Installation
Installing a pre-compiled binary release
In order to make it easy to install eXpress, we provide a few binary packages that save you the trouble of having to compile from source. To use the binary packages for OSX/Linux (Windows), simply download the appropriate one for your machine, untar (unzip) it, and make sure the express(.exe)
binary is in a directory in your PATH environment variable.
If using the Windows binary, you will also need to install the Visual C++ 2010 Runtime Library.
Installing from source
Note: Installing from source is often an unnecessary hassle. If a binary is available for your system, it is highly recommended that you use it. If one is not available, please send us a request to<EMAIL_ADDRESS>The instructions below include commands that are compatible with Unix/Linux/Mac. If compiling on a Windows machine, use the appropriate DOS commands instead. Assuming you are using the Visual C++ compiler, you must type your commands from the Visual Studio Command Prompt.
• Install C++ Compiler (if not already available)
While most Linux systems will already have GCC installed, Mac OS X and Windows do not have a C++ compiler installed by default. Free options include XCode for Mac OS X and Visual C++ Express for Windows.
• Download and extract the source
First you must download source code from the Download menu at the top of this page. Untar the file using:
$ tar -xf express-<EXPRESS_VERSION>-src.tgz
From now on we refer to the path to the directory that is created as <YOUR_EXPRESS_DIR>
.
• Install CMake
Fortunately, the developers of CMake provide simple installation packages for most architectures. Find the right package for your system at their website.
• Install BamTools
With CMake installed, BamTools installation is straightforward.
- Download the source from here and untar into
<YOUR_EXPRESS_DIR>
. - Navigate to
<YOUR_EXPRESS_DIR>/bamtools
.$ cd <YOUR_EXPRESS_DIR>/bamtools
- Make a new directory called build and navigate to it.
$ mkdir build
$ cd build - Have CMake generate the makefile.
$ cmake ..
- Build the BamTools libraries by calling make.
$ make
• Install BOOST
If you have a package manager such as yum (Linux) or MacPorts (OS X) installed, these will probably be the easiest way to install BOOST. Be sure to install boost-devel
so that the headers are included. If you are using Windows, you can use the installer found here. Otherwise, follow the instructions below.
- Download
Boost and the
bjam
build engine. - Unpack
bjam
and add it to your PATH. - Unpack the Boost tarball and
cd
to the Boost source directory. This directory is called theBOOST_ROOT
in some Boost installation instructions. - Build Boost. Note that you can specify where to
put Boost with the
--prefix
option. The default Boost installation directory is/usr/local
.
- If you are on Mac OS X, type:
$ bjam --prefix=<YOUR_BOOST_INSTALL_DIRECTORY> --toolset=darwin architecture=x86 address_model=32_64 link=static runtime-link=static --layout=versioned stage install
- If you are on a 32-bit Linux system, type:
$ bjam --prefix=<YOUR_BOOST_INSTALL_DIRECTORY> --toolset=gcc architecture=x86 address_model=32 link=static runtime-link=static stage install
- If you are on a 64-bit Linux system, type:
$ bjam --prefix=<YOUR_BOOST_INSTALL_DIRECTORY> --toolset=gcc architecture=x86 address_model=64 link=static runtime-link=static stage install
• Install eXpress
We are now ready to build and install eXpress!
- Navigate to
<YOUR_EXPRESS_DIR>
.$ cd <YOUR_EXPRESS_DIR>
- Make a new directory called build and navigate to it.
$ mkdir build
$ cd build - Have CMake generate the makefile.
$ cmake ..
- Build the eXpress binary by calling make.
$ make
- Copy the binary to /usr/lib/bin (or alternatively to another directory in your PATH).
$ sudo make install
You should now be able to type express from any directory and see a print-out of the usage and options. If you do not see this and there were no errors in the compilation, double check to see that the binary was copied into a directory in your PATH.
General Use Case: RNA-Seq abundances
Required input
eXpress requires two input files:
- A multi-FASTA file containing the transcript sequences. If the transcriptome of your organism is not annotated, you can generate this file from your sequencing reads using a de novo transcriptome assembler such as Trinity, Oases, or Trans-ABySS. If your organism has a reference genome you can assemble transcripts directly from mapped reads using Cufflinks. If your genome is already annotated (in GTF/GFF), you can generate a multi-FASTA file using the UCSC Genome Browser by uploading your annotation as a track and downloading the sequences under the "Tables" tab.
- Read alignments to the multi-FASTA file in SAM or BAM format. These can either be stored in a file or streamed directly from an aligner. It is important that you allow as many multi-mappings as possible. You can also allow many mismatches during mapping since eXpress builds an error model to probabalistically assign the reads, although this will increase mapping time. If you are combining reads from several library preparations or from sequencing runs using different read lengths, please see the Manual for important details on how the alignments should be input.
An example
In the following two sub-sections, you will run eXpress on a sample RNA-Seq dataset with simulated reads from UGT3A2 and the HOXC cluster using the human genome build hg18. Both the transcript sequences (transcripts.fasta) and raw reads (reads_1.fastq, reads_2.fastq) can be found in the <YOUR_EXPRESS_DIR>/sample_data directory. For this example to work, you will need to have both Bowtie and SAMtools installed, but in general any aligner will work and the conversion to BAM is not necessary unless you have insufficient disk space to store the uncompressed SAM.
Before you begin, you must prepare your Bowtie index. Since you wish to allow many multi-mappings, it is useful to build the index with a small offrate (in this case 1). The smaller the offrate, the larger the index and the faster the mapping. If you have disk space to spare, always use an offrate of 1. Build the index with the following commands.
$ cd <YOUR_EXPRESS_DIR>/sample_data
$ bowtie-build --offrate 1 transcripts.fasta transcripts
This command will populate your directory with several index files that allow Bowtie to more easily align reads to the transcripts.
You can now map the reads to the transcript sequences using the following Bowtie command, which outputs in SAM (-S
), allows for unlimited multi-mappings (-a
), and a maximum insert distance of 800 bp between the paired-ends (-X 800
). The first three options (a,S,X
) are highly recommended for best results. You should also allow for many mismatches, since eXpress models sequencing errors. Furthermore, you will want to take advantage of multiple processors when mapping large files using the -p option. See the Bowtie Manual for more details on various parameters and options.
The SAM output from Bowtie is piped into SAMtools in order to compress it to BAM format. This conversion is optional, but will greatly reduce the size of the alignment file.
$ bowtie -aS -X 800 --offrate 1 transcripts -1 reads_1.fastq -2 reads_2.fastq | samtools view -Sb - > hits.bam
Input from SAM/BAM file
Once you have aligned your reads to the transcriptome and stored them in a SAM or BAM file, you can run eXpress in default mode with the command:
$ express transcripts.fasta hits.bam
The default settings will be sufficient for most users, but please see the Manual for full descriptions of all available options.
Streaming input from aligner
If you do not wish to store an intermediate SAM/BAM file, you can pipe the Bowtie output directly into eXpress with the command:
$ bowtie -aS -X 800 --offrate 1 transcripts -1 reads_1.fastq -2 reads_2.fastq | express transcripts.fasta
This will give you the exact same output as the previous command, while avoiding the need potentially large amounts of disk space for storing the mapped reads.
Understanding the output
The output for eXpress is saved in a file called results.xprs in an easy-to-parse tab-delimited format. Since an output directory was not specified, it is simply placed in the working directory (<YOUR_EXPRESS_DIR>/sample_data). You can view the output for the run using the command:
$ less results.xprs
Your results should look like this:
bundle_id target_id length eff_length tot_counts uniq_counts est_counts eff_counts ambig_distr_alpha ambig_distr_beta fpkm fpkm_conf_low fpkm_conf_high solvable 1 NM_014620 2300 2123.247776 958 77 520.554767 563.888952 39.404036 38.861284 24516.910965 21256.860893 27776.961037 T 1 NM_153693 2072 1895.275039 481 5 119.916333 131.097934 19.284056 60.593282 6327.120373 4587.418700 8066.822046 T 1 NR_003084 1640 1463.326696 266 0 17.689103 19.824779 6.175215 86.684623 1208.828007 348.657645 2068.998369 T 1 NM_153633 1666 1489.323587 762 10 416.151511 465.518994 64.183691 54.654069 27942.316563 23818.690311 32065.942816 T 1 NM_018953 1612 1435.330044 228 91 212.893398 239.097731 3.148257 0.390173 14832.365459 11791.549012 17873.181907 T 1 NM_004503 1681 1504.321793 384 37 297.794888 332.770029 16.332212 5.398573 19795.956525 16293.650427 23298.262622 T 2 NM_014212 2037 1860.279224 55 55 55.000000 60.224830 0.000000 0.000000 2956.545409 1737.816128 4175.274690 T 3 NM_173860<PHONE_NUMBER>81 962<PHONE_NUMBER>00 1214.622475 0.000000 0.000000<PHONE_NUMBER>04<PHONE_NUMBER>11<PHONE_NUMBER>97 T 4 NM_022658 2288 2111.249211 4881 4881 4881.000000 5289.630397 0.000000 0.000000<PHONE_NUMBER>24<PHONE_NUMBER>27<PHONE_NUMBER>20 T 5 NM_017410 2396 2219.236296 42 42 42.000000 45.345329 0.000000 0.000000 1892.542947 953.138236 2831.947658 T 6 NM_006897 1541 1364.338534 664<PHONE_NUMBER>00 749.978084 0.000000 0.000000 48668.272828 42912.224535 54424.321120 T 7 NM_017409 1959 1782.288551 47 47 47.000000 51.659985 0.000000 0.000000 2637.058964 1581.064918 3693.053009 T 8 NM_001168316 2283 2106.249809 1552 12 443.212928 480.406032 86.571551 222.603290 21042.752189 18083.116378 24002.388000 T 8 NM_174914 2385 2208.237612 1745 38 1049.949880 1133.995024 74.786096 51.366264 47546.961175 43110.530924 51983.391426 T 8 NR_031764 1853 1676.301226 1243 7 270.837192 299.386118 100.878291 371.706966 16156.833161 13233.910476 19079.755847 T
While it may be difficult to read in your terminal, opening the file with R or Excel should help you to visualize the columns. An important column is FPKM, which reports the estimated abundance in expected Fragments Per Kilobase per Million mapped fragments. Other fields include the estimated counts (est_counts) and parameters for the the posterior count distribution (ambig_distr_alpha/beta), as well as the "effective" estimated counts (eff_counts) after correction for bias. Transcripts are sorted by their bundle_id, denoting which multi-mapping group the transcript belongs to and can help determine isoforms and gene families. More details on the output including full descriptions of all columns can be found in the Manual.
Calculating differential expression
If you have multiple samples and replicates, you may want to discover if there is a significant change in abundance of any genes or isoforms under different conditions. Unfortunately, a differential expression tool does not yet exist to take advantage of the full distribution on estimated counts that we output, but we are working on one that will be available soon. For now, we recommend inputting the rounded effective counts for your samples into a count-based differential expression tool such as DEGSeq or edgeR.
Additional Resources
Harold Pimentel has made an excellent walkthrough for today's *Seq I Meeting, which is available here. New users should have a look if you need help getting started with eXpress!


How can Karen handle hyperspace?
By thinking in multiple dimensions. Did you really think I did not know who would respond, and exactly when to post my conversation to entice that user.
Now their gift of sight can be expressed here for others.


There are four colored cards below. By clicking on any you will add a random ammount of points to your have count
You win the game by matching your want count to your have count, you lose a point if your have count goes above the want count.
The value of each card is hidden from you until you click on it, some cards may be more volatile than others.
Each time you pick a card, the game will change the values of each card
Want: 21
Wins: 0
Losses: 0
Have: 0


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This story is part of a series of specially designed stories that represents some of the best journalism The Post has to offer. Check out the rest of the special projects here.
Mingran Ma
11.06.17
After traveling the world and dabbling in politics throughout the U.S., Councilwoman Chris Fahl, D-4th Ward, came to Athens, where she has been an involved member of the community ever since.
She was elected to serve on Athens City Council in 2008, and she is running unopposed to keep her seat in the Nov. 7 election.
Fahl’s entry into Athens politics included campaigning for former Athens Mayor Paul Wiehl in 2007, according to a previous Post report. She has been on council since 2008 and said she had a rocky start to her career in politics.
“When I went out the first time … there were people who were yelling at me,” the fourth ward Democrat said in a previous Post report. “You (would) talk to some people in town, and they think I’m the worst thing that ever came. It was kind of ugly.”
She has advocated for recreational and cultural amenities, safe neighborhoods and a fair and effective city government. She believed those items are part of what attracts jobs to the city, according to a previous Post report.
“I think it is great so many people are running for office … township trustees to school board … that is great for our area and democracy.”– Chris Fahl
During her time on council, Fahl has worked on traffic calming, or the installation of safety solutions such as radar speed signs or speed bumps to reduce traffic, in residential neighborhoods. She has also worked toward pedestrian safety measures and has focused on city trash laws.
Her greatest accomplishment on city council, she said, has been working with residents to write “Chapter 47: Resource extraction and disposal monitoring and mitigation,” which protects Athens in terms of resource extraction and waste disposal by requiring reports and monitoring activities related to those processes.
Fahl ran to represent the fourth ward in 2009 and defeated Republican candidate Randy Morris. She won with about 53 percent of the vote.
In her role on council, she made $7,919.55 in calendar year 2017. She has served as the chair of council’s Planning & Development Committee. Before being elected to council, she was the chairwoman of the city’s Planning Commission.
In 2009, the OU College Democrats wrote to a letter to The Post supporting Fahl, reading that she is a “terrific addition to the Athens City Council's 4th Ward.”
“Chris has been a dedicated servant to the city,” the letter said. “We trust in her ability to continue to serve the people of Athens, including the students of Ohio University.”
Athens City Council President Chris Knisely, who was an at-large councilwoman at the time, said in 2014 that she appreciates Fahl’s laid-back mentality.
“She’s great,” Knisely said. “She brings a combination of being very factual and truthful with a sense of humor. And sometimes we need that.”
This year, Fahl said she is happy that so many candidates are running for office this election.
“I think it is great so many people are running for office … township trustees to school board … that is great for our area and democracy,” Fahl said in an email.
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Trigger validation of a dropdown box when tabbing over it in KnockoutJS
I had a problem to solve recently which was to change a form’s validation triggers in a front-end that uses KnockoutJS. When a user tabs through the required input elements of a form, the validation visuals of said elements get highlighted. That was not the case, however, with the dropdown boxes in the form as those didn’t highlight as invalid. The would still show the caption entry on the top (“Please select from the below”) which obviously is a prompt and that doesn’t meet the “required” criteria, but the validation would only trigger if a value was selected and then the caption entry on the top was selected again, which isn’t a usual user journey to start with.
The invalid dropdown entries would still be caught upon form submission. However, a quick feedback would provide consistency in the UI instead of some fields being highlighted when passed on with the tab key and some others not being hightlighted. More importantly, it’s also an accessibility issue as screen readers will wait until the end instead of picking up the invalid field as soon as they do with the text boxes.
Let’s look at an example. Here is a dropdown box whose markup and validation markers look like this:

State/Territory
 *

<select aria-invalid="false" name="expiry-month" id="selectState"
aria-describedby="selectStateErr"
data-bind="event: {blur: forceValidation.bind($data, state) }, value: state">
<option value="" selected>-Select-</option>
<option value="ACT">ACT</option>
<option value="NSW">NSW</option>
<option value="QLD">QLD</option>
<option value="VIC">VIC</option>
<option value="TAS">TAS</option>
<option value="NT">NT</option>
<option value="WA">WA</option>
<option value="SA">SA</option>
</select>

State

Validation rules for the dropdown:
this.state = ko.observable("")
.extend({
required: {
message: "You must select a state or territory",
}
});
The view model property state
is set up with an empty string value, same as the placeholder entry’s value. Were this not the case, the “required” validation of the dropdown would kick in immediately. This is because while the observable is initialized with another value, when Knockout evaluates the value binding it sets the state
field to the currently selected empty string value. Because undefined != “”, validation fails as knockout thinks that the property has been modified.
Then create a property with an observable that will trigger validation visuals in the DOM:
self.stateValid = ko.computed(function(){
return validateField(self.state);
});
Where validateField
is a generic validation function you would use with text fields too:
function validateField(field){
return !field.isValid() && field.isModified();
}
An additional step is required to trigger the validations of a dropdown when focusing and unfocusing from it.
The blur
event is fired when a user clicks out or tabs out from an input element. We need to let knockout know that it’s a event it should watch out for:
this.valueUpdate = ['blur']
Edit the selectState
opening dropdown tag as follows:
<select aria-invalid="false" name="expiry-month" id="selectState"
aria-describedby="selectStateErr"
data-bind="event:{blur: forceValidation.bind($data, state) }, value: state">
And add the following function:
self.forceValidation = function(field){
field.valueHasMutated();
};
Now when the dropdown loses focus its bound observable will have its valueHasMutated
function fired. valueHasMutated
normally notifies the observable’s subscribers that the value has changed but manually invoking it will have the same effect.
With that in place, you have a dropdown whose validation is also triggered by it losing focus.
- Installing the NewRelic .NET agent on an Alpine Docker image
- An implementation of the Capital Controls kata in Python
- Trigger validation of a dropdown box when tabbing over it in KnockoutJS
- The Capital Controls Kata
- Github Pages offers HTTPS for one custom domain. However you can point more than one domain to your pages (with caveats).


This page has a variety of scripts, including a big script, inline scripts, onclick handlers, dynamically added scripts, etc.


Please give me my {{ time }} massage :)
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Avishree Khare
Welcome! I am Avishree Khare, a Software Engineer at Google. I recently recieved a B.E. in Computer Science from BITS Pilani, K. K. Birla Goa campus. I am interested in Deep learning and its applications in Computer Vision. I have also recently started exploring Reasoning, Representation Learning and Incremental Learning in Neural Networks.
Prior to joining Google, I was an SWE Intern at Apple India. I have also been a summer intern at CSIR-IGIB, where I worked on Detection of Intracranial Hemorrhage from brain CT scans.
I enjoy reading and hence have a little section dedicated to my personal favourites (suggestions are always welcome ). I also enjoy writing articles on subjects that interest me (another little section dedicated to that).
News
| Jul 17, 2021 | Joined Google India as an SWE |
|---|---|
| May 2, 2021 | Our team (Het Shah and I) have been selected for ESOWC 2021 to work on the ML4Land project! |
| Jan 17, 2021 | Joined Apple India as an SWE Intern |
| Aug 17, 2020 | Selected for Google Research India AI Summer School 2020 (Vision track)! |


Kirk and I took our dogs (Daisy and Mason) out for a walk. We picked up one of the neighbors dogs along the way. (The owner forgot to put on the dog's invisible fence collar.) Our street ends at Durand Park. Its about a quarter of a mile away from our house and a lot of people bring their dogs there for walks. Once I get in the park, I often let Mason run off his leash so he can get the extra exercise. He loves to run around like a crazed hound sniffing and smelling everything. The neighbor's dog gave Mason an extra hard work out. Fortunately he's slightly larger than Mason and was able to stand up to Mason's abuse. I eventually had to put Mason back on his leash because he was getting too nippy.


This Alien: Isolation mod fills the space station with xenomorphs
by Fraser BrownLike the original movie, Alien: Isolation does a lot with a single monster that we're only exposed to in brief, terrifying moments before we run off and hide. Apparently some people think that's a walk in the park, though, and now a modder is catering to them by filling Sevastopol with more xenomorph chums.

Aliens: Isolation is still in development, so you can't experience the nightmare quite yet, but you can take a look at the short preview video below.
I found it stressful enough hiding from one xenomorph, so a whole hive of them is probably too much for my frayed nerves. Unlike Aliens, where an increase in action and decrease in tension accompanied the additional xenomorphs, Aliens: Isolation remains a survival horror romp, and just one of them spotting you will still be very bad news.

More details on Aliens: Isolation will be revealed soon.


Declaring a view is similar to declaring a type except the @x:view command is used instead of the @x:type command.
The @x:view command should contain a special sys:Is function that takes an object and returns true if that object is within the view.
The @x:self command for a view normally contains only functions not variables since the view itself in not instantiated to create an object. Instead an existing object is used which is referenced with self.
Views can have constructors. The special sys:Make function is called which must return an object that is within the view.
Functions within the @x:self command of a view that have option visible can be referenced by dot qualification of an object that is within the view where the name of the view is included following the # in <qualified>. Various other forms of sugared function calls also include the view following a #. For example the the ==#fold infix operator can be used to invoke the equality function for view fold.


There has long been a need for organized community around learning and teaching geospatial open source. It’s way too hard hard to learn about maps and geographic concepts. It’s way too hard to learn about geospatial technology. And it’s a huge bummer.
It’s also why Beth Schechter started MaptimeSF in the summer of 2013: she wanted to make it easier for herself to learn, and for her that meant working with other people. And then MaptimeSF grew in popularity, because it just makes sense to learn things in an accessible way with other people who want to learn. It’s the same reason I started MaptimePDX, and the same reason Will started OpenGeoCLE, and the reason that Maptime chapters are popping up all over the continent.
So what has Maptime become? Maptime is a collection of beginner-focused community groups for learning geospatial technology, techniques, and concepts, particularly using open source tools and hands-on practice and exercises. But Maptime is also an important idea: it shouldn’t be so hard to learn, and we have the power to make it easier – and teach ourselves in the process.
What is difficult for beginners in geospatial tech right now?
- The “geo community” as it stands includes developers who do mappy stuff, GIS professionals who largely work in government and utilities, OpenStreetMap editors and developers, and the academic community. Most meetups involve either networking / career seeking or presentations of recent work by experts who are to talk to other experts. If you’re new to the field and trying to learn, there isn’t a community for you.
- The “learning tools” for geospatial tech are largely written by developers and/or experts, the folks who perhaps wrote the tool or are active users/maintainers. Often these resources are well-written, but make too many assumptions and can alienate beginners. If you are a beginner and unsure how to learn, there is a lack of accessible and empowering resources.
- The “learning mechanisms” for geo involve blog posts scattered all around the web. There are some organied trainings, but they typically rely on learning one piece of software or one particular tool. If you want to learn how to make maps, there is neither a clearly defined starting point nor an intuitive flow so you can understand what to do next.
How does Maptime address these?
- Maptime creates community around learning geospatial as a beginner. By simply making a space weekly or monthly where folks can go to ask questions and hang out with other people in a welcoming environment, Maptime is already achieving one of its goals and making a difference.
- Maptime provides encouragement and a platform for creating beginner content that is clearly-written, accessible, and makes no assumptions about what the trainee is familiar with. Not only are tehse resources helpful for those learning, but all of the teaching and mentoring is coming from beginners as well. Maptime provides a platform for both learning and guiding, regardless of skill level.
- The Maptime community is slowly amassing content through its chapters to create a repository of resources and build a curriculum. We gain valuable insight from the hands-on work the chapters are doing to teach beginners, and from that we can determine what is helpful, what is not so helpful, and a logical path down which we can guide new Maptimers.
But these are just the first steps.
Where it was once very difficult for beginners in geospatial to get the help they need, there is now a place for them to go and resources for them to use. But there are lots of things left to do in order to make geo a welcoming field for everyone, regardless of background or skill level. We’re keeping a list of our goals, plans, and aspirations, and we want you to contribute. That list is over on the Issues page of the Maptime GitHub repo, and anyone is free to contribute.  Watch this quick video instead.)
Maptime exists because community, inclusivity, and accessibility are important and necessary components of positive learning experiences. The more we harp on this idea, the more it will become self-evident in communities everywhere. Plus, more people making maps is awesome. :)
Maptime chapters:
If you’re interested in starting a Maptime chapter in your area, shoot me an email. If you’re interested in getting in contact with an existing Maptime chapter in your area, check out the contact page here.


On November 15, 2019 a transcript of phone call between United States President Donald Trump and Ukraine President Volodymyr Zelensky was released. It revealed highly controversial contents of the phone call. Whistleblower stated that Trump violated his oath and tried to use his position in office for personal gain. Impeachment process started.
Although both articles talk about the phone call between President Trump and the President of Ukraine Volodymyr Zelensky both have a different take on if what was said qualifies as impeachable offenses. The New York Times article insists President Trump should be impeached by congress while the New York Post article brushes off the situation and urges that we shouldn’t overreact. Both articles helped us understand one crucial idea and it’s that we should look at this situation through unbiased eyes, wait for all the facts and what the hearings being held will reveal. If he is found guilty it’s a huge deal, knowing that the President of one of the most powerful countries in the world tried to leverage crucial government aid in exchange for information on a presidential candidate’s affairs should be impeachable.
Is Ukraine-Gate something we should brush off or is it and impeachable offense that should be agreed on by the Congress?


Isar735.github.io
In 1818, 20-year-old Mary Shelley published Frankenstein; or, The Modern Prometheus. This fall, the Department of English marks the book s 200th anniversary.
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This app takes as input a Github issue body and predicts a title for it. Behind the scenes it uses a Tensor2Tensor TensorFlow model, served via TF-Serving .
(Thanks to Hamel Husain for the original concept and source data.)
Enter the body of a github issue or the url of a github issue and click on Submit. The model then tries to generate a title or summary of the issue.
Populate Random Issue
Generate Title
This demo is run using Kubeflow - a machine learning toolkit for Kubernetes. Kubeflow is dedicated to making deployment of machine learning on Kubernetes simple, portable and scalable.


Simple, Secure, and Fast. P2P video calling provides quality and low latency, which is simply not available with traditional technology.


fnx_ui is a set of Desktop first Angular2.Dart components.
You are looking at the example ng2 application and "cookbook" for common UI components.


Hi everyone just wanted to let you know that this website is still under development and we are trying our absolute best to get it up and running but we are running into issues and we aren't sure what is going on :(
Anyway here is a button
Copyright 2020 ©


Donald Trump
Resume
“Get going. Move forward. Aim High. Plan a takeoff. Don’t just sit on the runway and hope someone will come along and push the airplane. It simply won’t happen. Change your attitude and gain some altitude. Believe me, you’ll love it up here.”
Experience
-
2017
Trump and his businesses had been involved in more than 4,000 state and federal legal actions, according to a running tally by USA Today
-
2016
In 2016, the New York State Attorney General's office notified the Trump Foundation that the foundation appeared to be in violation of New York laws regarding charities, ordering it to immediately cease its fundraising activities in New York.
-
2015
In February 2015, Trump stated that he was "not ready" to sign on for another season of the show because of the possibility of a presidential run.
-
2014
A February 2014 Quinnipiac poll had shown Trump losing to the more popular Cuomo by 37 points in a hypothetical election.
Business career
-
Real estate
In 1968, Trump began his career at his father Fred's real estate development company, E. Trump & Son, which, among other interests, owned middle-class rental housing in New York City's outer boroughs. Trump worked for his father to revitalize the Swifton Village apartment complex in Cincinnati, Ohio, which the elder Trump had bought in 1964. The management of the property was sued for racial discrimination in 1969; the suit "was quietly settled at Fred Trump's direction." The Trumps sold the property in 1972, with vacancy on the rise.
-
Manhattan developments
In 1978, Trump launched his Manhattan real estate business by purchasing a 50 percent stake in the derelict Commodore Hotel, located next to Grand Central Terminal. The purchase was funded largely by a $70 million construction loan that was guaranteed jointly by Fred Trump and the Hyatt hotel chain. When the remodeling was finished, the hotel reopened in 1980 as the Grand Hyatt Hotel.
-
Palm Beach estate
In 1985, Trump acquired the Mar-a-Lago estate in Palm Beach, Florida, for $10 million, $7 million for the real estate and $3 million for the furnishings. His initial offer of $28 million had been rejected, and he was able to obtain the property for the lower price after a real-estate market "slump". The home was built in the 1920s by heiress and socialite Marjorie Merriweather Post.
-
Atlantic City casinos
After New Jersey legalized casino gambling in 1977, Trump traveled to Atlantic City to explore new business opportunities. Seven years later, he opened Harrah's at Trump Plaza hotel and casino; the project was built by Trump with financing from the Holiday Corporation, who also managed its operation. It was renamed "Trump Plaza" soon after it opened.


6 times Tiwa Savage inspired fashion statement
Nigerian singer and mother of one, Tiwatope Savage, is not only known as a talented singer but she is also a known fashion icon. The iconic star is always an inspiration to her fans when it comes to fashion.
Savage knows how to entice her fans with lovely looks. She knows when to show little skin and when to risk it all just for the sake of fashion.
The mother of one rocks oversized clothes like they were tailored for her and she rocks fitted ones like they are made for her. At different times, Savage has wowed Nigerians with her fashionable style.
Legit.ng has compiled some of the photos of the times Savage made fashion statements.
1) The neon look
Tiwa Savage looked beautiful in a mesh crop top and trouser paired with a bra top all neon green from head to toe.
2) Cowries:
Savage looked stunning as she accessorised with cowries on her hair.
Teni - Billionaire will make you hit the dance floor
3) Oversized Pyer Moss look
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4) Oversized colour block look
Not many can pull off this bold look bu the singer sure knows how to make anything look gorgeous.
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5) The "yansh leather jacket"
This was indeed an iconic fashion statement.
6) The hot pant look
The singer looked beautiful and breathtaking as she showed off her legs in this daring look.
READ ALSO: NAIJ.com upgrades to Legit.ng: a letter from our Editor-in-Chief Bayo Olupohunda
Savage has always been a style icon, Legit.ng earlier reported that the singer stepped out in style while she was at the 2019 New York Fashion Week. The singer was spotted rocking three amazing outfits in New York City.
Top facts about the incredible Brianna Keilar: age, career and net worth
NAIJ.com (naija.ng) -> Legit.ng We have upgraded to serve you better
Do Tiwa Savage and Wizkid Make a 'Good' Couple? - Nigeria Street Gist - on Legit TV


Writable stream that concatenates strings or binary data and calls a callback with the result. Not a transform stream -- more of a stream sink.
Streams emit many buffers. If you want to collect all of the buffers, and when the stream ends concatenate all of the buffers together and receive a single buffer then this is the module for you.
Only use this if you know you can fit all of the output of your stream into a single Buffer (e.g. in RAM).
There are also objectMode
streams that emit things other than Buffers, and you can concatenate these too. See below for details.
var fs = require('fs')
var concat = require('concat-stream')
var readStream = fs.createReadStream('cat.png')
var concatStream = concat(gotPicture)
readStream.on('error', handleError)
readStream.pipe(concatStream)
function gotPicture(imageBuffer) {
// imageBuffer is all of `cat.png` as a node.js Buffer
}
function handleError(err) {
// handle your error appropriately here, e.g.:
console.error(err) // print the error to STDERR
process.exit(1) // exit program with non-zero exit code
}
var write = concat(function(data) {})
write.write([1,2,3])
write.write([4,5,6])
write.end()
// data will be [1,2,3,4,5,6] in the above callback
var write = concat(function(data) {})
var a = new Uint8Array(3)
a[0] = 97; a[1] = 98; a[2] = 99
write.write(a)
write.write('!')
write.end(Buffer('!!1'))
See test/
for more examples
var concat = require('concat-stream')
Return a writable
stream that will fire cb(data)
with all of the data that
was written to the stream. Data can be written to writable
as strings,
Buffers, arrays of byte integers, and Uint8Arrays.
By default concat-stream
will give you back the same data type as the type of the first buffer written to the stream. Use opts.encoding
to set what format data
should be returned as, e.g. if you if you don't want to rely on the built-in type checking or for some other reason.
string
- get a stringbuffer
- get back a Bufferarray
- get an array of byte integersuint8array
, u8
, uint8
- get back a Uint8Arrayobject
, get back an array of ObjectsIf you don't specify an encoding, and the types can't be inferred (e.g. you write things that aren't in the list above), it will try to convert concat them into a Buffer
.
concat-stream
does not handle errors for you, so you must handle errors on whatever streams you pipe into concat-stream
. This is a general rule when programming with node.js streams: always handle errors on each and every stream. Since concat-stream
is not itself a stream it does not emit errors.
MIT LICENSE


Fill in what YOUR favorite things are!
Where is your favorite place that you have traveled to?
What is the name of your favorite restaurant in Portland?
What is your favorite holiday of the year?
What is your favorite thing about Epicodus?
Show me my favorite things!
My favorite things:


- botLot - package botLot
-
- BotLot - Class in botLot
-
BotLot.java
- BotLot(BotLot) - Constructor for class botLot.BotLot
-
Constructor that takes in another BotLot object.
- BotLot(LotGraph, LotNode) - Constructor for class botLot.BotLot
-
Constructor that takes in a graph and a node.
- BotLot(LotGraph) - Constructor for class botLot.BotLot
-
Constructor to set the graph.
- BotLot() - Constructor for class botLot.BotLot
-
Basic constructor.
- botLot.dataSource - package botLot.dataSource
-
- botLot.lotGraph - package botLot.lotGraph
-
- botLot.pathFinding - package botLot.pathFinding
-
- botLot.pathFinding.Algorithms - package botLot.pathFinding.Algorithms
-
- BotLotDataSource - Class in botLot.dataSource
-
Abstract class for the different data source objects for BotLot.
- BotLotDataSource() - Constructor for class botLot.dataSource.BotLotDataSource
-
Basic constructor.
- BotLotDataSourceInterface - Interface in botLot.dataSource
-
Interface to define required functionality in data source objects.
- BotLotDS - Class in botLot.dataSource
-
Class BotLotDS "BotLot Data Source"
- BotLotDS(String, String) - Constructor for class botLot.dataSource.BotLotDS
-
Constructor for the data source object.
- BotLotDS() - Constructor for class botLot.dataSource.BotLotDS
-
Empty constructor, for initialization when you don't want to specify things
- BotLotDSException - Exception in botLot.dataSource
-
BotLotDataSourceException.java
- BotLotDSException() - Constructor for exception botLot.dataSource.BotLotDSException
-
Empty exception (just in case).
- BotLotDSException(String) - Constructor for exception botLot.dataSource.BotLotDSException
-
General exception, used for all exceptions made by the BotLotDataSource object
- BotLotException - Exception in botLot
-
BotLotException.java
- BotLotException() - Constructor for exception botLot.BotLotException
-
Empty exception (just in case).
- BotLotException(String) - Constructor for exception botLot.BotLotException
-
General exception, used for all exceptions made by the BotLot object
- BotLotPF - Class in botLot.pathFinding
-
BotLotPF.java
- BotLotPF() - Constructor for class botLot.pathFinding.BotLotPF
-
- BotLotPFAlgException - Exception in botLot.pathFinding.Algorithms
-
BotLotPFAlgException.java
- BotLotPFAlgException() - Constructor for exception botLot.pathFinding.Algorithms.BotLotPFAlgException
-
Empty exception (just in case).
- BotLotPFAlgException(String) - Constructor for exception botLot.pathFinding.Algorithms.BotLotPFAlgException
-
General exception, used for all exceptions made by the BotLotPF static object
- BotLotPFAlgInterface - Interface in botLot.pathFinding.Algorithms
-
The interface for Path Finding algorithms.
- BotLotPFAlgorithm - Class in botLot.pathFinding.Algorithms
-
Abstract class to set up the methods needed for all path generation.
- BotLotPFAlgorithm() - Constructor for class botLot.pathFinding.Algorithms.BotLotPFAlgorithm
-
Basic constructor for path finding.
- BotLotPFAlgorithm(BotLot) - Constructor for class botLot.pathFinding.Algorithms.BotLotPFAlgorithm
-
Constructor using a BotLot object.
- BotLotPFAlgorithm(BotLot, Collection<LotEdge>) - Constructor for class botLot.pathFinding.Algorithms.BotLotPFAlgorithm
-
Constructor using a BotLot object and a list of edges to avoid.
- BotLotPFAlgorithm(LotGraph, LotNode, LotNode) - Constructor for class botLot.pathFinding.Algorithms.BotLotPFAlgorithm
-
Constructor that initializes all the variables separately.
- BotLotPFAlgorithm(LotGraph, LotNode, LotNode, Collection<LotEdge>) - Constructor for class botLot.pathFinding.Algorithms.BotLotPFAlgorithm
-
Constructor that initializes everything, actually everything.
- BotLotPFDijkstra - Class in botLot.pathFinding.Algorithms
-
The implementation of Dijkstra's algorithm for this application.
- BotLotPFDijkstra() - Constructor for class botLot.pathFinding.Algorithms.BotLotPFDijkstra
-
Basic Constructor
- BotLotPFDijkstra(BotLot, Collection<LotEdge>) - Constructor for class botLot.pathFinding.Algorithms.BotLotPFDijkstra
-
Constructor using a BotLot object and a list of edges to avoid.
- BotLotPFDijkstra(LotGraph, LotNode, LotNode, Collection<LotEdge>) - Constructor for class botLot.pathFinding.Algorithms.BotLotPFDijkstra
-
Constructor taking in all the things needed for the thing to work.
- BotLotPFDijkstra(BotLot) - Constructor for class botLot.pathFinding.Algorithms.BotLotPFDijkstra
-
Constructor using a BotLot object.
- BotLotPFException - Exception in botLot.pathFinding
-
BotLotPFException.java
- BotLotPFException() - Constructor for exception botLot.pathFinding.BotLotPFException
-
Empty exception (just in case).
- BotLotPFException(String) - Constructor for exception botLot.pathFinding.BotLotPFException
-
General exception, used for all exceptions made by the BotLotPF static object
- BotLotPFRandom - Class in botLot.pathFinding.Algorithms
-
Finds a path by randomly selecting edges on each node.
- BotLotPFRandom(BotLot) - Constructor for class botLot.pathFinding.Algorithms.BotLotPFRandom
-
Constructor using a BotLot object.
- BotLotPFRandom(BotLot, Collection<LotEdge>) - Constructor for class botLot.pathFinding.Algorithms.BotLotPFRandom
-
Constructor using a BotLot object and a list of edges to avoid.
- BotLotPFRandom() - Constructor for class botLot.pathFinding.Algorithms.BotLotPFRandom
-
Basic constructor for path finding.
- BotLotPFRandom(LotGraph, LotNode, LotNode) - Constructor for class botLot.pathFinding.Algorithms.BotLotPFRandom
-
Constructor that initializes all the variables separately.
- BotLotPFRandom(LotGraph, LotNode, LotNode, Collection<LotEdge>) - Constructor for class botLot.pathFinding.Algorithms.BotLotPFRandom
-
Constructor that initializes everything, actually everything.
- BotLotPFWorkers - Class in botLot.pathFinding
-
Worker methods for path finding.
- BotLotPFWorkers() - Constructor for class botLot.pathFinding.BotLotPFWorkers
-
- BotLotXMLDS - Class in botLot.dataSource
-
Class to save BotLot/LotGraph data to XML.
- BotLotXMLDS(String) - Constructor for class botLot.dataSource.BotLotXMLDS
-
Constructor to take in a XML file location.
- BotLotXMLDS() - Constructor for class botLot.dataSource.BotLotXMLDS
-
Empty constructor.
- buildDBConnectionString() - Method in class botLot.dataSource.BotLotDS
-
Builds a valid connection string for use by the database connectors.


Linnerrud dataset¶
Notes¶
- Data Set Characteristics:
Number of Instances: 20 Number of Attributes: 3 Missing Attribute Values: None
The Linnerud dataset constains two small dataset:
- exercise: A list containing the following components: exercise data with 20 observations on 3 exercise variables: Weight, Waist and Pulse.
- physiological: Data frame with 20 observations on 3 physiological variables:
- Chins, Situps and Jumps.
References¶
- Tenenhaus, M. (1998). La regression PLS: theorie et pratique. Paris: Editions Technic.


\
Machine learning is the science of giving computers the ability to learn without being explicitly programmed. The idea is that there exist generic algorithms which can be used to find patterns in a broad class of data sets without having to write code specifically for each problem. The algorithm will build its own logic based on the data.
Machine learning is a subfield of computer science, and is closely related to computational statistics. It evolved from the study of pattern recognition in artificial intelligence (AI) research, and has made contributions to AI tasks like computer vision, natural language processing and speech recognition. It has also, especially in later years, found applications in a wide variety of other areas, including bioinformatics, economy, physics, finance and marketing.
The approaches to machine learning are many, but are often split into two main categories. In supervised learning we know the answer to a problem, and let the computer deduce the logic behind it. On the other hand, unsupervised learning is a method for finding patterns and relationship in data sets without any prior knowledge of the system. Some authours also operate with a third category, namely reinforcement learning. This is a paradigm of learning inspired by behavioural psychology, where learning is achieved by trial-and-error, solely from rewards and punishment.
Another way to categorize machine learning tasks is to consider the desired output of a system. Some of the most common tasks are:
This behaviour has inspired a simple mathematical model for an artificial neuron.
$$
\begin{equation}
y = f\left(\sum_{i=1}^n w_ix_i\right) = f(u)
\tag{1}
\end{equation}
$$
Here, the output \( y \) of the neuron is the value of its activation function, which have as input
a weighted sum of signals \( x_i, \dots ,x_n \) received by \( n \) other neurons.
An artificial neural network (NN), is a computational model that consists of layers of connected neurons, or nodes. It is supposed to mimic a biological nervous system by letting each neuron interact with other neurons by sending signals in the form of mathematical functions between layers. A wide variety of different NNs have been developed, but most of them consist of an input layer, an output layer and eventual layers in-between, called hidden layers. All layers can contain an arbitrary number of nodes, and each connection between two nodes is associated with a weight variable.
Nodes are represented by circles, while the arrows display the connections between the nodes, including the direction of information flow. Additionally, each arrow corresponds to a weight variable, not displayed here. We observe that each node in a layer is connected to all nodes in the subsequent layer, making this a so-called fully-connected FFNN.
A different variant of FFNNs are convolutional neural networks (CNNs), which have a connectivity pattern inspired by the animal visual cortex. Individual neurons in the visual cortex only respond to stimuli from small sub-regions of the visual field, called a receptive field. This makes the neurons well-suited to exploit the strong spatially local correlation present in natural images. The response of each neuron can be approximated mathematically as a convolution operation.
CNNs emulate the behaviour of neurons in the visual cortex by enforcing a local connectivity pattern between nodes of adjacent layers: Each node in a convolutional layer is connected only to a subset of the nodes in the previous layer, in contrast to the fully-connected FFNN. Often, CNNs consist of several convolutional layers that learn local features of the input, with a fully-connected layer at the end, which gathers all the local data and produces the outputs. They have wide applications in image and video recognition
So far we have only mentioned NNs where information flows in one direction: forward. Recurrent neural networks on the other hand, have connections between nodes that form directed cycles. This creates a form of internal memory which are able to capture information on what has been calculated before; the output is dependent on the previous computations. Recurrent NNs make use of sequential information by performing the same task for every element in a sequence, where each element depends on previous elements. An example of such information is sentences, making recurrent NNs especially well-suited for handwriting and speech recognition.
There are many other kinds of NNs that have been developed. One type that is specifically designed for interpolation in multidimensional space is the radial basis function (RBF) network. RBFs are typically made up of three layers: an input layer, a hidden layer with non-linear radial symmetric activation functions and a linear output layer (''linear'' here means that each node in the output layer has a linear activation function). The layers are normally fully-connected and there are no cycles, thus RBFs can be viewed as a type of fully-connected FFNN. They are however usually treated as a separate type of NN due the unusual activation functions.
Other types of NNs could also be mentioned, but are outside the scope of this work. We will now move on to a detailed description of how a fully-connected FFNN works, and how it can be used to interpolate data sets.
One use often so-called fully-connected feed-forward neural networks with three or more layers (an input layer, one or more hidden layers and an output layer) consisting of neurons that have non-linear activation functions.
Such networks are often called multilayer perceptrons (MLPs)
According to the Universal approximation theorem, a feed-forward neural network with just a single hidden layer containing a finite number of neurons can approximate a continuous multidimensional function to arbitrary accuracy, assuming the activation function for the hidden layer is a non-constant, bounded and monotonically-increasing continuous function. Note that the requirements on the activation function only applies to the hidden layer, the output nodes are always assumed to be linear, so as to not restrict the range of output values.
We note that this theorem is only applicable to a NN with one hidden layer. Therefore, we can easily construct an NN that employs activation functions which do not satisfy the above requirements, as long as we have at least one layer with activation functions that do. Furthermore, although the universal approximation theorem lays the theoretical foundation for regression with neural networks, it does not say anything about how things work in practice: A neural network can still be able to approximate a given function reasonably well without having the flexibility to fit all other functions.
$$
\begin{equation}
y = f\left(\sum_{i=1}^n w_ix_i + b_i\right) = f(u)
\tag{2}
\end{equation}
$$
In an FFNN of such neurons, the inputs \( x_i \)
are the outputs of the neurons in the preceding layer. Furthermore, an MLP is fully-connected,
which means that each neuron receives a weighted sum of the outputs of all neurons in the previous layer.
First, for each node \( i \) in the first hidden layer, we calculate a weighted sum \( u_i^1 \) of the input coordinates \( x_j \),
$$
\begin{equation}
u_i^1 = \sum_{j=1}^2 w_{ij}^1 x_j + b_i^1
\tag{3}
\end{equation}
$$
This value is the argument to the activation function \( f_1 \) of each neuron \( i \),
producing the output \( y_i^1 \) of all neurons in layer 1,
$$
\begin{equation}
y_i^1 = f_1(u_i^1) = f_1\left(\sum_{j=1}^2 w_{ij}^1 x_j + b_i^1\right)
\tag{4}
\end{equation}
$$
where we assume that all nodes in the same layer have identical activation functions, hence the notation \( f_l \)
$$
\begin{equation}
y_i^l = f_l(u_i^l) = f_l\left(\sum_{j=1}^{N_{l-1}} w_{ij}^l y_j^{l-1} + b_i^l\right)
\tag{5}
\end{equation}
$$
where \( N_l \) is the number of nodes in layer \( l \). When the output of all the nodes in the first hidden layer are computed,
the values of the subsequent layer can be calculated and so forth until the output is obtained.
The output of neuron \( i \) in layer 2 is thus,
$$
\begin{align}
y_i^2 &= f_2\left(\sum_{j=1}^3 w_{ij}^2 y_j^1 + b_i^2\right)
\tag{6}\\
&= f_2\left[\sum_{j=1}^3 w_{ij}^2f_1\left(\sum_{k=1}^2 w_{jk}^1 x_k + b_j^1\right) + b_i^2\right]
\tag{7}
\end{align}
$$
where we have substituted \( y_m^1 \) with.  The complete functional form is,
$$
\begin{align}
&y^{l+1}_1\! = \!f_{l+1}\!\left[\!\sum_{j=1}^{N_l}\! w_{1j}^3 f_l\!\left(\!\sum_{k=1}^{N_{l-1}}\! w_{jk}^2 f_{l-1}\!\left(\!
\dots \!f_1\!\left(\!\sum_{n=1}^{N_0} \!w_{mn}^1 x_n\! + \!b_m^1\!\right)
\!\dots \!\right) \!+ \!b_k^2\!\right)
\!+ \!b_1^3\!\right] &&
\tag{10}
\end{align}
$$
which illustrates a basic property of MLPs: The only independent variables are the input values \( x_n \).
This confirms that an MLP, despite its quite convoluted mathematical form, is nothing more than an analytic function, specifically a mapping of real-valued vectors \( \vec{x} \in \mathbb{R}^n \rightarrow \vec{y} \in \mathbb{R}^m \). In our example, \( n=2 \) and \( m=1 \). Consequentially, the number of input and output values of the function we want to fit must be equal to the number of inputs and outputs of our MLP.
Furthermore, the flexibility and universality of a MLP can be illustrated by realizing that the expression is essentially a nested sum of scaled activation functions of the form
$$
\begin{equation}
h(x) = c_1 f(c_2 x + c_3) + c_4
\tag{11}
\end{equation}
$$
where the parameters \( c_i \) are weights and biases. By adjusting these parameters, the activation functions
can be shifted up and down or left and right, change slope or be rescaled
which is the key to the flexibility of a neural network.
Additionally, we can represent the biases and activations as layer-wise column vectors \( \vec{b}_l \) and \( \vec{y}_l \), so that the \( i \)-th element of each vector is the bias \( b_i^l \) and activation \( y_i^l \) of node \( i \) in layer \( l \) respectively.
We have that \( \mathrm{W}_l \) is a \( N_{l-1} \times N_l \) matrix, while \( \vec{b}_l \) and \( \vec{y}_l \) are \( N_l \times 1 \) column vectors. With this notation, the sum in becomes a matrix-vector multiplication, and we can write the equation for the activations of hidden layer 2 in
$$
\begin{equation}
\vec{y}_2 = f_2(\mathrm{W}_2 \vec{y}_{1} + \vec{b}_{2}) =
f_2\left(\left[\begin{array}{ccc}
w^2_{11} &w^2_{12} &w^2_{13} \\
w^2_{21} &w^2_{22} &w^2_{23} \\
w^2_{31} &w^2_{32} &w^2_{33} \\
\end{array} \right] \cdot
\left[\begin{array}{c}
y^1_1 \\
y^1_2 \\
y^1_3 \\
\end{array}\right] +
\left[\begin{array}{c}
b^2_1 \\
b^2_2 \\
b^2_3 \\
\end{array}\right]\right).
\tag{12}
\end{equation}
$$
$$
\begin{equation}
y^2_i = f_2\Bigr(w^2_{i1}y^1_1 + w^2_{i2}y^1_2 + w^2_{i3}y^1_3 + b^2_i\Bigr) =
f_2\left(\sum_{j=1}^3 w^2_{ij} y_j^1 + b^2_i\right).
\tag{13}
\end{equation}
$$
This is not just a convenient and compact notation, but also
a useful and intuitive way to think about MLPs: The output is calculated by a series of matrix-vector multiplications
and vector additions that are used as input to the activation functions. For each operation
\( \mathrm{W}_l \vec{y}_{l-1} \) we move forward one layer.
A property that characterizes a neural network, other than its connectivity, is the choice of activation function(s). As described in, the following restrictions are imposed on an activation function for a FFNN to fulfill the universal approximation theorem
The second requirement excludes all linear functions. Furthermore, in a MLP with only linear activation functions, each layer simply performs a linear transformation of its inputs.
Regardless of the number of layers, the output of the NN will be nothing but a linear function of the inputs. Thus we need to introduce some kind of non-linearity to the NN to be able to fit non-linear functions Typical examples are the logistic Sigmoid
$$
\begin{equation}
f(x) = \frac{1}{1 + e^{-x}},
\tag{14}
\end{equation}
$$
and the hyperbolic tangent function
$$
\begin{equation}
f(x) = \tanh(x)
\tag{15}
\end{equation}
$$


2 projects
4 projects
8 projects
3 projects
3 projects
6 projects
4 projects
2 projects
07/2017 – 09/2017
Continental Automotive
07/2016 – 09/2016
Continental Automotive
10/2022 - 07/2025
Artificial Intelligence
Technical University of Iasi
10/2019 - 07/2021
Distributed Systems and Web Technologies
Technical University of Iasi
10/2015 - 07/2019
Information Technology
Technical University of Iasi
09/2007 - 07/2015
Computer Science
High School "Gr. Moisil"


| Do flowers need water to survive? | Is Sunlight beneficial for flower? | What season does flower remove old parts? |
|---|---|---|
| Yes they need water. | The sun is very Healthy for flowers | In the Spring season |
| Do weeds kill flowers and plants? | What are picked flowers used for? | How do you get flowers at home? |
|---|---|---|
| Yes they do | To give to loved ones | Plant seeds / roots of plant in soil and overtime flower will grow |
| Do bees like flowers? | Is green only colour for flowers ? | Can flowers survive indoors? |
|---|---|---|
| Yes they do. | Of course not, they come in many colours | Yes, if you water them and place them in front of direct sunlight. |


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valueChanged(ListSelectionEvent)
- Method in class domosaics.ui.views.domainview.components.
ShapeChooser
valueChanged(TreeSelectionEvent)
- Method in class domosaics.ui.workspace.
WorkspaceSelectionManager
valueForPathChanged(TreePath, Object)
- Method in class domosaics.ui.workspace.
WorkspaceTreeModel
This method should hopefully never be called.
valueOf(String)
- Static method in enum domosaics.ui.io.menureader.
MenuEntryType
Returns the enum constant of this type with the specified name.
valueOf(String)
- Static method in enum domosaics.ui.views.domainview.components.
DomainColors
Returns the enum constant of this type with the specified name.
valueOf(String)
- Static method in enum domosaics.ui.views.domainview.components.
DomainShapes
Returns the enum constant of this type with the specified name.
valueOf(String)
- Static method in enum domosaics.ui.views.domainview.manager.
DomainViewManagerFactory.DomainViewManager
Returns the enum constant of this type with the specified name.
valueOf(String)
- Static method in enum domosaics.ui.views.view.export.
GraphicFormats
Returns the enum constant of this type with the specified name.
valueOf(String)
- Static method in enum domosaics.ui.views.
ViewType
Returns the enum constant of this type with the specified name.
values()
- Static method in enum domosaics.ui.io.menureader.
MenuEntryType
Returns an array containing the constants of this enum type, in the order they are declared.
values()
- Static method in enum domosaics.ui.views.domainview.components.
DomainColors
Returns an array containing the constants of this enum type, in the order they are declared.
values()
- Static method in enum domosaics.ui.views.domainview.components.
DomainShapes
Returns an array containing the constants of this enum type, in the order they are declared.
values()
- Static method in enum domosaics.ui.views.domainview.manager.
DomainViewManagerFactory.DomainViewManager
Returns an array containing the constants of this enum type, in the order they are declared.
values()
- Static method in enum domosaics.ui.views.view.export.
GraphicFormats
Returns an array containing the constants of this enum type, in the order they are declared.
values()
- Static method in enum domosaics.ui.views.
ViewType
Returns an array containing the constants of this enum type, in the order they are declared.
VIEW
- Static variable in class domosaics.model.workspace.
WorkspaceElement
View
- Interface in
domosaics.ui.views.view
The View interface defining all methods which can be applied to each view.
VIEW_DESKTOP_KEY
- Static variable in class domosaics.ui.docking.
DefaultDockableResolver
VIEW_NAME
- Static variable in class domosaics.model.workspace.
WorkspaceElement
ViewActionManager
- Class in
domosaics.ui.views.view
The view specific action manager extending the
DefaultMenuActionManager
from
MenuReader
.
ViewActionManager()
- Constructor for class domosaics.ui.views.view.
ViewActionManager
ViewComponent
- Interface in
domosaics.ui.views.view.components
View components represent the graphical component for backend data models rendered within a view.
ViewElement
- Class in
domosaics.model.workspace
ViewElement describes workspace elements, representing views which were loaded.
ViewElement(ViewInfo)
- Constructor for class domosaics.model.workspace.
ViewElement
Constructor for a new ViewElement based on the ViewInfo about a view.
ViewHandler
- Class in
domosaics.ui
Class ViewManager manages the creation, adding and removing of view objects to Angstd and its main frame.
ViewInfo
- Class in
domosaics.ui.views.view
Class containing all needed information to create a new view or tool.
ViewInfo()
- Constructor for class domosaics.ui.views.view.
ViewInfo
ViewLayout
- Interface in
domosaics.ui.views.view.layout
The ViewLayout interface has to be implemented by all view specific classes which manage the layout process for their [@link ViewComponent}s.
ViewManager
- Interface in
domosaics.ui.views.view.manager
Views within Angstd display graphical components
ViewComponent
for backend data models.
ViewPanel
- Class in
domosaics.ui.views.view
Class ViewPanel is the basic class for embedding views, such as
DomainView
into the user interface.
ViewPanel(String, View)
- Constructor for class domosaics.ui.views.view.
ViewPanel
Basic constructor for a new ViewPanel.
ViewReader
- Class in
domosaics.ui.views.view.io
Class for reading a views property file.
ViewReader()
- Constructor for class domosaics.ui.views.view.io.
ViewReader
ViewType
- Enum in
domosaics.ui.views
An enumeration of all possible views, defined by their class path.
visualChange()
- Method in class domosaics.ui.views.view.manager.
DefaultViewManager
visualChange()
- Method in interface domosaics.ui.views.view.manager.
ViewManager
Shortcut method to notify the view that a visual change happened and therefore a repaint have to be triggered.
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STATION 1 of 5
Welcome to the primary station of the biophilic design trail. Find and scan the green QR codes located on this terrace to learn more about the specific biophilic design features related to the SDE4.
Click on the trails tab on the top right to find their locations. Start with the QR code to your left.


Kadun Zu Ketra:
I must find a worthy fighter to accomplish this mission. Unfortunately you don't seem up to the task.
(Only a character who possesses Ketra's friend - 3rd level can undertake this quest.)


VINAY KUMAR
FRONT END NINJA
Featured Work
Movie Website
Link to GitHub_StarterCode
Recipe
Link to GitHub_recipes
Own Adventure
Link to GitHub_create-your-own-adventure


{% extends '_base.html' %} {% block title %}Comparisons
{% endblock %} {% block content %}| ID | Updated | Project | Env | Version | Comparison title | Jobs | Tests | Tests with errors |
Test cases |
Test cases with errors |
|
|---|---|---|---|---|---|---|---|---|---|---|---|
| ID | Updated | Project | Env | Version | Comparison title | Jobs | Tests | Tests with errors |
Test cases |
Test cases with errors |


CustoIce - Personalized Whisky Ice Molds.
Based off OpenJsCad by Joostn.
How to use:
Plenty of text and pictures go here
Tools, Libraries
- OpenSCAD.jscad: Wrapper functions to ease the
translation from OpenSCAD to OpenJsCad, and command line rendering (with node.js) (by René K. Müller)
License
OpenJsCad is developed by Joost Nieuwenhuijse.
Based on initial CSG.js copyright (c) 2011 Evan Wallace.
Uses lightgl.js by Evan Wallace
and Tomi Aarnio for WebGL rendering.
Contributions by Alexandre Girard,
Tom Robinson,
jboecker,
risacher,
tedbeer,
bebbi,
Sahil Shekhawat.
All code released under MIT license.
Contributions are welcome! It's all written in Javascript, so if you know how to use it you
know how to modify it as well. Send me a pull request if you want to branch off and add features.


Vulnerability of agriculture and marine fisheries as a function of exposure, sensitivity, and adaptive capacity to the impacts of climate change. The bivariate map shows linked vulnerabilities of agriculture and fisheries for each country under RCP8.5. The 10 most vulnerable countries are indicated for agriculture (A) and marine fisheries (F). Right panel indicates latitudinal trends.


An SMS has been sent to the phone number you entered.
When you get the SMS,
enter the code in the SMS where it says "Enter your verification code" below.
{% elif 'sms_sent' in opts %}
There was an error sending you a SMS.
Please check that you entered your mobile phone number
correctly and try again.
{% endif %}
{% if opts['token_error'] %}
There was an error verifying your token. Please try again.
{% endif %}
{% if opts['user'].account['totp_enabled_via_sms'] %}
You are set up for Two-Factor Authentication via Twilio SMS!
{% else %}
Enabling SMS based Two-Factor Authentication on your account is a two
step process:
Enter your mobile phone number below and press the "Submit" button:
A 6-digit verification code will be sent to your mobile phone. When you
receive that number, enter it below:


This is essentially a placeholder that will hopefully be expanded. At this stage, see the tutorial page for some information.
Essentially, the purpose of the app is to record records to be submitted to the various projects at the virtual museum of the Animal Demography Unit (ADU) of the University of Cape Town (UCT).
To see a list of the projects, and more details about each project, see the project list.


var lastFPS: Int
Capture the last FPS to allow debouncing to notify the consumer only when an FPS change is actually detected.


|
Because
I Got High
(Afroman) |
|
It's
Like, I don't care about nothin man,
roll another blunt, Yea (ohh ohh ohh), La da da da da da La, Da Daaa, La da da da, La da da da, La da da daaa I was gonna clean my room until I got high I gonna get up and find the broom but then I got high my room is still messed up and I know why (why man?) yea heyy, - cause I got high [repeat 3X] (La da da da da da da da da) I was gonna go to class before I got high I coulda cheated and I coulda passed but I got high (La da da da da da da da da) I am taking it next semester and I know why, (why man?) yea heyy, - cause I got high [repeat 3X] (La da da da da da da da da) I was gonna go to work but then I got high I just got a new promotion but I got high now I'm selling dope and I know why (why man?) yea heayy, - cause I got high [repeat 3X] (La da da da da da da da da) I was gonna go to court before I got high I was gonna pay my child support but then I got high they took my whole paycheck and I know why (why man?) yea heayy, - cause I got high [repeat 3X] (La da da da da da da da da) I wasn't gonna run from the cops but I was high I was gonna pull right over and stop but I was high (La da da da da da da da da) Now I am a paraplegic and i know why (why man?) yea heayy, - because I got high [repeat 3X] (La da da da da da da da da) I was gonna pay my car note until I got high I was gonna gamble on the boat but then I got high now the tow truck is pulling away and I know why (why man?) yea heyy, - because I got high [repeat 3X] (La da da da da da da da da) I was gonna make love to you but then I got high I was gonna eat yo pussy too but then I got high now I'm jacking off and I know why, yea heyy, - cause I got high [repeat 3X] (La da da da da da da da da) I messed up my entire life because I got high I lost my kids and wife because I got high now I'm sleeping on the sidewalk and I know why (why man?) yea heyy, - cause I got high [repeat 3X] (La da da da da da da da da) I'm gonna stop singing this song because I'm high I'm singing this whole thing wrong because I'm high and if I dont sell one copy I know why (why man?) yea heyy, - cause I'm high [repeat 3X] La da da da da da, La da da da, Shoop shooby doo wop. |
|
|