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Read More...Lorem Ipsum is simply dummy text of the printing and typesetting industry. Lorem Ipsum has been the industry.
Read More...There are many variations of passages of Lorem Ipsum available, but the majority have alteration in some form, by injected
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DO NOT READ THIS FILE ON GITHUB, GUIDES ARE PUBLISHED ON https://guides.rubyonrails.org.
Ruby on Rails
7.1 Release Notes
Highlights in Rails
7.1:
Upgrading to Rails
7.1
If you're upgrading an existing application, it's a great idea to have good test
coverage before going in. You should also first upgrade to Rails
7.0 in case you
haven't and make sure your application still runs as expected before attempting
an update to Rails
7.1. A list of things to watch out for when upgrading is
available in the
Upgrading Ruby on Rails
guide.
Major Features
Railties
Please refer to the Changelog for detailed changes.
Removals
Deprecations
Notable changes
Action Cable
Please refer to the Changelog for detailed changes.
Removals
Deprecations
Notable changes
Action Pack
Please refer to the Changelog for detailed changes.
Removals
Remove deprecated behavior on
Request#content_type
Remove deprecated ability to assign a single value to
config.action_dispatch.trusted_proxies
.Remove deprecated
poltergeist
andwebkit
(capybara-webkit) driver registration for system testing.
Deprecations
Deprecate
config.action_dispatch.return_only_request_media_type_on_content_type
.Deprecate
AbstractController::Helpers::MissingHelperError
Deprecate
ActionDispatch::IllegalStateError
.
Notable changes
Action View
Please refer to the Changelog for detailed changes.
Removals
Remove deprecated constant
ActionView::Path
.Remove deprecated support to passing instance variables as locals to partials.
Deprecations
Notable changes
Action Mailer
Please refer to the Changelog for detailed changes.
Removals
Deprecations
Notable changes
Active Record
Please refer to the Changelog for detailed changes.
Removals
Remove support for
ActiveRecord.legacy_connection_handling
.Remove deprecated
::ActiveRecord::Base
config accessorsRemove support for
:include_replicas
onconfigs_for
. Use:include_hidden
instead.Remove deprecated
config.active_record.partial_writes
.Remove deprecated
Tasks::DatabaseTasks.schema_file_type
.
Deprecations
Notable changes
Active Storage
Please refer to the Changelog for detailed changes.
Removals
Remove deprecated invalid default content types in Active Storage configurations.
Remove deprecated
ActiveStorage::Current#host
andActiveStorage::Current#host=
methods.Remove deprecated behavior when assigning to a collection of attachments. Instead of appending to the collection, the collection is now replaced.
Remove deprecated
purge
andpurge_later
methods from the attachments association.
Deprecations
Notable changes
Active Model
Please refer to the Changelog for detailed changes.
Removals
Deprecations
Notable changes
Active Support
Please refer to the Changelog for detailed changes.
Removals
Remove deprecated override of
Enumerable#sum
.Remove deprecated
ActiveSupport::PerThreadRegistry
.Remove deprecated option to passing a format to
#to_s
inArray
,Range
,Date
,DateTime
,Time
,BigDecimal
,Float
and,Integer
.Remove deprecated override of
ActiveSupport::TimeWithZone.name
.Remove deprecated
active_support/core_ext/uri
file.Remove deprecated
active_support/core_ext/range/include_time_with_zone
file.Remove implicit conversion of objects into
String
by::ActiveSupport::SafeBuffer
.Remove deprecated support to generate incorrect RFC 4122 UUIDs when providing a namespace ID that is not one of the constants defined on
::Digest::UUID
.
Deprecations
Deprecate
config.active_support.disable_to_s_conversion
.Deprecate
config.active_support.remove_deprecated_time_with_zone_name
.Deprecate
config.active_support.use_rfc4122_namespaced_uuids
.
Notable changes
Active Job
Please refer to the Changelog for detailed changes.
Removals
Deprecations
Notable changes
Action Text
Please refer to the Changelog for detailed changes.
Removals
Deprecations
Notable changes
Action Mailbox
Please refer to the Changelog for detailed changes.
Removals
Deprecations
Notable changes
Ruby on Rails
Guides
Please refer to the Changelog for detailed changes.
Notable changes
Credits
See the
full list of contributors to Rails
for the many people who spent many hours making Rails
, the stable and robust
framework it is. Kudos to all of them.


--- layout: post esbma_id: 1788 title: Veracity Technology Overview date: 2010-07-19 20:13:45 ---
When I encounter a new piece of software, I usually ask, "What's in it?"
|
Tools and technologies we've been using to build Veracity |
|
|
C |
The core library and the command-line app are written entirely in C. Some folks won't like our coding conventions. I'll probably do a whole blog entry sometime to ((apologize for) && (defend)) the liberties we've taken with the C preprocessor. |
|
JavaScript jQuery |
On the browser side of things, Veracity is a web app written in JavaScript using jQuery. |
|
SVG |
Burn down charts and other web graphics are done using SVG. |
|
JSON |
Veracity uses JSON all over the place. All serialized structures in the repo are JSON. Database records and templates are JSON. We have a bunch of C code for parsing JSON, writing JSON, and dealing with JSON-like data in memory. |
|
SQLite |
We use SQLite in several places as a more scalable disk format, and also as an index. |
|
Mongoose |
The Veracity command-line app has an embedded web server for personal use. It's based on Mongoose. |
|
Curl |
The client side of push/pull is done by calling libcurl. |
|
UTF-8 ICU |
Early on in the Veracity project, we did a lot of work to make sure that stuff was done right with respect to Unicode. Our preferred encoding is UTF-8. The ICU library from IBM has been helpful in a number of places. |
|
REST |
The Veracity web API is very RESTy. And of course, everything serialized over the network is in JSON. |
|
CMake |
Our build system is CMake, with which we have a love/hate relationship. We love it because it generates makefiles, Xcode projects or Visual Studio solutions. We hate it because its language makes Forth look sane. |
|
CTest |
We have a huge suite of automated tests. CMake's integrated test stuff actually works pretty well. Just run 'ctest' at the top level directory. |
|
SpiderMonkey |
A lot of our tests are written in Javascript. We have a command-line executable called 'vscript' which is basically the Spidermonkey Javascript engine glued to the main Veracity library. |
|
Continuous Integration |
Our CI system system rebuilds from scratch and runs the main tests after every checkin, on Mac, Linux and Windows. Results are published to an internal web page and sent to the team by email. |
|
Scrum |
The more we use Scrum, the more I like it. We're patient with ourselves. We just try to get a little better in our Scrum practices each iteration. |
|
gcov lcov |
Nightly builds run the entire test suite with code coverage done by gcov. Our current coverage level is 81%. |
|
vcdiff |
For binary deltas, Veracity uses the algorithm described in RFC 3284. (Actually, the use of binary deltas is currently turned off by default, so if you notice that repositories seem big, that's why. All the plumbing is done. We're just not using it yet.) Anyway, we've got our own implementation of vcdiff. We may consider switching to Google's open-vcdiff at some point if its performance is better. |
|
zlib |
For simple non-deltified compression in repo implementations, Veracity uses zlib. |
|
Valgrind |
When coding in C, valgrind is indispensable. |
|
Shark |
I do most of my coding on the Mac, so I use the Shark profiler. Very cool. |
|
emacs vim Eclipse Visual Studio bash gdb |
Every developer on our team chooses their own tools. We have a good representation of most of the major religions. |
|
Firefox Safari Chrome |
Similarly, every developer chooses their own web browser. I'm not sure what feelings to have upon noticing that nobody is using Internet Explorer. It seems so wrong. And yet, so right. |
|
A few notable things we'll probably be using later |
|
|
.NET |
Even though Veracity was not built fundamentally on the .NET platform, we are committed to providing excellent support for Windows developers. Visual Studio integration is a high priority. |
|
Java |
Similarly, we didn't use Java to build the core libraries for Veracity, but we plan to deliver excellent integration into the Java world, including an Eclipse plugin. |
|
IIS Apache |
The embedded web server is fine for personal use on the desktop, but large teams will want to run a real web server for their central repository. We designed for this case early, but have not yet implemented something like an IIS plugin. |
|
A few notable things we are NOT using (and maybe never will) |
|
|
C++ |
Here's another blog article I need to write. Basically, we only considered two choices: C, and the C-like subset of C++. We chose plain C. I just wish the Microsoft C compiler supported C99. |
|
Flash Silverlight GWT |
We seriously considered other ways of building our web apps. We ended up choosing basic HTML/CSS/jQuery/Ajax. No regrets, but I sometimes wonder how things are going for people using GWT. |
|
NSPR APR |
Sorry folks, in an apparent fit of NIH syndrome, we wrote our own portability layer. And I am completely unrepentant. |
|
XML |
JSON won. What can I say? I just like curly braces a lot more than angle brackets. |


Because serialisation requires access to the private parts of a class definition, by default classdesc will emit actions for all members of class, whether private or public. Either all members of the class need to be declared public, or a friend declaration needs to be inserted so as to allow the action function to have access to the private members. The best way to do this is to put a CLASSDESC_ACCESS declaration in the class's definition, as in the following example:
class foo { int x, y CLASSDESC_ACCESS(foo); public: int z; } template <class T> class bar { T x, y CLASSDESC_ACCESS(bar); public: T z; }
The macro takes a single argument -- the typename of class in question. The program insert-friend parses it input, and outputs a copy with these declarations appropriately inserted. This may be used to automate the process, but is not 100% reliable.
The CLASSDESC_ACCESS_TEMPLATE is now deprecated, and is synonymous with CLASSDESC_ACCESS.
The header file classdesc_access.h
defines versions of these macros for the pack/unpack actions --
use this file as a guide for writing your own macros if you implement
other actions.
On the other hand, for descriptors like isa
, and for exposure
descriptors like TCL_obj
in Eco Lab, it is not desirable to
process private members. For these purposes, use
-respect_private
command line flag of classdesc
.


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Javascript & CSS Tab Menu with Sub-navigation
The "About" and "Our Work" tabs have sub-navigational elements that appear on-click.
Home
About
Our Work
Contact
Our Staff
Facilities
Certifications
Programs
Events
Cliccate su about o work per vedere i sottomenu


bgmm: Gaussian Mixture Modeling Algorithms and the Belief-Based
Mixture Modeling
Two partially supervised mixture modeling methods:
soft-label and belief-based modeling are implemented.
For completeness, we equipped the package also with the
functionality of unsupervised, semi- and fully supervised
mixture modeling. The package can be applied also to selection
of the best-fitting from a set of models with different
component numbers or constraints on their structures.
For detailed introduction see:
Przemyslaw Biecek, Ewa Szczurek, Martin Vingron, Jerzy
Tiuryn (2012), The R Package bgmm: Mixture Modeling with
Uncertain Knowledge, Journal of Statistical Software
<doi:10.18637/jss.v047.i03>.
Downloads:
Reverse dependencies:
Linking:
Please use the canonical form
https://CRAN.R-project.org/package=bgmm
to link to this page.


Nafplio, Greece
Vouleftikon (People's Assembly)
This 18th-century former mosque held the first Hellenic Parliament.
by gus619USAWhen the Ottoman Empire invaded the Kingdom of Morea in the Seventh Ottoman-Venetian War, many Venetians were forced to flee the area quickly. Legend has it that when two Venetian brothers went to Nafplio in 1730 to retrieve gold their father left behind, a sinister Agha murdered them and stole the gold. The Agha later felt guilty and decided to use his blood money to gain redemption by funding the construction of a new mosque.
Later that year, construction began on a mosque in the town square. It was built in the traditional Ottoman architecture of the time, using large blocks of gray stone. The main building was square and capped with a large dome, while on the west side there was an arcade that was capped with a smaller dome. A large terrace on the second floor showcased a view over a large portion of the city and port.
Shortly after the Greeks won their independence from the Ottomans, the building was converted for use as the first Hellenic Parliament and operated as such until the capital was moved to Athens in 1826. From 1828 to 1831, the building served as a public gathering place, hosting meetings and dances. In 1831 a Hellenic school opened upstairs but closed the following year. Also opening in 1831 was a prison in the downstairs areas. The prison ceased operating in 1834, but not before serving as the site for treason trials of several Greek revolutionaries who were charged with conspiring against King Otto, including Generals Theodoros Kolokotronis and Dimitrios Plapoutas.
The building mostly sat idle until suffering earthquake damage in 1928, which caused the smaller dome to collapse. The roof was repaired, but the building was still rarely used until the Greek government rehabilitated it and turned it over to the Ministry of Culture, which currently uses a portion to house the Municipal Art Gallery of Nafplio.
 The second floor terrace is accessible most days and evenings and has a nice view over a portion of the city. The outer courtyard of the property hosts a collection of rare relics.
Visit Greece with Atlas Obscura Trips
From Athens to the Islands: A Greek Easter Journey
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forum Visit our forums.


Anglo-Australian miner BHP appoints new global chief executive
by Edward ThicknesseBHP announced that it had appointed a new chief executive this morning, with current boss Andrew MacKenzie due to retire at the end of this year.
Mike Henry, the current head of BHP’s Australian operations, will take over from MacKenzie on 1 January 2020.
Read more: BHP shareholders vote against leaving industry groups
53-year-old Henry, who originally hails from Canada, takes over the world’s largest mining business at a time when the industry is feeling the pressure of rising environmental concerns, social issues and a slowdown of growth in China, BHP’s biggest customer.
Henry, who joined BHP in 2003, led the group’s Australian division for three years, leading 50,000 people across iron, coal, copper and nickel operations.
The division is the group’s single largest earner, generating profits of $16bn in 2019, roughly 70 per cent of the group’s total for the year.
Group chairman Ken MacKenzie said: “Mike’s deep operational and commercial experience, developed in a global career spanning the Americas, Europe, Asia and Australia, is the perfect mix for our next CEO.
“I am confident his discipline and focus will deliver a culture of high performance and returns for BHP.”
Read more: BHP to switch Chilean copper operations to renewables by 2021
However, some noted that Henry’s tenure had not been without its difficulties, especially relating to long-running problems at South Australia’s Olympic Dam copper and uranium mine.
In March, a runaway train was also forced to derail in the Australian outback at a cost of several hundred million dollars, and meant BHP had to suspend its rail freight operations in that area for several days.
Henry will be faced with some big decisions from the off, including a decision on whether to pursue a $17bn push into potash mining in Canada.
He will also face questions over the company’s environmental footprint. At November’s Sydney AGM, a motion calling on BHP to give up its membership in industry bodies which support policies which run counter to the Paris climate change treaty received the support of 30 per cent of stakeholders.
Read more: BHP boss has bonus slashed after death in Australian mine
Henry has yet to say whether he will step down from his position as vice chair of industry association Minerals Council of Australia.
There was some surprise over the timing of the decision, as MacKenzie had repeatedly said that he was keen to stay on.
Under his tenure BHP spun off a range of assets and sold its disastrous shale division, narrowing its focus to concentrate on iron ore, copper, coal and conventional oil and gas.
In 2015, the collapse of the company’s Samarco mine in Brazil killed 19 people and contaminated water sources across the area in what was the worst environmental disaster in Brazilian history.
In recent weeks BHP approved $44m to restart operations at the mine.
The appointment has met with a mixed reaction from investors, with opinion split between those who welcomed the continuity and those who were keen for the introduction of some fresh blood.
Read more: BHP owners pocked record dividend
Names mentioned included Anglo American chief executive Mark Cutifani and former Fortescue Metals chief executive Nev Power.
Analysts at Shore Capital said: “This appointment was a bit of a surprise for us in two ways. First, noises from the incumbent had led us to expect that he would be staying in the job a bit longer into 2020. Second, there had been chatter that an external candidate could be brought in, with Anglo American’s proven Cutifani particularly favoured.”
Shares in BHP initially fell 1.6 per cent on the announcement, before recovering.
Main image credit:


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Using Principal Component Analysis (PCA) Final(Methods, Code, Analysis/Conclusion, and Future Directions):The Human Microbiome Dataset
2018-08-01 | COSMOS Final Project CampAfter doing all the research needed for the project, our group now proceeded on the coding part of the project. Unlike the past two datasets I analyzed using PCA, for this specific dataset, I conducted a PCA within a PCA so that I can classify the specific regions from its general locations. So now let’s look into the code in which I classified the code in terms of its purpose.
Creating the Base PCA Solver
Importing Libraries
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import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from sklearn.preprocessing import StandardScaler
from sklearn.decomposition import PCA
To start the code, I imported the necessary libraries needed to perform the PCA. So I imported 4 main libraries: pandas, numpy, matplotlib, and sklearn. Pandas is mainly used to organize data structures so that one can view certain data in various persepctives. Numpy is mainly used for any type of calculations or math related since it can perform basically any types of mathematical functions. Matplotlib is mainly used to visualize a certain dataset so that one can physically see the results of a certain situation. Finally, I imported the StandardScaler and the PCA from sklearn.decomposition because these are the necessary components of PCA humans cannot physically do by hand yet only computers can handle.
Loading and Processing the Dataset
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dataF = pd.read_excel('otu_table_psn_v13_annotated_total.xlsx',header=None)
dataF = dataF.T
features = dataF.iloc[1:-2,5:].values
labels = dataF.iloc[1:-2,1].values
regionlabel = dataF.iloc[1:-2,3].values
specificregion = dataF.iloc[1:-2,2].values
In this part of the code, I created a pandas dataframe by loading the data as an excel file. In the parameters of the read_excel function, it includes the parameter header in which equalled None. The reason I set the header to None is because on the next line, I processed my dataframe so that it becomes the transpose of itself which basically means that the x and y axis switched. As shown in the picture above, taking the transpose of the dataset would follow our information section in that the independent variable would become the gender and the region the sample was taken and the dependent variable being different OTUs. Next, I split the dataframe to 4 different sub-dataframes called features, labels, regionlabel, and specificregion and the data contained in each of the dataframes are self-explanatory by their names.
PCA Solver
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numEvec=8
def PCASolver(X, numEvec):
pca = PCA(n_components= numEvec)
Y = pca.fit_transform(X)
return Y, pca.explained_variance_ratio_
Y, all_eigvalues = PCASolver(features, numEvec)
So the next part of the code deals with creating the base PCA Solver in which takes in the features dataframe and the number of principal components (numEvec) it will analyze. As this function runs, it runs PCA from the library we imported and also standardizes the matrix into the dataframe Y. Thus this specific function outputs the standardized dataframe Y and a list with all the explained varaince ratio of each principal component. What the explained variance can tell you about a principal component is that it shows what percent of the variance of the data each principal component explains.
Creating the Variance Plot
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numPCs = numEvec
percent = [i*100 for i in sorted(all_eigvalues, reverse=True)]
cum_var_exp = np.cumsum(percent[:numPCs])
plt.plot(np.arange(1,numPCs+1),cum_var_exp,color='r',marker='s')
plt.bar(np.arange(1,numPCs+1),percent)
plt.legend(['Cumulative Explained Variance','Eigenvalue'])
plt.title('Explained Variance by Different Principle Components')
plt.ylim([0,100])
plt.show()
As we ran the PCA solver with the raw dataset, we were able to generate a list called all_eigvalues with the explained variance ratio of each of the eigenvalues. So the next thing I did was to create another list called percent in which multiplied the ratio by 100 so that we can get the percentage as I ordered the percentages in descending order. By organizing the dataset from greatest to least, it would therefore categorize the principal components from the most important to the least important. This is the plot we get after running all the commands using the matplotlib library. Basically I created a plot and inside it created bar graphs that signified the explained variance in percent of each of the principal components from the most significant to the least. Also on the plot is a line graph that shows the cumulative explained variance in which shows the sum of the explained variance from pc1~pc(n). Normally, when using PCA and dealing with explained variance, data scientists would use the number of principal components that takes to explain at least 70% of the data. In this case, as shown by the graph the first four principal components represents approximately 70% of the data and we chose to analyze these four principal components.
PCA Amongst the General Regions
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n = 4
stri=[]
for i in range(n):
stri.append('PC' + str(i+1))
for i in range(n):
for j in range(i+1,n):
fig = plt.figure(figsize = (8,8))
ax = fig.add_subplot(1,1,1)
ax.set_xlabel(stri[i], fontsize = 15)
ax.set_ylabel(stri[j], fontsize = 15)
ax.set_title('2 component PCA', fontsize = 20)
targets = ['Urogenital_tract', 'Airways', 'Skin', 'Oral', 'Gastrointestinal_tract']
colors = ['r', 'g', 'b','c','m']
for target, color in zip(targets,colors):
indicesToKeep = regionlabel == target
ax.scatter(Y[indicesToKeep,i]
, Y[indicesToKeep,j]
, c = color
, s = 50)
ax.legend(targets)
ax.grid()
plt.show()
The next part of the code deals with the PCA for the general regions of where the samples were taken which includes the Urogenital tract, Airways, skin, the oral region, and the Gastrointestinal tract. Similar to the PCA Solver created for the past two datasets, the function takes the first n principal components it will try to compare with one another. First I created a separate list for the axes names called stri in which the values changed based on which principal component it was linked with as I was able to plot the graphs. As shown by these two graphs of comparing the first four principal components, different regions contain points that are located on a certain line in the PCA graph. Therefore, points that lie on one of the “spokes” of the graph can be classified with a high degree of certainty.
PCA Amongst the Specific Regions
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for target in targets:
Y, all_eigvalues = PCASolver(features, numEvec)
indicesToKeep = regionlabel == target
Y = Y[indicesToKeep][:]
shortenedspecific = specificregion[indicesToKeep][:]
unique = []
for x in specificregion[indicesToKeep]:
if x not in unique:
unique.append(x)
fig = plt.figure(figsize = (8,8))
plt.xlabel("PC1", fontsize = 15)
plt.ylabel("PC2", fontsize = 15)
plt.title(target+ " PCA", fontsize = 20)
for specific, color in zip(unique, colors):
indices = shortenedspecific == specific
plt.scatter(Y[indices,0], Y[indices,1], c = color, s = 50)
plt.legend(unique)
plt.grid()
plt.show()
Now in order to conduct a better PCA, our group decided to conduct PCA within each of the general regions since the dataset also provided the specific regions the samples were taken from. So by taking the regionlabel and specificregion list we created earlier, we were able to conduct PCA amongst each of the general locations. By creating two more lists, shortenedspecific and unique, I was able to categorize the dataset into which general location it came from so that the graphs would generate the principal components of the specific locations. As shown by the 5 graphs of each general location, there is very little horizontal variation along PC1 for samples taken from the Urogenital Tract region. Conversely, skin samples experience only slight vertical dispersal and vary almost exclusively in terms of PC1. Samples from the gastrointestinal region are clustered very tightly in the convergence of Oral, Urogenital Tract, and Skin samples. Airways samples nearly mirror those from Skin, but with less density/variation.
Future Directions
Future research might include applying our PCA algorithm to other sample sets from microbiomes found in the soil and ocean. Also, a more comprehensive dataset on the microbiome samples would allow researchers to recognize more patterns and clusters in the PCA graphs based on unexamined variables, such as age, location, and health. By further examining microbiomes, it could be possible to identify the different properties of the microbes and help cure/prevent certain illnesses.
Conclusion
As we predicted correctly in the hypothesis, communities of bacteria on different organs are very distinct. PCA showed us that samples from different regions varied directly to a their corresponding principal components; each region showed correlation to their specific OTUs. Our PCA solver ran smoothly, and we performed it on several reduced versions before plugging in the total dataset. We were initially confused about our graphs because unlike most PCA examples, our data did not group together in clusters. However, it makes sense because there is a linear variation of OTUs in specific areas. Some future research would include using our PCA solver on other sample sets from the Human Microbiome Project or from the soil and ocean microbiomes.
Works Cited
About the Human Microbiome. (2018). Retrieved from https://hmpdacc.org/hmp/overview
Klotter, J. (2010, July). NIH launches Human Microbiome Project. Townsend Letter, (324), 18. Retrieved from http://link.galegroup.com/apps/doc/A231544962/SUIC?u=pleasant_valhs&sid=SUIC&xid=c4109b64
Morgan XC, Huttenhower C (2012) Chapter 12: Human Microbiome Analysis. PLoS Comput Biol 8(12): e1002808. https://doi.org/10.1371/journal.pcbi.1002808
Savage DC. Microbial ecology of the gastrointestinal tract. Annu. Rev. Microbiol. 1977;31:107–133.
Comments


ME
Andres Segura Tinoco
Senior Software Developer at
OVS Group
Master of Information Engineering
Computer Engineer
Bogotá, Colombia
I was born in Colombia and I grew up in Venezuela.
I'm a Computer and information engineer, with more than 10 years of experience creating robust and high impact software solutions.
My research interests are in Data Compression, Data Science, Machine Learning, Deep Learning, Big Data, Text Processing, NLP, Cloud Computing and DataViz.
RESEARCH
- Andres Segura Tinoco, Vladimir E. Cuevas, Marly J. Piedrahita and William Rojas. "Proof of concept of the feasibility of creating a model that uses Machine Learning techniques to generate virtual metering for oil wells". As master's thesis in information engineering at the Universidad de los Andes. Bogotá, Colombia, 2019.
- Vladimir E. Cuevas, Juan Carlos Oyuela and Andres Segura Tinoco. "You can really learn about sexuality through a MOOC!". In: Visual Analytics course of Universidad de los Andes. Bogotá, Colombia, 2018.
- Andres Segura Tinoco and Vladimir E. Cuevas. "Co-Training implementation for objectivity detection". In: Knowledge Discovery from Social and Information Networks course of Universidad de los Andes. Bogotá, Colombia, 2018.
- Andres Segura Tinoco and Johanna Orozco Cacique. "Data analysis of patients at risk of presenting CHD using machine learning techniques". In: Intelligent Systems Architecture course of Universidad de los Andes. Bogotá, Colombia, 2017.
- Andres Segura Tinoco. "Development of an application for the compression of binary images using bidimensional cellular automata". As computer engineering thesis at the Universidad de Oriente. Barcelona, Anzoátegui, Venezuela, 2009.


Research Areas
I had successfully run a series of models, such as TOPMODEL, Xi'anjiang model, Sacromento model (SAC), Hydrologiska Byrans Vattenbalansavdelning (HBV) model, CESM1.0, Noah LSM, LPJ, Biome-BGC, Variable Infiltration Capacity (VIC) model, among them, VIC is my focus. My work mainly contains the following aspects.
- Integrating Noah LSM and Biome-BGC model.
- Development of a novel approach to evaluate the accuracy of two precipitation datasets.
- Runoff complexity analysis with multiscale entropy method.
- Investigating adaptability of VIC model in mountainous region.
Soil organic matter plays an import role in the process of water and heat conduction. Moreover, the leaf area index (LAI) is also very important in the process of energy and water exchange in earth surface. I took these two elements into account in Noah LSM by integrating Biome-BGC with Noah.
This approach includes multiple statistical metrics, multiple time scales, and wavelet transform and was applied to assess the accuracy of ITP and GLDAS precipitation data over the upper reach of Heihe River Basin. The proposed method in this study features four remarkable highlights: (1) including frequently-used metrics in evaluating precipitation data accuracy, (2) adopting three weather forecasting indicators, (3) Introducing wavelet transform, and (4) evaluating both magnitude and pattern of spatial distribution.
Multiscale entropy method is a kind of nonlinear dynamic analysis method. It can investigate the complexity of time series at different scales, thus the result maybe more credible than traditional complexity measure methods.
In this model, land surface is modeled as a grid of large, flat and uniform cells. It has been proved that VIC model have good performance on relativity flat ones. Although the subgrid variability of some variables has been taken into consideration in VIC model, there are still some variables that do not take subgrid variability into account. I chose a research area that located in mountainous region and had investigated whether or not errors will be produced or how large errors will be produced based on those hypotheses.
Attending Meetings
- Aug. 2009, Proceedings of the 9th National Congress on Hydrodynamics and 22th Conference on Hydrodynamics, Organised by Sichuan University.
- Apr. 2010, National Forum for Graduate Students of Water Conservancy Engineering, Organised by Wuhan University.
Projects
- Research on Photometric Characteristics of High-velocity Aerated Flow by CCD, (2008.10-2009.12, National Undergraduate Innovative Test Program (NUITP), project number 081070021) Main accomplisher. .
- A Decision Support System that is Based on Scientific Model and 3D Game Experience for Sustainable Development of Land and Water Resource in Heihe River Basin (HRB). (2008.6-2011.5, 863 High Technology Research & Development Project of China, project number 2008AA12Z205) Participant.
- An Integrated Watershed Management Spatial Decision Support System (SDSS) Service for Heihe River Basin (HRB) (2012.1-2014.12, Natural Science Foundation of China (NSFC), project number 91125006) Participant.


This essay describes a mathematical approach to reconciling various critical and philosophical perspectives on art that language has left us understanding as perhaps more disparate than they actually are. The work first makes use of the inclusion-exclusion principle to generalize comparisons of the expressive potential of different artistic media over time and space as explained by Gottfried Ephraim von Lessing. Then I proceed to a discussion of Kant’s distinction between the beautiful and the sublime, employing calculus concepts to explain the relationship between the two with respect to creative process and outcome. Finally, I explore Nietzsche’s famous assertion about the relationship between art and living, through the lens of number theory, showing the quick and severe emergence of complexity from seemingly shallow sets of descriptive vocabulary.


IT HAPPENED HERE Kevin Brownlow/Andrew Mollo -- 1964 -- UK -- B&W -- United Artists -- 99 minutes
Reviewed by David Gardiner
It's 1940 on the western seaboard of the continent of Europe. The retreating British army is driven back to the sea on the beaches of Dunkirk, and makes good its escape back to England carried by a makeshift fleet of sea-going pleasurecraft and small boats that previously carried parties of holidaymakers up and down the Thames or over and back to the Isle of Wight. But his time the advancing German army does not stop at the coast.....
The premise of this most unusual war film is that Britain was invaded and occupied after the Dunkirk retreat, and in a mixture of documentary and narrative styles it sets out to tell the story of the occupation that the country narrowly (some would say, unaccountably) escaped, up to and including the 'liberation', orchestrated by the efforts of local partisans with American assistance, in the war's closing year.
This is a low-key, reflective war drama, which follows its central character, an Irish-born district nurse working in a village near Salisbury, through the horrors of a partisan ambush that goes wrong, to a chilling Nazi-dominated vision of London, where she finds herself assimilated into the highly political "Immediate Action Organization" and receives her "political re-education", on to a rural medical centre specialising in euthenasia for "undesirables", through to the final chilling irony of "liberation" and the wholesale slaughter of "collaborators".
It is hard to believe that this film began its life as the spare-time project of 18-year-old Kevin Brownlow, a film enthusiast working in the cutting-room of a small London production company, and his 16-year-old schoolboy friend Andrew Mollo, who had a passion for military history and a collection of old German uniforms and regalia. Starting without a budget, using a borrowed 16 mm camera, the two doggedly pursued their dream of completing the project for almost eight years, finding actors, actresses, sets and backing as they went along. The incredible story of the film's birth is told in Kevin Brownlow's 1968 book "How It Happened Here" (Doubleday & Co., New York), now unfortunately out of print.
The most famous sequence in the work is a six-minute scene in which genuine Neo-Nazis expound their ideas. When the film was first previewed at various British film festivals, it was this scene that incurred the wrath of a number of Jewish organizations, who mantained that the film lacked balance in that no substantial counter-argument was presented to the opinions expressed by these monsters. Early performances were greeted by jeers, chants of "shame" and mass walk-outs from film theatres.
Kevin Brownlow maintained throughout that the Neo-Nazis condemned themselves out of their own mouths for anyone with an ounce of intelligence, but the topic was too hot to handle and in order to get the film accepted for distribution by United Artists the Directors had to agree to the removal of the sequence, which was not allowed back into the British release version until the distribution rights were taken over by Connoisseur in 1993. The Connoisseur video version of that date is a full, uncut transcription.
As well as the Jews, many others were offended by the film, and it was widely criticized on political grounds, as being deeply unpatriotic. The British public had been flattered by Churchillian rhetoric into believing itself exempt from the flaws of other national groups.  was being tapped in the shoulder and told: "Sorry, my friend, you are no different". There could be no more subversive artistic act than an attack on this smug self-image. Every individual and every group that the film portrays is tainted in some way with moral compromise. We are presented with an England peopled by cowed and unthinking conformists who just want to "get the country back on its feet"; fanatical resistance fighters who have no concern for the fate of ordinary people caught in the cross-fire; German soldiers who casually murder unarmed civilians, smile sheepishly, and continue on their way; American "forces of liberation" who do the same to groups of so-called collaborators as they carry out a scorched-earth de-Natzification policy of the newly "liberated" territory; mild-mannered old country doctors in genteel rural hospitals who calmly dispatch their patients with lethal injections before making their way downstairs for supper. Every incident portrayed is based on well-documented events that took place in other occupied countries. Nobody is spared in Brownlow's profoundly pacifist analysis of the military mind-set. War and military occupation are presented as pits of horror and despair that are without bottom. Ordinary, well-meaning people like Pauline Murray are swept along by forces far beyond their control into dark political whirkpools from which there is no escape. This is a chilling nightmare of a film that demands of every viewer that they ask themselves the question: What would I have done?
The ideas put forward in "It Happened Here" are so riveting even for a modern audience that it tends to be discussed mainly as a socio-political treatise and neglected as a film. And looked-at as a film, it undoubtedly has its faults. The acting, as one would expect from a group of amateurs, is uneven. Pauline Murray (who is very cleverly allowed to retain her own name in the film) was the wife of a doctor friend of the two Directors: she had never acted before. We see her technique develop as the story progresses (it was filmed more or less in sequence) and it is a process which, no doubt unintentionally, actually adds to the strength of the film. The story seems to emerge out of a naive daydream into something increasingly realistic and dreadful.
The film's mixture of documentary and narrative style, somewhat reminiscent of Citizen Kane, was an ambitious and thoroughly appropriate format, surperbly well-realized with the help of such genuine news-reel voice-over artists as Alvar Liddell and John Snagge (who gave their services free). The staged "archive footage" of the Flanders Field Christmas Day football match would do credit to any professional production company, as would the use of rousing military music to establish and maintain atmosphere. Brownlow's skill in carrying us along with his appalling fantasy is rooted in his commitment to the project: we see German tanks everywhere when we know in our heart of hearts that Kevin was only able to get hold of one, because this is the world that we have agreed to enter into. There is no real interest in looking for the seams and the improvisations. We are involved in the realization of a nightmare. It is the spirit and the atmosphere of the piece that seize hold of us, not the limitations of the production budget.
I found this film disturbing, unsettling, unfogettable. The scale of the achievement involved in the creation of a work of this quality from such humble beginnings can hardly be overstated. I sincerely hope that you may get the opportunity to see it, because it is, I know, something of a rarity.
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Make Emacs.app from Homebrew emacs-mac #!/usr/bin/env bash # Script to create Emacs.app that will be placed in Applications folder. # This is cobbled together from two sources. All credit to them, unless it # doesn't work, in which case, errors are my fault: # # (1) https://github.com/railwaycat/homebrew-emacsmacport/blob/master/docs/emacs-start-helpers.md # (2) https://mathiasbynens.be/notes/shell-script-mac-apps # You may need to make the script executable on your machine. In terminal # directory of this script: # $ chmod +x make_emacs_app.sh # (1) create an application that Spotlight will recogize from emacs-mac osacompile -o Emacs.app \ -e "tell application \"Finder\" to open POSIX file \"$(brew --prefix)/opt/emacs-mac/Emacs.app\"" # (2) move to /Applications mv Emacs.app /Applications # NB: To add icons # # 1. Copy Emacs.icns file images from /usr/local/opt/Emacs.app/Contents/Resources # 2. Right-click the Emacs.app file and select “Get Info” # 3. Select the app icon in the top left corner by clicking it once and paste


{% load static %}
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This is the record of an app service publisher that the module has. It should contain the most up to date information including the service’s active state.
@since RPC 5.1
Convenience init for all parameters.
- (nonnull instancetype)initWithServiceID:(nonnull NSString *)serviceID
serviceManifest:
(nonnull SDLAppServiceManifest *)serviceManifest
servicePublished:(BOOL)servicePublished
serviceActive:(BOOL)serviceActive;
init(serviceID: String, serviceManifest: SDLAppServiceManifest, servicePublished: Bool, serviceActive: Bool)
A unique ID tied to this specific service record
Manifest for the service that this record is for
If true, the service is published and available. If false, the service has likely just been unpublished, and should be considered unavailable
If true, the service is the active primary service of the supplied service type.
A SDLAppServiceRecord object
A unique ID tied to this specific service record. The ID is supplied by the module that services publish themselves.
String, Required
@property (nonatomic, strong) NSString *_Nonnull serviceID;
var serviceID: String { get set }
Manifest for the service that this record is for.
SDLAppServiceManifest, Required
@property (nonatomic, strong) SDLAppServiceManifest *_Nonnull serviceManifest;
var serviceManifest: SDLAppServiceManifest { get set }
If true, the service is published and available. If false, the service has likely just been unpublished, and should be considered unavailable.
Boolean, Required
@property (nonatomic, strong) NSNumber<SDLBool> *_Nonnull servicePublished;
var servicePublished: NSNumber & SDLBool { get set }
If true, the service is the active primary service of the supplied service type. It will receive all potential RPCs that are passed through to that service type. If false, it is not the primary service of the supplied type. See servicePublished for its availability.
Boolean, Required
@property (nonatomic, strong) NSNumber<SDLBool> *_Nonnull serviceActive;
var serviceActive: NSNumber & SDLBool { get set }


Resolve an options object based on configuration.
// This example assumes a Vinyl file
var createResolver = require('resolve-options');
var config = {
cwd: {
type: 'string',
default: process.cwd
},
sourcemaps: {
type: 'boolean',
default: false
},
since: {
type: ['date', 'number']
},
read: {
type: 'boolean'
}
};
var options = {
sourcemaps: true,
since: Date.now(),
read: function(file) {
return (file.extname !== '.mp4');
}
};
var resolver = createResolver(config, options);
var cwd = resolver.resolve('cwd', file);
// cwd === process.cwd()
var sourcemaps = resolver.resolve('sourcemaps', file);
// sourcemaps === true
var read = resolver.resolve('read', file);
// Given .mp4, read === false
// Given .txt, read === true
createResolver([config,] [options])
Takes a config
object that describes the options to accept/resolve and an options
object (usually passed by a user) to resolve against the config
. Returns a resolver
that contains a resolve
method for realtime resolution of options.
The config
object takes the following structure:
config {
[optionKey] {
type // string, array or function
default // any value or function
}
}
Each option is represented by its optionKey
in the config
object. It must be an object with a type
property.
The type
property must be a string, array or function which will be passed to the value-or-function
module (functions will be bound to the resolver to allow for dependent options).
A default
property may also be specified as a fallback if the option isn't available or is invalid. The default
value can be any value or a function (functions will be bound to the resolver to allow for dependent defaults). Note: default
values are not type-validated by the value-or-function
module.
resolver.resolve(optionKey, [...arguments])
Takes an optionKey
string and any number of arguments
to apply if an option is a function. Returns the resolved value for the optionKey
.
MIT


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etasonjb
INSTALLuntethered jailbreak for 8.4.1. (originally developed by @tihmstar) All 32-bit devices supported. (i'm just hosting the same ipa but signed with an enterprise cert so it can be downloaded without a computer) if you can consier a little donation as a thank you it will be really appreciated!
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CURLOPT_HTTP200ALIASES explained
NAME
CURLOPT_HTTP200ALIASES - specify alternative matches for HTTP 200 OK
SYNOPSIS
#include <curl/curl.h> CURLcode curl_easy_setopt(CURL *handle, CURLOPT_HTTP200ALIASES, struct curl_slist *aliases);
DESCRIPTION
Pass a pointer to a linked list of aliases to be treated as valid HTTP 200 responses. Some servers respond with a custom header response line. For example, SHOUTcast servers respond with "ICY 200 OK". Also some very old Icecast 1.3.x servers will respond like that for certain user agent headers or in absence of such. By including this string in your list of aliases, the response will be treated as a valid HTTP header line such as "HTTP/1.0 200 OK".
The linked list should be a fully valid list of struct curl_slist structs, and be properly filled in. Use curl_slist_append to create the list and curl_slist_free_all to clean up an entire list.
The alias itself is not parsed for any version strings. The protocol is assumed to match HTTP 1.0 when an alias match.
DEFAULT
PROTOCOLS
EXAMPLE
CURL *curl = curl_easy_init(); if(curl) { struct curl_slist *list; curl_easy_setopt(curl, CURLOPT_URL, "http://example.com"); list = curl_slist_append(NULL, "ICY 200 OK"); list = curl_slist_append(list, "WEIRDO 99 FINE"); curl_easy_setopt(curl, CURLOPT_HTTP200ALIASES, list); curl_easy_perform(curl); curl_slist_free_all(list); /* free the list again */ }
AVAILABILITY
RETURN VALUE
Returns CURLE_OK if HTTP is supported, and CURLE_UNKNOWN_OPTION if not.
SEE ALSO
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--- layout: post title: "Into Markov Chains" date: 2016-05-11 categories: twentysixteen ---
The last post talked about Markov Chains, so the next step is to use these chains to implement a reward process that is the basis for Reinforcement Learning. The reward process (Specifically the Markov Reward Process) is composed of a state space, a state transition matrix, a reward function, and a discount factor. The discount factor just represents how much importance the agent puts on getting a reward immediately versus getting a reward later. When the discount factor is 0, the agent will do an action that will give it a reward immediately, when the factor is 1, then the agent places an importance on delayed gratification. For example, if someone offers me a cake and my discount factor is 0, I am looking for an instant gratification, so I would eat the cake immediately. But when my discount factor is one, I politely say no to the cake and eat an apple instead. The second decision is better for my health so I was aiming for a long term gratification.
Every state in the state diagram should be labeled with a reward. This reward could be based on a point system. For the state diagram that I made in my last post, I would say that sleeping gives me a reward of 1 point, studying gives me a reward of 10 points, eating gives me a reward of 3 points and hanging out online is -14 points (because hanging out online tends to be a time-waster and it is the most addictive compared to other states on this diagram). The reward and discount factor can be mixed into one equation that keeps track of how much the agent is getting as it moves from one state to another. This equation is called the return. In any Markov Reward Process, we want to maximize our return.
Now that we have defined the return, we can move on to the value function. the value function represents the value of a function after many state transitions. It is dependent on the discount factor. If the discount factor is 0, then the value of that state is the same as the reward of that state since the agent wants an instant reward. But as the discount factor moves away from 0, the state value of a certain state will not be the same as the reward of that state since the long term reward will be of a higher priority to the agent.
You can make a Bellman function using the properties of a value function. The Bellman function states that the value of a state is based on the reward of that state added with the values of the outgoing states multiplied with the probabilities of coming out of your state and going to the next states.  The Bellman equation is very important because it breaks a big problem into “bite-sized” sub problems that we are capable of solving.
The concept of Reinforcement Learning is thankfully not complicated at all. In my last post, I was wondering how I was going to implement a reward process for my agent. David Silver’s second Reinforcement Learning lecture answered that. He said that all Reinforcement Learning problems can be conceptualized in the form of Markov Chains. An awesome tutorial for Markov chains that I used is here .
A Markov Chain is a process of a state space (just a list of states) that can be drawn as a graph. The process of a Markov Chain is based on a probability of a cursor moving from one state to another. I am familiar with state spaces because I studied Finite State Machines last semester. For my class final project, I created a state machine and I used it to implement a random number generator. The good thing about Markov Chains is that it does not need to remember all of the past events, it only uses that current event to decide the next state. This is called the Markov Property.
The Markov Chain can be modeled in 2 ways, with directed graphs; where vertices represent the state and the edges represent the probabilities, and a transition matrix. The transition matrix is used more often because a Markov Chain can get very complex. These complexities can get so big that it may be impossible or tedious to model a Markov Chain using a graph. This is why a transition matrix is almost always used to model Markov Chains. "Every state in the state space is included once as a row and again as a column. And each cell in the matrix tells you the probability of transitioning from its row state to its column state."- Powell.I won’t get into the specifics of transition matrix mathematics (because I don't know any of it yet), but I will create transition matrix. This transition matrix models my day to day life in the summer. The main activities that I do in the summer is sleep, eat, study, and hang out online. I am going to use numbers between 0 and 1 to model the probabilities where a 10 percent chance is 0.10 and a 50 percent chance is .50. I created this transition matrix by first creating a state diagram, but I am unable to put a state diagram on here at the moment. My state diagram starts from sleeping and it goes on from there. Every outgoing edge from any one of my states should add up to 1 because all probabilities are supposed to add up to 100 percent.
The transition matrix below conceptualizes a Markov Chain beautifully. It shows all of the transitions from a state to other states, for example the probability that I go from sleeping to sleeping again is 0.1, the probability that I go from eating to hanging out online is .75. The columns of the matrix are supposed to represent the arrows going from each state so each column should add up to 1 because there is 100 percent probability that something will happen no matter what state I am in.
| Sleeping | Eating | Studying | Hanging out online | |
| Sleeping | 0.1 | 0.25 | 0.50 | 0.075 |
| Eating | 0.30 | 0.05 | 0.10 | 0.75 |
| Studying | 0.02 | 0.175 | 0.30 | 0.15 |
| Hanging out online | 0.67 | 0.75 | 0.10 | 0.70 |
The next step is figuring out how to model the reward process using Markov Chains.
There are three methods of solving a Markov Decision process, these methods are, Dynamic Programming, Monte Carlo, and Temporal Difference Learning.
Dynamic Programming is a method where you know the full Markov Decision Process. The pros of this method, is that there is a specific algorithm that you can use to find your value function. The way this method is taught is “grid world” This is a grid that contains the value functions and determines the optimal policy that each state will contain until the state terminates. I am still trying to understand this grid world, so I can’t go into the specifics aspects of it.
Reinforcement learning depends on optimizing value functions. There also some situations where you don’t know what your Markov Decision Process is. This is an area that is suited for the Monte Carlo Method. The Monte Carlo Method relies on random sampling and it is also used for numerical integration. This random sampling is akin to the agent trying out everything in its environment and finding out what maximizes it’ s value function. This means that the Monte Carlo method learns from experience.
Temporal Difference Learning is a fundamental concept in reinforcement learning because it combines Dynamic Programming and the Monte Carlo Method. This method, like Monte Carlo also learns from experience, and it doesn’t have a Markov Decision Process. Unlike the Monte Carlo Method, Temporal Difference learning does not need the state to terminate to get the value function. Like Dynamic Programming, Temporal Difference Learning updates the value functions on the fly, the episode does not have to terminate. This is called bootstrapping.
The methods that solve a Markov Decision Process have the same steps. The first step is the policy evaluation that estimates the value function for a policy. The second step is finding the optimal policy, which is improving the policy when you have a value function.
Powell, Victor. "Markov Chains Explained Visually." Explained Visually. N.p., n.d. Web. 11 May 2016.
Silver, David. "Markov Decision Processes." SpringerReference (n.d.): n. pag. 2015. Web. 5 May 2016.
Sutton, Richard S., and Andrew G. Barto. Reinforcement Learning: An Introduction. Cambridge, MA: MIT, 1998. Print.


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| 007:171 Khan | : |
And (remember) when We raised the mountain over them as if it had been a canopy, and they thought that it was going to fall on them. (We said): "Hold firmly to what We have given you [i.e. the Taurat (Torah)], and remember that which is therein (act on its commandments), so that you may fear Allah and obey Him." |
| 007:171 Maulana | : |
And when We shook the mountain over them as if it were a covering, and they thought that it was going to fall down upon them: Hold on firmly that which We have given you, and be mindful of that which is in it, so that you may guard against evil. |
| 007:171 Pickthal | : |
And when We shook the Mount above them as it were a covering, and they supposed that it was going to fall upon them (and We said): Hold fast that which We have given you, and remember that which is therein, that ye may ward off (evil). |
| 007:171 Rashad | : |
We raised the mountain above them like an umbrella, and they thought it was going to fall on them: "You shall uphold what we have given you, strongly, and remember the contents thereof, that you may be saved." |
| 007:171 Sarwar | : |
When We raised the mount above them as a shadow and made it speak to them, they thought that it would fall on them. We told them to devote themselves decisively to what was given to them and follow its guidance so that they would have fear of God. |
| 007:171 Shakir | : |
And when We shook the mountain over them as if it were a covering overhead, and they thought that it was going to fall down upon them: Take hold of what We have given you with firmness, and be mindful of what is in it, so that you may guard (against evil). |
| 007:171 Sherali | : |
And when WE shook the mountain over them as though it were a covering and they thought it was going to fall on them, WE said, 'Hold fast that which WE have given you and remember what is therein that you may guard against evil.' |
| 007:171 Yusufali | : |
When We shook the Mount over them, as if it had been a canopy, and they thought it was going to fall on them (We said): "Hold firmly to what We have given you, and bring (ever) to remembrance what is therein; perchance ye may fear Allah." |
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.


You can synchronize your blog's folder to Blot with Dropbox, a file hosting service available on most platforms. You can switch your blog seamlessly to another client at any point.
No, Blot only requests access to your blog’s folder in your Dropbox by default. However, you can choose to grant Blot special access to your entire Dropbox. Only ever do this if you want to move your blog’s folder, or share it with other people who use Dropbox.
Dropbox’s traffic limits do not apply because Blot hosts the contents of your folder on its own server.
There are a number of text-editing apps for phones and tablets which integrate beautifully with Dropbox. I use iA Writer and I've heard from a number of customers who use Bear and Ulysses.
You can create more than one blog using Dropbox. When you add a second blog, Blot moves the existing blog folder into a subfolder of the Dropbox App folder, then creates a second subfolder for the new blog.
You can switch seamlessly from Dropbox to another client at any point:
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Title of your support desk, for example "My company Help Desk"
URL of the folder where your Hesk will be installed. Do NOT add a trailing / to the URL!
CORRECT: http://www.yourdomain.com/helpdesk
WRONG: http://www.yourdomain.com/helpdesk/ (don't end the URL with a / )
Name of the folder, where admin files are located.
The folder needs to exist within the main HESK folder. On most systems names are CaSe SeNSiTiVe.
Allowed chars (other will be removed): a-z A-Z 0-9 _ -
Default: admin
Name of the folder, where attachments will be stored.
The folder needs to exist within the main HESK folder and be writable by PHP. On most systems names are CaSe SeNSiTiVe.
Allowed chars (other will be removed): a-z A-Z 0-9 _ -
Default: attachments
The default number of tickets listed per page in admin panel. Use digits only.
Font size on the "Printer friendly version" pages of support tickets
If a ticket has no activity from the customer for X days it automatically closes. Set to 0 to disable autoclose.
A maximum number of open tickets a customer may have. When this limit is reached the customer will not be able to submit new tickets until existing ones are resolved. Affects only tickets submitted via online form.
Set to 0 to disable limiting maximum open tickets.
Choose whether you want most recent replies to appear at top or at bottom of the ticket page.
Choose whether you want the Add a reply form to appear at top or at bottom of the ticket page.
If set to YES staff will have an option to automatically login to their account every time they open HESK administration panel from their computer. If disabled HESK will only be able to remember usernames, not passwords.
Tickets will automatically be assigned to a staff member when submitted.
Several factors are taken into account when assigning tickets automatically, such as ticket category and number of currently open assigned tickets.
Set to ON to allow customers to reopen a ticket once it has been closed. When set to OFF once closed the customer will have to open a new ticket.
If set to ON customers will be able to mark staff replies as Helpful or Not helpful. This customer feedback will be used to rate staff.
If set to ON customers will be able to set priority/urgency for their tickets ranging from Low to High. If set to OFF all tickets will be submitted with the default priority (Low) and only staff will be able to change the priority level to a higher one.
If enabled, all tickets will also show a sequential ID number. This should allow easier customer-staff communication about a ticket (saying ticket ID 45 rather than ticket ID GHS-G5E-A6T8). However, only staff will be able to view tickets by sequential numbers.
If set to ON staff usernames be listed in a select box on the admin login page and they will only need to type their password in. Recommended setting: OFF (staff will have to type both their username and password to login)
Turns ON/OFF the debug mode. In normal usage debug mode should be turned OFF. Turn ON only if you are having problems and Hesk is not working properly. Don't forget to turn back OFF when Hesk is working normally again.
If turned ON, website links longer than 70 chars will be shortened for display.
For example a link like this:
http://www.some-long-domain.com/sub-folder/another-folder/and-yet-another/this-is-some-long-file-name.html?this_is_a_long_query=and_this_is_a_long_value
Would be displayed as:
http://www.some-long-domain.com/sub-folder/another-fol ... long_value
Toggles use of the anti-SPAM security image (captcha).
| Value | Meaning |
| OFF | anti-SPAM images disabled |
| ON - Customers | only customers will be required to solve the image |
| ON - All | both customer and staff will be required to solve it |
| Image type | |
| Simple image | a very basic image generated by PHP (GD library required) A simple image is not effective against advanced robots, consider using an anti-SPAM questions or ReCaptcha instead! |
| ReCaptcha | use a free service by Google (requires registration) |
ReCaptcha is a free anti-bot service by Google.
To use ReCaptcha you will need to:
Toggles use of the anti-SPAM security question. This is a very simple yet quite effective way of combating spammers (not if they decide to target your website specifically but it should block all the spambots travelling the web). The idea is to create a unique question that only humans can answer to. You can use HTML code in the question. Answers are not CaSe SeNSiTiVe. Some examples:
| -> Question | -> Answer |
| What color is water? | Blue |
| What is the next number after four? (use only digits) | 5 |
| Type access code ABCDE here: | ABCDE |
| Access code: (find it on the bottom of our links page) | Somecode |
Warning! DO NOT use these examples, use a unique question that will only be used on your website!
This feature limits brute-force attacks to either login to HESK or view tickets.
After Login attempts limit number of failed attempts to view a ticket or login to HESK the IP address will be banned for Ban time (minutes).
To disable failed login attempts limiting, set value to 0 (NOT recommended!).
If enabled (checked), customers will need to enter both their email address and ticket tracking ID to view the contents of a tickets. If disabled, only entering ticket tracking ID will suffice.
Select YES to enable file attachments. If enabled customers and support staff will have the option to attach files to support tickets.
NOTE: If and how file attachments will work depends on your server settings, please refer to readme.html for considerations and possible limitations of file attachments on your server.
Number of file attachments allowed per post. Optimal range is around 1 to 4. This setting is only available if "Use attachments" is set to YES.
Maximum size of attached files. Note that the maximum upload file size is limited by your server settings, please refer to readme.html for more information on file attachment limits. This setting is only available if "Use attachments" is set to YES.
Allowed file extensions for attaching files. To add more extensions use the same formatting .ext and separate them with a comma. This setting is only available if "Use attachments" is set to YES.


Quick Start
Installing JCAST
Requirements
Install Python 3.7+ and pip. See instructions on Python website for specific instructions for your operating system.
JCAST can be installed from PyPI via pip. We recommend using a virtual environment.
$ pip install jcast
Running JCAST
Launch JCAST as a module (Usage/Help):
$ python -m jcast
Alternatively:
$ jcast
Example command:
$ python -m jcast data/encode_human_pancreas/ data/gtf/Homo_sapiens.GRCh38.89.gtf data/gtf/Homo_sapiens.GRCh38.89.gtf data/genome/Homo_sapiens.GRCh38.dna.primary_assembly.fa -o encode_human_pancreas -q 0 1 -r 1 -m -c
To test that the installation can load test data files in tests/data (sample rMATS file and human chr 15 genome files)
$ pip install tox
$ tox
To run JCAST using the test files and print the results to Desktop
$ python -m jcast {j}/tests/data/rmats {j}/tests/data/genome/Homo_sapiens.GRCh38.89.chromosome.15.gtf {j}/tests/data/genome/Homo_sapiens.GRCh38.dna.chromosome.15.fa.gz -o ~/Desktop
where {j} is replaced by the path to JCAST.
Example Usage
The following is an example using JCAST to generate custom databases from ENCODE public RNA-seq dataset to generate a cardiac-specific database with JCAST.
Download RNA-Seq from ENCODE:
As an example, we will download the .fastq files from ENCODE adult human heart dataset 1 and dataset 2.
Align the FASTQ files to a reference genome
Read alignment can be performed using STAR >= v.2.5.0, e.g.,:
$ STAR --runThreadN 10 --genomeDir path/to/GRCh38/STARindex --sjdbGTFfile path/to/Homo_sapiens.gtf --sjdbOverhang 100 --readFilesIn ./ENCFF781VGS.fastq.gz ./ENCFF466ZAS.fastq.gz --readFilesCommand zcat --outSAMtype BAM SortedByCoordinate --outFileNamePrefix ./STAR_aligned/b1t1/
$ STAR --runThreadN 10 --genomeDir path/to/GRCh38/STARindex --sjdbGTFfile path/to/Homo_sapiens.gtf --sjdbOverhang 100 --readFilesIn ./ENCFF731CDK.fastq.gz ./ENCFF429YOS.fastq.gz --readFilesCommand zcat --outSAMtype BAM SortedByCoordinate --outFileNamePrefix ./STAR_aligned/b2t1/
Note: Arguments including runThreadN and sjdbOverhang should be customized to suit your system and data files. Please refer to the STAR documentations for details.
Identify transcript splice junctions
Splice junctions can be found using rMATS with the .bam files following STAR. Please refer to the rMATS instructions for latest commands. The following example was tested using rmats-turbo-0.1 running in Docker and using rMATS v.4.1.0/Python 3.7. Support for stringtie assembled transcripts will be implemented in a future version.
Set up a Virtual Environment for rMATS turbo 0.1 in Python 2.7 (only if needed)
Install the rMATS image
Follow instructions from rMATS and docker specific to your OS. E.g.:
$ sudo docker load -i rmats-turbo-0.1.tar
Prepare the /rMATS subdirectory
Copy the individual .bam files from STAR into the rMATS subdirectory and rename them b1t1.bam, b1t2.bam, b2t1.bam, b2t2.bam, etc. Copy the GTF file from the Genomes folder as GRCm38.gtf. Write a b1.txt file with a text editor containing the following docker virtual directories:
/data/b1t1.bam,/data/b1t2.bam
Write a b2.txt file
/data/b2t1.bam,/data/b2t2.bam
Go back to the data directory and run the rMATS image. The -v flag mounts the host directory into the docker container at /data, which corresponds to the visual directories in the b1.txt and b2.txt files.
$ sudo docker run -v path/to/data/directory:/data rmats:turbo01 --b1 /data/b1.txt --b2 /data/b2.txt --gtf /data/GRCh38.gtf --od /data/output -t paired --nthread 4 --readLength 101 --anchorLength 1
Run the JCAST Python program specifying the path to the rMATS output directory, the genome sequence, as well as the GTF annotation file:
$ python -m jcast path/to/rMATS/output/encode_human_heart/ path/to/gtf/Homo_sapiens.GRCh38.89.gtf path/to/genome/Homo_sapiens.GRCh38.dna.primary_assembly.fa -o encode_human_heart


Name: Mr. Ajay Kumar
Designation: Lecturer (Maths)
Qualification: M.Sc.(Maths)
Experience: 27+ Years
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Designation: Lecturer (English)
Qualification: Ph.D.
Experience: 12+ Years
Email Id<EMAIL_ADDRESS>Name: Mr. Jagmanadar
Designation: Lecturer (Physics)
Qualification: M. Sc(Physics), M.Phil.(Physics), B. Ed.
Experience: 6+ Years
Email Id: jagmendra11@gmail.com


This topic provides programming tips for development and deployment of Java and JavaFX applications that work in all execution environments.
This topic includes the following sections:
When an application is run in a browser, the application gets staged with predefined dimensions, which cannot be updated directly. Example 18-1 shows simple code to detect if a JavaFX application is embedded in a web page. The code can be used in either the main application or the JavaFX preloader start method.
Example 18-1 Detect if the JavaFX Application is Embedded in a Web Page
public void start(Stage stage) { boolean isEmbedded = (stage.getWidth() > 0); ... }
As an alternative, you can try to get a reference to the web context from the Application.getHostServices()
method. Null is returned if the application is not embedded.
JavaFX applications support both named and unnamed parameters that can be passed in a variety of ways:
Specified on the command line for a standalone launch.
Hardcoded in the application package (JAR and deployment descriptor).
Passed from the HTML page in which the application is embedded.
In a JavaFX application, access parameters from a preloader or main application using the getParameters()
method. For example, the code in Example 18-2 gets a list of all named parameters and their values:
Example 18-2 Get a List of Named Deployment Parameters and Values
Map m = getParameters().getNamed(); String labelText = "List of application parameters: \n"; for(String st: (Set<String>) m.keySet()) { labelText += " ["+st+"] : ["+m.get(st)+"]\n"; }
For Java applets, see Defining and Using Applet Parameters in the Java Tutorials for information on accessing application parameters.
For JavaFX applications, the Application.getHostServices()
method provides access to execution-mode-specific services, including:
Access to information about the code base and the document base.
For example, for embedded applications this is the URL of the application and URL of the host web page, respectively.
Access to the host web page using the JavaScript engine, only available to embedded applications.
Ability to open a web page in the browser.
Example 18-3 shows a few things you can do with getHostServices()
.
Example 18-3 Using getHostServices()
final HostServices services = getHostServices(); Button jsButton = new Button("Test Javascript"); jsButton.setOnAction(new EventHandler<ActionEvent>() { public void handle(ActionEvent t) { JSObject js = services.getWebContext(); js.eval("window.alert('Hello from JavaFX')"); } }); Button openButton = new Button("Test openDocument()"); openButton.setOnAction(new EventHandler<ActionEvent>() { public void handle(ActionEvent t) { services.showDocument("http://javafx.com/"); } });
Using the File
API and explicit relative references to external data files or resources might not work when the application is loaded from the web.
To refer to resources relative to your application, use the getResource()
method on one of the application classes, as shown in Example 18-4.
Example 18-4 Use the getResource() Method to Load Resources
scene.getStylesheets(). add(this.getClass().getResource("my.css").toExternalForm());
As an alternative, consider using getCodeBase()
or getDocumentBase()
from the HostServices
class to refer to resources relative to the application or the location where the application is used.
When a JavaFX application is embedded in a web page, the application cannot control stage dimensions. Dimensions specified at packaging time are preferences only and can be overridden by the user, for example if the user has custom browser zoom settings. Also, the stage can be resized at any time by the user.
To provide a good user experience, be prepared for arbitrary stage sizes. Otherwise, the application might be cropped, or garbage could be painted in the unused area of the stage.
If your application uses layouts, then you do not need to do anything. Layouts take care of resizing for you. Otherwise, implement resizing logic and listen to stage dimension changes to update the application, as shown in the simplified code in Example 18-5.
Example 18-5 Using Listeners to Resize an Embedded Application
public class ResizeFriendlyApp extends Application implements ChangeListener<Number> { private Stage stage; public void start(Stage stage) { //be resize friendly this.stage = stage; stage.widthProperty().addListener(this); stage.heightProperty().addListener(this); ... //resize content resize(stage.getWidth(), stage.getHeight()); stage.show(); } private void resize(double width, double height) { //relayout the application to match given size } public void changed(ObservableValue<? extends Number> ov, Number t, Number t1) { resize(stage.getWidth(), stage.getHeight()); } }


Distributed Data Parallel¶
Warning
The implementation of torch.nn.parallel.DistributedDataParallel
evolves over time. This design note is written based on the state as of v1.4.
torch.nn.parallel.DistributedDataParallel
(DDP) transparently performs
distributed data parallel training. This page describes how it works and reveals
implementation details.
Example¶
Let us start with a simple torch.nn.parallel.DistributedDataParallel
example. This example uses a torch.nn.Linear
as the local model, wraps
it with DDP, and then runs one forward pass, one backward pass, and an optimizer
step on the DDP model. After that, parameters on the local model will be
updated, and all models on different processes should be exactly the same.
import torch
import torch.distributed as dist
import torch.multiprocessing as mp
import torch.nn as nn
import torch.optim as optim
from torch.nn.parallel import DistributedDataParallel as DDP
def example(rank, world_size):
# create default process group
dist.init_process_group("gloo", rank=rank, world_size=world_size)
# create local model
model = nn.Linear(10, 10).to(rank)
# construct DDP model
ddp_model = DDP(model, device_ids=[rank])
# define loss function and optimizer
loss_fn = nn.MSELoss()
optimizer = optim.SGD(ddp_model.parameters(), lr=0.001)
# forward pass
outputs = ddp_model(torch.randn(20, 10).to(rank))
labels = torch.randn(20, 10).to(rank)
# backward pass
loss_fn(outputs, labels).backward()
# update parameters
optimizer.step()
def main():
world_size = 2
mp.spawn(example,
args=(world_size,),
nprocs=world_size,
join=True)
if __name__=="__main__":
# Environment variables which need to be
# set when using c10d's default "env"
# initialization mode.
os.environ["MASTER_ADDR"] = "localhost"
os.environ["MASTER_PORT"] = "29500"
main()
Internal Design¶
This section reveals how it works under the hood of
torch.nn.parallel.DistributedDataParallel
by diving into details of
every step in one iteration.
Prerequisite: DDP relies on c10d
ProcessGroup
for communications. Hence, applications must createProcessGroup
instances before constructing DDP.Construction: The DDP constructor takes a reference to the local module, and broadcasts
state_dict()
from the process with rank 0 to all other processes in the group to make sure that all model replicas start from the exact same state. Then, each DDP process creates a localReducer
, which later will take care of the gradients synchronization during the backward pass. To improve communication efficiency, theReducer
organizes parameter gradients into buckets, and reduces one bucket at a time. Bucket size can be configured by setting the bucket_cap_mb argument in DDP constructor. The mapping from parameter gradients to buckets is determined at the construction time, based on the bucket size limit and parameter sizes. Model parameters are allocated into buckets in (roughly) the reverse order ofModel.parameters()
from the given model. The reason for using the reverse order is because DDP expects gradients to become ready during the backward pass in approximately that order. The figure below shows an example. Note that, thegrad0
andgrad1
are inbucket1
, and the other two gradients are inbucket0
. Of course, this assumption might not always be true, and when that happens it could hurt DDP backward speed as theReducer
cannot kick off the communication at the earliest possible time. Besides bucketing, theReducer
also registers autograd hooks during construction, one hook per parameter. These hooks will be triggered during the backward pass when the gradient becomes ready.Forward Pass: The DDP takes the input and passes it to the local model, and then analyzes the output from the local model if
find_unused_parameters
is set toTrue
. This mode allows running backward on a subgraph of the model, and DDP finds out which parameters are involved in the backward pass by traversing the autograd graph from the model output and marking all unused parameters as ready for reduction. During the backward pass, theReducer
would only wait for unready parameters, but it would still reduce all buckets. Marking a parameter gradient as ready does not help DDP skip buckets as for now, but it will prevent DDP from waiting for absent gradients forever during the backward pass. Note that traversing the autograd graph introduces extra overheads, so applications should only setfind_unused_parameters
toTrue
when necessary.Backward Pass: The
backward()
function is directly invoked on the lossTensor
, which is out of DDP’s control, and DDP uses autograd hooks registered at construction time to trigger gradients synchronizations. When one gradient becomes ready, its corresponding DDP hook on that grad accumulator will fire, and DDP will then mark that parameter gradient as ready for reduction. When gradients in one bucket are all ready, theReducer
kicks off an asynchronousallreduce
on that bucket to calculate mean of gradients across all processes. When all buckets are ready, theReducer
will block waiting for allallreduce
operations to finish. When this is done, averaged gradients are written to theparam.grad
field of all parameters. So after the backward pass, the grad field on the same corresponding parameter across different DDP processes should be the same.Optimizer Step: From the optimizer’s perspective, it is optimizing a local model. Model replicas on all DDP processes can keep in sync because they all start from the same state and they have the same averaged gradients in every iteration.
Note
DDP requires Reducer
instances on all processes to invoke allreduce
in exactly the same order, which is done by always running allreduce
in the bucket index order instead of actual bucket ready order. Mismatched
allreduce
order across processes can lead to wrong results or DDP backward
hang.
Implementation¶
Below are pointers to the DDP implementation components. The stacked graph shows the structure of the code.
ProcessGroup¶
ProcessGroup.hpp: contains the abstract API of all process group implementations. The
c10d
library provides 3 implementations out of the box, namely, ProcessGroupGloo, ProcessGroupNCCL, and ProcessGroupMPI.DistributedDataParallel
usesProcessGroup::broadcast()
to send model states from the process with rank 0 to others during initialization andProcessGroup::allreduce()
to sum gradients.Store.hpp: assists the rendezvous service for process group instances to find each other.
DistributedDataParallel¶
distributed.py: is the Python entry point for DDP. It implements the initialization steps and the
forward
function for thenn.parallel.DistributedDataParallel
module which call into C++ libraries. Its_sync_param
function performs intra-process parameter synchronization when one DDP process works on multiple devices, and it also broadcasts model buffers from the process with rank 0 to all other processes. The inter-process parameter synchronization happens inReducer.cpp
.comm.h: implements the coalesced broadcast helper function which is invoked to broadcast model states during initialization and synchronize model buffers before the forward pass.
reducer.h: provides the core implementation for gradient synchronization in the backward pass. It has three entry point functions:
Reducer
: The constructor is called indistributed.py
which registersReducer::autograd_hook()
to gradient accumulators.autograd_hook()
function will be invoked by the autograd engine when a gradient becomes ready.prepare_for_backward()
is called at the end of DDP forward pass indistributed.py
. It traverses the autograd graph to find unused parameters whenfind_unused_parameters
is set toTrue
in DDP constructor.


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