Take this survey to find a friend!
Enter your name
Enter a URL to a picture of yourself
Dogs or cats?
only dogs
prefers dogs
likes both
prefers cats
only cats
Vegies or meat?
only vegies
prefers vegies
likes both
prefers meat
only meat
Liberal or conservative?
liberal all the way
mostly liberal
both
mostly conservative
conservative all the way
Oranges or apples?
only the apples
apples are good
both are good
oranges are tasty
eating oranges all day
Windows or Mac
Windows is the OS for me
I prefer Windows
I'll work with Windows, Mac, or even Linux
Macs are my preference
I only use Macs
Chocolate or peanut butter
Chocolate is my favorite!!!1
Chocolate is my preference
Peanut butter chocolate swirl!
I gotta go with the peanut butter
Peanut Butter all the way
Which color do you prefer?
yellow
orange
red
purple
blue
Fantasy or Science Fiction?
Fantasy genre rules!
I prefer Fantasy
I like both
I prefer Science Fiction
Science Fiction is the best!
Marvel or DC?
Marvel is the coolest
Marvel is preferred
I like both
DC is my prefernece
DC for sure
Pirates or ninjas?
arrrrr you kidding me? Pirates are the best!
Pirates are cool
Both are ok...
Ninjas are awesome
Ninjas forever!!!1


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BHP Plans to Increase Its Stake in Ecuador-Focused SolGold
by Thomas Biesheuvel and David Stringer(Bloomberg) — BHP Group, the world’s biggest miner, plans to increase its stake in SolGold Plc, the developer of projects in copper hot spot Ecuador, according to people familiar with the proposal.
Melbourne-based BHP will add to its existing 11% holding in SolGold and become the developer’s largest shareholder ahead of Newcrest Mining Ltd., the people said, asking not to be identified because the matter is confidential. An announcement detailing the size of the investment could be made within days.
BHP and SolGold declined to comment.
Brisbane-based SolGold’s interests in Ecuador include the flagship Alpala copper-gold project, which the company estimates has a potential 55-year life and is among the world’s best undeveloped deposits. The company is also studying 13 other priority targets in the nation, according to a presentation this month.
BHP, preparing to install Mike Henry as its new chief executive officer from January, is prioritizing future growth in copper and oil, and sees Ecuador as a key focus for new mining projects. Dwindling supply from aging copper mines and rising demand from renewable energy and the electric vehicle sector will combine to boost the metal’s outlook, the miner said in August.
The company is also partnering with Luminex Resources Corp. on a project in southeastern Ecuador and carrying out its own work in the country.
Exploration will be “the most cost effective way” of adding to the company’s growth pipeline, outgoing CEO Andrew Mackenzie said last month in a webcast. “We have positions in some very prospective parts of the world where we think we can add significantly to our resource base.”


Chat testing page
For anonymous user
Connect:
Disconnect:
Send Anonymously
For specific user
Connect To:
Disconnect:
Send:


Many naming services are extended with a
directory service.
A directory service associates names with objects and also associates such objects with
attributes.
directory service = naming service + objects containing attributes
You not only can look up an object by its name but also
get the object's attributes or search for the object based
on its attributes.
An example is the telephone company's directory service.
It maps a subscriber's name to his address and phone number.
A computer's directory service is very much like a telephone company's
directory service in that both can be used to store information such
as telephone numbers and addresses. The computer's
directory service is much more powerful, however, because
it is available online
and can be used to store a variety of information that
can be utilized by
users, programs, and even the computer itself and
other computers.
A directory object represents an
object in a computing environment.
A directory object can be used, for example, to represent a printer,
a person, a computer, or a network.
A directory object contains attributes
that describe the object that it represents.
Attributes
A directory object can have attributes.
For example, a printer might be represented by a directory
object that has as attributes its speed, resolution, and color. A
user might be represented by a directory object that has as
attributes the user's e-mail address, various telephone numbers, postal
mail address, and computer account information.
An attribute has an
attribute identifier and a set of attribute values.
An attribute identifier is a token that identifies an
attribute independent of its values.
For example, two different computer accounts might have
a "mail" attribute; "mail" is the attribute
identifier. An attribute value is the contents of
the attribute. The email address, for example, might have:
Attribute Identifier : Attribute Value
mail<EMAIL_ADDRESS>Directories and Directory Services
A
directory is a connected set of directory objects.
A
directory service is a service that
provides operations for creating,
adding, removing, and modifying the attributes associated with
objects in a directory.
The service is accessed through its own interface.
Many examples of directory services are possible.
- Network Information Service (NIS)
- NIS is a directory service available
on the Unix operating system for storing system-related information,
such as that relating to machines, networks, printers, and users.
-
Sun Java Directory Server
- The Sun Java Directory Server is a general-purpose directory service
based on the Internet standard LDAP
- Novell Directory Service (NDS)
- NDS is a directory service from Novell that
provides information about many networking services, such as
the file and print services.
Search Service
You can look up a directory object by supplying its name
to the directory service.
Alternatively, many directories, such as those based on the LDAP,
support the notion of
searches.
When you search, you can supply not a name but a
query
consisting of a logical expression in which you specify
the attributes
that the object or objects must have.
The query is called a
search filter.
This style of searching is sometimes called
reverse lookup or
content-based searching.
The directory service searches for and returns the objects that satisfy the
search filter.
For example, you can query the directory service to find:
- all users that have the attribute "age" greater than 40 years.
- all machines whose IP address starts with "192.113.50".
Combining Naming and Directory Services
Directories often arrange their objects in a hierarchy.
For example, the LDAP arranges all directory objects in a tree,
called a directory information tree (DIT).
Within the DIT,
an organization object, for example, might contain
group objects that might in turn contain person objects.
When directory objects are arranged in this way,
they play the role of naming contexts
in addition to that of containers of attributes.


The hostel is ideal for Kenyatta University (KU) Students. Located in Kahawa Sukari, 500km from Kenyatta Sacco, Bungoma Road 2nd North Avenue.
The hostel can accommodate 30 students, it is fully furnished with ample security and a caretaker.
• Breakfast, afternoon coffee/tea and dinner served daily
• Lunch during weekends and public holidays only
• Accommodation is mainly special single rooms
• Bed and mattress
Accommodation fees - per semester (3months) From May 2016
price:Ksh 3750
Tulivu Hostel for KU Students
Get one room per student, mattress, an elegant closet, own reading table and chair and a well-lit room with an awesome view of the horizon, own cloth line, hot showers, and many more.
Tulivu Hostels are located next to the Nyayo hostels so you don't have to walk long distances.
Prices per room:
Single (special) – ksh.10,000 per month
Two sharing – ksh.8,000 per month
Four sharing – ksh.4,500 per month
Meals provided:
Breakfast
Supper
Winsay Hostel in Ruiru
The hostel is ideal for students of Zetech and KU universities. Located 1 kilometer from the campus.
The hostel can accommodate 200 students, it has ample security and a caretaker. Offering bed, mattress, water and electricity and food at an extra cost of KShs 7000 per month.
Charges:


{% extends "home/base.html" %} {% load static %} {% block content %}
Enter the UNIPROT identifier of the protein
Enter the isoform ????
Is this there any mutation in this receptor?
Yes No
Enter the Name ????
Enter a brief description of the molecule
Molecule description
Enter the net charge of the molecule
Enter the InChI
Enter the InChI Key???
Enter the SMILES notation
Enter the RESNAME of the molecule
Enter the number of molecules in the system
Enter MAIN_PROTEIN
MAIN_PROTEIN
Enter MAIN_LIGAND
MAIN_LIGAND
Enter Resname of the Ion
Enter the number of each ion
Enter the type of MODEL
id Protein
id Complex molecule
Enter the Model Source
X-Ray structure NMR Homology Model
Enter the PDB code of the simulated crystal
Description of the Model
Describe the methodology applied in the generation of the model
Enter the Templates used in the generation of the model
Enter the simulation method
Molecular Mechanics (MM) MD QM/MM MD NMR Monte Carlo simulation MM MSM/HMM adaptive MM metadynamics MM umbrella sampling MM biased others MM scaled potentials
Software used for running the simulation
Version of the software used for running the simulation
Enter the Force Field
Enter the Force Field Version
Description of the Simulation
Describe the methodology applied in the simulation
Enter the membrane composition
POPC DOPC POPE
Solvent Type
TIPS TIP3 TIP5P SPC/E
Enter the input coordinates of the simulated system (PDB format)
Enter the input structure of the simulated system (PSF format)
Enter the topology of the simulated system (PSF/RTF file)
Enter the parameter files (PRM file)
Enter the trajectory file (DCD file)


Portfolio,
Category:
All
arrow_drop_down
ALL
ENTREPRENEURSHIP
CYBERSECURITY
BLOCKCHAIN
WEB
HARDWARE
MISC


Once Bitcoin wallet is created you can also start using a Lightning wallet by opening a payment channel with a peer of your choice and using it to send payments across a Lightning network.
Technically speaking it is a multisig lockbox on a Bitcoin blockchain which is mutually controlled by you and a peer of your choice. It is designed in such a way that once a certain amount of coins is locked in there you can use those coins to send and receive Lightning payments.
You can close a payment channel and get the rest of your locked coins back to your Bitcoin wallet at any time, either by agreeing with a peer on a mutual closing transaction or via forced channel closing if your peer becomes uncooperative.
This is an option which is always available to both you and your peer once a new payment channel is open. No matter what happens a channel can always be closed without peer’s permission with the rest of channel balance refunded back to your Bitcoin wallet.
That said, a refunding Bitcoin transaction created by forced channel closing is encumbered in a time lock of 144 Bitcoin blocks so closing a channel uncooperatively will take about one day of waiting before your channel coins are sent back to your Bitcoin wallet.
You may also want to watch this video which covers all the ways a payment channel could be closed.
An outgoing Lightning payment may get stuck due to a buggy or malicious node along a payment route. In such a case you would have to wait until it expires which may take quite a number of Bitcoin blocks. Once expiry happens a payment will be failed and you will get an in-flight funds back to your Lightning wallet.
…is tricky as there are some conditions:
Nothing can be received once a fresh channel is created since a room for incoming funds should be first made by spending. A payment channel can be thought of as a full bottle of water: in order to pour something in one first has to pour something out.
Each channel implicitly contains a reserve which is unspendable off-chain and typically takes about 1% of channel capacity. One has to spend that reserve before receiving is allowed. Unspendable channel reserve is the reason you see a negative receive limit when a new channel is full. It indicates how much you need to spend before anything could be received.
Every payment request is disposable, it can’t be fulfilled multiple times. So one would have to issue a payment request per each incoming payment.
Lightning wallet needs to be online in order to receive funds.
Sending each Lightning payment creates a valid Bitcoin transaction which is kept off-chain, in fact it never even leaves your device unless a forced channel closing happens. This commit transaction must have an appropriate fee for Bitcoin network to accept it in a timely manner.
As a consequence an actual amount available for spending in a payment channel will always be slightly less than a nominal amount because it always needs to contain enough funds for a current Bitcoin commit transaction fee.


Software Engineer | Maker
Cluj, Romania
Master’s Degree (MSc)
Computer Science
2013 - 2015
Engineer’s Degree (BSc)
Computer Science
2009 - 2013
English, Hungarian, Romanian
I'm an experienced Software Engineer with strong skills in the area of high performance distributed systems and Java based backend development.
In my free time, I like to do hardware projects involving IoT, Robotics, Computer Vision, ML & AI, Cloud Computing, FPGA and other related technologies.
Working on the Trados platform, a cloud based intelligent translation solution. As a Senior Software Engineer in the System Team (Agile), I'm working on core features of the platform.
Technologies: Java 11/17+, Spring Boot 3+, Kubernetes, Docker, Istio, AWS, Linux, MongoDB, Redis, ElasticSearch, RabbitMQ, Kafka, AI / ML, LLM-s, ARM64
Activities:Working on TORA's Order and Execution Management System (OEMS), a highly distributed system based on micro-services, with advanced fault-tolerance capabilities.
Technologies: Java 8+, Guava, PostgreSQL, TibcoRV, AWS, FIX, WebSocket, Linux, OpenTSDB, JavaScript, HTML5, CSS3, Blockchain
Activities:As an active member on Hackster.io and Element14 Community, I do various IoT and Electronics projects. Starting from March 2022, I'm also a member of Edge Impulse's expert network.
Some technologies involved are: Bluethooth Low Energy (BLE), WiFi, GSM, LoRa, SigFox OpenCV, GStreamer, DeepStream, C++ NodeJS, Python, Raspberry Pi, Blockchain, AI / ML, Tensorflow.
Occasionally, I participate in design contests and do RoadTest reviews on products like test instruments and IoT platforms.


Key Features
This project was really big, so I'll outline some of the main
functionalities:
- Portal: Login for Professors. Professors could start
and stop lectures
- Transcription : Real time speech to text
transcription of what the Professor is saying in
class that all students in the
class can view.
-
Phrase Analysis: Entity Analysis for the phrases
before they are transcribed. Articles that
are relevant
are found and then the phrase becomes a hyperlink in the
transcript.
-
Assisted Note taking: Professors can pass in a google
slides file and the text is written
onto
an edittable text file. This makes it so students do not
have to rush to write the bullet points on
a slide
and can instead focus on clarifications.
-
The Digest: At the end of each lecture session, a
digest is composed of 3 main parts, a
summary of the transcription of the lecture, the notes that
the student wrote in the notes tab, and
the important phrases along with a 2 sentence summary for
each one. This makes it optimal for
studying
before a test or just review in general.
How does Megafind work?
This project was fairly massive (for a hackathon) in terms of
the amount of features it had and the time it was built in.
To get the initial set up, we had to get an idea of what we
wanted to do. We started set up by getting a
hold
of the Google Speech to Text API and a summarizer API from
Intellexer.
We then used the Google Natural Language API to create an Entity
Extractor that would find relevant
phrases in our text during the real time transcription process.
Following this, we used the Google
Search API to
find a link for all the relevant phrases. The relevant phrases
would then be turned into hyperlinks when
being transcribed.
Using the Slides API from Google, we were able to parse the
provided powerpoint from teachers and write
it into an edittable text tab.
To create our digest that is sent to the students at the end of
the lecture session, we gathered the
following
data:
- A summary of the transcript
- Notes that the student wrote during the live lecture
- The key words and a short 2 sentence summary about them
We then wrote this data into a textfile and sent it. We gathered the
summary by using the Intellexer API,
the lecture
notes were gathered from the notes file, and the key words were also
go from storage; however, we had to use
the
Google search API to find 2 sentences about them. We stored the data
about which students to email
through Mlab and we actually sent the email by using the Sparkpost
API.


Not currently implemented Computes normalized STREAMWISE vorticity for the averaged cross-section based 3 different measures of secondary flow (Transverse (v), Secondary (zsd), Secondary (Roz)). This function uses the smoothed values of each component, and thus is called with each REPLOT. Vorticity (\omega) is normalized by the channel top width & average streamwise velocity: \omega = \tilde{\omega} frac{B}{U} FROM WIKIPEDIA: In fluid dynamics, the vorticity is a vector that describes the local spinning motion of a fluid near some point, as would be seen by an observer located at that point and traveling along with the fluid. Conceptually, the vorticity could be determined by marking the particles of the fluid in a small neighborhood of the point in question, and watching their relative displacements as they move along the flow. The vorticity vector would be twice the mean angular velocity vector of those particles relative to their center of mass, oriented according to the right-hand rule. This quantity must not be confused with the angular velocity of the particles relative to some other point. More precisely, the vorticity of a flow is a vector field (\omega), equal to the CURL (rotational) of its velocity field (v,w). Written by Frank L. Engel, USGS Last modified: F.L. Engel, USGS, 12/21/2012
0001 function [V] = VMT_Vorticity(V) 0002 % Not currently implemented 0003 % 0004 % Computes normalized STREAMWISE vorticity for the averaged cross-section 0005 % based 3 different measures of secondary flow (Transverse (v), Secondary 0006 % (zsd), Secondary (Roz)). This function uses the smoothed values of each 0007 % component, and thus is called with each REPLOT. 0008 % 0009 % Vorticity (\omega) is normalized by the channel top width & 0010 % average streamwise velocity: 0011 % \omega = \tilde{\omega} frac{B}{U} 0012 % 0013 % FROM WIKIPEDIA: In fluid dynamics, the vorticity is a vector that 0014 % describes the local spinning motion of a fluid near some point, as would 0015 % be seen by an observer located at that point and traveling along with the 0016 % fluid. Conceptually, the vorticity could be determined by marking the 0017 % particles of the fluid in a small neighborhood of the point in question, 0018 % and watching their relative displacements as they move along the flow. 0019 % The vorticity vector would be twice the mean angular velocity vector of 0020 % those particles relative to their center of mass, oriented according to 0021 % the right-hand rule. This quantity must not be confused with the angular 0022 % velocity of the particles relative to some other point. More precisely, 0023 % the vorticity of a flow is a vector field (\omega), equal to the CURL 0024 % (rotational) of its velocity field (v,w). 0025 % 0026 % Written by Frank L. Engel, USGS 0027 % Last modified: F.L. Engel, USGS, 12/21/2012 0028 0029 % Begin code 0030 0031 B = V.dl; 0032 U = nanmean(V.u(:)); 0033 0034 [V.vorticity_vw,~]= curl(... 0035 V.mcsDist,... 0036 V.mcsDepth,... 0037 V.vSmooth,... 0038 V.wSmooth); 0039 V.vorticity_vw = -V.vorticity_vw.*B./U; % reverse sign to keep RH coordinates 0040 0041 [V.vorticity_zsd,~]= curl(... 0042 V.mcsDist,... 0043 V.mcsDepth,... 0044 V.vsSmooth,... 0045 V.wSmooth); 0046 V.vorticity_zsd = -V.vorticity_zsd.*B./U; 0047 0048 [V.vorticity_roz,~]= curl(... 0049 V.mcsDist,... 0050 V.mcsDepth,... 0051 V.Roz.usSmooth,... 0052 V.wSmooth); 0053 V.vorticity_roz = -V.vorticity_roz.*B./U; 0054 0055 % Vertical vorticity -- not saved in V struct, experiemental only 0056 [vorticity_uv,~]= curl(... 0057 V.mcsDist,... 0058 V.mcsDepth,... 0059 V.uSmooth,... 0060 V.vSmooth); 0061 vorticity_uv = -vorticity_uv.*B./U; % reverse sign to keep RH coordinates


Second update before MECSPE 2017, where we will be thanks to Confartigianato Padova. In this video Giacomo Ballotta , ex technical manager and R&D Engineer in Dallara, explains us his experience in Race Up team.
Enjoy!


Start the Ping Pong number game. Enter a number betweeen 1 and 100 and see! All the multiples of 3, 5 and 15 change!


Fade out
|
Fade in
|
Set opacity = 50%
Element to fade
Slide to: 200, 200
|
Slide by: 200, 200
|
Slide from 200, 200 to 500, 500
I slide
I fade to my BG!
I fade from my BG!
Wipe out
wipe wipe. It is so fun to... Wipe wipe wipe. It is so fun to... Wipe wipe wipe. It is so fun to... Wipe wipe wipe. It is so fun to...
This one does a wipeOut+wipeIn that you could use to replace the content. Go ahead, give it a
try
Non-wipe...
EXPLODE!!!
Explode!
Implode!


Transients and Other Time Series Science¶
Focusing on the detection and analysis of any object that shows flux variation.
For example: variable stars, TDE, and eclipsing binaries.
Exoplanet and supernova are also trasients, but they have much broader impact therefore they have their own topics.
Transient Notification¶
pycgn - Python package for processing Gamma-ray Coordinates Network (GCN) notices and circulars
Anonymous VOEvent client for receiving GCN/TAN notices in XML format
Data Access¶
ztfquery - Access ZTF data from Python
By Mickael Rigault. ztfquery is a python tool to download ZTF (and SEDM) data
Differential Photometry¶
lemon - Differential photometry for humans (and astronomers)
By Víctor Terrón. LEMON is a differential-photometry pipeline, written in Python, that determines the changes in the brightness of astronomical objects over time and compiles their measurements into light curves.
hotpants - High Order Transform of Psf ANd Template Subtraction code
By Andy Becker.
astrobase - Python modules for light curve work and variable star astronomy
By Waqas Bhatti. It includes implementations of several period-finding algorithms, batch work drivers for working on large collections of light curves, and a small web-app useful for reviewing and classifying light curves by stellar variability type.
Trasient Identification and Classification¶
-
Avocado is a general photometric classification code that is designed to produce classifications of arbitrary astronomical transients and variable objects.
The original codebase of avocado was developed for and won the 2018 Kaggle PLAsTiCC challenge.
-
By Daniel Muthukrishna. RAPID (Real-time Automated Photometric IDentification) can classify multiband photometric light curves into several different transient classes. It uses a deep recurrent neural network to produce time-varying classifications.
astrodash - Deep learning for the automated spectral classification of supernovae
By Daniel Muthukrishna. Software to classify the type, age, redshift and host for any supernova spectra. Two platforms exists: a python library that enables a user to classify several spectra (can classify thousands of spectra in seconds), and also a graphical interface that enables a user to view and classify a spectrum.
UPSILoN- Automated Classification of Periodic Variable Stars Using Machine Learning
UPSILoN (AUtomated Classification of Periodic Variable Stars using MachIne LearNing)
SuperNNova - Open Source Photometric classification
Using recurrent network technique. Based on SuperNNova: an open-source framework for Bayesian, Neural Network based supernova classification
Lightcurve and Exoplanet¶
lightkurve - A friendly package for Kepler & TESS time series analysis in Python
Lightkurve is a community-developed, open-source Python package which offers a beautiful and user-friendly way to analyze astronomical flux time series data, in particular the pixels and lightcurves obtained by NASA’s Kepler and TESS exoplanet missions.
eleanor - light curves from TESS
eleanor is a python package to extract target pixel files from TESS Full Frame Images and produce systematics-corrected light curves for any star observed by the TESS mission.
starry - Analytic occultation light curves for astronomy
By Rodrigo Luger. Based on a very nice paper
everest - EPIC Variability Extraction and Removal for Exoplanet Science Targets
A pipeline for de-trending K2 light curves with pixel level decorrelation and Gaussian processes.]
wotan - Automagically remove trends from time-series data
By Michael Hippke. Offers free and open source algorithms to automagically remove trends from time-series data.


Creates a new Vector2 from the given x and y coordinates
defines the first coordinate
defines the second coordinate
defines the first coordinate
defines the second coordinate
Copy the current vector to an array
a new array with 2 elements: the Vector2 coordinates.
Gets a new Vector2 copied from the Vector2
a new Vector2
Updates the Vector2 from the sparsely populated value.
The sparsely populated value to read from.
Sets the Vector2 coordinates with the given floats
defines the first coordinate
defines the second coordinate
the current updated Vector2
Gets a boolean if two vectors are equals
defines the other vector
true if the given vector coordinates strictly equal the current Vector2 ones
Gets a boolean if two vectors are equals (using an epsilon value)
defines the other vector
defines the minimal distance to consider equality
true if the given vector coordinates are close to the current ones by a distance of epsilon.
Gets a new Vector2 from current Vector2 floored values
a new Vector2
Gets a new Vector2 from current Vector2 floored values
a new Vector2
Gets class name
the string "Vector2"
Gets current vector hash code
the Vector2 hash code as a number
Gets the length of the vector
the vector length (float)
Gets the vector squared length
the vector squared length (float)
Gets a new Vector2 set with the Vector2 coordinates multiplied by the given floats
defines the first coordinate
defines the second coordinate
a new Vector2
Gets a new Vector2 with current Vector2 negated coordinates
a new Vector2
Normalize the vector
the current updated Vector2
Returns a new Vector2 scaled by "scale" from the current Vector2
defines the scaling factor
a new Vector2
Multiply the Vector2 coordinates by scale
defines the scaling factor
the current updated Vector2
Sets the Vector2 coordinates with the given floats
defines the first coordinate
defines the second coordinate
the current updated Vector2
Sets the Vector2 coordinates in the given array or Float32Array from the given index.
defines the source array
defines the offset in source array
the current Vector2
Returns a JSON representation of this vector. This is necessary due to the way Actors detect changes on components like the actor's transform. They do this by adding properties for observation, and we don't want these properties serialized.
Gets a string with the Vector2 coordinates
a string with the Vector2 coordinates
Gets a new Vector2 located for "amount" (float) on the CatmullRom spline defined by the given four Vector2
defines 1st point of control
defines 2nd point of control
defines 3rd point of control
defines 4th point of control
defines the interpolation factor
a new Vector2
Returns a new Vector2 set with same the coordinates than "value" ones if the vector "value" is in the square defined by "min" and "max". If a coordinate of "value" is lower than "min" coordinates, the returned Vector2 is given this "min" coordinate. If a coordinate of "value" is greater than "max" coordinates, the returned Vector2 is given this "max" coordinate
defines the value to clamp
defines the lower limit
defines the upper limit
a new Vector2
Gets the shortest distance (float) between the point "p" and the segment defined by the two points "segA" and "segB".
defines the middle point
defines one point of the segment
defines the other point of the segment
the shortest distance
Gets a new Vector2 set from the given index element of the given array
defines the data source
defines the offset in the data source
a new Vector2
Sets "result" from the given index element of the given array
defines the data source
defines the offset in the data source
defines the target vector
Returns a new Vector2 located for "amount" (float) on the Hermite spline defined by the vectors "value1", "value3", "tangent1", "tangent2"
defines the 1st control point
defines the outgoing tangent
defines the 2nd control point
defines the incoming tangent
defines the interpolation factor
a new Vector2
Gets a new Vector2(1, 1)
a new Vector2
Determines if a given vector is included in a triangle
defines the vector to test
defines 1st triangle point
defines 2nd triangle point
defines 3rd triangle point
true if the point "p" is in the triangle defined by the vertors "p0", "p1", "p2"
Gets a new Vector2(0, 0)
a new Vector2
Class representing a vector containing 2 coordinates


Scope
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Participants are responsible for their own transportation and accommodation. Registration to the event is free and will cover the meals during the time of the meeting. The organising committee have informed the venue (Chateauform Les Berges de Seine) of this event. Participants should make their hotel reservation at this venue.
There will be a minimum of fifteen travel rewards for the invited applicants. The travel rewards will be offered on a case-by-case basis based on the application’s relevance to ELIXIR’s technical activities and strategy.
ELIXIR platforms, ELIXIR use cases, ELIXIR France, as well as members of the Japanese BioHackathon organising committee are welcomed to put forward hacking topic proposals for the BioHackathon. These proposals should ideally be aligned with existing ELIXIR technical activities. We especially welcome hacking sessions focusing on upcoming ELIXIR deliverables. We have created an online form to collect the proposals. The proposals will be evaluated and selected by the BioHackathon organising committee, with the help of the ELIXIR platform and community coordinators. We will notify applicants shortly after the deadline.
May 23th, 2018.
Campus des Berges de Seine (South Paris)
1 Route de Beaulieu, 77240 Seine-Port, France


Issue: TAILP-NILReferences: TAILP (p275)
Category: CLARIFICATION/CHANGE
Edit History: 13-Sep-88, version 1 by Walter van Roggen,
13-Sep-88, version 2 by Pitman
18-Oct-88, version 3 by van Roggen (just one proposal)
01-Dec-88, version 4 by Pitman (minor edits per discussion)
9-Dec-88, Version 5 by Masinter (clarify EQL)
Problem Description:
CLtL (p275) specifies TAILP as:
TAILP sublist list [Function]
This predicate is true if SUBLIST is a sublist of LIST (i.e.,
one of the conses that makes up LIST); otherwise, it is false.
Another way to look at this is that TAILP is true if
(NTHCDR n list) is SUBLIST, for some value of N. See LDIFF.
Two implementations of this definition might be:
(defun tailp (sublist list) ;Definition "A"
(return t))))
(defun tailp (sublist list) ;Definition "B"
((atom list) (eql list sublist))
(return t))))
They differ only in their treatment of the atomic case.
At issue is the fact that () is a list, and hence some would
argue that it is a sublist of all other lists. On the other hand,
the definition of TAILP seems to imply that being a cons is a
necessary precondition of being a "sublist".
Also at issue is the question of whether dotted lists are permissible
second arguments.
Proposal (TAILP-NIL:T):
Strike any text in the definition of TAILP which suggests that a
sublist must be a cons.
Clarify that (TAILP sublist list) returns true iff (NTHCDR n list) is
sublist for some value of n, and false otherwise.
Note, however, that since the list may be dotted, so the end test
used by TAILP must be ATOM, not ENDP. That is, if (TAILP x l)
returns true, it means there was n such that (NTHCDR n list) would
return x; however, it doesn't follow that if TAILP returns false,
it is safe to go blithely NTHCDR's into the list looking for it,
since the list might be dotted and NTHCDR might hit bad data.
Note that TAILP uses EQL or equivalent to compare
sublist to list in the case where sublist is a number, etc.
Rationale:
This is more consistent with the definition of LDIFF, which
gives a useful meaning to arbitrary atomic SUBLIST arguments.
This gives a more elegant definition of SUBLIST, allowing it to
refer to any list -- including the empty list -- which is a
part of another list.
Some reasons for preferring an ATOM check to ENDP include:
- The name TAILP suggests tails, not sublists. Some users might
expect this distinction to mean that data more general than
proper sublists.
- Making TAILP require lists limits its usefulness. If we did
not make this choice, some users would end up having to write
their own extended TAILP which we could just as well have
provided compatibly.
- TAILP is not considered to be used frequently enough in code
that the minor loss in speed to an ATOM check is worth the
lost functionality. Indeed, expanding the scope of its
capabilities may lead to more uses for TAILP.
- Other operators (eg, APPEND) have recently been extended to
treat dotted lists in an interesting way because users have
expressed a desire for this. As such, this change is
culturally consistent.
- Some implementations already support this feature, and the
wording in CLtL is sufficiently ambiguous that some users
might think it appropriate to depend on the feature. In the
absence of compelling efficiency reasons to the contrary,
we should lean toward extending some implementations rather
than tightening others in our efforts to achieve cross-dialect
consistency.
Examples:
#1: (LET ((X '(B C))) (TAILP X (CONS 'A X)))
should return T in all implementations.
#2: (TAILP '(X Y) '(A B C))
should return NIL in all implementations.
#3: (TAILP '() '(A B C))
returns T under this proposal.
#4: (TAILP 3 '(A B C))
returns NIL under this proposal.
#5: (TAILP 3 '(A B C . 3))
returns T under this proposal.
#6: (TAILP '(X Y) '(A B C . 3))
returns NIL under this proposal.
Current Practice:
Symbolics Genera is consistent with TAILP-NIL:T.
Neither Lucid nor VAX LISP currently implements TAILP-NIL:T.
VAX LISP effectively implements definition "A" from the
Problem Description above.
Cost to Implementors:
An implementation of TAILP-NIL:T is given as Definition "B" in the
problem description.
Some implementations might have compiler optimizers for these definitions
as well, so a slight amount of additional effort might be required.
Cost to Users:
Given that current practice varies widely on the disputed case,
this is unlikely to have a practical effect on existing portable code.
Benefits:
Avoids unnecessary incompatibilities between implementations.
Non-Benefits:
Slows down TAILP slightly in some implementations because ENDP is
potentially faster than ATOM.
Discussion:
This issue was first raised in ">GLS>clarifications.text" of 12/06/85.
It recommended an earlier proposal, TAILP-NIL:NIL, which is effectively
equivalent to Definition "A" from the Problem Description.
Pitman introduced TAILP-NIL:T as an alternative, arguing that the
definition TAILP-NIL:NIL offered no practical value to programmers
in the disputed situations, while TAILP-NIL:T was of arguable usefulness.
Pitman and van Roggen support TAILP-NIL:T.
It was suggested more than once by more than one cleanup
committee member that we remove TAILP from the language
"since almost nobody uses it".


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