, and Holt-Winters + GARCH for predicting future number of COVID-19 cases in Nigeria. As at the time of writing, it has achieved a 100% hit-rate
Check it outThis project involves Augmented reality without the use of markers. An image of an advert or video frames can be projected on a surface by persistent frame-by-frame homography tracking.
Check it outExposure fusion creates a single image with optimal detail from a set of multi-exposed images. As developed by Tom Mertens et al., quality measures; Contrast, Saturation, and Well-Exposedness are used to create a weight map that blends each of the multi-exposed images to a single image with best exposure.
Check it outObject tracking was performed using the Particle Filter algorithm. For robustness, higher level motion dynamics can be incorporated in the algorithmic formulation.
PrivateThe Particle filter algorithm is used to track a Pedestrian using a template sample. Detection also works well when HOG features are used with a Kalman filter. This has applications in traffic analysis and self-driving cars.
PrivateHistorical market data is used to train a Random Forest algorithm in-sample. It is then tested by simulating the possible portfolio performance using a market simulator. Portfolio gains around 200% was achieved for AAPL stock in 2008 (in-sample) vs 2009 (out-sample)
PrivateThe objective of this project is to showcase different indicators used in the stock market for trading decisions: Bollinger bands, %b, Relative Strength Index, and Moving Average Convergence Divergence. These help in identifying Bullish and Bearish market, and in deciding when to Short, Long, or Hold a position.
Check it outThis Portfolio simulator builds the performance of a portfolio for a given stock(s) and market data. It receives an "Orders" file containing actions: "BUY", "SELL", Symbol of the stock, Dates, Transaction costs, and Starting value. The simulator then builds the portfolio returns
PrivateRavens progressive matrices (RPM) is a cognitive test. This work develops a Knowledge-based AI Agent to solve the RPM problems using visual heuristics. There are three steps: re-representation - a global contractive transformation, followed by a prediction stage, and finally an optimization (matching) stage. It is one of the simplest agents and can be implemented in just 5 lines of code!
Check it outFace detection was performed as described in the Viola-Jones paper. Haar features create representations for human faces while Boosting enhances the performance of the learner by concentrating on difficult samples.
PrivateMotion detection using the Lucas Kanade (LK) algorithm which is based on the least squares solution of NxN Optical flow equations. It is assumed that objects move slowly, however, for objects with more displacement, the Heirarchical LK performs better .
PrivateHyperparameters are very important for optimum performance of Deep learning models. These include: number of layers, number of units, dropout, momentum, epochs, batch-size, learning-rate. This work explores different values of these parameters and the corresponding performance on an MNIST-DNN model.
PrivateTrigger word detection is the technology that allows devices like Samsung Bixby, Amazon Alexa, Google Home, Apple Siri, and Baidu DuerOS to wake up upon hearing a certain word. The model built uses 1-D convolutional layers, GRU layers, and dense layers on audio segments converted to spectrograms.
PrivateA computer vision algorithm is designed to classify sea lions from aerial images into the classes: adult males, sub adult males, adult females, juveniles and pup respectively. First, a high-resolution aerial image is cut into smaller images. Next, feature points are used to extract training samples for a Convolutional Neural Network
Check it outThe Model-free reinforcement Q-learning algorithm was programmed for navigating a simple maze. A Q-table is built based on immediate and discounted rewards using experience tuples as the bot interacts with the world.
PrivateAn image is converted to a HSV color space for color based segmentation. The system emphasizes body tones that can be used for tracking, AR applications and so on.
Check it outReccurent Neural Networks (RNN) are excellent for sequence-to-sequence learning. In this cool project, they are used to generate Yoruba names in a closed-loop fashion with inspiration drawn from Control theory.
PrivateADRC uses an Extended state observer to linearize the Quadrotor's Nonlinear dynamics (similar to Feedback linearization). This makes it capable of eliminating disturbances (robustness).
Check it outYOLO is a popular algorithm because it achieves high accuracy while also being able to run in real-time. This algorithm (you-) "only looks once" at the image in the sense that it requires only one forward propagation pass through the network to make predictions. After non-max suppression, it then outputs recognized objects together with the bounding boxes.
PrivateRecurrent Neural Networks (RNN) are excellent for sequence-to-sequence learning. In this interesting project, they are used to generate names for Dinosaurs.
PrivateNeural Style Transfer (NST) is one of the un techniques in deep learning. It merges two images, namely, a "content" image (C) and a "style" image (S), to create a "generated" image (G). The generated image G combines the "content" of the image C with the "style" of image S.
PrivateGiven two images, how can a bot tell if they are the same person (?). Due to variations in lighting, orientation of the person's face, even minor changes in head position, and so on, directly comparing pixel values won't work. Instead, learning an encoding gives more accurate judgements as to whether two pictures are of the same person.
PrivateThe altitude control of a quadrotor unmanned aerial vehicle is treated using its altitude dynamics in hover mode. An LMS-based Adaptive Active disturbance rejection control (AADRC) is proposed to compensate for undesirable effects resulting in smoother control.
Check it outThe SIGNS dataset is a collection of 6 signs representing numbers from 0 to 5. A ConvNet was built with TensorFlow for solving this classification problem. Steps include: Creating placeholders, initializing parameters, forward propagation, computing cost, and then optimization.
PrivateGenerating Jazz music with deep learning. An LSTM model is trained on random snippets of values taken from a much longer piece of music to learn the patterns of the jazz soloist. This would be used to synthesize new music.
PrivateWord vector representations were used to build an Emojifier which automatically appends emojis to text: "Congratulations on the promotion! 👍. Lets get coffee and talk. ☕ Love you! ❤"
PrivateA Neural Machine Translation (NMT) model is built to translate human readable dates ("25th of June, 2009") into machine readable dates ("2009-06-25") using an attention model, (one of the most sophisticated sequence to sequence models).
PrivateThis work deals with very deep convolutional networks, using Residual Networks (ResNets). In theory, very deep networks can represent very complex functions; but in practice, they are hard to train (vanishing gradients). ResNets, introduced by He et al., allow training much deeper networks using the idea of skip-connections.
PrivateLyapunov-based based backstepping controller was developed to control an induction motor. Induction Motors are reliable, rugged and cheap. Main problem in control arises from: coupled highly nonlinear model, unavailability of certain parameters and time varying nature of some parameters.
Check it outA HDR image is made by smartly combining multiple photographs of a scene at different exposures. It involves three main steps: Calibration, Radiance Map, and Tone Mapping. The Camera response curve is calculated in the first step, followed by a the computation of radiance map. Finally, correction of the color tones in the image is performed.
PrivateFaceNet learns a neural network that encodes a face image into a vector of 128 numbers. By comparing two such vectors, an algorithm can determine if two pictures are of the same person. A triplet loss function was used on a pretrained model with encodings to perform face verification and face recognition
PrivateVideo textures are created through 3 steps. First is computing a similarity metric. Second is to compute the transition difference. Third involves finding and synthesizing recurrent loops
PrivateClassification of "Cat vs non-Cat" dataset using Deep neural networks. Compared to simple logistic regression, the DNN yields better classification accuracy. Performance of 70% with logistic regression, 72% with 2 layers, and 80% with a 4 layer network without tuning.
Check it outUsing ORB feature detector, matched source and destination keypoints are used to calculate the homography transform between input images. The homography is then used to warp the images, which are finally blended together
PrivateImplementing the paper "A multiresolution spline with application to image mosaics". A cool framework was developed to blend images using a predefined mask. This can be used for interesting image editing such as face swaps
Check it outReproducing the paper "Content-aware image resizing" by Shai-Avidan et al. Using the seam carving algorithm, three images are subject to: (1) aspect ratio change by seam removal, (b) aspect ratio change by seam insertion, and (c) aspect ratio change by optimal retargeting.
Check it outExtrinsic noise is everywhere. Using a microphone and a Voltage controlled crystall oscillator, an RF circuit was built to jam cellular communications at the GSM band using destructive interference.
Check it outWith Virtual Robot Experimentation Platform, a Quadrotor can be controlled using a dedicated controller such as PID in a Lua script. Quadrotor incorporates visual feedback to detect the desired landing spot.
Check it outBackstepping is a Nonlinear controller based on Lyapunov functions. Four controllers are developed for the Altitude, Yaw, Pitch, and Roll attitudes of the quadrotor.
Check it outBasic algorithms in Image processing. Using Matlab code to explore different Edge detection algorithms and parameters.
Check it out


Click one of the geometry buttons.
A box will appear showing the coordinates in
the map's projection
, as well as the
projected results
from a geometry service and from
Proj4JS
.
Point
Polyline
Polygon


Welcome to your new digital library!
BookLib lets you organize your books in a smart way. Take a tour around
the application and see what amazing features you can discover.
Books
Here you find ALL of your books. You can see them in tiles or in a
list, organize them by name, author, language or year. You can also
filter them, search them and add new books manually. This is where
your library starts!
Collections
You can organize your books in collections, like series of books,
trilogies or books that have something in common. Find all of your
collections here, add new ones and manage your current ones.
Authors
Here you can find info about authors you follow. (This feature is
still under development!)
Goodreads
Here you can connect to the Goodreads platform, import your library
from your account, find books and authors and connect your current
books to the Goodreads database. (This feature is still under
development!)
Import
Here you can import your personal library from a customized
spreadsheet and have all your books very easily uploaded to your
digital library.


This page tracks the new paper links made to my list of I3D 2014 papers.
Page maintained by Ke-Sen Huang.
If you have additions or changes, send an e-mail.
March 28, 2014
- Stochastic Activity Authoring with Direct User Control
February 14, 2014
February 6, 2014
- A GPU accelerated algorithm for 3D Delaunay triangulation
January 30, 2014
- Sub-Pixel Shadow Mapping
- Efficient Virtual Shadow Maps for Many Lights
- Per-Triangle Shadow Volumes Using a View-Sample Cluster Hierarchy
January 13, 2014
- Prefiltered Single-Scattering
- Multi-Layer Alpha Blending
- Adaptive Depth Bias for Shadow Maps
- Dynamic Radial View Based Culling for Continuous Self-Collision Detection
- Real-Time Low-Frequency Signal Modulated Texture Compression using Intensity Dilation
January 11, 2014
- Sound Localization and Multi-Modal Steering for Autonomous Virtual Agents
- A Hybrid Image-CAD Based System for Modeling Realistic Hairstyles
- FORKS: Interactive compliant mechanisms with parallel state computation
- Fast Rotation Search for Real-Time Interactive Point Cloud Registration
- k+-buffer: Fragment Synchronized k-buffer
December 15, 2013
- Fast Rotation Search for Real-Time Interactive Point Cloud Registration
- Fracture Animation Based on High-Dimensional Voronoi Diagrams
- Page Created and Published
kesen.huang@gmail.com


Copy the link below to your clipboard and add it as a new repository to the 'Help > Install New Software...' Eclipse menu.
Or download an archive for instance for offline installation and pass it via 'Archive' in the 'Help > Install new Software...' Eclipse menu. Note however, that you will miss automatic update notifications this way.


{% extends "layouts/learn.html" %} {% load layout_extras %} {% load menus %} {% load i18n %} {% block title %}{% block title-twitter %}{% trans "Open Source Software for EZID" %}{% endblock %}{% endblock %} {% block heading %}{% content_heading _("Open Source Software for EZID") %}{% endblock %} {% block content %} {% comment %}Don't need to translate this{% endcomment %} {% learn_breadcrumb _("Open Source Software for EZID") 'Documentation' '#04' %}
TRANSLATE TEXT WRAPPED BY HTML TAGS
i.e.: PLEASE PRESERVE HTML TAGS {% endcomment %} {% blocktrans trimmed %} IMPORTANT: The software listed below is not endorsed or supported by the EZID Team. It is listed here solely as a courtesy. If you are the author of a resource listed here and you wish us to remove it, please contact us. If you are the author of software that you wish to be listed, please contact us.


Your organisation must be identified as a UK partner in the grant agreement to receive funding.
If your organisation type is not listed, please ring 0300 321 4357 or email EUGrantsFunding@ukri.org.


Teacher respect day was orgazine so many year.This day was for respect the teacher like teacher appile to be stundent in this university.
Read MoreThis activities let the parents of the student know about the faculty.
Read MoreThis workshop give knowledge to student about how to create the vidioes game and mechanic of virtual reality.how to use it and how to set up device.
Read MoreThis workshop give student how to connect all your thoughts into visually organize information in mindmap.
Read MoreOpen house begin in 29-31 of january 2020 to outside student know about our faculty..
Read Moreko kret field trip has give student about thai culture and know how people live in kor kret.
Read More


This is the third time I quit smoking, so I hope I can provide some assistance to others. Chantix won't make you quit, but it helps reduce the cravings and takes the edge off those cravings. No vivid dreams, but experienced nausea. Taking the morning pill at least an hour after breakfast and plenty of water beforehand helped me.
When I get a craving, I take a deep breathe and hold it as long as I can.
SENT5
It kind of simulates the smoke going into my lungs and satisfies my craving. Brushing my teeth often also helped me.


FINN.no
Applying the function
> myFunction 1 2
8
> myPartiallyAppliedFunction = myFunction 1
> myPartiallyAppliedFunction 2
8
You must have an else
.
Return types must match.
There are no for/while loops in haskell.
Recursively looping through a list and changing nothing.
$ stack repl ./src/Part1.hs
(...)
*Part1>
Unloading all modules:
*Loaded Modules> :load
Ok, no modules loaded.
Prelude>
Loading single module:
Prelude> :load Part1
Ok, one module loaded.
*Part1>
Reloading current modules:
*Part1> :reload
Ok, one module loaded.
*Part1>
>1 + 1
2
>:type 1
1 :: Num a => a
First shell
stack repl ./src/Part1.hs
Second shell
./runtests 1
Functions are values and can be passed as arguments to, and be returned from, other functions.
You can turn an infix function into a prefix function by wrapping it in parantheses.
This is required to pass it as an argument.
You can include one operand inside the parantheses to create a partially applied function.
cylinderVolume :: Float -> Float -> Float
cylinderVolume diameter height =
let radius = diameter / 2
area = pi * radius ^ 2
in area * height
Variables can not be reassigned.
First shell
stack repl ./src/Part2.hs
Second shell
./runtests 2
First shell
stack repl ./src/Part3.hs
Second shell
./runtests 3
Sometimes more convenient than creating a named function, or partially applying an existing function.
Use an underscore to tell the compiler (and yourself) that an argument is intentionally not used.
First shell
stack repl ./src/Part4.hs
Second shell
./runtests 4
(:)
is a function.
> :type (:)
(:) :: a -> [a] -> [a]
Product of two types (which may be different)
First shell
stack repl ./src/Part5.hs
Second shell
./runtests 5


{% extends "kadmin/base.html" %} {% block content_title %}
Page last rendered: {{ now }} {{ settings.TIME_ZONE }}
{{ msg }}
{% endfor %}Auto-refreshing every 30 seconds :: Refresh page
| batch | name | created | start | end | message | delta |
|---|---|---|---|---|---|---|
| {{ record.batch_id }} | {{ record.name }} | {{ record.creation_time }} | {{ record.start_time }} | {{ record.end_time }} | {{ record.message }} | {{ record.delta }} |
| message | start time |
|---|---|
| {{ record.text }} | {{ record.starttime }} |
Note: The number of records may not line up with the number of outstanding indexing tasks because records are created when the task starts.
Settings at the time this page was loaded:
| ES_LIVE_INDEXING | {{ settings.ES_LIVE_INDEXING }} |
|---|---|
| ES_INDEX_PREFIX | {{ settings.ES_INDEX_PREFIX }} |
| ES_INDEXES | {{ settings.ES_INDEXES }} |
| ES_WRITE_INDEXES | {{ settings.ES_WRITE_INDEXES }} |
| Elasticsearch version | {{ es_deets.version.number }} |
All available indexes:
| Index Name | Documents | Type | Delete | {% for index_name, index_count in indexes %}|
|---|---|---|---|---|
| {{ index_name }} | {{ index_count }} | {% if index_name in read_indexes and index_name in write_indexes %} READ/WRITE {% else %} {% if index_name in read_indexes %} READ {% else %} {% if index_name in write_indexes %} WRITE {% endif %} {% endif %} {% endif %} | {% if index_name not in read_indexes %}{% else %} | Disabled | {% endif %}
Index does not exist.
{% else %}| doctype | count |
|---|---|
| {{ doctype }} | {{ count }} |
Write indexes are the same as the read indexes.
{% else %} {% for index, stats in doctype_stats.items %}Index does not exist.
{% else %}| doctype | count |
|---|---|
| {{ doctype }} | {{ count }} |
REINDEX into existing index |
|
|
Reindex into the existing WRITE index. Don't do this if you've made mapping changes since this does not recreate the index with the new mappings. {% if outstanding_chunks %}WARNING! There are outstanding index tasks! Don't launch another indexing pass unless you really know you want to. {% endif %} {% if not settings.ES_LIVE_INDEXING %}WARNING! ES_LIVE_INDEXING is False so you can't reindex via the admin. Either enable ES_LIVE_INDEXING or use the command line ./manage.py esreindex. {% endif %} |
{% if doctype_write_stats != None %} {% endif %} |
DELETE existing index group's write index, recreate it and reindex |
|
|
This DELETES the existing WRITE index for a group, recreates it with the mappings, and indexes into the new index. You should have to do this only when the search mapping changes or when setting up the site for the first time. {% if read_indexes == write_indexes %}WARNING! All read and write indexes are the same! Deleting and rebuilding the index would be really bad! {% endif %} |
|
RESET records and mark as failed |
|
|
This marks outstanding records as fail. This allows you to run a new reindexing pass. |
| batch | name | created | start | end | status | message | delta |
|---|---|---|---|---|---|---|---|
| {{ record.batch_id }} | {{ record.name }} | {{ record.creation_time }} | {{ record.start_time }} | {{ record.end_time }} | {{ record.status }} | {{ record.message }} | {{ record.delta }} |


How to Test Android's Parcelable Interface
Android applications invariably seem to have many places where you have to pack data into an Intent or Bundle in order to pass it on to another component. I have found that packing a single Java object in the payload is far more maintainable than using several primitive key/value pairs.  In order to do this, the object must implement either Serializable or Parcelable. I generally prefer Parcelable, even though it takes a bit more code to implement.
Unfortunately, I recently ran into a huge pitfall with Parcelable objects. To ensure that my Parcelable implementation is correct, I had been using a very basic unit test–but I didn’t realize that the test wasn’t actually proving everything was working as intended. One night back in February, I was refactoring some code and seeing ClassCastExceptions when pulling a particular Parcelable object out of a Bundle. I couldn’t figure out where the type was getting switched in my code, but it never occurred to me that the actual Parcelable implementation was at fault because I had too much faith in my unit tests. After a couple hours of digging, I figured out that the naive unit tests I had been using for Parcelable don’t actually ensure anything. Hopefully this post can help you avoid the same mistake.
First, a quick summary of implementing Parcelable if you have not done it before. In order to make an object Parcelable, you must handle writing to the Parcel, creating an object from the Parcel, and provide a CREATOR factory. This code is pretty straightforward, although there are a few gotchas. Take a look at the implementation below for class ‘Foo’. In order to test these types of classes, I always make sure to write (or generate) a proper equals() method.
In order to effectively unit test the parceling objects, you must implement a deep comparison equals() method. This ensures that all fields are tested. This is simple to autogenerate in any Java IDE, or you can use the Guava equals helpers for a cleaner implementation.
(Bonus rant: why did the Android team not include writeBoolean() and readBoolean() methods? I generally pack booleans as an int, but you can also use a byte or a String to pass them. Thanks to this answer on StackOverflow for the inspiration on how to pass them cleanly.)
My original unit test naively mirrored how you actually use Parcelable objects. Stick it in a Bundle, pull it out of a Bundle, and check equality.
Thus, the above test passed even though the Foo class’s CREATOR was written originally as shown below. Note that CREATOR is returning type Bar instead of type Foo. This is one easy pitfall when writing the boilerplate associated with Parcelable.
After finding the actual bug, my concern switched to figuring out why the unit tests hadn’t caught it. It turns out that Bundle doesn’t actually serialize/de-serialize each value until the Bundle itself is parceled. Thankfully James Wilson has a nice solution that correctly tests the object.
In fact, with this code the test suite won’t even build because CREATOR’s return value is type checked at compile time, rather than casting from Bundle’s getParcelable() at runtime.
I would recommend writing that unit test anytime you are creating a Parcelable object.
Republished from Kevin’s blog.


The Lazy Saturday
Men's - $ 90 USD (Coming Soon)
DESCRIPTION
Your new weekend shoe. We borrowed the woven upper of a Mexican huarache sandal and combined it with the sillhouette of an English slipper and the outsole of a boat shoe. Because casual should still turn heads.
MATERIALS


Bargaining and negotiation can be used interchangeably; they mean virtually the same thing. Bargaining or Negotation refer to a strategy that is very similar to Collaboration, except that the expectations of the parties are lower as they enter the conflict arena. Instead of seeking solutions that are mutually fulfilling to both or all parties, people who use Negotiation are trying to get as much as they can, assuming that they will not get everything they want (but at least they will get some of what they want, as the others get some of what they want). In other words, where Collaboration is a win-win strategy, Negotiation is a sorta-win-sorta-lose strategy.
Negotiation can be used at all levels. People are used to it, they expect it, and they usually know how to deal with it. A frequent problem with the use of Negotiation, however, is the fact that many issues or problems are not negotiable.
To bargain, the "prize" must be something that is divisible or items one can trade. Negotation is often a good compromise between Collaboration and Compelling. It is necessary for all the parties to be willing to bargain. All parties must come to the table and join in the give and take.
Bargaining is not a good idea in situations where there is a great power disparity between parties (unless the power disparity is compensated in some way). What may officially be designated as bargaining does not feel like bargaining when one of the parties perceives that they must go along with the proposals suggested by the other. (This is not Negotiating; it is Compelling.)
Finally, bargaining is not a good strategy to use when any of the parties has a high level of fear and perceptual distortion about the other and the situation. This is a condition in which Negotiation is not possible but in which it is best postponed until all sides feel a modicum of safety and each is able to approach the problems somewhat rationally and with a clear head.


MILLER'S CROSSING (1990) A Film Review by Ted Prigge Copyright 1998 Ted Prigge
Director: Joel Coen Writers: Joel and Ethan Coen Starring: Gabriel Byrne, Albert Finney, Marcia Gay Harden, Jon Polito, John Turturro, J.E. Freeman, Mike Starr, Al Mancini, Steve Buscemi, Michael Jeter, Danny Aiello III, Richard Woods, Frances McDormand
With the Brothers Coen finally getting the recognition they deserve, what with the whole "Fargo" thing and "The Big Lebowski" opening in a couple days in wide release, so it's really a blast going back and retracing their entire filmography to see how exactly they arrived at what seems to be the pinnacle of their career thus far (back to the whole "Fargo" spiel). "Miller's Crossing," their third film, is a nice combination of story, substance, and style, as well as being one of the most underrated films dealing with gangsters, and is actually one of their best films.
 The protagonist is Tom Reagan (Gabriel Byrne), an Irish gangster working for the big political boss in town, Leo (Albert Finney), who not only controls the police, but also the various mafiasos in town.
 In the first scene, an almost-parody of the opening of "The Godfather," Casper asks Leo to have him killed, but Leo won't budge. However, Leo doesn't know that his right-hand man, Tom, is screwing Verna on the side, and she seems to the person who the characters revolve around.
Soon, Tom and Leo find themselves at odds over her, and Leo kicks Tom out of his job, so he crosses over to Casper's side, who's trying to overthrow Leo throughout the film with the help of his menacing henchman, Eddie Dane (J.E. Freeman). As the film goes on, Tom tries to manipulate everything, but towards the middle of the film, rats on where Bernie is, and finds himself with the task of shooting him at Miller's Crossing. Here's where the big twist lies, and it sets the stage for the rest of this film, which reaches a fateful conclusion.
The story, which is extremely labyrinthine and complex, is nevertheless one that is endlessly fascinating, as we're introduced to a wide variety of wacky and eccentric supporting characters, such as the homosexual annoyance Mink (Steve Buscemi, in a one-scene role), and two hitmen working for Casper, named Frankie and Tic-Tac (Mike Starr and Al Mancini, respectively). The world of the Coen brother is always intriguing in all their films, as they allow every character to have a distinct personality and various weird quirks. While some may argue that the world of David Lynch's is much more weird and thus more interesting, the Coens combine style with a better grasp on all their characters and an overall better control over their world.
What's most interesting about "Miller's Crossing" is how it's always interesting and fun to watch, but how it also has an interesting take on the world of classical gangsterdom. The men in this movie may seem to dominate, but most of them have a soft spot for women, especially seductive women. Poor Marcia Gay Harden is burdened with the role of being the woman who is manipulating everyone whether she likes it or not. Not only is Leo reluctant to have her brother killed for fear that she won't marry him, but also Tom, who seems all cold, is smitten by her. What do you think is on his mind in the famous Miller's Crossing scene of this film? Is he really influenced by Bernie's "Look into your heart" pleas? Gangster pics should always present an interesting and if possible original take on gangsters, and that's what "Miller's Crossing" does. It's a film that shows that women really ran the show.
But besides that, the style of the Coens, which is eccentric and wild, is clearly here, not only in how they handle the camera and cinematography (by none other than director Barry Sonnenfeld), but also in how they present their story.  Who can keep a straight face in the scene where a little boy steals the toupee off of a corpse in the streets?  Even the violence in this film is handled with cartoonish effect: take a scene where Tom is beaten around by a bunch of guys and all he gets is a lip bleed - this film is almost as cartoonishly violent as another 1990 release, "Dick Tracy" (although this is considerably more graphic).
The acting in Coen Brothers films is always over-the-top and just this shade of camp, but they're always pretty amazing.  Also, look for Coen wife (I can't believe I forget which one she's married to) Frances McDormand in a small role as a mayoral secretary.
And what about all those magnificent cinematic treats, like the scene where Leo beats up Tom after discovering he's been sleeping with his girl; the scene where Tom is forced to go kill Bernie in the woods; a later intense scene where Tom is dragged back to the woods; a masterfully frightening scene in a dark room with a blazing fire where Eddie tries to kill Tom; and, of course, the famed "Danny Boy" sequence, which is one of the best directed scenes I've ever viewed. The Coen Brothers definitely have a knack for creating beautifully layered films with lots of cinematic goodies, like these, which linger in the mind long after the film has ended.
I remember watching "Miller's Crossing" years ago when I was watching all of their films for the first time, and not being really impressed with it. But rewatching it, I was absolutely blown away. It's a film that almost requires multiple viewings, not because of the complex story, but for the sheer pleasure of watching it. It's the kind of film that blows you away with its amazing style, and intriguing and darkly comical take on the role of the gangster, and even if it doesn't impress you the first time, trust me and give it another try. Trust me anyway.
MY RATING (out of 4): ****
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@(title: String)
@main("Originator - Test Requests") {
This site does the following:
- Get a new chain from the originator
- Encrypt the request 3 times
- The target of the request is the Quote service
- Send the request to the entry node
- Wait for a response
Received Quotes
}


--- layout: page title: About typeof: AboutPage ProfilePage permalink: /about/ ---
I am Marvin, a researcher and software engineer in the area of the Semantic Web.
This web site serves as a place for posts about my PhD thesis, the adventures as a Linked Data engineer and other all the other things out there a nerd like me is interested in.
You know Marvin the Paranoid Android from The Hitchhiker's Guide to the Galaxy? No?! Then go for a read. Or if you are more like me and too lazy to read a book, watch the amusing movie. However, his name is Marvin and mine too, so one thing led to another.
This site is:


'Denied for three years': Kaepernick auditions for NFL comeback
Colin Kaepernick said he was ready to play anywhere in the NFL after a long-awaited tryout at an Atlanta area high school on Saturday.
"I've been ready for three years. I've been denied for three years," said the former San Francisco 49ers quarterback who made headlines by kneeling at games during the US anthem to protest against racial injustice.
He has said teams would not sign him because they wanted to distance themselves from the protests, which President Donald Trump criticised as unpatriotic and disrespectful.
"So we are waiting for the 32 owners, 32 teams and [league commissioner] Roger Goodell to stop running," the 32-year-old said.
"Stop running from the truth, stop running from the people.
"We are ready to play. We are ready to go anywhere."
The NFL had arranged the tryout at the Atlanta Falcons training facility, but at the last minute the quarterback's representatives moved the workout to a high school stadium.
Kaepernick said it was done so media could be present.
Live stream video showed Kaepernick making a variety of throws to a group of receivers to applause from onlookers.
His arm talent remains "elite" and is the same as when he came out of college, ESPN reported an NFL executive commenting after the workout, adding Kaepernick threw the ball well.
Kaepernick's representatives made the move to the high school after accusing the league of not acting in a forthright manner in organising the workout.
Kaepernick also had requested all media be allowed into the workout to observe and film it, but the NFL denied the request, his representatives said in a statement.
The NFL said it was informed of Kaepernick's decision 30 minutes before the workout was to begin.
Representatives from at least 25 of the NFL's 32 teams had been scheduled to attend the workout. Several made it to the new location.
Kaepernick, 32, has played six seasons in the NFL and participated in six post-season games, including Super Bowl XLVII in 2013, when the 49ers lost 34-31 to the Baltimore Ravens.
The Nevada product, who was a second-round draft pick by the 49ers in 2011, has passed for 12,271 yards in his career with 72 touchdowns and 30 interceptions.
He also rushed for 2300 yards and 13 more touchdowns.
Reuters


|
IPASS Dylan Griffioen
|
Public Member Functions | |
| tests (const i2c_ipass &i2c_ipass_object, const ADXL345 &ADXL345_object) | |
| Constructor for a tests object. More... | |
| bool | test_i2c_ipass_read () |
| Tests the read function from the i2c_ipass class. More... | |
| bool | test_i2c_ipass_write () |
| Tests the write function form the i2c_ipass class. More... | |
| bool | test_ADXL345_measuring () |
| Tests wether or not the sensor can start measuring. More... | |
| bool | test_ADXL345_set_standby_mode () |
| Test if the set_standby_mode function can return the sensor to standby mode without touching the other bits in the register. More... | |
| void | print_test_results () |
| This function runs all tests and prints the results. More... | |
Constructor for a tests object.
The constructor requires an i2c_ipass object and an ADXL345_object.
| void tests::print_test_results | ( | ) |
This function runs all tests and prints the results.
Example: tests_oject.print_test_results();
| bool tests::test_ADXL345_measuring | ( | ) |
Tests wether or not the sensor can start measuring.
The sensor starts in standby mode and untill it gets into measure mode the axis data is 0. What this test does is that it first checks wether or not they are 0. Then if all axis are 0 it uses the setup funciton to calibrate the sensor and put it in measure mode. The setup function just contains a bunch of i2c_ipass write functions which we already tested. Once the sensor has entered measure mode it starts to fill the axis registers with data. Do keep in mind that when the sensor is hold upright the X and Y axis are 0 so in order for this funciton to work the sensor needs to be at an angle.
| bool tests::test_ADXL345_set_standby_mode | ( | ) |
Test if the set_standby_mode function can return the sensor to standby mode without touching the other bits in the register.
So the set_stanby_mode function is designed to only clear bit D3 which is the measure mode bit. To test this we write 12 to the register which is 00001100. Then after we exectute the set_standby_mode function it should clear bit D3 leaving 00000100 which is 4. Then it writes 0 to the POWER_CTL register to ensure we don't leave any unwanted bits in there.
| bool tests::test_i2c_ipass_read | ( | ) |
| bool tests::test_i2c_ipass_write | ( | ) |
Tests the write function form the i2c_ipass class.
We tested the read function in the previous test so we can use that to check our write function. For this test the function writes 8 to the z offest register so if we read that register it should containt the value 8. Daarna zetten we de byte weer op 0 om te voorkomen dat het ergens anders problemen veroorzaakt.


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In setback for Trump, Louisiana re-elects Democratic governor
John Bel Edwards wins a second term, narrowly beating Eddie Rispone in a hotly contested poll in the southern US state.
The Democratic governor of the US state of Louisiana John Bel Edwards has been re-elected for a second term in office following a hotly contested race.
Edwards on Saturday narrowly defeated Eddie Rispone, a Republican opponent who had closely aligned himself with US President Donald Trump.
More:
Edwards, a conservative Democrat, is the party's only governor in the Deep South. He campaigned on the state's strengthening economy.
Trump, who had won 58 percent of the popular vote in Louisiana in the 2016 presidential election, had travelled to the state three times to support Rispone, a Republican businessman, and repeatedly supported the candidate on Twitter.
On Saturday afternoon he urged voters on Twitter: "Louisiana, 3 hours left, get out and Vote for @EddieRispone for Governor. Lower taxes and much more!"
In conceding the election, Rispone said he was disappointed in the result.
"We have nothing to be ashamed of," he told supporters, and thanked Trump for campaigning for him in the state.
"He (Trump) came down here three times specifically to try to help us," he said.
The Democrat victory comes after mixed results in other recent elections.
In Kentucky's gubernatorial election last week, Democratic challenger Andy Beshear defeated Republican Governor Matt Bevin despite the president's endorsement, while Democrats have also taken full control of state government in Virginia.
In Mississippi earlier this month, Republicans comfortably kept control of the statehouse following strong support from Trump.
Tom Perez, chairman of the DNC, said Edwards' victory was another sign of the "surging strength" of Democrats.
"Louisiana, Kentucky, and Virginia have delivered a devastating warning to Donald Trump's Republican Party," he said in a statement.
"Democrats are competing and winning in every corner of the country. Less than a year before the 2020 election, the wind is firmly at our backs," he added.
The DNC invested heavily in the Louisiana Democratic Party to support Democrats' election efforts.


|
qLibc
|
Tree Table container that implements the Left-Leaning Red-Black BST algorithm. More...
Go to the source code of this file.
Functions | |
| qtreetbl_t * | qtreetbl (int options) |
| Initialize a tree table. | |
| void | qtreetbl_set_compare (qtreetbl_t *tbl, int(*cmp)(const void *name1, size_t namesize1, const void *name2, size_t namesize2)) |
| qtreetbl->set_compare(): Set user comparator. | |
| bool | qtreetbl_put (qtreetbl_t *tbl, const char *name, const void *data, size_t datasize) |
| qtreetbl->put(): Put an object into this table with string type key. | |
| bool | qtreetbl_putstr (qtreetbl_t *tbl, const char *name, const char *str) |
| qtreetbl->putstr(): Put a string into this table. | |
| bool | qtreetbl_putstrf (qtreetbl_t *tbl, const char *name, const char *format,...) |
| qtreetbl->putstrf(): Put a formatted string into this table. | |
| bool | qtreetbl_putobj (qtreetbl_t *tbl, const void *name, size_t namesize, const void *data, size_t datasize) |
| qtreetbl->putobj(): Put an object data into this table with an object key. | |
| void * | qtreetbl_get (qtreetbl_t *tbl, const char *name, size_t *datasize, bool newmem) |
| qtreetbl->get(): Get an object from this table. | |
| char * | qtreetbl_getstr (qtreetbl_t *tbl, const char *name, const bool newmem) |
| qtreetbl->getstr(): Finds an object and returns it as string type. | |
| void * | qtreetbl_getobj (qtreetbl_t *tbl, const void *name, size_t namesize, size_t *datasize, bool newmem) |
| qtreetbl->getobj(): Get an object from this table with an object name. | |
| bool | qtreetbl_remove (qtreetbl_t *tbl, const char *name) |
| qtreetbl->remove(): Remove an object from this table. | |
| bool | qtreetbl_removeobj (qtreetbl_t *tbl, const void *name, size_t namesize) |
| qtreetbl->remove(): Remove an object from this table with an object name. | |
| bool | qtreetbl_getnext (qtreetbl_t *tbl, qtreetbl_obj_t *obj, const bool newmem) |
| qtreetbl->getnext(): Get next element. | |
| void * | qtreetbl_find_min (qtreetbl_t *tbl, size_t *namesize) |
| qtreetbl->find_min(): Find the name of the leftmost object. | |
| void * | qtreetbl_find_max (qtreetbl_t *tbl, size_t *namesize) |
| qtreetbl->find_max(): Find the name of the rightmost object. | |
| qtreetbl_obj_t | qtreetbl_find_nearest (qtreetbl_t *tbl, const void *name, size_t namesize, bool newmem) |
| qtreetbl->find_nearest(): Find an object with matching key or nearest. | |
| size_t | qtreetbl_size (qtreetbl_t *tbl) |
| qtreetbl->size(): Returns the number of keys in the table. | |
| void | qtreetbl_clear (qtreetbl_t *tbl) |
| qtreetbl->clear(): Clears the table so that it contains no keys. | |
| void | qtreetbl_lock (qtreetbl_t *tbl) |
| qtreetbl->lock(): Enter critical section. | |
| void | qtreetbl_unlock (qtreetbl_t *tbl) |
| qtreetbl->unlock(): Leave critical section. | |
| void | qtreetbl_free (qtreetbl_t *tbl) |
| qtreetbl->free(): De-allocate the table. | |
| int | qtreetbl_byte_cmp (const void *name1, size_t namesize1, const void *name2, size_t namesize2) |
| bool | qtreetbl_debug (qtreetbl_t *tbl, FILE *out) |
| qtreetbl->debug(): Print the internal tree structure in text. | |
| int | node_check_root (qtreetbl_t *tbl) |
| Verifies that root property of the red-black tree is satisfied. | |
| int | node_check_red (qtreetbl_t *tbl, qtreetbl_obj_t *obj) |
| Verifies that red property of the red-black tree is satisfied. | |
| int | node_check_black (qtreetbl_t *tbl, qtreetbl_obj_t *obj, int *path_len) |
| Verifies that black property of the red-black tree is satisfied. | |
| int | node_check_llrb (qtreetbl_t *tbl, qtreetbl_obj_t *obj) |
| Verifies that LLRB property of the left-leaning red-black tree is satisfied. | |
| int | qtreetbl_check (qtreetbl_t *tbl) |
| Verifies that the invariants of the red-black tree are satisfied. | |
Tree Table container that implements the Left-Leaning Red-Black BST algorithm.
qtreetbl implements a binary search tree that allows efficient in-order traversal with O(log n) search time.
The algorithm qtreetbl specifically implements is the left-leaning red-black tree algorithm invented in 2008 by Robert Sedgewick. A left-leaning red–black tree is a type of self-balancing binary search tree and it is a variant of the red–black tree which was invented in 1972 by Rudolf Bayer.
References:
(E) ______________|______________ / \ (C) (R) / \ / \ (A,B) (D) (I,N) (S,X) <2-3-4 Tree Data Structure> E ______________|______________ / \ C R _______|_______ _______|_______ / \ / \ B D N X// // // A* I* S*
<Left-Leaning Red-Black Tree Data Structure>Nodes A, I and S are nodes with RED upper link. Others are BLACK
The Red-Black BST algorithm has been one of the popular BST algorithms especially for in-memory operation. The Left-Leaning version of Red-Black especially improves performance and reduces overall complexity.
Since it's relatively new algorithm, there's not many practically functional working codes yet other than proof of concept kinds. Here's one of fully functional codes and I, Seungyoung Kim, would like to dedicate this code to the genius inventor Robert Sedgewick and to all the great qLibc users.
Additional features:
Definition in file qtreetbl.c.
| qtreetbl_t * qtreetbl | ( | int | options | ) |
Initialize a tree table.
| options | combination of initialization options. |
| errno | will be set in error condition.
|
Definition at line 182 of file qtreetbl.c.
| void qtreetbl_set_compare | ( | qtreetbl_t * | tbl, |
| int(*)(const void *name1, size_t namesize1, const void *name2, size_t namesize2) | cmp | ||
| ) |
qtreetbl->set_compare(): Set user comparator.
| tbl | qtreetbl_t container pointer. |
| cmp | a pointer to the user comparator function. |
Definition at line 250 of file qtreetbl.c.
| bool qtreetbl_put | ( | qtreetbl_t * | tbl, |
| const char * | name, | ||
| const void * | data, | ||
| size_t | datasize | ||
| ) |
qtreetbl->put(): Put an object into this table with string type key.
| tbl | qtreetbl_t container pointer. |
| name | key name. |
| data | data object. |
| datasize | size of data object. |
| errno | will be set in error condition.
|
Definition at line 269 of file qtreetbl.c.
| bool qtreetbl_putstr | ( | qtreetbl_t * | tbl, |
| const char * | name, | ||
| const char * | str | ||
| ) |
qtreetbl->putstr(): Put a string into this table.
| tbl | qtreetbl container pointer. |
| name | key name. |
| str | string data. |
| errno | will be set in error condition.
|
Definition at line 287 of file qtreetbl.c.
| bool qtreetbl_putstrf | ( | qtreetbl_t * | tbl, |
| const char * | name, | ||
| const char * | format, | ||
| ... | |||
| ) |
qtreetbl->putstrf(): Put a formatted string into this table.
| tbl | qtreetbl_t container pointer. |
| name | key name. |
| format | formatted string data. |
| errno | will be set in error condition.
|
Definition at line 305 of file qtreetbl.c.
| bool qtreetbl_putobj | ( | qtreetbl_t * | tbl, |
| const void * | name, | ||
| size_t | namesize, | ||
| const void * | data, | ||
| size_t | datasize | ||
| ) |
qtreetbl->putobj(): Put an object data into this table with an object key.
| tbl | qtreetbl_t container pointer. |
| name | key name. |
| namesize | key size. |
| data | data object. |
| datasize | size of data object. |
| errno | will be set in error condition.
|
Definition at line 336 of file qtreetbl.c.
| void * qtreetbl_get | ( | qtreetbl_t * | tbl, |
| const char * | name, | ||
| size_t * | datasize, | ||
| bool | newmem | ||
| ) |
qtreetbl->get(): Get an object from this table.
| tbl | qtreetbl_t container pointer. |
| name | key name. |
| datasize | if not NULL, object size will be stored. |
| newmem | whether or not to allocate memory for the data. |
| errno | will be set in error condition.
|
Definition at line 392 of file qtreetbl.c.
| char * qtreetbl_getstr | ( | qtreetbl_t * | tbl, |
| const char * | name, | ||
| const bool | newmem | ||
| ) |
qtreetbl->getstr(): Finds an object and returns it as string type.
| tbl | qtreetbl_t container pointer. |
| name | key name. |
| newmem | whether or not to allocate memory for the data. |
| errno | will be set in error condition.
|
Definition at line 417 of file qtreetbl.c.
| void * qtreetbl_getobj | ( | qtreetbl_t * | tbl, |
| const void * | name, | ||
| size_t | namesize, | ||
| size_t * | datasize, | ||
| bool | newmem | ||
| ) |
qtreetbl->getobj(): Get an object from this table with an object name.
| tbl | qtreetbl_t container pointer. |
| name | key name. |
| namesize | key size. |
| datasize | if not NULL, oject size will be stored. |
| newmem | whether or not to allocate memory for the data. |
| errno | will be set in error condition.
|
Definition at line 443 of file qtreetbl.c.
| bool qtreetbl_remove | ( | qtreetbl_t * | tbl, |
| const char * | name | ||
| ) |
qtreetbl->remove(): Remove an object from this table.
| tbl | qtreetbl_t container pointer. |
| name | key name. |
| errno | will be set in error condition.
|
Definition at line 474 of file qtreetbl.c.
| bool qtreetbl_removeobj | ( | qtreetbl_t * | tbl, |
| const void * | name, | ||
| size_t | namesize | ||
| ) |
qtreetbl->remove(): Remove an object from this table with an object name.
| tbl | qtreetbl_t container pointer. |
| name | key name. |
| name | key size. |
| errno | will be set in error condition.
|
Definition at line 491 of file qtreetbl.c.
| bool qtreetbl_getnext | ( | qtreetbl_t * | tbl, |
| qtreetbl_obj_t * | obj, | ||
| const bool | newmem | ||
| ) |
qtreetbl->getnext(): Get next element.
| tbl | qtreetbl_t container pointer. |
| obj | found data will be stored in this object. |
| newmem | whether or not to allocate memory for the data. |
| errno | will be set in error condition.
|
Definition at line 580 of file qtreetbl.c.
| void * qtreetbl_find_min | ( | qtreetbl_t * | tbl, |
| size_t * | namesize | ||
| ) |
qtreetbl->find_min(): Find the name of the leftmost object.
| tbl | qtreetbl_t container pointer. |
| namesize | if not NULL, the size of key name will be stored. |
Definition at line 634 of file qtreetbl.c.
| void * qtreetbl_find_max | ( | qtreetbl_t * | tbl, |
| size_t * | namesize | ||
| ) |
qtreetbl->find_max(): Find the name of the rightmost object.
| tbl | qtreetbl_t container pointer. |
| namesize | if not NULL, the size of key name will be stored. |
Definition at line 662 of file qtreetbl.c.
| qtreetbl_obj_t qtreetbl_find_nearest | ( | qtreetbl_t * | tbl, |
| const void * | name, | ||
| size_t | namesize, | ||
| bool | newmem | ||
| ) |
qtreetbl->find_nearest(): Find an object with matching key or nearest.
find_nearest() returns matching key or nearest key object. If there's no keys in the table. It'll return empty qtreetbl_obj_t object with errno ENOENT.
| tbl | qtreetbl_t container pointer. |
| name | key name. |
| namesize | key size. |
| newmem | whether or not to allocate memory for the data. |
| errno | will be set in error condition.
|
Definition at line 711 of file qtreetbl.c.
| size_t qtreetbl_size | ( | qtreetbl_t * | tbl | ) |
qtreetbl->size(): Returns the number of keys in the table.
| tbl | qtreetbl_t container pointer. |
Definition at line 775 of file qtreetbl.c.
| void qtreetbl_clear | ( | qtreetbl_t * | tbl | ) |
qtreetbl->clear(): Clears the table so that it contains no keys.
| tbl | qtreetbl_t container pointer. |
Definition at line 784 of file qtreetbl.c.
| void qtreetbl_lock | ( | qtreetbl_t * | tbl | ) |
qtreetbl->lock(): Enter critical section.
| tbl | qtreetbl_t container pointer. |
Definition at line 805 of file qtreetbl.c.
| void qtreetbl_unlock | ( | qtreetbl_t * | tbl | ) |
qtreetbl->unlock(): Leave critical section.
| tbl | qtreetbl_t container pointer. |
Definition at line 818 of file qtreetbl.c.
| void qtreetbl_free | ( | qtreetbl_t * | tbl | ) |
qtreetbl->free(): De-allocate the table.
| tbl | qtreetbl_t container pointer. |
Definition at line 827 of file qtreetbl.c.
| int qtreetbl_byte_cmp | ( | const void * | name1, |
| size_t | namesize1, | ||
| const void * | name2, | ||
| size_t | namesize2 | ||
| ) |
Definition at line 833 of file qtreetbl.c.
| bool qtreetbl_debug | ( | qtreetbl_t * | tbl, |
| FILE * | out | ||
| ) |
qtreetbl->debug(): Print the internal tree structure in text.
| tbl | qtreetbl_t container pointer. |
| out | output stream. |
| errno | will be set in error condition.
|
[]
. In this example, 5 and 8 are Red nodes. Definition at line 871 of file qtreetbl.c.
| int node_check_root | ( | qtreetbl_t * | tbl | ) |
Verifies that root property of the red-black tree is satisfied.
Root property: The root is black.
| tbl | qtreetbl_t container pointer. |
Definition at line 890 of file qtreetbl.c.
| int node_check_red | ( | qtreetbl_t * | tbl, |
| qtreetbl_obj_t * | obj | ||
| ) |
Verifies that red property of the red-black tree is satisfied.
Red property: If a node is red, then both its children are black.
| tbl | qtreetbl_t container pointer. |
| obj | qtreetbl_obj_t object pointer. |
Definition at line 909 of file qtreetbl.c.
| int node_check_black | ( | qtreetbl_t * | tbl, |
| qtreetbl_obj_t * | obj, | ||
| int * | path_len | ||
| ) |
Verifies that black property of the red-black tree is satisfied.
Black property: For each node, all simple paths from the node to descendant leaves contain the same number of black nodes.
| tbl | qtreetbl_t container pointer. |
| obj | qtreetbl_obj_t object pointer. |
| path_len | black path length. |
Definition at line 940 of file qtreetbl.c.
| int node_check_llrb | ( | qtreetbl_t * | tbl, |
| qtreetbl_obj_t * | obj | ||
| ) |
Verifies that LLRB property of the left-leaning red-black tree is satisfied.
LLRB property: 3-nodes always lean to the left and 4-nodes are balanced.
| tbl | qtreetbl_t container pointer. |
| obj | qtreetbl_obj_t object pointer. |
Definition at line 971 of file qtreetbl.c.
| int qtreetbl_check | ( | qtreetbl_t * | tbl | ) |
Verifies that the invariants of the red-black tree are satisfied.
Root property: The root is black. Red property: If a node is red, then both its children are black. Black property: For each node, all simple paths from the node to descendant leaves contain the same number of black nodes. LLRB property: 3-nodes always lean to the left and 4-nodes are balanced.
| tbl | qtreetbl_t container pointer. |
Definition at line 1001 of file qtreetbl.c.


Hi!
I am Yash Salunke
I am studying in BE class of Computer Department at " SSBT COET,Bambhori,Jalgaon ".
I Completed my 12th (HSC) from " M.R.Mahajan Jr. College, Anturli ".
Also I secured 87.20% in 10th (SSC) from " M. F. Taral
Highschool, Anturli".
My hobby is Playing Chess.


ISABEL CAMPOS-NOONAN
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Syracuse pulls away to defeat Cornell 72-53
by MARK FRANKSYRACUSE, N.Y. (AP) - Elijah Hughes scored 22 points, including 16 in the second half, Buddy Boeheim had 16, and Syracuse pulled away from stubborn Cornell in the second half, defeating the Big Red 72-53 on Wednesday night.
Syracuse had a 44-41 lead with just under 10 minutes to go but outscored Cornell 28-12 the rest of the way.
The win for Syracuse (3-1) was the Orange’s 40th straight over their upstate New York neighbor dating back to 1968.
Jimmy Boeheim scored 25 to lead Cornell (1-4) in the second matchup of the Boeheim brothers. Jordan Jones added 15.
Both teams struggled on offense.
The Orange started the game 5 for 10 overall and 4 for 6 from 3-point range but then went 12 for 33 overall before heating up near the end. Syracuse finished 10 for 27 from 3, going 6 for 21 after the hot start.
Cornell shot 35% for the game on 14-of-40 shooting, just 6 for 27 from 3 (22%). Cornell entered the game hitting just 29% from beyond the arc.
After hitting their first 4 of 6 3-pointers, the Orange missed their next eight 3s in the half and 1 of 9 overall from the field during an eight-minute span, allowing Cornell to get back in the game. The Big Red took a 25-24 lead on a layup by Jimmy Boeheim with more than a minute remaining, but brother Buddy broke a nearly six-minute scoring drought to give Syracuse a 26-25 lead heading into the break.
BIG PICTURE:
Cornell: The Big Red showed a lot of heart, hanging with Syracuse for 30 minutes, but was outmanned at the end.
Syracuse: After two strong showings, the Orange took several steps back against Cornell and is still searching for consistency on the offensive end.
UP NEXT:
Cornell plays its third of four straight road games Saturday against Coppin State.
Syracuse concludes its season-opening five-game home stand Saturday against Bucknell, the Orange’s second Patriot League opponent of the young season.
For more AP college basketball: https://apnews.com/Collegebasketball and https://twitter.com/AP_Top25
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About
This website provides a collection of ebooks specially for students at UC Berkeley. For certain reasons, we don't provide google drive link on this webpage. To obtain download links through google drive, scan the QR code on the right to follow us on wechat. Detailed instructions will be provided. I will continue to update the book list according to your feedback.
Anthropology
- Anthro1------> 度盘 密码:j4ul
- Essentials of Physical Anthropology: Discovering Our Origins | Larsen | 1st edtion
- Anthro2AC------> 度盘 密码:n1o5
- the Land of Open Graves | León | N/A
- Anthro3AC------> 度盘 密码:huu7
- Discovering our Past: a Brief Introduction to Archaeology | Ashmore, Sharer | 5th edtion
Astron
- Astron7A------> 度盘 密码:rq96
- an Introduction to Modern Astrophysics | Carroll, Ostilie | 2nd edtion
- AstronC10------> 度盘 密码:hoci
- the Cosmos: Astronomy in the New Millenium(.equb) | Pasachoff | 4th edtion
Bio/MCB
- Bio1A/1AL/1B------> 度盘 密码:3qrt
- Campbell Biology | Reece, Wasserman | 10th edition
- Campbell Biology | Reece, Wasserman | 11th edition
- MCB32------> 度盘 密码:dnmh
- Principles of Human Biology | Stanfield | 5th edition
- MCB102------> 度盘 密码:io9j
- Lehninger Principles of Biochemistry | Nelson, Cox | 6th edition
- MCB104------> 度盘 密码:paxd
- Essential Cell Biology | Alberts | 4th edition
- Genetics: from Genes to Genomes | Hartwell | 4th edition
- MCB110------> 度盘 密码:ukwp
- Molecular Biology: Principles of Genome Function | Craig | N/A
Chem
- Chem1A------> 度盘 密码:zzi4
- Chemistry: a Molecular Approach | Tro | 3rd edition
- Chemistry: a Molecular Approach | Tro | 4th edition
- Chemical Principles: the Quest for Insight | Atkins, Jones, Laverman | 6th edition
- Chem1B------> 度盘 密码:awyz
- Principles of Modern Chemistry | Oxtoby, gillis, Campion | 7th edition
- Chem3AL------> 度盘 密码:ozp4
- Understanding the Principles of Organic Chemistry: a Laboratory Course | Pedersen, Myers | N/A
- Chem3A/3B------> 度盘 密码:oxul
- Organic Chemistry: Structure and Function | Vollhardt, Schore | 7th edtion
- Chem4A/4B------> 度盘 密码:67pz
- Quantitative Chemistry Analysis | Harris | 8th edtion
- General Chemistry: Principles and Modern Application | Petrucci, Herring | 11th edition
Cogsci
- Cogsci1B------> 度盘 密码:26g2
- Cognitive Science: an Introduction to the science of the Mind | Bermúdez | 2nd edtion
- Cogsci127------> 度盘 密码:tjmn
- Cognitive Neuroscience: the Biology of the Mind | Gazzaniga, Ivry, Mangun | 4th edtion
CS
- CS61B------> 度盘 密码:8dfc
- Algorithms | Sedgewick, Waynee | 4th edition
- Head First Java | Sierra, Bates | 2nd edition
- Data Structures & Algorithms in Java | Goodrich, Tamassia, Goldwasser | 6th edition
- CS61C------> 度盘 密码:16bo
- the C Programming Language | Kernighan, Ritchie | 2nd edition
- Computer Organization and Design: the Hardware/Software Interface | Patternson, Hennesy | 5th edtion
- the Datacenter as a Computer: an Introduction to the Design of Warehouse-Scale Machines | Barroso, Hölzle | N/A
- CS162------> 度盘 密码:b9tu
- Operating Systems: Principles and Practice | Anderson, Dahlin | N/A
- CS168------> 度盘 密码:yqlg
- Computer Networking: a Top-Down Approach | Kurose, Ross | 6th
- CS170------> 度盘 密码:8eb5
- Algorithms | Dasgupta, Papadimitriou, Vairani | 1st edition
- CS186------> 度盘 密码:mgz1
- Database System Concepts | Siberschatz, Korth, Sudarshan | 6th edition
- Database Management Systems | Ramakrishnan, Gehrke | 3rd edition
- CS188------> 度盘 密码:w163
- Artificial Intelligence: a Modern Approach | Russel, Norvig | 3rd edition
Econ
- Econ1/2------> 度盘 密码:nljb
- Macroeconomics as a Second Language | Olney | N/A
- Principles of Economics | Frank, Bernanke | 5th edition
- Econ100A------> 度盘 密码:h40j
- Microeconomics: Theory & Applications with Calculus | Perloff | N/A
- Econ100B------> 度盘 密码:8t1x
- Macroeconomics: Policy and Practices | Mishkin | 2nd
- Econ101A------> 度盘 密码:7eb7
- Microeconomic Theory: Basic Principles and Extensions | Nicholson, Snyder | 11th edtion
- Econ140------> 度盘 密码:tzib
- Introduction to Econometrics | Stock | 3rd edtion
- Econ141------> 度盘 密码:c7lj
- Introductory Econometrics: a Modern Approach | Wooldridge | 6th edtion
EE
- EE16A------> 度盘 密码:vbb7
- Schaum's outlines: Electric Circuits | Nahvi, Edminister | 6th edtion
- Schaum's outlines: Linear Algebra | Lipschutz, Lipson | 4th edtion
- EE126------> 度盘 密码:r1xn
- Introduction to Probability | Bertsekas, Tsitsiklis | 2nd edtion
French
- French1/2------> 度盘 密码:4wxt
- Chez Nous: branché sur le monde francophone | Valdman, Pons, Scullen | 4th edtion
Math
- Math1A------> 度盘 密码:hf49
- Single Variable Calculus | Stewart | 7th edtion
- Calculus: Early Transcendentals | Stewart | 8th edtion
- Math1B------> 度盘 密码:vlnq
- Single Variable Calculus | Stewart | 7th edtion
- Multivariable Calculus | Stewart | 7th edtion
- Calculus: Early Transcendentals | Stewart | 8th edtion
- Math10A/10B------> 度盘 密码:xhbk
- Calculus for the Life Sciences | Schreiber, Smith, Getz | N/A
- Math16A/16B------> 度盘 密码:6zyx
- Calculus & its applications | Goldstein, Lay, Schneider, Asmar | 13th edition
- Calculus with applications | Lial, Greenwell, Ritchey | 11th edition
- Math32------> 度盘 密码:efwp
- Precalculus: a Prelude to Calculus | Axler | 2nd edition
- Math53------> 度盘 密码:3lir
- Multivariable Calculus | Stewart | 7th edtion
- Calculus: Early Transcendentals | Stewart | 8th edtion
- Math54------> 度盘 密码:4ss0
- Fundamentals of Differential Equations | Snider | 8th edtion
- Linear Algebra and its Applications | Lay | 4th edtion
- Math55------> 度盘 密码:8mz0
- Discrete Mathematics and its Applications | Rosen | 7th edtion
- Math104------> 度盘 密码:54f1
- Elementary Analysis: the Theory of Calculus | Ross | 2nd edtion
- Math110------> 度盘 密码:lla0
- Linear Algebra | Friedberg, Insel, Spence | 4th edtion
- Math113------> 度盘 密码:1c0w
- Abstract Algebra | Herstein | 3rd edtion
- a First Course in Abstract Algebra | Fraleign | N/A
- Math128A------> 度盘 密码:xbp4
- Numerical Analysis | Burden, Faires | 9th edtion
- Math185------> 度盘 密码:wd3a
- Notes on Complex Function Theory | Sarason | N/A
- Schaum's Outlines: Complex Variables | Spiegel, Lipschutz | 2nd edition
- Visual Complex Analysis | Needham | N/A
- Complex Analysis | Stein, Shakarchi | N/A
- Introduction to Complex Analysis | Priestley | 2nd
- Complex Variables and Applications | Brown, Churchill | 9th
Physics
- Physics7A/7B------> 度盘 密码:5ukh
- Physics for Scientists & Engineers with modern physics | Giancoli | 4th edition
- Physics7C------> 度盘 密码:1jiy
- Physics for Scientists & Engineers with modern physics | Giancoli | 4th edition
- Modern Physics | Tipler, Liewellyn | 6th edition
- Physics8A/8B------> 度盘 密码:vwxk
- Essential University Physics | Wolfson | 2nd edition
Psychology
- Psych1------> 度盘 密码:o7h8
- Introduction to Psychology | Kalat | 9th edition
- Psych2------> 度盘 密码:rs5t
- Psychology | Myers | 10th edition
- Psych101------> 度盘 密码:zjkp
- Statistics for Psychology | Aron, Coups | 6th edition
- Statistics for the Behavioral Sciences | Nolan, Heinzen | 2nd edition
Stat
- Stat2/20/21------> 度盘 密码:wnuv
- Statistics | Freedman, Pisani, Purves | 4th edtion
- Stat134------> 度盘 密码:ufdk
- Probablity | Pitman | 1st edtion
- Stat135------> 度盘 密码:i5hn
- Stat labs: Mathematical Statistics through Applications | Nolan, Speed | 2nd edtion
- Mathematical Statistics and Data Analysis | Rice | 3rd edtion
- Introductory Statistics with R | Dalgaard | 2nd edtion
- Stat150------> 度盘 密码:0pi0
- An Introduction to Stochastic Modeling | Pinsky, Karlin | 4th edtion
- Basic Stochastic Process: a Course through Exercises | Breźniak, Zastawniak | N/A
- Stat153------> 度盘 密码:6t7e
- Time Series Analysis and its Applications with R Examples | Shumway, Stoffer | 3rd edition
- Time Series Analysis with Applications in R | Cryer, Chan | 2nd
- Stat154------> 度盘 密码:pmbc
- an Introduction to Statistical Learning with Applications in R | James, Witten, Hastie, Tibshirani | N/A
- Stat155------> 度盘 密码:xe5a
- Game Theory | Ferguson | N/A
- Game Theory | Karlin, Peres | N/A
UGBA
- UGBA101B------> 度盘 密码:4qo4
- Macroeconomics: Policy and Practice | Mishkin | 2nd edtion
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If you have any advice or find any mistake I've made (even a typo), please don't hesitate to contact me<EMAIL_ADDRESS>Maintained by RN


HeroWork’s 12th project transforms support centre
by Roxanne Egan-ElliottStaff at a grassroots agency in Esquimalt that provides programming and support to sex workers have been serving up to 200 meals a day with an oven that never heated up enough and a broken dishwasher.
After a massive renovation by hundreds of volunteers, Peers Victoria Resources Society has a new kitchen, new floors, new offices, a new classroom and an entirely new space to support clients.
“There’s only one bathroom I think that really didn’t change,” said Rachel Phillips, executive director of Peers. “It was a gut job.”
On Saturday, the agency held an event for the community to visit the revamped space.
The transformation by HeroWork, a charity that completes radical renovations of other charities, will give staff more time and space to support the community.
“Because our space is organized better, there’s room for growth now, so we can add more programs and have the space to deliver them,” Phillips said.
Watching 500 volunteers dedicate thousand of working hours to transform the space showed Phillips that the stigma against sex workers isn’t as strong as it once was.
“This says the community supports sex workers in a very profound kind of way,” she said.
Sarah Smith, who works at Peers as a drop-in coordinator, said she is most excited about a new heated closet. Smith said many clients come in out of the rain, drenched without a change of clothes. The heated closet means they can dry their clothes and shoes before going back outside.
Peers supports nearly 600 current and former sex workers annually, providing a range of services from housing and health care to clothing and meals.
Volunteers started the renovations, valued at more than $625,000, in the first week of October. About 115 companies donated supplies to the renovation, which is HeroWork’s 12th project.
“The transformation of charity buildings helps to transform charities,” said HeroWork founder and CEO Paul Latour. “When you’re serving in a building that doesn’t work, and it’s ugly and debilitating and inefficient, it’s hard for a charity to operate well.”
Peers is recruiting new volunteers to help provide programming to the community.


Model
Mathematical models and computer simulations provide a great way to describe the function and operation of BioBrick Parts and Devices. Synthetic Biology is an engineering discipline, and part of engineering is simulation and modeling to determine the behavior of your design before you build it. Designing and simulating can be iterated many times in a computer before moving to the lab. This award is for teams who build a model of their system and use it to inform system design or simulate expected behavior in conjunction with experiments in the wetlab.
Gold Medal Criterion
Convince the judges that your project's design and/or implementation is based on insight you have gained from modeling. This could be either a new model you develop or the implementation of a model from a previous team. You must thoroughly document your model's contribution to your project on your team's wiki, including assumptions, relevant data, model results, and a clear explanation of your model that anyone can understand.
The model should impact your project design in a meaningful way. Modeling may include, but is not limited to, deterministic, exploratory, molecular dynamic, and stochastic models. Teams may also explore the physical modeling of a single component within a system or utilize mathematical modeling for predicting function of a more complex device.
Best Model Special Prize
To compete for the Best Model prize, please describe your work on this page and also fill out the description on the judging form. Please note you can compete for both the gold medal criterion #3 and the best model prize with this page.