Joao Moutinho signs news Wolverhampton Wanderers contract
Wolves midfielder Joao Moutinho has signed a new contact with the club until 2022.
The Portuguese, who joined Wolves upon promotion to the Premier League in 2018, has made 66 appearances for the club and scored in the 2-1 win at Bournemouth on Saturday.
"It was a very easy decision because I feel very good here," Moutinho told the official Wolves website.
"It's a big club that wants to do something good and I want to do my best to help.
"We did a great job last season and this season we'll try to do better.
"That's what we're going to do and I'm going to try 100 per cent to help the club to improve.
"I feel very good. We won today, a very important win, and I signed a new contract.
"I'm very happy to help the team today and I hope to help for the new two, three years."
Moutinho began his career at Sporting Lisbon before moving on to Porto and Monaco.
The 33-year-old, who has won over 100 caps for his country and helped Portugal to Euro 2016 glory, has featured in every Premier League fixture since moving to Molineux.
Wolves sporting director Kevin Thelwell said: "It's terrific news. In the time he's been here, everyone has been able to see he's a top player.
"Top performances on the pitch, top professionalism off the pitch. He has an amazing mentality and has made a really big impression on everyone at the football club."
body check tags ::


--- notes: | Below are some examples of how the One Wiley Visual Language (also known as the network) can be used to add decoration to elements. The network should be used sparingly within UI design and adopted primarily to draw focus to key information. Please consult the One Wiley Brand Guidelines for information on marketing collatoral. ---


|
Panda3D
|
An implementation of a very simple LRU algorithm. More...
Public Member Functions | |
| __init__ (str name, int max_size) | |
| beginEpoch () | |
| Marks the end of the previous epoch and the beginning of the next one. More... | |
| considerEvict () | |
| Evicts a sequence of objects if the queue is full. More... | |
| int | countActiveSize () |
| Returns the total size of the pages that were enqueued since the last call to begin_epoch(). More... | |
| evictTo (int target_size) | |
| Evicts a sequence of objects until the queue fits within the indicated target size, regardless of its normal max size. More... | |
| int | getMaxSize () |
| Returns the max size of all objects that are allowed to be active on the LRU. More... | |
| int | getTotalSize () |
| Returns the total size of all objects currently active on the LRU. More... | |
| output (Ostream out) | |
| setMaxSize (int max_size) | |
| Changes the max size of all objects that are allowed to be active on the LRU. More... | |
| bool | validate () |
| Checks that the LRU is internally self-consistent. More... | |
| write (Ostream out, int indent_level) | |
| Public Member Functions inherited from Namable | |
| __init__ (const Namable) | |
| __init__ (str initial_name) | |
| clearName () | |
| Resets the Namable's name to empty. More... | |
| str | getName () |
| bool | hasName () |
| Returns true if the Namable has a nonempty name set, false if the name is empty. More... | |
| output (Ostream out) | |
| Outputs the Namable. More... | |
| setName (str name) | |
Additional Inherited Members | |
| Static Public Member Functions inherited from Namable | |
| static TypeHandle | getClassType () |
| Public Attributes inherited from Namable | |
| const String | name |
An implementation of a very simple LRU algorithm.
Also see AdaptiveLru.
| __init__ | ( | str | name, |
| int | max_size | ||
| ) |
| beginEpoch | ( | ) |
Marks the end of the previous epoch and the beginning of the next one.
This will evict any objects that are pending eviction, and also update any internal bookkeeping.
| considerEvict | ( | ) |
Evicts a sequence of objects if the queue is full.
| int countActiveSize | ( | ) |
Returns the total size of the pages that were enqueued since the last call to begin_epoch().
| evictTo | ( | int | target_size | ) |
Evicts a sequence of objects until the queue fits within the indicated target size, regardless of its normal max size.
| int getMaxSize | ( | ) |
Returns the max size of all objects that are allowed to be active on the LRU.
| int getTotalSize | ( | ) |
Returns the total size of all objects currently active on the LRU.
| output | ( | Ostream | out | ) |
| setMaxSize | ( | int | max_size | ) |
Changes the max size of all objects that are allowed to be active on the LRU.
If the size is (size_t)-1, there is no limit.
| bool validate | ( | ) |
Checks that the LRU is internally self-consistent.
Returns true if successful, false if there is some problem.
| write | ( | Ostream | out, |
| int | indent_level | ||
| ) |


Great Laura!
Play!
Is Laura Great?
Yes!
No!
Surely you're thinking of a different Laura?!
Try again!
Yes, Laura is great!
Play again!


|
Panda3D
|
Main class used for handling PSSM. More...
Public Member Functions | |
| __init__ (const PSSMCameraRig) | |
| __init__ (int num_splits) | |
| Constructs a new PSSM camera rig. More... | |
| NodePath | getCamera (int index) |
| Returns the n-th camera. More... | |
| const PTALMatrix4 | getMvpArray () |
| Returns a handle to the MVP array. More... | |
| const PTALVecBase2 | getNearfarArray () |
| Returns a handle to the near and far planes array. More... | |
| reparentTo (NodePath parent) | |
| Reparents the camera rig. More... | |
| resetFilmSizeCache () | |
| Resets the film size cache. More... | |
| setBorderBias (float bias) | |
| Sets the border bias for each split. More... | |
| setLogarithmicFactor (float factor) | |
| Sets the logarithmic factor. More... | |
| setPssmDistance (float distance) | |
| Sets the maximum pssm distance. More... | |
| setResolution (int resolution) | |
| Sets the resolution of each split. More... | |
| setSunDistance (float distance) | |
| Sets the suns distance. More... | |
| setUseFixedFilmSize (bool flag) | |
| Sets whether to use a fixed film size. More... | |
| setUseStableCsm (bool flag) | |
| Sets whether to use stable CSM snapping. More... | |
| update (NodePath cam_node, const LVecBase3 light_vector) | |
| Updates the PSSM camera rig. More... | |
Main class used for handling PSSM.
This is the main class for supporting PSSM, it is used by the PSSM plugin to compute the position of the splits.
It supports handling a varying amount of cameras, and fitting those cameras into the main camera frustum, to render distant shadows. It also supports various optimizations for fitting the frustum, e.g. rotating the sources to get a better coverage.
It also provides methods to get arrays of data about the used cameras view-projection matrices and their near and far plane, which is required for processing the data in the shadow sampling shader.
In this class, there is often referred to "Splits" or also called "Cascades". These denote the different cameras which are used to split the frustum, and are a common term related to the PSSM algorithm.
To understand the functionality of this class, a detailed knowledge of the PSSM algorithm is helpful.
| __init__ | ( | const | PSSMCameraRig | ) |
| __init__ | ( | int | num_splits | ) |
Constructs a new PSSM camera rig.
This constructs a new camera rig, with a given amount of splits. The splits can not be changed later on. Splits are also called Cascades.
An assertion will be triggered if the splits are below zero.
| num_splits | Amount of PSSM splits |
| NodePath getCamera | ( | int | index | ) |
Returns the n-th camera.
This returns the n-th camera of the camera rig, which can be used for various stuff like showing its frustum, passing it as a shader input, and so on.
The first camera is the camera which is the camera of the first split, which is the split closest to the camera. All cameras follow in descending order until to the last camera, which is the split furthest away from the camera.
If an invalid index is passed, an assertion is thrown.
| index | Index of the camera. |
| const PTALMatrix4 getMvpArray | ( | ) |
Returns a handle to the MVP array.
This returns a handle to the array of view-projection matrices of the different splits. This can be used for computing shadows. The array is a PTALMatrix4 and thus can be directly bound to a shader.
| const PTALVecBase2 getNearfarArray | ( | ) |
Returns a handle to the near and far planes array.
This returns a handle to the near and far plane array. Each split has an entry in the array, whereas the x component of the vecto denotes the near plane, and the y component denotes the far plane of the split.
This is required because the near and far planes of the splits change constantly. To access them in a shader, the shader needs access to the array.
| reparentTo | ( | NodePath | parent | ) |
Reparents the camera rig.
This reparents all cameras to the given parent. Usually the parent will be ShowBase.render. The parent should be the same node where the main camera is located in, too.
If an empty parrent is passed, an assertion will get triggered.
| parent | Parent node path |
| resetFilmSizeCache | ( | ) |
Resets the film size cache.
In case PSSMCameraRig::set_use_fixed_film_size is used, this resets the film size cache. This might lead to a small "jump" in the shadows, because the film size changes, however it leads to a better shadow distribution.
This is the case because when using a fixed film size, the cache will get bigger and bigger, whenever the camera moves to a grazing angle. However, when moving back to a normal angle, the film size cache still stores this big angle, and thus the splits will have a much bigger film size than actualy required. To prevent this, call this method once in a while, so an optimal distribution is ensured.
| setBorderBias | ( | float | bias | ) |
Sets the border bias for each split.
This sets the border bias for every split. This increases each splits frustum by multiplying it by (1 + bias), and helps reducing artifacts at the borders of the splits. Artifacts can occur when the bias is too low, because then the filtering will go over the bounds of the split, producing invalid results.
If the bias is below zero, an assertion is thrown.
| bias | Border bias |
| setLogarithmicFactor | ( | float | factor | ) |
Sets the logarithmic factor.
This sets the logarithmic factor, which is the core of the algorithm. PSSM splits the camera frustum based on a linear and a logarithmic factor. While a linear factor provides a good distribution, it often is not applicable for wider distances. A logarithmic distribution provides a better distribution at distance, but suffers from splitting in the near areas.
The logarithmic factor mixes the logarithmic and linear split distribution, to get the best of both. A greater factor will make the distribution more logarithmic, while a smaller factor will make it more linear.
If the factor is below zero, an ssertion is triggered.
| factor | The logarithmic factor |
| setPssmDistance | ( | float | distance | ) |
Sets the maximum pssm distance.
This sets the maximum distance in world space until which shadows are rendered. After this distance, no shadows will be rendered.
If the distance is below zero, an assertion is triggered.
| distance | Maximum distance in world space |
| setResolution | ( | int | resolution | ) |
Sets the resolution of each split.
This sets the resolution of each split. Currently it is equal for each split. This is required when using PSSMCameraRig::set_use_stable_csm, to compute how bix a texel is.
It has to match the y-resolution of the pssm shadow map. If an invalid resolution is triggered, an assertion is thrown.
| resolution | The resolution of each split. |
| setSunDistance | ( | float | distance | ) |
Sets the suns distance.
This sets the distance the cameras will have from the cameras frustum. This prevents far objects from having no shadows, which can occur when these objects are between the cameras frustum and the sun, but not inside of the cameras frustum. Setting the sun distance high enough will move the cameras away from the camera frustum, being able to cover those distant objects too.
If the sun distance is set too high, artifacts will occur due to the reduced range of depth. If a value below zero is passed, an assertion will get triggered.
| distance | The sun distance |
| setUseFixedFilmSize | ( | bool | flag | ) |
Sets whether to use a fixed film size.
This controls if a fixed film size should be used. This will cause the camera rig to cache the current film size, and only change it in case it gets too small. This provides less flickering when moving, because the film size will stay roughly constant. However, to prevent the cached film size getting too big, one should call PSSMCameraRig::reset_film_size once in a while, otherwise there might be a lot of wasted space.
| flag | Whether to use a fixed film size |
| setUseStableCsm | ( | bool | flag | ) |
Sets whether to use stable CSM snapping.
This option controls if stable CSM snapping should be used. When the option is enabled, all splits will snap to their texels, so that when moving, no flickering will occur. However, this only works when the splits do not change their film size, rotation and angle.
| flag | Whether to use stable CSM snapping |
Updates the PSSM camera rig.
This updates the rig with an updated camera position, and a given light vector. This should be called on a per-frame basis. It will reposition all camera sources to fit the frustum based on the pssm distribution.
The light vector should be the vector from the light source, not the vector to the light source.
| cam_node | Target camera node |
| light_vector | The vector from the light to any point |


Creates div containing children elements of sidebar panel
Creates div containing children elements of main panel
Creates grid layout composed of sidebar and main panels
sidebar_panel(..., width = 1) main_panel(..., width = 3) sidebar_layout( sidebar_panel, main_panel, mirrored = FALSE, min_height = "auto", container_style = "", area_styles = list(sidebar_panel = "", main_panel = "") ) sidebarPanel(..., width = 6) mainPanel(..., width = 10) sidebarLayout( sidebarPanel, mainPanel, position = c("left", "right"), fluid = TRUE )
| ... | Container's children elements |
|---|---|
| width | Width of main panel container as relative value |
| sidebar_panel | Sidebar panel component |
| main_panel | Main panel component |
| mirrored | If TRUE sidebar is located on the right side, if FALSE - on the left side (default) |
| min_height | Sidebar layout container keeps the minimum height, if specified. It should be formatted as a string with css units |
| container_style | CSS declarations for grid container |
| area_styles | List of CSS declarations for each grid area inside |
| sidebarPanel | same as |
| mainPanel | same as |
| position | vector with position of sidebar elements in order sidebar, main |
| fluid | TRUE to use fluid layout; FALSE to use fixed layout. |
Container with sidebar and main panels
if (interactive()){ library(shiny) library(shiny.semantic) ui <- semanticPage( titlePanel("Hello Shiny!"), sidebar_layout( sidebar_panel( shiny.semantic::sliderInput("obs", "Number of observations:", min = 0, max = 1000, value = 500), width = 3 ), main_panel( plotOutput("distPlot"), width = 4 ), mirrored = TRUE ) ) server <- function(input, output) { output$distPlot <- renderPlot({ hist(rnorm(input$obs)) }) } shinyApp(ui, server) }


Accessibility resources
Wear something purple on Thursday 16 May 2019 to mark the eighth Global Accessibility Awareness Day (GAAD).
Things to try
- go mouse-free – try unplugging your mouse and only using your keyboard for an hour
- set your browser zoom to 400% and check if the fonts resize – to find out if a page meets the Web Content Accessibility Guidelines
- try some of the Windows accessibility features, like Windows Narrator
- if you have an Apple device, try the Mac accessibility features
Government guidance and blogs
- Make your public sector website or app accessible
- Publishing accessible documents
- Making your service accessible
- Designing for different browsers and devices
- Running research sessions with people with disabilities
- Testing for accessibility
- Understanding disabilities and impairments: user profiles
- WCAG 2.1 Primer: help understanding the Web Content Accessibility Guidelines
- Office for National Statistics guide on using accessible colours for data visualisation
- GOV.UK accessibility blog
- Home Office's accessibility posters blog


Volkswagen to invest $66 billion in e-mobility, digital tech by 2024
Plans 75 EV and 60 hybrid variants
by ReutersFRANKFURT — Volkswagen will invest 60 billion euros ($66.12 billion) by 2024 to develop electric and hybrid cars as well as digital technologies, the multi-brand car and truckmaking group said on Friday.
Volkswagen plans to build 75 variants of electric car and around 60 hybrid vehicle models, it said. By 2029 around 26 million electric cars will be built, of which 20 million vehicles will use VW's electric MEB vehicle platform, the carmaker said.
Volkswagen plans to retool its plant in Emden, Germany to build an electric sports utility vehicle (SUV) from 2022 onwards, and a decision on where to locate a new factory will be taken by year-end, the carmaker said.
Reporting by Edward Taylor.


Statnet is a collection of packages for statistical network analysis that are
designed to work together because they share common data representations and 'API'
design. They provide an integrated set of tools for the representation,
visualization, analysis, and simulation of many different forms of network data.
This package is designed to make it easy to install and load the
key 'statnet' packages in a single step. Learn more about 'statnet'
at <http://www.statnet.org>. For an introduction to functions in this package,
type help(package='statnet').
| Version: |
2018.10 |
| Depends: |
R (≥ 3.0), tergm (≥ 3.5.2), ergm.count (≥ 3.3), sna (≥
2.4), tsna (≥ 0.2) |
| Imports: |
ergm (≥ 3.9.4), network (≥ 1.13), networkDynamic (≥ 0.9), statnet.common (≥ 4.1.4) |
| Suggests: |
ergm.rank (≥ 1.1), ergm.ego (≥ 0.4), EpiModel (≥ 1.6.5), degreenet (≥ 1.3.1), latentnet (≥ 2.9.0), networksis (≥
2.1.3), relevent (≥ 1.0.4), ndtv (≥ 0.12.3) |
| Published: |
2018-10-22 |
| Author: |
Mark S. Handcock [aut],
David R. Hunter [aut],
Carter T. Butts [aut],
Steven M. Goodreau [aut],
Pavel N. Krivitsky [aut],
Skye Bender-deMoll [aut],
Martina Morris [aut, cre] |
| Maintainer: |
Martina Morris <morrism at uw.edu> |
| BugReports: |
https://github.com/statnet/statnet/issues |
| License: |
GPL-3 + file LICENSE |
| URL: |
http://statnet.org |
| NeedsCompilation: |
no |
| Citation: |
statnet citation info |
| Materials: |
README |
| In views: |
SocialSciences |
| CRAN checks: |
statnet results |


Make a differnce
We exist for non-profits social enterprises activists. Lorem politicians and individual citizens.
Active Care For Those In NEED (ACRT) is a registered non - profit organization that has
Lorem ipsum dolor sit amet, consectetur adipiscing elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua. Quis ipsum suspendisse ultrices gravida. Risus commodo viverra maecenas accumsan lacus vel facilisis. Lorem ipsum dolor sit amet, consectetur adipiscing elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua. Quis ipsum suspendisse ultrices gravida. Risus commodo viverra maecenas accumsan lacus vel facilisis.
Lorem ipsum dolor sit amet, consectetur adipiscing elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua. Quis ipsum suspendisse ultrices gravida. Risus commodo viverra maecenas accumsan lacus vel facilisis. Lorem ipsum dolor sit amet, consectetur adipiscing elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua. Quis ipsum suspendisse ultrices gravida. Risus commodo viverra maecenas accumsan lacus vel facilisis.
For over 10 years, we have focused on HIV/ AIDS prevention and screening! Although we do have more programs, we are still geared towards health & wellness in all aspects!
Our community is so important to us ! We have a fantastic local footprint here in Windsor, ON with our seniors program, Thai Migrant Worker program as well as our Youths Program!
 We have travelled all over the world to help those who have sufferred from natural disasters, offering our help and services whichever way we can!
We exist for non-profits social enterprises activists. Lorem politicians and individual citizens.
We exist for non-profits social enterprises activists. Lorem politicians and individual citizens.
We exist for non-profits social enterprises activists. Lorem politicians and individual citizens.


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about total management
Total Management is a free, tableless, W3C-compliant web design layout by Template World. This template has been tested and proven compatible with all major browser envrionments and operating systems. You are free to modify the design to suit your tastes in any way you like.
We only ask you to not remove "Design by Template World" and the link http://www.templateworld.com from the footer of the template.
If you are interested in seeing more of our free web template designs feel free to visit our website, Template World. We intend to add at leas 25 new free templates in the coming month.


You can use this website to upload photos. You can drag and drop photos in grey area or click it to add photos. You can upload one or two photos every time.


{'Here you can edit the states on the site'|lang}
{'Add state'|lang}
{'Are you sure you want to remove a state'|lang}: "{$item.name}"?


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Happy to have you here!
Keep Calm And Help The Elderly!
"Let's Donate to spread some gladness."

Donate NowThis is our Payment Gateway Website.
Established in 2010 ours beginnings were humble.
Continiously we are improving our website to provide
a better online payment platform so donation will be
easy and safe.


Custom partitioning and data colocation can be used separately or in conjunction with one another.
Custom Partitioning
Use custom partitioning to group like entries into region buckets within a region. By default, Geode assigns new entries to buckets based on the entry key’s hash code. With custom partitioning, you can assign your entries to buckets in whatever way you want.
You can generally get better performance if you use custom partitioning to group similar data within a region. For example, a query run on all accounts created in January runs faster if all January account data is hosted by a single member. Grouping all data for a single customer can improve performance of data operations that work on customer data. Data aware function execution also takes advantage of custom partitioning.
With custom partitioning, you have two choices:
- Standard custom partitioning. With standard custom partitioning, you group entries into buckets, but you do not specify where the buckets reside. Geode always keeps the entries in the buckets you have specified, but may move the buckets around for load balancing. See Standard Custom Partitioning for implementation and configuration details.
Fixed custom partitioning. With fixed custom partitioning, you specify the exact member where each region entry resides. You assign an entry to a partition and then to a bucket within that partition. You name specific members as primary and secondary hosts of each partition.
This gives you complete control over the locations of your primary and any secondary buckets for the region. This can be useful when you want to store specific data on specific physical machines or when you need to keep data close to certain hardware elements.
Fixed partitioning has these requirements and caveats:
- Geode cannot rebalance fixed partition region data, because it cannot move the buckets around among the host members. You must carefully consider your expected data loads for the partitions you create.
- With fixed partitioning, the region configuration is different between host members. Each member identifies the named partitions it hosts, and whether it is hosting the primary copy or a secondary copy. You then program a fixed-partition resolver to return the partition id, so the entry is placed on the right members. Only one member can be primary for a particular partition name, and that member cannot be the partition’s secondary.
See Fixed Custom Partitioning for implementation and configuration details.
Data Colocation Between Regions
With data colocation, Geode stores entries that are related across multiple data regions in a single member. Geode does this by storing all of the regions’ buckets with the same ID together in the same member. During rebalancing operations, Geode moves these bucket groups together or not at all.
So, for example, if you have one region with customer contact information and another region with customer orders, you can use colocation to keep all contact information and all orders for a single customer in a single member. This way, any operation done for a single customer uses the cache of only a single member.
This figure shows two regions with data colocation where the data is partitioned by customer type.
Data colocation requires the same data partitioning mechanism for all of the colocated regions. You can use the default partitioning provided by Geode or any of the custom partitioning strategies.
You must use the same high availability settings across your colocated regions.
See Colocate Data from Different Partitioned Regions for implementation and configuration details.


PrimitiveFace is a tool for generated primitive, allowing users (for some specifics primitives) to get the face polygon array
to have an easier manipulation.
public var aPolygons:Array
The array containing the polygon instances own by this primitive face
appearance:Appearance
[read-write]
Implementation
public function get appearance():Appearance
public function set appearance(value:Appearance):void
primitive:Shape3D
[read-only]
Implementation
public function get primitive():Shape3D
public function PrimitiveFace(p_iPrimitive:Shape3D)
PrimitiveFace class
This class is a tool for the primitives. It will stores all the polygons that are owned by the visible primitive face.
Parameters
| p_iPrimitive:Shape3D — The primitive this face will be linked to
|
public function addPolygon(p_oPolyId:uint):void
Parameters
API documentation for Sandy 3D Engine, Ver. 3.1.2


--- title: BART vs Causal forests showdown author: Gertjan Verhoeven date: '2018-12-15' summary: In this post, we test both `Bayesian Additive Regression Trees (BART)` and `Causal forests (grf)` on four simulated datasets of increasing complexity. May the best method win! slug: bart-vs-grf-showdown draft: FALSE categories: - causal inference - machine learning tags: - causal inference - BART - grf header: image: "headers/actor-adult-ancient-236171.jpg" preview: FALSE ---
# library(devtools)
#devtools::install_github("vdorie/dbarts")
library(dbarts)
library(ggplot2)
library(tidyverse)
## ── Attaching packages ─────────────────────────────────────── tidyverse 1.3.0 ──
## ✔ tibble 3.0.4 ✔ dplyr 1.0.2
## ✔ tidyr 1.1.2 ✔ stringr 1.4.0
## ✔ readr 1.4.0 ✔ forcats 0.5.0
## ✔ purrr 0.3.4
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ tidyr::extract() masks dbarts::extract()
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag() masks stats::lag()
library(grf)
#devtools::install_github("vdorie/aciccomp/2017")
library(aciccomp2017)
library(cowplot)
source("CalcPosteriors.R")
fullrun <- 0
This is not a causal problem but a prediction problem.
## y = f(x) + epsilon , epsilon ~ N(0, sigma)
## x consists of 10 variables, only first 5 matter
f <- function(x) {
10 * sin(pi * x[,1] * x[,2]) + 20 * (x[,3] - 0.5)^2 +
10 * x[,4] + 5 * x[,5]
}
set.seed(99)
sigma <- 1.0
n <- 100
x <- matrix(runif(n * 10), n, 10)
Ey <- f(x)
y <- rnorm(n, Ey, sigma)
df <- data.frame(x, y, y_true = Ey)
if(fullrun){
## run BART
set.seed(99)
bartFit <- bart(x, y)
saveRDS(bartFit, "s1.rds")
} else { bartFit <- readRDS("s1.rds")}
plot(bartFit)
MCMC or sigma looks ok.
df2 <- data.frame(df,
ql = apply(bartFit$yhat.train, length(dim(bartFit$yhat.train)), quantile,probs=0.05),
qm = apply(bartFit$yhat.train, length(dim(bartFit$yhat.train)), quantile,probs=.5),
qu <- apply(bartFit$yhat.train, length(dim(bartFit$yhat.train)), quantile,probs=0.95)
)
bartp <- ggplot(df2, aes(x= y, y = qm)) + geom_linerange(aes(ymin = ql, ymax = qu), col = "grey") +
geom_point() + geom_smooth() +
geom_abline(intercept = 0, slope = 1, col = "red", size = 1)
bartp
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
This looks nice.
From the manual: Trains a regression forest that can be used to estimate the conditional mean function mu(x) = E[Y | X = x]
if(fullrun){
reg.forest = regression_forest(x, y, num.trees = 2000)
saveRDS(reg.forest, "s00.rds")
} else {reg.forest <- readRDS("s00.rds")}
df3 <- CalcPredictionsGRF(x, reg.forest)
df3 <- data.frame(df3, y)
ggplot(df3, aes(x= y, y = qm)) + geom_linerange(aes(ymin = ql, ymax = qu), col = "grey") +
geom_point() +
geom_abline(intercept = 0, slope = 1, col = "red", size = 1)
This is pretty bad compared to BART. What's wrong here?
From reference.md: GRF isn't working well on a small dataset
If you observe poor performance on a dataset with a small number of examples, it may be worth trying out two changes:
Dataset is pretty small (n=100). Maybe turn of honesty? We cannot turn off variance estimate computation, because we want the CI's
if(fullrun){
reg.forest2 = regression_forest(x, y, num.trees = 2000,
honesty = FALSE)
saveRDS(reg.forest2, "s001.rds")
} else {reg.forest2 <- readRDS("s001.rds")}
df2 <- CalcPredictionsGRF(x, reg.forest2)
df2 <- data.frame(df2, y)
grfp <- ggplot(df2, aes(x= y, y = qm)) + geom_linerange(aes(ymin = ql, ymax = qu), col = "grey") +
geom_point() + geom_smooth() +
geom_abline(intercept = 0, slope = 1, col = "red", size = 1)
grfp
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
Ah! better now. But Grf still worse than BART. We ran with 2000 trees and turned of honesty. Perhaps dataset too small? Maybe check out the sample.fraction parameter? Sample.fraction is set by default at 0.5, so only half of data is used to grow tree. OR use tune.parameters = TRUE
gp <- plot_grid(bartp, grfp)
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
gp
This is a bigger dataset, N=4302.
head(input_2017[, c(3,24,14,15)])
## x_3 x_24 x_14 x_15
## 1 20 white 0 2
## 2 0 black 0 0
## 3 0 white 0 1
## 4 10 white 0 0
## 5 0 black 0 0
## 6 1 white 0 0
Check transformed covariates used to create simulated datasets.
# zit hidden in package
head(aciccomp2017:::transformedData_2017)
## x_1 x_3 x_10 x_14 x_15 x_21 x_24 x_43
## 2665 -1.18689448 gt_0 leq_0 leq_0 gt_0 J E -1.0897971
## 22 -0.04543705 leq_0 leq_0 leq_0 leq_0 J B 1.1223750
## 2416 0.13675482 leq_0 leq_0 leq_0 gt_0 J E 0.6136700
## 1350 -0.24062700 gt_0 leq_0 leq_0 leq_0 J E -0.2995632
## 3850 1.02054653 leq_0 leq_0 leq_0 leq_0 I B 0.6136700
## 4167 -1.18689448 gt_0 leq_0 leq_0 leq_0 K E -1.5961206
So we find that we should not take the functions in Dorie 2018 (debrief.pdf) literately. x_3 used to calculate the treatment effect is derived from x_3 in the input data. x_24 used to calculate the treatment effect is derived from x_24 in the input data. Both have become binary variables.
Turns out that this was a feature of the 2016 ACIC and IS mentioned in the debrief.pdf
We pick the iid, strong signal, low noise, low confounding first. Actually from estimated PS (W.hat) it seems that every obs has probability of treatment 50%.
parameters_2017[21,]
## errors magnitude noise confounding
## 21 iid 1 0 0
# easiest?
Grab the first replicate.
sim <- dgp_2017(21, 1)
Need also counterfactual predictions. Most efficient seems to create x.test with Z reversed. This will give use a y.test as well as y.train in the output. We expect draws for both. Plotting a histogram of the difference gives us the treatment effect with uncertainty.
From the MCMC draws for sigma we infer that we need to drop more "burn in" samples.
Prepare data for BART, including x.test with treatment reversed:
# combine x and y
y <- sim$y
x <- model.matrix(~. ,cbind(z = sim$z, input_2017))
# flip z for counterfactual predictions (needed for BART)
x.test <- model.matrix(~. ,cbind(z = 1 - sim$z, input_2017))
## run BART
#fullrun <- 0
if(fullrun){
set.seed(99)
bartFit <- bart(x, y, x.test, nskip = 350, ntree = 1000)
saveRDS(bartFit, "s2.rds")
} else { bartFit <- readRDS("s2.rds")}
plot(bartFit)
This means switching z from 0 to 1 and looking at difference in y + uncertainty in y.
#source("calcPosteriors.R")
sim <- CalcPosteriorsBART(sim, bartFit, "z")
sim <- sim %>% arrange(alpha)
bartp <- ggplot(sim, aes(x = 1:nrow(sim), qm)) +
geom_linerange(aes(ymin = ql, ymax = qu), col = "grey") +
geom_point() +
geom_smooth() + geom_point(aes(y = alpha), col = "red") + ylim(-2.5, 4.5)
bartp
## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'
## Warning: Removed 1 rows containing missing values (geom_smooth).
This looks sort of ok, but still weird. Some points it gets REALLY wrong.
sim <- sim %>% mutate(in_ci = ql < alpha & qu > alpha)
mean(sim$in_ci)
## [1] 0.4363087
Pretty bad coverage. Look into whats going on here. Here it should be 0.9
The iid plot for method 2 gives coverage 0.7 (where it should be 0.95)
sqrt(mean((sim$alpha - sim$ite)^2))
## [1] 0.1587338
For All i.i.d. (averaged over 250 replicates averaged over 8 scenarios) method 2 (BART should have RMSE of CATE of 0.35-0.4)
need large num.trees for CI.
# prep data for Grf
# combine x and y
sim <- dgp_2017(21, 1)
Y <- sim$y
X <- model.matrix(~. ,input_2017)
W = sim$z
# Train a causal forest.
#fullrun <- 0
if(fullrun){
grf.fit_alt <- causal_forest(X, Y, W, num.trees = 500)
saveRDS(grf.fit_alt, "s3.rds")
} else{grf.fit_alt <- readRDS("s3.rds")}
It appears that using 4000 trees consumes too much memory (bad_alloc)
df_sep2 <- CalcPredictionsGRF(X, grf.fit_alt)
df_sep2 <- data.frame(df_sep2, Y, W, TAU = sim$alpha)
df_sep2 <- df_sep2 %>% arrange(TAU)
grfp <- ggplot(df_sep2, aes(x = 1:nrow(df_sep2), y = qm)) +
geom_linerange(aes(ymin = ql, ymax = qu), col = "grey") + geom_point() + geom_smooth() +
geom_point(aes(y = TAU), col = "red") + ylim(-2.5, 4.5)
grfp
## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'
This works ok now.
gp <- plot_grid(bartp, grfp)
## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'
## Warning: Removed 1 rows containing missing values (geom_smooth).
## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'
gp
THis dataset is used in the Grf manual page. Size N = 2000. Probability of treatment function of X1. Treatment effect function of X1.
# Generate data.
set.seed(123)
n = 2000; p = 10
X = matrix(rnorm(n*p), n, p)
# treatment
W = rbinom(n, 1, 0.4 + 0.2 * (X[,1] > 0))
# outcome (parallel max)
Y = pmax(X[,1], 0) * W + X[,2] + pmin(X[,3], 0) + rnorm(n)
# TAU is true treatment effect
df <- data.frame(X, W, Y, TAU = pmax(X[,1], 0))
Default settings are honesty = TRUE.
# Train a causal forest.
if(fullrun){
tau.forest = causal_forest(X, Y, W, num.trees = 2000)
saveRDS(tau.forest, "s4.rds")
} else {tau.forest <- readRDS("s4.rds")}
From the GRF manual:
Given a test example, the GRF algorithm computes a prediction as follows:
For each tree, the test example is 'pushed down' to determine what leaf it falls in.
Given this information, we create a list of neighboring training examples, weighted by how many times the example fell in the same leaf as the test example.
A prediction is made using this weighted list of neighbors, using the relevant approach for the type of forest. In causal prediction, we calculate the treatment effect using the outcomes and treatment status of the neighbor examples.
Those familiar with classic random forests might note that this approach differs from the way forest prediction is usually described. The traditional view is that to predict for a test example, each tree makes a prediction on that example. To make a final prediction, the tree predictions are combined in some way, for example through averaging or through 'majority voting'. It's worth noting that for regression forests, the GRF algorithm described above is identical this 'ensemble' approach, where each tree predicts by averaging the outcomes in each leaf, and predictions are combined through a weighted average.
# Estimate treatment effects for the training data using out-of-bag prediction.
tau.hat.oob = predict(tau.forest)
res <- data.frame(df, pred = tau.hat.oob$predictions)
ggplot(res, aes(x = X1, y = pred)) + geom_point() + geom_smooth() + geom_abline(intercept = 0, slope = 1) +
geom_line(aes(y = TAU), col = "red", size = 1)
## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'
# Estimate the conditional average treatment effect on the full sample (CATE).
average_treatment_effect(tau.forest, target.sample = "all")
## estimate std.err
## 0.37316437 0.04795009
mean(res$TAU)
## [1] 0.4138061
# Estimate the conditional average treatment effect on the treated sample (CATT).
# Here, we don't expect much difference between the CATE and the CATT, since
# treatment assignment was randomized.
average_treatment_effect(tau.forest, target.sample = "treated")
## estimate std.err
## 0.47051526 0.04850751
mean(res[res$W == 1,]$TAU)
## [1] 0.5010723
# Add confidence intervals for heterogeneous treatment effects; growing more
# trees is now recommended.
if(fullrun){
tau.forest_big = causal_forest(X, Y, W, num.trees = 4000)
saveRDS(tau.forest_big, "s5.rds")
} else {tau.forest_big <- readRDS("s5.rds")}
## PM
#source("CalcPosteriors.R")
df_res <- CalcPredictionsGRF(df, tau.forest_big)
grfp <- ggplot(df_res, aes(x = X1, y = qm)) +
geom_linerange(aes(ymin = ql, ymax = qu), col = "grey") +
geom_point() +
geom_smooth() + geom_line(aes(y = TAU), col = "red", size = 1) +
ylim(-1,3.5)
grfp
## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'
x.train <- model.matrix(~. ,data.frame(W, X))
x.test <- model.matrix(~. ,data.frame(W = 1 - W, X))
y.train <- Y
if(fullrun){
bartFit <- bart(x.train, y.train, x.test, ntree = 2000, ndpost = 1000, nskip = 100)
saveRDS(bartFit, "s10.rds")
} else {bartFit <- readRDS("s10.rds")}
plot(bartFit)
#source("calcPosteriors.R")
sim <- CalcPosteriorsBART(df, bartFit, treatname = "W")
bartp <- ggplot(sim, aes(x = X1, qm)) + geom_linerange(aes(ymin = ql, ymax = qu), col = "grey") +
geom_point() + geom_smooth() +
geom_line(aes(y = TAU), col = "red", size = 1) + ylim(-1,3.5)
bartp
## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'
gp <- plot_grid(bartp, grfp)
## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'
## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'
gp
Here Grf appears more accurate. Mental note: Both W and TAU function of X1.
This dataset has a complex propensity of treatment function (Exponential of X1 and X2), as well as hetergeneous treatment effect that is exponential function of X3. It has size N=4000.
# In some examples, pre-fitting models for Y and W separately may
# be helpful (e.g., if different models use different covariates).
# In some applications, one may even want to get Y.hat and W.hat
# using a completely different method (e.g., boosting).
set.seed(123)
# Generate new data.
n = 4000; p = 20
X = matrix(rnorm(n * p), n, p)
TAU = 1 / (1 + exp(-X[, 3]))
W = rbinom(n ,1, 1 / (1 + exp(-X[, 1] - X[, 2])))
Y = pmax(X[, 2] + X[, 3], 0) + rowMeans(X[, 4:6]) / 2 + W * TAU + rnorm(n)
df_sep4 <- data.frame(X, TAU, W, Y)
Regression forest to predict W from X. This is a propensity score.
if(fullrun){
forest.W <- regression_forest(X, W, tune.parameters = c("min.node.size", "honesty.prune.leaves"),
num.trees = 2000)
saveRDS(forest.W, "s6.rds")
} else {forest.W <- readRDS("s6.rds")}
W.hat = predict(forest.W)$predictions
This predict Y from X, ignoring treatment.
if(fullrun){
forest.Y = regression_forest(X, Y, tune.parameters = c("min.node.size", "honesty.prune.leaves"),
num.trees = 2000)
saveRDS(forest.Y, "s7.rds")
} else {forest.Y <- readRDS("s7.rds")}
Y.hat = predict(forest.Y)$predictions
forest.Y.varimp = variable_importance(forest.Y)
# Note: Forests may have a hard time when trained on very few variables
# (e.g., ncol(X) = 1, 2, or 3). We recommend not being too aggressive
# in selection.
selected.vars = which(forest.Y.varimp / mean(forest.Y.varimp) > 0.2)
This selects five variables of 20. Indeed these are the variables that were used to simulate Y.
if(fullrun){
tau.forest2 = causal_forest(X[, selected.vars], Y, W,
W.hat = W.hat, Y.hat = Y.hat,
tune.parameters = c("min.node.size", "honesty.prune.leaves"),
num.trees = 4000)
saveRDS(tau.forest2, "s8.rds")
} else {tau.forest2 <- readRDS("s8.rds")}
df_sep2 <- CalcPredictionsGRF(df_sep4[,selected.vars], tau.forest2)
grfp <- ggplot(df_sep2, aes(x = X3, y = qm)) +
geom_linerange(aes(ymin = ql, ymax = qu), col = "grey") + geom_point() +
geom_smooth() +
geom_line(aes(y = TAU), col = "red", size = 1) + ylim(-0.7,2)
grfp
## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'
This looks ok.
x.train <- model.matrix(~. ,data.frame(W, X))
x.test <- model.matrix(~. ,data.frame(W = 1 - W, X))
y.train <- Y
if(fullrun){
bartFit <- bart(x.train, y.train, x.test, ntree = 4000)
saveRDS(bartFit, "s9.rds")
} else {bartFit <- readRDS("s9.rds")}
plot(bartFit)
#source("calcPosteriors.R")
sim <- CalcPosteriorsBART(df_sep4, bartFit, treatname = "W")
bartp <- ggplot(sim, aes(x = X3, qm)) + geom_linerange(aes(ymin = ql, ymax = qu), col = "grey") +
geom_point() + geom_smooth() +
geom_line(aes(y = TAU), col = "red", size = 1) + ylim(-0.7,2)
bartp
## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'
gp <- plot_grid(bartp, grfp)
## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'
## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'
gp
Very similar results. BART appears slightly more accurate, especially for low values of X3.


| DOSE: T+ 0:00 |
0.5 hits |
oral |
LSD |
(blotter / tab) |
| T+ 7:00 |
0.5 hits |
oral |
LSD |
(blotter / tab) |
| T+ 8:50 |
1 tablet |
oral |
MDMA |
(pill / tablet) |
The setting and mood was joyful. I had been away for about six months and could only stay a couple of days.  I have mild stimulation and am intigued by the most simple objects. The music is wonderful and I complement my friend on his excellent taste as we drive to the mall. I arrive at the mall with all intents a tripping teenager. Not many visuals but I felt a sense of joy which I had not grasped in some time; a piece of myself had come back.
9:00 p.m. - ate the rest of the gel
9:50 p.m. - I did not know that I could 'build' on (the effect) with acid, from what someone else had told me, but I was now experiencing the total ramification of a single hit. Laughing; tripping; insane.
10:50 p.m. - took the roll. The name, I presume, was 'Lemondrop'. The guy supposedly swore by them as the best to hit the area in a while.  I was very psyched about candyflipping. The fractals were off the wall, literally. I was at light speed.
11:45 - my face is flat on the floor. The roll hit me like a Mack Truck and I'm left smiling; saying 'oh my god'. Massages and Vicks Vapor sticks were all around me. Comforting me.  The beanbag chair seemed to hug me; love me. Everything was so beautiful. The colors; the faces; even persons I do not like I liked. It was perfect. The CD player didn't work so I took my music to my car and the sonics poured out of the speakers with the intensity of lava. I made statements; regrets; apologies; and intentions to my friends. I loved him.
We were now nomads. I was treking up a hill when these sudden panic attacks pelted me. I had been through these before (bad trip episodes) and could now handle them very well. I turned away from the fear and searing heat that attempted to take hold. I came again and I raised my hand. I sat down and stared at the stars as they were smeared across the sky. I felt my feelings being reflected back from them; I felt spectacular. The facts came to me and flowed like a playful brook about my head. I made mindless assumptions which have left me since then. The X was talking. [I talked to my friends about stuff and blah blah blah, the story goes on]
2:05 a.m. - we went back to the cave to meet with MacCleod. We puffed on him and he told of a cat and good days past. I was now uncontrollably laughing and the colors were in a rage. Red, Green, and Purple were shooting everywhere; wrapping around me. I tasted them. It was the taste of color. Take all four tastes: Salt, Sweet, Sour, and Bitter; and then add a fifth, Color. It was creamy and melting, smothering my head with this other candy.
I mistook the glowstick for a lighter and my hand for the bong. The colors were getting in the way as I was shifting from reality to candyland. I saw my hand melt into space-time? am I here. I'm back again and so is MacCleod. Cool; soothing (Note to guys: on drugs or not, put Gold Bond foot powder on your balls. No joke. I am serious. Just a small handful. There is nothing better than pineapple sherbert on your pieces). 'Wait, did I ask you?'.'Where did you come from?', and the story writes itself. My mind has been running to so many places that it is getting tired. It will have to take a break. The couch is very comfortable.
I wake up.
Beaten and exhausted.

Experience Reports are the writings and opinions of the individual authors who submit them.
Some of the activities described are dangerous and/or illegal and none are recommended by Erowid.


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Object
# File lib/itsumade/store.rb, line 47 47: def <<(hours) 48: @hours << hours 49: @hours.sort! { |x, y| x.start <=> y.start } 50: changed 51: notify_observers 52: end
# File lib/itsumade/store.rb, line 54 54: def [](index) 55: @hours[index] 56: end
# File lib/itsumade/store.rb, line 58 58: def []=(index, hours) 59: @hours[index] = hours 60: changed 61: notify_observers 62: end
# File lib/itsumade/store.rb, line 41 41: def delete_at(index) 42: @hours.delete_at index 43: changed 44: notify_observers 45: end
# File lib/itsumade/store.rb, line 24 24: def each 25: @hours.each { |hours| yield hours } 26: end
# File lib/itsumade/store.rb, line 28 28: def include?(weekday, hour, minute) 29: @hours.each do |hours| 30: if hours.include?(weekday, hour, minute) 31: return true 32: end 33: end 34: false 35: end
# File lib/itsumade/store.rb, line 37 37: def length 38: @hours.length 39: end
# File lib/itsumade/store.rb, line 18 18: def name=(new_name) 19: @name = new_name 20: changed 21: notify_observers 22: end
# File lib/itsumade/store.rb, line 64 64: def notify_observers 65: @observers.each do |observer| 66: observer.update 67: end 68: end
# File lib/itsumade/store.rb, line 70 70: def status 71: now = Time.now 72: relative = TimeBuilder.build(now.wday, now.hour, now.min) 73: @hours.each do |hours| 74: if hours.end > relative 75: if hours.start < relative 76: return "#{ANSI.green} open until #{hours.end.strftime('%l:%M %p')}#{ANSI.clear}" 77: else 78: return "#{ANSI.red }closed until #{hours.start.strftime('%l:%M %p')}#{ANSI.clear}" 79: end 80: end 81: end 82: return "" 83: end
Disabled; run with --debug to generate this.
Generated with the Darkfish Rdoc Generator 1.1.6.


Creation and deletion of logical system objects, such as user accounts or workstations, should be logged.
This functionality should be implemented by the centralized log server solution. Examine the logs to verify that creation and deletion of system objects events are logged.
This functionality should be implemented by the centralized log server solution. Configure the log server to log creation and deletion of system objects by following the vendor's instructions.


All The Football (Offline)
You're offline, but here's a hot rumour for free...
Allan to Newcastle. Done deal.


{% extends "base.html" %} {% import "bootstrap/wtf.html" as wtf %} {% import "_macros.html" as macros %} {% block title %}MorphBlog - Write a blog!{% endblock %} {% block page_content %}


Press n or j to go to the next uncovered block, b, p or k for the previous block.
| File | Statements | Branches | Functions | Lines | |||||
|---|---|---|---|---|---|---|---|---|---|
| index.js | 100% | 1/1 | 100% | 0/0 | 100% | 0/0 | 100% | 1/1 | |
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| swsHapi.js | 21.31% | 13/61 | 0% | 0/9 | 10% | 1/10 | 21.31% | 13/61 | |
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| swsUtil.js | 65.45% | 36/55 | 50% | 12/24 | 85.71% | 6/7 | 70% | 35/50 | |
| swsegress.js | 50% | 12/24 | 50% | 1/2 | 33.33% | 2/6 | 50% | 12/24 | |
| swsmetrics.js | 90.32% | 28/31 | 100% | 7/7 | 100% | 3/3 | 90.32% | 28/31 | |
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| swssysstats.js | 97.62% | 41/42 | 60% | 6/10 | 83.33% | 5/6 | 97.62% | 41/42 |


|
AssemblerEmulator - Nikola Bebic
Assembler and emulator used for systems software class
|
Represents an exception with a string message. More...
#include <MessageException.h>
Public Member Functions | |
| MessageException (std::string message) noexcept | |
| Constructs a MessageException object with the message. More... | |
| std::string | message () const noexcept |
| Gets the message of the exception. More... | |
| const char * | what () const noexcept override |
Private Attributes | |
| std::string | message_ |
Represents an exception with a string message.
Definition at line 11 of file MessageException.h.
|
explicitnoexcept |
Constructs a MessageException object with the message.
| message | Message |
Definition at line 5 of file MessageException.cpp.
|
noexcept |
Gets the message of the exception.
Definition at line 7 of file MessageException.cpp.
References message_.
Referenced by bnssassembler::FirstPass::execute(), bnssassembler::SecondPass::execute(), and bnssassembler::Parser::parse().
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overridenoexcept |
|
private |
Definition at line 25 of file MessageException.h.


Trevor Ogg
Aspiring Web Developer
Front End
HTML5
CSS3
Javascript
jQuery
React.js
Express.js
Back End / Other
MySQL
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C/C++
Education
DU Coding Bootcamp - January 2018 - July 2018
University of Colorado - BS Electrical Engineering - Fall 2016
Some of my work
Yearbook()
A website that allows users to create a profile, search for jobs, and create a page that displays their employment status and contact information.
Clicky
A simple memory game using React.js.
Nite Owl
A website that uses restaurant,bar, and Movie APIs to show users nearby dining and movie options.


When giving workshops or presenting online tutorials, it's frequently the case that the system setup can take longer than the actual learning exercises. Using Docker to provide a learning sandbox solves this problem while avoiding changing the learner's system in potentially destructive ways.


The following list of data files is in alphabetical order by region, country and city. The file names are listed after each available city and use the following format: SSCCCCCC.txt (SS-Country abbreviation CCCCCC-City abbreviation up to 6 letters).
Africa
Algeria
Burundi
Benin
Central African Republic
Congo
Egypt
Ethiopia
Gabon
Gambia
Guinea
Guinea-Bissau
Ivory Coast
Kenya
Morocco
Madagascar
Mauritania
Malawi
Mozambique
Namibia
Nigeria
Senegal
Sierra Leone
South Africa
Togo
Tunisia
Tanzania
Uganda
Zambia
Asia
Bangladesh
China
Hong Kong
India
Indonesia
Japan
Kazakhstan
Kyrgyzstan
Laos
Malaysia
Mongolia
Myanmar (Burma)
Nepal
North Korea
Pakistan
Philippines
Singapore
South Korea
Sri Lanka
Taiwan
Tajikistan
Thailand
Turkmenistan
Uzbekistan
Vietnam
Australia/South
Pacific
Australia
New Zealand
Europe
Albania
Austria
Belarus
Belgium
Bulgaria
Croatia
Cyprus
Czech Republic
Denmark
Finland
France
Germany
Georgia
Greece
Hungary
Iceland
Ireland
Italy
Latvia
Macedonia
The Netherlands
Norway
Poland
Portugal
Romania
Russia
Serbia-Montenegro
Slovakia
Spain
Sweden
Switzerland
Ukraine
United Kingdom
Yugoslavia
Middle East
Bahrain
Israel
Jordan
Kuwait
Lebanon
Oman
Qatar
Saudi Arabia
Syria
Turkey
United Arab Emirates
North
America
Canada
Mexico
South/Central America & Carribean
Argentina
Bahamas
Bermuda
Belize
Bolivia
Barbados
Brazil
Colombia
Costa Rica
Cuba
Dominican Republic
Equador
Guatemala
Guyana
Haiti
Honduras
Nicaragua
Panama
Peru
Suriname
Uruguay
Venezuela


Class DefaultAccountManager
DefaultAccountManager
- Native5\Api\ApiClient
- Native5\Services\Account\DefaultAccountManager implements Native5\Services\Account\AccountManager
Namespace: Native5\Services\Account
Author: Shamik Datta<EMAIL_ADDRESS>Version: $id$
Uses:
Uses:
Located at Native5/Services/Account/DefaultAccountManager.php
Author: Shamik Datta<EMAIL_ADDRESS>Version: $id$
Uses:
Native5\Api\ApiClient
Uses:
Native5\Services\Account\AccountManager
Located at Native5/Services/Account/DefaultAccountManager.php
Methods summary
public
|
|
public
|
|
public
|
|
public
|
Methods inherited from Native5\Api\ApiClient
Magic methods summary
Constants summary
string |
APP_FTP_SERVER_DN
|
'uploads.native5.com' |
|
integer |
APP_FTP_SERVER_PORT
|
22 |
|
string |
APP_FTP_SERVER_USER
|
'n5ftpadmin' |
|
string |
APP_FTP_SERVER_USER_KEYNAME
|
'n5sandboxftp' |
|
string |
APP_FTP_SERVER_DIRECTORY
|
'/home/n5ftpadmin/sandboxApps' |


WAP to find the roots of the quadratic equation with real roots using math library.
Hint:
$$ ax^2+bx+c=0 $$
$$ x = \frac{-b \pm \sqrt{b^2 - 4ac}}{2a} $$
WAP to read weekday number and print weekday name using switch.
Note: Week starts from monday -> 0, tuesday -> 1, etc.
WAP to design calculator with basic operations using switch.
Modify the previous program to run repeatedly along with a exit condition using goto.
WAP to find the height of the building when the user enters the distance from the building and the angle of elevation in degrees.


Scenario: First, let's plan what we want to do and what this example is about. In this example we want to show two samples of content targeting.
(1) Advertising, we have a database which contains our ads and a knowledge base that includes some logic that relates the ads to property values. For example our knowledge base would tell us "people who have old and low-end phones are more likely to be interested in new high-end smart phones" or "those who have specific devices (brand, model, type of device, etc.) would probably be interested in specific devices or accessories". DeviceAtlas can be used to get device properties, then used against the knowledge base to get the set of ads the user would probably be interested in. In this example we use a small array and a few if conditions to mimic the knowledge base.
(2) Downloading an app, we have an app written in different platforms, when a user comes to download the app we can use DeviceAtlas to detect user's operating system and automatically show him the link to the app which is created for her/his device.


Crystals Collector
Instructions:
- You will be given a random number at the start of the game.
- There are four crystals below. By clicking on a crystal you will add a specific amount of points to your total score.
- You win the game by matching your total score to a random number. You lose the game if your total score goes above the random number.
- The value of each crystal is hidden from you until you click on it.
- Each time when the game starts, the game will change the values of each crystal.


[1]
Great picture, nice apps; Wifi is in and out, expensive, too many different setting issues
[2]
Easy wifi hookup, Netflix, excellent audio and video characteristics.; There are no cons


Introduction
Org Configuration
Config Management
User Configuration
Provider Configuration
Gallery Setup
Devops Roles


Getting your bearings
| So you want to... | |
| Install node | http://nodejs.org |
| - Windows | Pre-built binary for v0.5.4 (unstable) |
| - OS X | >> brew install node |
| Package manager | http://npmjs.org |
| - Install packages | >> npm install mongoose |
| REPL | >> node |
| Run a script | >> node foo.js |
| Debug GDB-style | >> node debug foo.js Add debugger statements in code |
| Debug GUI | Install node-inspector |
| Cloud deployment | heroku, no.de, nodester |


Ex-Notts County manager Kevin Nolan odds-on favourite to take charge of Grimsby Town
Former Newcastle midfielder is keen on a return to the dugout
by Leigh CurtisFormer Notts County manager Kevin Nolan is the overwhelming favourite to take charge of Grimsby Town as the Mariners search for a successor to Michael Jolley.
Nolan is currently 5/6 favourite to land the job at Blundell Park after Jolley left the League Two club by mutual consent last week.
The former Newcastle captain has been without a club since he was sacked by Notts last August having been appointed John Sheridan’s successor by previous owner Alan Hardy in January 2017.
After Nolan took over, he led the Magpies to safety in League Two before guiding them to the play-offs which they lost to eventual winners Coventry City in the semi-finals.
He was then instructed to bring down the average of the squad following that disappointment, and the former West Ham and Newcastle captain was then sacked after losing four of his opening five games.
Many fans still feel that his departure was the catalyst for Notts sliding out of the Football League and he left Meadow Lane with a win ratio of more than 40 per cent.
But the 37-year-old is keen on a return to the dugout and has been linked with Lincoln City and Bolton Wanderers in recent months.
Should he take over at Grimsby he will inherit a side that is currently 18th in League Two.


Preparation time
less than 30 mins
Cooking time
30 mins to 1 hour
Serves
Serves 2
Strips of chicken breast are tossed with spiced rice, flaked almonds, sliced apple, cherry tomatoes and long stem broccoli to make a colourful, filling and portable lunch. Simply pack into a plastic container, pop into a cool bag with a couple of ice blocks and keep well chilled.
With a GI of 51, this meal is
high protein, low GI
and provides 354 kcal per portion.
Half-fill a medium saucepan with water and bring to the boil. Add the rice and one teaspoon of curry powder. Cook for 25 minutes, or until tender. Add the broccoli and cook for a further two minutes. Drain in a colander and rinse under cold running water until cold.
Meanwhile half-fill a small saucepan with water, add the stock cube and bring to the boil, stirring until the stock dissolves. Add the chicken, the sliced onion and bay leaf and reduce the heat. Simmer for 15 minutes or until the chicken is cooked through. Drain in a sieve, reserving the cooking liquor. Set the chicken aside to cool.
Return the pan to the heat, spray with oil and add the chopped onion. Cook over a low heat for three minutes, stirring. Add the remaining curry powder and stir for a further few seconds.
Pour 200ml/7fl oz of reserved cooking liquor into the pan and bring to a simmer. Cook the onion for 5-6 minutes, or until most of the liquid has evaporated, stirring occasionally. Remove from the heat, spread across a plate and leave to cool for five minutes before transferring to the fridge until cold.
Tear the chicken into pieces and place in a bowl. Tip the rice and broccoli on top, add the cooled spiced onions, almonds, apple and tomatoes. Scatter with the coriander and sprinkle with lemon juice. Season to taste and serve.


---
title: "Arbor Day!"
date: "2019-04-26 00:00:00 -06:00"
categories:
- whats-blooming
layout: "post"
blog-banner: "whats-blooming-now-spring.jpg"
post-date: "April 26, 2019"
post-time: "12:00 PM"
blog-image: "2019-04-26-wbn.jpg"
published: true
---
Happy Arbor Day!!
As Utah’s very own arboretum, there’s no place better than Red Butte Garden to celebrate Arbor Day! Visit the Garden for a day filled with "tree-rific" fun as you discover the wonderful world of trees. Don't forget to pick up your FREE tree seedling courtesy of Rocky Mountain Power (while supplies last). Thanks to funding in part by Salt Lake County Zoo, Arts, and Parks Program (ZAP) Arbor Day is a ZAP Free Day! Enjoy free Garden admission all day.
This beauty is one that lines both sides of the path of the Serviceberry Tunnel on the Floral Walk.
Hundreds of this spring bulb fill the space under the oaks next to the Oak Tunnel path.
A newcomer to the Garden, look for this handsome bulb in the Water Conservation Garden.
This double-flowered variety was discovered in the wild in northern Minnesota. You can discover ours in the Rose Garden.
Delicate pink flowers cover this shrub first thing in spring.
This is one of the most popular and widely planted flowering cherries, also known as Sakura in Japan.
Get ready for the Lilac bloom. The first flowers opened this week and they will all open shortly, so visit soon so you don't miss these beloved blooms.


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Helping Others Can Help You
Volunteering has a meaningful, positive impact on your community. But did you know that it can have many benefits for you, too? You may have heard that volunteering helps you get into college, but keep in mind they're not just looking for a list of organizations and dates. Colleges want to see a complete picture of you, and real examples of your commitment, dedication, and interests. Reasons to Volunteer Gain Valuable Life Experiences and Skills Whether you build houses for the homeless or mail flyers for a local politician, you'll experience the real world through hands-on work. You can use this experience to explore your major or career interests. Meet Interesting People Volunteering brings together a variety of people. Both the recipients of your volunteer efforts and your co-workers can be rich sources of insight. For example, maybe you'll learn about the legal profession from a former lawyer you visit at a convalescent center. Get Academic Credit Some schools offer academic credit for volunteer work through service-learning. This is a teaching method that integrates hands-on learning (through service to the community) into the school curriculum. It's available in high schools and colleges, as well as in earlier grades. To find out if your school offers service-learning, visit the Learn and Serve America website. Send a Signal to Colleges Colleges pay attention to your life inside and outside the classroom. Your extracurricular activities reveal a great deal about you, such as what your interests are, whether you can manage your priorities and maintain a long-term commitment, what diversity you'd bring to the student body, and how you've made a meaningful contribution to something. Keep in mind, colleges are not interested in seeing you do it all. It's more meaningful to colleges to see your dedication to one or two causes or activities than to see that you've spread yourself thin. Volunteering has many other intangible benefits. It can help you give back to society, break down barriers of misunderstanding or fear, explore personal issues, and even have fun. "Community service, which was required at my high school, was a big wow with interviewers. It's even better if you can match your service with your career interest. For example, volunteer at a hospital if you're planning on med school," says Faith, a college student. How to Get Involved There are many people, places, and organizations that need volunteers. Here are some tips for getting started:
Source: CollegeBoard.com |
|||||
| If investment, financial, mortgage or insurance planning and/or products are required, College and Retirement Solutions, LLC may refer you to one of its affiliates, including The Ziering Insurance Agency. Any discussion of the Tax implications of planning is strictly for general purposes. Neither "CRS" nor its representatives may give specific tax advice. One should seek the advice and counsel of their accountant and/or tax advisor for information regarding their particular situation. |


My First Simle One Ecommerce template
$230.00
Old price : $345.00
`
h2 tag will be appear
Lorem Ipsum is simply dummy text of the printing and typesetting industry. Lorem Ipsum has been the industry's standard dummy text ever since the 1500s, when an unknown printer took a galley of type and scrambled it to make a type specimen book. It has survived not only five centuries, but also the leap into electronic typesetting, remaining essentially unchanged. It was popularised in the 1960s with the release of Letraset sheets containing Lorem Ipsum passages, and more recently with desktop publishing software like Aldus PageMaker including versions of Lorem Ipsum- Lorem ipsum dolor sit amet Consectetur adipiscing elit
- Integer molestie lorem at massa Facilisis in pretium nisl aliquet
- Nulla volutpat aliquam velit
- Faucibus porta lacus fringilla vel Aenean sit amet erat nunc Eget porttitor lorem
- Product Code: Product 16
- Reward Points: 60
- Availability: In Stock
- Old Price: $500.00
- Ex Tax: $500.00
- Ex Tax: $500.00
- Product Code: Product 16
- Reward Points: 60
Write a Review
Lorem Ipsum is simply dummy text of the printing and typesetting industry. Lorem Ipsum has been the industry's standard dummy text ever since the 1500s, when an unknown printer took a galley of type and scrambled it to make a type specimen book. It has survived not only five centuries, but also the leap into electronic typesetting, remaining essentially unchanged. It was popularised in the 1960s with the release of Letraset sheets containing Lorem Ipsum passages, and more recently with desktop publishing software like Aldus PageMaker including versions of Lorem Ipsum


Request.BidAgentResponse
based on custom strategy name from Request.BidAgentRequest
.
Supported custom strategies are the following:
Flat Price Strategy - FlatPriceStrategy
Random Price Strategy - RandomPriceStrategy
Retargeting Strategy - RetargetingStrategy
Request.BidAgentRequest
and returns a Request.BidAgentResponse
AdcandidateOuterClass.Bidding.CustomStrategy
which is defined using Buzz API.AdcandidateOuterClass.Bidding.CustomStrategy.Params
, it simply uses the first one
when iterating through the param list.ChannelPipeline
.
Full HTTP response may have following error codes:
200 if it processed request successfully.
204 if request has no content.
400 if there is a IllegalArgumentException
.
500 if there is an internal error.HexbidHandlerFactory
for the list of end points
currently supported by this server.HttpServerCodec
- decodes/encodes ByteBuf
into/from HttpRequest
and
HttpContent
HttpObjectAggregator
- aggregates an HttpMessage
and its following HttpContent
s into a
single FullHttpRequest
or FullHttpResponse
.
GlobalHandler
- handles request processing for all end points.ProtobufDecoder
Request.BidAgentResponse
successfully.
204 if no bid is made for this request
400 if there is a parsing error Request.BidAgentRequest
or it fails to get bidding strategy.
500 if server experienced an error.AdcandidateOuterClass.Bidding.CustomStrategy.Params
, it simply uses the first one when iterating
through param list.ServerConfig
loads configurations from a specified property file and falls back to default
value when the file is missing or not found.
Server Configurable values:
Port
- This is the port that the HTTP server is listening on to accept new connections.
Max request size
- maximum length of aggregated content in HTTP request
Boss group size
- number of threads used by boss NioEventLoopGroup
which only accepts
incoming connection and registers the connection to the worker.
Backlog size
- the maximum queue length for incoming connection indications(a request to connect)
TCP nodelay
- option to disable Nagle's algorithm to achieve lower latency on every packet sent
Keep alive
- option to enable keep-alive packets for a socket connectionCopyright © 2017. All rights reserved.


|
Biomedical Image Analysis Library
The Biomedical Image Analysis Library is a poweful tool for developers, physicians, researchers, engineers, and so on.
|
Go to the source code of this file.
Macros | |
| #define | const /* note: need a more gentle solution here */ |
| #define | z_const |
| #define | MAX_MEM_LEVEL 9 |
| #define | MAX_WBITS 15 /* 32K LZ77 window */ |
| #define | OF(args) () |
| #define | Z_ARG(args) () |
| #define | ZEXTERN extern |
| #define | ZEXPORT |
| #define | ZEXPORTVA |
| #define | FAR |
| #define | SEEK_SET 0 /* Seek from beginning of file. */ |
| #define | SEEK_CUR 1 /* Seek from current position. */ |
| #define | SEEK_END 2 /* Set file pointer to EOF plus "offset" */ |
| #define | z_off_t long |
| #define | z_off64_t z_off_t |
Typedefs | |
| typedef unsigned char | Byte |
| typedef unsigned int | uInt |
| typedef unsigned long | uLong |
| typedef Byte | Bytef |
| typedef char | charf |
| typedef int | intf |
| typedef uInt | uIntf |
| typedef uLong | uLongf |
| typedef Byte * | voidpc |
| typedef Byte * | voidpf |
| typedef Byte * | voidp |
| typedef unsigned long | z_crc_t |
| #define SEEK_END 2 /* Set file pointer to EOF plus "offset" */ |


Road traffic is a huge problem in LA and cities all across the world. It decreases productivity and steals otherwise fun or useful time. We aim to solve this problem through the use of blockchain. Crowdchain leverages the power of blockchain to create distributed, enforceable contracts and the ability of large groups of people to make change happen. Click the pink button to the right and join our fight to destroy traffic once and for all.
We espouse an all or nothing methodology. After you sign up, there is no committment. However, when participation hits 80% of LA residents, our smart contract kicks in. You will receive an award for participating in a huge shift to public transportation along with the vast majority of our city's population. The roads will be for the first time, clear.
Scan your phone at QR codes available on buses and subways.  In return, we will make the cryptocurrency flow. Thanks again for participating, and we hope you join our movement.


loan calculator
loan amount
interest percent (%)
no. months to pay
calculate
clear
interest amount
total loan amount
amount to pay per month


- bartMachine - package bartMachine
-
The code for the implementation of the BART algorithm as described in detail in:
- bartMachine_a_base - Class in bartMachine
-
The base class for any BART implementation.
- bartMachine_a_base() - Constructor for class bartMachine.bartMachine_a_base
-
- bartMachine_b_hyperparams - Class in bartMachine
-
This portion of the code controls hyperparameters for the BART
algorithm as well as properties and transformations of the response variable.
- bartMachine_b_hyperparams() - Constructor for class bartMachine.bartMachine_b_hyperparams
-
- bartMachine_c_debug - Class in bartMachine
-
This portion of the code used to have many debug functions.
- bartMachine_c_debug() - Constructor for class bartMachine.bartMachine_c_debug
-
- bartMachine_d_init - Class in bartMachine
-
This portion of the code initializes the Gibbs sampler
- bartMachine_d_init() - Constructor for class bartMachine.bartMachine_d_init
-
- bartMachine_e_gibbs_base - Class in bartMachine
-
This portion of the code performs everything in
the Gibbs sampling except for the posterior sampling itself
- bartMachine_e_gibbs_base() - Constructor for class bartMachine.bartMachine_e_gibbs_base
-
- bartMachine_f_gibbs_internal - Class in bartMachine
-
This portion of the code that performs the posterior sampling
in the Gibbs sampler except for the Metropolis-Hastings tree search step
- bartMachine_f_gibbs_internal() - Constructor for class bartMachine.bartMachine_f_gibbs_internal
-
- bartMachine_g_mh - Class in bartMachine
-
This portion of the code performs the Metropolis-Hastings tree search step
- bartMachine_g_mh() - Constructor for class bartMachine.bartMachine_g_mh
-
- bartMachine_g_mh.Steps - Enum in bartMachine
-
the types of steps in the Metropolis-Hastings tree search
- bartMachine_h_eval - Class in bartMachine
-
This portion of the code performs the evaluation / prediction on the BART model
- bartMachine_h_eval() - Constructor for class bartMachine.bartMachine_h_eval
-
- bartMachine_i_prior_cov_spec - Class in bartMachine
-
This portion of the code implements the informed prior information on covariates feature.
- bartMachine_i_prior_cov_spec() - Constructor for class bartMachine.bartMachine_i_prior_cov_spec
-
- bartMachineClassification - Class in bartMachine
-
The class that is instantiated to build a binary classification BART model
- bartMachineClassification() - Constructor for class bartMachine.bartMachineClassification
-
- bartMachineClassificationMultThread - Class in bartMachine
-
This class handles the parallelization of many Gibbs chains over many CPU cores
to create one BART regression model.
- bartMachineClassificationMultThread() - Constructor for class bartMachine.bartMachineClassificationMultThread
-
- bartMachineRegression - Class in bartMachine
-
The class that is instantiated to build a Regression BART model
- bartMachineRegression() - Constructor for class bartMachine.bartMachineRegression
-
Constructs the BART classifier for regression.
- bartMachineRegressionMultThread - Class in bartMachine
-
This class handles the parallelization of many Gibbs chains over many CPU cores
to create one BART regression model.
- bartMachineRegressionMultThread() - Constructor for class bartMachine.bartMachineRegressionMultThread
-
the default constructor sets the number of total iterations each Gibbs chain is charged with sampling
- bartMachineTreeNode - Class in bartMachine
-
The class that stores all the information in one node of the BART trees
- bartMachineTreeNode() - Constructor for class bartMachine.bartMachineTreeNode
-
- bartMachineTreeNode(bartMachineTreeNode, bartMachine_b_hyperparams) - Constructor for class bartMachine.bartMachineTreeNode
-
Creates a new node
- bartMachineTreeNode(bartMachineTreeNode) - Constructor for class bartMachine.bartMachineTreeNode
-
Creates a new node
- bartMachineTreeNode(bartMachine_b_hyperparams) - Constructor for class bartMachine.bartMachineTreeNode
-
Creates a new node
- binary_add_arrays(int[], int[]) - Static method in class bartMachine.Tools
-
Adds one array to another after first converting each addend to binary
(1 if the value > 0, 0 otherwise)
- Build() - Method in class bartMachine.bartMachine_e_gibbs_base
-
Builds a BART model by unleashing the Gibbs sampler
- Build() - Method in class bartMachine.bartMachineRegressionMultThread
-
This function actually initiates the Gibbs sampling to build all the BART models
- Build() - Method in class bartMachine.Classifier
-
build the machine learning classifier (implemented by a daughter class), you must
set the data
first


About
Entrepreneur, UI/UX Designer & Fitness Enthusiast.
- Birthday: 18 January 1998
- Website: www.iamdharmveer.github.io
- Phone: +91 99837 91397
- City: Pune, Maharashtra, India
- Age: 24
- Degree: Bachelor of Technology
- Email<EMAIL_ADDRESS>- Freelance: Available
Hardworking FinTech Founder passionate about building companies from within by attracting intelligent and productive professionals. Quality-driven leader focused ongrowth and experienced in customer acquisition and executive management.
Proactive UI/UX designer with 3+ years of experience in delivering enjoyable web and mobile products for the FinTech industry. Currently working with IFAS Edutech as a UX Designer. Skilled with Adobe XD, and Figma
Achievement
Satisfied Clients
Year of Service
Hours Of Support
Client Retention
Techinal Skills
Scripting Language & Software for Designing
Education & Work Experience
Seasoned professional with 3+ years of experience. Expert in User Interface Designing, User Experience Design, UX Research, Usability Analysis, Prototyping.
Sumary
Dharmveer Sharma
Innovative and deadline-driven UX Designer with 3+ years of experience designing and developing user-centered Web/App/digital/print marketing material from initial concept to final, polished deliverable.
- Cross Road Mall- 514, Vidhyadhar Nagar,Jaipur, India
- +91 99837 91397
-<EMAIL_ADDRESS>Education
Bachelor of Technology in Computer Science & Engineering
Aug,2014 - May,2018
Shri Mata Vaishno Devi University, Jammu
Senior Secondary School in Science-Mathematics(RBSE XI-XII)
July,2011 - May,2013
CCA Sr. Sec. School,Ringus(Sikar), Rajasthan
Secondary School(RBSE IX-X)
July,2009 - May,2011
CTP Sr. Sec. School,Ringus(Sikar), Rajasthan
Jr. High School(RBSE VI-VIII)
July,2006 - May,2009
Government Seth Surajmal Satbhai Upper Primary School,Ringus(Sikar), Rajasthan
Primary School(RBSE I-V)
July,2001 - May,2006
Government Seth Surajmal Satbhai Upper Primary School,Ringus(Sikar), Rajasthan
Professional Experience
UX Designer
Jan,2022 - Onwards
IFAS Pune, IN
- Responsible for UX research, design research and developing User Interface & prototypes.
- Implements company’s long and short term goals.
Managing Director
Sep,2019 - Dec,2021
Proftech Advisors LLP Jaipur, IN
- Co-founded startup which focuses on the Business Consultancy of new Startups registration and maintenance of finical department that significantly increased efficiency, safety, quality.
- Responsible for the day-to-day running of the business with a particular emphasis on sales and business development.
- Making sure that the business continues to grow by way of developing new clients whilst maintaining its existing customer base.
Sr. UX Researcher
May,2019 - Nov,2021
Bradvice Solutions PVT Ltd. Jaipur, IN
- Co-Founded startup which focuses on the development of new fintech software that significantly increased efficiency, safety, quality, and transparency of the online payment systems.
- Participated in all aspects of business development from market research and finance to operations and marketing, ensuring that the company's vision was followed.
- Formulated the company's vision, goals, and objectives; hired and trained new employees.
- Grew company from idea to recognize business within 2 years.
UI UX Designer
Sep,2018 - Oct,2021
Sprouting Wings Jaipur, IN
- Founded startup which focuses on the development of App & Web software that significantly increased efficiency, safety, quality, and transparency of the online systems.
- Participated in all aspects of business development from market research and finance to operations and marketing, ensuring that the company's vision was followed.
- Formulated the company's vision, goals, and objectives; hired and trained new employees.
- Built and maintained professional relationships with potential investors and partners; attended various startup and business events all around the world.
- Grew company from idea to recognize business within 3 years.
- Shortlisted by SiliconIndia Startup City Magazine as 10 Best Startups in Advertising in 2019 and 2020.
Head Design & Research,Partner
Jan,2019 - Feb,2020
kalpanaa Delhi, IN
- Developed Women Entrepreneur Empowerment program jointly with CodeBinary Initiatives and Sprouting Wings.
- Responsible for market research, design research and developing User Interface & prototypes.
- Implements company’s long and short term goals.
Chief Operating Officer
Sep,2018 - Jan,2020
CodeBinary Initiatives Jaipur, IN
- Responsible for market research, brand management, and acquiring and maintaining new customers.
- Facilitated full SDLC ensuring 100% on-time delivery of projects.
- Interfaced with clients, maintaining 98% satisfaction scores.
- Formulated the company's vision, goals, and objectives; hired and trained 100+ students.
- Translated requirements into designs. Collaborated closely with designers to achieve high usability scores.
Data Analysis Intern
May,2017 - July,2017
AVK Sharma & Comapny Jaipur, IN
- This internship was done under CA.Vinay Kumar Sharma, Chartered Accountant, AVK Sharma & Company, Jaipur, India.
- Identified, analyzed and interpreted trends or patterns in complex data sets by finding correlations and visualizing with charts.
- Increased annual revenue by 20% by recommending improvements in data entry efficiency.
Cyber Security Intern
Jun,2016 - July,2016
Gurugram Police Gurugram, IN
- Social Media Crime and Investigation (Jun’16 - Jul’16) The Internship was done under Rakshit Tandon (Cyber Expert,Govt. of India)
- We investigated more than 100+ cases on cyber crimes focused on social networking sites.
- Design and developed SMC to reduce 50 % time taken in crime reporting and helped 10K+ people to aware about social crimes.
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Librarians stalk the halls in search of you! Sneak through the library and don’t get caught. Traps are scattered throughout the maze, where stepping on them will make noise and attract the librarian’s attention.
Find Out MoreWe developed this game for a class project on Oct 2017 with Unity 5.6 using C# in 3D. Wanting to improve upon our initial prototype, we participated in the IGM Imagine Cup, a 24 hour hackathon, and won 1st Runner Up.
Group Lead/2D Artist
AI Pathfinding
Trap Engineer/Audio Master
Lead Programmer
3D Artist/Menu Designer/The Best


Before Change
def get_input(self, train=False):
if hasattr(self, "previous"):
return self.previous.get_output(train=train)
elif hasattr(self, "input"):
return self.input
else:
raise Exception("Layer is not connected" +
After Change
// to avoid redundant computations,
// layer outputs are cached when possible.
if hasattr(self, "layer_cache"):
previous_layer_id = id(self.previous)
if previous_layer_id in self.layer_cache:
return self.layer_cache[previous_layer_id]
previous_output = self.previous.get_output(train=train)
if hasattr(self, "layer_cache"):
previous_layer_id = id(self.previous)
self.layer_cache[previous_layer_id] = previous_output


While a sophisticated animation API is planned for Bokeh, it is already possible to create animated plots just by updating a glyph’s data source periodically. This requires running bokeh-server so that plots are notified of the updates to the data. Below is a video capture of an animated plot in the ipython notebook (which may be found in examples/plotting/notebook).
Note that all the tools, zoom, pan, resize function normally and the plot continues to animate while the tools are used. Currently in order to animate, you must grab the glyph renderer off a plot, update its data source, then store the data source on the session. The code to animate the above plot is shown here:
renderer = p.select(dict(type=GlyphRenderer))
ds = renderer[0].data_source
while True:
rmin = ds.data["inner_radius"]
rmin = roll(rmin, 1)
ds.data["inner_radius"] = rmin
rmax = ds.data["outer_radius"]
rmax = roll(rmax, -1)
ds.data["outer_radius"] = rmax
cursession().store_objects(ds)
time.sleep(.10)
It is possible to use Bokeh to create dashboard-like applets. These applets can be served directly from the Bokeh Server, or they may be embedded in you own web applications. In addition to the standard Bokeh interactive plot tools, Bokeh applets can contain widgets such as drop downs, date selectors, and sliders. The values are from these widgets are made available to the applet code, which can add, remove, or update plots or otherwise inform the application view based on user input.
One example is the stocks correlation applet pictured below:
This applet allows a user to pick between pairs of stocks to display correlation plots for. The subplots below show histograms for each time series as well as the time series themselves. These plots have linked panning and selections; making a selection on the correlation plot will highlight the selected points on the time series, as well as update the histograms to only show binnings got the selected points.
The code and instructions for running this example can be found at examples/app/stock_applet.
Another example is the sliders applet that presents several sliders to modify parameters of a sin function:
The code and instructions for running this example can be found at examples/app/sliders_applet.
Bokeh is designed to allow you to flexibly compose many different glyph types into one plot. As an example, we will step through how to reproduce Will Burtin’s antibiotics chart, shown below, using Bokeh.
This first block defines the data and computes some derived quantities used in the plot using NumPy and Pandas:
from collections import OrderedDict
from math import log, sqrt
import numpy as np
import pandas as pd
from six.moves import cStringIO as StringIO
from bokeh.plotting import *
antibiotics = """
bacteria, penicillin, streptomycin, neomycin, gram
Mycobacterium tuberculosis, 800, 5, 2, negative
Salmonella schottmuelleri, 10, 0.8, 0.09, negative
Proteus vulgaris, 3, 0.1, 0.1, negative
Klebsiella pneumoniae, 850, 1.2, 1, negative
Brucella abortus, 1, 2, 0.02, negative
Pseudomonas aeruginosa, 850, 2, 0.4, negative
Escherichia coli, 100, 0.4, 0.1, negative
Salmonella (Eberthella) typhosa, 1, 0.4, 0.008, negative
Aerobacter aerogenes, 870, 1, 1.6, negative
Brucella antracis, 0.001, 0.01, 0.007, positive
Streptococcus fecalis, 1, 1, 0.1, positive
Staphylococcus aureus, 0.03, 0.03, 0.001, positive
Staphylococcus albus, 0.007, 0.1, 0.001, positive
Streptococcus hemolyticus, 0.001, 14, 10, positive
Streptococcus viridans, 0.005, 10, 40, positive
Diplococcus pneumoniae, 0.005, 11, 10, positive
"""
drug_color = OrderedDict([
("Penicillin", "#0d3362"),
("Streptomycin", "#c64737"),
("Neomycin", "black" ),
])
gram_color = {
"positive" : "#aeaeb8",
"negative" : "#e69584",
}
df = pd.read_csv(StringIO(antibiotics), skiprows=1, skipinitialspace=True)
width = 800
height = 800
inner_radius = 90
outer_radius = 300 - 10
minr = sqrt(log(.001 * 1E4))
maxr = sqrt(log(1000 * 1E4))
a = (outer_radius - inner_radius) / (minr - maxr)
b = inner_radius - a * maxr
def rad(mic):
return a * np.sqrt(np.log(mic * 1E4)) + b
big_angle = 2.0 * np.pi / (len(df) + 1)
small_angle = big_angle / 7
x = np.zeros(len(df))
y = np.zeros(len(df))
Configure Bokeh to generate static HTML output using output_file:
output_file("burtin.html", title="burtin.py example")
We are going to be combining several glyph renderers on to one plot, so let define a figure:
p = figure(plot_width=width, plot_height=height, title="",
x_axis_type=None, y_axis_type=None,
x_range=[-420, 420], y_range=[-420, 420],
min_border=0, outline_line_color=None,
background_fill="#f0e1d2", border_fill="#f0e1d2")
Next we add the first glyph:
p.line(x+1, y+1, alpha=0)
then the red and blue regions using annular_wedge:
# annular wedges
angles = np.pi/2 - big_angle/2 - df.index.to_series()*big_angle
colors = [gram_color[gram] for gram in df.gram]
p.annular_wedge(
x, y, inner_radius, outer_radius, -big_angle+angles, angles, color=colors,
)
finally some others small wedges representing the antibiotic effectiveness and the radial axes:
# small wedges
p.annular_wedge(x, y, inner_radius, rad(df.penicillin),
-big_angle+angles+5*small_angle, -big_angle+angles+6*small_angle,
color=drug_color['Penicillin'])
p.annular_wedge(x, y, inner_radius, rad(df.streptomycin),
-big_angle+angles+3*small_angle, -big_angle+angles+4*small_angle,
color=drug_color['Streptomycin'])
p.annular_wedge(x, y, inner_radius, rad(df.neomycin),
-big_angle+angles+1*small_angle, -big_angle+angles+2*small_angle,
color=drug_color['Neomycin'])
# radial axes
p.annular_wedge(x, y, inner_radius-10, outer_radius+10,
-big_angle+angles, -big_angle+angles, color="black")
Then we add some text labels for the bacteria using text:
# bacteria labels
xr = radii[0]*np.cos(np.array(-big_angle/2 + angles))
yr = radii[0]*np.sin(np.array(-big_angle/2 + angles))
label_angle=np.array(-big_angle/2+angles)
label_angle[label_angle < -np.pi/2] += np.pi # easier to read labels on the left side
p.text(xr, yr, df.bacteria, angle=label_angle,
text_font_size="9pt", text_align="center", text_baseline="middle")
some legends using circle, text, and rect:
# OK, these hand drawn legends are pretty clunky, will be improved in future release
p.circle([-40, -40], [-370, -390], color=list(gram_color.values()), radius=5)
p.text([-30, -30], [-370, -390], text=["Gram-" + gr for gr in gram_color.keys()],
text_font_size="7pt", text_align="left", text_baseline="middle")
p.rect([-40, -40, -40], [18, 0, -18], width=30, height=13,
color=list(drug_color.values()))
p.text([-15, -15, -15], [18, 0, -18], text=list(drug_color.keys()),
text_font_size="9pt", text_align="left", text_baseline="middle")
and we get rid of the grid lines:
p.xgrid.grid_line_color = None
p.ygrid.grid_line_color = None
Finally, show the plot:
show(p)


Start Getting started using metrics Maven dependency and tile Gradle plugin Built-in JVM metrics for GC, Threads, Logging Using @Timed
About Provides low cost simple metrics for JVM applications. @Timed for fast simple timing metrics Counter metrics Gauge metrics Built-in JVM metrics for GC, Threads, Logging


| #define SDMMC004_BLOCK_ADDRESSING (8) |
Card supports Block addressing. It means Address will be taken in terms of Sector Numbers
Definition at line 180 of file SDMMC004.h.
| #define SDMMC004_MAX_PASSWD_LENGTH (16) |
Maximum Password Length for locking
Definition at line 190 of file SDMMC004.h.
| #define SDMMC004_MMC (16) |
MMC card
Definition at line 184 of file SDMMC004.h.
| #define SDMMC004_SD_HIGH_CAPACITY (4) |
High Capacity Card .
Definition at line 175 of file SDMMC004.h.
| #define SDMMC004_SD_STANDARD_CAPACITY_V1x (2) |
Standard Capacity Card of Version1x.
Definition at line 171 of file SDMMC004.h.
| #define SDMMC004_SD_STANDARD_CAPACITY_V2 (1) |
Standard Capacity Card of Version2
Definition at line 167 of file SDMMC004.h.
| #define SDMMC004_STATE_CARD_INITIALIZED (1) |
Card is initialized
Definition at line 137 of file SDMMC004.h.
| #define SDMMC004_STATE_CARD_LOCKED (64) |
Card is Locked.
Definition at line 161 of file SDMMC004.h.
| #define SDMMC004_STATE_CARD_WRITE_PROTECT (16) |
Card is Write Protected. Cannot write on the card.
Definition at line 153 of file SDMMC004.h.
| #define SDMMC004_STATE_CMD_ACTIVE (4) |
Command Line is in Use.
Definition at line 145 of file SDMMC004.h.
| #define SDMMC004_STATE_DATA_ACTIVE (8) |
Data Line is in Use.
Definition at line 149 of file SDMMC004.h.
| #define SDMMC004_STATE_HC_INITIALIZED (32) |
Host Controller is Initialized
Definition at line 157 of file SDMMC004.h.
| #define SDMMC004_STATE_NO_CARD (2) |
No Card Present
Definition at line 141 of file SDMMC004.h.
Card Lock/Unlock Operation Enumeration.
Definition at line 216 of file SDMMC004.h.
Data Transfer Direction Enumeration.
Definition at line 201 of file SDMMC004.h.
This enumerates SDMMC LLD Errors.
Definition at line 231 of file SDMMC004.h.
This enumerates some of the SDMMC Interrupts.
| SDMMC004_CARD_REMOVAL |
Card Removal Interrupt |
| SDMMC004_CARD_INSERTION |
Card Insertion Interrupt |
Definition at line 794 of file SDMMC004.h.


Newport crash victim 'denied justice on technicality'
by Caleb SpencerA man injured so badly in a suspected hit-and-run that he was given a 2% chance to live, said he has been denied justice "on a technicality".
John Conibeer spent six weeks in a coma and needed multiple life-saving operations after he was hit by a van on the A48 near Chepstow last year.
The man charged with the hit and run walked free because of a clerical error, he said.
The Crown Prosecution Service said it would be "inappropriate" to comment.
Gwent Police said it had "followed the standard procedures" while investigating and had sought advice early from the CPS.
The hit-and-run
In the early hours of 17 February 2018, Mr Conibeer, then 32, was one of three passengers in a car being driven by his partner Emma Ross on the way home from a meal together in Chepstow.
Ms Ross lost control of the car and crashed into a wall. There were no injuries and Mr Conibeer got out of the car to check for damage.
As he was attempting to push the car back on to the road, he was hit from behind by a Ford Transit van.
Mr Conibeer became trapped between the van's wheel and the wheel arch and was dragged around the wheel twice before being thrown to the side of the road.
He was left with 24 broken bones, including a fractured pelvis, hip, shoulder and four vertebrae.
He also suffered a punctured lung, a lacerated kidney and liver and injuries to his bowel, urethra and bladder.
Mr Conibeer was taken to the Royal Gwent Hospital, but needed transferring to specialists at the University Hospital of Wales (UHW) in Cardiff.
He had to be resuscitated twice during his first operation, which lasted 24 hours as surgeons worked in shifts, and Ms Ross estimates her partner was operated on for about 80 hours.
The investigation
After a lengthy appeal to catch the suspect, police made an arrest in March, but no charges were brought until September.
The BBC has learned the suspect was facing three charges - causing serious injury through careless driving, failing to stop at the scene of an accident and failing to report an accident.
Mr Conibeer said police had told him the suspect had admitted he was driving the van.
The defendant was due to appear at Newport Magistrates' Court on 14 November but the case, which was being prosecuted by the police, was withdrawn after the district judge ruled the case had "timed out".
With certain motoring offences, prosecutors have six months to charge someone, at which point the offence becomes "timed out" unless the prosecutor issues a certificate of extension.
Ms Ross said the certificate was not present in the case file, which allowed the defence to call on the judge to throw it out.
One of the charges - causing serious injury through careless driving - was also missing from the charges on the court sheet, Ms Ross said.
The couple claim police and the CPS have "blamed each other" and Ms Ross has complained to the CPS and is considering complaining to the Independent Office for Police Conduct.
"I have lost out on justice because of a technicality," Mr Conibeer said.
The recovery
The physical and mental recovery has been an arduous one for Mr Conibeer.
"It's set him back years.
Mr Conibeer said: "It makes me really angry when people say 'you're so lucky to be alive'. Part of me wishes I had died that night because I don't feel lucky.
"I have battled with suicidal thoughts - the pain was in every part of my body."
Despite the struggle to overcome the lasting consequences of that night, Mr Conibeer said he was genuinely grateful, particularly to his family, friends and the medical staff who saved his life.
Ms Ross was asked what the couple would like to see happen.
"I would like to see him admit what he did in front of a judge, and then face us and hear how it has affected him," she said.
"If you look at him, he looks brilliant now. But his life will never be the same."
A spokesman for the CPS said: "We have received a complaint in relation to this case. It would be inappropriate for us to comment further at this stage."
Gwent Police said: "We note the legal arguments raised by the defence. It would be inappropriate to comment further."


---
layout: page
title: 作品
permalink: /work/
desc: ワークス・TBC
---
Personal Projects
{% for post in site.tags.works_personal %}
{% include themes/{{ site.amsf_theme }}/includes/page-item.html %}
{% endfor %}
Community Work
{% for post in site.tags.works_community %}
{% include themes/{{ site.amsf_theme }}/includes/page-item.html %}
{% endfor %}
Tools
{% for post in site.tags.works_tools %}
{% include themes/{{ site.amsf_theme }}/includes/page-item.html %}
{% endfor %}
Graphic Design
{% for post in site.tags.works_graphic %}
{% include themes/{{ site.amsf_theme }}/includes/page-item.html %}
{% endfor %}
Photographs
{% for post in site.tags.works_photo %}
{% include themes/{{ site.amsf_theme }}/includes/page-item.html %}
{% endfor %}


Insert Record
Filter By Name:
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Book Category
Choose category
Category is required.
Book Sub-Category
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Subcategory is required.
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Book title is required.
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Repetition number must be between 1 and 5.
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{{formStatusText}}


Observing attribute changes
[{
"items": [
{
"id": 1
"name": "Fahrrad",
"price": 500,
"currency": "Euro"
},
{
"id": 2
"name": "Laptop"
},
]
}, {...}]
This complexity is difficult to handle as we're mixing two concepts that are very hard for the human mind to reason about: mutation and asynchronicity.
|
Shared?
|
Mutable? | ||
|---|---|---|---|
| no | yes | ||
| no | OK | OK | |
| yes | OK | CHAOS |
getSafes(): Observable<Safe[]> {
getSafe(safeId: string): Observable<Safe> {
getItems(safeId: string): Observable<SafeItem[]> {
getItem(safeId: string, itemId: string): Observable<SafeItem> {
Services manage state using RxJS
Services are for behavior, not for state.
RxJS HELL
Architecture Pattern to solve this: flux
console.log(store.getState())
Enforcing every change is described as an action lets us have a clear understanding of what’s going on in the app.
The only way to change the state is to dispatch an action, an object describing what happened.
export const ItemActionTypes = {RemoveItem: '[Items] Remove item'}
export class RemoveItem implements Action {
type = ItemActionTypes.RemoveItem;
constructor(public payload: {id: number}) {}
}
store.dispatch(new ItemActionTypes.RemoveItem({id: 1}));
To specify how the state tree is transformed by actions, you write pure reducer functions.
export function reducer(state = initialState, action: itemActions.ItemActions): State {
switch (action.type) {
case ItemActionTypes.RemoveItem:
return handleRemoveItem(state, action);
default:
return state;
}
}
function handleRemoveItem(state: State, action: itemActions.RemoveItem): State {
return {
...state,
items: state.items.filter((item) => item.id === action.payload.id)
}
}
More details on flux, redux and application architecture on our additional redux training.