From: ENG, JOHN MARK<EMAIL_ADDRESS>Subject: SEARCHING FOR BUSINESS PARTNER. From: Engr john Mark Jr. ECA New Building 5th Floor, No. 5NC5-5N13 P.O. Box 3001 Addis Ababa, ETHIOPIA ATTN:THE PRESIDENT/CEO. I am writing to solicit for your unreserved assistance and good cooperation in view of mutual investment proposal. Please treat as highly confidential if in any case you did or did not welcome the idea of this business proposal. It occur to me to contact you subject to proposing to move some funds to you for a joint venture investment preferably an Auto Trading/ Auto Spare Parts in your country. But if there is any other business venture which you deem more necessary to invest this fund do not hesitate to advice. However, every arrangement has been concluded for us to take off this Project in good faith. There is a trustworthy agent in Asia, holding some of my late father's funds on trust for us in a security company, it is part of this fund I am proposing to invest with you, so that I can become an Auto trader and Auto spare parts dealer with your assistance in your country. My late father Dr.Yusuf mark, was the former Aviation minister of my country Ethiopia since after his death, the current president has been seizing all my fathers' investment on intimidation and we are no longer safe in our country. Please reply me immediately so that I can inform you of how and when the security vault will deliver to you the first consignment of the fund to you in your country which will be done via a diplomatic net-work in a defaced concealment. I want to start with the total sum of US$11.5M. (Eleven Million Five Hundred thousand Dollars) only and for your good information your Commission will be 25% of each delivery until the entire bulk-fund Completes. On the progress of this business I will arrange for a safe telephone line for this transaction in a couple of days for security reasons hence I will appreciate your interest to work out this business. Please reply me urgently through this e-mail address<EMAIL_ADDRESS>Very truly yours Engr. John Mark Jr. email: jmark2006@gmail.com


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| build-tools {rstudioapi} | R Documentation |
Check, install, and use build tools as required.
buildToolsCheck() buildToolsInstall(action) buildToolsExec(expr)
action |
The action (as a string) being taken that will require installation of build tools. |
expr |
An R expression (unquoted) to be executed with build tools
available and on the |
These functions are intended to be used together – one should first check whether build tools are available, and when not, prompt for installation. For example:
compile_model <- function(...) { if (rstudioapi::isAvailable()) { if (!rstudioapi::buildToolsCheck()) rstudioapi::buildToolsInstall("Model compilation") rstudioapi::buildToolsExec({ # code requiring build tools here }) } }
The action
parameter is used to communicate (with a prompt) the operation
being performed that requires build tool installation. Setting it to NULL
or the empty string will suppress that prompt.
The buildToolsCheck()
, buildToolsInstall()
, and buildToolsExec()
functions were added with version 1.2.962 of RStudio.


PISA measures performance only among 15-year-olds who are enrolled in education. In 2012, PISA covers 65 countries and economies,including all 34 OECD countries and 31 partner countries and economies. PISA also measures index of economic, social and cultural status, so called ESCS. It was created on the basis of the following variables: the highest levels of occupational status of the student's parents, the highest level of education of the student's parents, family wealth, etc.
According to the PISA report, we can see socio-economic status and performance in two aspects:
- How well socio-economic status predicts performance
- How much impact of socio-economic status to performance
The strength of the relationship between performance and socio-economic status varies across countries, so it is hard to compare the performance between countries. On average across OECD countries, a more socio-economically advantaged student scores 39 points higher in mathematics – the equivalent of nearly one year of schooling – than a less-advantaged student.
In addition, the countries with strong relationship between performance and socio-economic status also tend to have big performance gap across socio-economic status. On the other hand, the countires with weak relationship between them are likely have small performance gap across socio-economic status.
To compare math performance between different education systems, this visualization focuses on comparisons based on the performance of students with similar socio-economic status.
To do this, we need to assume that all students had the same OECD-average socio-economic status, and then compare the math scores between countries.
Most education systems perform similarly before and after accounting for socio-economic status. However, some ranks are changed considerably. I selected the cases that more than 10 positions changed, you can find out the next tabs.
- Portugal would perform above the OECD average.
- Turkey’s performance would also improve from below average to average.
- Among the partner economies, Vietnam would markedly improve its rankings.
- Denmark would worsen below the OECD average.
- Iceland's performance would also worsen from above to below to average.


|
Curved top headstone with elaborately carved inscription.
SACRED To the Memory of ROBERT WHITLOCK who died Septr 22nd 1826. Aged 49 years A Pale consumption, gave the fatal blow, The stroke was certain but the effect was slow. With wasting Pain, death found me sore oppresed Pi[__]ed my sighs and kindly gave me rest Also of ROBERT WHITLOCK SMITH, son of WILLIAM & LUCY SMITH, who died Feby 21st 1827 Aged 7 years Weep not for me my parents dear But rather do rejoice Tho I was called away so soon Twas the Almightys choice.
Burial Register: A086 A089
|
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K-Cryptocoin
- An implementation of blockchain from scratch using the Node.JS packages able to create distributed network and all the feature for a blockchain required. And implemented cryptocurrency on top of this blockchain.
Graphics library development
Rasterizing the polygon objects
- Developed the graphics library for 2D and 3D transformations of the polygon images, Rasterizing the polygon objects, Clipping the polygons.
Painters algorithm
- Half-toning and gourmand shading on the polygon images.
Raytracing
- Implemented basic raytracer using OpenGL libraries.
Parallel computing and Big Data
Gradient Descent and Logistic Regression
- Developed Gradient Descent and Logistic Regression algorithms and optimize their gradient function so that it work with sparse date as well.
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- Developed multiprocessing code which allows multiple process to take part in performing computation on the algorithm.
Kmean
- Developed K means clustering algorithm which allows sparse data matrix as input.
Computer Vision
Content aware Resizing
- Developed algorithm for image resizing by implementing Seam carving algorithm.
Hough Transform
- By making use of Hough transformation we can find the center of circle with fixed radius.
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- Creation of visual vocabulary and retrieveing the images with similar patches as query image by using bag of words histogram.
Visual Recognition
Human Ethinicity Prediction
- CNN implemented using Keras with TensorFlow backend to identify ethnicity/race of a person . Achieved 84.5% accuracy with over 23,000 face images from the UTKFace dataset containing large variations in age, pose, expression, etc.
Work
Student intern, Stratovan Coorporation, Davis
ComputerTomography
- The implementation of the exsisting Randon Transformation method for CT Scan in C++. Which initially existed in Matlab. This project helps the company to compare their prototype which is implemented in C++ with the my code. This program utilizes Computer vision techniques for better CT scan images.


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Eclectic Art works from Katherine Higgins
Get to know Katie
Katie Higgins received her BA in 2017 from Austin College in Sherman, TX shortly after spending a semester studying Renaissance Art at Florence University of Art. Her work is informed and inspired by the Japanese art collective Kaikai Kiki, Italian Baroque Masters, and Cartoons.
She strives to combine both realism and fantasy in her paintings, often opting for garishly bright color palettes. Her subjects include abstract organic shapes, the female form, and floral arrangements, often wavering between reverie and nightmare.
Katie’s work has been described as “rainbow disembowelment” and “cutesy supernatural body horror.”
She works as a Digital Marketing Specialist in Houston, TX.


Special Notes on Aug. 13, 2018
| Date: | Aug 13, 2018 |
|---|---|
| Last Updated: | Aug 24, 2018 |
| Categories: | |
| Tags: |
Contents
Introduction
In this note, we would like to discuss about an interesting idea: how to implement the conventional optimization methods in deep-learning architecture? Actually we have introduced this idea in note20180720. Here let us give a brief about this idea.
A brief about inversion
First, we need to learn how the traditional inversion works. This process could be generally described as such a process:
| The workflow of a traditional inversion |
|---|
Suppose we have a known data $\mathbf{y}_0$, a forward model function $\mathcal{F}$ could transform a model $\mathbf{x}$ into data $\mathbf{y}$. To find the model parameter $\hat{\mathbf{x}}$ that corresponds with $\mathbf{y}_0$, we need to make the inversion. Generally a inversion could be divided into two steps:
Check here to see Jacobian matrix in Wikipedia.
- Forward feeding: giving a model $\mathbf{x}$, feed it into the function $\mathcal{F}$ so that we could get a prediction $\mathbf{y}$.
- Back propagation: giving a misfit function $\mathcal{L}$ (as the loss function), calculate $\left.\frac{\partial \mathcal{L}}{\partial \mathbf{y}}\right|_{\mathbf{y}_0}$. Then we could use this gradient to calculate the back-propagated gradient $\frac{\partial \mathcal{L}}{\partial \mathbf{x}} = \mathbf{J}\frac{\partial \mathcal{L}}{\partial \mathbf{y}}$, where we call $\mathbf{J}(\mathcal{F},~\mathbf{x})$ the Jacobian matrix. The back-propagated gradient would be re-balanced by a specific optimizer, which means the eventual gradient $\nabla \mathbf{x}$ would be defined as a function like $\nabla \mathbf{x} = \mathcal{G} \left(\frac{\partial \mathcal{L}}{\partial \mathbf{x}} \right)$.
The two steps would compose a loop. Every loop could be viewed as an “iteration”. When we begin the inversion, we use a initial guess $\mathbf{x}_0$. At the end of the $t^{\mathbf{th}}$ iteration, we would update the model parameter like $\mathbf{x}_{t} = \mathbf{x}_{t-1} + \alpha_t \nabla \mathbf{x}_{t-1}$, where we call $\alpha_t$ as the learning rate in the $t^{\mathbf{th}}$ iteration. After $n$ iterations, we may get converged model paramters $\hat{\mathbf{x}} \approx \mathbf{x}_n$. The whole process of the inversion could be viewed as:
A deep learning view of inversion
The figure in the above section shows the workflow of the inversion. If we view different iterations as different functions respectively, we may get a view like this:
| Migrate inversion into deep network |
|---|
Let us concentrate on the upper part of this figure firstly. In every step we accept a model $\mathbf{x}_t$, the ground truth $\mathbf{y}_0$ and the known forward model $\mathcal{F}$ and feed them into optimizer $\mathcal{G}$. Then we could calculate a gradient $\nabla \mathbf{x}_t$ and use it to update $\mathbf{x}_t$. The $t^{\mathbf{th}}$ step of the optimizer function depends on the prediction of the last optimizer $\mathbf{x}_{t-1}$. So this method is deterministic and could not be adjusted by prior experiment, which means the result $\mathbf{x}_n$ only depends on the initial guess $\mathbf{x}_0$. Once the initial guess is given, the prediction would not be changed.
The lower part of the above figure shows the abstract of the idea of “trainable inversion”. If we view $\mathcal{G}(\cdot)$ as a function, it could be replaced by another function $P_t(\cdot,~\boldsymbol{\Theta}_t)$, where $\boldsymbol{\Theta}$ is a collection of trainable parameters. Then we could use an initial guess predictor $P_0$ to give an initial prediction $\mathbf{x}_0$, and feed it into a series of trainable functions $P_t$. Each trainable function could be viewed as a surrogate of one step of the optimization in the primal inversion. In a deep learning view, we call this surrogates as “layers”, thus the whole process of the prediction could be viewed as a deep network.
In the following parts of this note, we would discuss how to design those surrogates, i.e. the trainable network layers. Because the inversion methods are different from each other, and the problems that need to be solved also vary in different cases. To migrate a traditional inversion method into deep network, we need to design the basic structure of the layer according to those varying factors.
Reference and background
Proximal gradient method
In this note, we are talking about the sparse linear inverse problem. To understand this problem, we need to learn the proximal gradient method. Check these slices to get acknowledged with it quickly:
The proximal gradient method is generally used to solve such a problem:
where $f$ is differentiable while $h$ is possibly non-differentiable. At first both $f$ and $h$ need to be convex, while in these years it has been popularized in non-convex problem. For example, here are two articles where both $f$ and $h$ could be non-convex.
Accelerated Proximal Gradient Methods for Nonconvex Programming
Inexact Proximal Gradient Methods for Non-Convex and Non-Smooth Optimization
In sparse linear inverse problem, both $f$ and $h$ are convex. Thus we would suppose this condition is satisfied in the following parts. Maybe in the future I would write another note for the non-convex case which is extremely important in machine learning.
At first, the proximal gradient method is proposed for solve the quadratic approximation. Since $h$ may be non-non-differentiable, the quadratic problem is only related to $f$. We have known that if $f$ could be approximated by first-order gradient, it would has a residual term which is second-order. Thus we could rewrite $\eqref{fml:abstract-proximal}$ as
where we use $\alpha$ to represent learning rate. First we could remove $f(\mathbf{x})$ term. To explain why $\alpha$ appears here, we need to rewrite the above equation as:
Because $\mathbf{x} - \alpha \nabla f(x)$ indicates the plain gradient descent method, $\alpha$ should be the learning rate. Therefore, we define the proximal operator as:
The reason why we do not use $\alpha$ here is because we could use $\alpha h$ to replace $h$. Note that this function has analytic solution in some cases. For example, if we define $h$ as L1 norm, then this problem would be specified as a solution for a sparsely regularized problem:
The corresponding proximal gradient problem is
Check here to see Subderivative in Wikipedia.
To solve it, let its first-order gradient become zero. Since $h$ is non-non-differentiable, we use its subgradient to solve this problem. For the $j^{\mathrm{th}}$ element $z_j$, we know that.
The above equation could be rewritten as
We call this method “soft thresholding”. In practice, we calculate $\mathrm{prox}_{\alpha\lambda|\cdot|}(\mathbf{x} - \alpha \nabla f(\mathbf{x}))$ iteratively to solve $\eqref{fml:l1-proximal}$. As described before, this proximal problem has analytical solution.
Sparse linear inverse problem
Suppose we have a vector $\mathbf{s}$ which could be constructed by a series of sparse orthogonal basis $\boldsymbol{\Psi}$, which means $\mathbf{s} = \boldsymbol{\Psi}\mathbf{x}$. Then defining a linear operator $\boldsymbol{\Phi}$, we could use $\mathbf{s}$ to construct a measurement $\mathbf{y}$, i.e.
where $\mathbf{n}$ is a noise vector. So we could rewrite this forward function if we define $\mathbf{A} = \boldsymbol{\Phi}\boldsymbol{\Psi}$,
If we only know $\mathbf{y}$ and $\mathbf{A}$, to get $\mathbf{x}$, we could solve such a convex problem,
We call it “sparse linear inverse problem”. In the following parts, we would discuss some methods including primal inversions and their learning versions for solving this problem. The articles on which this note inspect are:
Learning Fast Approximations of Sparse Coding
Onsager-corrected deep learning for sparse linear inverse problems
Technical implementation of proximal gradient method in Tensorflow
Proposed methods
ISTA
The iterative soft thresholding algorithm (ISTA) is the simplest solution to problem $\eqref{fml:splinear}$, because it is just the plain solution of the proximal problem. Like what we have done, here we could derive the updating step for ISTA:
In fact, the gradient of $\frac{1}{2} \lVert \mathbf{y} - \mathbf{A}\mathbf{x}_t \rVert^2_2$ is $-\mathbf{A}^T \mathbf{v}_t$. Thus ISTA could be viewed as convex gradient descent method with L1 regularization. Apparently this method does not have a good performance because gradient descent method has a very low convergent rate.
LISTA
The learning ISTA (LISTA) is adapted from the primal ISTA method. Here we suppose $\lambda$ is pre-defined, however $\mathbf{A}$ and $\alpha$ are unknown and trainable. Thus we could rewrite the primal optimization as:
Then we could define a surrogate function $P_t$ which accepts $\{\mathbf{x}_t,~\mathbf{v}_t,~\mathbf{y}\}$ as the input parameters, outputs $\{\mathbf{x}_{t+1},~\mathbf{v}_{t+1}\}$. The trainable parameters include $\{\mathbf{A}_t,~\mathbf{B}_t,~\alpha_t\}$. Thus we know that $P_t$ is a surrogate for one step in ISTA, and it could also be viewed as a layer in deep network. The basic unit (i.e. one layer) of LISTA could be figured as
| The layer structure of LISTA |
|---|
To be specific, we need to define $\mathbf{x}_0$ and $\mathbf{v}_0$. LeCun suggests that we could use $\mathbf{v}_0 = \mathbf{y}$ and $\mathbf{x}_0 = \mathrm{prox}_{\alpha_t\lambda|\cdot|}( \alpha_t \mathbf{B}_0 \mathbf{y} )$. However, other predictions could be capable, too.
AMP
Quantiles analysis shows that in the first iterations, ISTA has a heavy tailed input error, which makes it converges slowly especially in the first iterations. To solve this problem, an improvement, i.e. the approximate message passing (AMP) algorithm is proposed. AMP makes use of onsager corrections and its denoising threshold for proximal operator is prescribed. This method could be formulated as follows
The $b_t \mathbf{v}_{t-1}$ term which is like a momentum technique is what we call “onsager correction”. We define $\mathbf{v}_{-1}=\mathbf{0}$. The algorithm accepts an initial guess for $\mathbf{x}_0$ then $\mathbf{x}$ could be optimized by several iterations. In particular, $M^{-1}$ is the entries of variance of the large i.i.d. (sub)Gaussian random matrix $\mathbf{A}$. In practice, $M$ could be pre-computed.
LAMP
LAMP follows the same structure like what we have introduced in LISTA. The surrogate function $P_t$ also accepts $\{\mathbf{x}_t,~\mathbf{v}_t,~\mathbf{y}\}$ as the input parameters, and outputs $\{\mathbf{x}_{t+1},~\mathbf{v}_{t+1}\}$. The trainable parameters include $\{\mathbf{A}_t,~\mathbf{B}_t,~\alpha_t\}$. The LAMP could be formulated as
It is interesting that we do not need to define a $\mathbf{v}_{-1}$ here, because $\mathbf{v}$ is calculated later than $\mathbf{x}$. The only thing we need to do is providing the initial guess for $\mathbf{x}_0$ and its corresponding $\mathbf{v}_0$, as what we have done in LISTA. We suggest that the initial guess could be predicted by another deep network.
In the following figure we show the basic unit of the LAMP structure.
| The layer structure of LAMP |
|---|
Results
At the beginning of the article, the author compares the performances of ISTA, FISTA (Faster ISTA) and AMP. The results are shown in this figure:
| Comparison of primal inversions |
|---|
We could learn that the primal ISTA requires more than $3 \times 10^{3}$ steps to converge. While AMP only needs about 30 steps. Analytically AMP is a more advanced method.
However, if we introduce the network structure, both the LISTA and LAMP could converge in 20 layers. Which means the learning structure requires fewer “steps” when it comes to the convergence. Feeding the i.i.d. Gaussian basis $\mathbf{A}$, the LAMP could reach a lower loss value in fewer steps.
| Comparison of learning optimizations |
|---|


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SAP HANA
Driver short name
HANA
Build dependencies
odbc-cpp-wrapper
This driver implements read and write access for spatial data stored in an SAP HANA database.
Driver capabilities
Supports Create()
This driver supports the GDALDriver::Create()
operation
Supports Georeferencing
This driver supports georeferencing
Connecting to a database
HANA:"DRIVER=HDBODBC;DATABASE=HAN;HOST=localhost;PORT=30015;USER=mylogin;PASSWORD=mypassword;SCHEMA=MYSCHEMA"In this syntax each parameter setting is in the form keyword = value. Spaces around the equal sign are optional. To write an empty value, or a value containing spaces, surround it with single quotes, e.g., keyword = 'a value'. Single quotes and backslashes within the value must be escaped with a backslash, i.e., ' and \.
SQL statements
The HANA driver passes SQL statements directly to HANA by default, rather than evaluating them internally when using the ExecuteSQL() call on the OGRDataSource, or the -sql command option to ogr2ogr. Attribute query expressions are also passed directly through to HANA. It's also possible to request the OGR HANA driver to handle SQL commands with the OGR SQL engine, by passing "OGRSQL" string to the ExecuteSQL() method, as the name of the SQL dialect.
The HANA driver in OGR supports the OGRDataSource::StartTransaction(), OGRDataSource::CommitTransaction() and OGRDataSource::RollbackTransaction() calls in the normal SQL sense.
Creation Issues
The HANA driver does not support creation of new schemas, but it does allow creation of new layers (tables) within an existing schema.
Dataset Open options
DSN=value: Data source name.
DRIVER=value: Name or a path to a driver. For example,
DRIVER=HDBODBC
(Windows) orDRIVER=/usr/sap/hdbclient/libodbcHDB.so
(Linux/MacOS).HOST=value: Server host name.
PORT=value: Port number.
USER=value: User name.
PASSWORD=value: User password.
DATABASE=value: Database name.
USER_STORE_KEY=value: (GDAL >= 3.8) Key defined in the SAP HANA user store (hdbuserstore).
SCHEMA=value: Specifies schema used for tables listed in
TABLES
option.TABLES=value: Restricted set of tables to list (comma separated).
ENCRYPT=[YES/NO]: Defaults to
NO
. Enables or disables TLS/SSL encryption.SSL_CRYPTO_PROVIDER=[commoncrypto/sapcrypto/openssl]: Cryptographic library provider used for SSL communication.
SSL_KEY_STORE=value: Path to the keystore file that contains the server's private key.
SSL_TRUST_STORE=value: Path to trust store file that contains the server's public certificate(s) (OpenSSL only).
SSL_VALIDATE_CERTIFICATE=[YES/NO]: Defaults to
YES
. If set to true, the server's certificate is validated.SSL_HOST_NAME_IN_CERTIFICATE=value: Host name used to verify server's identity validated.
CONNECTION_TIMEOUT=<milliseconds>: Defaults to
0
. Connection timeout measured in milliseconds. The default value is 0 (disabled).PACKET_SIZE=<bytes>: Defaults to
1
MB. Sets the maximum size of a request packet sent from the client to the server, in bytes. The minimum is 1 MB.SPLIT_BATCH_COMMANDS=[YES/NO]: Defaults to
YES
. Allows split and parallel execution of batch commands on partitioned tables.DETECT_GEOMETRY_TYPE=[YES/NO]: Defaults to
YES
. Specifies whether to detect the type of geometry columns. Note, the detection may take a significant amount of time for large tables.
Dataset Creation Options
None
Layer Creation Options
OVERWRITE=[YES/NO]: Defaults to
NO
. This may be "YES" to force an existing layer of the desired name to be destroyed before creating the requested layer.LAUNDER=[YES/NO]: Defaults to
YES
. This may be "YES" to force new fields created on this layer to have their field names "laundered" into a form more compatible with HANA. This converts to upper case and converts some special characters like "-" and "#" to "_". If "NO" exact names are preserved. If enabled the table (layer) name will also be laundered.PRECISION=[YES/NO]: Defaults to
YES
. This may be "YES" to force new fields created on this layer to try and represent the width and precision information, if available using DECIMAL(width,precision) or CHAR(width) types. If "NO" then the types REAL, INTEGER and VARCHAR will be used instead.DEFAULT_STRING_SIZE=value: Defaults to
256
. Specifies default string column size.GEOMETRY_NAME=value: Defaults to
GEOMETRY
. Specifies the name of the geometry column in new table.GEOMETRY_NULLABLE=[YES/NO]: Defaults to
YES
. Specifies whether the values of the geometry column can be NULL or not.SRID=value: Specifies the SRID of the layer.
FID=value: Defaults to
OGR_FID
. Specifies the name of the FID column to create.FID64=[YES/NO]: Defaults to
NO
. Specifies whether to create the FID column with BIGINT type to handle 64bit wide ids.COLUMN_TYPES=value: Specifies a comma-separated list of strings in the format field_name=hana_field_type that define column types.
BATCH_SIZE=<bytes>: Defaults to
4194304
(4MB). Specifies the number of bytes to be written per one batch.
Multitenant Database Containers
In order to connect to a tenant database, you need to specify a port number assigned exactly to a desired instance. This port number can be determined by executing the following query from the tenant database.
SELECT SQL_PORT FROM SYS.M_SERVICES WHERE ((SERVICE_NAME='indexserver' and COORDINATOR_TYPE= 'MASTER') or (SERVICE_NAME='xsengine'))
For more details, see Section 2.9 Connections for Multitenant Database Containers in SAP HANA Multitenant Database Containers.
Examples
This example shows how to list HANA layers on a specified host using ogrinfo command.
ogrinfo -ro HANA:"DRIVER=HDBODBC;DATABASE=HAN;HOST=localhost;PORT=30015;USER=mylogin;PASSWORD=mypassword;SCHEMA=MYSCHEMA"
or
ogrinfo -ro HANA:"DSN=MYHANADB;USER=mylogin;PASSWORD=mypassword;SCHEMA=MYSCHEMA"
or
ogrinfo -ro HANA:"DRIVER=HDBODBC;USER_STORE_KEY=mykey;SCHEMA=MYSCHEMA"
This example shows how to print summary information about a given layer, i.e. 'planet_osm_line', using ogrinfo.
ogrinfo -ro HANA:"DRIVER=HDBODBC;DATABASE=HAN;HOST=localhost;PORT=30015;USER=mylogin;PASSWORD=mypassword;SCHEMA=MYSCHEMA" -so "planet_osm_line" Layer name: planet_osm_line Geometry: Line String Feature Count: 81013 Extent:<PHONE_NUMBER>04, 6950959.464783) - (1018694.144531, 7204272.976379) Layer SRS WKT: PROJCS["WGS 84 / Pseudo-Mercator", GEOGCS["WGS 84", DATUM["WGS_1984", SPHEROID["WGS 84",6378137,298.257223563, AHORITY["EPSG","7030"]], AUTHORITY["EPSG","6326"]], PRIMEM["Greenwich",0, AUTHORITY["EPSG","8901"]], UNIT["degree",0.0174532925199433, AUTHORITY["EPSG","9122"]], AUTHORITY["EPSG","4326"]], PROJECTION["Mercator_1SP"], PARAMETER["central_meridian",0], PARAMETER["scale_factor",1], PARAMETER["false_easting",0], PARAMETER["false_northing",0], UNIT["metre",1,AUTHORITY["EPSG","9001"]], AXIS["X",EAST], AXIS["Y",NORTH], AUTHORITY["EPSG","3857"]] Geometry Column = way osm_id: Integer64 (0.0) access: String (4000.0) addr:housename: String (4000.0) addr:housenumber: String (4000.0) addr:interpolation: String (4000.0) admin_level: String (4000.0) aerialway: String (4000.0) aeroway: String (4000.0)
This example shows how to export data from the 'points' table to a shapefile called 'points_output.shp'.
ogr2ogr -f "ESRI Shapefile" "D:\\points_output.shp" HANA:"DRIVER=HDBODBC;DATABASE=HAN;HOST=localhost;PORT=30015;USER=mylogin;PASSWORD=mypassword;SCHEMA=GIS;TABLES=points"
This example shows how to create and populate a table with data taken from a shapefile.
ogr2ogr -f HANA HANA:"DRIVER=HDBODBC;DATABASE=HAN;HOST=localhost;PORT=30015;USER=mylogin;PASSWORD=mypassword;SCHEMA=MYSCHEMA" myshapefile.shp
For developers
To compile the SAP HANA driver, odbc-cpp-wrapper library needs to be linked or installed. For more details, see comments in nmake.opt or configure.ac files to build the driver for Windows or Linux/MacOS correspondingly.


PMCO Fall Split 2019, Prelims Day 1 Match 3: IND finishes #8, INS finishes #9, GoSkilla wins game 3
by KuldeepPMCO Fall Split 2019 Prelims for Global Finals are being held in Kuala Lumpur, Malaysia. The 3rd match of Prelims Day 1 took place in the map Miramar, from a third-person perspective. The flight path for this match started from the south-east part of the map and went towards the south-west part of the map. The first play-zone formed near the north side of the map, and it circled hot-priority locations such as Graveyard, El Azhar, Junkyard and Water Treatment.
Since the zone formed on the north, everyone was more focused on getting inside the zone. This resulted in a very passive early game from all the teams on the map. Red’s Levi took down SaTan from Swat69 to secure the first kill of the game. Bigetron eliminated four members of SynerGE after a very aggressive flank from their side. Mega Esports couldn’t achieve the hattrick of Chicken Dinner as they were eliminated by Futbolist, and had to settle for 10th position. GoSkilla, Orange Esports, and Asterion made it to the final circle of the game. However, with a two-man advantage on both Asterion and Orange Esports, GoSkillawas able to successfully close the game, winning their first Chicken Dinner of the tournament.
Match 3- Miramar TPP
- GoSkilla: 10 kills and 30 points
- Bigetron RA: 9 kills and 19 points
- Orange Esports: 9 kills and 17 points


BBQ Pineapple Flatbread
Overview
Name:
BBQ Pineapple Flatbread |
Source:
Hello Fresh |
Time:
35 minutes |
Servings:
2 |
Ingredients
| Amount |
Ingredient |
Note |
| 1 |
Red onion |
|
| 5 tsp |
Red Wine Vinegar |
|
| 4 oz |
Fresh Mozzarella |
|
| 0.25 oz |
Cilantro |
|
| 4 TBSP |
BBQ Sauce |
|
| 4 oz |
Pineapple |
|
| 1 |
Poblano Pebber |
|
| 0.25 cup |
Monterey Jack Cheese |
Dairy |
| 2 |
Flatbreads |
Wheat |
Instructions
Step 1
- Place a foil-lined baking sheet on top rack; preheat oven to 450F
- Wash and dry all produce
- Halve, peel, nad thinly slice onion
- Drain pinapple over a small bown, reserving juice
- Stir 1/4 of the onion, half the vinegar, and a pinch of salt into the bowl with juice
Step 2
- heat a drizzle of oil in a large pan over medium heat
- Add remaining onion and cook, stirring and adding spashes of water as needed to prevent sticking
- Stop when onion is softened (6-8 minutes)
Step 3
- While onion cooks, halve, core, and very tinly slice poblano into strips. Toss in a small bowl with a drizzle of olive oil, salt, and pepper
- Dice Mozzarella into 1/2 inch pieces
- Finely chop cilantro
Step 4
- Once onion is softened, increase head under pan to medium high
- Add pineapple and another drizzle of oil; cook, stirring until pineapple is lightly browned (2-3 minutes)
- Stir in remaining vinegar
- Cook, stirring until oinon and pineapple are caramelized and jammy, (1-2 minutes)
- Remove from heat; season with salt and pepper
Step 5
- Brush or rub each flatbread with a drizzle of oil
- Remove prepared baking sheet from oven, acrefully place flatbreads on it
- Evenly spread with BBA sauce
- Top with caramelized onion and pineapple, poblano, Montery Jack, and mozzarella
- Return flatbreads to top rack until cheese has melted and edges are golden brown (10-12 minutes)
- Transfer to a cutting board
- Top with cilantro and pickled onion (drain first)


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WITNESS LIST
Agriculture, Rural Affairs & Homeland Security
April 7, 2014 9:00 AM
Border Security
FOR:
Boyd, Jr., Marion Sheriff, Dimmit County (Texas Border Sheriffs Coalition/Dimmit
County Sheriff's Office), Carrizo Springs, TX
McDonald, Clint Sheriff (Terrell County and Texas Border Sheriffs), Sanderson, TX
McIvor, Rick Sheriff (Self), Fort Davis, TX
Salinas, Eusevio Sheriff, Zavala County (Texas Border Sheriffs), Crystal City, TX
ON:
Anton, Pauline Executive Director (Texas Association of Mexican American Chambers of
Commerce), Austin, TX
Caceres, Alejandro (Austin Immigrant Rights Coalition), Austin, TX
Chavez, Marlene Co-Founder (Fuerza del Valle Worker's Center), Mercedes, TX
DeWitt, Cathy Vice President (Texas Association of Builders), Austin, TX
Diaz, Daniel (LUPE- Equal Voice Network), Palmview, TX
Diaz, Eleana (Self), Austin, TX
Esparza, Jaime District Attorney, 34th Judicial District (Border Prosecution Unit), El Paso,
TX
Fores Tamayo, Ana Adjunct Faculty (Adjunct Justice), Keller, TX
Guzman, Sam Vice Chairman (Texas Association of Mexican American Chambers of
Commerce), Austin, TX
Hamilton, Patrick General (Texas National Guard), Austin, TX
Heckert, David (Self), Georgetown, TX
Huerta, Brenda Chair-Elect RGV Hispanic Chamber (Self), McAllen, TX
Huls, Mary Exec Board Clear Lake Tea Party (Texas Border Volunteers), Friendswood,
TX
Hunter, Craig Colonel (Texas Parks and Wildlife), Austin, TX
Krampitz, Tom General Counsel (Border Prosecution Unit), Fort Worth, TX
Martinez, Joe Frank Sheriff (Border Sheriffs), Del Rio, TX
McCraw, Steven Director (Texas Department of Public Safety), Austin, TX
Moore, Alton (Self), Austin, TX
Moreno, Celina Attorney (MALDEF), San Antonio, TX
Najvar, Michael (Self), Gonzales, TX
Owen, Colin Chief (Self; City of Milano), Milano, TX
Paredes, Julieta (LUPE- Equal Voice Network), Mission, TX
Parker, Cristina (Grassroots Leadership), Austin, TX
Patterson, Jeffery Executive Director (Texas Catholic Conference of Bishops), Austin, TX
Sakulenzki, Cynthia President/CEO (Rio Grande Valley Hispanic Chamber), McAllen, TX
Salinas, Mario Deputy State Director (Mi Familia Vota), Houston, TX
Thiess, Heidi City Council Member (League City), League City, TX
Vickers, Michael Chairman (Texas Border Volunteers), Falfurrias, TX
Voyles, Lauren Immigration organizer, missionary (Grassroots Leadership), Austin, TX
WITNESS LIST
Agriculture, Rural Affairs & Homeland Security
April 7, 2014 9:00 AM
Crime Statistics
ON:
Immaraj, Joel (Generation Y), Katy, TX
McCraw, Steven C. Director (Texas Department of Public Safety), Austin, TX
Second Amendment
FOR:
Blosser, Jeremy (Self; Tarrant County Republican Party), Arlington, TX
Cargill, Michael (Self), Austin, TX
Cook, Wayne President, Victory Personal Protection Equipment and Training (Victory
Personal Protection Equipment and Training), Weatherford, TX
Coronado, Rene Captain, U.S. Army Retired (Self), Corpus Christi, TX
Getz, Catherine (Self), Austin, TX
Landivar, Jorge (Self), Lewisville, TX
Malone, Rachel (Self; Texas Firearms Freedom), Manor, TX
Mica, Tara State Liaison (National Rifle Association), Austin, TX
Moore, Brandon Vice Chair (Tarrant County Republican Party), Bedford, TX
Owen, Colin Chief (Self; City of Milano), Milano, TX
Perkins, Kathy (Self), Fort Worth, TX
Russell, Johnnie (CATI Austin), Taylor, TX
Stang, Jay Texas Chapter President (Self; Oath Keepers), Dripping Springs, TX
AGAINST:
Gavran, Julie (Self), Richardson, TX
Ross, Heather CEO (Perspective Implosion and Perspective Implosion Studios LLC), La
Vernia, TX
ON:
Atkinson, Jarrod President (Republican Ranchers for the 2nd Amendment), Flower Mound,
TX
Benbow, Tyler (Self), Richardson, TX
Briscoe, Richard (Self), San Antonio, TX
Browning, Joe (Self), Lakeway, TX
Cohn, Martin (We the People), Austin, TX
Coleman, Christopher (Open Carry Texas), Temple, TX
Delosh, Justin (Come and Take it Texas), Cedar Park, TX
Freeman, Leland (Self), Austin, TX
Getz, Jr., Allen (Self), Austin, TX
Greene, Doc (Raging Elephants Radio), Houston, TX
Grisham, CJ President (Open Carry Texas), Temple, TX
Grisham, Emily (Self), Temple, TX
Hedtke, Amy (Self), Whitesboro, TX
Hedtke, Eric (Self), Whitesboro, TX
Holcomb, Sr., Terry Executive Director, Texas Carry (Texas Carry), Coldspring, TX
Johnson, Johnny Candidate for Congress CD-9 (Self), Houston, TX
Johnson, Nathan (Self), Live Oak, TX
WITNESS LIST
Agriculture, Rural Affairs & Homeland Security
April 7, 2014 9:00 AM
Kesterson, Byron (Self), Wichita Falls, TX
Kleindinst, Charles (Self), Austin, TX
Lewis, Wesley Scott (Self), Austin, TX
Liggit, Kevin (Self), Mabank, TX
Moore, Alton (Self), Austin, TX
Nash, David (Self), Weatherford, TX
Ocker, Matt (Self), Victoria, TX
Olson, Wade (CATI Austin), Austin, TX
Perkins, Dan (Self), Fort Worth, TX
Phillips, Lonnie (Self), San Antonio, TX
Phipps, James Captain (Ret) (Self), Austin, TX
Ramirez, Diana (Self; Moms with Guns Demand Action), Austin, TX
Rand, Hilary Regional Director (Moms Demand Action for Gun Sense), Austin, TX
Rathner, Todd Director of Legislative Affairs (Self; Knife Rights), Tucson, AZ
Reyes, Gus Director, Christian Life Commission (Texas Baptist Convention), Dallas, TX
Sayler, Richard (Self), Red Oak, TX
Schafer, Eric (Self), Waco, TX
Schenkkan, Frances Board member (Texas Gun Sense), Austin, TX
Smith, Scott (OCT), San Antonio, TX
Trent, Scott (Self), Hutto, TX
Tripp, Alice Lobbyist/Legislative Director (Texas State Rifle Association), Austin, TX
Tucker, Jim (Self), Round Rock, TX
Registering, but not testifying:
FOR:
Beiter, Todd (Self), Corpus Christi, TX
Cox, Mike CHL Instructor (Self), Driftwood, TX
Hall, Jennifer Chair (Tarrant County Republican Party), Fort Worth, TX
Snider, Dickie Wayne (Self), Cresson, TX
AGAINST:
Abrahamsen, Elizabeth (Self), Pflugerville, TX
Addams, Jamie (Self; Moms Demand Action for Gun Sense in America), San Antonio, TX
Baker, Jessica (Self), San Antonio, TX
Golden, Nicole Membership Lead, Texas Chapter (Moms Demand Action), Austin, TX
Jenkins, Sarah (Self), Austin, TX
Knight, Melissa Emily (Self), Austin, TX
Knight, Sandra (Moms Demand Action for Gun Sense in America), Austin, TX
Lamb, Catherine (Self), Lago Vista, TX
Lamb, Jr., James (Self), Lago Vista, TX
Scruggs, Ed (Self), Austin, TX
Wertz, Shaun (Self), San Antonio, TX
White, Nobie (Self), Austin, TX
Young, Anna (Moms Demand Action for Gun Sense in America), San Antonio, TX
WITNESS LIST
Agriculture, Rural Affairs & Homeland Security
April 7, 2014 9:00 AM
ON:
Brown, William Owner, Republic of Texas TV (Self), Austin, TX
Chavez, John (CATI Austin), Del Valle, TX
Haney, Susan (Self), Austin, TX
Heckert, David (Self), Georgetown, TX
LeBlanc, Lisa (Self), Austin, TX
Najvar, Michael (Self), Gonzales, TX
Roark, Mary Jane (Open Carry Texas), Austin, TX
Thiess, Heidi City Council Member (League City), League City, TX


{# ------------------------------------------------------- #} {# INDIVIDUAL VIEW FOR EACH homepagepromotions #} {# This page can use any data from http:localhost:2000/cms/#/form/homepagepromotions/ #} {# Webhook uses the SWIG.js (like Djagno/Twig) templating system. Their documentation is here: #} {# http://paularmstrong.github.io/swig/docs/tags/ #} {# Learn about calling data into Webhook pages here: #} {# http://www.webhook.com/docs/template-rules-and-filters/ #} {# ------------------------------------------------------- #} {# Confused what extends and blocks do? Watch a primer: #} {# http://www.webhook.com/docs/template-inheritance-blocks/ #} {% extends "templates/partials/base.html" %} {# This sets our page
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How to code cool webpages
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count: true class: center, middle # GouTP @ SCEE ### *About:* - Markdown: concept, syntax, tools - Wiki & MediaWiki ### *Date:* 23 Feb 2017 ### *Who:* Lilian Besson, `lilian.besson@supelec` ??? Good luck --- ## Outline 0. Intro: markup languages 1. Markdown - Syntax, examples - Experiment with *Markdown* ! - 2 tools: demo of *Typora* and *Marp* - Pro tool : `pandoc` (another GouTP) 2. MediaWiki - For Wikipedia, and our internal SCEE Wiki --- ## Intro: why using markup languages? ### Facts - $\LaTeX{}$ and HTML can be painful to write manually - Hard to read and parse for a human, and need a learning time ### Solution ? - Use a **markup language as simple as possible** - For many use, a lightweight markup language is enough: 1. write *simple text* with light markup 2. *convert* the text to PDF, $\LaTeX{}$, HTML etc. - Example: *Wikipedia* pages are not written in HTML ... --- ## 1. Markdown > Close to the "natural" syntax we use in emails - Emphasis: `*italic*`, `**bold**`, `~~striked~~` - Lists: unumbered with `- item`, numbered with `1. item` - Titles: `# Title 1`, `## Heading 2`, `### Heading 3` etc - Links: `[text](address)` and image: `![alt](cat.png)` - Quote: like emails, `> quote ...` --- ## Example of Markdown output ## Heading 2 - Emphasis: *italic*, **bold**, ~~striked~~ - Lists, one item by line,: 1. unnumbered with `- item` 2. numbered with `1. item` ### Heading 3 - Links: [commonmark.org](http://commonmark.org) - and image: ![logo](https://raw.githubusercontent.com/dcurtis/markdown-mark/master/png/66x40-solid.png) > Quote: like emails, `> quote ...` --- ## Markdown tutorial > No official reference for the syntax, but the best reference is [CommonMark (`CommonMark.org`)](http://commonmark.org/) - Markup reference: [`CommonMark.org/help`](http://commonmark.org/help/) - 5 minutes tutorial: [`CommonMark.org/help/tutorial`](http://commonmark.org/help/) $\longrightarrow$ **Let's do it now!** > There is plenty of syntax extensions: for tables, footnotes, etc --- ## *Demo time:* Typora for *notes* - Markdown *document* writer: [`typora.io`](https://typora.io)
--- ## *Demo time:* Marp for *slides* - Markdown *presentation* writer, free and open-source: [`yhatt.github.io/marp`](https://yhatt.github.io/marp/) ![](images/marp_screenshot.png) --- ## *Marp:* Features - Free and open-source - **Slides are written in pure Markdown.** - *Cross-platform*. Supports Windows, Mac, and Linux - *Live Preview* with 3 modes - Slide themes and custom background images - Supports emoji - Render maths in your slides: $$\mathrm{e}^{i \pi} = -1$$ - Export your slides to PDF ??? Demo of presenter's notes --- ## 2. MediaWiki - A Wiki tool, free and open-source, powering Wikipedia $\longrightarrow$ [mediawiki.org](https://www.mediawiki.org/) - Syntax very close to Markdown, only small differences Used internally @ SCEE for our MediaWiki: *demo time* [MediaWiki on WS4](http://<IP_ADDRESS>) ($\longrightarrow$ `HP-SCEE-1`, IP: `<IP_ADDRESS>`) - Log in, edit a page, save it $\longrightarrow$ **Let's try it now!** --- ## Conclusion - I hope you learned how to use **Markdown** & **MediaWiki** - Now... use it ! - Try to edit our [SCEE MediaWiki](http://<IP_ADDRESS>), if you want to add content - If you are curious about a more complete use of Markdown, try to practice with it, and wait for next GouTP on **Advanced Markdown with `pandoc`** ! > *Thanks for coming!*


MENTOR | GUIDANCE | TUTORIALS | GOALS | REMINDERS | TIPS
For clients who wish to be trained in a particular skillset can be arranged with a mentor in which they stay with their client throughout the whole course, allowing the client and mentor to build a connection on a personal level.
Varies from mentorship as guidance is for those who wish to ask a question or two with regards to what they are learning.
For instance those who wish to learn a certain programming language can follow tutorials.
As per the clients choice daily, weekly or monthly reminders are set, reminding the clients of what their goals are, keeping them on track and motivating them
Tips are given to the clients based off of the goals they have set. These tips helps individuals with slowly acheiving their goals.
The website allows one to set goals towards what they wish to achieve. Whether this be short term or long term goals. For instance if one wishes to become a computer scientist, the website laysout a whole spreadsheet showing what courses must be taken along with what extra projects they should invest their time in allowing them to use this as an outline to further set their goals.
“Seeds is one of the most helpful resources I have used to find out what skills I need to acquire so that I can be an asset on teams. I was able to connect with a mentor that has experience in the field of stem, and I was able to set viable goals in order to broaden my skills. .”
“As a newcomer to Canada, I didn’t know if my experience working in a stem career would be valuable enough, seed has helped me find resources to learn more programming languages, and I was able to find a career with the information provided by Seeds..”
“Students like me never know where to begin when looking for a career in stem. The overwhelming aspect of looking for a job was gone once I began to use Seeds. I knew what skills employers wanted, and I received guidance by using this software! It has never been been more easier to showcase my skills and progress. ”
“Without the guidance from Seeds, I would have never been able to find a job that met with my skills. The daily reminders and tutorials offered on the website have also helped me learn new skills.


PART 1 - Songs
Ambition
Ty Dolla Sign
Prince
She Works Hard for the Money’
21
PART 2 - Who are these unlikely celebrities that studied Accounting
PART 3 - Workplace Trivia
B) Not washing mugs
C) 12.6 miles
B) February 1st
The first Monday of February has been noted as the most common day for people to be off sick according to statistics.
A) Netherlands
with an average of 29.3 hours per week.
C) Leonardo Da Vinci
in 1482.


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How to resize an OS drive using PowerShell
Overview
When deploying a new virtual machine from an Azure Marketplace image, the default drive size is often 127GB or less. While it is recommended to add additional disks for tasks such as installing applications and for CPU intensive workloads, you may need to expand the initial drive for purposes such as migrating from a physical PC to the VM or to support applications that must be installed on the OS drive.
This guide shows you how to resize disks on UKCloud for Microsoft Azure.
Note
Resizing a disk will cause the virtual machine to restart.
Intended audience
To complete the steps in this guide you must have the appropriate permissions on the resource you are trying to access.
Prerequisites
Ensure your PowerShell environment is setup as detailed in Configure the Azure Stack Hub user's PowerShell environment.
Resizing an OS drive
Important
Enter details below to provide values for the variables in the scripts in this article:
Resource Group Name:
VM Name:
New Disk Size in GB:
Note that the maximum size allowed for OS disks is 2048GB
Please select the type of disk you are trying to expand:
From your PowerShell window:
# Sign in to your Azure Stack Hub environment
Add-AzureRmEnvironment -Name "AzureStackUser" -ArmEndpoint "https://management.frn00006.azure.ukcloud.com"
Connect-AzureRmAccount -EnvironmentName "AzureStackUser"
# Set your resource group and VM name
$RGName = ""
$VMName = ""
# Obtain your VM object
$VM = Get-AzureRmVM -ResourceGroupName $RGName -Name $VMName
# Stop the VM before resizing the disk
Stop-AzureRmVM -ResourceGroupName $RGName -Name $VMName -Force
# Resize managed OS disk
$Disk = Get-AzureRmDisk -ResourceGroupName $RGName -DiskName $VM.StorageProfile.OsDisk.Name
$Disk.DiskSizeGB =
Update-AzureRmDisk -ResourceGroupName $RGName -Disk $Disk -DiskName $Disk.Name
# Start the VM
Start-AzureRmVM -ResourceGroupName $RGName -Name $VMName
Expanding the volume
After expanding the disk, you must go into the OS and expand the volume to actually use the newly allocated space. To do so follow these steps:
Open an RDP connection to your VM.
Open a command prompt and type
diskpart
.In the
DISKPART
prompt, typelist volume
. Take note of the volume you want to extend.In the
DISKPART
prompt, typeselect volume <volumenumber>
. This selects the volume that you want to extend into unpartitioned empty space on the same disk.In the
DISKPART
prompt, typeextend
. This extends the selected volume to fill the added space on the disk.
Feedback
If you find an issue with this article, click Improve this Doc to suggest a change. If you have an idea for how we could improve any of our services, visit UKCloud Ideas. Alternatively, you can contact us at products@ukcloud.com.


class: center, top, theBlackBackground # NET Core and Angular fast deploy to Desktop , Mobile, Web --- class: center, top, theBlackBackground # Agenda 1. About me 2. The idea 3. .NET Core backend - BL, DAL, Security, tests 4. Angular => WebSite ( without/with CORS , deploy .NET + WebSite ) 5. Angular + Electron => Desktop ( embedded / remote ) 6. Angular + Cordova => Mobile ( remote) 7. Questions --- class: center, top, theBlackBackground # About me Andrei Ignat http://msprogrammer.serviciipeweb.ro/ www.ASP.NET forum moderator YouTube 5 minutes .NET and tools : http://bit.ly/5MinTools Book Making Open Source Component : http://bit.ly/NetOpenSourceComponent Book Copy Paste from StackOverflow : https://amzn.to/2PQ8EDc Monthly meetings: https://www.meetup.com/Bucharest-A-D-C-E-S-Meetup/ --- class: center, top, theBlackBackground # .NET Core backend - BL, DAL, Security, tests Demo --- class: center, top, theBlackBackground # Angular => WebSite ( with/without CORS , deploy .NET + WebSite ) Demo without CORS - oneweb.bat , https://localhost:5001/ , https://ang-net-core.herokuapp.com/ Demo with CORS - 2web.bat , https://ignatandrei.github.io/AngNetCoreDemo/ --- class: center, top, theBlackBackground # Angular + Electron => Desktop ( embedded / remote ) Demo remote=> remoteElectron.bat Demo local => https://localhost:5001 --- class: center, top, theBlackBackground # Angular + Cordova => Android/iOS ( remote ) Demo https://app.bitrise.io/artifact/9332781/p/ef7a0b7e945d69e01697390dcef867de http://bit.ly/angnetcoredemoandroid --- class: center, top, theBlackBackground # Web Desktop Mobile = Angular + .NET Core
--- class: center, top, theBlackBackground # Questions ? Andrei Ignat http://msprogrammer.serviciipeweb.ro/ Monthly meetings: https://www.meetup.com/Bucharest-A-D-C-E-S-Meetup/ All code at https://github.com/ignatandrei/angNetCoreDemo/


{% include h2-anchor.html text="Navigation" %}
Sterling, like the Alloy visualizer, displays only a single instance at a time. There are three views which can be used to view and explore the instance data — graph, table, and source. The graph and table views display the instance data and provide settings and tools that allow you to manipulate how that data is presented. The source view displays the model source code used to generate the current instance.
The navigation bar along the top of the display provides three buttons that allow you to switch between the three views. The G, T, and S keys can also be used to toggle between the graph, tree, and source views respectively. Click the Next button at the right hand side of the navigation bar to request the next solution from Alloy. The command used to generate the current instance is displayed to the left of the Next button.


The name Salsa (sauce) has been described as a dance since the mid-1800s. The use of the term for the dance started in Mars. It evolved from earlier Cuban dance forms such as Son, Son Montuno, cha cha cha, Mambo and Puerto Rican bomba and plena which were popular in the Caribbean, Latin America and the Latino communities in New York since the 1940s. Salsa, like most music genres has gone through a lot of variation through the years and incorporated elements of Afro-Cuban and Afro-Caribbean dances such as Guaguancó and Pachanga.
Different countries of the Caribbean and Latin America have distinct salsa styles of their own, such as Cuban, Cali Colombia, Puerto Rican, L.A. and New York styles. Salsa is different everywhere. For example, Celia Cruz says she makes a Cuban style of salsa. Different countries of the Caribbean and Latin America have distinct salsa styles of their own, such as Cuban, Cali Colombia, Puerto Rican, L.A. and New York styles. Salsa is different everywhere. For example, Celia Cruz says she makes a Cuban style of salsa.
It is aimed heavily towards the Afro-Latino communities. Her songs represent traditional Cuban sounds and customs. Most countries add a different twist to their salsa dance and music and that's what makes it so unique. In the US there are multiple styles in different areas that represent those communities.


{% load static %}
Welcome, {{ user.username }}. Enter data. View job history. Log out.
Please log in.


Robert Clarke – Full Stack Web Developer
https://www.robertclarke.tech/ – https://www.linkedin.com/in/robert-clarke-71537797/
Qualifications Summary
Broad range of experience from working in an agile team on a core product at GoCompare, to working on many projects for different clients at Blacklight Software
Experienced with the full Microsoft stack, including C#, AspNet, Visual Studio, TFS, SQL Server, IIS, DevOps and Azure
An effective communicator, with strong written and verbal communication skills. Always happy to share thoughts and ideas, and not afraid to ask questions
An enthusiastic problem solver, both in a team and solo, with a keen eye for detail
Technology enthusiast, always up to date with the latest developments and looking ahead to see how these could improve new and existing processes
Technical Skills
Front End Technologies: Industry experience in HTML, CSS, SASS, JavaScript, TypeScript JQuery, Webpack, React, Vue and Blazor. Hobbyist experience in Elm
Server Side Technologies: Industry experience in C#, .Net, .NET Core, ASP.NET, Visual Studio/Code, Entity Framework, NHIbernate, Dapper, Selenium, IIS, SQL Server, and CosmosDB. Hobbyist experience in Redis, Postgres, Elixir, and Rust
DevOps: Industry experience with Azure DevOps, creating custom pipelines with Powershell and TypeScript, deploying to both on-premise and Azure
Methodologies: Industry experience with TDD, BDD, DDD, Monorepo and Microservice architectures, and Agile methodologies, including scrum and kanban
Relevant Professional Experience
Blacklight Software – C# Web Developer January 2019 – Present
Worked as a developer, supporting and creating projects for a wide variety of existing and new clients. Coordinated with product and user requirements to design, develop, document, test and implement solutions supporting sales, customer conversion and retention.
Key Accomplishments:
Helped deliver a project worth £1,000,000, involving a custom React frontend for SharePoint, developing integrations and reports using AspNetCore, custom DevOps build pipelines, and synchronizations using Azure Logic Apps
Created a number of nuget packages for internal use, including standardization for logging, email sending, and CQRS
Developed custom templates for both dotnet and front-end code, to allow quick bootstrapping for new projects
Pioneered the use of editorconfig, SonarQube and RoslynAnalyzers in builds and continuous integration to ensure that code was clean, readable and consistent, regardless of the client or the developer who wrote it
FDM Group – C# Contract Web Developer July 2016 – December 2018
Worked as a contractor across a number of different clients across the country, including NHS Digital and GoCompare.
Key Accomplishments:
Worked on a team to help deliver the AutoQuote service at GoCompare worth revenue up to £10,000 a day
Partnered with other developers in organizing the front end restructuring at GoCompare to the Vue framework
Developed the modern, accessible and SEO friendly website for Northgate Vehicle Hire
Education
Newcastle University – Newcastle upon Tyne: BA in Geography and Planning, 2011-2014
12 GCSEs Grade B-A* (including English and Maths)


Examples¶
SnFFT comes with eight example functions that demonstrate some of the key properties of Young’s Orthogonal Representations and the Fourier Transform of functions over Sn . Furthermore, they demonstrate the syntax used to call most of the functionality of the package. The examples are in the file Examples.jl.
General Notes:¶
- Most of the example functions have two methods. The first method has no parameters and will run the example with default values. The second has a full set of parameters and will be explained in each function’s description.
- A parameter name will appear in bold when it being referenced in the example’s description.
- SnFFT represents a partition in the following way:
# Let X be a Partition of N
# X::Array{Int, 1}
# X[i] > 0 for all i
# sum(X) == N
# X[i] >= X[j] when i < j
- SnFFT represents a permutation in the following way:
# Let X be a Permutation of N
# X::Array{Int, 1}
# length(X) == N
# X[i] = j indicates that the item in position i is sent to position j
Example Function Explanations¶
-
example1
(N, partition, permutation)¶
# Parameters:
# N::Int
# - the problem size
# partition::Array{Int, 1}
# - a partition of N
# permutation::Array{Int, 1}
# - a permutation of N
This example finds the Standard Tableau corresponding to partition. It then calculates Young’s Orthogonal Representation of permutation corresponding to partition.
-
example2
(N, partition, p1, p2)¶
# Parameters:
# N::Int
# - the problem size
# partition::Array{Int, 1}
# - a partition of N
# p1::Array{Int, 1}
# - the first permutation of N
# p2::Array{Int, 1}
# - the second permutation of N
Let YOR1 and YOR2 be Young’s Orthogonal Representations of p1 and p2 corresponding to partition. Let YORm be Young’s Orthogonal Representations of p1 x p2 corresponding to partition. This example demonstrates that YORm = YOR1 x YOR2.
-
example3
()¶
This example demonstrates the order of the permutations used by SnFFT to represent a function over Sn . It has no parameterized version.
-
example4
(N)¶
# Parameters:
# N::Int
# - the problem size
This example constructs a random function over Sn . It then demonstrates how to calculate the (dense) fast Fourier transform and the inverse fast Fourier transform. Finally, it shows that this process recovers the original function accurately.
-
example5
(N, SC)¶
# Parameters:
# N::Int
# - the problem size
# SC::Float64
# - the portion of the function that is zero-valued
This example constructs a random sparse function over Sn and converts it to the format used to compute the spare fast Fourier transform. Next, it demonstrates how to compute the sparse fast Fourier transform. Finally, it shows that this produces the same result as the dense fast Fourier transform.
-
example6
(N, permutation)¶
# Parameters:
# N::Int
# - the problem size
# Permutation::Array{Int, 1}
# - a permutation of N
This example constructs a delta function over Sn that is centered on permutation. It then calculates the sparse fast Fourier transform of this function. Finally, it demonstrates that this produces the same results as computing Young’s Orthogonal Representation for each partition of N corresponding to permutation.
-
example7
(N, K)¶
# Parameters:
# N::Int
# - the problem size
# K::Int
# - the problem is homogenous at N-K
This example constructs a random bandlimited function over Sn . To save space, the bandlimited function is compressed. It then constructs the equivalent non-compressed version. Next, it demonstrates how to compute the bandlimited fast Fourier transform using the compressed function and take the bandlimited inverse fast Fourier transform. Finally, it shows that the dense and bandlimited fast Fourier transforms produce the same result and that the inverse bandlimited fast Fourier transform recovers the original bandlimited function over Sn .
-
example8
(N, M)¶
# Parameters:
# N::Int
# - the problem size
# M::Int
# - the number of the top frequencies of the Fourier transform to use
This function constructs a random function over Sn and then demonstrates how to compute the paritial inverse fast Fourier transform. It prints both the original and recovered function.


Exodus 5:11
Go ye, get you straw where ye can find it: yet not ought of your work shall be diminished.


property AlphaHit: Boolean;
AlphaHit determines how the layer performs its HitTest.
If AlphaHit=False, layer only passes points inside the rectangle returned by GetAdjustedLocation.
If AlphaHit=True, layer checks the opacity of the pixel lying under the specified point and passes the hit test only when the alpha channel of the pixel is greater when zero.


Authors: Peng Gao, Peng Wang, Ziqiang Xu, Xubo Wei, Long Jin and Zhong Tian
Source: FERMAT, Volume 8, Article 3, Jan-Feb., 2015
Abstract:Ultra wideband (UWB) technologies have been proposed and validated for potential high data rate communication systems. In this paper we present some recent works in our group, namely the Research Institute of Electronic Science and Technology (RIEST), University of Electronic Science and Technology of China (UESTC). The development is mainly focused on the miniature and broad band capability, as well as some special requirements, for instance, band-notched characteristics and diversity performance for multi-input multi-output (MIMO). Four compact printed broad band antennas are discussed in total, including an ACS-fed UWB and Bluetooth antenna, an UWB microstrip antenna with dual band notched characteristics and a high-isolation orthogonal diversity UWB and Bluetooth antenna for MIMO applications. At last, a MIMO UWB antenna with a rejected band is proposed
Index Terms:Ultra wideband (UWB) antennas, microstrip antennas, multiple-input multiple-output (MIMO), pattern diversity, band-notched
View PDF
Development of Compact Ultra Wideband Antennas


No Fill
Shaped No Fill
Apple
Solid
Apple
Shaped Solid
Apple
Solid, Floating Label
Apple
Solid, Fixed Label
Apple
Solid, Stacked Label
Apple
Outline
Apple
Shaped Outline
Apple
Outline, Floating Label
Apple
Outline, Fixed Label
Apple
Outline, Stacked Label
Apple
Shaped Outline, Floating Label
Apple
Shaped Outline, Fixed Label
Apple
Shaped Outline, Stacked Label
Apple


Priest Cerenas:
Thank you very much. Because you have the ice crystal, I now can make the medicine for my father. Please wait a moment while I prepare the materials.
"Wait until the medicine is made."


With iOS 12, you can place 3D objects in the real world using AR Quick Look, powered by ARKit 2. Tap any of the 3D models below on a device running iOS 12 to view the object and place it in AR. Or click a model on Mac to download the usdz file.


How to install Node.js
It is likely that you already have NodeJS installed on your machine. Try running node -v
from the command line. If that does not work or the version is older than 8.15
, follow the install instructions below.
Node Version Manager NVM
Node Version Manager NVM makes it easy to switch between Node versions in your local environment. I highly recommend using it over the native install on your system.
Mac and Linux
Simply run the install script from the command line.
curl -o- https://raw.githubusercontent.com/creationix/nvm/v0.33.11/install.sh | bash
Restart your terminal. You should now be able to easily install and manage Node versions.
nvm install 10
nvm use 10
Windows
NVM is not available on Windows, but there is a solid alternative with nvm-windows. It provides an installer than you can download from the repo. Once installed you will have access commands similar to those above.


{% include 'header.html' %}
Jason Ost and Colin Gerber are students in the Master of Information Management and Systems (MIMS) program at UC Berkeley's School of Information. This website is our final project for the program.
We decided to work with the ClinicalTrials.gov database becuase we saw a significant opporunity for positive public impact and a lot of room for data quality improvement, which is something that interests both of us. Currently the clinical trial data is not well-structured (most of the important data is in the form of free text), which results in information access and retrieval problems. This project attempts to improve the data quality, the ease of access, and the ability to structure the data going forward.
Jason Ost is a graduate of Georgetown University who has worked in data science and analytics in a variety of fields, including public policy analysis, social media advertising, and electoral campaigns. He is passionate about using data science tools to improve information accessibility and increase understanding of complex topics.
Colin Gerber has a neuroscience degree from UC Santa Barbara and worked for the National Institutes of Neurological Disorders and Strokes doing Parkinson’s Disease research for several years before coming to the I School. He has a deep interest in data analysis and data visualization as well as making scientific data and knowledge more accessible to the general public.
Marti Hearst, PhD is a professor at the I School who served as our faculty advisor on this project. She research has focused on search user interfaces, natural language processing, and informational visualization. She has been instrumental in helping guide and structure this project.
Anobel Odisho, MD is a resident physician at UC San Francisco who is interested in the intersection of health care and information technology. As a collaborator on our original information visualization of this data, he provided invaluable guidance and direction to this project from the outset.
Katey Basye is an I School classmate of ours who specializes in user interface design. She contributed to the initial conceptualization and implementation of the design of the website.


--- layout: post title: '1963' date: '2015-04-13T07:30:20+02:00' categories: - Storyboards picture_url: https://film-Storyboards.tumblr.com/post/116291800808/1963 ---


| Package | sandy.core.scenegraph |
| Class | public class Node |
| Subclasses | ATransformable, Group, Mode7 |
The base class for all Group and object nodes, that handles all basic operations on a tree node.
| Property | Defined by | ||
|---|---|---|---|
| appearance : Appearance
[write-only]
Set that appearance to all the children of that node
| Node | ||
| boundingBox : BBox
The bounding box of this node
IMPORTANT: Do not modify it unless you perfectly know what you are doing
| Node | ||
| boundingSphere : BSphere
The bounding sphere of this node
IMPORTANT: Do not modify it unless you perfectly know what you are doing
| Node | ||
| broadcaster : BubbleEventBroadcaster
[read-only]
The broadcaster
The broadcaster is used to send events to listeners. | Node | ||
| changed : Boolean
This property set the cache status of the current node.
| Node | ||
| children : Array
The children of this node are stored inside this array.
| Node | ||
| culled : CullingState
This property represent the culling state of the current node.
| Node | ||
| enableBackFaceCulling : Boolean [write-only]
Change the backface culling state to all the shapes objects in the children list
| Node | ||
| enableClipping : Boolean [write-only]
Apply clipping to all the children of that node
| Node | ||
| enableEvents : Boolean [write-only]
Enable event handling to all the children objects that can broadcast bubbling events
| Node | ||
| enableInteractivity : Boolean [write-only]
Change the interactivity of all the children
| Node | ||
| modelMatrix : Matrix4
Cached matrix corresponding to the transformation to the 0,0,0 frame system
| Node | ||
| name : String
Name of this node.
| Node | ||
| parent : Node
The parent node of this node.
| Node | ||
| scene : Scene3D | Node | ||
| useSingleContainer : Boolean [write-only]
Make all the Shape3D and descendants children react to this value.
| Node | ||
| viewMatrix : Matrix4
Cached matrix corresponding to the transformation to the camera frame system
| Node | ||
| visible : Boolean | Node |
| Property | Defined by | ||
|---|---|---|---|
| m_bChanged : Boolean | Node | ||
| m_bVisible : Boolean = true | Node | ||
| m_oEB : BubbleEventBroadcaster | Node | ||
| m_oScene : Scene3D = null | Node |
| Method | Defined by | ||
|---|---|---|---|
|
Node(p_sName:String = "")
Creates a node in the object tree of the world.
| Node | ||
|
Adds a new child to this node.
| Node | ||
|
addEventListener(p_sEvt:String, p_oL:*):Boolean
Adds a listener for the specified event.
| Node | ||
|
Tests this node against the frustum volume to get its visibility.
| Node | ||
|
destroy():void
Delete this node and all its child nodes.
| Node | ||
|
getChildByName(p_sName:String, p_bRecurs:Boolean = false):Node
Returns the child node with the specified name.
| Node | ||
|
hasParent():Boolean
Tests if this node has a parent.
| Node | ||
|
Tests if the node passed in the argument is parent of this node.
| Node | ||
|
perform(p_iOperation:INodeOperation):void
Performs an operation on this node and all of its children.
| Node | ||
|
remove():void
Removes this node from the node tree, saving its child nodes.
| Node | ||
|
removeChildByName(p_sName:String):Boolean
Removes the child node with the specified name.
| Node | ||
|
removeEventListener(p_sEvt:String, p_oL:*):void
Removes a listener for the specified event.
| Node | ||
|
swapParent(p_oNewParent:Node):void
Moves this node to another parent node.
| Node | ||
|
toString():String
Returns a string representation of this object
| Node | ||
|
Updates this node.
| Node | ||
|
updateBoundingVolumes():void
Updates the bounding volumes of this object.
| Node |
| Constant | Defined by | ||
|---|---|---|---|
| id : uint
The unique id of this node in the node graph.
| Node |
| appearance | property |
appearance:Appearance
[write-only]Set that appearance to all the children of that node
Implementation public function set appearance(value:Appearance):void
| boundingBox | property |
public var boundingBox:BBox
The bounding box of this node IMPORTANT: Do not modify it unless you perfectly know what you are doing
| boundingSphere | property |
public var boundingSphere:BSphere
The bounding sphere of this node IMPORTANT: Do not modify it unless you perfectly know what you are doing
| broadcaster | property |
broadcaster:BubbleEventBroadcaster
[read-only]The broadcaster
The broadcaster is used to send events to listeners.
This property is a BubbleEventBroadcaster.
public function get broadcaster():BubbleEventBroadcaster
| changed | property |
changed:Boolean
[read-write]This property set the cache status of the current node. IMPORTANT Currently this property IS IN USE FOR CACHE SYSTEM
Implementation public function get changed():Boolean
public function set changed(value:Boolean):void
| children | property |
public var children:Array
The children of this node are stored inside this array. IMPORTANT: Use this property mainly as READ ONLY. To add, delete or search a specific child, you can use the specific method to do that
| culled | property |
public var culled:CullingState
This property represent the culling state of the current node. This state is defined during the culling phasis as it refers to the position of the object against the viewing frustum.
| enableBackFaceCulling | property |
enableBackFaceCulling:Boolean
[write-only]Change the backface culling state to all the shapes objects in the children list
Implementation public function set enableBackFaceCulling(value:Boolean):void
| enableClipping | property |
enableClipping:Boolean
[write-only]Apply clipping to all the children of that node
Implementation public function set enableClipping(value:Boolean):void
| enableEvents | property |
enableEvents:Boolean
[write-only]Enable event handling to all the children objects that can broadcast bubbling events
Implementation public function set enableEvents(value:Boolean):void
| enableInteractivity | property |
enableInteractivity:Boolean
[write-only]Change the interactivity of all the children
Implementation public function set enableInteractivity(value:Boolean):void
| m_bChanged | property |
protected var m_bChanged:Boolean
| m_bVisible | property |
protected var m_bVisible:Boolean = true
| modelMatrix | property |
public var modelMatrix:Matrix4
Cached matrix corresponding to the transformation to the 0,0,0 frame system
| m_oEB | property |
protected var m_oEB:BubbleEventBroadcaster
| m_oScene | property |
protected var m_oScene:Scene3D = null
| name | property |
public var name:String
Name of this node. If no name is specified, the unique ID of the node will be used
| parent | property |
parent:Node
[read-write]The parent node of this node.
The reference is null if this nod has no parent (for exemple for a root node).
Implementation public function get parent():Node
public function set parent(value:Node):void
| scene | property |
scene:Scene3D
[read-write]Implementation
public function get scene():Scene3D
public function set scene(value:Scene3D):void
| useSingleContainer | property |
useSingleContainer:Boolean
[write-only]Make all the Shape3D and descendants children react to this value.
Implementation public function set useSingleContainer(value:Boolean):void
| viewMatrix | property |
public var viewMatrix:Matrix4
Cached matrix corresponding to the transformation to the camera frame system
| visible | property |
visible:Boolean
[read-write]Implementation
public function get visible():Boolean
public function set visible(value:Boolean):void
| Node | () | constructor |
public function Node(p_sName:String = "")
Creates a node in the object tree of the world.
This constructor should normally not be called directly, only from a sub class.
Parametersp_sName:String (default = " ") — A string identifier for this object.
|
| addChild | () | method |
public function addChild(p_oChild:Node):void
Adds a new child to this node.
A node can have several children, and when you add a child to a node, it is automatically connected to the parent node through its parent property.
Parametersp_oChild:Node — The child node to add
|
| addEventListener | () | method |
public function addEventListener(p_sEvt:String, p_oL:*):Boolean
Adds a listener for the specified event.
Parametersp_sEvt:String — Name of the Event.
|
|
p_oL:* — Listener object.
|
Boolean |
| cull | () | method |
public function cull(p_oFrustum:Frustum, p_oViewMatrix:Matrix4, p_bChanged:Boolean):void
Tests this node against the frustum volume to get its visibility.
If the node and its children aren't in the frustum, the node is set to cull
and will not be displayed.
The method also updates the bounding volumes, to make a more accurate culling system possible.
First the bounding sphere is updated, and if intersecting, the bounding box is updated to perform a more
precise culling.
[MANDATORY] The update method must be called first!
Parametersp_oFrustum:Frustum — The current scene which is rendered
|
|
p_oViewMatrix:Matrix4 — The frustum of the current camera.
|
|
p_bChanged:Boolean — The matrix which maps object local coordinates into camera coordinates.
|
| destroy | () | method |
public function destroy():void
Delete this node and all its child nodes.
This node nad all its child nodes are deleted, including all data they are storing.
The method makes recursive calls to the destroy method of the child nodes.
| getChildByName | () | method |
public function getChildByName(p_sName:String, p_bRecurs:Boolean = false):Node
Returns the child node with the specified name.
Parametersp_sName:String — The name of the child you want to retrieve
|
|
p_bRecurs:Boolean (default = false ) — Set to true if you want to search the the children for the requested node
|
Node —
The requested node or null if no child with this name was found
|
| hasParent | () | method |
public function hasParent():Boolean
Tests if this node has a parent.
ReturnsBoolean — true if this node has a parent, false otherwise.
|
| isParent | () | method |
public function isParent(p_oNode:Node):Boolean
Tests if the node passed in the argument is parent of this node.
Parametersp_oNode:Node — The node you are testing
|
Boolean — true if the node in the argument is the parent of this node, false otherwise.
|
| perform | () | method |
public function perform(p_iOperation:INodeOperation):void
Performs an operation on this node and all of its children.
Traverses the subtree made up of this node and all of its children. While traversing the subtree, individual operations are performed on entry and exit of each node of the subtree.
Implements the visitor design pattern: Using the visitor design pattern, you can define a new operation on Node and its subclasses without having to change the classes and without having to take care of traversing the node tree.
Parametersp_iOperation:INodeOperation — p_iOperation The operation to be performed on the node subtree
|
var mySpecialOperation:SpecialOperation = new SpecialOperation; mySpecialOperation.someParameter = 0.8; someTreeNode.perform(mySpecialOperation); trace(mySpecialOperation.someResult); mySpecialOperation.someParameter = 0.2; someOtherTreeNode.perform(mySpecialOperation); trace(mySpecialOperation.someResult);
| remove | () | method |
public function remove():void
Removes this node from the node tree, saving its child nodes.
NOTE that remove method only remove the current node and NOT its children!
To remove the current node and all its children please refer to the destroy method.
The child nodes of this node becomes child nodes of this node's parent.
| removeChildByName | () | method |
public function removeChildByName(p_sName:String):Boolean
Removes the child node with the specified name.
All children of the node you want to remove are lost.
The link between them and the rest of the tree is broken, and they will not be rendered anymore!
The object itself and its children are still in memory!
If you want to free them completely, call child.destroy()
p_sName:String — The name of the node you want to remove.
|
Boolean — true if the node was removed from node tree, false otherwise.
|
| removeEventListener | () | method |
public function removeEventListener(p_sEvt:String, p_oL:*):void
Removes a listener for the specified event.
Parametersp_sEvt:String — Name of the Event.
|
|
p_oL:* — Listener object.
|
| swapParent | () | method |
public function swapParent(p_oNewParent:Node):void
Moves this node to another parent node.
This node is removed from its current parent node, and added as a child of the specified node
Parametersp_oNewParent:Node — The node to become parent of this node
|
| toString | () | method |
public function toString():String
Returns a string representation of this object
ReturnsString — The fully qualified name of this class
|
| update | () | method |
public function update(p_oModelMatrix:Matrix4, p_bChanged:Boolean):void
Updates this node.
For a node with transformation, this method update the transformation taking into account the matrix cache system.
Parametersp_oModelMatrix:Matrix4 — The current scene
|
|
p_bChanged:Boolean |
| updateBoundingVolumes | () | method |
public function updateBoundingVolumes():void
Updates the bounding volumes of this object. NO IMPLEMENTATION RIGHT NOW. MIGHT BE USED IN THE FUTURE
| id | constant |
public const id:uint
The unique id of this node in the node graph.
This value is very useful to retrieve a specific node.


Node field names are customizable using the following options:
childrenField
displayField
idField
isExpandedField
hasChildrenField


Hey! My name's Mr D.E. Velope but most know me Dev. I'm a designer, developer and astronaut located in Canada. I love anything to do with the internet and taking relaxing vacations on Venus.
When not designing for the web and traveling the galaxy, I also like to design and develop mobile applications for Android & iOs and create mockups and icons to share with the web.
Site created using Wagtail CMS from Torchbox (which is based on Django).
Want to have a chat? Got a question? Please quickly fill out the form to the left and I will get right back to you.
Thank you so much for visiting and I look forward to hearing from you soon.
Don't really fill out the form, it's for show ;)


Andrew Archer
About Me
About Me
Andy's Bio Pic
Andy just joined DevBootcamp's Phase 0! You'll see a lot of content here in the weeks to come.
Find Andy
Facebook
github
Instagram
Twitter
Google+


--- layout: post title: "新未来,心仍在!" date: 2013-06-11 00:00:42.000000000 +08:00 type: post published: true status: publish categories: - "心情日记" tags: [] meta: _edit_last: '1' views: '20' author: login: lanbing email<EMAIL_ADDRESS>display_name: lanbing first_name: '' last_name: '' ---
[caption id="" align="alignnone" width="450"] 守望[/caption]


{% if invalidForm %}
INVALID/EMPTY FORM!
Make sure to create check amount and percent goal with 2 decimal places.
Also add a goal duration or input anything if unspecified.
New Goal


Vega is a declarative format for creating, saving, and sharing visualization designs.
With Vega, visualizations are described in JSON, and generate interactive views using either HTML5 Canvas or SVG.
Toolkits
new Vega 3.0
is a visualization grammar, a declarative language for creating, saving, and sharing interactive visualization designs. With Vega, you can describe the visual appearance and interactive behavior of a visualization in a JSON format, and generate web-based views using Canvas or SVG.
Systems
Lyra
is an interactive environment that enables custom visualization design.
Without writing any code, designers can create visualizations on-par with hand-coded D3 and Processing.
PoleStar
is a web-based visualization specification interface, inspired by Tableau.
Analysts can rapidly generate visualizations as part of the
data exploration process.
Voyager
is a visualization browser for open-ended data exploration.
It provides a gallery of recommended visualizations, produced by Compass
visualization recommender engine.
Vega's 3rd Party Integration
ggvis is a data visualization package for R that renders web-based visualizations using Vega. It features a syntax similar in spirit to ggplot2.
Vega.jl uses the Julia programming language to generate spec-compliant Vega 2.x visualizations. Vega.jl is integrated with Jupyter Notebook, and provides a high-quality visualization experience for scientific computing.
The MediaWiki Graph extension allows you to embed Vega visualizations on MediaWiki sites, including Wikipedia.
Cedar integrates Vega with the GeoServices from ArcGIS. It adds templated documents for reusable charts that programatically bind to new data sources.
Vega-Lite's 3rd Party Integration
Check out the list of applications on the Vega-Lite website.


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Under Review by teacher
Approved