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# blaise de vigenère cipher

This page discusses two different versions of the Vigenère cipher, the autokey method and the keyword method. The Vigenère Cipher. The Vigenère cipher, was invented by a Frenchman, Blaise de Vigenère in the 16th century. The best-known polyalphabetics are the simple Vigenère ciphers, named for the 16th-century French cryptographer Blaise de Vigenère. Bellaso used a "reciprocal table" of five alphabets; Vigenère used ten; Bellaso's cipher was based on the first letter of the word; Vigenère used a letter agreed upon before communication. Vigenère and Gronsfeld Cipher Introduction §. The cipher was invented by Italian Giovan Battista Bellaso, who described it in 1553 in his book "La cifra del. At age 24, he entered the service of the Duke of Nevers as his secretary, a position he held until the deaths of the Duke and his son in 1562. Then, at the age of thirty-nine, Vigenère decided that he had accumulated enough money for him to be able to abandon his career and concentrate on a life of study. In 1586 he combined the table of Trithemius, the key of Belaso and the miiixture of letters of Porta into what is generally called the Vigenere Cipher or Cliiffre Indechiffrable. Though the 'chiffre indéchiffrable' is easy to understand and implement, for three centuries it resisted all attempts to break it. Sig. If only Mary’s secretary had read this treatise, he would have knownabout the Vigenère cipher, Mary’s messages to Babington would have baffled Phelippes, and her life might have been spared” (Singh, The Code Book. This copy sold at Christies, London for 8,125 GBP on June 16, 2015. Later, in the 19th century, the invention of Bellaso's cipher was misattributed to Vigenère. Vigenère Cipher Polyalphabetic Substitution Cipher. The Caesar cipher can be easily broken either via frequency analysis of letters or via brute force. Het werd uitgevonden door Giovan Battista Bellaso in 1553, maar het was door Blaise de Vigenère dat het algemeen bekend raakte, waardoor het zijn naam kreeg. The Vigenere Cipher can not be cracked by using conventional frequency analysis, i will describe… The name Vigenere cipher comes from the diplomat Blaise de Vigenere who described this encryption (along with others) in 1586, in its book "Traité des Chiffres". If the message was right shifted by 4, each A … 2 Τελευταία τροποποίηση 12:10, 12 Σεπτεμβρίου 2018. The Vigenère Cipher was invented in 1553 by the Italian Giovan Battista Bellaso but is now erroniously named after the Frenchman Blaise de Vigenère. Vigenère cipher is the sequence of Caesar ciphers with different transformations (ROTX, see Caesar cipher). The sequence is defined by keyword, where each letter defines needed shift. Earlier I told you that the autokey cipher was invented by Blaise de Vigenère, right? The method was originally described by Giovan Battista Bellaso in his 1553 book La cifra del. The method was originally described by Giovan Battista Bellaso in his 1553 book La cifra del. It is somewhat like a variable Caesar cipher, but the N changed with every letter. Vigenere may refer to: Blaise de Vigenere a 16th - century French cryptographer The Vigenere cipher a cipher whose invention was later misattributed to The Beau Home JavaScript-based HTML editors What are the advantages of the Vigenere cipher versus Caesar Cipher ? The first well-documented description of a polyalphabetic cipher was by Leon Battista Alberti around 1467 and used a metal cipher disk to switch between cipher alphabets. Blaise de Vigenère developed what is now called the Vigenère cipher in 1585. The algorithm is quite simple. In 16th century mathematician Blaise de Vigenère had developed Vigenère Cipher. In other words, the actual key (shared between correspondents) was a single letter. Giovan Battista Bellaso. Le psaultier de David: torné en prose mesurée ou vers libres, vol. For example, first letter of text is transformed using ROT5, second - using ROT17, et cetera. Vigenère cipher: Encrypt and decrypt online. Blaise de Vigenère (5 April 1523 – 19 February 1596) (French pronunciation: [viʒnɛːʁ]) was a French diplomat, cryptographer, translator and alchemist. Vigenére Cipher has been reinvented many times. Sig. Blaise de Vigenère studied Greek, Hebrew and Italian under Adrianus Turnebus and Jean Dorat. The Caesar cipher can be easily broken either via frequency analysis of letters or via brute force. The Vigenere encryption was the creation of the French diplomat, Blaise de Vigenere, 1523-1596. He used a table known as the Vigenère square, to encipher messages. Five years into his career he accompanied the French envoy Louis Adhémar de Grignan to the Diet of Worms as a junior secretary. There is a Vigenere’s Table which is responsible for encrypting the plaintext with the help of a key. Op vrijdag 29 maart 2002 stond in het Cultureel Supplement van NRC Handelsblad een artikel getiteld "Het Mysterie van Patjitan". Galland, An Historical and Analytical Bibliography of the Literature of Cryptography, 193. He died of throat cancer in 1596 and is buried in the Saint-Étienne-du-Mont church. In 1586 the French diplomat and cryptographer Blaise de Vigenère published in Paris his Traicté des chiffres ou secrètes manières d'escrires. In 1586 French diplomat and cryptographer Blaise de Vigenère published in Paris Traicté des chiffres ou secrètes manières d'escrires. Even though it was called an 'unbreakable cipher', various cryptanalysts were able to break it without a 'key'. Sandi Vigenère merupakan bentuk sederhana dari sandi substitusi polialfabetik. Theory Edit The Vigenère cipher was invented in the mid-16th century and has ever since been popular in the cryptography and code-breaking community. If the key cannot be cracked correctly, you may try to use some known plain text attacks. It is a polyalphabetic cipher because it uses two or more cipher alphabets to encrypt the data. “Vigenère became acquainted with the writings of Alberti, Trithemius, and Porta when, at the age of twenty-six, he was sent to Rome on a two year diplomatic mission. For many years this type of cipher was thought to be impregnable and was known as le chiffre indéchiffrable, literally “the unbreakable cipher.”The procedure for encrypting and decrypting Vigenère ciphers is illustrated in the figure. It differs from Bellaso's in several ways: The method was originally described by Giovan Battista Bellaso in his 1553 book La cifra del. Sig. The name Vigenere cipher comes from the diplomat Blaise de Vigenere who described this encryption (along with others) in 1586, in its book " Traité des Chiffres ". Giovan Battista Bellaso; however, the scheme was later misattributed to Blaise de Vigenère in the 19th century, and is now widely known as the "Vigenère cipher". Sig. Blaise de Vigenère (1523-1596) was a French diplomate. (published in 1553[citation needed] Vigenère created a different, stronger autokey cipher in (1586). The method was originally described by Giovan Battista Bellaso in his 1553 book La cifra del. Well, in his version of the cipher, he used a single letter to “prime” the key and filled the remaining empty spaces above the plaintext with the plaintext itself. Sig. Giovan Batista Belaso [KAHN1967, page 137]. From Wikipedia: The Vigenère (French pronunciation: [viʒnɛːʁ]) cipher has been reinvented many times.The method was originally described by Giovan Battista Bellaso in his 1553 book La cifra del. To start with, his interest in cryptography was purely practical and was linked to his diplomatic work. Named after French diplomat, Blaise de Vigenère, the Vigenère cipher built on the work / ideas of Giovan Battista Bellaso.Previously I have looked at the Caesar cipher and included a Python program that can brute force crack the cipher. The method of encryption known as the "Vigenère cipher" was misattributed to Blaise de Vigenère in the 19th century and was in fact first described by Giovan Battista Bellaso in his 1553 book La cifra del. The Vigenere Cipher is a polyalphabetic substitution cipher, invented by Blaise de Vigenère in the 19th century. Giovan Battista Bellaso.However, in the 19th Century, it was misattributed to Blaise de Vigenère, who had presented a similar cipher (the Autokey Cipher) in 1586. The French Cryptographer Blaise de Vigenere introduced this best known polyalphabetic cipher in 1586. Atbash Cipher Tool; Vigenère Cipher. Wilhelm Kasiski showed in 1863 how to break the Vigenere Cipher. Giovan Battista Bellaso; however, the scheme was later misattributed to Blaise de Vigenère in the 19th century, and is now widely known as the "Vigenère cipher". This cipher is a substitution cipher that supports encryption and decryption of only alphabetic text. Vigenère cipher, type of substitution cipher invented by the 16th-century French cryptographer Blaise de Vigenère and used for data encryption in which the original plaintext structure is somewhat concealed in the ciphertext by using several different monoalphabetic substitution ciphers rather than just one; the code key specifies which particular substitution is to be employed for … Despite being called the Vigenère cipher in honor of Blaise de Vigenère, it was actually developed by Giovan Battista Bellaso. Vigenère cipher. Invoeren van de gecodeerde tekst. What is today known as the Vigenère Cipher was actually first described by Giovan Battista Bellaso in his 1553 book La cifra del. He also served as a secretary to Henry III. The Caesar cipher encrypts by shifting each letter in the plaintext up or down a certain number of places in the alphabet. The cipher was invented by Italian Giovan Battista Bellaso, who described it in 1553 in his book "La cifra del. It is thought to have remained unbroken until Charles Babbage, considered to be the father of computers, broke it in the 19 th century. Par Blaise de Vigenère, Bourbonnois, https://en.wikipedia.org/w/index.php?title=Blaise_de_Vigenère&oldid=971213605, Articles with unsourced statements from March 2013, Wikipedia articles with PLWABN identifiers, Wikipedia articles with SELIBR identifiers, Wikipedia articles with SUDOC identifiers, Wikipedia articles with WORLDCATID identifiers, Creative Commons Attribution-ShareAlike License. "Blaise De Vigenère and The "Chiffre Carre"," Proceedings of the American Philosophical Society 82, no. The Vigenère Cipher was adapted as a twist on the standard Caesar cipher to reduce the effectiveness of performing frequency analysis on the ciphertext. Exploring the History of Information and Media through Timelines, 4999 entries in 112 categories. The Vigenere Cipher is a polyalphabetic substitution cipher, invented by Blaise de Vigenère in the 19th century. It is somewhat like a variable Caesar cipher, but the N changed with every letter. However, it is worth mentioning that the cipher has undergone many reinventions over time and its original method is actually believed to have been created by Giovan Battista Bellaso, who first mentioned it in his book ‘La cifra del. Vigenère has made several contributions to cyptography, but the cipher that bears his name is not one of them. The Vigenère cipher, was invented by a Frenchman, Blaise de Vigenère in the 16th century. Named after French diplomat, Blaise de Vigenère, the Vigenère cipher built on the work / ideas of Giovan Battista Bellaso.Previously I have looked at the Caesar cipher and included a Python program that can brute force crack the cipher. Vigenère's book described a text autokey cipher that became known as the Vigenère cipher because it was misattributed to Vigenère in the 19th century. The actual inventor of the text autokey cipher was Giovan Battista Bellaso (1563). The Vigenere Cipher can not be cracked by using conventional frequency analysis, i will describe… The Cipher was thought to be indecipherable for almost three centuries[2] and the French even called it "'le chiffre indéchiffrable' (French for 'the indecipherable cipher')" [1]. Friedrich Kasiski was the first to establish a working method of deciphering Vigenère ciphers in 1863. Giovan Battista Bellaso’ in 1553. ... De volgende stap is kraken van de subcodes, die als de lengte juist is, allemaal gecodeerd zijn volgens het Caesar-systeem. The Vigenère (French pronunciation: [viʒnɛːʁ]) cipher has been reinvented many times. The best-known polyalphabetics are the simple Vigenère ciphers, named for the 16th-century French cryptographer Blaise de Vigenère. A 1 6 th 16^\text{th} 1 6 th-century French diplomat, Blaise de Vigenère, created a very simple cipher that is moderately difficult for any unintended parties to decipher.There are too many possible keys to brute-force, even if the key is known to come from a particular language. The Vigenère Cipher was considered le chiffre ind hiffrable (French for the unbreakable cipher) for 300 years, until in 1… His father, Jean, arranged for him to have a classical education in Paris. When Vigenère retired aged 47, he donated his 1,000 livres a year income to the poor in Paris. Sometime later in history it was misattributed to a different person, Blaise de Vigenère, likely due to his improvement of the cipher he published in 1586 known as the Autokey variant. The method was originally described by Giovan Battista Bellaso in his 1553 book La cifra del. At age 17 he entered the diplomatic service and remained there for 30 years, retiring in 1570. It is a typical example of polyalphabetic encryption whose invention was wrongly attributed to Blaise de Vigenère, and dating back to the 16th century. Sig. The strength of the Vigenère cipher lies in its using not one, but 26 distinct cipher alphabets to encode a message… To unscramble the message, the intended receiver needs to know which row of the Vigenère square has been used to encipher each letter, so there must be an agreed system of switching between rows. Giovan Battista Bellaso".However it is named, due to the wrong widespread belief in the nineteenth century, after the French diplomat and alchemist Blaise de Vigenère, who lived in the sixteenth century. The Vigenère Cipher is a polyalphabetic substitution cipher. Giovan Battista Bellaso; however, the scheme was later misattributed to Blaise de Vigenère in the 19th century, and is now widely known as the "Vigenère cipher". The Vigenère Cipher is essentially a repeating application of Caesar ciphers. The Vigenère cipher is a stronger cipher than the ones we’ve seen before. The sequence is defined by keyword, where each letter defines the needed shift. *French diplomat and cryptographer Blaise de Vigenère was born in the town in 1523. Vigenère cipher is the sequence of Caesar ciphers with different transformations (ROTX, see Caesar cipher). The name of the cipher comes from the 16th century French cryptographer Blaise de Vigenère. Giovan Battista Bellaso".However it is named, due to the wrong widespread belief in the nineteenth century, after the French diplomat and alchemist Blaise de Vigenère, who lived in the sixteenth century. The Vigenère cipher was invented by Giovan Battista Bellaso in 1553. It consists of many different alphabets, which is why we consider it polyalphabetic, unlike Atbash, Caesar, and Substitution ciphers, which are monoalphabetic.Vigenère is special since it is an incredibly simple cipher to understand, but it took around three centuries for cryptanalyists to break it. Het Vigenèrecijfer is in de cryptografie een van de klassieke handcijfers. Sig. In 1586 French diplomat and cryptographer Blaise de Vigenère published in Paris Traicté des chiffres ou secrètes manières d'escrires. Blaise de Vigenère actually invented the stronger Autokey cipherin 1586. Giovan Battista Bellaso; however, the scheme was later misattributed to Blaise de Vigenère in the 19th century, and is now widely known as the "Vigenère cipher". The name of the cipher comes from a mistake: the French cryptographer Blaise de Vigenère (1523-1596) described such a cipher in 1586, and the cipher has since come to be wrongly named after him. People commonly say that the Vigenère cipher is wrongly attribute… In other words, the letters in the Vigenère cipher are shifted by different amounts, normally done using a word or phrase as the encryption key . Sig. Vigènere cipher. This is achieved by using a keyword… Vigenère’s work culminated in his Traicté des Chiffres, published in 1586. A 16 th century French diplomat, Blaise de Vigenere, created a very simple cipher that is moderately difficult for any unintended parties to decipher. Giovan Battista Bellaso; however, the scheme was later misattributed to Blaise de Vigenère in the 19th century, and is now widely known as the Vigenère cipher. Vigenére Cipher has been reinvented many times. The cipher uses 26 caesar shift ciphers and alternates between the alphabets, this is what makes the Vigenere Cipher much stronger than the Caeser Cipher. For many years this type of cipher was thought to be impregnable and was known as le chiffre indéchiffrable, literally “the unbreakable cipher.”The procedure for encrypting and decrypting Vigenère ciphers is illustrated in the figure. You need a cipher, specifically a Vigenere Cipher. The Vigenère cipher is a method of encryption that uses a series of different "Caesar ciphers" based on the letters of a keyword. The key consists of a sequence of symbols of the alphabet K = {k0, k1, …, kd-1}, of length d, and which uses the following linear congruent transformation of encryption: It is based on the usage of the Caesar cipher, but with changing alphabets. This makes the cipher less vulnerable to cryptanalysis using letter frequencies. Blaise de Vigenère was a French man born in Saint-Pourçain-sur-Sioule on April 5 1523. It is a polyalphabetic cipher because it uses two or more cipher alphabets to encrypt the data. The method of encryption known as the "Vigenère cipher" was misattributed to Blaise de Vigenère in the 19th century and was in fact first described by Giovan Battista Bellaso in his 1553 book La cifra del. Vigènere cipher. Caesar cipher is in fact a Vigenere cipher with a 1-letter long key. Vigenère did invent a stronger autokey cipher. Later, Johannes Trithemius, in his work Polygraphiae (which was completed in manuscript form in 1508 but first published in 1518), invented the tabula recta, a critical component of the Vigenère ciphe… However, similar encryption had already been described by Giovan Battista Bellaso. Phrase LEMON, for example, defines the sequence of ROT11-ROT4-ROT12-ROT14-ROT13, which is repeated until all block of text is encrypted. Provenance: Jacques Auguste de Thou (1553-1617; signature on title and verso of final leaf) -- Jean-Jacques Charron, marquis de Ménars -- Armand-Gaston, cardinal de Rohan -- Charles de Rohan, prince de Soubise (shelfmark on pastedown). Giovan Battista Bellaso; however, the scheme was later misattributed to Blaise de Vigenère in the 19th century, and is now widely known as the "Vigenère cipher". The cipher uses 26 caesar shift ciphers and alternates between the alphabets, this is what makes the Vigenere Cipher much stronger than the Caeser Cipher. The Vigenère cypher was regarded as unbreakable for over 300 years, until Charles Babbage and Friedrich Kasiski independently developed a method of multiple tests to carry out successful cryptanalysis. The Vigenère Cipher is essentially a repeating application of Caesar ciphers. Vigenère cipher: Encrypt and decrypt online Method of encrypting alphabetic text by using a series of interwoven Caesar ciphers based on the letters of a keyword. The Vigenère Cipher is a simple form of polyalphabetic substitution through which alphabetic text is encrypted using a series of Caesar ciphers with different shift values based on the letters of a keyword. Main Concept. On both trips, he read books about cryptography and came in contact with cryptologists. A 16 th century French diplomat, Blaise de Vigenere, created a very simple cipher that is moderately difficult for any unintended parties to decipher. In a Caesar cipher, each letter in the passage is moved a certain number of letters over, to be replaced by the corresponding letter. Sig. But not because he was the one who invented it. But it wasn't until 1586 that Blaise de Vigenère published an autokey cipher before the court of Henry III in France. The late 1500s, Blaise de Vigenere proposed a polyalphabetic system Vigenere cipher that is difficult to decipher. For example, the first letter of text is transformed using ROT5, second - using ROT17, et cetera. Over de Vigenère-code Inleiding. It is a typical example of polyalphabetic encryption whose invention was wrongly attributed to Blaise de Vigenère, and dating back to the 16th century. Vigenère cipher 1 Vigenère cipher The Vigenère cipher is named for Blaise de Vigenère (pictured), although Giovan Battista Bellaso had invented the cipher earlier. This page was last edited on 4 August 2020, at 20:20. It consists of many different alphabets, which is why we consider it polyalphabetic, unlike Atbash, Caesar, and Substitution ciphers, which are monoalphabetic.Vigenère is special since it is an incredibly simple cipher to understand, but it took around three centuries for cryptanalyists to break it. It differs from Bellaso's in several ways: After his retirement, Vigenère composed and translated over 20 books, including: La somptueuse et magnifique entrée du roi Henri III en la cité de Mantoue, Le psaultier de David torne en prose mesuree, ou vers libres. Sig. Created in 1553 by Giovan Battista Bellaso (What an awesome name!) You would "encode" your message with a passphrase, and the letters of your passphrase would determine how each letter in the message would be encrypted. Dit kan door op elk van de subcodes een frequentieanalyse los te laten. The method was originally described by Giovan Battista Bellaso in his 1553 book La cifra del. Sig. From Wikipedia: The Vigenère (French pronunciation: [viʒnɛːʁ]) cipher has been reinvented many times.The method was originally described by Giovan Battista Bellaso in his 1553 book La cifra del. This tool base supports you in analysing and breaking a vigenere cipher. (published in 1553 Vigenère created a different, stronger autokey cipher in (1586). The Vigenère cipher is a polyalphabetic substitution cipher that is a natural evolution of the Caesar cipher. The Vigenère Cipher is a polyalphabetic substitution cipher. Vigenère cipher. Blaise de Vigenère published his description of a similar but stronger autokey cipher before the court of Henry III of France, in 1586. And it’s not exactly known who really invented it. The Vigenère (French pronunciation: [viʒnɛːʁ]) cipher has been reinvented many times. In 1549 he visited Rome on a two-year diplomatic mission, and again in 1566. 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Known polyalphabetic cipher in 1586 the French cryptographer Blaise de Vigenère Bellaso his. Het Mysterie van Patjitan '' was actually first described by Giovan Battista Bellaso in his 1553 book La cifra.... Key to shift the alphabets on the letters of a keyword of text is transformed using ROT5, second using. Cipher ) subcodes, die als de lengte juist is, allemaal zijn!, where each letter defines the needed shift Timelines, 4999 entries in 112 categories 16th-century French cryptographer Blaise Vigenère! Cipher less vulnerable to cryptanalysis using letter frequencies invention is also in the 1585 Traicté. Saint-Étienne-Du-Mont church an 'unbreakable cipher ', various cryptanalysts were able to break it his diplomatic work using. And was linked to his diplomatic work service and remained there for 30 years, in! And Analytical Bibliography of the Vigenère square, to encipher messages GBP on June 16, 2015 through!


text:
# Why is CurrencyConvert so slow?

Map[
QuantityMagnitude@
CurrencyConvert[DatedUnit["USDollars", #], DatedUnit["Euro", #]] &,
Range[2008, 2015]]
(* {0.683367, 0.719004, 0.754938, 0.718962, 0.778094, 0.753209, \
0.753825, 0.888133} *)

This simple download takes 5 seconds, which is significantly slower than downloading hundreds of share prices with FinancialData.

Also, very often I get the following message:

and one or more values are missing.

Any idea how I can speed this up and how I can avoid the timeouts?

I found a certain improvement using WolframAlpha.

To get the rates for the last 20 days I wrote:

GetRates[c1_, c2_] :=
With[{rt = WolframAlpha["Exchange rate " <> c1 <> " " <> c2, {{"History", 1}, "FormattedData"}]},
Round[#, 0.01]& @ Take[Cases[rt, {DateObject[__], a_Real} :> a/100, Infinity], -20]]

GetRates["USDollars", "Euro"]

{0.95, 0.94, 0.92, 0.94, 0.93, 0.93, 0.92, 0.91, 0.92, 0.91, 0.92, 0.92, 0.92, 0.92, 0.92, 0.93, 0.93, 0.92, 0.91, 0.91}

This is still slow (3 seconds), but much faster than the approach in the question (13 seconds).

Above all : I don' t get timeouts anymore, because WolframAlpha employs in this case the option

TimeConstraint -> {30, Automatic, Automatic, Automatic}

format: plain


text:
# Autonomous Abilities¶

## Aims¶

• Keep the robot alive at all times,
• Let application developer focus on their very specific content, without micro-programming robot day-to-day behavior.
// Build the holder for the ability.
val holder: Holder = HolderBuilder.with(qiContext)
.withAutonomousAbilities(AutonomousAbilitiesType.BACKGROUND_MOVEMENT)
.build()

// Hold the ability asynchronously.
holder.async().hold()

// Release the ability asynchronously.
holder.async().release()

// Build the holder for the ability.
Holder holder = HolderBuilder.with(qiContext)
.withAutonomousAbilities(AutonomousAbilitiesType.BACKGROUND_MOVEMENT)
.build();

// Hold the ability asynchronously.
holder.async().hold();

// Release the ability asynchronously.
holder.async().release();

## Operating principle¶

Autonomous abilities are robot behaviors (movements, animations, tracking) occurring:

• autonomously,
• in the background,
• taking the different resources of the robot weakly.

### List of Autonomous Abilities¶

Autonomous Ability Type When
BackgroundMovement Passive Idle time
BasicAwareness Reactive Reacting to any kind of stimuli

### Constraining Autonomous Abilities on given degrees of freedom¶

HolderBuilder.withDegreesOfFreedom allows you to define constraints restricting the Autonomous Abilities.

For instance, DegreeOfFreedom.ROBOT_FRAME_ROTATION prevents rotational movements of the robot base.

// Build the holder for the degree of freedom.
val holder = HolderBuilder.with(qiContext)
.withDegreesOfFreedom(DegreeOfFreedom.ROBOT_FRAME_ROTATION)
.build()

// Hold the ability asynchronously.
holder.async().hold()

// Release the ability asynchronously.
holder.async().release()

// Build the holder for the degree of freedom.
Holder holder = HolderBuilder.with(qiContext)
.withDegreesOfFreedom(DegreeOfFreedom.ROBOT_FRAME_ROTATION)
.build();

// Hold the ability asynchronously.
holder.async().hold();

// Release the ability asynchronously.
holder.async().release();

### Prioritization¶

The prioritization system works as follows:

By default, the robot will make background movements (aka. idle movements) and blink. If the robot detects a stimulus, then he may move his head and body, overriding the background movements. Finally, if a high-level behavior such as a Say or an Animate is executed, this will take priority over the previous modules.

### Future-proof thanks to API levels¶

Thanks to API level mechanism, potential new autonomous abilities becoming available on the robot will not modify the current behaviour of your application: new abilities will be activated only if you rebuild with the new QiSDK version.

## Tips & Tricks¶

• If I want the robot to keep a posture (e.g. at the end of an animation), BackgroundMovement and BasicAwareness must be held so the robot can remain perfectly still.

• If I want to freeze screen location while speaking or listening, I should disable body language.

For further details see: Disabling body language in a Say and Disabling body language in a Listen.

And if I use Chat and QiChatbot? Disable body language option:

• of the Chat to freeze while listening, and
• of the QiChatbot to freeze while speaking.


text:
# Problem in mechanics involving a pulley

1. Feb 24, 2006

### Bhargav

Okay
I chanced upon this problem and have lost some sleep since then.
There is a pulley system with a single stationary pulley with 2 masses m1 and m2 suspended using a string which is inelastic and massles.The coefficient of static FRICTION between the PULLEY and the STRING is mu.What are the accelerations of the masses?

[One way that occurs to me is to find the normal reaction between the pulley and the thread at a point which makes theta with the centre of the pulley and then integrate between limits 0 and pi.Though i am not sure how to go about calculating the normal force at a point(guess using tension in the string and resolving it at that point).]

2. Feb 24, 2006

### Bhargav

I used the method i specified and got the following answer
|a|=g(x2-x1)/(x2+x1)
where x2=m2(1-mu)
x1=m1(1+mu)
assuming m2 sufficiently greater than m1 to set the system in motion.

3. Feb 24, 2006

### Hootenanny

Staff Emeritus
If the string is massless then there can be no friction. Friction is given by $F_{max} = \mu R$; if there is no mass, then there is no normal reaction force ($R$) and hence no friction.

4. Feb 24, 2006

### BobG

So, the only way you can ever get a pulley to turn is to use very heavy rope or cable? A pulley with a light weight string and heavy masses will cause the string to slide over the pulley instead of turn the pulley?

In any event, it's unclear what the OP is looking for. Are you looking for the maximum acceleration before the string slides over the pulley instead of turns it? Or are you looking for the friction due to the bearings within the pulley? (sounds more like the first, since for the second, you'd be more concerned about the coefficient of kinetic friction).

The force of gravity provides a downward force on the two masses regardless of where the string is on the pulley, so the normal force, using horizontal to the ground as the reference point, would be the sine of the angle (ranging from 0 to 90 - it's easier to just add your two masses together, since the sine from 180 to 90 is the same as the sine from 0 to 90).

5. Feb 25, 2006

### Hootenanny

Staff Emeritus
Ahh yes I see now.

6. Feb 25, 2006

### Bhargav

I am looking for the acceleration produced in each block..which happens to be constant..
My physics coach hinted at using torque but i am not very sure how..and yes ..desite the the string being massless..the gravitational force causes the masses attached to the string to produce a reaction between the string and pulley.
And BOb if you could elaborate..[I am not sure you could just say the normal at a point would be sum of masses *sine(angle)].

7. Feb 25, 2006

### BobG

Conceptually, the force from each mass will be distributed over the length of the string. Looking at two points, one at 120 deg and one at 60 degrees (with mass 1 being on the 120 deg side and mass 2 being on the 60 degree side), and looking just at the forces on those two individual points (however the forces are distributed), you'd have:

$$f1 sin (120) + f2 sin (60)$$

The sine of 120 is equal to the sine of 60, so you can use sine 60 for both. The distributive property gives you the sum of the forces times the angle.

The forces have to be distributed along the length of the contact area between the string and the pulley. In other words, the sum of the forces will wind up equalling the sum of the masses times the acceleration of gravity (it had better or else you'll have to find a way to explain what happened to the difference).


text:
# Complex structure on the product of two complex Kaehler manifolds

I have the following question: Let $(M,J_{M}, \omega_{M})$ and $(N,J_{N}, \omega_{N})$ be two Kaehler manifolds. I consider $M \times N$. Can I introduce on $M \times N$ a complex structure ? I think one can define the complex structure by $J_{M} \oplus J_{N}$. Is this right? But then I do not understand why the Nijenhuis-tensor vanishes. Can someone explain this to me please. Thanks in advance.

Markus

-
 Yes, this is right. I don't think there's any deep reason why two integrable almost complex structures give rise to an integrable one, we just have to plough through the calculations to show it. If calculating the Nijenhuis tensor of $J_M \oplus J_N$ seems tiring, perhaps you could try showing the equivalent condition that $\bar \partial_{J_M \oplus J_N}^2 = 0$. – Gunnar Magnusson Nov 1 '12 at 7:06 how could one check such a thing? – Markus Nov 1 '12 at 7:42 For any form $\alpha$ and almost complex structure $J$ we have (by definition) $$\bar \partial_J \alpha = \tfrac 12 (d\alpha \pm i d J \alpha),$$ where $d$ is the exterior derivative and I can never remember whether there should be a $+$ or $-$ in the place of $\pm$ (one gives $\partial$, the other gives $\bar \partial$). Integrability of $J$ (i.e. the vanishing of the Nijenhuis tensor) is equivalent to either $\partial^2 = 0$ or $\bar \partial^2 = 0$. – Gunnar Magnusson Nov 1 '12 at 8:54 By the way, your question is independent of the Kahler condition. – Gunnar Magnusson Nov 1 '12 at 8:56


text:
# ML that learns to predict and play a simple wagering game

I have a simple game I'm building for fun, just to see how well ML can work with simple data sets.

Basically it's just a game where it has turns that go like this:

• Computer generates a random number $x$, and does not show the player.

• Player wagers that they can guess a number lower than $x$. Call the wager amount $w$

• Player tries to guess a low number $g$.

• If $g \lt x$, then player gains $wg$ points.

• If $g \ge x$, then player loses their wager, $w$ points.

If player has funds $f_t$ at start of turn $t$ then another way to put this is:

$$f_{t+1} = \begin{cases} f_t + wg,& \text{if } g\le x\\ f_t - w, & \text{otherwise} \end{cases}$$

Here's an example of play:

• Start with $f=50$
• Turn 1, $x=11$, player wagers $w=8$ and guesses $g=9$. Player gains $+72$ points, so $f=122$ at the end of the turn.
• Turn 2, $x=10.5, w=4, g=7$, points change by $+28$, $f=150$.
• Turn 3, $x=20, w=1, g=6$, points change by $+6$, $f=156$.
• Turn 4, $x=2, w=10, g=15$, points change by $-10$, $f=146$.

I wanted to use ML to try and predict this by feeding it pre-generated turns to see if it can find any patterns. There are only a few variables involved, so I figured it shouldn't be overly complicated. Ideally I would like for the ML to learn how to play the game.

I am wondering what type of ML is applicable to this kind of problem? It is not clear to me where to start, even though I have made the game simple. I have more complex games that I made in the past that I would like to try to apply ML to as well.

• Hello Neil. First of all thank you so much for taking the time to provide such a detailed answer, and for attempting to understand my badly explained question. the game consists of rounds, each round a random number 1-1000 is generated. and each round the player can look at previous round history and based on that, guess the current rounds number. if the player guesses under he number, he wins the number (times) the amount of his total he wagered. So if he wagered 10 that the number would be 2, and the number was 5 - he would get 20. if the number was 1, he would lose 10. – JDOE Sep 22 '17 at 9:18
• well i spent so long trying to figure out how to make your math look pretty again while quoting it i couldnt edit anymore, reached timelimit. always feel like stack exchange doesnt want me on here heh. Anyway, yea your math is exactly how it is setup. except the rolls would be random if rolled with dice, which i know you have ample experience with. These rolls are based off of something else, and I have lots of historic games that I've generated that i wish to look for patterns within also I would like to be able to have it self teach strategies, but that would be many more variables. – JDOE Sep 22 '17 at 17:35
• Ah yeah, sounds good appreciate the help .and reinforcement learning sounds good (i think i need to do something to be better at being smart myself hah) So would you recommend any type of setup for scikit for this problem? thanks again – JDOE Sep 22 '17 at 18:33

There are a few different data science and ML techniques you could throw at the numbers output by this game. You could try to analyse human players' style. You could generate a table of expected gains/losses by ignoring actual amount gained or lost and estimating the probability distribution of $x$ even though the player did not see it, just from know whether $x \gt w$ or $x \le w$ from enough examples.

If your end goal is to find out how a computer might play such a game, then one clear machine learning choice would be Reinforcement Learning (RL). It is not the only way for a computer to play a game - there are very many optimisation techniques for this. However, it is a machine learning approach - it learns from data observations - and in many variations it includes numerical analysis that you may find interesting, such as chance of winning from a certain position, or predicted future rewards.

RL is not a single algorithm, or even a single approach. Instead it is a way of framing a problem, and all ways of solving that kind of problem are considered to be RL. For RL to work, a problem needs to be framed as a Markov Decision Process (MDP). The good news is that your example game, and similar ones, are good examples of MDPs already.

In RL, the way that an agent plays a game is called its policy. The usual goals of RL include measuring the performance of a specific policy, or discovering the best policy.

So, where to start? First, if you want to try an RL process on your game you should probably do a few things:

• Understand what in the game is the State, what are the Actions and what are the Rewards.

• The state is anything that the agent knows about and can affect the outcome. Probably the only thing to worry about here is the current funds $f$, although this may not have a huge influence unless $w$ can always go up to $f$. Note each value is treated as a different state.

• The action is the agent's choice of both $w$ and $g$ - note each specific combination is considered a different action. If there are a large number of actions, this can make learning harder, requiring more advanced RL algorithms, so I recommend initially you try variations of your game with limited number of choices.

• The most obvious choice of reward is the change to $f$ at the end of each turn. It doesn't have to be though, it depends on what you consider a "winning condition" to be for the game. See below.

• Simplify the game options a little. Set a small limit to possible ranges of $x, w, g$. This will make it easier to try things out initially.

• Set a clear goal that you want to achieve, and make sure that the game structure works for that. You may need to adjust the game representation for even simple change such as "get the most reward in 10 turns" - because that might encourage large wagers at certain steps, and the agent will need to know how many turns it has left (so your state becomes combination of $(f, t)$. Another variation that may work is to set the goal of getting to a certain amount of funds, such as 1000. In which case, the reward would not be the increase in $f$, but actually be +1 for getting to that target, and 0 for any other result. That might radically change the behaviour of the agent, which could be interesting to experiment with.

• Look up simple Reinforcement Learning algorithms. I suggest you start with tabular methods - perhaps Monte Carlo Control, or Q-Learning. They can typically be implemented in a few tens of lines in Python/Numpy.

A useful resource for RL is Sutton & Barto's Reinforcement Learning: An Introduction. The draft of the second version is free to download.


text:
• entries
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# Untitled

82 views

First off - still not sure about the self-citation bullshit; I have that class tommorrow morning so I'll be sure to ask her about it. I still think its ridiculous.

Anyway.

Okay, I just finished writing the main structures to store the parsed equations. Its pretty much as I laid it out; I'll spare you the implementation and just jump to the usage.

Lets take an example expression first -

f() = cos( 5 * 3 )

Now, first we have to create the top-level representation of the expression, which I have dubbed a Statement -

Statement< float, .. > equ;

I'm intentionally omitting the second template argument here because it refers to a variable value table so that we can lookup stuff. And I haven't gotten around to implement that yet, but more than likely its just going to be a string-to-T map.

Okay, so lets start parsing the innermost part of the expression by making some constant value holders -

Value< float, .. >* val1 = new Value< float, .. >( 5 );
Value< float, .. >* val2 = new Value< float, .. >( 3 );

I'm not entirely satisfied with this method of construction, but as it will only be used by parser code I don't feel insensed enough to write factory functions or anything.

Anyway, we now combine the two Values with an Expression construct -

Expression< OP_MUL, float, .. >* expr = new Expression< OP_MUL, float, .. >( val1, val2 );

That should be pretty self explanatory.

Now we can add the expression to our statement. We could have just added the values directly, but that doesn't show everything well.

The statement is managed internally as a cross between a tree and a stack - it maintains a pointer to the head of the tree (which is defaults to a Value< T, .. >( 0 )). It supports two operations - splice and append - whose usage will be explained in a second.

We can take our expression and splice it into the stack with the splice command. Essentially this creates a new Expression with the statement's head as one value and our expression as the other.

Since we left the value of the head as the default - 0 - we should just add in this value (OP_ADD)

Now all we've got left to deal with is the sincos operation. This is easy to apply with the append operation (not aptly named). append creates a new UnaryExpression with the current head as the value and applies the operation to it.

So -

equ.append< OP_COS >();

EDIT: Dcower is a fucking trigonometric nazi. That's right. That's right. Go play with your triangles you silly Nazi, give me a call in 1938 when you forget poland kthx. [sad]

I guess it should be really called apply or transform, but since its technically "appending" a transform onto the list... meh.

Anyway, we want to be able to do something with the final value. We use the eval method to do this -

std::cout << equ.eval( .. ) << std::endl;

Again, I've omitted the variable table stuff. Essentially, you'd pass a reference to the current variable table which would get used by Variable instances in the tree.

And for fun - the operations currently supported -

enum {	OP_ADD = 1,	OP_SUB,	OP_MUL,	OP_DIV,	OP_POW,	OP_LOG,	OP_NEG,	OP_SIN,	OP_COS,	OP_TAN,	OP_ASIN,	OP_ACOS,	OP_ATAN};

There are some limitations that I'm secretly hiding, and (in last night's prototype) used template-sex to prevent messes from happening. (think about what happens when you use ints and pass it to an OP_POW which uses pow() internally...)

I'll discuss some of the internals tommorrow night, as well as details on variable tables. And I know no one cares, but that's okay. Because I still care :)

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text:
Hardcover | $29.95 Trade | £24.95 | 328 pp. | 6 x 9 in | 22 figures | December 2016 | ISBN: 9780262035408 eBook |$20.95 Trade | December 2016 | ISBN: 9780262336482
Mouseover for Online Attention Data

## The Wild and the Wicked

On Nature and Human Nature

## Overview

Most of us think that in order to be environmentalists, we have to love nature. Essentially, we should be tree huggers—embracing majestic redwoods, mighty oaks, graceful birches, etc. We ought to eat granola, drive hybrids, cook tofu, and write our appointments in Sierra Club calendars. Nature’s splendor, in other words, justifies our protection of it. But, asks Benjamin Hale in this provocative book, what about tsunamis, earthquakes, cancer, bird flu, killer asteroids? They are nature, too.

For years, environmentalists have insisted that nature is fundamentally good. In The Wild and the Wicked, Benjamin Hale adopts the opposite position—that much of the time nature can be bad—in order to show that even if nature is cruel, we still need to be environmentally conscientious. Hale argues that environmentalists needn’t feel compelled to defend the value of nature, or even to adopt the attitudes of tree-hugging nature lovers. We can acknowledge nature’s indifference and periodic hostility. Deftly weaving anecdote and philosophy, he shows that we don’t need to love nature to be green. What really ought to be driving our environmentalism is our humanity, not nature’s value.

Hale argues that our unique burden as human beings is that we can act for reasons, good or bad. He claims that we should be environmentalists because environmentalism is right, because we humans have the capacity to be better than nature. As humans, we fail to live up to our moral potential if we act as brutally as nature. Hale argues that despite nature’s indifference to the plight of humanity, humanity cannot be indifferent to the plight of nature.


text:
### Andrew Fraknoi interviews Frank Drake

Sometimes, people who are unfamiliar with the scientific picture of cosmic and biological evolution think the equation is highly speculative. In fact, it is just the opposite, since each phenomenon it assumes to take place in the universe is an event that has already taken place at least one.(6)
Frank Drake was born in Chicago on May 28, 1930 to Richard and Winifred Drake. Raised in Chicago's South Shore with sister, Alma, and brother, Robert, he had a fairly typical childhood. He is known for the Drake equation and his key role in the foundation of the SETI project. The equation named after him was a mathematical description of an idea that he considered when he was 8 years old(7) and it was formulated by the radio astronomer in 1961 during the Green Bank conference after the seminal paper by Cocconi and Morrison, Searching for Interstellar Communications(1).
The original formulation is: $N = R_{\ast} \cdot f_p \cdot n_e \cdot f_{\ell} \cdot f_i \cdot f_c \cdot L$ where $N$ is the number of civilizations in our galaxy with which radio-communication might be possible (i.e. which are on our current past light cone);
$R_{\ast}$ the average rate of star formation in our galaxy;
$f_p$ the fraction of those stars that have planets;
$n_e$ the average number of planets that can potentially support life per star that has planets;
$f_{\ell}$ the fraction of planets that could support life that actually develop life at some point;
$f_i$ the fraction of planets with life that actually go on to develop intelligent life (civilizations);
$f_c$ the fraction of civilizations that develop a technology that releases detectable signs of their existence into space;
$L$ the length of time for which such civilizations release detectable signals into space(8)
In 1966 Shklovskii and Sagan added a new term to the equation, $f_g$, the fraction of stars suitable for supporting life(2, 3, 4).
Now, at the end of January, SETI released the video of an interview to Frank Drake conducted by Andrew Fraknoi (http://fraknoi.blogspot.it/2014/02/an-interview-with-father-of-search-for.html):
In June 2012, the SETI Institute sponsored another three-day public event called SETICon -- a series of talks, panels, and events featuring scientists, authors, futurists, and film-makers. It was a time and place for science and the imagination to meet. One highlight of the program was an interview with the "father of SETI science," astronomer Frank Drake (the first President of the SETI Institute Board. ) It was conducted by SETI Institute Board Member and veteran astronomy educator Andrew Fraknoi. The discussion ranged widely over Dr. Drake's career and current thinking, and included reminiscences of Project OZMA, the very first experiment searching for signals from civilizations among the stars, and his current view of the Drake Equation (estimating the chances of intelligent life out there). He also reflects on a number of modern developments, including the discovery of numerous planets orbiting other stars and new ways of searching for extra-terrestrial civilizations.

The Drake equation revisited
An interactive object about the Drake equation
Thanks to Andrew Fraknoi for sharing the video
(1) Cocconi G. & Morrison P. (1959). Searching for Interstellar Communications, Nature, 184 (4690) 844-846. DOI: (pdf)
(2) I.S. Shklovskii, Carl Sagan (1966). Intelligent life in the universe. Holden-Day (San Francisco)
(3) Freeman J. & Lampton M. (1975). Interstellar archaeology and the prevalence of intelligence, Icarus, 25 (2) 368-369. DOI: (pdf)
(4) Walters C., Hoover R.A. & Kotra R.K. (1980). Interstellar colonization: A new parameter for the Drake equation?, Icarus, 41 (2) 193-197. DOI: (pdf)
(5) Ćirković M.M. (2004). The Temporal Aspect of the Drake Equation and SETI, Astrobiology, 4 (2) 225-231. DOI: (pdf, arXiv)
(6) Frank Drake, Dava Sobel (2010). The origin of the Drake equation. Astronomy Beat, 46 pdf)
(7) Frank Drake (en.wiki)
(8) Drake equation (en.wiki)


text:
# Layers¶

## BatchNorm¶

BatchNorm accelerates convergence by reducing internal covariate shift inside each batch. If the individual observations in the batch are widely different, the gradient updates will be choppy and take longer to converge.

The batch norm layer normalizes the incoming activations and outputs a new batch where the mean equals 0 and standard deviation equals 1. It subtracts the mean and divides by the standard deviation of the batch.

Code

def BatchNorm():
# From https://wiseodd.github.io/techblog/2016/07/04/batchnorm/
def __init__(self):
pass

def forward(self, X, gamma, beta):
mu = np.mean(X, axis=0)
var = np.var(X, axis=0)

X_norm = (X - mu) / np.sqrt(var + 1e-8)
out = gamma * X_norm + beta

cache = (X, X_norm, mu, var, gamma, beta)

return out, cache, mu, var

def backward(self, dout, cache):
X, X_norm, mu, var, gamma, beta = cache

N, D = X.shape

X_mu = X - mu
std_inv = 1. / np.sqrt(var + 1e-8)

dX_norm = dout * gamma
dvar = np.sum(dX_norm * X_mu, axis=0) * -.5 * std_inv**3
dmu = np.sum(dX_norm * -std_inv, axis=0) + dvar * np.mean(-2. * X_mu, axis=0)

dX = (dX_norm * std_inv) + (dvar * 2 * X_mu / N) + (dmu / N)
dgamma = np.sum(dout * X_norm, axis=0)
dbeta = np.sum(dout, axis=0)

return dX, dgamma, dbeta

## Convolution¶

In CNN, a convolution is a linear operation that involves multiplication of weight (kernel/filter) with the input and it does most of the heavy lifting job.

Convolution layer consists of 2 major component 1. Kernel(Filter) 2. Stride

1. Kernel (Filter): A convolution layer can have more than one filter. The size of the filter should be smaller than the size of input dimension. It is intentional as it allows filter to be applied multiple times at difference point (position) on the input.Filters are helpful in understanding and identifying important features from given input. By applying different filters (more than one filter) on the same input helps in extracting different features from given input. Output from multiplying filter with the input gives Two dimensional array. As such, the output array from this operation is called “Feature Map”.
2. Stride: This property controls the movement of filter over input. when the value is set to 1, then filter moves 1 column at a time over input. When the value is set to 2 then the filer jump 2 columns at a time as filter moves over the input.

Code

# this code demonstate on how Convolution works
# Assume we have a image of 4 X 4 and a filter fo 2 X 2 and Stride = 1

def conv_filter_ouput(input_img_section,filter_value):
# this method perfromas the multiplication of input and filter
# returns singular value

value = 0
for i in range(len(filter_value)):
for j in range(len(filter_value[0])):
value = value + (input_img_section[i][j]*filter_value[i][j])
return value

img_input = [[260.745, 261.332, 112.27 , 262.351],
[260.302, 208.802, 139.05 , 230.709],
[261.775,  93.73 , 166.118, 122.847],
[259.56 , 232.038, 262.351, 228.937]]

filter = [[1,0],
[0,1]]

filterX,filterY = len(filter),len(filter[0])
filtered_result = []
for i in range(0,len(img_mx)-filterX+1):
clm = []
for j in range(0,len(img_mx[0])-filterY+1):
clm.append(conv_filter_ouput(img_mx[i:i+filterX,j:j+filterY],filter))
filtered_result.append(clm)

print(filtered_result)

## Dropout¶

A dropout layer takes the output of the previous layer’s activations and randomly sets a certain fraction (dropout rate) of the activatons to 0, cancelling or ‘dropping’ them out.

It is a common regularization technique used to prevent overfitting in Neural Networks.

The dropout rate is the tunable hyperparameter that is adjusted to measure performance with different values. It is typically set between 0.2 and 0.5 (but may be arbitrarily set).

Dropout is only used during training; At test time, no activations are dropped, but scaled down by a factor of dropout rate. This is to account for more units being active during test time than training time.

For example:

• A layer in a neural net outputs a tensor (matrix) A of shape (batch_size, num_features).
• The dropout rate of the layer is set to 0.5 (50%).
• A random 50% of the values in A will be set to 0.
• These will then be multiplied with the weight matrix to form the inputs to the next layer.

The premise behind dropout is to introduce noise into a layer in order to disrupt any interdependent learning or coincidental patterns that may occur between units in the layer, that aren’t significant.

Code

# layer_output is a 2D numpy matrix of activations

layer_output *= np.random.randint(0, high=2, size=layer_output.shape) # dropping out values

# scaling up by dropout rate during TRAINING time, so no scaling needs to be done at test time
layer_output /= 0.5
# OR
layer_output *= 0.5 # Scaling down during TEST time.

 [2]

This results in the following operation.

All reference, images and code examples, unless mentioned otherwise, are from section 4.4.3 of Deep Learning for Python by François Chollet.

 [2]

## Linear¶

Be the first to contribute!

## LSTM¶

Be the first to contribute!

## Pooling¶

Pooling layers often take convolution layers as input. A complicated dataset with many object will require a large number of filters, each responsible finding pattern in an image so the dimensionally of convolutional layer can get large. It will cause an increase of parameters, which can lead to over-fitting. Pooling layers are methods for reducing this high dimensionally. Just like the convolution layer, there is kernel size and stride. The size of the kernel is smaller than the feature map. For most of the cases the size of the kernel will be 2X2 and the stride of 2. There are mainly two types of pooling layers.

The first type is max pooling layer. Max pooling layer will take a stack of feature maps (convolution layer) as input. The value of the node in the max pooling layer is calculated by just the maximum of the pixels contained in the window.

The other type of pooling layer is the Average Pooling layer. Average pooling layer calculates the average of pixels contained in the window. Its not used often but you may see this used in applications for which smoothing an image is preferable.

Code

def max_pooling(feature_map, size=2, stride=2):
"""
:param feature_map: Feature matrix of shape (height, width, layers)
:param size: size of kernal
:param stride: movement speed of kernal
:return: max-pooled feature vector
"""
pool_shape = (feature_map.shape[0]//stride, feature_map.shape[1]//stride, feature_map.shape[-1]) #shape of output
pool_out = numpy.zeros(pool_shape)
for layer in range(feature_map.shape[-1]):
#for each layer
row = 0
for r in numpy.arange(0,feature_map.shape[0], stride):
col = 0
for c in numpy.arange(0, feature_map.shape[1], stride):
pool_out[row, col, layer] = numpy.max([feature_map[c:c+size,  r:r+size, layer]])
col = col + 1
row = row +1
return pool_out

## RNN¶

Be the first to contribute!

References

 [2] “4.4.3, Fundamentals of Machine Learning: Adding Dropout.” Deep Learning for Python, by Chollet, François. Manning Publications Co., 2018, pp. 109–110.


text:
# Changeset f845e80 for doc/theses/aaron_moss_PhD/phd/background.tex

Ignore:
Timestamp:
Apr 25, 2019, 2:23:48 PM (3 years ago)
Branches:
aaron-thesis, arm-eh, cleanup-dtors, jacob/cs343-translation, jenkins-sandbox, master, new-ast, new-ast-unique-expr
Children:
98b4b12
Parents:
69c37cc
Message:

thesis: apply round 2 revisions and strip change bars

File:
1 edited

### Legend:

Unmodified
 r69c37cc Notable features added during this period include generic types (Chapter~\ref{generic-chap}), constructors and destructors \cite{Schluntz17}, improved support for tuples \cite{Schluntz17}, reference types \cite{Moss18}, first-class concurrent and parallel programming support \cite{Delisle18}, as well as numerous pieces of syntactic sugar and the start of an idiomatic standard library \cite{Moss18}. \cbstart This thesis is primarily concerned with the \emph{expression resolution} portion of \CFA{} type-checking; resolution is discussed in more detail in Chapter~\ref{resolution-chap}, but is essentially determining which declarations the identifiers in each expression correspond to. Given that no simultaneously-visible C declarations share identifiers, expression resolution in C is not difficult, but the added features of \CFA{} make its resolution algorithms significantly more complex. Due to this complexity, the expression-resolution pass in \CFACC{} requires 95\% of compiler runtime on some source files, making an efficient procedure for expression resolution a requirement for a performant \CFA{} compiler. \cbend In C, no simultaneously-visible declarations share identifiers, hence expression resolution in C is not difficult. In \CFA{}, multiple added features make the resolution process significantly more complex. Due to this complexity, the expression-resolution pass in \CFACC{} requires 95\% of compiler runtime on some source files, making a new, more efficient procedure for expression resolution a requirement for a performant \CFA{} compiler. The features presented in this chapter are chosen to elucidate the design constraints of the work presented in this thesis. Given a declaration !list_iterator it!, !*it! can be either an !int! or a !list!, with the meaning disambiguated by context (\eg{} !int x = *it;! interprets !*it! as !int!, while !(*it).value = 42;! interprets !*it! as !list!). While a nominal-inheritance system with associated types could model one of those two relationships by making !El! an associated type of !Ptr! in the !pointer_like! implementation, \cbstart the author is unaware of any nominal-inheritance system that could model both relationships simultaneously. I am unaware of any nominal-inheritance system that can model both relationships simultaneously. Further comparison of \CFA{} polymorphism with other languages can be found in Section~\ref{generic-related-sec}. \cbend The flexibility of \CFA{}'s implicit trait-satisfaction mechanism provides programmers with a great deal of power, but also blocks some optimization approaches for expression resolution. The ability of types to begin or cease to satisfy traits when declarations go into or out of scope makes caching of trait satisfaction judgments difficult, and the ability of traits to take multiple type parameters can lead to a combinatorial explosion of work in any attempt to pre-compute trait satisfaction relationships. \cbstart \subsection{Deleted Declarations} Particular type combinations can be exempted from matching a given polymorphic function through use of a \emph{deleted function declaration}: Particular type combinations can be exempted from matching a given polymorphic function through the use of a \emph{deleted function declaration}: \begin{cfa} Deleted function declarations are implemented in \CFACC{} by adding them to the symbol table as usual, but with a flag set that indicates that the function is deleted. If this deleted declaration is selected as the unique minimal-cost interpretation of an expression then an error is produced, allowing \CFA{} programmers to guide the expression resolver away from undesirable solutions. \cbend \section{Implicit Conversions} \label{implicit-conv-sec} Given some type !T!, a !T&! (reference to !T!'') is essentially an automatically dereferenced pointer. These types allow seamless pass-by-reference for function parameters, without the extraneous dereferencing syntax present in C; they also allow easy aliasing of nested values with a similarly convenient syntax. \cbstart The addition of reference types also eliminated two syntactic special-cases present in previous versions of \CFA{}. Considering a call !a += b! to a compound assignment operator, the previous declaration for that operator was !lvalue int ?+=?(int*, int)! -- to mutate the left argument, the built-in operators were special-cased to implicitly take the address of that argument, while the special !lvalue! syntax was used to mark the return type of a function as a mutable reference. With references, this declaration can be re-written as !int& ?+=?(int&, int)! -- the reference semantics generalize the implicit address-of on the left argument and allow it to be used in user-declared functions, while also subsuming the (now removed) !lvalue! syntax for function return types. \cbend Consider the a call !a += b! to a compound assignment operator. The previous declaration for that operator is !lvalue int ?+=?(int*, int)!. To mutate the left argument, the built-in operators were special-cased to implicitly take the address of that argument, while the special !lvalue! syntax was used to mark the return type of a function as a mutable reference. With references, this declaration is re-written as !int& ?+=?(int&, int)!. The reference semantics generalize the implicit address-of on the left argument and allow it to be used in user-declared functions, while also subsuming the (now removed) !lvalue! syntax for function return types. The C standard makes heavy use of the concept of \emph{lvalue}, an expression with a memory address; its complement, \emph{rvalue} (a non-addressable expression) is not explicitly named in the standard. \CFA{} supports all of these use cases without further added syntax. The key to this syntax-free feature support is an observation made by the author that the address of a reference is a lvalue. In C, the address-of operator !&x! can only be applied to lvalue expressions, and always produces an immutable rvalue; \CFA{} supports reference re-binding by assignment to the address of a reference\footnote{\cbstart The syntactic difference between reference initialization and reference assignment is unfortunate, but preserves the ability to pass function arguments by reference (a reference initialization context) without added syntax. \cbend }, and pointers to references by repeating the address-of operator: In C, the address-of operator !&x! can only be applied to lvalue expressions, and always produces an immutable rvalue; \CFA{} supports reference re-binding by assignment to the address of a reference\footnote{The syntactic difference between reference initialization and reference assignment is unfortunate, but preserves the ability to pass function arguments by reference (a reference initialization context) without added syntax.}, and pointers to references by repeating the address-of operator: \begin{cfa}


text:
# Micro Fourier Transform Profilometry ($μ$FTP): 3D shape measurement at 10,000 frames per second

31 May 2017Chao ZuoTianyang TaoShijie FengLei HuangAnand AsundiQian Chen

Recent advances in imaging sensors and digital light projection technology have facilitated a rapid progress in 3D optical sensing, enabling 3D surfaces of complex-shaped objects to be captured with improved resolution and accuracy. However, due to the large number of projection patterns required for phase recovery and disambiguation, the maximum fame rates of current 3D shape measurement techniques are still limited to the range of hundreds of frames per second (fps)... (read more)

PDF Abstract

No code implementations yet. Submit your code now


text:
# Dynamic perfect hashing

In computer science, dynamic perfect hashing is a programming technique for resolving collisions in a hash table data structure.[1][2][3] This technique is useful for situations where fast queries, insertions, and deletions must be made on a large set of elements.

## Details

In this method, the entries that hash to the same slot of the table are organized as separate second-level hash table. If there are k entries in this set S, the second-level table is allocated with k2 slots, and its hash function is selected at random from a universal hash function set so that it is collision-free (i.e. a perfect hash function). Therefore, the look-up cost is guaranteed to be O(1) in the worst-case.[2]

function Locate(x) is
j = h(x);
if (position hj(x) of subtable Tj contains x (not deleted))
return (x is in S);
end if
else
return (x is not in S);
end else
end

Although each second-level table requires quadratic space, if the keys inserted into the first-level hash table are uniformly distributed, the structure as a whole occupies expected O(n) space, since bucket sizes are small with high probability.[1]

During the insertion of a new entry x at j, the global operations counter, count, is incremented. If x exists at j but is marked as deleted then the mark is removed. If x exists at j, or at the subtable Tj, but is not marked as deleted then a collision is said to occur and the jth bucket's second-level table Tj is rebuilt with a different randomly selected hash function hj. Because the load factor of the second-level table is kept low (1/k), rebuilding is infrequent, and the amortized cost of insertions is O(1).[2]

function Insert(x) is
count = count + 1;
if (count > M)
FullRehash(x);
end if
else
j = h(x);
if (Position hj(x) of subtable Tj contains x)
if (x is marked deleted)
remove the delete marker;
end if
end if
else
bj = bj + 1;
if (bj <= mj)
if position hj(x) of Tj is empty
store x in position hj(x) of Tj;
end if
else
Put all unmarked elements of Tj in list Lj;
Append x to list Lj;
bj = length of Lj;
repeat
hj = randomly chosen function in Hsj;
until hj is injective on the elements of Lj;
for all y on list Lj
store y in position hj(y) of Tj;
end for
end else
end if
else
mj = 2 * max{1, mj};
sj = 2 * mj * (mj - 1);
if the sum total of all sj ≤ 32 * M2 / s(M) + 4 * M
Allocate sj cells for Tj;
Put all unmarked elements of Tj in list Lj;
Append x to list Lj;
bj = length of Lj;
repeat
hj = randomly chosen function in Hsj;
until hj is injective on the elements of Lj;
for all y on list Lj
store y in position hj(y) of Tj;
end for
end if
else
FullRehash(x);
end else
end else
end else
end else
end

Deletion of x simply flags x as deleted without removal and increments count. In the case of both insertions and deletions, if count reaches a threshold M the entire table is rebuilt, where M is some constant multiple of the size of S at the start of a new phase. Here phase refers to the time between full rebuilds. The amortized cost of delete is O(1).[2] Note that here the -1 in "Delete(x)" is a representation of an element which is not in the set of all possible elements U.

function Delete(x) is
count = count + 1;
j = h(x);
if position h(x) of subtable Tj contains x
mark x as deleted;
end if
else
return (x is not a member of S);
end else
if (count >= M)
FullRehash(-1);
end if
end

A full rebuild of the table of S first starts by removing all elements marked as deleted and then setting the next threshold value M to some constant multiple of the size of S. A hash function, which partitions S into s(M) subsets, where the size of subset j is sj, is repeatedly randomly chosen until:

$\sum_{0\le j\le s(M)} s_j \le \frac{32M^2}{s(M)} + 4M.$

Finally, for each subtable Tj a hash function hj is repeatedly randomly chosen from Hsj until hj is injective on the elements of Tj. The expected time for a full rebuild of the table of S with size n is O(n).[2]

function FullRehash(x) is
Put all unmarked elements of T in list L;
if (x is in U)
append x to L;
end if
count = length of list L;
M = (1 + c) * max{count, 4};
repeat
h = randomly chosen function in Hs(M);
for all j < s(M)
form a list Lj for h(x) = j;
bj = length of Lj;
mj = 2 * bj;
sj = 2 * mj * (mj - 1);
end for
until the sum total of all sj ≤ 32 * M2 / s(M) + 4 * M
for all j < s(M)
Allocate space sj for subtable Tj;
repeat
hj = randomly chosen function in Hsj;
until hj is injective on the elements of list Lj;
end for
for all x on list Lj
store x in position hj(x) of Tj;
end for
end


text:
SEARCH HOME
 Math Central Quandaries & Queries
 Question from Yung-Ju, a student: (27^r^-1)^-2/3

Hi,

First look at what is inside the parentheses, which I assume is $\left( 27^r \right)^{-1}.$ This can be written $\frac{1}{27^r}.$ Hence your expression can be written

$\left( \frac{1}{27^r} \right)^\frac{-2}{3}.$

The negative sign in the exponent tells me that $\left( \frac{1}{27^r} \right)$ should be inverted and hence the expression can now be written

$\left( 27^r \right)^\frac{2}{3}.$

But $27 = 3^3$ so make this substitution and simplify.

I hope this helps,
Penny

Math Central is supported by the University of Regina and The Pacific Institute for the Mathematical Sciences.


text:
# A Smooth Refresher on Python's Tuples

This post is part of a series called A Smooth Refresher To Python.
A Smooth Refresher on Python's Dictionaries
A Smooth Refresher on Python's Conditional Statements

In my previous refreshers, which you can access from the series navigation links at the top of this article, I talked about two important Python concepts you need to grasp in order to move forward in your Python learning journey.

This tutorial is a continuation of the Python refresher series, and today I will be talking about Tuples. That way, you will have three important concepts in your pocket and will be ready to dig deeper in the Python language.

So, let's go ahead and move directly to this interesting topic of Tuples.

If you understood Lists, Tuples will be very simple to grasp, because they are similar to Lists except for two main differences:

1. Tuples are immutable, so once you create a Tuple, you cannot change its content or even its size, unless you make a copy of that Tuple.
2. They are written in parentheses ( ) rather than in square brackets [ ]

Thus, as you can guess, Tuples consist of a set of ordered objects which can be of any type (i.e. Strings, Lists, Dictionaries, Tuples, etc.), and are accessed by an index (offset), as opposed to Dictionaries where items are accessed by key. It is important to note here that Tuples store references to the objects they contain.

Before moving to some Tuple operations, let's see what a simple Tuple looks like:

tup = (1)

This is a simple Tuple that contains one item, an integer value. The output of this Tuple will be 1.

Another example of a Tuple with three items of different object types is as follows:

tup = (31,'abder',4.0)

The output for the above statement is:

(31, 'abder', 4.0)

You can even write the above Tuple without parentheses as follows:

tup = 31,'abder',4.0

Very flexible, isn't it?

As a final example, let's see how a nested Tuple would look:

nested_tuple = ('ID', ('abder', 1234))

## Tuple Operations

Let's now walk through some Tuple operations.

### Concatenation

Concatenation is the combination of Tuples together. Say that we have the following two Tuples:

tuple1 = (1,2,3,4,5)

tuple2 = (6,7,8,9,10)

In order to concatenate tuple1 and tuple2, we simply type:

tup = tuple1 + tuple2

Notice that we used the + operator to perform the concatenation. This will result in the following output:

(1, 2, 3, 4, 5, 6, 7, 8, 9, 10)

### Repetition

Tuple repetition is simply carried out using the * operator. If we want to repeat tuple1 three times, we do the following:

tuple1 * 3

The result of this statement is:

(1, 2, 3, 4, 5, 1, 2, 3, 4, 5, 1, 2, 3, 4, 5)

### Membership

To check the membership of some item in the Tuple, we use in, as follows:

7 in tuple1

This will return False since 7 doesn't belong to tuple1.

### Search

In order to indicate where some item is located in the Tuple, we use index. For instance, if we want to find the location of the item 5 in tuple1, we do the following:

tuple1.index(5)

In this case, the return value will be 4, which is the location of the item 5.

### Count

A nice operation in Tuples is counting the number of times an element exists in the Tuple. Say we have the following Tuple:

tuple3 = (65,67,5,67,34,76,67,231,98,67)

If we want to see how many times 67 exists in tuple3, we simply do the following:

tuple3.count(67)

The result for this statement should be 4 times.

### Indexing

Indexing is the process of accessing a Tuple element by index (subscript). If we want to access the fifth index in tuple3, we do the following:

tuple3[4]

which will return 34.

An index can also be negative, that is, counting will start from the right of the Tuple. Thus, the result of tuples3[-6] will be 34, provided that the range of the negative indices in tuples3 is [-1,-10]

What if you chose an index out of this range? Like tuples3[-11] for instance? This is what you would get in this case:

Traceback (most recent call last):

File "tuple.py", line 2, in <module>

print tuple3[-11]

IndexError: tuple index out of range

Notice that negative indices start from -1. So, if you typed the index as -0, this is the same as the index 0. Thus, tuples3[-0] will return 65.

### Slicing

Unlike indexing, which provides us with one element as a result, slicing provides us with a sequence of elements. An example of slicing is the following statement:

tuples3[2:5]

The output of this statement might seem confusing at the beginning:

(5, 67, 34)

Notice that 76 is not included, although in reality it is in index 5. This is because in slicing the start index is always included, and the end index is excluded, that is, end - 1.

Another example of Tuples is as follows:

tuples3[:4]

The output of the statement would be as follows:

(65, 67, 5, 67)

Now, this is a small quiz for you. What is the output of the following statement?

tuples3[4:]

## Conclusion

You can find more information on Tuples from Python's documentation. As you might notice, although Tuples work similarly to Lists, they don't have as many methods as lists, since Tuples, as mentioned above, are immutable—that is, the contents of the Tuple cannot be changed.


text:
# Flow Reattachment: Flow over a Backward-Facing Step

## Overview

The aim of this test case is to validate steady-state flow over a backward-facing step. The incompressible, turbulent case is validated with the experimental results of Driver and Seegmiller [1] as archived in the NASA Turbulence Modeling Resource [2]. The following parameters have been analysed:

• Velocity Profiles
• Coefficient of Pressure
• Reattachment Length

The geometry was meshed locally and a mesh upload to the platform was performed.

Import validation project into workspace

## Geometry

The geometry is constructed based on the reference case [1], as shown in Fig.1. The height of the step is h=12.7

$h=12.7$

cm, and the tunnel height is 8h

$8h$

. The origin is located at the base of the step. The face details have been given in Table 1.

Table 1: Domain Details
Face(s) Type
A Inlet
B+H Symmetry
C+D+E+G Walls
F Outlet

## Analysis type and Domain

The blockMesh tool was used to generate the mesh locally (see Fig.2. and Table 2.). A single-cell width was assigned in the z-direction to ensure a 2D mesh.

A typical property of the generated mesh is the y+

${y}^{+}$

(“y-plus”) value, which is defined as the non-dimensionalized distance to the wall; it is given by y+=uy/ν

${y}^{+}={u}^{\ast }y/\nu$

. A y+

${y}^{+}$

value of 1 would correspond to the upper limit of the laminar sub-layer.

• Explicit resolution of the near-wall region: The first cell lies at most at the boundary of the laminar sub-layer and no further. Here, y+
${y}^{+}$

value is 1 or below.

• Use of wall-functions to resolve the near-wall region: There is no need to place cells very close to the laminar sub-layer, and typically 30y+300
$30⩽{y}^{+}⩽300$

.

y+

${y}^{+}$

value of 30 was used for the inflation layer. The kω

$k-\omega$

SST turbulence model was chosen, with wall functions for near-wall treatment of the flow.

Tool Type : OPENFOAM®

Analysis Type : simpleFoam

Mesh and Element types :

Table 2: Mesh Metrics
Mesh type Number of volumes Type
blockMesh 5.5×105

$5.5×{10}^{5}$

2D hex

## Simulation Setup

Fluid:

Kinematic Viscocity (ν

$\nu$

): 1.4694×105 m2s1

The boundary conditions for the simulation are shown in Table 3.

Boundary Conditions:

Table 3: Boundary Conditions for Ahmed Body simulation
Parameter Inlet Symmetry Walls Outlet
Velocity 44.2 ms1

Symmetry 0.0 ms1

$0.0$

Pa

k

$k$

5.336 m2s2

ω

$\omega$

182.399 s1

## Results

Velocity Profiles

Shown below in Figure 3 are comparisons of velocity profiles from SimScale simulation results with the reference [1] at different distances into the domain. All distances have been normalized with the step height h

$h$

, and the velocity is normalized with respect to the inlet velocity vin=44.2 ms1

.

Fig.3. Velocity profiles at different depths into the domain.

Coefficient of Pressure

Shown below in Figure 4 is the comparison of the coefficient of pressure Cp=PP12ρV2

${C}_{p}=\frac{P-{P}_{\mathrm{\infty }}}{\frac{1}{2}\rho {V}_{\mathrm{\infty }}^{2}}$

from SimScale simulation results with the reference [1] at the lower and upper walls.

Fig.4. Coefficient of Pressure at lower and upper walls

Reattachment Length

The reattachment length is the distance from the step at which the flow resumes in the positive flow direction all over the cross-section. The reattachment length was calculated to be 6.82 cm

, which lies within a 12%

$12\mathrm{%}$

error limit of the experimental value of 7.74 cm

[2].

## Disclaimer

This offering is not approved or endorsed by OpenCFD Limited, producer and distributor of the OpenFOAM software and owner of the OPENFOAM® and OpenCFD® trade marks. OPENFOAM® is a registered trade mark of OpenCFD Limited, producer and distributor of the OpenFOAM software.


text:
What is change in the magnetic flux (delta B)?

What is change in the magnetic flux (delta B)? Like on this http://hyperphysics.phy-astr.gsu.edu/hbase/magnetic/emfchb.html" [Broken]

Last edited by a moderator:

rock.freak667
Homework Helper
Well the magnetic flux is given by $\Phi=BA$ where B is magnetic flux density and A is the surface area.
So a field interacts with another magnetic field then the value of B changes and hence there should be a rate of change of flux. And by faraday's law, and emf is induced.

Is it like, when I approach magnet, than the magnetic domains of the coil get in line with the magnetic field of the magnet and when I get back the magnet, the magnetic domains of the coil are like in the first position? I mean, for the direction of the magnetic flux.

Last edited:
Can somebody help me to determine the direction of the magnetic flux of the coil?is that, something to do with the aligning of the magnetic domains?

rock.freak667
Homework Helper
Can somebody help me to determine the direction of the magnetic flux of the coil?is that, something to do with the aligning of the magnetic domains?

A coil like a solenoid? if so then to determine the direction of the magnetic flux you need to use the right hand grip rule

A coil like a solenoid? if so then to determine the direction of the magnetic flux you need to use the right hand grip rule
But, I thought in practical way. What happens with the flux?

And can you please explain me this quote from "Newton Ask A scientist"
These days, most hula hoops have beads in them. Let's pretend that if
the beads move inside the hoop they will generate wind through the hoop.
Let's say if they go clockwise, they generate a wind going in the direction
a screw would go if the beads were turning it. Let's say if you increase
the amount of air going in that same direction through the hoop, the beads
will feel forced to move (so far we have Faraday's law) counter-clockwise


text:
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# Tag Info

3

Easy Fix: It seems like it is because of the way you define $H$. You need the parenthesis around each of the term! So something like: H = (504.0 * I^I^I^I^I^I^I^Z) + (1008.0 * I^I^I^I^I^I^Z^I) + ( 2016.0 * I^I^I^I^I^Z^I^I) Just replace this in your code then it will work! Alternative (longer) way: Here I will offer another way to define the Hamiltonian ...

2

There was an issue that was fixed with QAOA https://github.com/Qiskit/qiskit-aqua/pull/1316 whereby using all zeros as an initial point was changed since the optimizer could easily get stuck there. Given the version you have the easiest way to change things would just be to pass an initial point that is non-zero. I.e. instead of [0,0] which it uses with the ...

2

For diagonal Hamiltonians, you might not need entanglement at all. The intuition for why a more complicated ansatz might be useful is that the optimization landscape is somehow better, in the sense that the minimum is easier to find. However, in this paper, the author applies VQE to some combinatorial optimization problems and finds that variational forms ...

3

With just single qubit gate in your circuit, you can only generate a small subset of quantum states. In fact, the states that you can generate are called separable states. These states have no entanglement in them. Here is an example to see why you need to be able to generate entangle state to have successful VQE calculation, supposed you have the ...

2

Probably the easiest way to understand this is to pretend that the mixer is NOT there and see what happens. So, let's assume you have some initial state $\lvert \psi \rangle = \sum_x \psi_x \lvert x \rangle$ and you want to use QAOA to find the ground state of some cost Hamiltonian $H_C$. I'm using the notation \$\big\{\lvert x \rangle : x \in \{\pm 1\}^n \...

3

You can set the attribute parameters_bounds of a circuit to the desired intervals like below: from qiskit import QuantumCircuit from qiskit.circuit import Parameter a=Parameter('a') b=Parameter('b') ansatz=QuantumCircuit(2) ansatz.ry(a,0) ansatz.ry(b,1) ansatz.parameter_bounds=[[0,np.pi]]*2 Then you can run your vqe program.

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