The actor, 54, has explained that the ghost lives in his house and is a rather noisy uninvited guest.
Read more: EastEnders fans predict yet another twist in Sharon baby plot after Lisa reveals truth
Shaun, who played infamous Walford "doormat" Barry Evans in the BBC soap as well as a satirical version of himself in Ricky Gervais' Extras, made the startling revelation on Richard Herring's Leicester Square Theatre podcast.
According to Metro.co.uk, Shaun told shocked listeners that he'd had to call on ghost expert Derek Acorah for advice about the unwelcome guest.
He revealed that he and his wife moved into their new house and very quickly started hearing noises, which TV ghosthunter Derek attributed to a poltergeist.
Read more: EastEnders fans pay tribute to 'iconic' Mel Owen after she dies in horrific accident
Shaun said: "I was in Celebrity Big Brother with Derek Acorah and I believe he sees and feels everything he believes he sees and feels.
"We moved into a new house, myself and my current wife Adele. And straight away there were banging noises - we'd got a ghost."
Shaun then admitted that Derek said the ghost was called Rosemary.
He added: "We do have a noisy ghost in our house and according to Derek, it's called Rosemary.
"I do believe the spirits of people linger where they were and all the time there is more evidence of poltergeists."
While taking part in CBB together in 2017, Most Haunted star Derek told Shaun they were 'guided by spirits' when they were in the house.
But the spirits sadly didn't help them win, with Shaun evicted by a secret 'back door eviction' just 24 hours before the final and Derek coming in fourth place.
Soap fans will remember the iconic death of Shaun's character Barry in January 2004, when his spiteful wife Janine pushed him to his death off a cliff and watched him die.
Not quite as dramatic as Mel Owen's horrific death this week, though!
Do you believe in ghosts? Let us know on our Facebook page @EntertaimentDailyFix .


Break Time(in minutes):
-
5
+
Work Time(in minutes):
-
25
+
Work Time!
25:00
Coded and designed by
Gilbert


New Google Adwords Editor Version 11 Features Wordstream
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Adwords Editor 11 3 3 For Windows Download
Ginny Marvin On Twitter A Brand New Google Ads Editor 1 0 Out Now If You Re Tryiing To Download Googleads Editor And Still See Adwords Messaging Like This Below Fear Not The Branding


Grand Master Vitus:
Why have you come here? You seem too weak to walk the same path we do. Perhaps one day when you are stronger... (This quest is for characters level 39 and above who have completed the 1st class transfer.)


You hired a helicopter just to make Cardi B recognize you – Archipalago to Shatta Wale
by Qwame BenedictArchipalago aka Palago Mufasa has attacked Shatta Wale for hiring a chopper just to get Cardi B’s attention.
Dancehall artiste Shatta Wale yesterday pulled a surprise after he showed up at the Accra Sports stadium for his soundcheck with a helicopter before heading to his hotel room to rest ahead of the concert later in the evening.
Some few hours after this, Shatta Wale took to social media to laugh at the celebrities who had gone to the Kempinski Grand Hotel to have a ‘Meet and Greet’ session with Cardi B but were disappointed.
That didn’t end there as he posted on his page again hammering on the fact that Ghana is still a village.
He posted: “WHEN I SAY GHANA BE VILLAGE…..DEM GO TALK SAY WHY”
But Archipalago who has been trolling the celebrities after the incident sent an epic respond to Shatta Wale
He responded: “U made it look more village by hiring a helicopter just for #CardiB to recognize you ???Kwasia Banku Abeteku”
See screenshot below:


Total number of sequencing reads from raw fastq file
Percentage of reads uniquely mapped to the reference genome.
Percentage of reads mapped equally well to multiple locations on the reference genome.
Percentage of reads not mapped to the reference genome.
Number of uniquely mapped reads.
Percentage of reads mapped to unique gene region, including Exons, UTRs and Introns.
Percentage of reads mapped to gene-overlapping region and can't be unambiguously assigned to either gene.
Percentage of reads mapped to unannotated genomic regions.
Total number of tags (reads, one paired-end read counted as two reads)
Percentage of mapped reads on Exons.
Percentage of mapped reads on 5' UTR.
Percentage of mapped reads on 3' UTR.
Percentage of mapped reads on Introns.
Percentage of mapped reads on Intergenic regions.
Reads counted by featureCounts (=uniquely mapped read * uniq_rate)
Normalization factor calculated by edgeR
Average correlation where this sample is involved
Avg_Corr : Average correlation where this sample is involved;
Avg_Rest : Average correlation where this sample is NOT involved;
Corr_diff : The difference between Avg_Corr and Avg_Rest.
(1) For each sample, calculate the correlation difference. This is simply a difference between the average of all the pair wise correlations that involve the sample (for the same group) and the average of all the pair wise correlations that do not involve the sample. For example, if we have a, b, c, d for group 1, the correlation difference of sample a is:
The difference of Average (correlation(a, b), correlation(a, c), correlation(a, d)) and Average (correlation(b, c), correlation (b, d), correlation(c, d)). You can see that if sample a is an outlier, then the difference will be negative.
(2) Now we have a vector of values (one for each sample). We simply convert this vector to MAD scores (robust Z-scores) by subtracting the medians then dividing it by median absolute deviations (MAD). We use a standard MAD cutoff (e.g. -5) to determine the outliers.
| Search: | Right click on a column header to show the explanation. |


--- layout: post title: Buzzwords is Amazon Free App of the Day 2/10/12 category : news image: tagline: tags : [buzzwords, android, amazon] ---
We are so excited to announce that Buzzwords is the FREE App of the Day on Amazon today!
http://www.amazon.com/mobile-apps/b/ref=topnav_storetab_mas?ie=UTF8&node...
If there's ever been a time to try out Buzzwords, now is it. So bust it out this weekend with some friends and see what all the buzz is about ;)


Ethan's Blog
Proper Feedback
April 19, 2015
ASK (Actionable, Specific, Kind) feedback is kind in that it is phrased to respect the receiver. It is actionable in that it formulates a concrete plan of action. Finally, it is specific in that it is narrowly tailored to one aspect, and does not attempt to address everything.
The majority of feedback at Devbootcamp is positive. Based on the Student and Guided feedbacks, people are able to discover their strengths and weaknesses. To improve, one should continue asking questions for things they do not understand.
At the same time, some feedback maybe overly positive. Rather than try to shade the truth, DBC Students should be more honest with their opinions.
As a personal aside, I felt frustrated with one Pairing session in particular. My Partner kept mixing up the Driver/Navigator roles. This led to general confusion as I lost kept of the Code that I wrote during the session. Therefore, making my concerns clear to that person through ASK Feedback would help them improve.


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In this post, I'm going to present how to reuse the trained model on few detection layers.
As we built and trained the FaceNet (using ResNet101 via Base101) with 6 detection layers.
- The first detection layers will detect the faces of approximately (16x16) in 160x160 cells if the input image size is 640x640 (the feature maps size of this detection layer).
- The second detection layers will detect the faces of approximately (32x32) in 80x80 cells if the input image size is 640x640 (the feature maps size of this detection layer).
In practical use, we rarely need to detect the very tiny faces like this. The purpose of two layers is mainly for achieving high accuracy in WiderFace dataset.
Removing the first detection layer or both layers will reduces a lot of time on making detections via forward pass.
There are 3 main things we need to care when removing these layers.
1. Build the truncated models.
A. Removing these detection layers is simply "don't create DetectionBlock
for these layers".
When we define FaceNet model, I tried to make it flexible as possible. As we see in the code,
the number of detection blocks depends on the length of the list num_anchors_per_cells
.
* num_anchors_per_cells
: list of values representing for the number of anchors per cell in each
detection layer (because we have only square anchor - then the values are 1 for all layers).
class FaceNet():
def __init__(self,
num_classes=2,
num_anchors_per_cells=[1, 1, 1, 1, 1, 1],
base=Base101(pretrained=True)):
# ...
self.num_det_layers = len(num_anchors_per_cells)
for i, num_anchor_per_cell in enumerate(self.num_anchors_per_cells):
self.det_blocks += [DetectionBlock(num_anchor_per_cell, self.num_classes)]
If we remove the first detection layer, simply set num_anchors_per_cells=[1, 1, 1, 1, 1]
. Then,
there will be 5 DetectionBlock
created.
B. We also need to determine the the input detection layer for each DetectionBlock
we created.
The feature maps of 6 detection layers we extract from the self.extractor
are [conv2, conv3,
conv4, conv5, conv6, conv7]
.
If we remove the first detection layer, so we need to pass [conv3, conv4, conv5, conv6, conv7]
to the self.det_blocks
.
class FaceNet():
def __init__(self):
...
def forward(self, input):
# Get [conv2, conv3, conv4, conv5, conv6, conv7]
ftmaps = self.extractor(input)
# If we remove the first detection layers, we only need to keep
# [conv3, conv4, conv5, conv6, conv7].
start_idx = (len(ftmaps) - self.num_det_layers)
ftmaps = ftmaps[start_idx : ]
# Then, make the detections
for i in range(self.num_det_layers):
res_dets = self.det_blocks[i](ftmaps[i])
2. Get the weights for these truncated models.
We trained the FaceNet (ResNet101 backbone) model with 6 detection layers and save the weights to
the pickle file. Essentially, the pickle file is just a dictionary (OrderedDict
) with key :
value is weight_name
: weight_value
.
The DetectionBlock
contains several conv
layers, and the weights of conv
layers
would start with the prefix which is the name of its DetectionBlock
.
We define DetectionBlock
in a list with the attribute self.det_blocks
:
class FaceNet():
def __init__(self, ...):
...
for i, num_anchor_per_cell in enumerate(self.num_anchors_per_cells):
self.det_blocks += [DetectionBlock(num_anchor_per_cell, self.num_classes)]
Therefore, the weights of all conv
layers in the first DetectionBlock
would start with the
prefix: det_blocks.0.
. The second DetectionBlock
starts with det_blocks.1
, and so on.
If we remove the first detection layer, we need to:
* Remove the first DetectionBlock
from the weight dictionary. Remove all keys and weights which
start with det_blocks.0.
- Change the key name of the other detection blocks.
'det_blocks.0' - remove
'det_blocks.1' -> change to 'det_blocks.0'
'det_blocks.2' -> change to 'det_blocks.1'
'det_blocks.3' -> change to 'det_blocks.2'
'det_blocks.4' -> change to 'det_blocks.3'
'det_blocks.5' -> change to 'det_blocks.4'
If we remove the first and second layers:
'det_blocks.0' - remove
'det_blocks.1' - remove
'det_blocks.2' -> change to 'det_blocks.0'
'det_blocks.3' -> change to 'det_blocks.1'
'det_blocks.4' -> change to 'det_blocks.2'
'det_blocks.5' -> change to 'det_blocks.3'
Finally, we save the weights dictionary to a new file.
The implementation of the code is located in '/utils/model_utils.py'.
3. Make detections with the truncated models.
The example below is for the truncated model of 5 detection layers (remove the first detection layer).
# 1. Build model
net = FaceNet(num_classes=2,
num_anchors_per_cells=[1, 1, 1, 1, 1], # 5
base=torch_object_detection.models.Base101(pretrained=False)
)
# 2. Load truncated weights
net.load_state_dict(torch.load(truncated_model_path))
# 3. Make detections and suppose we have an image -> convert to a tensor
loc_dets, cls_dets, fm_sizes = net(tensor, fm_sizes_return=True)
Now, after doing forward pass to the model, we need to decode
loc_dets, cls_dets, fm_sizes
into the boxes, labels, and confidence scores. It means that we
need to define the BoxCoder
for it.
If we remove the first layers, we need to redefine the values of anchor_sizes
, aspect_ratios
,
and fm_sizes
. The example below is for the input image of (640x640)
# Load and image and preprocess it to tensor
image = torch_object_detection.utils.load_image('./images/2.jpg', height=640, width=640)
tensor = torch_object_detection.utils.preprocess_image(image)
# Define some argument values for BoxCoder
img_size = (640, 640)
anchor_sizes = [32, 64, 128, 256, 512]
aspect_ratios = [[1,],[1,], [1,], [1,], [1,]]
fm_sizes = [[80, 80],
[40, 40],
[20, 20],
[10, 10],
[5, 5]]
box_coder = torch_object_detection.utils.BoxCoder(img_size=img_size,
anchor_sizes=anchor_sizes,
aspect_ratios=aspect_ratios,
fm_sizes=fm_sizes)
The fm_sizes
can be changed if we change the input image size. For example, if we get the image
of (320, 480) but we don't know what is the values of fm_sizes
, so there is 1 way to do it:
# Load and image and preprocess it to tensor
image = torch_object_detection.utils.load_image('./images/2.jpg', height=640, width=640)
tensor = torch_object_detection.utils.preprocess_image(image)
# Do forward pass, we get the fm_sizes if `fm_sizes_return=True`
loc_dets, cls_dets, fm_sizes = net(tensor, fm_sizes_return=True)
In detect
method, I already implement it for you, but you must define anchor_sizes
,
aspect_ratio
for it while fm_sizes=None
.
# Load and image and preprocess it to tensor
image = torch_object_detection.utils.load_image('./images/2.jpg', height=480, width=320)
tensor = torch_object_detection.utils.preprocess_image(image)
# Define some argument values for BoxCoder
img_size = (image.size[1], image.size[0])
anchor_sizes = [32, 64, 128, 256, 512]
aspect_ratios = [[1,],[1,], [1,], [1,], [1,]]
box_coder = torch_object_detection.utils.BoxCoder(img_size=img_size,
anchor_sizes=anchor_sizes,
aspect_ratios=aspect_ratios,
fm_sizes=None)
4. Complete exmple:
# Build model
net = FaceNet(num_classes=2,
num_anchors_per_cells=[1, 1, 1, 1, 1], # 5
base=torch_object_detection.models.Base101(pretrained=False)
)
# Load truncated weights
net.load_state_dict(torch.load(truncated_model_path))
# Load and image and preprocess it to tensor
image = torch_object_detection.utils.load_image('./images/2.jpg', height=480, width=320)
tensor = torch_object_detection.utils.preprocess_image(image)
# Define some argument values for BoxCoder
img_size = (image.size[1], image.size[0])
anchor_sizes = [32, 64, 128, 256, 512]
aspect_ratios = [[1,],[1,], [1,], [1,], [1,]]
box_coder = torch_object_detection.utils.BoxCoder(img_size=img_size,
anchor_sizes=anchor_sizes,
aspect_ratios=aspect_ratios,
fm_sizes=None)
# Make detection with `detect` method
det_boxes, det_scores, det_labels = torch_object_detection.utils.detect(tensor, net,
box_coder=box_coder,
device='cpu',
score_thresh=0.3,
nms_thresh=0.3)
# Draw the detections
drawn_image = torch_object_detection.utils.draw_detections(image, det_boxes, det_scores, det_labels)
The scripts of examples can be found at
examples/find_faces_with_original_model.py
examples/find_faces_with_notop1_model.py
examples/find_faces_with_notop12_model.py


Inheritance can be done in a number of ways. Till now, we have come across different types of inheritances in different examples.
The different types of inheritances which we have come across are:
Single Inheritance
In single inheritance, a class inherits another class.
Multilevel Inheritance
In this type of inheritance, one class inherits from another class. This base class inherits from some other class.
Hierarchical Inheritance
In hierarchical inheritance, more than one class inherit from a base class.
Multiple Inheritance
In this chapter, we will be studying about multiple inheritance.
In multiple inheritance, a class can inherit from more than one classes. In simple words, a class can have more than one parent classes. This type of inheritance is not present in Java.
Suppose we have to make two classes A and B as the parent classes of class C, then we have to define class C as follows.
class C: public A, public B
{
// code
};
Let's see an example of multiple inheritance
#include <iostream>
using namespace std;
class Area
{
public:
int getArea(int l, int b)
{
return l * b;
}
};
class Perimeter
{
public:
int getPerimeter(int l, int b)
{
return 2*(l + b);
}
};
class Rectangle : public Area, public Perimeter
{
int length;
int breadth;
public:
Rectangle()
{
length = 7;
breadth = 4;
}
int area()
{
return Area::getArea(length, breadth);
}
int perimeter()
{
return Perimeter::getPerimeter(length, breadth);
}
};
int main()
{
Rectangle rt;
cout << "Area : " << rt.area() << endl;
cout << "Perimeter : " << rt.perimeter() << endl;
return 0;
}
Perimeter : 22
In this example, class Rectangle has two parent classes Area and Perimeter. Class 'Area' has a function getArea(int l, int b) which returns area. Class 'Perimeter' has a function getPerimeter(int l, int b) which returns the perimeter.
When we created the object 'rt' of class Rectangle, its constructor got called and assigned the values 7 and 4 to its data members length and breadth respectively. Then we called the function area() of the class Rectangle which returned getArea(length, breadth) of the class Area, thus calling the function getArea(int l, int b) and assigning the values 7 and 4 to l and b respectively. This function returned the area of the rectangle of length 7 and breadth 4.
Similarly, we returned the perimeter of the rectangle by the class Perimeter.
Let's see one more example.
#include <iostream>
using namespace std;
class P1
{
public:
P1()
{
cout << "Constructor of P1" << endl;
}
};
class P2
{
public:
P2()
{
cout << "Constructor of P2" << endl;
}
};
class A : public P2, public P1
{
public:
A()
{
cout << "Constructor of A" << endl;
}
};
int main()
{
A a;
return 0;
}
Constructor of P1
Constructor of A
Here, when we created the object 'a' of class 'A', its constructor got called. As seen before, the compiler first calls the constructor of the parent class. Since class 'A' has two parent classes 'P1' and 'P2', so the constructors of both these classes will be called before executing the body of the constructor of 'A'. The order in which the constructors of the two parent classes are called depends on the following code.
class A : public P2, public P1
The order in which the constructors are called depends on the order in which their respective classes are inherited. Since we wrote 'public P2' before 'public P1', therefore the constructor of P2 will be called before that of P1.
Words may lie but actions will always tell the truth.


--- title: ellipse sidebar: function_reference_sidebar permalink: ellipse.html folder: function_reference toc: false ---
annotate an ellipse in a spherical plot
| r | rotation i.e. matrix of three orthogonal vectors specifying ellipse position |
| a,b | long/short half axes of ellipse in radian |


--- layout: page_en ---
As this is an open source project we want to encourage you to get involved.
Become a member of the OSO family.
We are happy about any support we get.
There are a lot of possibilities to support this project.
Feel free to contact us on any platform


What is Woogle?
A search engine, which tries to index all wicket related stuff for a more dedicated result.
My
first test of
Nutch
.
What technologies is used?
Nutch (Lucene)
,
Wicket
,
Hibernate
and
Spring
What are indexed?
[Site]
How often are they indexed?
Once every day.
Is the sourcecode available?
Yes, of course. It's under the Apache 2.0 license, so knock yourself out.
Right now I have just used my own local SF installation:
http://sf.billen.dk/sf/projects/woogle
Why isn't this cool YYY Wicket related site indexed?
Because you haven't
submitted it
yet ;)


Examples of what CodeMirror can do, and how to do it
-
Bundling with Rollup
How to create a script file that you can load in the browser.
-
Document Changes
How to write code that changes the editor document.
-
Handling Tab
If you're confused or annoyed that the editor doesn't handle Tab by default, read this.
-
Config: Changing Language
Modifying the language configuration in an existing state.
-
Internet Explorer 11
Setting up your editor so that it loads in Internet Explorer.
-
Writing a Language Package
Adding support for a new language to CodeMirror.
-
Extension: Zebra Stripes
Constructing an extension that styles alternating lines.
-
Split View
How to set up two editor views looking at the same document.
-
Huge Document
This demo creates an editor with a document of several million lines, just because we can.


This is paragraph 72203440-6047-4f5b-8814-72d35ec4e854
This is text f84227a0-6c5c-4097-b496-8f828ba5aefe This is text baf0273b-1b4f-4c24-afef-3ec8b6c30231 This is text dca38a71-a041-4ab0-829e-2cefec9f0e1b


MonitorCommand
MONITOR is a debugging command that outputs the whole sequence of commandsreceived by the Redis server. is very handy in order to understandwhat is happening into the database. This command is used directlyvia telnet.
% telnet <IP_ADDRESS> 6379
Trying <IP_ADDRESS>...
Connected to segnalo-local.com.
Escape character is '^]'.
MONITOR
+OK
monitor
keys *
dbsize
set x 6
foobar
get x
del x
get x
set key_x 5
hello
set key_y 5
hello
set key_z 5
hello
set foo_a 5
hello
The ability to see all the requests processed by the server is useful in orderto spot bugs in the application both when using Redis as a database and asa distributed caching system.
In order to end a monitoring session just issue a QUIT command by hand.
Non standard return value, just dumps the received commands in an infinite flow.


The artist wanted to redesign her website for a more professional and cleaner look. The website also needed an update to include newer projects that she could show to clients. Here's what it looked like before:
And here is what it looks like after:
The artist created her own design and I translated it into HTML, CSS, and some JavaScript. I used JavaScript to create a drop down menu with features such as closing all other tabs when one is opened and highlighting the tab that is being hovered over. I also included a Jquery library that scrolls to to the top of the page when a "top" link in the footer is clicked.


Interface AdapterInterface
Interface of the adapter to handle specified SMS gateway
Direct known implementers
Indirect known implementers
SmsZilla\Adapter\CiscoAdapter
,
SmsZilla\Adapter\ClickatellAdapter
,
SmsZilla\Adapter\FileAdapter
,
SmsZilla\Adapter\InfobipAdapter
,
SmsZilla\Adapter\MockAdapter
,
SmsZilla\Adapter\SerwerSmsAdapter
,
SmsZilla\Adapter\SmsApiAdapter
,
SmsZilla\Adapter\SmsCenterAdapter
Namespace: SmsZilla\Adapter
Author: Jarosław Wasilewski<EMAIL_ADDRESS>Used by:
Located at SmsZilla/Adapter/AdapterInterface.php
Author: Jarosław Wasilewski<EMAIL_ADDRESS>Used by:
SmsZilla\SmsSender::send()
Using specific adapter classLocated at SmsZilla/Adapter/AdapterInterface.php
Methods summary
public
|
|
public
mixed
|
|
public
boolean
|
#
send(
Send message with given SMS gateway |
public
|


Prerna Kaul
Project Part 2
Model and Clustering
Before conducting the analysis, the categorical values were converted to dummy variables and then the dataset was normalized. Since there was a high dimensionality in the dataset the PCA projection of clusters was plotted.
The observations and conclusions for different clustering methods are as follows:
The K Means was carried out for different k values ranging from 2 to 10. The best clustering occurred when the number of clusters = 3 and the average silhouette score = 0.4188064504340923.
The agglomerative clustering was carried out for different n_clusters i.e. the number of cluster values ranging from 2 to 10. The best clustering occurred when the number of clusters = 3 and the average silhouette score was = 0.40560405413301365.
The DBSCAN clustering was performed for different values of ‘eps’ i.e. the radius of a cluster, keeping the number of minimum samples same. The number of clusters was estimated using the cluster labels that were generated. It was inferenced that the best fit clustering occurred when the estimated number of clusters = 4 and the average silhouette_score for the estimated number of clusters = 0.4002130195933568.
In general, it was seen that the data was best fit into 3 or 4 clusters. Plotting the PCA projection of clusters gave a better insight into the clusters that were formed by these various methods - it was seen that for a high value of the number of clusters i.e. k, the clusters were overlapping each other and hence were not considered good clustering, even though the silhouette score was high. By seeing the clusters visually, a better and correct result was reached.


ASPELL(1) Aspell Abbreviated User's Manual ASPELL(1) NAME aspell - interactive spell checker SYNOPSIS aspell [options] <command> DESCRIPTION aspell is a utility program that connects to the Aspell library so that it can function as an ispell -a replacement, as an independent spell checker, as a test utility to test out Aspell library features, and as a utility for managing dictionaries used by the library. The Aspell library contains an interface allowing other programs direct access to its functions and therefore reducing the complex task of spell checking to simple library calls. The default library does not contain dictionary word lists. To add language dictionaries, please check your distro first for modified dictionaries, otherwise look here for base lan‐ guage dictionaries <http://aspell.net>. The following information describes the commands and options used by the Aspell Utility. This manual page is maintained separately from the official documentation so it may be out of date or incomplete. The official documentation is maintained as a Texinfo manual. See the `aspell' entry in info for more complete documentation. COMMANDS <command> is one of: usage, -? Send a brief Aspell Utility usage message to standard output. This is a short summary listing more common spell-check commands and options. help Send a detailed Aspell Utility help message to standard output. This is a complete list showing all commands, options, filters and dictionaries. version, -v Print version number of Aspell Library and Utility to standard output. check <file>, -c <file> Spell-check a single file. pipe, -a Run Aspell in ispell -a compatibility mode. list Produce a list of misspelled words from standard input. [dump] config Dump all current configuration options to standard output. config <key> Send the current value of <key> to standard output. soundslike Output the soundslike equivalent of each word entered. munch Generate possible root words and affixes from an input list of words. expand [1-4] Expands the affix flags of each affix compressed word entered. clean [strict] Cleans an input word list so that every line is a valid word. munch-list [simple] [single|multi] [keep] Reduce the size of a word list via affix compression. conv <from> <to> [<norm-form>] Converts <from> one encoding <to> another. norm (<norm-map>|<from> <norm-map> <to>) [<norm-form>] Perform Unicode normalization. [dump] dicts|filters|modes Lists available dictionaries, filters, or modes. dump|create|merge master|personal|repl <wordlist> dump, create, or merge a master, personal, or replacement word list. DICTIONARY OPTIONS The following options may be used to control which dictionaries to use and how they behave. --master=<name>, -d <name> Base name of the dictionary to use. If this option is specified then Aspell will either use this dictionary or die. --dict-dir=<directory> Location of the main dictionary word list. --lang=<string>, -l <string> Language to use. It follows the same format of the LANG environ‐ mental variable on most systems. It consists of the two letter ISO 639 language code and an optional two letter ISO 3166 country code after a dash or underscore. The default value is based on the value of the LC_MESSAGES locale. --size=<string> The preferred size of the dictionary word list. This consists of a two char digit code describing the size of the list, with typi‐ cal values of: 10=tiny, 20=really small, 30=small, 40=med-small, 50=med, 60=med-large, 70=large, 80=huge, 90=insane. --variety=<string> Any extra information to distinguish this variety of dictionary from other dictionaries which may have the same lang and size. --jargon=<string> Please use the variety option since it replaces jargon as a better choice. jargon will be removed in the future. --word-list-path=<list of directories> Search path for word list information files. --personal=<file>, -p <file> Personal word list file name. --repl=<file> Replacements list file name. --extra-dicts=<list> Extra dictionaries to use. --ignore-accents This option is not yet implemented. CHECKER OPTIONS These options control the behavior of Aspell when checking documents. --ignore=<integer>, -W <integer> Ignore words <= <integer> characters in length. --ignore-case, --dont-ignore-case Ignore case when checking words. --ignore-repl, --dont-ignore-repl Ignore commands to store replacement pairs. --save-repl, --dont-save-repl Save the replacement word list on save all. --sug-mode=<mode> Suggestion <mode> = ultra|fast|normal|bad-spellers FILTER OPTIONS These options modify the behavior of the various filters. --add-filter=<list>, --rem-filter=<list> Add or remove a filter. --add-filter-path=<paths>, --rem-filter-path=<paths> Add or remove paths searched for filters. --mode=<string>, -e, -H, -t, -n Sets the filter mode. Mode is one of none, url, email, html, tex or nroff. The alternative shortcut options are '-e' for email, '-H' for Html/Sgml, '-t' for Tex or '-n' for Nroff. --encoding=<string> encoding the document is expected to be in. The default depends on the current locale. --add-email-quote=<list>, --rem-email-quote=<list> Add or Remove a list of email quote characters. --email-margin=<integer> Number of chars that can appear before the quote char. --add-html-check=<list>, --rem-html-check=<list> Add or remove a list of HTML attributes to always check. For example, look inside alt= tags. --add-html-skip=<list>, --rem-html-skip=<list> Add or remove a list of HTML attributes to always skip while spell checking. --add-sgml-check=<list>, --rem-sgml-check=<list> Add or remove a list of SGML attributes to always check for spell‐ ing. --add-sgml-skip=<list>, --rem-sgml-skip=<list> Add or remove a list of SGML attributes to always skip while spell checking. --sgml-extension=<list> SGML file extensions. --tex-check-comments, --dont-tex-check-comments Check TeX comments. --add-tex-command=<list>, --rem-tex-command=<list> Add or Remove a list of TeX commands. RUN-TOGETHER WORD OPTIONS These may be used to control the behavior of run-together words. --run-together, --dont-run-together, -C, -B Consider run-together words valid. --run-together-limit=<integer> Maximum number of words that can be strung together. --run-together-min=<integer> Minimal length of interior words. MISC OPTIONS Miscellaneous options that don't fall under any other category. --conf=<file name> Main configuration file. This file overrides Aspell's global defaults. --conf-dir=<directory> Location of main configuration file. --data-dir=<directory> Location of language data files. --keyboard=<keyboard> Use this keyboard layout for suggesting possible words. These spelling errors happen if a user accidently presses a key next to the intended correct key. --local-data-dir=<directory> Alternative location of language data files. This directory is searched before data-dir. --home-dir=<directory> Directory Location for personal wordlist files. --per-conf=<file name> Personal configuration file. This file overrides options found in the global config file. ASPELL UTILITY OPTIONS These options are part of the aspell Utility and work independently of the library. --backup, --dont-backup, -b, -x The aspell utility creates a backup file by making a copy and appending .bak to file name. This only applies when the command is check <file> and the backup file is only created if any spell‐ ing modifications take place. --byte-offsets, --dont-byte-offsets Use byte offsets instead of character offsets. --guess, --dont-guess, -m, -P Create missing root/affix combinations not in the dictionary in pipe mode. --keymapping=aspell, --keymapping=ispell The keymapping to use, either aspell for the default mapping or ispell to use the same mapping that the Ispell utility uses. --reverse, --dont-reverse Reverse the order of the suggestions list in pipe mode. --suggest, --dont-suggest Suggest possible replacements in pipe mode. If false, Aspell will simply report the misspelling and make no attempt at suggestions or possible corrections. --time, --dont-time Time the load time and suggest a time in pipe mode. In addition Aspell will try to make sense out of Ispell's command line options so that it can function as a drop in replacement for Ispell. If Aspell is run without any command line options it will display a brief help screen and quit. CONFIGURATION Aspell can accept options via global or personal configuration files so that you do not need to specify them each time at the command line. The default global configuration file is /etc/aspell.conf or another file specified by option --conf and is checked first. The default per user configuration file ~/.aspell.conf located in the $HOME directory (or another file specified by option --per-conf) is checked next and over‐ rides options set in the global config file. Options specified at either the command line or via an environmental variable override those speci‐ fied by either configuration file. Each line of the configuration file has the format: option [value] where option is any one of the standard library options above without the leading dashes. For example the following line will set the default lan‐ guage to Swiss German: lang de_CH There may be any number of spaces between the option and the value, how‐ ever it can only be spaces, i.e. there is no '=' between the option name and the value. Comments may also be included by preceding them with a '#' as anything from a '#' to a newline is ignored. Blank lines are also allowed. The /etc/aspell.conf file is a good example of how to set these options and the Aspell Manual has more detailed info. SEE ALSO aspell-import(1), prezip-bin(1), run-with-aspell(1), word-list-com‐ press(1) Aspell is fully documented in its Texinfo manual. See the `aspell' entry in info for more complete documentation. SUPPORT Support for Aspell can be found on the Aspell mailing lists. Instruc‐ tions for joining the various mailing lists (and an archive of them) can be found off the Aspell home page at <http://aspell.net>. Bug reports should be submitted via the Sourceforge Tracker rather than being posted to the mailing lists. AUTHOR This manual page was written by Brian Nelson<EMAIL_ADDRESS>based on the Aspell User's Manual, Copyright © 2002 Kevin Atkinson. Updated Nov 2006 by Jose Da Silva<EMAIL_ADDRESS>and Dec 2006 by Kevin Atkin‐ son<EMAIL_ADDRESS>GNU 2006-12-10 ASPELL(1)


How values can help combat Stereotype Threat at Dev Bootcamp
12/13/2014
If I have to pick one single value that is important to me that would be Achievement. The drive to achieve is an overwhelming feeling that forces me to do more and go beyond what a typical person would consider sufficient. Over my career, I have had to learn different programming languages and platforms (PowerBuilder, Java, PERL, JSP, J2EE). In our company I have had to wear many hats as a developer, support specialist, software architect. While it was very stressful, at the end of the day when our software went from being sold to a handful of small companies to being used at every Fortune 500 company as well as many small companies in every corner of the world, the sense of the achievement has made the scarifies well worth it. As an immigrant, I think my personal experience confirms that anything can be achieved through hard work and perseverance. So the best way to combat stereotypes at DBC will be through hard work and achieving results.


Monday's Best Bet: Manny Wah gets right setup in Steel Valley Sprint
by Jarrod HorakMonday’s 7th race from Mahoning Valley is the $250,000 Steel Valley Sprint Stakes for 3-year-olds dashing six furlongs on the main track (Post Time 3:33 p.m. ET).
Click here for Mahoning Valley Entries & Results
#5 MANNY WAH (8-1) turned back and smartly captured an optional claiming sprint at this distance Sept. 27. He should sit a nice trip behind the speed at square odds.
#1 Bourbon Calling (4-1) exits back-to-back dirt sprint wins from off the pace. He almost always finishes in the top three at this distance and should get pace to chase.
#2 Call Paul (5-2) is a talented PA-bred dirt sprinter with early-pressing versatility. The consistent colt looks like a solid contender at low odds.
#12 Preamble (5-1) won his first three starts before throwing in a clunker as the chalk in the Chick Lang (G3) May 18. He drilled a Keeneland bullet for this Nov. 18 and figures to chase the pace from the outside post.
Wagers:
#5 to win
Exacta 5/1-2-12
Exacta 1-2-12/5
Trifecta 1-5/1-2-5-12/1-2-5-12
November 2019 Best Bet Winners
Nov 4 - KARSAVINA ($9.60), Quinella ($15.80)
Nov 5 - MISCHEVIOUS ALEX ($7.00)
Nov 6 - FINAL CUT ($8.00), Trifecta ($47.80)
Nov 7 - HAY FIELD ($7.50), Exacta ($20.80)
Nov 10 - FLY SO PRETTY ($23.20), Exacta ($79.50)
Nov 17 - QUEEN BEE TO YOU ($8.00)
Nov 19 - MESS'N AROUND ($6.00)
Nov 23 - MIDCOURT ($7.60), Quinella ($14.00)
Nov 24 - Fashionably Fast/Oliver Quinella ($5.20)


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|---|---|---|---|---|---|
|
|
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|
{{physician.specialization}}
|
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|
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|


Resources¶
File ID¶
Classes like dataloder.Dataloader
usually need file_id
to locate
resources. The string will be passed to file_utils.file_utils.get_resource_file_path()
or
file_utils.file_utils.import_local_resources()
.
The format of file_id
is name[@source][#processor]
,
where source
and processor
are optional.
name
can be:A string start with “resources://”, indicating predefined resources.
A string start with “https://” indicating online resources.
A string indicating local path, absolute or relative to cwd.
source
only works whenname
indicates a predefined resources, wheresource
means where to download the dataset. See the following sections of a specific resource for all availiablesource
. If not specified, a default source will be chosen (the first one in the list showing source).preprocessor
is necessary whenname
is not a predefined resources. It has to be one of the subclass offile_utils.file_utils.ResourceProcessor
.
Examples:
file_id |
name |
source |
processor |
|---|---|---|---|
resources://MSCOCO |
MSCOCO |
default(amazon) |
Default(MSCOCOResourceProcessor) |
resources://MSCOCO@tsinghua |
MSCOCO |
tsinghua |
Default(MSCOCOResourceProcessor) |
https://cotk-data.s3-ap-northeast-1.amazonaws.com/mscoco.zip#MSCOCO |
MSCOCO |
None |
MSCOCOResourceProcessor |
./mscoco.zip#MSCOCO |
MSCOCO |
None |
MSCOCOResourceProcessor |
Download / Import Resources Manually¶
If you want to use CoTK resources in an offline environment, you can download resources and import them to cotk manually.
The download urls are placed under
./cotk/resource_config
, see Resource Configs.The following command can import a local file of resources into cache:
cotk import <file_id> <file_path>
where file_id
should start with resources://
, and file_path
is the path to the local resource. For example:
cotk import resources://MSCOCO ./MSCOCO.zip
More CLI commands here.
Predefined Resources
Word Vector¶
Glove50d¶
name:
Glove50d
processor:
_utils.resource_processor.Glove50dResourceProcessor
source:
stanford
used by:
wordvector.Glove
- Introduction
This is a file containing vector representation for words pretrained by GloVe, which is an unsupervised learning algorithm for obtaining vector representations for words. Training is performed on aggregated global word-word co-occurrence statistics from a corpus, and the resulting representations showcase interesting linear substructures of the word vector space.
This file is a 50 dimension version from
glove.6B
, which is trained on Wikipedia2014 and Gigaword5.- Statistic
Property
Dimension
50
Vocabulary Size
400,000
- Reference
[1] GloVe: Global Vectors for Word Representation. https://nlp.stanford.edu/projects/glove/
[2] Pennington J, Socher R, Manning C. Glove: Global vectors for word representation //Proceedings of the 2014 conference on empirical methods in natural language processing (EMNLP). 2014: 1532-1543.
Glove100d¶
name:
Glove100d
processor:
_utils.resource_processor.Glove100dResourceProcessor
source:
stanford
usage:
wordvector.Glove
- Introduction
This is a file containing vector representation for words pretrained by GloVe.
This file is a 100 dimension version from
glove.6B
.Refer to Glove50d for more information and references.
- Statistic
Property
Dimension
100
Vocabulary Size
400,000
Glove200d¶
name:
Glove200d
processor:
_utils.resource_processor.Glove200dResourceProcessor
source:
stanford
usage:
wordvector.Glove
- Introduction
This is a file containing vector representation for words pretrained by GloVe.
This file is a 200 dimension version from
glove.6B
.Refer to Glove50d for more information and references.
- Statistic
Property
Dimension
200
Vocabulary Size
400,000
Glove300d¶
name:
Glove300d
processor:
_utils.resource_processor.Glove300dResourceProcessor
source:
stanford
usage:
wordvector.Glove
- Introduction
This is a file containing vector representation for words pretrained by GloVe.
This file is a 300 dimension version from
glove.6B
.Refer to Glove50d for more information and references.
- Statistic
Property
Dimension
300
Vocabulary Size
40,000
Glove50d_small¶
name:
Glove50d_small
processor:
_utils.resource_processor.Glove50dResourceProcessor
source:
amazon
usage:
wordvector.Glove
- Introduction
This is a file containing vector representation for words pretrained by GloVe.
This file is a 50 dimension version from
glove.6B
and only contains the most frequency 40,000 words.Refer to Glove50d for more information and references.
- Statistic
Property
Dimension
50
Vocabulary Size
40,000
Datasets¶
MSCOCO¶
name:
MSCOCO
processor:
_utils.resource_processor.MSCOCOResourceProcessor
source:
amazon
,tsinghua
usage:
dataloader.MSCOCO
- Introduction
MSCOCO is a new dataset gathering images of complex everyday scenes containing common objects in their natural context. We neglect the images and just employ the corresponding caption.
The original data is 2017 Train/Val annotations [241MB] . We use the same train set as original data, but split the val set into dev(odd-numbered sentences) and test set(even-numbered sentences). We extract the caption and use
nltk.tokenize.word_tokenize
for tokenization. We also capitalize each sentence and add full stop to it if it does not have one.- Statistic
Property
Train
Dev
Test
Numbers of Sentences
591,753
12,507
12,507
Minimum length of Sentences
8
10
10
Maximum length of Sentences
50
48
50
Average length of Sentences
13.55
13.55
12.52
Std Deviation of Length of Sentences
2.51
2.44
2.44
Vocabulary Size
33,241
5,950
5,869
Frequency Vocabulary Size (times>=10)
8,064
1,050
1,034
- Reference
[1] COCO: Common Objects in Context. http://cocodataset.org
[2] Chen X, Fang H, Lin T Y, et al. Microsoft COCO Captions: Data Collection and Evaluation Server. arXiv:1504.00325, 2015.
MSCOCO_small¶
name:
MSCOCO_small
processor:
_utils.resource_processor.MSCOCOResourceProcessor
source:
amazon
usage:
dataloader.MSCOCO
OpenSubtitles¶
name:
OpenSubtitles
processor:
_utils.resource_processor.OpenSubtitlesResourceProcessor
source:
amazon
,tsinghua
usage:
dataloader.OpenSubtitles
- Introduction
Opensubtitle dataset is collected from movie subtitles. To construct this dataset for single-turn dialogue generation, we follow [3] and regard a pair of adjacent sentences as one dialogue turn. We set the former sentence as a post and the latter one as the corresponding response. We remove the pairs whose lengths are extremely long or short, and randomly sample 1,144,949/20,000/10,000 pairs as our train/dev/test set. The dataset is tokenized and all the tokens are in lower-case.
- Statistic
Property
Train
Dev
Test
Numbers of Post/Response Pairs
1,144,949
20,000
10,000
Average Length (Post/Response)
9.08/9.10
9.06/9.13
9.04/9.05
Vocabulary Size
104,683
18,653
12,769
Frequency Vocabulary Size (times>=10)
31,039
2,030
1,241
- Reference
[1] OpenSubtitles. http://opus.nlpl.eu/OpenSubtitles.php
[2] J. Tiedemann, 2016, Finding Alternative Translations in a Large Corpus of Movie Subtitles. In Proceedings of the 10th International Conference on Language Resources and Evaluation (LREC 2016)
[3] Vinyals O, Le Q. A Neural Conversational Model. arXiv preprint arXiv:1506.05869, 2015.
OpenSubtitles_small¶
name:
OpenSubtitles_small
processor:
_utils.resource_processor.OpenSubtitlesResourceProcessor
source:
amazon
usage:
dataloader.OpenSubtitles
- Introduction
The data is random uniformed sampled from OpenSubtitles.
Refer to OpenSubtitles for more information and references.
- Statistic
Property
Train
Dev
Test
Numbers of Post/Response Pairs
11,449
2,000
1,000
Vocabulary Size
13,984
4,661
3,079
Frequency Vocabulary Size (times>=10)
1,333
367
211
SST¶
name:
SST
processor:
_utils.resource_processor.SSTResourceProcessor
source:
stanford
usage:
dataloader.SST
- Introduction
Stanford Sentiment Treebank is the first corpus with fully labeled parse trees that allows for a complete analysis of the compositional effects of sentiment in language.
We remain the original split of dataset. The dataset is tokenized and contains capitalized letters.
- Statistic
Property
Train
Dev
Test
Numbers of Sentences
8,544
1,101
2,210
Average Length
19.14
19.32
19.19
Vocabulary Size
16,586
5,043
7,934
Frequency Vocabulary Size (times>=10)
1,685
232
441
- Reference
[1] Recursive Deep Model for Sematic Compositionality over a Sentiment Treebank. https://nlp.stanford.edu/sentiment/
[2] Socher R, Perelygin A, Wu J, et al. Recursive deep models for semantic compositionality over a sentiment treebank //Proceedings of the 2013 conference on empirical methods in natural language processing. 2013: 1631-1642.
SwitchboardCorpus¶
name:
SwitchboardCorpus
processor:
_utils.resource_processor.SwitchboardCorpusResourceProcessor
source:
amazon
usage:
dataloader.SwitchboardCorpus
- Introduction
Switchboard is a collection of about 2,400 two-sided telephone conversations among 543 speakers (302 male, 241 female) from all areas of the United States. A computer-driven robot operator system handled the calls, giving the caller appropriate recorded prompts, selecting and dialing another person (the callee) to take part in a conversation, introducing a topic for discussion and recording the speech from the two subjects into separate channels until the conversation was finished. About 70 topics were provided, of which about 50 were used frequently. Selection of topics and callees was constrained so that: (1) no two speakers would converse together more than once and (2) no one spoke more than once on a given topic.
We introduce the data processed by Zhao, Ran and Eskenazi[4], which construct the set
multi_ref
for one-to-many dialog evaluation (One context, multiple responses).multi_ref
was constructed by extracting multiple responses for single context with retrieval method and annotation on the other test set. For the details, please refer to [4].We used their training set, dev set and test set. That is, capitalization, tokenization, splits of dataset and any other aspects of our data are the same as theirs (in their version, utterances are lowercase and are not tokenized).
However, there are two differences between our data with theirs:
We ensure that any two consecutive utterances come from different speakers, by concatenating the original consecutive utterances from the same speakers during pre-processing of
dataloader.SwitchboardCorpus
. (This is because we want to be compatible with other multi-turn dialog set.)To avoid the gap between training and test, we have to remove some samples from
multi_ref
, where the target speaker is the same as the last one in the context.- Statistic
Property
Train
Dev
Test
Numbers of Sessions
2,316
60
62
Minimum Number of Turns (per session)
3
19
8
Maximum Number of Turns (per session)
190
144
148
Average Number of Turns (per session)
59.47
58.92
58.95
Std Deviation of Number of Turns
27.50
26.91
32.43
Minimum Length of Sentences
3
3
3
Maximum Length of Sentences
401
185
333
Average Length of Sentences
19.03
19.12
20.15
Std Deviation of Number of Sentences
20.25
19.65
21.59
Vocabulary Size
24,719
7,704
6,203
Frequency Vocabulary Size (times>=10)
9,508
1,021
1,095
- Reference
[1] Switchboard-1 release 2. https://catalog.ldc.upenn.edu/LDC97S62
[2] John J G and Edward H. Switchboard-1 release 2. Linguistic Data Consortium, Philadelphia 1997.
[3] NeuralDialog-CVAE. https://github.com/snakeztc/NeuralDialog-CVAE
[4] Zhao, Tiancheng and Zhao, Ran and Eskenazi, Maxine. Learning Discourse-level Diversity for Neural Dialog Models using Conditional Variational Autoencoders. ACL 2017.
SwitchboardCorpus_small¶
name:
SwitchboardCorpus_small
processor:
_utils.resource_processor.SwitchboardCorpusResourceProcessor
source:
amazon
usage:
dataloader.SwitchboardCorpus
- Introduction
The data is random uniformed sampled from SwitchboardCorpus.
Refer to SwitchboardCorpus for details and references.
- Statistic
Property
Train
Dev
Test
Numbers of Sessions
463
12
12
Vocabulary Size
13,235
5,189
4,729
Frequency Vocabulary Size (times>=10)
3,923
341
317
Ubuntu¶
name:
Ubuntu
processor:
_utils.resource_processor.UbuntuResourceProcessor
source:
amazon
,tsinghua
usage:
dataloader.UbuntuCorpus
- Introduction
Ubuntu Dialogue Corpus 2.0 is a dataset containing a mass of multi-turn dialogues. The dataset has both the multi-turn property of conversations in the Dialog State Tracking Challenge datasets, and the unstructured nature of interactions from microblog services such as Twitter.
We build the dataset using the ubuntu-ranking-dataset-creator[1], without tokenization, lemmatization or stemming. The dataset contains capital character. The positive example probability is set to 1.0 for training set. The examples for training/dev/test sets are set to 1,000,000/19,560/18,920 as default. Other settings are default.
- Statistic
Property
Train
Dev
Test
Numbers of Sessions
1,000,000
19,560
18,920
Minimum Number of Turns (per session)
3
3
3
Maximum Number of Turns (per session)
19
19
19
Average Number of Turns (per session)
4.95
4.79
4.85
Std Deviation of Number of Turns
2.97
2.79
2.85
Minimum Length of Sentences
2
2
2
Maximum Length of Sentences
977
343
817
Average Length of Sentences
17.98
19.40
19.61
Std Deviation of Number of Sentences
16.26
17.25
17.94
Vocabulary Size
576,693
47,288
47,847
Frequency Vocabulary Size (times>=10)
23,498
2,568
2,565
- Reference
[1] Ubuntu Dialogue Corpus v2.0. https://github.com/rkadlec/ubuntu-ranking-dataset-creator
[2] R. Lowe, N. Pow, I. Serban, and J. Pineau. The ubuntu dialogue corpus: A large dataset for research in unstructured multi-turn dialogue systems. In Special Interest Group on Discourse and Dialogue (SIGDIAL), 2015a.
Ubuntu_small¶
name:
Ubuntu_small
processor:
_utils.resource_processor.UbuntuResourceProcessor
source:
amazon
usage:
dataloader.UbuntuCorpus


Contents
Cover
Title Page
Copyright Page
Table of Contents
Chapter 1
Epilogue
Colophon
Cover
Title Page
Copyright Page
Table of Contents
Half Title Page
Chapter 1
Epilogue
Colophon


public class Chart extends java.lang.Object
Chart()
static org.jfree.chart.JFreeChart
produce(Month[] months)
clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait
public Chart()
public static org.jfree.chart.JFreeChart produce(Month[] months)
months


Sixten is a programming language I've worked on as a hobby project for around six years now. The ideas and focus areas have crystallised and evolved over time, although it started mostly as aimless exploration.
In this post I'd like to say a few words about what's special about Sixten, why I'm doing it, and what I find important to focus on when implementing programming languages.
Unboxed data
When writing code in high-level languages I sometimes find myself missing the low-level control over the memory layout of data that languages that are "closer to the metal" give you, because it's often essential for performance to avoid heap allocations in hot spots of your programs.
In Haskell you can use the UnboxedSums and UnboxedTuples extensions which allow you to write code that uses unboxed values, but these types have a kind that is not generally compatible with ordinary polymorphic types and functions, making the support feel second class.
In Sixten the aim is to make the support for unboxed data first class.
Concretely, the benefits of this are
- Being able to write code that doesn't do heap allocations, thus giving the programmer the tools they need to reduce garbage collection overheads.
- Increasing cache locality since data is packed tighter together.
- Saving memory wasted on indirections.
Type representation polymorphism
Most high-level languages with parametrically polymorphic (or generic) data types and functions, even if it is offered under a different name like templates, fall into one of the following two categories:
They use a uniform representation for polymorphic data, which is usually word-sized. If the data is bigger than a word it's represented as a pointer to the data.
They use monomorphisation or template instantiation, meaning that new code is generated statically whenever a polymorphic function is used at a new type.
Neither of these approaches is perfect: With the uniform representation approach we lose control over how our data is laid out in memory, and with the template instantiation approach we lose modularity and polymorphic recursion:
With a uniform representation we cannot for example define polymorphic intrusive linked lists, where the node data is stored next to the list's "next pointer". Given the (Haskell) list definition
The representation in memory of the list Cons x (Cons y Nil)
in languages with a uniform representation is something like:
We cannot define a polymorphic list whose representation is intrusive:
What we gain from using a uniform representation is modularity: A polymorphic function, say map : forall a b. (a -> b) -> List a -> List b
, can be compiled once and used for any types a
and b
.
With monomorphisation, we are able to define intrusive lists, like in the following C++-like code:
However, unless we know all the types that A
will be instantiated with in advance, we have to generate new code for every instantiation of the function, meaning that we have partly lost modular compilation. We also can't have polymorphic recursion since that would require an unbounded number of instantiations. Template instantiation also leads to bigger code since it generates multiple versions of the same function.
What is gained is the ability to more finely express how our data is laid out in memory, which for instance means that we can write code that is cache-aware and which uses fewer memory allocations.
Sixten gives us both: it allows us to control the memory layout of our data all the while retaining modularity.
A definition of the list type in Sixten is
The difference between Sixten and (for instance) Haskell is that everything is unboxed by default, meaning that the a
field in the Cons
constructor is not represented by a pointer to an a
, but it is an a
. This also means that we have to mark where we actually want pointers with the Ptr
type constructor. The Cons
constructor has to hold a pointer to the tail of the list because we would otherwise create an infinite-size datatype, which is not allowed in Sixten.
The novel feature that allows this is type representation polymorphism. Types are compiled to their representation in Sixten. In the current implementation of Sixten the representation consists only of the type's size in memory, so e.g. Int
is compiled to the integer 8
on a 64-bit system. A polymorphic function like map : forall a b. (a -> b) -> List a -> List b
implicitly takes the types a
and b
as arguments at runtime, and its compiled form makes use of the type representation information to calculate the memory offsets and sizes of its arguments and results that are needed to be representation polymorphic.
Sixten's type representation polymorphism is closely related to research on intensional polymorphism. What sets Sixten apart is the way type representations are used in the compiled code. Sixten doesn't need to use type representations to perform code selection, but rather compiles polymorphic functions to single implementations that leverage the information in the type representation to be general enough to work for all types. Type representations are also not structural in Sixten, but consist simply of the size of the type.
TODO mention Swift
Optional monomorphisation
This kind of polymorphism is potentially slower than specialised functions since it passes around additional implicit arguments and does more calculation at runtime. Some of this inefficiency should be offset by having better memory layout than systems using uniform representations, meaning better cache behaviour. Also note that type representation polymorphism does not preclude creating specialised versions of functions known to be performance-critical, meaning that we can choose to use monomorphisation when we want to.
TODO mention C#
Dependent types
- Pi types
- Inductive families
Dependent types and unboxed data seem like a combination that could open up for implementing some low-level tricks safely, such as strings or arrays that are stored unboxed up to a certain size.
Quote conclusion of http://www.cs.nott.ac.uk/~psztxa/publ/ydtm.pdf
Editor support
- incremental compiler built on a query-based architecture.
- Language server built in from the start.
Fast type checking
- smalltt
- variable representations
- gluing
- parallel type checking


The Image Viewer allows users to view a diffraction image located on the file server at SSRL. The image files are subject to the Unix security restriction. The user must have a read permission to the image file in order to load and view the image.
The user may use this viewer without having a beam time.
The viewer comprises of three main frames: left, right and bottom. The left frames consists of a JPEG image and a control panel for adjusting the image display, such as zooming and paning. The left frames either displays an image header or an image analysis result. The bottom frames is a file browser that allows the user to select an image file and explore directories.
Image files must be located on the smbfs file server to be viewable by the Image Viewer. There are three ways to select an image to be displayed. One is to type in an image path in the text box in the left frame. The file path typically begins with /home or /data and followed by the user's login name, for example, /home/joeuser/images/remo_1_001.img.
The second way to select an image is to user the file browser in the bottom frame.
The third way is to click on "Prev" or "Next" button in the image control panel, which is located on the right side of the display image. These buttons only work if an image has already been loaded. The "Prev" button loads an image whose index in the file name is immediately lower than the current image. For example, if the current image is remo_3_015.img, clicking the "Prev" button will load remo_3_014.img or clicking on the "Next" button will load remo_3_015.img. If the current image is the first image in the dataset, clicking the "Prev" button yields no effect. Same go with the "Next" button if the current image is the last in the dataset.
The file path of the image is displayed above the JPEG image.
The user must have a read permission to the image file to view the image.
The file browser displays a list of sub directories and image files (file extension .img) of a given directory on smbfs. The directory is set to /data/userid by default. The user can save the current directory for use in a future session by clicking the Save button in the Preference tab.
The first line in the file browser displays the current directory. The second line is an HTML link [Up] that allows thw user to change directory up one level to the parent of the current directory. The following lines are sub directories or image files. Each line contains file permissions, file/directory name, size in bytes, and last modification date and time. To go down to a sub directory, click the HTML link for desired sub directory. To select an image for display, click the HTML link for the desired image file.
Note that the user can only browse files and directories that have a read permission for his Unix login id.
The user can zoom, pan, adjust brightness and set display size of the image by using the image control panel located on the right side of the displayed image. The control panel consists of (from top to bottom) a thumbnail image, pan buttons, zoom buttons, brightness buttons, image size drop down menu, Analyze Image button, and Prev/Next button.
There are three ways to pan the image. One is by clicking on the image. The image will be moved so that the mouse click position will be come the center of the display.
The second way to pan the image is by using the thumbnail located at the top of the image control panel. When the zoom level is greater than 1 (the image is zoomed in), the thumbnail displays a red regtangle representing the area of the image being displayed. The more the image is zoomed in, the smaller the rectangle. The user can click anywhere in the thumnail to move the rectangle.
The third way to pan the image is by clicking one of the pan buttons, which are located below the thumbnail and above the zoom buttons.
The user can zoom the image in and out by using the zoom buttons or by entering a zoom level in the zoom text box. When the image is zoomed out to the maximum, the zoom is level is 1.0; it can not go lower than this. The higher the zoom level the more zoomed in the image. The left arrow button is for zooming out and the right is for zooming in.
Brightness of the image is controlled by the Brightness buttons and text box below the zoom buttons. The left arrow button makes the image darker and the right button makes it lighter. The brightness range is usually between 100 - 2000. The lower the number the darker the image. Brightness of 200-400 is typically good for most images.
By default the image size is 400 by 400 pixels. The user can change the
image display size by selecting a different size from the drop down
menu
located under the brightness buttons.
The button "Last Image" loads the last collected image at the
selected beamline. The button is disabled if this webice session
is not connected to a beamline. See Connecting to a Beamline section to
learn how to connect to a beamline in order to get live data.
Prev and Next button load previous image and next image in the
dataset, respectively. A dataset is a series of images in a
directory, whose file names share the same prefix. For example,
infl_1_001.img and infl_1_020.img are in the same dataset, whereas
infl_2_001.img is not. If the currently displayed image is
infl_1_005.img, "Prev" button will load infl_1_004.img and "Next"
button will load infl_1_006.img.
Image information is displayed in the frame on the right side. It has a grey background. There are two types of information that can be viewed: image header and image analysis result. At the top of the frame, there are two tabs, "Image Header" and "Analyze Image", that the user can click in order to select the type of information to be displayed. The selected tab will have a white background and the unselected tab will have a grey background.
When an image is loaded, the "Image Header" tab is automatically selected and the image header information is displayed. The "Analyze Image" tab is automatically selected and the image analysise result is displayed when the user clicks the "Analyze Image" button in the image control panel.
Image header information is retrieved via the image server. These are headers stored in the actual image file. The following describes what they are:
| Header Name | Description | Example |
|---|---|---|
| OVERLOAD_CUTOFF | The highest intensity value for each pixel | 65535 |
| HEADER_BYTES | ??? | 512 |
| DIM | ??? | 2 |
| BYTE_ORDER | Machine byte order (little or big endian) | little_endian |
| TYPE | Type of value stored for each pixel | unsigned_short |
| SIZE1 | Width of the image in pixel | 2304 |
| SIZE2 | Height of the image in pixel | 2304 |
| PIXEL_SIZE | Size of the pixel in cm | 0.0816 |
| BIN | ??? | none |
| DETECTOR_SN | ??? | 403 |
| TIME | Exposure time | 30.041451 |
| DISTANCE | Detector distance to sample | 175.000000 |
| PHI | Phi | 89.000000 |
| OSC_START | Phi | 89.000000 |
| OSC_RANGE | Oscillation range | 1.500000 |
| WAVELENGTH | Wavelength in Anstrom | 0.979413 |
| BEAM_CENTER_X | Beam center X in cm | 94.000000 |
| BEAM_CENTER_Y | Beam center Y in cm | 94.000000 |
| PIXEL SIZE | Size of the pixel in cm | 0.0816 |
| OSCILLATION RANGE | Oscillation range | 1.5 |
| EXPOSURE TIME | Exposure time in seconds??? | 30.0415 |
| TWO THETA | ??? | 0 |
| DETECTOR | Name of detector | ADSC QUANTUM4 |
| BEAM CENTRE | Beam center X and Y | 94 94 |
Below the image resize drop down menu in the control panel, the user will find a button that can be toggled between "Analyze Image" and "Display Image" button text. When an image is loaded, the button text shows the "Analyze Image" text.
When the user clicks on "Analyze Image", WebIce will run a program called spotfinder to analyze the image. spotfinder generates an image with highlighted features and a log files containing the analysis result. When the program finishes, WebIce will display the image generated by spotfinder. The button text will change to "Display Image". In the image info frame on the right side, the "Analyze Image" tab will be automatically selected to display the content of the log file under.
The user can still manually select the "Header" tab to see the image header information.
When the user clicks on "Display Image" button, WebIce will display the original image. The "Header" tab in the right frame will be selected automatically to display the image header. Once the image has been analyzed, the user can click on the "Analyze Image" tab next to the "Header" tab to see the analysis result at any time.
Once the image has been analyzed, it will not be analyzed again when the user clicks the "Analyze Image" button. WebIce will simply load the result image and log file stored in /data/userid/webice/image directory (unless the time stamp of the original image is newer than these files).
The log file and image file generate by spotfinder are saved in /data/userid/webice/image directory, where userid is the user login name. For example, if joeuser, currently viewing /data/joeuser/dataset1/inf_1_001.img image, clicks the "Analyze Image" button, output files inf_1_001.log and inf_1_001.spt.img will be saved in /data/joeuser/webice/image directory. The content of the log file is displayed in the image info frame with a grey background, located on the right side.
This is the result generated by spotfinder saved in the log file (see picture below).
In the output IMG image, the following features are indicated:
The program shows a brief summary of each image it has processed. Statistics shown include:
The most useful criteria to determine the quality of an image are:


Amazon GameLift Alias RoutingStrategy
RoutingStrategy
is a property of the AWS::GameLift::Alias resource
that configures the routing strategy for an Amazon GameLift (GameLift) alias. For
more information, see
the RoutingStrategy data type in the
Amazon GameLift API Reference.
Syntax
Properties
Message
-
A text message that GameLift displays for the
Terminal
routing type.Required: Conditional. If you specify
TERMINAL
for theType
property, you must specify this property.Type: String
Type
-
The type of routing strategy. For the
SIMPLE
type, traffic is routed to an active GameLift fleet. For theTerminal
type, GameLift returns an exception with the message that you specified in theMessage
property.Required: Yes
Type: String


PETE'S DRAGON A film review by David Landers Copyright 1997 David Landers
RATING: ** 1/2 out of **** - 6.0 out of 10.0 CAST: Helen Reddy (Nora), Mickey Rooney (Lampie), Red Buttons (Hoagy), Jim Dale (Dr. Terminus), Sean Marshall (Pete), Charlie Callas (Elliot - Voice) DIRECTOR: Don Chaffey CERTIFICATION: G (USA) for very mild violence YEAR OF PRODUCTION: 1977 ACADEMY AWARD NOMINATIONS: Music
PETE'S DRAGON is a Children's Adventure/Musical that follows the events of a young orphan, Pete (Sean Marshall). This film is an entertaining movie that is a combination of live action and animation. It can be viewed as a WHO FRAMED ROGER RABBIT type movie with less animation.
The movie begins with Pete trying to get away from his cruel owners, the Gogan family. He finds his way into a secluded area where we see him speaking to someone. But who? This invisible character then is seemingly responsible for knocking over a few members of the Gogan family, despite Pete's warning to not swing his "tail". After the Gogans give up looking for Pete, the two companions decide to call it a night.
The next morning, we are able to meet Elliot, a flying dragon. He's a large green mass who seems playful and timid. After breakfast, which consists of cooked apples (thanks to Elliot and his flaming ability), Pete decides to journey to the local town of Pasamaquadoy. That might not be the correct spelling, but as we'll find out, I'm not the only one who has problems with the name.
Once the two make it to the small fishing town, Elliot makes a bad entrance, causing all kinds of havoc and destruction. The townspeople think that Pete is the one causing all the trouble, when it's actually his invisible dragon. With this explanation, they think he's either lying or crazy. Pete eventually finds his way to a cave near a lighthouse, operated by Nora (Helen Reddy) and her dad, Lampie (Mickey Rooney), a drunk who is scared half to death by Elliot. Of course, when he tries to explain this to his drinking friends, they give him the same treatment as Pete.
Pete is eventually taken in by Nora and is allowed to stay at the lighthouse. Elliot has to stay in the cave, though, but the two still get to spend time together. Soon thereafter, Dr. Terminus (Jim Dale) and his assistant Hoagy (Red Buttons) arrive in the town in an attempt to sell their magical medications. The townspeople are obviously very familiar with their "work" and do not show the two a kind welcome, either. The Doc is eventually able to convince them to buy his remedies, but his biggest potential seller is not owned by him yet. When Lampie informs him that Elliot, a dragon, is staying in a cave by his lighthouse, Dr. Terminus begins his efforts to capture Elliot and use him to make more medicines. The Gogans, as well, continually attempt to steal Pete back so that he can work their farm all by himself like he used to. Elliot's mission, apparently to help orphaned kids, is extended when Nora informs Pete that her soon-to-husband, Paul (Cal Bartlett) has been missing for some time. Pete is sure that Elliot can find Paul and return him home.
PETE'S DRAGON is a fun movie, especially for younger kids. Complete with the Disney-standard songs throughout the movie, PETE'S DRAGON is a very enjoyable movie that you won't grow too tired of half way through. Older kids and adults should find it a good movie, but not as much as younger kids will.
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


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This is a random paragraph I'm writing as I think of it because I was too lazy to go copy lorem ipsum text.


First off, we had to find the rules and limits for the MakerBot Replicator 5th Gen printers. The first rule is that you can't have to object to big because the printer has to have enough room for it to print the object precisely and accurately. also, the object has to be small enough for the printer to print the whole entire thing. Next you cannot have multiple materials printing at the same time because well the machine won't be able to print them. Because the printer is an additive one it has to print with layers.
Second, We were required to scan our heads and shoulder area with a i pad looking device. The device would give a 3D model of yourself. After that we had to use meshmixer a program to fill in any holes the device missed. Sizing the head was a difficult and time consuming process... umfortunantly my head was never printed but i do have an image of what it looked like on a computer!
Third, we had to design an object that could not be made subractively which was actually a very challenging task. This means you could not take a block and shave stuff off of it to make something. For this i decided to make a large ringe shaped donut. The way that i designed it was going on fusion making a ring then i hollowed it out. I do not have the original files unfortunantly.... But it turned out great!
Finally, J Gro and i created a drip irrigation cap. What this does is when put on a water bottle it drips out water to like water plants. Ours turned out succeful first try even though it was very basic!


--- layout: post title: no title date: '2014-10-23T07:30:34+02:00' categories: - Atoleiros Battle - Storyboards picture_url: https://film-Storyboards.tumblr.com/post/100744416564 ---


本示例将为您展示NanUI激动人心的功能。
从现在开始，您能够使用您所熟悉的HTML5、CSS3以及Javascript技术来设计您的界面。然后透过NanUI界面引擎，将为您带来华丽而丰富的WinForm界面效果。告别只有黑与灰的WinForm吧！
现在就使用NanUI，为您的软件界面带来无限可能吧！
MIT 开源许可协议(MIT) 版权所有 (c) 2014 - 2016, Net Dimension Studio, Mr.Json(林选臣) 特此向任何得到本软件副本或相关文档的人授权：被授权人有权使用、复制、修改、 合并、 出版、发布、散布、再授权和/或贩售软件及软件的副本，及授予被供应人 同等权利，只 需服从以下义务： 在软件和软件的所有副本中都必须包含以上版权声明和本许可声明。 该软件是"按原样"提供的，没有任何形式的明示或暗示，包括但不限于为特定目的和不侵权 的适销性和适用性的保证担保。在任何情况下，作者或版权持有人，都无权要求任何索赔， 或有关损害赔偿的其他责任。无论在本软件的使用上或其他买卖交易中， 是否涉及合同，侵 权或其他行为。 The MIT License (MIT) Copyright (c) 2014 - 2016, Net Dimension Studio, Mr.Json(林选臣) Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.  IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
CEF Copyright (c) 2008-2014 Marshall A. Greenblatt. Portions Copyright (c) 2006-2009 Google Inc. All rights reserved. Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met: * Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer. * Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution. * Neither the name of Google Inc. nor the name Chromium Embedded Framework nor the names of its contributors may be used to endorse or promote products derived from this software without specific prior written permission.
ChromiumFX Copyright (c) 2014-2015 Wolfgang Borgsmüller All rights reserved. Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met: 1. Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer. 2. Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution. 3. Neither the name of the copyright holder nor the names of its contributors may be used to endorse or promote products derived from this software without specific prior written permission.


--- layout: application ---
The Asteroids Have Gone Missing! Where'd they go?
Maybe we'll find them back at the start ...


A report from the course XXX
S01 report
I have previously been working with gitbash and github although most of my experience has been with the VCS in intellij. Before this lecture introduction and first lab I hadn't had any experience with Markdown Github pages. During our software engineering 2 course one of the subgroups in our project group developed an website and everyone had to do a lab where we learned the basics although we used JSP and no html, css or js. We have also had labs, during the Network application course DA374C, about developing network application such as RESTful apis on servers. During our project we also used the MQTT protocol. I don't have much experience with web development but I have some experience with some of the protocols that can be used in web development such as REST and MQTT. At the moment I don't have much experience with JS, HTML or css but have seen a little bit of it. Using a texteditor such as Visual Studio Code is also new to me as we have mainly been using Intellij or android studio when working with android development. Recently I have done some selfstudying on AWS where the focus have been on the solution architect point of view. So I have some experience with webservices from AWS and which features to use, such as load balancing, domain names, dns, caching etc. For this course section my TIL is how to use Github page to create webpages, how index files works and how to use Visual Studio Code. I also refreshed my skills in gitbash as I haven't used that in a long time.
S02
Prior to this course I didn't have any experience with HTML, CSS or javascript. I had heard of them of course but hadn't done any work using it. The role of HTML is to structure the content of the website. This is done by the use of headers, sections etc inside the body. The role of CSS is to style the website. This is things like fonts, colors and layout. Javascript is used to change a website from being static to dynamic. It helps to give feedback to the user and makes it possible to for example use functions and operations without having to reload the page. The browser is the client that request recources from the server. This is done using the HTTP protocol using CRUD. This could be to create a resource using push, read a resource using get. Update a resource using put or delete a resource using Delete. In this section I learned about html, css and javascript. I didn't know much about html or css before and didn't know exactly when its used and what for. Javascript I had some knowledge of but I didnt know exactly how it interacts with html.
S03
I don't have any previous experience with client side Javascript or javascript in any context. I feel like it's a bit like java. But loosely typed since we can just use var or const here without needing to declare which variable it is. Such as if statements and how to write methods seems similar and familiar to when you do GUIS in java or in android using either java or kotlin. I actually believe that kotlin seems more similar since it is also loosely typed but without the need of ";". For this exercise I used w3schools.com alot to learn how to do css styling and how to properly implement some of the divs. Other than that I used your coding examples. for example my ducks movement is based on the code that you have shared in the examples which was really helpful, I aim for a 4 at the moment since I have some trouble with the transition on the flags. I believe my code turned out alright but I'm sure there can be plenty of improvement which I might come back and do later before the final hand in. My TIL for this course is how to use all the different elements when building a website. It gave me a better view of how to actually use css and apply it and how to interact with your html document using javascript.
S04
Here is the text for this section.
S05
Here is the text for this section.
S06
Here is the text for this section.
S07
Here is the text for this section.
S08
Here is the text for this section.
S09
Here is the text for this section.
S10
Here is the text for this section.


---
layout: post
title: I Broke Google!
date: '2009-05-18T18:00:00.005+01:00'
author: Darren Bishop
tags:
modified_time: '2009-05-18T18:10:56.412+01:00'
thumbnail: http://3.bp.blogspot.com/_Q_F_ea5EKi0/ShGU7FXX_RI/AAAAAAAAAE0/zOI4OXjsevc/s72-c/2009-05-18+18.00_ScreenShot_001.jpg
blogger_id: tag:blogger.com,1999:blog-3826577489563855618.post-451634586200145026
blogger_orig_url: http://blog.darrenbishop.com/2009/05/i-broke-google.html
---
... but it seems that the query is working now, lol.
Never seen this error page before... and I'll probably never see it again.
I'm having images of an old desktop in a very large air-craft hanging going, "Pooof"!
Update: Further more, I discovered I can't spell, lol (best of breed)


Filters:
{% for key in distinct_param_keys %}
{{ key }}
{% endfor %}
Custom Filter:
Post Processor for Legends:
Custom Series Split By:
Clip absolute value:
Plot width:
Plot height:
Y-Axis Attribute:
{{ plot_key }}
{% for plottable_key in plottable_keys %}
{{ plottable_key }}
{% endfor %}
Use Median
Only show best
Only show best final
Only show best so far
Filter NaN
Smooth Curve
Normalize Error Bar
(Figure) Split by:
(None)
(None)
{% for key in distinct_param_keys %}
{{ key }}
{% endfor %}
(Series) Split by:
{{ distinct_param_keys[0] }}
(None: split by experiment name)
{% for key in distinct_param_keys %}
{{ key }}
{% endfor %}
Update
Plot EPS
{{ plot_div|safe }}


We are a group of nonpartisan web-developers dedicated to making it easier for you to be politically active on a day to day basis. Our app streamlines political involvement so you can know you representatives, view upcoming bills, and attend political events in your community -- all in one place.
The United States Congress is the bicameral legislature of the Federal government of the United States consisting of two chambers: the Senate and the House of Representatives. The Congress meets in the United States Capitol in Washington, D.C. Both senators and representatives are chosen through direct election, though vacancies in the Senate may be filled by a gubernatorial appointment. Congress has 535 voting members: 435 Representatives and 100 Senators. The House of Representatives has six non-voting members representing Puerto Rico, American Samoa, Guam, the Northern Mariana Islands, the U.S. Virgin Islands, and Washington, D.C. in addition to its 435 voting members. Although they cannot vote, these members can sit on congressional committees and introduce legislation.
- Charles De Gaulle
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Build a reaction queue for a target compound
Summarizes the total of all resources currently in a colony store structure
Figure out which basic minerals are missing and how much
Generate a queue of reactions to produce the most needed compound
Get the required amount of basic minerals for a reaction queue
Recursively generate a list of ingredients required to produce a compound
Generated using TypeDoc
Abathur is responsible for the evolution of the swarm and directs global production of minerals. Abathur likes efficiency, XGHO2, and high lab uptime, and dislikes pronouns.


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SOLARCAINE COOL ALOE- lidocaine hydrochloride gel
MSD Consumer care, Inc.
Disclaimer: Most OTC drugs are not reviewed and approved by FDA, however they may be marketed if they comply with applicable regulations and policies. FDA has not evaluated whether this product complies.
Active ingredient
Lidocaine hydrochloride 0.5%
Purpose
External analgesic
Uses
temporarily relieves pain and itching due to:
- minor skin irritations
- minor burns
- sunburn
- scrapes
- minor cuts
- insect bites
Warnings
For external use only
Do not use in large quantities, particularly over raw surfaces or blistered areas
When using this product keep out of eyes
Stop use and ask a doctor if
- condition gets worse
- symptoms last more than 7 days
- symptoms clear up and occur again in a few days
Keep out of reach of children. If swallowed, get medical help or contact a Poison Control Center right away.
Directions
- adults and children 2 years of age and older: apply to affected area not more than 3 to 4 times daily
- children under 2 years of age: ask a doctor
Inactive Ingredients
aloe barbadensis leaf juice, water, propylene glycol, glycerin, triethanolamine, isopropyl alcohol (0.06% v/v), polysorbate 80, carbomer, diazolidinyl urea, menthol, disodium EDTA, yellow 5, blue 1
© Copyright & Distributed by MSD
Consumer Care, Inc., PO Box 377,
Memphis, TN 38151 USA, a subsidiary
of Merck & Co., Inc.,
Whitehouse Station, NJ USA
PRINCIPAL DISPLAY PANEL - 226g Bottle Label
Cools Instantly!
#1
SUNBURN
RELIEF BRAND
Solarcaine®
cool aloe
BURN RELIEF FORMULA
Pain Relieving Gel with Lidocaine HCl
Restores Moisture
to Sunburned Skin
Doctor Tested
25897-02
NET WT 8 OZ (226g)
MSD Consumer care, Inc.


Freelanced building small apps for local businesses
Applied to jobs across the country
Tech recruiter connected me to a startup in CA
Technical Screen
function fizzBuzz (number) {
const fizz = number % 3 === 0
const buzz = number % 5 === 0
if (fizz && buzz) return 'FizzBuzz'
if (fizz) return 'Fizz'
if (buzz) return 'Buzz'
return number
}
Flew me to San Francisco
Interview for Software Engineer Position
What did the interview test me on?
Communications skills
Knowledge of the Ruby programming language
Understanding of distributed web applications
Problem-solving and collaboration abilities
Interest and enthusiam for educational technology
Got the Job and moved to San Francisco
5 years later
Senior Software Engineer & Engineering Manager
What are some cool things happening in technology?
Artificial Intelligence
3D Printing
Robotics
Electric Vehicles
Fusion Power
Gaming & Virtual Reality
Music and video production
Space Exploration
Controlled Environment Agriculture
What companies are working on this stuff?
OpenAI
Makerbot
SpaceX
NASA
Telsa
Farmbot
Netflix
What jobs are there in tech?
Designer
Software Engineer
UI/UX Researcher
Research Scientist/Engineer
Product Manager / Project Manager
Customer Success
Sales Representative
Information Technology (IT)
Roboticist
Video Producer / Cinematographer
Animator
Data Scientist
Game Developer
Creativity
Design
Design
Design / Creative
Designer
UI / UX Researcher
Video Producer / Cinematographer / Editor
Animator
Technical / Scientific
Engineer (Software, Mechanical, Electrical, etc)
Research Scientist / Engineer (MS, Phd)
Roboticist
IT Specialist
Data Scientist / Machine Learning
Quality Assurance Analyst
Business / Management / Non-Technical
Product Manager / Project Manager
Customer Success
Sales
Marketing
Human Resources
Salaries
Job Title
Salary
Growth
Designer
$68,106
24%
Software Engineer
$109,642
21%
Project Manager
$86,526
33%
Data Scientist
$122,250
37%
https://www.indeed.com
What are the main business areas in tech?
Freelance & Contract work
Startups
Enterprise Companies
Non-profit
Government
Academia
What is a freelancer (independent contractor)?
A term commonly used for a person who is self-employed and not necessarily committed to a particular employer long-term.
What is a startup?
A company or project initiated by an entrepreneur to seek, effectively develop, and validate a scalable business model.
What is the difference between a startup and small business?
Startup is a hypothesis for what a customer or client is willing to pay for (new cool app)
A small business has a concrete model for a business (pizza shop)
What is an Enterprise Company?
1000 employees
1 Billion+ in Revenue
What is venture capital?
A form of financing that is provided by firms or funds to startups, early-stage, and emerging companies that have been deemed to have high growth potential or which have demonstrated high growth (in terms of number of employees, annual revenue, or both).
What is a bootstrapped startup?
A startup that takes little or no venture capital or outside investment. It means relying on your own savings and revenue to operate and expand
What opportunities are there in government and academia?
Research (STEM and humanities)
Cybersecurity
IT for government and educational institutions
Military Support
What kind of company is Artemis?
Artemis is an agricultural technology startup
Less than 20 employees
Company is valued at under $10 million
In AgTech new technologies are applied to agricultural processes to increase production, increase safety, and improve sustainability
Other related fields including AgTech, Biotech, and Edtech
What are the educational paths to get a tech job?
Community college
4 year University
Coding Bootcamps
Massive open online course (MOOC)
Teach yourself
What are some degrees that would be helpful to get into tech?
Computer Science & IT
Engineering
Psychology
Business & Entrepreneurship
Communications & Multimedia
Mathematics
What are some alternative and emerging paths for getting into tech?
You can override background transitions per-slide.
<section data-background-transition="zoom">
Iframe Backgrounds
Since reveal.js runs on the web, you can easily embed other web content. Try interacting with the page in the background.
Marvelous List
No order here
Or here
Or here
Or here
Fantastic Ordered List
One is smaller than...
Two is smaller than...
Three!
Clever Quotes
These guys come in two forms, inline: The nice thing about standards is that there are so many to choose from and block:
“For years there has been a theory that millions of monkeys typing at random on millions of typewriters would
reproduce the entire works of Shakespeare. The Internet has proven this theory to be untrue.”
Intergalactic Interconnections
You can link between slides internally,
like this.
Speaker View
There's a speaker view. It includes a timer, preview of the upcoming slide as well as your speaker notes.
Set data-state="something" on a slide and "something"
will be added as a class to the document element when the slide is open.
State Events
Additionally custom events can be triggered on a per slide basis by binding to the data-state name.


| 054:037 Khan | : |
And they indeed sought to shame his guest (by asking to commit sodomy with them). So We blinded their eyes, "Then taste you My Torment and My Warnings." |
| 054:037 Maulana | : |
And certainly they endeavoured to turn him from his guests, but We blinded their eyes; so taste My chastisement and My warning. |
| 054:037 Pickthal | : |
They even asked of him his guests for an ill purpose. Then We blinded their eyes (and said): Taste now My punishment after My warnings! |
| 054:037 Rashad | : |
They negotiated with him about his guests; we blinded them. Suffer My retribution; you have been warned. |
| 054:037 Sarwar | : |
They demanded that he turn over his guests to them. |
| 054:037 Shakir | : |
And certainly they endeavored to turn him from his guests, but We blinded their eyes; so taste My chastisement and My warning. |
| 054:037 Sherali | : |
And they deceitfully sought to turn him away from his guests.  (They heard:) "Now taste ye My Wrath and My Warning." |
Concepts/Themes Covered:
This tool uses eight English parallel translations for the Holy Quran imported from Mr. Clay Smith. You can also visit Qurany Concepts for browsing the Quran themes or topics. If you want to search the Holy Quran for keywords, please visit Qurany Keywords.


Now is a good time to pause and discuss units. Physical quantities are described by a number followed by a unit of measurement. In scientific discussion, you cannot just give answers with numbers, you need to specify the unit.
If we ask you how much energy there is in your soda can and you answer 1... we then wait ... and follow by 1 what?
It could be 1 Joule, 1 Calorie or maybe 1 orange worth of energy? 1 is not an answer, you need a unit.
Units are often abbreviated. For example a second becomes s.
Usain Bolt ran the race in 9.69 s
We can also modify the units by adding a prefix. Here is a list of common prefixes that can be added to any unit to decrease or increase its value by the corresponding factor (expressed in scientific notation)
| name | abbreviation | factor |
| :--- | :--- | :--- |
| pico | p |
Usain Bolt ran the 100 m race. This is the same as the 0.1 km (0.1 kilometer) race.
Over time, physicists have invented many different units for the same physical quantities.
The horsepower is the typical power (energy per time) of a working horse. The unit is abbreviated by hp. It is often used to measure power output of engines instead of the more standard Watt (W) for electricity.
1 hp = 745 W
Here are other well-known unit conversions between inch (in), miles (mi) and meters (m)
All three units in, mi and m represents the same physical quantity of length. They are just different measurement standard.
In science, we need to choose a set of units and stick to it in order to not be constantly confused. The standard currently used is the SI unit (for Systeme International).
Here are the SI units we have seen up to this point in this class.
| SI unit | abbreviation | physical quantity represented | | :--- | :--- | :--- | | second | s | time | | meter | m | length | | gram | g | mass | | joule | J | energy | | watt | W | power |
You still need to know about other (non SI) units like the Calorie. You do not need to remember that a Calorie is 4184 Joules, but you need to know that a Calorie is a unit of measurement for the physical quantity known as energy.
Many units can be expressed in term of others. The easiest example that we have just seen is the unit for power the Watt.
Since power = energy/time, it means that
1 Watt = 1 Joule/second (pronounced 1 Joule per second). One can say that a lightbulb is rated at 60 W or it is rated at 60 J/s. These are equivalent ways of stating the same physical quantity.
Another well known example is speed which is in m/s (meters per second). We do not have a special name for it, we just always express speed in m/s or km/h or mi/h. The last one is often written as mph (miles per hour).
note that hour (h) is not a SI unit and must be converted to second 1 h = 3600 s
Finally, one can go in reverse. If power = energy/time, we can also write that
energy = power * time
Instead of saying we have 1 Joule of energy, we can say we have 1 Ws (pronounced 1 Watt second). We can use Wh (watt hour) or kWh (kiloWatt hour). Because these are units of power multiplied by units of time, the result are units of energy.
Finally at the end of this page, we get to the kWh which is the unit of energy used in your bills at home by power companies. It is almost a SI unit (the kilo-Watt part) but not quite since hour is not a SI unit, it should be second. It is a unit of energy not power! This is what you buy from power from power companies, you buy energy which is a real countable physical quantity just like mass.


Your hair is beautiful, but this tool can only help you if you
have curly hair.
If your hair is not straight, try a picture with a different
angle or lighting.
{% elif predicted_class == 'unsure' %}
Try a picture with a different angle or lighting.
{% elif predicted_class == 'nonhair' %}
If this is a picture of your hair, try a picture with a different angle or lighting.
{% else %}
According to the curly people of Reddit who have hair like yours, you should start
with these products:
Shampoo: {{ shampoo }} (link)
Conditioner: {{ conditioner }} (link)
Leave-In: {{ leavein }} (link)
Give these styling products a try:
Gel: {{ gel }} (link)
Serum: {{ serum }} (link)
Cream: {{ cream }} (link)
If you feel like your curls are a little limp or dry, try a
one-time treatment such as:
Protein: {{ protein }} (link)
Deep Treatement: {{ deep }} (link)
Clarifying Shampoo: {{ clarify }} (link)
{% endif %}
Try another
photo!