Contains an abstract base class that supports chemically aware data splits.
deepchem.splits.splitters.
ButinaSplitter
(verbose=False)[source]¶Bases: deepchem.splits.splitters.Splitter
Class for doing data splits based on the butina clustering of a bulk tanimoto fingerprint matrix.
k_fold_split
(dataset, k, directories=None, **kwargs)¶| Parameters: | |
|---|---|
| Returns: | |
| Return type: | list of length k tuples of (train, cv) |
split
(dataset, frac_train=None, frac_valid=None, frac_test=None, log_every_n=1000, cutoff=0.18)[source]¶Splits internal compounds into train and validation based on the butina clustering algorithm. This splitting algorithm has an O(N^2) run time, where N is the number of elements in the dataset. The dataset is expected to be a classification dataset.
This algorithm is designed to generate validation data that are novel chemotypes.
Note that this function entirely disregards the ratios for frac_train, frac_valid, and frac_test. Furthermore, it does not generate a test set, only a train and valid set.
Setting a small cutoff value will generate smaller, finer clusters of high similarity, whereas setting a large cutoff value will generate larger, coarser clusters of low similarity.
train_test_split
(dataset, train_dir=None, test_dir=None, seed=None, frac_train=0.8, verbose=True)¶Splits self into train/test sets. Returns Dataset objects.
train_valid_test_split
(dataset, train_dir=None, valid_dir=None, test_dir=None, frac_train=0.8, frac_valid=0.1, frac_test=0.1, seed=None, log_every_n=1000, verbose=True)¶Splits self into train/validation/test sets.
Returns Dataset objects.
deepchem.splits.splitters.
FingerprintSplitter
(verbose=False)[source]¶Bases: deepchem.splits.splitters.Splitter
Class for doing data splits based on the fingerprints of small molecules O(N**2) algorithm
k_fold_split
(dataset, k, directories=None, **kwargs)¶| Parameters: | |
|---|---|
| Returns: | |
| Return type: | list of length k tuples of (train, cv) |
split
(dataset, frac_train=0.8, frac_valid=0.1, frac_test=0.1, log_every_n=1000)[source]¶Splits internal compounds into train/validation/test by fingerprint.
train_test_split
(dataset, train_dir=None, test_dir=None, seed=None, frac_train=0.8, verbose=True)¶Splits self into train/test sets. Returns Dataset objects.
train_valid_test_split
(dataset, train_dir=None, valid_dir=None, test_dir=None, frac_train=0.8, frac_valid=0.1, frac_test=0.1, seed=None, log_every_n=1000, verbose=True)¶Splits self into train/validation/test sets.
Returns Dataset objects.
deepchem.splits.splitters.
IndexSplitter
(verbose=False)[source]¶Bases: deepchem.splits.splitters.Splitter
Class for simple order based splits.
k_fold_split
(dataset, k, directories=None, **kwargs)¶| Parameters: | |
|---|---|
| Returns: | |
| Return type: | list of length k tuples of (train, cv) |
split
(dataset, seed=None, frac_train=0.8, frac_valid=0.1, frac_test=0.1, log_every_n=None)[source]¶Splits internal compounds into train/validation/test in provided order.
train_test_split
(dataset, train_dir=None, test_dir=None, seed=None, frac_train=0.8, verbose=True)¶Splits self into train/test sets. Returns Dataset objects.
train_valid_test_split
(dataset, train_dir=None, valid_dir=None, test_dir=None, frac_train=0.8, frac_valid=0.1, frac_test=0.1, seed=None, log_every_n=1000, verbose=True)¶Splits self into train/validation/test sets.
Returns Dataset objects.
deepchem.splits.splitters.
IndiceSplitter
(verbose=False, valid_indices=None, test_indices=None)[source]¶Bases: deepchem.splits.splitters.Splitter
Class for splits based on input order.
k_fold_split
(dataset, k, directories=None, **kwargs)¶| Parameters: | |
|---|---|
| Returns: | |
| Return type: | list of length k tuples of (train, cv) |
split
(dataset, seed=None, frac_train=0.8, frac_valid=0.1, frac_test=0.1, log_every_n=None)[source]¶Splits internal compounds into train/validation/test in designated order.
train_test_split
(dataset, train_dir=None, test_dir=None, seed=None, frac_train=0.8, verbose=True)¶Splits self into train/test sets. Returns Dataset objects.
train_valid_test_split
(dataset, train_dir=None, valid_dir=None, test_dir=None, frac_train=0.8, frac_valid=0.1, frac_test=0.1, seed=None, log_every_n=1000, verbose=True)¶Splits self into train/validation/test sets.
Returns Dataset objects.
deepchem.splits.splitters.
MolecularWeightSplitter
(verbose=False)[source]¶Bases: deepchem.splits.splitters.Splitter
Class for doing data splits by molecular weight.
k_fold_split
(dataset, k, directories=None, **kwargs)¶| Parameters: | |
|---|---|
| Returns: | |
| Return type: | list of length k tuples of (train, cv) |
split
(dataset, seed=None, frac_train=0.8, frac_valid=0.1, frac_test=0.1, log_every_n=None)[source]¶Splits internal compounds into train/validation/test using the MW calculated by SMILES string.
train_test_split
(dataset, train_dir=None, test_dir=None, seed=None, frac_train=0.8, verbose=True)¶Splits self into train/test sets. Returns Dataset objects.
train_valid_test_split
(dataset, train_dir=None, valid_dir=None, test_dir=None, frac_train=0.8, frac_valid=0.1, frac_test=0.1, seed=None, log_every_n=1000, verbose=True)¶Splits self into train/validation/test sets.
Returns Dataset objects.
deepchem.splits.splitters.
RandomGroupSplitter
(groups, *args, **kwargs)[source]¶Bases: deepchem.splits.splitters.Splitter
k_fold_split
(dataset, k, directories=None, **kwargs)¶| Parameters: | |
|---|---|
| Returns: | |
| Return type: | list of length k tuples of (train, cv) |
train_test_split
(dataset, train_dir=None, test_dir=None, seed=None, frac_train=0.8, verbose=True)¶Splits self into train/test sets. Returns Dataset objects.
train_valid_test_split
(dataset, train_dir=None, valid_dir=None, test_dir=None, frac_train=0.8, frac_valid=0.1, frac_test=0.1, seed=None, log_every_n=1000, verbose=True)¶Splits self into train/validation/test sets.
Returns Dataset objects.
deepchem.splits.splitters.
RandomSplitter
(verbose=False)[source]¶Bases: deepchem.splits.splitters.Splitter
Class for doing random data splits.
k_fold_split
(dataset, k, directories=None, **kwargs)¶| Parameters: | |
|---|---|
| Returns: | |
| Return type: | list of length k tuples of (train, cv) |
split
(dataset, seed=None, frac_train=0.8, frac_valid=0.1, frac_test=0.1, log_every_n=None)[source]¶Splits internal compounds randomly into train/validation/test.
train_test_split
(dataset, train_dir=None, test_dir=None, seed=None, frac_train=0.8, verbose=True)¶Splits self into train/test sets. Returns Dataset objects.
train_valid_test_split
(dataset, train_dir=None, valid_dir=None, test_dir=None, frac_train=0.8, frac_valid=0.1, frac_test=0.1, seed=None, log_every_n=1000, verbose=True)¶Splits self into train/validation/test sets.
Returns Dataset objects.
deepchem.splits.splitters.
RandomStratifiedSplitter
(verbose=False)[source]¶Bases: deepchem.splits.splitters.Splitter
RandomStratified Splitter class.
For sparse multitask datasets, a standard split offers no guarantees that the splits will have any activate compounds. This class guarantees that each task will have a proportional split of the activates in a split. TO do this, a ragged split is performed with different numbers of compounds taken from each task. Thus, the length of the split arrays may exceed the split of the original array. That said, no datapoint is copied to more than one split, so correctness is still ensured.
Note that this splitter is only valid for boolean label data.
TODO(rbharath): This splitter should be refactored to match style of other splitter classes.
get_task_split_indices
(y, w, frac_split)[source]¶Returns num datapoints needed per task to split properly.
k_fold_split
(dataset, k, directories=None, **kwargs)[source]¶Needs custom implementation due to ragged splits for stratification.
train_test_split
(dataset, train_dir=None, test_dir=None, seed=None, frac_train=0.8, verbose=True)¶Splits self into train/test sets. Returns Dataset objects.
deepchem.splits.splitters.
ScaffoldSplitter
(verbose=False)[source]¶Bases: deepchem.splits.splitters.Splitter
Class for doing data splits based on the scaffold of small molecules.
k_fold_split
(dataset, k, directories=None, **kwargs)¶| Parameters: | |
|---|---|
| Returns: | |
| Return type: | list of length k tuples of (train, cv) |
split
(dataset, frac_train=0.8, frac_valid=0.1, frac_test=0.1, log_every_n=1000)[source]¶Splits internal compounds into train/validation/test by scaffold.
train_test_split
(dataset, train_dir=None, test_dir=None, seed=None, frac_train=0.8, verbose=True)¶Splits self into train/test sets. Returns Dataset objects.
train_valid_test_split
(dataset, train_dir=None, valid_dir=None, test_dir=None, frac_train=0.8, frac_valid=0.1, frac_test=0.1, seed=None, log_every_n=1000, verbose=True)¶Splits self into train/validation/test sets.
Returns Dataset objects.
deepchem.splits.splitters.
SingletaskStratifiedSplitter
(task_number=0, verbose=False)[source]¶Bases: deepchem.splits.splitters.Splitter
Class for doing data splits by stratification on a single task.
Example:
>>> n_samples = 100
>>> n_features = 10
>>> n_tasks = 10
>>> X = np.random.rand(n_samples, n_features)
>>> y = np.random.rand(n_samples, n_tasks)
>>> w = np.ones_like(y)
>>> dataset = DiskDataset.from_numpy(np.ones((100,n_tasks)), np.ones((100,n_tasks)), verbose=False)
>>> splitter = SingletaskStratifiedSplitter(task_number=5, verbose=False)
>>> train_dataset, test_dataset = splitter.train_test_split(dataset)
k_fold_split
(dataset, k, directories=None, seed=None, log_every_n=None, **kwargs)[source]¶Splits compounds into k-folds using stratified sampling. Overriding base class k_fold_split.
| Parameters: |
|
|---|---|
| Returns: | fold_datasets – List containing dc.data.Dataset objects |
| Return type: | List |
split
(dataset, seed=None, frac_train=0.8, frac_valid=0.1, frac_test=0.1, log_every_n=None)[source]¶Splits compounds into train/validation/test using stratified sampling.
| Parameters: |
|
|---|---|
| Returns: | retval – Tuple containing train indices, valid indices, and test indices |
| Return type: | Tuple |
train_test_split
(dataset, train_dir=None, test_dir=None, seed=None, frac_train=0.8, verbose=True)¶Splits self into train/test sets. Returns Dataset objects.
train_valid_test_split
(dataset, train_dir=None, valid_dir=None, test_dir=None, frac_train=0.8, frac_valid=0.1, frac_test=0.1, seed=None, log_every_n=1000, verbose=True)¶Splits self into train/validation/test sets.
Returns Dataset objects.
deepchem.splits.splitters.
SpecifiedSplitter
(input_file, split_field, verbose=False)[source]¶Bases: deepchem.splits.splitters.Splitter
Class that splits data according to user specification.
k_fold_split
(dataset, k, directories=None, **kwargs)¶| Parameters: | |
|---|---|
| Returns: | |
| Return type: | list of length k tuples of (train, cv) |
split
(dataset, frac_train=0.8, frac_valid=0.1, frac_test=0.1, log_every_n=1000)[source]¶Splits internal compounds into train/validation/test by user-specification.
train_test_split
(dataset, train_dir=None, test_dir=None, seed=None, frac_train=0.8, verbose=True)¶Splits self into train/test sets. Returns Dataset objects.
train_valid_test_split
(dataset, train_dir=None, valid_dir=None, test_dir=None, frac_train=0.8, frac_valid=0.1, frac_test=0.1, seed=None, log_every_n=1000, verbose=True)¶Splits self into train/validation/test sets.
Returns Dataset objects.
deepchem.splits.splitters.
Splitter
(verbose=False)[source]¶Bases: object
Abstract base class for chemically aware splits..
k_fold_split
(dataset, k, directories=None, **kwargs)[source]¶| Parameters: | |
|---|---|
| Returns: | |
| Return type: | list of length k tuples of (train, cv) |
split
(dataset, frac_train=None, frac_valid=None, frac_test=None, log_every_n=None, verbose=False)[source]¶Stub to be filled in by child classes.
deepchem.splits.splitters.
TimeSplitterPDBbind
(ids, year_file=None, verbose=False)[source]¶Bases: deepchem.splits.splitters.Splitter
k_fold_split
(dataset, k, directories=None, **kwargs)¶| Parameters: | |
|---|---|
| Returns: | |
| Return type: | list of length k tuples of (train, cv) |
split
(dataset, seed=None, frac_train=0.8, frac_valid=0.1, frac_test=0.1, log_every_n=None)[source]¶Splits protein-ligand pairs in PDBbind into train/validation/test in time order.
train_test_split
(dataset, train_dir=None, test_dir=None, seed=None, frac_train=0.8, verbose=True)¶Splits self into train/test sets. Returns Dataset objects.
train_valid_test_split
(dataset, train_dir=None, valid_dir=None, test_dir=None, frac_train=0.8, frac_valid=0.1, frac_test=0.1, seed=None, log_every_n=1000, verbose=True)¶Splits self into train/validation/test sets.
Returns Dataset objects.
Contains an abstract base class that supports chemically aware data splits.
deepchem.splits.task_splitter.
TaskSplitter
[source]¶Bases: deepchem.splits.splitters.Splitter
Provides a simple interface for splitting datasets task-wise.
For some learning problems, the training and test datasets should have different tasks entirely. This is a different paradigm from the usual Splitter, which ensures that split datasets have different datapoints, not different tasks.
k_fold_split
(dataset, K)[source]¶Performs a K-fold split of the tasks for dataset.
If split is uneven, spillover goes to last fold.
| Parameters: |
|
|---|
split
(dataset, frac_train=None, frac_valid=None, frac_test=None, log_every_n=None, verbose=False)¶Stub to be filled in by child classes.
train_test_split
(dataset, train_dir=None, test_dir=None, seed=None, frac_train=0.8, verbose=True)¶Splits self into train/test sets. Returns Dataset objects.
train_valid_test_split
(dataset, frac_train=0.8, frac_valid=0.1, frac_test=0.1)[source]¶Performs a train/valid/test split of the tasks for dataset.
If split is uneven, spillover goes to test.
| Parameters: |
|
|---|
Gathers all splitters in one place for convenient imports


Benelli Leoncino 250 review, test ride
by Vishal VenugopalThe Leoncino 250 is the smallest of the company's Scrambler line-up, but does it really fit into that segment?
- Make : Benelli
- Model : Leoncino
Benelli lists its Leoncino line-up under the Scrambler tab on its website, and we were a bit apprehensive of this when we rode the Leoncino 500 a couple of months ago. Now this might be true for the Trail variant of the 500 that’s sold overseas, but we’ve just experienced the smallest of the Leoncino family and the only ‘scrambling’ this bike has been designed for is through the concrete jungle.
How does it look?
The Leoncino 250, as the name suggests, is a smaller iteration of the Leoncino 500. It shares an almost identical design philosophy and it looks like a slightly shrunken version of its elder sibling.  However, the 250 uses a pill-shaped LED headlamp whereas the 500 gets a round unit. While these characteristics add to the visual presence of the bike, some of them take a toll on the ergonomics. The seat is a bit too narrow and could have done with a more comfortable padding, as well as more space for a pillion. Most of all, the foot pegs are aggressively rear-set and result in a cramped riding position.
What’s the engine like?
Having said that, I wouldn’t categorise the Leoncino 250 as uncomfortable – the ergonomics are just not what you’d expect from a motorcycle in the category. Under the skin, the 249cc single-cylinder powering the bike is a fairly punchy unit and while it’s certainly not a performance benchmark, there’s enough on offer to keep you happy; that, and a bassy exhaust note!
It’s not a very refined engine and you will feel (and hear!) vibrations higher in the rev band, but not at levels that will bother you. In our performance tests, the Leoncino 250 managed the 0-60kph sprint in 3.49sec, while a 100kph took 9.68sec. These figures aren’t exemplary and are almost identical with those of the Suzuki Gixxer 250. However, the Honda CB300R, which falls in the same bracket as the Leoncino, is vastly quicker.
What’s it like to ride?
Barring its ergonomics, the Leoncino 250's ride quality is firm, but not punishingly so. Its suspension comprises a 41mm USD fork and a preload-adjustable monoshock, a set-up that’s quite common for bikes in this price bracket. The riding position and the suspension setup both feel more in line with a sporty naked, which means that standing up and riding through broken stretches (as you would on a scrambler) isn’t a very natural-feeling process.
What other features does it have?
Most of what the Leoncino offers to stand out from its competition comes in the form of small details that aim to give it a premium touch. While aspects like the basic digital instrument console are a letdown, the bike does give you some things to be happy about – the tyres, for instance, are premium Metzeler M5s and the design language is unique enough to turn heads.
Should I buy one?
As a motorcycle, the Leoncino 250 has its charms, but the price is a deal breaker. An ex-showroom price tag of Rs 2.5 lakh puts it a whole Rs 1 lakh above the Suzuki Gixxer 250, which has very similar levels of performance and puts it in the space of much more accomplished machines. For a similar budget, you could get yourself the KTM 390 Duke, the Honda CB300R, or even the Royal Enfield Interceptor 650 – all motorcycles going up against whom the little Leoncino would be seriously out of its depth. If (or rather, when) Benelli figures out a way to bring the price down to an appropriate level, the Leoncino 250 will make for a quirky, left-of-field alternative to the established crowd.
Tech Specs
| PRICE | Petrol | Petrol AT | Diesel | Diesel AT | Electric |
|---|---|---|---|---|---|
| Ex-showroom - Delhi | Rs 2.5 lakh |
| ENGINE | Petrol | Petrol AT | Diesel | Diesel AT | Electric |
|---|---|---|---|---|---|
| Cubic Capacity (cc) | 249cc | ||||
| Engine Layout | Single-cylinder | ||||
| Max Power (hp @ rpm) | 25.8hp at 9250rpm | ||||
| Max Torque (nm @ rpm) | 21Nm at 8000rpm |
| BRAKES | Petrol | Petrol AT | Diesel | Diesel AT | Electric |
|---|---|---|---|---|---|
| Front Brake Type | Disc | ||||
| Front Brake Size (mm) | 280mm | ||||
| Rear Brake Type | Disc | ||||
| Rear Brake Size (mm) | 240mm |
| SUSPENSION | Petrol | Petrol AT | Diesel | Diesel AT | Electric |
|---|---|---|---|---|---|
| Front Suspension | USD fork | ||||
| Rear Suspension | Monoshock |


--- title: "Blog" linkTitle: "Blog" aliases: [ "/pt-br/blog/", "/zh-cn/blog/", "/ja/blog/", "/other/blog/", ] --- {{< blocks/section color="selenium-green" height="min" >}}
Keep up to date with all Selenium news here!


Webmining/Jsminer/bot-net Policy
There has been an increase in VPS, bot-net, and webminers, We will have to either ban and/or lock the payments for anything that is considered ddos/abusive traffic ! If it consists of bot-nets and Webminers.
If you have a high amount of workers that are legitimately yours. (At least 50 miners!), Use a proxy like
xmr-node-proxy . Its made to specificly connect to our pool type and works very efficent!
This proxy has several advantages over other software. It appears to the pool as a single miner and doesnt consume resources!


Packet queues may form at both the input ports and the output ports, and queuing will happen at each location biased on the amount of traffic, the speed of the switch and the speed of the connection.
● Roundabout Analogy
Queues can only be so big without running out of buffer and packets being lost. Input port queue’s don't form as often as output port queue’s because the computer acting as the receiver is normally faster at processing packets than the network is at transporting packets.
One of the problems of output port queuing is that the packet scheduler at the output port must choose one packet among those queued to be transmission. This selection might be done using one of several methods:
Another problem with output port queuing is that if there is not enough memory to add an incoming packet to the queue than a decision must be made to drop a packet. This can be done by:
Ideally this issue gets resolved before it happens using one of the congestion reduction methods we have already talked about.


DataChannel BitPlayCoin
balance:
remittee
None
amount
$
Send
Home
RAW
BlockChain
Miner
...
...
Transaction
When you click title of Block, you can see transactions in the block.
×
Transaction
TX In
Tx Out


|
| function | mlunit_test_nostorage (in varargin) |
| |
| function | setUp (in self) |
| |
| function | test_putGetWithMetaData (in self) |
| |
| function | runAndCheckError (in self, in commandStr, in expIdentifierList, in varargin) |
| | RUNANDCHECKERROR executes the specifies command and checks that it throws an exeption with an identifier containing the specified marker. More...
|
| |
| function | checkCode (in inpMeObj, in fieldName, in codeList) |
| |
| function | getIsCodeMatch (in inpMeObj, in checkDepth, in fieldName, in codeStr) |
| |
| function | runAndMeasureTime (in self, in varargin) |
| | RUNANDMEASURETIME executes the specified command and displayes a profiling report using the specified name as a marker. More...
|
| |
- Author
- Peter Gagarinov, PhD pgaga.nosp@m.rino.nosp@m.v@gma.nosp<EMAIL_ADDRESS>- Copyright
- 2015-2016 Peter Gagarinov, PhD
2012-2015 Moscow State University,
Faculty of Applied Mathematics and Computer Science,
System Analysis Department
Definition at line 7 of file mlunit_test_nostorage.m.
◆ mlunit_test_nostorage()
| function mxberry::core::cont::test::ondisk::mlunit_test_nostorage::mlunit_test_nostorage |
( |
in |
varargin | ) |
|
◆ setUp()
| function mxberry::core::cont::test::ondisk::mlunit_test_nostorage::setUp |
( |
in |
self | ) |
|
◆ test_putGetWithMetaData()
| function mxberry::core::cont::test::ondisk::mlunit_test_nostorage::test_putGetWithMetaData |
( |
in |
self | ) |
|
◆ map
| Property mxberry::core::cont::test::ondisk::mlunit_test_nostorage::map |
|
private |
The documentation for this class was generated from the following file:


ACCORDING TO ANDY's NOTES:
www:Computer that communicate with each other
How the web works: "When a person goes to a web page like www.google.com, their computer sends a HTTP Request to a server. The server finds the appropriate HTML document and sends it back to the user's computer where a web browser interprets the page and displays it on the user's screen
HTML: "Hypertext Markup Language"
HTML documents contain text content which describes "what you see" and markup which describes "how it looks", references to other documents and links to other pages
Tags and Elements: HTML documents are made of HTML elements. When writing HTML, we tell browsers the type of each element by using HTML tags.
Inline vs Block Elements:HTML elements are either inline or block. Block elements form an "invisible box" around the content inside of them. Eg: Inline: br;b;em;img. Block: p.
The "tree-like structure" of HTML:he "tree-like structure" comes from the fact that HTML elements can have other elements inside of them.
Text Editors: Programmers use text editors sush as Sublime Text or Codepen.
CSS: Cascading Style sheets (cascading: most specific rule)
To place a comment: /* comment */
Box Sizing: Html elements are boxes and each box has 4 components (magin,border,padding and content); there are 2 techniques so you can use to helpdeal with sizing issues:1) set sizes in terms of percentages rather than pixels.2) Set the box sizing attribute to border box for every element. 3) Different brwsers work slightly differently sometimes this causes different browsers to display the same code differently.
Box Positioning: 1)Divs are blocks elements (as opposed to inline) so by default they take up the entire widht of a page. 2) Adding the rule (display: flex;) to the appropiate CSS will override this behavior and let divs appear next to each other.
Code, Test, Refine: 1) look for natural boxes; 2) look for repeated styles and semantic elements; 3) write your html; 4) apply styles (from biggest to smallest); 5) fix things.


---
title: Matches
layout: default
menu: matches
permalink: matches
---
{% include clickable-row.html %}
{% include page-title.html title="Matches" %}
{% for group in site.data.matchGroups %}
{% assign gtitle = group[0] %}
{% assign gmatches = group[1].Matches %}
{% include cards/matchlist.html title=gtitle matches=gmatches %}
{% endfor %}


Recently I acquired 10g of shredded Brugmansia, and having a gap between studies, wanted to try it out. Having experimented with a wide range of chemicals, psychedelics in particular, I was intrigued by what a 'deliriant' hallucinogen had to offer. I had experienced a threshold dose of Datura previous to this a few years ago, but I wanted to push the boat out a bit.
Two good friends would be taking this journey with me. I had very recently turned my friends onto the wonders of psychedelic chemicals: LSD, mescaline and mushrooms, which they had dug a lot. Besides that, we drink, and smoke my home grown ganja fairly often.
To prepare the concoction I simply boiled a kettle, let it sit for a while, and then poured the still hot water into a cup with the shredded Brugmasia flowers (there were just under 3, as I had smoked some of the material previously). This was stirred occasionally, the water was allowed to cool, and then the potion was filtered with kitchen roll. The resulting dark brown liquid was a lot less offensive to the palate than ayahuasca, but here was something evil about it. This was split three ways and rapidly downed. We also took a few caps of dream herb, Calea zacatechichi to aid the dream effects of the Brugmansia.
I wasnít sure what to expect-if anything. After a while of nothing happening, we all realised that we were actually very sizzled, in a weird way. A bit of a stoned feeling, but also like an alcohol intoxication, very different to a psychedelic. Although not the most pleasant sort of intoxication, it really wasnít that bad either. On standing the effects were even more apparent, in that it was hard to walk. After a joint, we retired to bed.
This is when it really started to get weird. Although I felt very tired, I couldnít sleep for a long time. Later, I fell into some kind of dream/hallucitory state. This was strange. I had forgotten I had taken something, and for the life of me couldnít tell whether I was awake or dreaming for the entire night. Things that had been on my mind earlier would surface in my dreams/visions. I had long in depth talks to both of my friends, who sat on the end of my bed. Of course, they were only in my mind. Both my friends reported the same thing on waking.
Iíve been trying to induce lucid dreams recently, and several times I became lucid, realising I was dreaming--but not that I was intoxicated on Brugmansia. Several times I woke up and no idea where I was, or thought I was sleeping at the other end of my bed for some reason.
I had a long lie in and on waking didnít feel bad at all. Both of my friends had also had a strange night, filled with vivid, waking dreams. The vivid dreams continued in the second night. The next day I felt fine, but not sober. My friends were the same. Trying to study was a fruitless exercise. My friend said that when intoxicated his nerves felt excited and energised, and he would like to have hit something. I recall feared warriors somewhere in South America using this plant before battle.
For the next few days a weird side effect was noticed by all of us--we couldnít focus on anything near to our eyes, so reading was made difficult.
I was glad to have a breakthrough experience with the Brugmasia. It was less of an ordeal than I expected it to be from reports, and the dream state was interesting. I donít think I have any desire to repeat the experience though, having done it. Maybe sometime in the future. This plant definitely has a more toxic, threatening vibe than the psychedelics. I can see this plant would change the nature of ayahuasca dramatically if used in preparation.
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.


Next:
General Overview
Up:
userhtml
Previous:
Abstract
Contents
General Overview
Code Distribution
Configuring and Building MLD2P4
Prerequisites
Optional third party libraries
Configuration options
Bug reporting
Example and test programs
Multigrid Background
AMG preconditioners
Smoothed Aggregation
Smoothers and coarsest-level solvers
Getting Started
Examples
User Interface
Method init
Method set
Method hierarchy_build
Method smoothers_build
Method build
Method apply
Method free
Method descr
Auxiliary Methods
Adding new smoother and solver objects to MLD2P4
Error Handling
License
Bibliography


This module presents the functionalities offered by TRSL.
Systematic sampling is provided through trsl::is_picked_systematic, a predicate functor to use in combination with trsl::persistent_filter_iterator to form a sample iterator. The sample iterator accesses a population of elements through a range defined by a pair of Forward Iterators (begin/end), and provides on-the-fly iteration through a sample of the population.
Systematic sampling may perform very badly if the order in which the input population is presented is following a pattern. If a pattern is likely to occur in the population, the user may want to pipe the sample iterator after a trsl::random_permutation_iterator, or use is_picked_systematic with ppfilter_iterator. The resulting iterator effectively implements probability sampling. The price to pay for probability sampling is a Random Access Iterator. While persistent_filter_iterator will work e.g. with std::list
, ppfilter_iterator requires e.g. std::vector
or std::deque
.
Iteration through an index-based reordering of a range can be obtained with trsl::reorder_iterator. TRSL provides several functions that generate reorder iterators for common reorderings.
trsl::random_permutation_iterator provides an iterator over a random permutation of a range.
trsl::sort_iterator provides an iterator over a sorted permutation of a range.


class: center, middle, inverse, title-slide # STA 235 - Wrapping up ## Spring 2021 ### McCombs School of Business, UT Austin ---  .center2[ .box-4LA[We have seen a lot of topics this semester]] --- .pull-left[ .box-1[Regression] ] --- .pull-left[ .box-1[Regression]
 .box-2[Potential Outcomes framework] ] --- .pull-left[ .box-1[Regression]
 .box-2[Potential Outcomes framework]
 .box-3[Randomized Controlled Trials] ] --- .pull-left[ .box-1[Regression]
 .box-2[Potential Outcomes framework]
 .box-3[Randomized Controlled Trials]
 .box-4[Observational studies] ] --- .pull-left[ .box-1[Regression]
 .box-2[Potential Outcomes framework]
 .box-3[Randomized Controlled Trials]
 .box-4[Observational studies] ] .pull-right[ .box-5[Model selection] ] --- .pull-left[ .box-1[Regression]
 .box-2[Potential Outcomes framework]
 .box-3[Randomized Controlled Trials]
 .box-4[Observational studies] ] .pull-right[ .box-5[Model selection]
 .box-6[Regularization] ] --- .pull-left[ .box-1[Regression]
 .box-2[Potential Outcomes framework]
 .box-3[Randomized Controlled Trials]
 .box-4[Observational studies] ] .pull-right[
 .box-5[Model selection]

 .box-6[Regularization]

 .box-7[Prediction] ] --- .center2[ .box-2LA[How do we bring everything together?] ] --- # Case Study .pull-left[ **.darkorange[The use of shared bikes]** - Q1: How to predict demand? - Q2: How to incentivize use? ] .pull-right[ .center[ ![:scale 100%](https://raw.githubusercontent.com/maibennett/sta235/main/exampleSite/content/Classes/Week14/2_Wrapup/images/bikeshare.jpg)] ] --- # Q1: How to predict demand? - What type of problem is it? -- - How would you approach this problem? -- - What is your outcome variable? -- - What data would you ask for? - Think about granularity (level), time scope, variables, other data sources. --- # Let's look at the data ```r bikedc <- read.csv("https://raw.githubusercontent.com/maibennett/sta235/main/exampleSite/content/Classes/Week14/2_Wrapup/data/bikesharedc.csv") head(bikedc) ``` ``` ## datetime season holiday workingday weather temp atemp humidity ## 1 2011-01-01 00:00:00 1 0 0 1 9.84 14.395 81 ## 2 2011-01-01 01:00:00 1 0 0 1 9.02 13.635 80 ## 3 2011-01-01 02:00:00 1 0 0 1 9.02 13.635 80 ## 4 2011-01-01 03:00:00 1 0 0 1 9.84 14.395 75 ## 5 2011-01-01 04:00:00 1 0 0 1 9.84 14.395 75 ## 6 2011-01-01 05:00:00 1 0 0 2 9.84 12.880 75 ## windspeed casual registered count ## 1 0.0000 3 13 16 ## 2 0.0000 8 32 40 ## 3 0.0000 5 27 32 ## 4 0.0000 3 10 13 ## 5 0.0000 0 1 1 ## 6 6.0032 0 1 1 ``` --- # Let's look at the data - Would you **.darkorange[transform]** some of this data? - What **.darkorange[prediction method]** would you use? ```r head(bikedc) ``` ``` ## datetime season holiday workingday weather temp atemp humidity ## 1 2011-01-01 00:00:00 1 0 0 1 9.84 14.395 81 ## 2 2011-01-01 01:00:00 1 0 0 1 9.02 13.635 80 ## 3 2011-01-01 02:00:00 1 0 0 1 9.02 13.635 80 ## 4 2011-01-01 03:00:00 1 0 0 1 9.84 14.395 75 ## 5 2011-01-01 04:00:00 1 0 0 1 9.84 14.395 75 ## 6 2011-01-01 05:00:00 1 0 0 2 9.84 12.880 75 ## windspeed casual registered count ## 1 0.0000 3 13 16 ## 2 0.0000 8 32 40 ## 3 0.0000 5 27 32 ## 4 0.0000 3 10 13 ## 5 0.0000 0 1 1 ## 6 6.0032 0 1 1 ``` --- # Q2: How to incentivize use? - What type of problem is it? -- - How would you approach this problem? -- - What is your outcome variable? -- - What data would you ask for? - Think about granularity (level), time scope, variables, other data sources. --- # Q2: How to incentivize use? - If you only had access to the previous data structure, **.darkorange[what intervention could you design?]** -- - Imagine now that bike share is present in **.darkorange[two cities:]** DC and NYC. Would this change your approach? -- - Now imagine that in 2012, **.darkorange[NYC modified their prices for casual and registered riders]**. What approach could you use now? --- # Review Material .pull-left[ ```r bikenyc <- read.csv("https://raw.githubusercontent.com/maibennett/sta235/main/exampleSite/content/Classes/Week14/2_Wrapup/data/bikesharenyc.csv") ggplot(bikenyc, aes(x = date, y = count)) + geom_smooth(method = "loess", color = "blue") + geom_smooth(data = bikedc, aes(x = date, y = count), method = "loess", color = "red") + theme_bw() ``` ] .pull-right[  ] --- background-position: 50% 50% class: left, bottom, inverse .big[ Let's wrap things up ] --- # Objectives of this course - **.darkorange[Learn to think about causality]**: - Is this correlation or causation? - What assumptions need to hold? - How can I answer a causal question? -- - **.darkorange[Learn to think about prediction]**: - What do we care when thinking about a good prediction model? - What type of data do I need? - How can I use prediction to answer some pressing questions? --- # Objectives of this course
 .box-4LA[Even if you don't do these studies, learn how to be CRITICAL CONSUMER of these products/analysis] --
 .box-4tL[Same with coding!] --- # Do you wish we would have done more or less R coding?  --- # How comfortable are you with R?  --- # Remember for the final exam... .pull-left[ - You will need a **.darkorange[webcam]**. - Try to have a **.darkorange[stable internet connection]**. - The final exam will cover everything we have seen in the class (with an emphasis on prediction). - **.darkorange[Attend office hours if you have questions!]** ] .pull-right[ .center[ ![:scale 70%](https://raw.githubusercontent.com/maibennett/sta235/main/exampleSite/content/Classes/Week14/2_Wrapup/images/bob.jpg)] ] ---

 .box-7[We're very close to the finish line!
Good luck!] .center[![](https://media.giphy.com/media/NiBMdMizycNCE/giphy.gif)]


===Management (Executive, middle)===
Employees working directly for the company as described above. Given their position and function within the company they oftentimes have access to privileged information and may


The ggf-section class adds a 16px top and bottom margin to any HTML element, and can be used to separate sections of HTML content:


Single element Radial Progress Indicator
Normal
Disc variant
Dynamically updated (CSS custom property)


I am proficient at PHP, Laravel, AngularJS, Ionic Framework HTML5, CSS3 and jQuery-Javascript. As well as database design and management and Also user interface design (Web application, Moible) and user experience design. Please see my proficientcy below.
In my current job at Super Seven Stars as Senior Web Developer and Mz Online Market as Web Development Team Leader, I am responsible to, communicate between Operation Team and Web Developer Team for day-to-day issues of development, operations and development, Developing the Ecommerce Website base on Magento Platform, Developing the CMS Websites for the companies that exist under the Super Seven Stars Group, maintaining and system administration for the Web Servers.
I spent my leisure time as a freelance web & mobile developer. I’m also a great volunteer as a senior developer and admin for www.mmunicode.org (Myanmar Unicode Area) for supporting Myanmar Unicode Development in Myanmar. As you know, sharing is caring. I also participate www.coronahelpmm.org (CoronHelp MM) as web developer and admin for supporting Covic-19 information.
I also contributed and developed some plugin and library for Myanmar Unicode Migration process. E.g. MyanmarUnipress, WordPress plugin that received an award from Myanmar Unicode Migration Committee, MUA Web Unicode Converter and ViramaJS


What is a component state? Let's take a look at buttons - the most basic building blocks on any UI toolkit. Buttons usually have icons and texts so that the users know what will happen when they click them. In addition, modern UI toolkits provide rich texturing capabilities that allow skinning different parts of the button visuals - such as background, border and focus ring. These visuals usually depend on the current state of the button.
If a button does not respond to UI events (such as mouse click, for instance), it is said to be disabled - and usually has a lighter or partially translucent appearance to match its disabled state. A toggle button can be in a selected state, conveying that certain application parameter is toggled on. A selected button is usually painted with different hue to facilitate quick scanning of the application state. There are more button states - rollover when the mouse is moved over it, pressed when the user pressed but has not yet released the mouse and default button which is invoked when the user presses the Enter key. The following screenshot shows how the same button can look like in different states:
Different controls have different states. For example, a progress bar can be determinate or indeterminate - depending on whether the application code can reliably assess the overall length of the work to be performed. A text component can be editable or uneditable. Input controls can be marked as required - to prevent the user from leaving the screen until he makes a selection on that control.
Component states in Substance are managed by the
org.pushingpixels.substance.api.ComponentState
class. Instances of this class correspond to states of Swing core and custom controls.
This class provides a number of predefined static instances to cover most action-based
controls such as buttons, check boxes and menu items. In addition, application code
can define custom component states that create fine grained mapping between arbitrary
states of controls and specific color scheme bundles in custom skins.
Each component state is defined by two arrays of component state facets (available in
ComponentStateFacet
class).  For example,
when a selected toggle button is pressed, it transitions to
PRESSED_SELECTED
state. This state
has ComponentStateFacet.ENABLE
,
ComponentStateFacet.SELECTION
and
ComponentStateFacet.PRESS
as its on facets.
If a selected toggle button is disabled, it has
ComponentStateFacet.SELECTION
in
its on facets and ComponentStateFacet.ENABLE
in its
off facets.
The ComponentStateFacet
class defines
a number of core facets. The ComponentStateFacet.ENABLE
facet is universal - it is relevant for all Swing controls. Other facets apply to a
wider range of controls. For example,
ComponentStateFacet.ROLLOVER
facet applies
to all controls that can show rollover effects - including buttons, menu items,
comboboxes, sliders, scrollbars and many more. Some facets apply to a very narrow
range of controls. For example,
ComponentStateFacet.EDITABLE
is only relevant
for editable controls, such as text components, editable comboboxes or spinners.
The static instances of ComponentState
defined in
this class do not aim to cover all possible combinations of on and off facets.
In addition to making this class to unwieldy, it is not possible to do since
application code can define its own facets. Instead, Substance provides three ways to
fine-tune the mapping between the component states and the color schemes used to paint
the components.
ComponentState.PRESSED_SELECTED
- the skinning
layer first looks for the exact state (as passed to
SubstanceColorSchemeBundle.registerColorScheme(SubstanceColorScheme, ColorSchemeAssociationKind, ComponentState)
or similar APIs). If the exact match is found, it is used. If there is no exact match,
the skinning layer will look at all color schemes registered for the specific color
scheme association kind in the matching color scheme bundle. The decision is made
based on how "close" the registered component state is to the component state of the
currently painted component. For example,
ComponentState.PRESSED_SELECTED
is a better
match for ComponentState.PRESSED_UNSELECTED
than
ComponentState.ROLLOVER_SELECTED
- since the
ComponentStateFacet.PRESS
has more weight
than the ComponentStateFacet.ROLLOVER
in the
decision process. The skinning layer will choose the "closest" registered component
state that is sufficiently close. For example,
ComponentState.DISABLED_SELECTED
will never
be chosen for ComponentState.SELECTED
, even
if there are no other registered component states. This way the application
code can register a few color schemes in the specific bundle, and have all
other states "fall back" to the smaller subset of states.ComponentStateFacet.DETERMINATE
or
ComponentStateFacet.EDITABLE
are relevant
only for a small subset of controls. In order to simplify the API signature of
ComponentState
, these facets are not
part of any of the predefined static states in this class. Instead, they are
used internally in the matching UI delegates (such as for progress bar or text
components) to find the best match among all the registered states of the current
skin. The specific skin can define its own
ComponentState
instances that use these facets.
For example, Nebula defines a number of
component states that use the
ComponentStateFacet.DETERMINATE
facet, and maps the matching color schemes. At runtime, the procedure described
in the previous item will match the state of the specific progress bar to the
states defined in this skin, and use the matching color schemes.ComponentStateFacet
class. In this case, the application code can create its own facet instances,
and its own component states that use those facets in the on and off lists.
Part of the custom code will be in the UI delegates that compute the current
state of the custom component using the new facets. Other part of the custom
code will be in the skin definition that maps the component states defined with
the new facets to the specific color schemes.Note that you do not have to create explicit dependency between custom component states used in the skin definition and custom component states used in the painting routines (in the UI delegates). In fact, the custom component states defined in the Substance UI delegate for progress bar are not accessible to the application code. The recommended way to separate the skin definition from the model lookups in the painting is:
ComponentStateFacet
class.
When this (perhaps elaborate) state is passed to
SubstanceColorSchemeBundle.getColorScheme(ColorSchemeAssociationKind, ComponentState)
API, the the procedure described above will match the this state to one of the "base"
states defined in your skin, and use the matching color scheme.Note that the matching algorithm only looks at the facets in the on and off lists, and ignores the component state name. This allows you to create a broad component state in your skin, and a number of narrow component states during the painting - and have the Substance skinning layer find the best match.
When the matching algorithm cannot find a sufficiently close match, the skinning
layer will fall back on one of the three base color schemes passed to the
SubstanceColorSchemeBundle.SubstanceColorSchemeBundle(SubstanceColorScheme, SubstanceColorScheme, SubstanceColorScheme)
constructor. States with ComponentStateFacet.ENABLE
in their off list will fall back to the disabled color scheme. The
ComponentState.ENABLED
will fall back
to the enabled color scheme. The rest of the states will fall back to the active
color scheme. To change the fallback behavior pass a non-null fallback color scheme
to the
ComponentState.ComponentState(String, ComponentState, ComponentStateFacet[], ComponentStateFacet[])
constructor as the second parameter.
Let's see two examples. As mentioned above, the Nebula skin defines custom color schemes
for progress bars using the ComponentStateFacet.DETERMINATE
:
ComponentState determinateState = new ComponentState("determinate",
new ComponentStateFacet[] { ComponentStateFacet.ENABLE,
ComponentStateFacet.DETERMINATE }, null);
ComponentState indeterminateState = new ComponentState("indeterminate",
new ComponentStateFacet[] { ComponentStateFacet.ENABLE },
new ComponentStateFacet[] { ComponentStateFacet.DETERMINATE });
SubstanceColorScheme determinateScheme = schemes
.get("Nebula Determinate");
SubstanceColorScheme determinateBorderScheme = schemes
.get("Nebula Determinate Border");
defaultSchemeBundle.registerColorScheme(determinateScheme,
determinateState, indeterminateState);
defaultSchemeBundle.registerColorScheme(determinateBorderScheme,
ColorSchemeAssociationKind.BORDER, determinateState,
indeterminateState);
ComponentState determinateDisabledState = new ComponentState(
"determinate disabled",
new ComponentStateFacet[] { ComponentStateFacet.DETERMINATE },
new ComponentStateFacet[] { ComponentStateFacet.ENABLE });
ComponentState indeterminateDisabledState = new ComponentState(
"indeterminate disabled", null, new ComponentStateFacet[] {
ComponentStateFacet.ENABLE,
ComponentStateFacet.DETERMINATE });
SubstanceColorScheme determinateDisabledScheme = schemes
.get("Nebula Determinate Disabled");
SubstanceColorScheme determinateDisabledBorderScheme = schemes
.get("Nebula Determinate Disabled Border");
defaultSchemeBundle.registerColorScheme(determinateDisabledScheme,
determinateDisabledState, indeterminateDisabledState);
defaultSchemeBundle.registerColorScheme(
determinateDisabledBorderScheme,
ColorSchemeAssociationKind.BORDER, determinateDisabledState,
indeterminateDisabledState);
And the resulting visuals - note that the progress bars use brown color scheme, while all the other controls use gray colors:
The second example is from the Magellan skin that registers a specific color scheme to be used for painting uneditable text components:
ComponentState uneditable = new ComponentState("uneditable",
new ComponentStateFacet[] { ComponentStateFacet.ENABLE },
new ComponentStateFacet[] { ComponentStateFacet.EDITABLE });
SubstanceColorScheme uneditableControls = colorSchemes
.get("Magellan Uneditable Controls");
defaultColorSchemeBundle.registerColorScheme(uneditableControls,
ColorSchemeAssociationKind.FILL, uneditable);
Note that if your custom components use the DETERMINATE
or
EDITABLE
facets in the computation of their states, they
will get the matching visuals from the corresponding core Substance skins.


On its long way to version 1.0.
LML is XML stored as expressions. If an expression is not a text string, the first element of an expression must be the tag name as a symbol, followed by optional attributes as keyword arguments and then its children.
The tag and attribute names are converted to lower camel case.
Function $$ is used to convert LML into a DOM tree. This:
(princ ($$ `(html :lang "en"
(head
(title "Hello world!")
(script :src "/main.js"
:defer nil ; Attribute without value.
"")) ; Ensure closing tag.
(body
"Hi there!"))))
will print
<html lang="en"
<head>
<title>Hello world!</title>
<script src="/main" defer></script>
</head>
<body>
Hi there!
</body>
</html>
Functions to convert between LML expressions and a DOM tree.
A simple React-like component that doesn't come with reconciliation.
LML macro expansion (including some to add event listeners) + LML component expansion.
Automatic form generation based on data descriptions and widget set.


Heavenly Father, so often I lose sight of eternity. Often my mind dwells on the momentary. In an instant I am captured by the temporal, and the vast expanse of eternity dims. As looking through a crack in the door, I see but a sliver of life and forget the endless span You inhabit.
How easily I give into hopelessness, I give into anxiety, into sin.
Teach me, Father, to see my existence as but a breath. As a blade of grass I stand for a moment before I am scorched away and return to dust. Why then do I fill my ephemeral existence with so much folly. Why do I not use every precious moment You have bestowed to me in wholehearted devotion to You?
This is my ailment: my narrow vision.
When I succumb to sin, I believe it will provide me lasting happiness. Yet, sin is but a fleeting pleasure. It has always left me hollow. If I would consider the enduring satisfaction, indelible joy, supreme pleasure found in You, how could I ever give into sin?
O that I would look back to eternity past, and see how You, in love, predestined me. See how You chose to place Your affections upon me, for no worth of my own. That You would choose me to be included in Your plan for maximal glory.
O that I would look at the breadth of history, and see how time and again You have proven faithful. How You have continually nurtured Your people, even when they forsook You.   All this so that He might bear my sins, I might be counted righteous, and You be just and the justifier. What greater love is there than this!
O that I would look at the short years my life has inhabited, and see how You have shown unfathomable love towards me. That even in my rebellion You loved me, and called me, and justified me. That day after day You sanctify me and conform me to the image of Your radiant Son. That You have been my comforter day after day. My peace in troubled times. My refuge in the storms of life. My truth and my anchor. My eternal joy!
O that I would look to my future days on earth. Each and every one of them written in Your book, though not one has come to pass. And in each day You have promised to work all things for my good.
O that I would look beyond my earthly life to eternity future. For You have promised that my mortal flesh will be raised immortal, and my sin stained garments will be traded for pure white robes. For You shall wipe away every tear, and sin shall be no more, and the anguish of this life will fade away.  And I shall behold my radiant Redeemer at Your side. And my sinless tongue shall cry out in a heavenly timbre, “To Him who sits on the throne and to the Lamb be blessing and honor and glory and might forever and ever!” And I shall fall at Your feet and worship You with joy inexpressible and filled with glory!
When the fog of temptation settles upon me, and the allure of sin seems too great to overcome, I must look beyond the temporal to the eternal. If only I would consider eternity past to eternity future, I would see sin as but a momentary gratification. A fleeting sliver of hollow happiness, that is but an infinitesimal instant in comparison to the great span of eternity. And were I to set my mind upon Your inexhaustible goodness from eternity to eternity, I would see all sin, all anxiety, all doubt as exceedingly worthless.
Father in Heaven, broaden my narrow vision to look behind and beyond and to behold my glorious Savior above.


WATCH | Death toll rises to 29 in DR Congo plane crash
At least 29 people were killed on Sunday when a small plane crashed after takeoff into a densely populated area of the city of Goma in eastern Democratic Republic of Congo.
A survivor among the 19 people - 17 passengers and two crew members - who were aboard the plane was taken to hospital along with 16 others injured on the ground, the North Kivu regional government said.
"At this stage, 29 bodies have been found in the rubble," the statement said.
The Busy Bee Dornier-228 smashed into two houses near the airport, the deputy transport minister Jacques Yuma Kipuya said earlier.
The first images from the scene showed smoke billowing above the neighbourhood and the aircraft in flames with local people throwing buckets of water on it.
A video seen by AFP showed the cabin of the plane still smouldering embedded in the wall of the house.
The aircraft had been headed for the city of Beni, 350km north of Goma, when it went down after takeoff in the residential area.
Busy Bee airline staff member Heritier Said Mamadou had earlier confirmed that 19 were on board the flight scheduled from Goma around 09:00.
Busy Bee, a recently established company, has three planes serving routes in the North Kivu province.
The pilot "failed in his takeoff", Nord Kivu governor Carly Nzanzu Kasivita said in a statement.
One of the airline's maintenance workers at the site, quoted by news site actualite.cd, blamed a "technical problem".
Among the victims of the Goma crash was a woman who was the coordinator of an association for the defence of women's rights, Mambo Zawadi, her NGO said.
There were also three civil servants in the housing sector "who were returning to their posts in Butembo and Beni after work sessions that I had organised," their supervisor Molendo Sakombi said in a statement.
The UN mission deployed in DR Congo, MONUSCO, sent two fire engines to support local rescue services.
Aircraft accidents are common in the vast, conflict-wracked central African country.
Last month an Antonov-72 cargo plane that was providing logistical assistance for a trip by Congolese President Felix Tshisekedi and carrying eight crew and passengers went missing after taking off from Goma.
Troubled province
Goma is the capital of troubled North Kivu province near the border with Rwanda.
The plane's destination, Beni, has been the scene of other tragedies in the province plagued with fighting between rival militias. More than 60 civilians have been massacred since October 30.
The failure of the army and police to stop the killings has prompted protests by civilians, the latest one on Friday.
The Nord Kivu governor went to Beni to calm the citizens and on Sunday offered condolences to the families of the victims of the plane crash.
Beni has also been at the epicentre of an Ebola epidemic which so far has killed around 2 200 people in DR Congo in the last year.


Objectives
I am fortunate enough to know the utter delight of figuring out why programs make the
choices they do, and have discovered how to test them to make sure the quality is high. I
have seen many areas that could benefit from well-written software, and want to be a
part of making sure that it exists, and can be used by those of any age.
Project Contributions
Qit
- Documentation
- Jest Testing
- Test plan creation and updating
Cat Jokes
- Manual testing
- Test plan creation
- Results delivery
End Rating
- Documentation
- Codeless Testing
- Test plan creation
- Results and risk planning
EverGarden
Languages and Frameworks
- Selenium
- Jest/Enzyme
- HTML/CSS
- Python/Flask
- MSSQL
Experience with:
- C++
- PHP
- Security considerations & GDPR
- SQLAlchemy
- PyTest
- Markdown
Repositories
Self-Study Focus
Interpersonal skills
- Self discipline and time management
- Concise, accurate writing
- Remote work
- Interpersonal communication
- Customer service: written, by telephone and voice
Education
AA, Office Systems, Laramie County Community College, Cheyenne WY
Previous Work History
I was self-employed with my father as The Wood Shop, and then continued under the name Rings and Reads
after his death.
- Maintaining a database of both customers and inventory
- Production of wooden and knitted items
- Planning layout and design
- Accurate time estimation for finished items
- Storage and shipping of items and supplies
- Record keeping for tax and budgeting
Previous to this, we had a small employment agency, doing both local business and US-wide
agricultural placements.
- Maintaining records of clients, including confidentiality
- Contacting businesses with new clients of potential interest
- Maintaining records for legal purposes
- Contract enforcement
These two businesses we supplemented during slow points, but I was able to maintain contributing to
them.
Other positions include:
- Remote Intern with Basketball Wales Cheyenne, WY September 2018- current
- Tax Preparation with Liberty Tax Service Cheyenne, WY October 2011-April 2012
- Office Assistant with Zenith Security Cheyenne, WY July 2011-September 2011
- Cashier/Maintenance with Pilot Travel Center Cheyenne, WY July 2009-July 2011


Full Maps are Awesome
Close to You
By default the map you see here is an embeded Google Map. You can add your own API for maps and generate places.
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John's Pub
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Peer Reviewed Publications
- Milstead, W. B., J.W. Hollister, R. B. Moore, H. A. Walker (2013).
Estimating Summer Nutrient Concentrations in Northeastern Lakes from SPARROW
Load Predictions and Modeled Lake Depth and Volume. PLoS ONE 8(11):
Link
- Hollister, J. W., W.B. Milstead, M.A. Urrutia (2011). Predicting Maximum
Lake Depth from Surrounding Topography. PLoS ONE 6(9):
Link
- Hollister, J. W., W.B. Milstead (2010). Using GIS to Estimate Lake Volume from Limited Data. Lake and
Reservoir Management. 26(3)194-199.
Abstract
Self-archived
Supplemental 1
Supplemental 2
- Morzillo, A. T., A. G. Mertig, J. W. Hollister, N. Garner, J. Liu (2010). Socioeconomic Factors Affecting
Local Support for Black Bear Recovery Strategies. Environmental Management. 45:1299-1311.
Self-archived
- Benyi, S. J., J. W. Hollister, J. A. Kiddon, H. A. Walker. (2009). A Process for Comparing and Interpreting
Differences in Two Benthic Indices in New York Harbor. Marine Pollution Bulletin. 59:65-71
Abstract
- Hale, S. S. and J. W. Hollister. (2009) Beyond Data Management: How Ecoinformatics Can Benefit
Environmental Management Programs. Environmental Monitoring and Assessment. 150:227-235.
Abstract
Self-archived
- Hollister, J. W., J. F. Paul, and H. A. Walker (2008). CProb: A Computational Tool for Conducting
Conditional Probability Analysis. Journal of Environmental Quality. 37(6):2392-2396.
Abstract
Self-archived
- Hollister, J. W., P. V. August, J. F. Paul, and H. A. Walker. (2008). Predicting Estuarine Sediment Metal
Concentrations and Inferred Ecological Conditions: An Information Theoretic Approach. Journal of
Environmental Quality. 37(1):234-244.
Self-archived
- Hollister, J. W., P. V. August, and J. F. Paul. (2008). Effects of Spatial Extent on Landscape Structure and
Sediment Metal Concentration Relationships in Small Estuarine Systems of the United States’ Mid-Atlantic
Coast. Landscape Ecology. 23(SI):91-106.
Abstract
- Hollister, J. W., M. L. Gonzalez, J. F. Paul, P. V. August, J. L. Copeland (2004). Assessing the Accuracy of
the National Land Cover Dataset at Multiple Spatial Extents. Photogrammetric Engineering and Remote
Sensing. 70(4):405-414.
Link
- Paul, J. F., J. L. Copeland, M. Charpentier, P. V. August, and J. W. Hollister (2003). Overview of Geographic
Information Systems applications in Estuarine Monitoring and Assessment Research. Marine Geodesy.
26:63-72.
Abstract
- Eubanks, J. O., J. W. Hollister C. Guyer, and W.K. Michener. (2002). Reserve Area Requirements for Gopher
Tortoises (Gopherus polyphemus). Chelonian Conservation and Biology. 4(2).
Self-archived
Book Chapters:
- Cormier, S. and J. W. Hollister (2011) Chapter 6: Types of Spatial and Landscape Data and Sampling Designs.
In U.S. EPA (Environmental Protection Agency). (2011) Landscape and predictive tools: a guide to spatial
analysis for environmental assessment. Risk Assessment Forum. Washington, DC. EPA/100/R-11/002.
Link
- Hollister, J. W. and Cormier, S. (2011) Chapter 7: Methods and Tools for Analyzing Spatially Explicit Information
In U.S. EPA (Environmental Protection Agency). (2011) Landscape and predictive tools: a guide to spatial
analysis for environmental assessment. Risk Assessment Forum. Washington, DC. EPA/100/R-11/002.
Link
- Michener, W. K., J. B. Atkinson, D. G. Edwards, J. W. Hollister, P. F. Houhoulis, P. M. Johnson, and R. N.
Smith. (2000). Habitat Characteristics of Northern Bobwhite Quail-Hunting Party Encounters: A Landscape
Perspective. Pages 173-182 in L.A. Brennan, W.E. Palmer, L.W. Burger, Jr., and T.L. Pruden (eds.). Quail
IV: Proceedings of the Fourth National Quail Symposium, Tall Timbers Research Station, Tallahassee, FL.
Link
Non Peer Reviewed Submissions (Letters, Guest Blog Posts, Popular Press articles, etc.):
- Hollister, J.W. (2013). SPARROWs, Lakes, and Nutrients? US EPA It All Starts With Science Blog.
Link
- Hollister, J.W. (2010). Ecology on the Web – 3 Years Old. Submitted to Ecology on the Web, Bulletin of the
Ecological Society of America. 91(2).
Link
- Morzillo, A. T., J. W. Hollister, C. A. Drew, M. E. Rocca, M. E. Baker, J. M. Bossenbroek, C. A. Mazzarella.
(2008). A Young Scientist’s Guide to Gainful Employment: Recent Graduates’ Experiences and Successful
Strategies. Bulletin of the Ecological Society of America. 89(2)193-203.
Full text
- Hollister, J. W. (2007). Moving Forward with Ecological Informatics and Reproducibility. ESA News and
Views Blog. Link.
- Hollister, J. W. (2007). Natural Resource Management Partnership Annotated Website Link. Submitted to
Ecology on the Web, Bulletin of the Ecological Society of America. 88(2).
Link
- Hollister, J. W. and H. A. Walker (2007). Beyond Data: Reproducible Research in Ecology and
Environmental Science. Frontiers in Ecology and the Environment. 5(1):11-12.
Link
- Hollister, J. W. and L. M. Ernst (2001) Eelgrass Habitats get a Boost from Geographic Information Systems.
Maritimes. 43(1):16-18.
Self-archived
Landscape Ecology 2.0 Blog Posts (five relevant posts)
- Hollister, J. W. (2013) Rstudio, and presentations, and git! Oh my!
Link
- Hollister, J. W. (2013) An R function to download shapefile
Link
- Hollister, J. W. (2013) Things I Forget: Reading a Shapefile in R with readOGR
Link
- Hollister, J. W. (2013) Open Access and Landscape Ecology
Link
- Hollister, J. W. (2013) Things I Forget: Installing new python packages on Windows
Link
Thesis/Dissertations:
- Hollister, J. W. (2004). Predicting Condition of Small Estuarine Systems Along the United States’ Atlantic
Coast. Ph.D. Dissertation, University of Rhode Island. 143 pp.
File set
- Hollister, J. W. (1997). An Analysis of Red Spruce Establishment Success in Highland County, Virginia.
Masters of Environmental Management Project, Duke University.
Self-archived


Dec 14, 2018
I led a discussion on several intriguing papers on methods to address network interference in the seminar class provided by Prof. Sinan Aral. Topics include network exposure, graph cluster randomization, and bias reduction in networked experiments. I am happy to explore in this research area in the future. Please let me know if you have any thoughts to discuss with me!
I share my notes here.
References:
[1] Peter M. Aronow, and Cyrus Samii. "Estimating average causal effects under general interference, with application to a social network experiment." The Annals of Applied Statistics 11.4 (2017): 1912-1947.
[2] Johan Ugander, et al. "Graph cluster randomization: Network exposure to multiple universes." Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining. ACM, 2013.
[3] Dean Eckles, Brian Karrer, and Johan Ugander. "Design and analysis of experiments in networks: Reducing bias from interference." Journal of Causal Inference 5.1 (2017).
[4] Sinan Aral. Networked experiments. Oxford, UK: Oxford University Press, 2016.


Apache Cassandra is an open source distributed database management system designed to handle large amounts of data across many commodity servers, providing high availability with no single point of failure. Cassandra offers robust support for clusters spanning multiple datacenters, with asynchronous masterless replication allowing low latency operations for all clients.
Cassandra also places a high value on performance. In 2012, University of Toronto researchers studying NoSQL systems concluded that "In terms of scalability, there is a clear winner throughout our experiments. Cassandra achieves the highest throughput for the maximum number of nodes in all experiments."
Cassandra's data model is a partitioned row store with tunable consistency. Rows are organized into tables; the first component of a table's primary key is the partition key; within a partition, rows are clustered by the remaining columns of the key. Other columns may be indexed separately from the primary key.
Tables may be created, dropped, and altered at runtime without blocking updates and queries.
Cassandra does not support joins or subqueries, except for batch analysis via Hadoop. Rather, Cassandra emphasizes denormalization through features like collections.


Measles are a childhood infection caused by a virus. Once quite common, measles can now almost always be prevented with a vaccine.
Measles signs and symptoms appear around 10 to 14 days after exposure to the virus.
 Review your family's vaccination records with your doctor, especially before your children start elementary school or college and before international travel.
There's no specific treatment for an established measles infection. However, some measures can be taken to protect vulnerable individuals who have been exposed to the virus.


Server Filtering, Sorting and Paging with Custom element DataTable.
Data Source: "http://services.odata.org"


How to Create Cartesian Products in Python
Manual Creation of Cartesian Product
Manual Creation of Cartesian Product:
- We can break down a cartesian product to its rawest form, just the combination of each element in the set in a product set
- We can get the desired outcome by using multiple for loops
- This works, however python for loops are notoriously clunky and present a challenge in efficienctly doing this operation whenever the data set size increases.
Cartesian Product with itertools
Clean Cartesian Product:
- The itertools package gives us tools which behave a lot nicer computationly compared to multiple nested for loops.
- Here we're just using the product function to achieve the desired result, which we would of previous done with three for loops ( one for each set you're wishing to include in your cartesian product)
The full code from this video:
Project videos using this concept:
{% endblock %}


Caching a report source in the session variable
allows it to be used multiple times efficiently. When a report source is not
cached, the process of creating a new report source multiple times becomes
fairly expensive. Furthermore, caching a report source allows reports with or
without saved data to be refreshed.
Once you have obtained an object (called
reportSource) that
represents a report source, you can cache the report source using the following
code:
session.setAttribute("reportSource", reportSource);
Note: If you are using a cached report
source, the viewer or the report source’s
dispose
methods should not be called until the report source is no longer being used.
It is highly recommended that you cache report
sources in order to ensure data consistency when viewing reports. Using an
uncached report source causes a new report source to be created when any action
is performed in the viewer or export control. As a result, using uncached
report sources causes the viewer to display some pages with saved data and
other pages with live data.


| Mixin | Description |
|---|---|
base |
Sets the font to Roboto |
mdc-typography($style) |
Applies one of the typography styles, including setting the font to Roboto |
overflow-ellipsis |
Truncates overflow text to one line with an ellipsis |
baseline-top($distance) |
Sets the baseline height of a text element from top. |
baseline-bottom($distance) |
Sets the distance from text baseline to bottom. This mixin should be
combined with mdc-typography-baseline-top
when setting baseline distance to following text element. |


Coraline has discovered how noisy a stockpot can be when rolled on the concrete floor. Put toy inside, and it's EVEN LOUDER!
3/9/2009, 7:59:06 PM
Favs: 0
Retweets: 0
link--- layout: tweet ---
Coraline has discovered how noisy a stockpot can be when rolled on the concrete floor. Put toy inside, and it's EVEN LOUDER!
3/9/2009, 7:59:06 PM
Favs: 0
Retweets: 0
link


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Although I have been advised that Conquering The Command Line, by Mark Bates, is a great resource for learning Unix and Linux Commands for Developers, I find myself referring to The Command Line Crash Course, by Zed. A. Shaw, when I have a command line brain fart. It is a very informative tutorial for n00bs to get the basics of navigating unix-like command line ecosystems.
For maximum value ..
He then goes on to explain:
Sorry to be mean, but that's what you have to do. If you have an irrational fear of the command line, the only way to conquer an irrational fear is to just shut up and fight through it.
You are not going to be thrown into some jail at the bottom of Microsoft's Redmond campus.
Why? Because if you want to learn to code, then you must learn this. Programming languages are advanced ways to control your computer with language. The command line is the baby little brother of programming languages. _Learning the command line teaches you to control the computer using language.
in other wordz ..
get ur big girl panties out of the hamper .. pull em up and make it happen
.. can i get an Amen ..
One of the goals of the aforementioned pearls of wisdom is to encourage you to consciously work on the development of your fingers muscle memory so that it coincides with programming skill development. FYI .. as a dev, you will be blessed with this advise .. A-lot .. Nuff said.


The current index in the array its analyzing
Unsorted area
Already in order. (Not interested anymore)


OWN TERMS
crypto OWNERS CAN LEND crypto
BORROWERS CAN ASK for crypto
OPEN MARKET
unlimited - all requests are on the market
PRIVACY
only account address
CRYPTO SECURED LOANS
loans secured by tokens and crypto
TRULY DECENTRALIZED
Only you owns your crypto, DEMI not interfere and not block loan proposals


Problem Solving
One time I got stuck on trying to push my local repository to GitHub. To solve the problem I applied the ‘reading error messages’, ‘googling’ & ‘asking your peers for help’ techniques.
The ‘reading error messages’ technique was helpful as I was able to see the errors that would come when I was using the Terminal. Therefore, I was able realise that I have made an error & read the ‘error messages’ as too why the command had not gone through. I need to remember to practise breathing
and mindfulness when there’s a complex error message as it can be frustrating when you spent over an hour trying to figure out an error. Remembering I’m not the first developer ever to be faced with this error, and you won't be the last. No matter what happens, me and my team can handle it. The second
technique I used was ‘Googling’. I would research this error in Google too read articles/ forums or watch videos on YouTube on ‘how to push to GitHub’. Remembering that programmers at the highest level of development do it all the time. The true crime would be to have a massive resource of knowledge at your
disposal, and not to use it! However forming the correct search can be tricky, especially as a beginner. It can be hard to verbalize what you don’t understand. Even if it seems fruitless, put together a few key words: it’s likely some answer will be returned. The final technique was ‘asking your peers for help’.
On the Cohort Slack Channel I reached out & I ended up getting an answer on the channel and then further having a direct conversation with one of the Cohort members. The other member was very helpful and by discussing & bouncing ideas of each other we managed to figure out the problem which was I wasn’t in the correct
folder to push the local repository to GitHub. Sharing knowledge & providing guidance is powerful tool for problem solving as often the other member has solved the same or a similar issue, they go over the concept again to gain further clarification & it also provides more collaboration & trust between you and that person.
Although a problem may be very frustrating at the time of solving the issue, I learnt that there’s many different problem solving techniques and process’s that can be used to help solve the problem.
Firstly, remember too properly formatted your code. This can't be emphasised enough. Properly formatted code will allow you to see your work (and that of others) more clearly. It will make some mistakes obvious, and reduce the chance of making others. Here are some other steps to try:
- Pseudocode
- Try something!
- Simplify
- Read error message
- Improve visibility of problems using console output
- Debuggers
- Rubber ducky
- Google
- Blocked? Read out to Peers, Coaches & utilise the Slack Channel
- Reflect


Adekamie works on all modern browsers including Chrome, Safari, Firefox, and IE version 9 and above
Currently Adekamie saves your work in your browser. To access saved work, you must use the same browser and computer as you saved the work with. To keep your work private, we recommend you use Adekamie on your personal computer
Contact us for assistance and feedback here


HOMICIDAL (director:William Castle; writer: Robb White; cinematographer: Burnett Guffey; cast: Glenn Corbett (Karl), Patricia Breslin (Miriam Webster), Eugenie Leontovich (Helga), Alan Bunce (Doctor Jones), Richard Rust (Jim Nesbitt), James Westerfield (Mr. Adrims), Gilbert Green (Lt. Miller), Jean Arless (Emily/Warren), 1961)
Reviewed by Dennis Schwartz
A wonderfully hokey B-movie, that is a derivative thriller from the director with all the gimmicks, William Castle (Tingler). Here he uses a fright-clock on screen, giving those audience members who are too frightened to see the scary conclusion, a chance to leave the theater. They would get their money back by walking in a yellow line, with the spotlight on them, to a yellow booth labeling them as cowards, where there would be a cashier to give them a refund. We don't have showmen like Castle around anymore, instead we got corporate executive types running the studios, looking only at the bottom line. They have taken the fun out of promoting a movie and have made it just another stale business marketing venture.
Castle also effectively features in Homicidal a lot of copycat things from other films, such as the psychotic mannerisms of the killer, which was lifted straight out of Psycho. That the story makes absolutely no sense and has more holes in it than Swiss cheese, is besides the point.
The film opens in 1948, showing a young brother and sister in conflict over the doll he took from her and is squeezing maliciously before ripping it up, causing her to cry.
The next shot tells us that we are in today's Ventura, California, as a strikingly attractive blonde, Miss Miriam Webster, is checking into the Ventura Hotel. She soon waylays the bellboy, Jim Nesbitt, and offers him $2,000 to marry her, but also tells him that after the ceremony they will annul the marriage. This strikes Jim as being a little odd, but, what the hell, he thinks, she must have her reasons; and, he says to himself aloud, "I guess, I have nothing to lose."
At the justice of the peace's home, late at night, the marriage is made official and before you can say Jackie Robinson, the bride pulls out a long surgical knife and she carves up Mr. Adrims (Westerfield), before he can even kiss the bride, and he is now the late justice of the peace.
After fleeing alone in Jim's car and then abandoning it, Miriam returns home, where she is taking care of Helga (Leontovich), a mute paralysed Danish woman who is in a wheelchair and can only communicate by tapping on the table. It also turns out that the murderer's name is not Miriam but Emily. Emily has been hired as a live-in nurse by the boy Helga raised, Warren, who now feels obligated to take care of her.
Entering the house for a visit to Helga is the real Miriam Webster (Breslin), the brunette half sister to Warren (the boy who squeezed her doll but who has now grown up to be a seemingly solid young man of almost 21-years). He has since that time developed a good relationship with Miriam, as they both realize they were brought up in a dysfunctional household. It seems that Warren's father was a monster who demanded his wife give birth to a boy, when Miriam was born he dumped her mother and married again. This time his new wife gave birth to Warren and Helga was hired as a nanny. The father, though, was disappointed in Warren, he wanted his son to grow up strong and to eventually run his successful business. He hired a boy named Karl (Corbett) just to pick fights with Warren, to toughen him up, as he would sit back and laugh as he watched them in action.
The pyscho-dynamics of this twisted yarn, as they unfold, are a real hoot to watch. Emily and Warren (Jean Arless) speak in unnatural stilted tones, while Karl and Miriam go out together and are so sugary sweet that one could hyperventilate by just watching them.
The good Dr. Jones (Bunce), the family doctor, unfortunately was not present when Warren was delivered by his nurse Helga. He always wondered why Helga left him and went to work for Warren' family right after the delivery. And when Warren's folks died soon after in a car crash, she assumed responsibility in raising the two children. Helga eventually took Warren back home to Denmark with her, returning only after she mysteriously got crippled.
On Warren's 21st birthday, which is in two days, he stands to inherit 10 million dollars that his father left to his only son.
Homicidal is a classical horror film, but featuring mediocre acting and plot; its one gigantic plus is the interesting characterization by Jean Arless, who seems to be swimming in a different stream than the rest of the cast. The ending is predictable but fun to watch, and for that matter, this shamelessly exploitative film is a total gas. Even the serious explanation for what a homicidal psychopath is, as summed up by Karl after talking to the police investigator, is one that should be treated with benign amusement, as Karl states, "Anybody can kill that doesn't have an outlet for their hate."
Castle himself introduces the film, and thereby gets you in the right mood to see this shocker. If you are in the proper humor for a movie that doesn't take itself that seriously, you have nothing to lose by seeing it, which is the attitude the bellboy had when he mumbled just those sentiments to himself before accepting the bizarre marriage proposal offered him by the seemingly attractive blonde. This is the kind of film that defies critical analysis, if you're in the mood for schlock-shock, this could be a most entertaining film.
REVIEWED ON 7/19/99 GRADE: C
Dennis Schwartz: "Ozus' World Movie Reviews"
http://www.sover.net/~ozus
<EMAIL_ADDRESS>© ALL RIGHTS RESERVED DENNIS SCHWARTZ
The review above was posted to the
rec.arts.movies.reviews newsgroup (de.rec.film.kritiken for German reviews).
The Internet Movie Database accepts no responsibility for the contents of the
review and has no editorial control. Unless stated otherwise, the copyright
belongs to the author.
Please direct comments/criticisms of the review to relevant newsgroups.
Broken URLs inthe reviews are the responsibility of the author.
The formatting of the review is likely to differ from the original due
to ASCII to HTML conversion.
Related links: index of all rec.arts.movies.reviews reviews


A modern architecture to help you create fast and secure sites and dynamic apps with JavaScript, APIs, and prerendered Markup, served without web servers.
jamstack.org
So what are the benefits ?
Developer Experience
Great plugins Ecosystem
Amazing Performance
Pre-rendered HTML
Automatic Code Splitting
Smart link prefetching
Progressive Images
Safe
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Deployment
Netlify
AWS
Zeit
S3
BUT WHY gridsome ?
VuePress is amazing for docs
Nuxt works amazingly for application (static or not)


The quantity of greenhouse gases (GHGs) generated by our food can vary considerably across the global food supply chain...
Food’s carbon footprint, or foodprint, is the greenhouse gas emissions produced by growing, rearing, farming, processing, transporting,...
Greenhouse gases trap heat and make the planet warmer. Human activities are responsible for almost all of the increase in...
The key to stopping or slowing global warming may lie with cities—particularly large and wealthy ones. That was the major finding ...


An R wrapper to the Julia word2vec.jl pacakge.
Being a wrapper to a Julia package, word2vec.r
requires the latter to be installed.
# install.packages("remotes")
remotes::install_github("news-r/word2vec.r") # github
You must run setup_word2vec
at the begining of every session, you will otherwise encounter errors and be prompted to do so.
word2vec.r::setup_word2vec() # setup word2vec Julia dependency
#> Julia version 1.1.1 at location /home/jp/Downloads/julia-1.1.1-linux-x86_64/julia-1.1.1/bin will be used.
#> Loading setup script for JuliaCall...
#> Finish loading setup script for JuliaCall.


Identity Provider Home (local)
Identity Provider Logout (local)
Service Provider Home (local)
Service Provider Home (remote)


GreenMe is a simple logic game. The only thing you have to do is make all the dots become green. (I love green.) When you touch a circle, circles around it will be changed to another color. The color transformation is GREEN => RED => YELLOW => GREEN again. Just 3 colors. You will lose 5 points for each touch, and 100 points when reloading a level, so play carefully. Have fun!
install from google play


gift certificate
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This is a "mock" sign-in page, built for development purposes!
Please enter the information of a user to impersonate:


The cowboy_spdy
module implements SPDY/3 as a Ranch protocol.
Configuration for the SPDY protocol handler.
This configuration is passed to Cowboy when starting listeners using the cowboy:start_spdy/4
function.
It can be updated without restarting listeners using the Ranch functions ranch:get_protocol_options/1
and ranch:set_protocol_options/2
.
The default value is given next to the option name.
Initial middleware environment.
List of middlewares to execute for every requests.
Fun called every time a request is received.
Fun called every time a response is sent.
None.


I was born and raised in a small, country town in the heart of NC.
I attended college at the University of North Carolina at Wilmington, where I graduated in 2011 with my BS in Public health.
After graduation, I moved to Blacksburg, VA to attend Virginia Tech's Master's of Public Health program, concentrating in Epidemiology. However, upon visiting the financial aid office to apply for a Stafford loan of $20,000/year + a Grad Plus loan for $20,000/year, bringing my total to $40,000/year for a MPH....I nearly had a panic attack and decided to drop out and actually work in public health for a while before committing to that much student loan debt. BEST DECISION EVER!
Shortly, after I moved to super-fun Austin, TX!
Working in public health was sucking the life out of me. So, I knew I had to do something else. Given my education background, and love for hard sciences (biochem, bio, human anatomy and physiology, etc.), I figured going to Med School was my best option. Then Phillip (my then boyfriend, now husband, who is a software engineer) asked me the question that changed my life, "Why not do software development like me?"
I couldn't afford much, so I did the self-taught route. I studided my bum off for 6 months, and eventually landed an internship through networking and attending career fairs geared towards interns/students.
I worked as in intern for the past year; however, the internship I was promised was not the reality. I worked alone, not having anyone to learn from, and was expected to build a full-stack Groovy and Grails application, which I had absolutely no experience doing. I only knew SQL and Java, a little Ruby, and had never actually built an application before. I somehow managed to get something up and working, but it was untested, spaghetti code. Eventually, the application started falling apart(bugs, bugs everywhere!).
My internship brunt me out. I needed change in my life. So, I quit my internship, got married (August 2014), and moved to Boulder (November 2014)!
I grew up doing musical theater. I love to sing and dance.
My husband, Phillip, and I were child-hood sweethearts. He's been my BFF since we were 6.
I am a vegetarian, and a bit of a health freak :-)
Wine and chocolate always put me in a better mood...on that note, I actually make my own dark chocolate.
My super, duper-duper great uncle is Benjamin Franklin.