Features
For core part-of-speech categories, see the universal POS tags. The features listed here distinguish additional lexical and grammatical properties of words, not covered by the POS tags.
| Lexical features | Inflectional features | |
| Nominal* | Verbal* | |
| PronType | Gender | VerbForm |
| NumType | Animacy | Mood |
| +NumForm | Number | Tense |
| +NumValue | Case | Aspect |
| Poss | +PrepCase | Voice |
| Reflex | +Variant | |
| +NameType | Polarity | |
| +AdpType | Degree | Person |
| +ConjType | +Gender[psor] | |
| Abbr | +Number[psor] | |
| +Hyph | ||
| Foreign | ||
| +Style |
* The labels Nominal and Verbal are used as approximate categories only. There is no universal rule that a particular feature can only occur with verbs or nominals (although language-specific rules may define such constraints). Even the boundary between lexical and inflectional features is sometimes blurred: for example, gender is a lexical feature of nouns but an inflectional feature of adjectives or verbs.
Abbr
: abbreviation
| Values: | Yes |
Boolean feature. Is this an abbreviation? Note that the abbreviated word typically belongs to a part of speech other than cs-pos/X.
Yes
: it is abbreviation
Examples
- Acronyms: ČR (Česká republika) “Czech Republic”, LN (Lidové noviny) (a newspaper), ODS (Občanská demokratická strana) “Civic Democratic Party”, OSN (Organizace spojených národů) “United Nations Organization”, ODA (Občanská demokratická aliance) “Civic Democratic Alliance”
- Initials: J, M, V, A, C
- Abbreviations: r. (rok) “year”, např. (například) “for example”, tzv. (takzvaný) “so-called”, a. s. (akciová společnost) “joint-stock company”, tel. (telefon) “phone”
AdpType
: adposition type
| Values: | Comprep | Prep | Voc |
Czech has neither postpositions nor circumpositions but there are several forms of prepositions that this feature distinguishes.
Prep
: (normal) preposition
Examples
- v “in”, na “on”, o “about”, z “of”, s “with”, do “into”, k “to”, pro “for”, za “behind”, po “after”
Voc
: vocalized preposition
Some Czech prepositions are non-syllabic and their form has to be changed in some contexts to facilitate pronunciation. Moreover, some syllabic prepositions are altered too, if the following word starts with certain consonant clusters.
Examples
The first line shows examples of vocalized preposition forms, the second line shows corresponding base forms.
- ve, se, ze, ke, ode, beze, ku, skrze, přede, nade
- v, s, z, k, od, bez, k, skrz, před, nad
Comprep
: dependent part of compound preposition
This value marks dependent first part of a compound preposition. This word cannot occur alone.
Not all compound prepositions contain words marked Comprep
.
Many compound prepositions consist of two normal prepositions and a noun
(an example is na rozdíl od “in contrast to”).
Sometimes there are just two words, the second one is normal preposition and the first one
is a secondary preposition (etymologically some other part of speech, but it has been frozen as
a preposition).
Examples
- vzhledem k(e) “due to”, nehledě na “regardless of”, narozdíl od “in contrast to”
Animacy
: animacy
| Values: | Anim | Inan |
Similarly to Gender, animacy is a lexical feature of nouns and inflectional feature of other parts of speech that mark agreement with nouns. It is independent of gender, therefore it is encoded separately in some tagsets (e.g. all the Multext-East tagsets). On the other hand, in Czech the (almost) only grammatical implications occur within the masculine gender, which is why the PDT tagset does not have animacy as separate feature and instead defines four genders: masculine animate, masculine inanimate, feminine and neuter.
Anim
: animate
Human beings, animals, fictional characters, names of professions etc. are all animate. Even nouns that are normally inanimate can be inflected as animate if they are personified. For instance, consider a children’s story about cars where cars live and talk as people; then the cars may become and be inflected as animates.
PDT examples of masculine animate nouns:
- člověk “man”, ministr “minister”, prezident “president”, předseda “chairman”, ředitel “director”
Inan
: inanimate
Nouns that are not animate are inanimate.
PDT examples of masculine inanimate nouns:
- rok “year”, zákon “law”, stát “state”, případ “case”, milión “million”
Aspect
: aspect
| Values: | Imp | Perf |
Aspect is a feature that specifies duration of the action in time, whether the action has been completed etc.
In Czech, aspect is considered a lexical feature of verbs. While many imperfective verbs have morphologically related perfective counterparts, it is not a regular system and the two verbs are represented by different lemmas.
Imp
: imperfect aspect
The action took / takes / will take some time span and there is no information whether and when it was / will be completed.
Examples
- péci “to bake” (Imp); pekl chleba “he baked / was baking a bread”
Perf
: perfect aspect
The action has been / will have been completed. Since there is emphasis on one point on the time scale (the point of completion), this aspect does not work well with the present tense. Czech morphology can create present forms of perfective verbs but these actually have a future meaning.
Examples
- upéci “to bake” (Perf); upekl chleba “he baked / has baked a bread”
Diffs
Prague Dependency Treebank
The PDT tagset does not encode aspect. However, verb lemmas in PDT contain their own features
that encode the aspect: _:T
= Imp
and _:W
= Perf
. These lemma features were removed
during conversion, and the Aspect
feature was introduced instead.
Unfortunately the morphological lexicon underlying the PDT annotation is incomplete and numerous verbs lack the aspect information. Without this imperfection there would be only a tiny group of verbs that work with both aspects.
Case
: case
| Values: | Acc | Dat | Gen | Ins | Loc | Nom | Voc |
Case is an inflectional feature of nouns and other parts of speech (adjectives, numerals) that mark agreement with nouns. It is also valency feature of prepositions (saying that the preposition requires its argument to be in that case).
Case helps specify the role of the noun phrase in the sentence. For example, the nominative and accusative cases often distinguish subject and object of the verb, while in fixed-word-order languages these functions would be distinguished merely by the positions of the nouns in the sentence.
Czech morphology distinguishes seven cases:
Nom
, Gen
, Dat
, Acc
, Voc
, Loc
and Ins
(this ordering is fixed in the grammar and the cases are also referred to by numbers 1–7).
Examples
- singular nominative matka “mother”, genitive matky , dative matce, accusative matku, vocative matko, locative matce, instrumental matkou
- plural nominative matky, genitive matek, dative matkám, accusative matky, vocative matky, locative matkách, instrumental matkami
The descriptions of the individual case values below include semantic hints about the prototypical meaning of the case. Bear in mind that quite often a case will be used for a meaning that is totally unrelated to the meaning mentioned here. Valency of verbs, adpositions and other words will determine that the noun phrase must be in a particular grammatical case to fill a particular valency slot (semantic role).
Nom
: nominative
The base form of the noun, also used as citation form (lemma). This is the word form used for subjects of clauses.
Gen
: genitive
Prototypical meaning of genitive is that the noun phrase somehow belongs to its governor; it would often be translated by the English preposition of.
Note that despite considerable semantic overlap, the genitive case is not the same as the feature of possessivity (Poss). Possessivity is a lexical feature, i.e. it applies to lemma and its whole paradigm. Genitive is a feature of just a subset of word forms of the lemma. Semantics of possessivity is much more clearly defined while the genitive (as many other cases) may be required in situations that have nothing to do with possessing. For example, bez prezidentovy dcery “without the president’s daughter” is a prepositional phrase containing the preposition bez “without”, the possessive adjective prezidentovy “president’s” and the noun dcery “daughter”. The possessive adjective is derived from the noun prezident but it is really an adjective (with separate lemma and paradigm), not just a form of the noun. In addition, both the adjective and the noun are in their genitive forms (the nominative would be prezidentova dcera). There is nothing possessive about this particular occurrence of the genitive. It is there because the preposition bez always requires its argument to be in genitive.
Examples
- Praha je hlavní město České republiky. “Prague is the capital of the Czech Republic.”
Dat
: dative
This is the word form often used for indirect objects of verbs.
Examples
- Dal jsem dárek svému bratrovi. “I gave my brother a present.” (svému bratrovi “my brother” is dative and dárek “present” is accusative.)
Acc
: accusative
Perhaps the second most widely spread morphological case. This is the word form most frequently used for direct objects of verbs.
Voc
: vocative
The vocative case is a special form of noun used to address someone. Thus it predominantly appears with animate nouns (see the feature of Animacy). Nevertheless this is not a grammatical restriction and inanimate things can be addressed as well.
Examples
- Co myslíš, Filipe? “What do you think, Filip?”
Loc
: locative
The locative case often expresses location in space or time, which gave it its name. As elsewhere, non-locational meanings also exist and they are not rare. On the other hand, some location roles may be expressed using other cases (e.g. because those cases are required by a preposition).
This is the only Czech case that is used exclusively in combination with prepositions.
Examples
- V červenci jsem byl ve Švédsku. “In July I was in Sweden.”
- Mluvili jsme tam o morfologii. “We talked there about morphology.” (Non-locational non-temporal example)
Ins
: instrumental
The role from which the name of the instrumental case is derived is that the noun is used as instrument to do something (as in psát perem “to write using a pen”). Many other meanings are possible, for example the instrumental is required by the preposition s “with” and thus it includes the meaning expressed in other languages by the comitative case.
In Czech the instrumental is also used for the agent-object in passive constructions (cf. the English preposition by).
Examples
- Tento zákon byl schválen vládou. “This bill has been approved by the government.” (Passive example)
ConjType
: conjunction type
| Values: | Oper |
This feature further subclassifies the parts of speech cs-pos/CCONJ and cs-pos/SCONJ;
in Czech, it is used only with CCONJ
. The main distinction between coordinating and
subordinating conjunctions is done already at the part-of-speech level.
Oper
: mathematical operator
Note that operators can be expressed either using symbols or using words.
The words are considered special kind of coordinating conjunctions and they are marked using
ConjType=Oper
.
Examples
- x “×”, krát “times”, plus “plus”, minus “minus”, kráte “times”
Degree
: degree of comparison
| Values: | Cmp | Pos | Sup |
Degree of comparison is inflectional feature of some adjectives and adverbs.
Pos
: positive, first degree
This is the base form that merely states a quality of something, without comparing it to qualities of others. Note that although this degree is traditionally called “positive”, negative properties can be compared, too.
Examples
- mladý muž “young man”
Cmp
: comparative, second degree
The quality of one object is compared to the same quality of another object.
Examples
- ten muž je mladší než já “the man is younger than me”
Sup
: superlative, third degree
The quality of one object is compared to the same quality of all other objects within a set.
Examples
- toto je nejmladší muž v našem týmu “this is the youngest man in our team”
Foreign
: is this a foreign word?
| Values: | Yes |
Boolean feature. Is this a foreign word? Not a loan word but a genuinely foreign word appearing inside native text, e.g. inside direct speech, titles of books etc.
Note that Czech data (especially those from the PDT) often indicate the original part of speech of foreign words. Thus this feature may occur with any POS tag. If the original part of speech is not known, the feature will accompany the cs-pos/X tag.
Yes
: it is foreign
Examples
- … nese jméno VLIW (Very Long Instruction Word – velmi dlouhé instrukční slovo)
Diffs
Prague Dependency Treebank
For proper nouns the borderline between foreign words and loan words is somewhat fuzzy, so e.g. the English personal name George is marked as foreign even though it would not normally be translated (except for names of rulers and saints, which would become Jiří).
Articles in foreign names (the, die, le) are tagged cs-pos/ADJ, not cs-pos/DET.
Gender
: gender
| Values: | Fem | Masc | Neut |
Gender is a lexical feature of nouns and inflectional feature of other parts of speech (adjectives, verbs) that mark agreement with nouns. There are three values of gender: masculine, feminine, and neuter.
See also the related feature of Animacy.
 Other nouns may be also grammatically masculine, without any relation to sex.
Examples
- pán “gentleman”
- hrad “castle”
- muž “man”
- stroj “machine”
- předseda “chairman”
- soudce “judge”
Fem
: feminine gender
Nouns denoting female persons are feminine. Other nouns may be also grammatically feminine, without any relation to sex.
Examples
- žena “woman”
- růže “rose”
- píseň “song”
- kost “bone”
Neut
: neuter gender
This third gender is for nouns that are neither masculine nor feminine (grammatically). Nouns whose nominative suffix is -o or -í (including a large group of deverbative nouns denoting actions) are usually neuter.
Examples
- město “city”
- moře “sea”
- kuře “chicken”
- stavení “building”
Gender[psor]
: possessor’s gender
| Values: | Fem | Masc |
Possessive adjectives and pronouns may have two different genders: that of the
possessed object (gender agreement with modified noun) and that of
the possessor (lexical feature, inherent gender). The Gender[psor]
feature captures the possessor’s gender.
In the Czech examples below, the masculine gender implies using one of the suffixes -ův, -ova, -ovo, and the feminine gender implies using one of -in, -ina, -ino.
Masc
: masculine possessor
Examples
- otcův syn “father’s son”
Gender[psor]=Masc|Gender=Masc
- otcova dcera “father’s daughter”
Gender[psor]=Masc|Gender=Fem
- otcovo dítě “father’s child”
Gender[psor]=Masc|Gender=Neut
Fem
: feminine possessor
Examples
- matčin syn “mother’s son”
Gender[psor]=Fem|Gender=Masc
- matčina dcera “mother’s daughter”
Gender[psor]=Fem|Gender=Fem
- matčino dítě “mother’s child”
Gender[psor]=Fem|Gender=Neut
Hyph
: hyphenated compound or part of it
| Values: | Yes |
Boolean feature. Is this the first part of a hyphenated compound?
Compound adjectives with hyphens, such as česko-slovenský “Czech-Slovak” get split during tokenization. The last part, slovenský, is an independent adjective with full inflection paradigm. However, the first part, česko, is a form that does not occur elsewhere than in compounds (the independent form would be český).
Yes
: it is part of hyphenated compound
Examples
- česko-slovenský “Czecho-Slovak”
Mood
: mood
| Values: | Cnd | Imp | Ind |
Mood is a feature that expresses modality and subclassifies finite verb forms.
Ind
: indicative
The indicative can be considered the default mood. A verb in indicative merely states that something happens, has happened or will happen, without adding any attitude of the speaker.
Examples
- Studuješ na univerzitě. “You study at the university.”
Imp
: imperative
The speaker uses imperative to order or ask the addressee to do the action of the verb.
Czech verbs (except for modal verbs) have imperative forms of the second person singular, first person plural and second person plural.
Examples
- Studuj na univerzitě! “Study at the university!”
Cnd
: conditional
The conditional mood is used to express actions that would have taken place under some circumstances but they actually did not / do not happen.
Czech has present conditional and past conditional, both formed periphrastically using the past participle of the content verb, and a special form of the auxiliary verb být. The special form resembles forms of the historic aorist tense, which disappeared from Czech during the 15th century.
The past participle of the content verb is not marked as conditional because it can also be used in past indicative.
Examples
- Kdybych byl chytrý, studoval bych na univerzitě. “If I were smart I would study at the university.”
NameType
: type of named entity
| Values: | Com | Geo | Giv | Nat | Oth | Pro | Prs | Sur |
Classification of named entities (token-based, no nesting of entities etc.)
The feature applies mainly to the cs-pos/PROPN tag;
in multi-word foreign names, adjectives may also have this feature
(they preserve the ADJ
tag but at the same time they would not exist in Czech
otherwise than in the named entity).
Conversion from the Prague Dependency Treebank
Lemmas in PDT contain features
that also encode types of named entities. When converting the PDT annotation
to UD, these lemma features are removed and the feature
NameType
is added to the universal features to preserve the type.
The following table lists the name types together with the most frequent examples. See http://ufal.mff.cuni.cz/techrep/tr27.pdf, page 8, section 2.1 (Lemma structure) for more details.
| _;Y | given name | Jan, Jiří, Václav, Petr, Josef | “Jan, Jiří, Václav, Petr, Josef” |
| _;S | surname | Klaus, Havel, Němec, Jelcin, Svoboda | “Klaus, Havel, Němec, Yeltsin, Svoboda” |
| _;E | member of a particular nation, inhabitant of a particular territory | Němec, Čech, Srb, Američan, Slovák | “German, Czech, Serbian, American, Slovak” |
| _;G | geographical name | Praha, ČR, Evropa, Německo, Brno | “Prague, CR, Europe, Germany, Brno” |
| _;K | company, organization, institution | ODS, OSN, Sparta, ODA, Slavia | “ODS, UN, Sparta, ODA, Slavia” |
| _;R | product | LN, Mercedes, Tatra, PC, MF | “LN, Mercedes, Tatra, PC, MF” |
| _;m | other proper name: names of mines, stadiums, guerilla bases etc. | US, PVP, Prix, Rapaport, Tour | “US, PVP, Prix, Rapaport, Tour” |
Geo
: geographical name
Names of cities, countries, rivers, mountains etc.
Examples
- Praha “Prague”, Kostelec nad Černými lesy, Německo “Germany”
Prs
: name of person
This value is used if it is not known whether it is a given or a family name, but it is known that it is a personal name.
Giv
: given name of person
Given name (not family name). This is usually the first name in European and American names. In Chinese names, the last two syllables (of three) are usually the given name.
Examples
- Jan, Jiří, Václav
Sur
: surname / family name of person
Family name (surname). This is usually the last name in European and American names. In Chinese names, the first syllable (of three) is usually the surname.
Examples
- Klaus, Havel, Němec
Nat
: nationality
Name denoting a member of a particular nation, or inhabitant of a particular territory. This does not include derived adjectives, nor nouns denoting languages (both groups are written in lowercase). Thus Čech “Czech [man]” belongs here but český “Czech” and čeština “Czech [language]” do not.
Examples
- Čech “Czech”, Němec “German”, Pražan “Praguer”
Com
: company, organization
Pro
: product
Oth
: other
Names of stadiums, guerilla bases, events etc.
NumForm
: numeral form
| Values: | Digit | Roman | Word |
Feature of cardinal and ordinal numbers. Is the number expressed by digits or as a word?
Word
: number expressed as word
Examples
- jeden “one”, dva “two”, tři “three”
Digit
: number expressed using digits
Examples
- 1, 2, 3
Roman
: roman numeral
Examples
- I, II, III
NumType
: numeral type
| Values: | Card | Frac | Mult | Ord | Sets |
Czech has a complex system of numerals. For example, in the school grammar of Czech, the main part of speech is “numeral”, it includes almost everything where counting is involved and there are various subtypes. It also includes interrogative, relative, indefinite and demonstrative quantifiers (words like kolik “how many”, tolik “so many”, několik “several”), so at the same time we may have a non-empty value of PronType.
From the syntactic point of view, some numtypes behave like adjectives
and some behave like adverbs. We tag them cs-pos/ADJ and
cs-pos/ADV respectively. Thus the NumType
feature applies to
several different parts of speech:
- cs-pos/NUM: cardinal numerals
- cs-pos/DET: quantifiers
- cs-pos/ADJ: adjectival ordinal and some generic numerals
- cs-pos/ADV: adverbial (e.g. ordinal and multiplicative) numerals
Card
: cardinal number or corresponding interrogative / relative / indefinite / demonstrative word
Examples
- jeden, dva, tři “one, two, three”
- kolik “how many”
- několik “several”, mnoho “many”, málo “few”
- tolik “so many”
- čtvero, patero, desatero (specific forms of four, five, ten; they are morphologically, syntactically and stylistically distinct from the default forms čtyři, pět, deset)
Ord
: ordinal number or corresponding interrogative / relative / indefinite / demonstrative word
This is a subtype of adjective or adverb.
Adjectival examples
- první “first”; druhý “second”, třetí “third”
- kolikátý lit. how manieth “which rank”
- několikátý “some rank”
- tolikátý “this/that rank”
Adverbial examples
- poprvé “for the first time”; podruhé “for the second time”; potřetí “for the third time”
- pokolikáté “for which time”
- poněkolikáté “for x-th time”
- potolikáté “it has been so many times”
Mult
: multiplicative numeral or corresponding interrogative / relative / indefinite / demonstrative word
This is subtype of adjective or adverb.
Examples
- dvojí, trojí, čtverý (twofold, threefold, fourfold; these are morphologically and syntactically adjectives)
- jednou “once”; dvakrát “twice”; třikrát “three times”
- kolikrát “how many times”
- několikrát “several times”
- tolikrát “so many times”
Frac
: fraction
This is a subtype of cardinal numbers. It may denote a fraction or just the denominator of the fraction.
Examples
- půl / polovina “half”; třetina “one third”; čtvrt / čtvrtina “quarter”
Sets
: number of sets of things; collective numeral
Morphologically distinct class of numerals used to count sets of things, or nouns that are pluralia tantum.
Examples
- dvoje / troje boty “two / three [pairs of] shoes”; as opposed to normal cardinal numbers: dvě / tři boty “two / three shoes”
NumValue
: numeric value
| Values: | 1 | 2 | 3 |
In Czech, number “one” agrees with the counted noun in Gender, Number and Case. Number “two” agrees in gender and case and numbers “three” and “four” agree in case. These numerals behave similarly to adjectives. Numbers “five”, “six” etc. behave differently. If the case of the counted phrase is genitive, dative, locative or instrumental, the numeral agrees in case with the noun. However, if the case of the whole phrase is nominative, accusative or vocative, then the numeral dictates that the noun is in genitive. This behavior is similar to nouns modified by other nouns in genitive. (Note that this is why in the Czech PDT some numeral nodes are annotated as governing nouns instead of modifying them.) In addition, the whole phrase (number + counted noun) together behaves as neuter singular (this is important for subject-verb agreement).
Specific behavior of low-value numerals is the reason why there is a separate feature to mark these numerals.
1
: numeric value 1
- jeden, jedna, jedno “one”
2
: numeric value 2
- dva, dvě “two”
3
: numeric value 3 or 4
- tři “three”, čtyři “four”
Number
: number
| Values: | Coll | Dual | Plur | Ptan | Sing |
Number
is an inflectional feature of nouns and
other parts of speech (adjectives,
verbs) that mark agreement with nouns.
Sing
: singular number
A singular noun denotes one person, animal or thing.
Examples
- starý muž přišel “an old man came”
- mladá žena přišla “a young woman came”
- malé kuře přišlo “a small chicken came”
Plur
: plural number
A plural noun denotes several persons, animals or things.
Examples
- staří muži přišli “old men came”
- mladé ženy přišly “young women came”
- malá kuřata přišla “small chickens came”
Dual
: dual number
A dual noun denotes two objects. The dual number has almost vanished from Czech with the exception of special instrumental case suffixes for body parts that occur in pairs, and any adjectives that modify them.
Examples
The noun noha means either “leg” of a human, or of a table. Dual is used for the former and plural for the latter:
- holka s dlouhýma nohama “a girl with long legs”
- stůl s dlouhými nohami “a table with long legs”
The numeral sto “hundred” has also a special form of plural that is actually the dual:
- dvě stě “two hundred”
- tři sta “three hundred”
Ptan
: plurale tantum
Some nouns appear only in the plural form even though they denote one
thing (semantic singular); some tagsets mark this distinction.
Grammatically they behave like plurals, so Plur
is obviously the
back-off value here; however, the
non-existence of singular form sometimes means that the gender is
unknown. In Czech, special type of numerals is used when counting
nouns that are plurale tantum (NumType=Sets
).
Examples
- nůžky, kalhoty “scissors, pants”
Coll
: collective / mass / singulare tantum
Collective or mass or singulare tantum is a special case of singular. It applies to words that use grammatical singular to describe sets of objects, i.e. semantic plural. Although in theory they might be able to form plural, in practice it would be rarely semantically plausible. Sometimes, the plural form exists and means “several sorts of” or “several packages of”.
Examples
- lidstvo “mankind”
Diffs
Prague Dependency Treebank
The PDT tagset does not distinguish Ptan
from Plur
and Coll
from Sing
,
therefore this distinction is not being made in the converted data.
Number[psor]
: possessor’s number
| Values: | Plur | Sing |
Possessives may have two different numbers: that of the possessed object (number
agreement with modified noun) and that of the possessor. The
Number[psor]
feature captures the possessor’s number.
Sing
: singular possessor
Examples
- můj pes “my dog”
Number[psor]=Sing|Number=Sing
- mí psi “my dogs”
Number[psor]=Sing|Number=Plur
Plur
: plural possessor
Examples
- náš pes “our dog”
Number[psor]=Plur|Number=Sing
- naši psi “our dogs”
Number[psor]=Plur|Number=Plur
Person
: person
| Values: | 1 | 2 | 3 |
Person is a feature of personal and possessive pronouns, and of verbs. On verbs it is in fact an agreement feature that marks the person of the verb’s subject. Person marked on verbs makes it unnecessary to always add a personal pronoun as subject and thus subjects are sometimes dropped (Czech is a pro-drop language).
1
: first person
In singular, the first person refers just to the speaker / author. In plural, it must include the speaker and one or more additional persons.
Examples
- dělám “I do”
- děláme “we do”
2
: second person
In singular, the second person refers to the addressee of the utterance / text. In plural, it may mean several addressees and optionally some third persons too.
Examples
- děláš “you.
Sing
do” - děláte “you.
Plur
do”
3
: third person
The third person refers to one or more persons that are neither speakers nor addressees.
Examples
- dělá “he/she/it does”
- dělají “they do”
Polarity
: whether the word can be or is negated
| Values: | Neg | Pos |
In Czech, negation is mostly done using the bound morpheme ne-, and an independent negating particle (equivalent to English “not”) is rarely seen. Words that can take the morpheme of negation have the feature of polarity.
It applies to verbs,
adjectives, sometimes also adverbs and
even nouns.
(Most nouns have just Polarity=Pos
;
deverbative nouns can have also Polarity=Neg
.
Occasionally, other nouns can be negated to denote the complement of the original noun:
plavec “swimmer” vs. neplavec “non-swimmer”.)
Note that Polarity=Neg
is not the same thing as
PronType=Neg
. For pronouns and other pronominal parts of speech
there is no such binary opposition as for verbs and adjectives. (There
is no such thing as “affirmative pronoun”.)
Pos
: positive, affirmative
Examples
- přišel “he came”
- rozumný “wise”
- pěkně “nicely”
- přijetí “acceptance”
Neg
: negative
Examples
- nepřišel “he did not come”
- nerozumný “unwise”
- nepěkně “nastily”
- nepřijetí “non-acceptance, rejection”
Poss
: possessive
| Values: | Yes |
Boolean feature of pronouns, determiners or adjectives. It tells whether the word is possessive.
While many tagsets would have “possessive” as one of the various pronoun types, this feature is intentionally separate from PronType, as it is orthogonal to pronominal types. Several of the pronominal types can be optionally possessive, and adjectives can too.
Yes
: it is possessive
Note that there is no No
value. If the word is not possessive, the
Poss
feature will just not be mentioned in the FEAT
column. (Which
means that empty value has the No
meaning.)
Examples
- possessive personal determiners: můj, tvůj, jeho, její, náš, váš, jejich “my, your, his, her, our, your, their”
- possessive reflexive determiner: svůj “one’s own”
- possessive relative determiner: jehož “whose”
- possessive adjectives: otcův “father’s”, matčin “mother’s”
PrepCase
: case form sensitive to prepositions
| Values: | Npr | Pre |
Some personal pronouns have different forms depending on whether they are objects of prepositions or not.
Default empty value means that the word form is neutral w.r.t. prepositions.
Npr
: non-prepositional case
This word form must not be used after a preposition.
Examples
- jeho, jemu, jím “him” (
Gen,Acc
,Dat
,Ins
)
Pre
: prepositional case
This word form must be used after a preposition.
Examples
- něho, němu, něm, ním “him” (
Gen,Acc
,Dat
,Loc
,Ins
)
PronType
: pronominal type
| Values: | Dem | Emp | Ind | Int | Neg | Prs | Rel | Tot |
This feature typically applies to pronouns, determiners, pronominal numerals (quantifiers) and pronominal adverbs.
Prs
: personal or possessive personal pronoun or determiner
See also the Poss feature that distinguishes normal personal
pronouns from possessives. Note that Prs
also includes reflexive
personal/possessive pronouns (e.g. se / svůj; see the
Reflex feature).

Examples:
- kdo “who”
- co “what”
- jaký “what kind of”
- který “which”
- čí “whose”
- kolik “how many”
- kolikátý “how-maniest” (ordinal number)
- kolikrát “how many times”
- kde “where”
- kam “where to”
- kdy “when”
- jak “how”
- proč “why”
Rel
: relative pronoun, determiner, numeral or adverb
Note that this class heavily overlaps with interrogatives, yet there are pronouns that are only relative.
Examples:
- jenž, což “which, that” (relative but not interrogative pronouns)
- jehož “whose” (possessive relative pronoun)
Dem
: demonstrative pronoun, determiner, numeral or adverb
These are to some extent parallel to interrogatives.
Examples
- tento “this”
- tamten “that”
- takový “such”
- týž “same”
- tolik “so many”
- tolikátý “so-maniest” (ordinal number)
- tolikrát “so many times”
- tady “here”
- tam “there”
- teď “now”
- tehdy “then”
- tak “so”
Emp
: emphatic determiner
Emphatic pro-adjectives (determiners) emphasize the nominal they depend on. There are similarities with reflexive and demonstrative pronouns / determiners.
Examples
- sám as in Sám prezident se přišel podívat. “President himself came to see it.”
Tot
: total (collective) pronoun, determiner or adverb
Examples
- každý “every, everybody, everyone, each”
- všechno “everything, all”
- všude “everywhere”
- vždy “always”
Neg
: negative pronoun, determiner or adverb
Examples
- nikdo “nobody”
- nic “nothing”
- nijaký “no (kind)”
- ničí “no one’s”
- žádný “no, none”
- nikde “nowhere”
- nikam “(to) nowhere”
- nikdy “never”
- nijak “no way” (lit. “no-how”)
Ind
: indefinite pronoun, determiner, numeral or adverb
Examples
- někdo “somebody”; kdokoli “anybody”; málokdo “few people”; leckdo “quite a few people”; kdosi “somebody”
- něco “something”; cokoli “anything”; máloco “few things”; lecco “quite a few things”; cosi “something”
- nějaký “some kind of”; jakýkoli “any kind of”; lecjaký “just any”; jakýsi “some, certain”
- některý “some”; kterýkoli “any”; málokterý “few”; leckterý “quite a few”; kterýsi “some”
- něčí “someone’s”; číkoli “anyone’s”; lecčí “of quite a few people”; čísi “someone’s”
- několik “several”; málo “few”; mnoho “many”
- několikátý “severalth” (indefinite ordinal numeral)
- několikrát “several times”
- někde “somewhere”; kdekoli “anywhere”; málokde “few places”; leckde “quite a few places”; kdesi “somewhere”
- někam “(to) somewhere”; kamkoli “(to) anywhere”; kamsi “(to) somewhere”
- někdy “sometimes”; kdykoli “anytime”; málokdy “few times”; leckdy “quite a few times”; kdysi “once (long ago)”
- nějak “somehow”; jakkoli “anyhow”; lecjak “quite a few ways”; jaksi “somehow”
Reflex
: reflexive
| Values: | Yes |
Boolean feature of pronouns or determiners. It tells whether the word is reflexive, i.e. refers to the subject of its clause.
In Czech, reflexive pronouns have various functions:
- Reflexive object of a verb means that the object is the same entity as the subject: Jan si koupil auto = “Jan bought himself a car” vs. Jan mu koupil auto = “Jan bought him [someone else] a car”
- Reflexive object of a verb in plural may also indicate a reciprocal action. This usage of the reflexive pronoun is translated to English as “each other”. Unlike e.g. German, Czech does not have a special reciprocal pronoun and the reflexive pronoun is used instead: Jan a Marie se milují = “Jan and Mary love each other”
- Reflexive pronoun in a subjectless clause constitutes so-called reflexive passive: To se napíše zítra (reflexive passive, the verb is morphologically in active form) vs. To bude napsáno zítra (normal passive, with auxiliary finite verb and a passive participle) “That will be written tomorrow”
- Some verbs are mandatorily reflexive, i.e. they never occur without the reflexive pronoun. The pronoun does not alter the meaning in any way, but without it the sentence would not be grammatical: Jan se směje “Jan laughs”
Reflexive possessives indicate that the subject of the clause is the possessor:
- Jan prodal své auto. “Jan sold his [own] car.”
- Jan prodal jeho auto. “Jan sold his [someone else’s] car.”
Yes
: it is possessive
Note that there is no No
value. If the word is not reflexive, the
Reflex
feature will just not be mentioned in the FEAT
column. (Which means that empty value has the No
meaning.)
Examples
- reflexive personal pronouns: se, si, sebe, sobě, sebou (occurs in various cases but not in nominative and vocative; does not distinguish Number)
- reflexive possessive determiner: svůj
Style
: style or sublanguage to which this word form belongs
| Values: | Arch | Coll | Expr | Rare | Slng | Vrnc | Vulg |
This may be a lexical feature (some words-lemmas are archaic, some are colloquial) or a morphological feature (inflectional patterns may systematically change between dialects or styles).
Arch
: archaic, obsolete
Examples
- biblí, bukův, dubův, činějí
Rare
: rare
- causa (rare spelling)
- kauza (neutral spelling)
Coll
: colloquial
Examples
- normal paradigm of hard adjectives: mladý, mladého, mladému, mladém, mladým, mladí, mladých, mladým, mladé, mladými “young”
- colloquial paradigm of hard adjectives: mladej, mladýho, mladýmu, mladým, mladým, mladý, mladejch, mladejm, mladý, mladejma “young”
Vrnc
: vernacular
- kotáry, žehnáčky, husličky, nesúdí
Slng
: slang
- kšeft, záklaďák, čtvrtkařka, štafl
Expr
: expressive, emotional
Typical examples of expressive words in the Czech data are diminutives:
- jezírko, okénko, semínko, hovínko, miminko
Vulg
: vulgar
- parchant, suchoprd, bordel, nablito
Tense
: tense
| Values: | Fut | Past | Pres |
Tense is a feature that specifies the time when the action took / takes / will take place, in relation to the current moment or to another action in the utterance.
Past
: past tense
The past tense denotes actions that happened before the current moment. Past tense in Czech consists of the past participle (also called active participle or l-participle), which is accompanied by a present auxiliary verb in the first and second persons, and stands alone in the third person.
The auxiliary (if any) is in its present form, so it will have Tense=Pres
.
The participle has Tense=Past
, even though it can also be used to form present conditional.
Examples
- Šel jsem domů. “I have gone home.”
- Šel jsi domů. “You have gone home.”
- Šel domů. “He has gone home.”
Pres
: present tense
The present tense denotes actions that are happening right now or that usually happen.
Note that morphologically present forms of perfective verbs have actually a future meaning
but they will still be marked Tense=Pres
.
Examples
- Přicházím domů. “I come / am coming home.” (Přicházet is an imperfective verb.)
- Přijdu domů. “I will come home.” (Přijít is a perfective verb.)
- Jdu domů. “I go / am going home.” (Jít is an imperfective verb.)
Fut
: future tense
The future tense denotes actions that will happen after the current moment. Future tense in Czech is formed in one of three ways, depending of the verb:
- Present forms of perfective verbs have future meaning. These forms are tagged
Tense=Pres
, notTense=Fut
(see above). - The verb být “to be” has a set of distinct future forms. They combine a future stem bud with present suffixes.
A small set of verbs (mostly motion verbs) have also future forms. These are formed as the present form (present stem and suffix)
with the prefix po-.
Although these forms are morphologically very close to the present forms, they are tagged
Tense=Fut
because the same lemma has also present forms and the feature must distinguish the two. - The remaining imperfective verbs have periphrastic future forms, consiting of the future form of the auxiliary být,
and the infinitive of the content verb. Only the auxiliary will have
Tense=Fut
, while there will be no tense information at the infinitive.
Examples
- Půjdu domů. “I will go home.” (Jít is an imperfective verb, phonological rule transformed the prefix po- to pů-.)
- Budu přicházet domů. “I will be coming home.” (Přicházet is an imperfective verb and it forms future periphrastically.)
Variant
: alternative form of word
| Values: | Short |
Sometimes there are multiple word forms for the same lemma and set of features.
The Variant
feature helps distinguish alternate forms.
In Czech there are two groups of words where double forms are regular and worth capturing:
short forms of adjectives and short (clitic) forms of personal pronouns.
This feature only marks the non-standard short forms, hence there is only one value, Short
.
For the long standard forms the Variant
feature remains unspecified.
Short
: short form of adjectives
The short form is called nominal form of adjective (jmenný tvar přídavného jména), as opposed to the long form, which is pronominal because it originated as a combination of a nominal form and a personal pronoun. But this is ancient history of the language. In modern Czech, only a subset of the nominal forms survive, and using them sometimes sounds slightly archaic. They are used as nominal predicates with copula, but they do not appear as premodifiers of nouns. The pronominal forms are considered standard, except for two frequent adjectives that do not have them: třeba, rád.
Examples
- možno “possible”, schopen “able”, nutno “necessary”, znám “known”, spokojen “satisfied”, povinen “supposed to”, ochoten “willing”, jist “sure”, vědom “knowing”, přítomen “present”, roven “equal”, patrno “apparent”, hotov “finished”, spjat “connected”, vinen “guilty”
- Long equivalents: možné, schopný, nutné, známý, spokojený, povinný, ochotný, jistý, vědomý, přítomný, rovný, patrné, hotový, spjatý, vinný
Short
: short (clitic) form of personal pronouns
Some personal pronouns in dative and accusative Case have double forms. The normal (long) form is more independent in terms of positions it can take in word order. The short forms are clitics (http://cs.wikipedia.org/wiki/P%C5%99%C3%ADklonka). They are separate words (unlike in some other languages) but in the word order they usually stick to the second position.
- mi, mě, ti, tě, mu, ho, si, se
- mně, mne, tobě, tebe, jemu, jeho, sobě, sebe
- “me, me, you, you, him, him, oneself, oneself”
VerbForm
: form of verb or deverbative
| Values: | Conv | Fin | Inf | Part |
Even though the name of the feature seems to suggest that it is used
exclusively with verbs, it is not the case.
The Part
value can be used also with adjectives.
It distinguishes participles from other verb forms,
and participial adjectives from other adjectives.
Fin
: finite verb
Rule of thumb: if it has non-empty Mood, it is finite. In Czech this applies to indicative and imperative forms, and to the special conditional forms of the auxiliary verb být.
 It is also used with the auxiliary být to form periphrastic future tense, and it appears as the argument of modal and other verbs.
Examples
- nést “to carry”
- být “to be”
Part
: participle
Participle is a non-finite verb form that shares properties of verbs and adjectives. Czech has two types of participles:
- The past participle (also called active participle or l-participle) is used to form the past tense, and the conditional mood in present or past tense.
- The passive participle is used to form the passive voice (in any tense or mood).
Participles inflect for Gender and Number but not for Person.
Examples
- nesl, nesla, neslo, nesli, nesly “carried” (past participle in different genders and numbers)
- nesen, nesena, neseno, neseni, neseny “carried” (passive participle in different genders and numbers)
- byl, byla, bylo, byli, byly “was/been” (past participle in different genders and numbers)
Conv
: converb, transgressive
The converb, also called transgressive, adverbial participle or gerund, is a non-finite verb form that shares properties of verbs and adverbs.
Imperfective verbs form present converb, meaning “while doing”.
Perfective verbs form past converb, meaning “having done”.
Examples
- nesa, nesouc, nesouce “carrying” (present transgressive in different genders and numbers)
- přines, přinesši, přinesše “having brought” (past transgressive in different genders and numbers)
- jsa, jsouc, jsouce “being” (present transgressive in different genders and numbers)
- byv, byvši, byvše “having been” (past transgressive in different genders and numbers)
- zírali na mne, pevně svírajíce své zbraně “they stared at me while gripping their guns firmly”
- udělavši večeři, zavolala rodinu ke stolu “having prepared the dinner, she called her family to the table”
Voice
: voice
| Values: | Act | Pass |
Voice is a feature of verbs that helps map the traditional syntactic functions, such as subject and object, to semantic roles, such as agent and patient.
Act
: active voice
The subject of the verb is the doer of the action (agent), the object is affected by the action (patient).
All finite verb forms and the active/past participles are tagged Voice=Act
.
Examples
- Napadli jsme nepřítele. “We attacked the enemy” (the active participle napadli can be used to form either past tense or conditional mood; here it forms the past tense.)
Pass
: passive voice
The subject of the verb is affected by the action (patient). The doer (agent) is either unexpressed or it appears as an object of the verb.
Only the passive participle is tagged Voice=Pass
.
Examples
- Jsme napadeni nepřítelem. “We are attacked by the enemy” (the passive participle napadeni is used to form passive in all tenses; here it forms the present passive.)


The Khelo India programme has been introduced to revive the sports culture in India at the grass-root level by building a strong framework for all sports played in our country and establish India as a great sporting nation.Talented players identified in priority sports disciplines at various levels by the High-Powered Committee will be provided annual financial assistance of INR 5 lakh per annum for 8 years
Read More‘Seva Bhoj Yojna’ is a Central Sector Scheme of the Ministry of Culture, Government of India. It envisages to reimburse the Central Government share of Central Goods and Services Tax (CGST) and Integrated Goods and Service Tax (IGST) so as to lessen the financial burden of such Charitable Religious Institutions who provide Food/Prasad/Langar (Community Kitchen)/Bhandara free of cost without any discrimination to Public/Devotees.
Read MoreRashtriya Sanskriti Mahotsav is the flagship festival of the Ministry of Culture.It has been organized since 2015 with the active participation of Seven Zonal Culture Centres.It was started with an intention to exhibit the rich cultural heritage of the country in all its rich and varied dimensions namely, Handicrafts, Cuisine, Painting, Sculpture and Performing Arts- Folk, Tribal, Classical and Contemporary- all at one place.
Read MoreIncredible India (styled as Incredıble!ndıa) is the name of an international tourism campaign maintained by the Government of India since 2002, to promote tourism in India. The "Incredible India" title was officially branded and promoted since 2002
Read More


Sqlmap Overview
A quick presentation of sqlmap, a popular SQL injection tool
Sqlmap is an open source SQL injection tool that automates the process of testing and exploiting SQL injection vulnerabilities. The tool being developed in Python, you can use it on any operating system as long as you have a Python interpreter.
Supported DBMS
Sqlmap supports a wide range of DBMS. At the time of this writing it fully supports the following databases: MySQL, Oracle, PostgreSQL, Microsoft SQL Server, Microsoft Access, IBM DB2, SQLite, Firebird, Sybase and SAP MaxDB.
Sqlmap Features
From detection to exploit, sqlmap has rich features for penetration testers.
Supported techniques
Sqlmap supports different attack vectors. The most known functionalities are classic SQL injection techniques such as UNION query and stacked queries attacks. But sqlmap is also capable of retrieving data using a different channel and supports out-of-band attacks. Finally, this SQL injection tool can also achieve error-based, boolean-based blind and time-based blind SQL injections.
Enumeration
One key functionality of sqlmap is its capacity to enumerate automatically database information. Precious data such as users, roles, privileges, databases, tables, columns and even password hashes can be retrieved from a simple command. It also combines the automatic recognition of password hash format. It is even possible for the tester to automatically launch a dictionary based attack to crack them.
Attacks
Pretty handy functionalities are offered by sqlmap. To name a few, you can :
Execute arbitrary commands and retrieve their outpout (MySQL, PostgreSQL and SQL Server).
Establish a TCP connection between the testers’ machine and the database server.
Download or upload any file from the database server (MySQL, PostgreSQL and SQL Server).
Escalate privilege by launching Metasploit’s Meterpreter getsystem command.
To get the extended list of features refer to sqlmap features page.
Limitation
Like every sql injection scanner, sqlmap will be somehow limited in terms of finding vulnerabilities. On the other, side its enumeration functionalities are really powerful. In addition, attack features will allows the attacker to exploit most systems with a sql injection flaw.
Getting Started
After downloading sqlmap, take a quick look at our sqlmap tutorial to get started!


| Time | Sender | Message |
|---|---|---|
| 1:00 AM | Jim Brown | Go Browns!!! |
| 2:00 AM | Mickey Mouse | Minnie is goofy looney. |
| 3:00 AM | Tom Brady | I like having six. |
| 4:00 AM | The Most Interesting Man | Beer me. |
| 5:00 AM | Bob Marley | Bag me. |
| 6:00 AM | Slim Shady | I'd walk 8 miles, then 8 miles more. |
| 7:00 AM | Tom Hanks | Run, Private Ryan, Run! |
| 8:00 AM | Thomas the Tank Engine | Diesel can't touch this caboose. |
| 9:00 AM | Mr. Incredible | I am incredible. |
| 10:00 AM | Lady Gaga | *noises weakly resembling a musical score* |


The African National Congress and its allies do much better "under siege", said the party's secretary-general Gwede Mantashe in Pretoria on Tuesday.
"The most important thing about the new leadership of the ANC is that it is the most scrutinised one, that makes us more stronger because we get different views and we get to know what people want."
He said the possible breakaway party being formed did not keep them awake at night: "But what keeps us awake is our ability to deliver to the people of this country."
Speaking during a discussion on the country's political landscape, Mantashe said the ANC's campaign for next year's elections would be based on its programme, not on a defence against the splinter group.
"We remain focused, embarking on moral regeneration and targeting the young professionals, black and white."
He said support for the ANC in white communities had increased substantially.
ANC members attending the upcoming convention held by party dissidents would be indicating their intention to join the new movement.
He said former president Thabo Mbeki had written to the party to say he did not bless the initiative: "I believe what he said in that letter, and I have no reason not to believe him, because he showed his loyalty to the ANC by stepping down with dignity when he was asked to do so by the party." - Sapa


head: "blocks/multiple.html"
header: "blocks/multiple.html"
header: "append-block-multiple.html"
body: "blocks/multiple.html"
body: "append-block-multiple.html"
footer: "blocks/multiple.html"
footer: "append-block-multiple.html"
foot: "blocks/multiple.html"


Class FlatSDKInternal.IRenderer
Global Objects for rendering
Inheritance
System.Object
FlatSDKInternal.IRenderer
Inherited Members
System.Object.ToString()
System.Object.Equals(System.Object)
System.Object.Equals(System.Object, System.Object)
System.Object.ReferenceEquals(System.Object, System.Object)
System.Object.GetHashCode()
System.Object.GetType()
System.Object.MemberwiseClone()
Assembly: FlatPMSDK.dll
Syntax
public static class IRenderer
Fields
_black
Declaration
public static SolidBrush _black
Field Value
| Type |
Description |
| SolidBrush |
|
_blackOpaq
Declaration
public static SolidBrush _blackOpaq
Field Value
| Type |
Description |
| SolidBrush |
|
_blue
Declaration
public static SolidBrush _blue
Field Value
| Type |
Description |
| SolidBrush |
|
_cyan
Declaration
public static SolidBrush _cyan
Field Value
| Type |
Description |
| SolidBrush |
|
_fcustom
Declaration
public static Font _fcustom
Field Value
_font
Declaration
Field Value
_fontsmall
Declaration
public static Font _fontsmall
Field Value
_graphics
Declaration
public static Graphics _graphics
Field Value
| Type |
Description |
| Graphics |
|
_gray
Declaration
public static SolidBrush _gray
Field Value
| Type |
Description |
| SolidBrush |
|
_grayOpaq
Declaration
public static SolidBrush _grayOpaq
Field Value
| Type |
Description |
| SolidBrush |
|
_grayOpaq2
Declaration
public static SolidBrush _grayOpaq2
Field Value
| Type |
Description |
| SolidBrush |
|
_green
Declaration
public static SolidBrush _green
Field Value
| Type |
Description |
| SolidBrush |
|
_opakwhite
Declaration
public static SolidBrush _opakwhite
Field Value
| Type |
Description |
| SolidBrush |
|
_orange
Declaration
public static SolidBrush _orange
Field Value
| Type |
Description |
| SolidBrush |
|
_pink
Declaration
public static SolidBrush _pink
Field Value
| Type |
Description |
| SolidBrush |
|
_red
Declaration
public static SolidBrush _red
Field Value
| Type |
Description |
| SolidBrush |
|
_trans
Declaration
public static SolidBrush _trans
Field Value
| Type |
Description |
| SolidBrush |
|
_white
Declaration
public static SolidBrush _white
Field Value
| Type |
Description |
| SolidBrush |
|
_yellow
Declaration
public static SolidBrush _yellow
Field Value
| Type |
Description |
| SolidBrush |
|


| Sweep Type
selects either continuous AC Sweeps or a single Slow Sweep. The AC
Sweep runs continuously at 60Hz but data is only collected when the Run
Sweep button is clicked. The Slow Sweep runs once when Run Sweep is
clicked. The slow sweep values are entered in the Slow Sweep Area. Run Sweep initiates acquisition of a trace by sending the SWP command to Mini_CT. Received data is calibrated, saved and plotted. Live starts a background timer that runs at the Rate setting. The timer initiates a Run Sweep at each timer interval. Avg 8 runs eight sweeps and averages the values. Use this to lower noise. Filter On enables a running averaging filter that is applied to the incoming data. The filter is a three point average of adjacent data points. AC Waveforms are selected by clicking on the desired waveform type. The selected type is highlighted in blue and the text is shown below the selection image. When waveform is clicked, the WAV command is sent to Mini_CT. Main |


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Install the Bit-Player Launcher extension
Please wait...
Developer? I'll be glad for some help :)
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https://n0where.net/understanding-the-ldap
Ports
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Modules | |
| USB CDC Specification Release Numbers | |
| This section list the CDC Spec. | |
| CDC Descriptor Types | |
| This section lists CDC descriptor types. | |
| CDC Descriptor Subtypes | |
| This section lists CDC descriptor sub types. | |
| USB CDC Device Descriptor Values | |
| This section lists the values for CDC Device Descriptor. | |
| USB CDC Communication Interface Descriptor | |
| This section lists the values for CDC Communication Interface Descriptor. | |
| USB CDC Data Interface Values | |
| This section lists the values for CDC Data Interface Descriptor. | |
| USB CDC CallManagement Capabilities | |
| This section lists CDC CallManagement Capabilities. | |
| USB CDC ACM Capabilities | |
| This section lists the capabilities of the CDC ACM. | |
| CDC SerialState bits | |
| This page lists the bit map for CDC Serial States. | |
| USB CDC Request Codes | |
| This section lists USB CDC Request Codes. | |
| USB CDC ControlLineState bitmap | |
| This section lists CDC ControlLineState bitmap. | |
| USB CDC LineCoding StopBits | |
| This section lists Stop Bits for CDC Line Coding. | |
| USB CDC LineCoding ParityCheckings | |
| This section lists Parity checkings for CDC Line Coding. | |
Classes | |
| struct | _CDCHeaderDescriptor |
| struct | _CDCUnionDescriptor |
| struct | _CDCCallManagementDescriptor |
| struct | _CDCAbstractControlManagementDescriptor |
| struct | _CDCSerialState |
| USB CDC SerialState struct (bitmap) More... | |
| struct | _CDCLineCoding |
Typedefs | |
| typedef struct _CDCHeaderDescriptor | CDCHeaderDescriptor |
| Marks the beginning of the concatenated set of functional descriptors for the interface. More... | |
| typedef struct _CDCUnionDescriptor | CDCUnionDescriptor |
| Describes the relationship between a group of interfaces that can be considered to form a functional unit. More... | |
| typedef struct _CDCCallManagementDescriptor | CDCCallManagementDescriptor |
| Describes the processing of calls for the communication class interface. More... | |
| typedef struct _CDCAbstractControlManagementDescriptor | CDCAbstractControlManagementDescriptor |
| Describes the command supported by the communication interface class with the Abstract Control Model subclass code. More... | |
| typedef struct _CDCSerialState | CDCSerialState |
| USB CDC SerialState struct (bitmap) More... | |
| typedef struct _CDCLineCoding | CDCLineCoding |
| Format of the data returned when a GetLineCoding request is received. More... | |
Functions | |
| void | CDCLineCoding_Initialize (CDCLineCoding *lineCoding, uint32_t bitrate, uint8_t stopbits, uint8_t parity, uint8_t databits) |
| Initializes the bitrate, number of stop bits, parity checking and number of data bits of a CDCLineCoding object. More... | |
| uint8_t | CDCSetControlLineStateRequest_IsDtePresent (const USBGenericRequest *request) |
| Notifies if the given request indicates that the DTE signal is present. More... | |
| uint8_t | CDCSetControlLineStateRequest_ActivateCarrier (const USBGenericRequest *request) |
| Notifies if the given request indicates that the device carrier should be activated. More... | |
USB CDC Subclass IDs | |
| #define | CDC_SUBCLASS_DLCM 0x01 |
| Direct Line Control Model. More... | |
| #define | CDC_SUBCLASS_ACM 0x02 |
| Abstract Control Model. More... | |
| #define | CDC_SUBCLASS_TCM 0x03 |
| Telephone Control Model. More... | |
| #define | CDC_SUBCLASS_MCCM 0x04 |
| Multi-Channel Control Model. More... | |
| #define | CDC_SUBCLASS_CCM 0x05 |
| CAPI Control Model. More... | |
| #define | CDC_SUBCLASS_ETH 0x06 |
| Ethernet Networking Control Model. More... | |
| #define | CDC_SUBCLASS_ATM 0x07 |
| ATM Networking Control Model. More... | |
USB CDC Communication Interface Protocol IDs | |
| #define | CDC_PROTOCOL_V25TER 0x01 |
| Common AT commands. More... | |
USB CDC Data Interface Protocol IDs | |
| #define | CDC_PROTOCOL_I430 0x30 |
| ISDN BRI. More... | |
| #define | CDC_PROTOCOL_HDLC 0x31 |
| HDLC. More... | |
| #define | CDC_PROTOCOL_TRANS 0x32 |
| Transparent. More... | |
| #define | CDC_PROTOCOL_Q921M 0x50 |
| Q.921 management protocol. More... | |
| #define | CDC_PROTOCOL_Q921 0x51 |
| Q.931 [sic] Data link protocol. More... | |
| #define | CDC_PROTOCOL_Q921TM 0x52 |
| Q.921 TEI-multiplexor. More... | |
| #define | CDC_PROTOCOL_V42BIS 0x90 |
| Data compression procedures. More... | |
| #define | CDC_PROTOCOL_Q931 0x91 |
| Euro-ISDN protocol control. More... | |
| #define | CDC_PROTOCOL_V120 0x92 |
| V.24 rate adaption to ISDN. More... | |
| #define | CDC_PROTOCOL_CAPI20 0x93 |
| CAPI Commands. More... | |
| #define | CDC_PROTOCOL_HOST 0xFD |
| Host based driver. More... | |
| #define | CDC_PROTOCOL_PUFD 0xFE |
| Describes the Protocol Unit Functional Descriptors [sic] on Communication Class Interface. More... | |
| #define CDC_PROTOCOL_CAPI20 0x93 |
CAPI Commands.
| #define CDC_PROTOCOL_HDLC 0x31 |
HDLC.
| #define CDC_PROTOCOL_HOST 0xFD |
Host based driver.
| #define CDC_PROTOCOL_I430 0x30 |
ISDN BRI.
| #define CDC_PROTOCOL_PUFD 0xFE |
Describes the Protocol Unit Functional Descriptors [sic] on Communication Class Interface.
| #define CDC_PROTOCOL_Q921 0x51 |
Q.931 [sic] Data link protocol.
| #define CDC_PROTOCOL_Q921M 0x50 |
Q.921 management protocol.
| #define CDC_PROTOCOL_Q921TM 0x52 |
Q.921 TEI-multiplexor.
| #define CDC_PROTOCOL_Q931 0x91 |
Euro-ISDN protocol control.
| #define CDC_PROTOCOL_TRANS 0x32 |
Transparent.
| #define CDC_PROTOCOL_V120 0x92 |
V.24 rate adaption to ISDN.
| #define CDC_PROTOCOL_V25TER 0x01 |
Common AT commands.
| #define CDC_PROTOCOL_V42BIS 0x90 |
Data compression procedures.
| #define CDC_SUBCLASS_ACM 0x02 |
Abstract Control Model.
| #define CDC_SUBCLASS_ATM 0x07 |
ATM Networking Control Model.
| #define CDC_SUBCLASS_CCM 0x05 |
CAPI Control Model.
| #define CDC_SUBCLASS_DLCM 0x01 |
Direct Line Control Model.
| #define CDC_SUBCLASS_ETH 0x06 |
Ethernet Networking Control Model.
| #define CDC_SUBCLASS_MCCM 0x04 |
Multi-Channel Control Model.
| #define CDC_SUBCLASS_TCM 0x03 |
Telephone Control Model.
Describes the command supported by the communication interface class with the Abstract Control Model subclass code.
Describes the processing of calls for the communication class interface.
Marks the beginning of the concatenated set of functional descriptors for the interface.
Format of the data returned when a GetLineCoding request is received.
| typedef struct _CDCSerialState CDCSerialState |
USB CDC SerialState struct (bitmap)
Describes the relationship between a group of interfaces that can be considered to form a functional unit.
| void CDCLineCoding_Initialize | ( | CDCLineCoding * | lineCoding, |
| uint32_t | bitrate, | ||
| uint8_t | stopbits, | ||
| uint8_t | parity, | ||
| uint8_t | databits | ||
| ) |
Initializes the bitrate, number of stop bits, parity checking and number of data bits of a CDCLineCoding object.
| lineCoding | Pointer to a CDCLineCoding instance. |
| bitrate | Bitrate of the virtual COM connection. |
| stopbits | Number of stop bits (USB CDC LineCoding StopBits CDC LineCoding StopBits). |
| parity | Parity check type (USB CDC LineCoding ParityCheckings CDC LineCoding ParityChecking). |
| databits | Number of data bits. |
| uint8_t CDCSetControlLineStateRequest_ActivateCarrier | ( | const USBGenericRequest * | request | ) |
Notifies if the given request indicates that the device carrier should be activated.
| request | Pointer to a USBGenericRequest instance. |
| uint8_t CDCSetControlLineStateRequest_IsDtePresent | ( | const USBGenericRequest * | request | ) |
Notifies if the given request indicates that the DTE signal is present.
| request | Pointer to a USBGenericRequest instance. |


|
Peak
to average power ratio reduction of OFDM signals using
Peak Reduction Carriers
|
E.
Lawrey, C. J. Kikkert
Electrical and Computer Engineering
James Cook University, Douglas Campus, Townsville, Queensland,
4814, Australia
Email<EMAIL_ADDRESS><EMAIL_ADDRESS>|
Published: Fifth
International Symposium on Signal Processing and its
Applications,
ISSPA ’99, Brisbane, Australia, 22-25 August, 1999
Organised by the Signal Processing Research Centre,
QUT,
Brisbane, Australia
|
Abstract
A technique is described for a better than 5 dB reduction in
the Peak to Average Power Ratio (PAPR) of an OFDM signal. The
optimal amplitude and phase of additional peak reduction carriers
(PRC) is obtained using a code-book, which is obtained using
a search of all possible signal combinations.
1 Introduction
Wireless digital communications is rapidly expanding, resulting
in a demand for wireless systems that are reliable and have
a high spectral efficiency. Orthogonal Frequency Division Multiplexing
(OFDM) has a high tolerance to multipath signals and is spectrally
efficient making it a good candidate for future wireless communication
systems.
One disadvantage of OFDM is that the peak of the signal can
be up to N times the average power (where N is the number
of carriers). These large peaks increase the amount of intermodulation
distortion resulting in an increase in the error rate. The
average signal power must be kept low in order to prevent
the transmitter amplifier limiting. Minimising the PAPR allows
a higher average power to be transmitted for a fixed peak
power, improving the overall signal to noise ratio at the
receiver. It is therefore important to minimise the PAPR.
A large amount of research has been done on broadcast OFDM
systems, however most wireless communication systems must
support multiple users. One application that a multi-user
OFDM system would be suitable for is fixed wireless telephony
applications. In such a system each user is allocated a small
percentage of the system carriers, typically 4-16 depending
on the symbol rate used. This paper presents a method for
reducing the PAPR of a OFDM signal that contains data. It
is optimised for a low number of carriers making it a suitable
technique for multi-user OFDM.
2 Background
The PAPR of an OFDM signal can be reduced in several ways. Selective
mapping [1, 2] involves generating a large set of data vectors
all representing the same information. The data vector with
the lowest resulting PAPR is selected. Information about which
particular data vector was used is sent as additional carriers.
However there may be potential problems with decoding the signal
in the presence of noise with selective mapping. Errors in the
reverse mapping would result in the data of whole symbols being
lost.
Another technique similar to selective mapping is using Golay
sequences [6]. Information is transmitted by mapping each
data word with a Golay sequence. Using Golay sequences result
in a low PAPR, typically 3-6 dB, however the coding rate is
poor, typically half, resulting in a large bandwidth increase.
Cyclic coding [3] involves adding extra carriers in which
the phase of every fourth carrier is calculated based on an
algorithm using the phase of the previous three information
carriers. This method is similar to the presented technique
except that it gives sub optimal results.
3 Peak Reduction Carriers
This paper presents a technique that combines selective mapping
and cyclic coding. A reduction in the PAPR is achieved by adding
extra carriers referred to as Peak Reduction Carriers (PRC).
The phase and amplitude of the PRCs is varied to minimise the
overall PAPR. The original information carriers are unaffected
and can be decoded normally. The receiver can disregard the
PRCs, or they can be used for error detection. The frequency
of PRCs, or relative positioning of the PRCs can be varied with
respect to the information carriers depending on the application.
The results presented were found using a computationally intensive
exhaustive search to find the optimal setting for the PRCs.
However it is assumed that further work will allow a more efficient
algorithm to be found.
An optimal setting for the PRCs corresponds to the combination
of phase and amplitude that achieves the lowest PAPR of the
overall OFDM symbol (information carriers and PRCs). In this
paper the phase and amplitude of the PRCs was set in a coarse
quantised manner to minimise the number of combinations needed
to be searched. The phase of the PRCs was set to 0° or
180° and the carriers were turned on or off. There are
therefore 3M combinations for the PRCs for each
information code word (where M is the number of PRCs). This
level of quantisation was found to be appropriate for BPSK
information carriers. Finer quantisation may produce improved
results for higher modulation schemes.
An exhaustive search of all combinations of allowable phase
and amplitude gives optimal PRCs, but is computationally intensive.
This method can be used for small numbers of carriers where
the optimal PRC coding can be stored in a look up table or
code-book. This is impractical for more than 16 information
carriers or for more than 10 PRCs as the number of combinations
becomes too large to store and calculate. However for some
multi-user OFDM applications 16 or less carriers per user
is sufficient.
The results shown were calculated based on all combinations
of information code words, thus will give a good indication
of the practical PAPR improvement.
For each experiment the inverse fast Fourier transform (IFFT)
of the carrier configuration was used to give a complex base
band signal. Let the complex base band signal be defined as
in eqn. 1.
|
|
(1)
|
When this is quadrature modulated to RF the signal can be written
in polar form as:
|
|
(2)
|
where a(t) is the amplitude and q(t) is the phase of the signal.
Thus:
|
|
(3)
|
The definition of the PAPR in eqn 4, where T is the OFDM symbol
period, can be used for RF as well as base band [1].
|
|
(4)
|
For the simulations carried out, the base band carriers were
centred on DC and the size of the IFFT was made at least 8 times
greater than the number of carriers, oversampling the time domain
signal. This ensures that peaks in the signal were accurately
represented to get an accurate PAPR [4, 5].
3.1 Results
The simplest arrangement for the relative positioning of
the data and PRCs is to have a block of data carriers immediately
followed by a block of PRCs. This arrangement was used for
the results shown in figures 1-3.
Figure 1. PAPR verses number of edge grouped
PRCs (8 BPSK data carriers)
Figure 2. Maximum PAPR verses number of data
carriers and edge grouped PRCs, where M is the number of
PRCs.
Figure 1 shows the worst case PAPR and the 90% point in the
cumulative distribution of PAPR as the number of PRCs is increased.
The maximum PAPR for the 8 information carriers is reduced
by >5.5dB for the addition of 10 PRCs. Selecting the optimal
amplitude and phase of the PRC improves the performance significantly
as compared with only setting the phase as used in cyclic
coding [3]. For this reason phase and amplitude modulation
of the PRCs was used in all later experiments due to the improved
performance.
Figure 2 shows the effect of adding PRCs to the PAPR as the
number of information carriers is varied. The improvement
in PAPR remains relatively constant as the number of information
carriers is increased. This shows that this technique gives
consistent performance gains as the number of information
carriers is varied.
Adding PRCs reduces the PAPR at the expense of additional
transmission power. Figure 3 shows the net improvement in
PAPR due to the addition of PRCs. The PAPR reduction was calculated
as the difference between the PAPR results for zero PRCs and
the PAPR results with the addition of PRCs. The loss in signal
power due to the PRCs was then subtracted from the PAPR reduction
in order to give the net PAPR improvement. If the data signal
power lost due to the transmission of the PRCs was more than
the PAPR gain then there would be little point in adding the
PRCs. It can be seen that for 10 BPSK carriers there is little
improvement in adding more than 2 PRCs. In fact adding more
than 5 PRCs results in a worsening of the average (50%) PAPR.
This is due to the power cost of the PRCs.
4 Effect of PRC Position
Previous results are shown for grouped PRCs that were positioned
immediately after the data carriers as shown in Figure 4a. Two
different positioning tests were performed. One test kept the
PRCs grouped together as in Figure 4a, however they were moved
with respect to the data carriers as in Figure 4b.The second
test positioned the PRCs in a spread out manner. The best spread
pattern was established using a randomised search.
Figure 4. PRC position combinations
|
Adding PRCs use a significant amount of additional bandwidth.
It is therefore important to minimise the number used, or to
position the PRCs so that the bandwidth can be reused. For example,
in a multi-user OFDM system where each user transmits a block
of carriers, the PRCs can be overlapped, i.e. they are transmitted
at the same frequency, effectively halving the bandwidth.
4.1 Grouped PRCs
In this scheme the PRCs were maintained as a group of carriers.
They were repositioned by sliding them with respect to the
data carriers. Figure 5 and 6 show the effect of the position
on effectiveness of the PRCs. Figure 5 shows for a small number
of PRCs the position of the PRCs within the data carriers
has little effect on the performance. However with 4 or more
PRCs the position has a significant effect on the performance
of the PRCs.Placing the PRCs within the data carriers with
an off centre of 3 carriers gives the best results. This gives
a further reduction of 1dB as compared with edge grouped PRCs.
Not having edge grouped PRCs would prevent overlapping of
the PRCs for a multi-user OFDM, thus doubling the bandwidth
used by the PRCs.
Figure 5. PAPR verses position of 2 grouped PRCs
(10 BPSK data carriers)
Figure 6. PAPR verses position of 4 grouped PRCs
(10 BPSK data carriers)
4.2 Spread PRCs
The position of the 4 grouped PRCs had a significant effect
on the PRC performance, thus it seemed likely that spreading
the PRCs out might lead to further improvements. The exact relationship
between the position of the PRCs and the PAPR distribution is
currently unknown and so a random search was used for optimisation.
The PRCs and data carriers were positioned randomly to form
a block of carriers with no gaps as shown in figure 4d. For
each position combination the PAPR distribution was found and
the combination which resulted in the lowest maximum PAPR was
selected as the optimised PRCs position.
The PAPR distribution was found by testing all
combinations of the data code words. For each data code word
combination the optimum PRCs were found as described in section
3. The PAPR distribution verses the number of PRCs is shown
in figure 7. This result is for 10 data carriers and shows
that spreading the PRCs can result in large reductions in
the PAPR of the OFDM symbol. A reduction of > 6dB is possible.
Figure 7. PAPR verses the number of spread PRCs
(10 BPSK data carriers)
Figure 8. Net improvement in PAPR, position optimised
PRCs (10 BPSK data carriers)
Figure 8 shows the overall net improvement in the PAPR using
position optimised PRC. This can be directly compared to figure
3 which shows the results for edge grouped PRCs. The maximum
net gain for position optimised PRCs is nearly double that
of the edge grouped PRCs. Figure 8 shows that the net PAPR
gain increases rapidly up to 4 PRCs, after which the gain
is minimal. Thus the optimal number of PRCs would be 4 for
10 data carriers. Other tests also show that the number of
PRCs needs to be approximately 40% of the number of data carriers
in order to get significant improvements in the PAPR.
Overlapping of the spread PRCs in a multi-user OFDM system
is more difficult as most of the PRCs will be bounded with
data carriers. Thus simple overlapping may not be possible.
This would be the case if the data and PRC are grouped. However
if the carriers for each user are spread out it might be possible
have spread out PRCs that overlap between the users, but still
provide a large reduction in the PAPR.
The addition of 4 PRCs with 10 data carriers results is a
large net gain of 4.5 dB, allowing more power to be transmitted.
For a transmission with no PRCs at an error rate of 1x10-3,
adding the PRCs and maintaining the same peak power the error
rate would be decreased to 1x10-7 [7]. This is
more efficient than adding error correcting bits at the same
coding rate. For example using Hamming coding at a rate of
4 parity bits for 11 data bits gives a gain of only 1.2 dB
at a bit error rate of 1x10-4 [7] which is significantly
less than 4.5dB.
Table 1 shows the number PRC positions tested, and the best
combination found.
Table 1. Optimised positions found for
10 BPSK data carriers |
5 Conclusion
Adding peak reduction carriers can significantly improve the
PAPR of an OFDM signal. The PRCs can result in a reduction of
>6dB in the maximum PAPR and a net reduction of >4.5dB when
the additional power for PRCs is taken into account. It was
found that varying the amplitude as well as phase for the PRCs
gave improved performance over just phase variation. It was
also found that spreading the position of the PRCs resulted
in a better performance than grouped PRCs. Adding more PRCs
results in a lower PAPR, however the use of large numbers of
PRCs is limited by the cost of additional transmission power,
bandwidth and complexity limits.
6 References
1. Bauml, R.W., Fischer, R.F.H., Huber, J.B., Reducing the
peak-to-average power ratio of multicarrier modulation by selected
mapping, Electronic Letters, 1996, Vol. 32, pp. 2056-2057
2. Van Eetvelt, J., Wade, G., Tomlinson, M., Peak to average
power reduction for OFDM schemes by selective scrambling,
Electronic Letters, 1996, Vol. 32, pp. 1963-1964
3. Wulich, D., Reduction of peak to mean ratio of
multicarrier modulation using cyclic coding, Electronic
Letters, 1996, Vol. 32, pp. 432-433
4. Tellambura, C., Use of m-sequences for OFDM peak-to-average
power ratio reduction, Electronic. Letters, 1997, Vol.
33, pp. 1300-1301
5. Tellambura, C., Phase optimisation criterion
for reducing peak-to-average power ratio in OFDM, Electronic
Letters, 1998, Vol. 34, pp. 169-170
6. Davis, J.A., Jedwab, J., Peak-to-mean power control
and error correction for OFDM transmission using Golay sequences
and Reed-Muller codes, Electronic Letters, 1997, Vol.
33, pp. 267-268
7. Sklar, B., Digital Communications Fundamentals and
Applications, Prentice Hall, 1988, pp. 300
|


San Diego YMCA proudly offers state-of-the-art fitness equipment, group exercise classes, free child watch, and programs for all ages.
The YMCA of San Diego County has tasked Jacob Tyler Agency with enhancing its website by creating an impactful, relevant and attractive online presence so that they may continue their path for success, credibility and new opportunities for expansion and profit. Jacob Tyler Agency will create an online experience which allows all Y locations to work together in unison, keeping the brand and experience consistent across the San Diego system to support the “Join One, Join All” membership benefits.
The first step in the process was to create User Flows. It’s important to think about the objectives that users are trying to accomplish when they land on the website. This will help us design an experience that fulfills multiple user objectives that lead to successful interactions. Not all website visitors are alike, so we had to prioritize the flows and focus our efforts on a few that would have the most impact. We reached that decision through performing an exercise with the client during a discovery meeting. The client receives a lot of feedback from Y members each year, so they were able to share such pain points. Along with their business objectives in mind, we decided to focus on these flows:
We examined how these 4 scenarios currently flowed on the website, and looked for ways in which it could be improved. With the advent of technology, we now live in a instant gratification society. The evolution of consumer products have always seen iterations in which a user could complete a task faster and more efficiently. If we can help users complete an objective in 2 clicks versus 5 clicks, then we should do so.
Next, we created an information architecture (IA). This was actually a beast to tackle due to the enormous size of their website. Performing a detailed audit and analysis of the existing content was no easy task. They had at least 2,000 webpages because it had just accumulated over the years with individual pages being created each time they had new information instead of being added to an existing page. This certainly posed a challenge as we had to figure out what was the best way to present this deliverable. We couldn’t follow standard conventions where it would look like a family tree. In the image below, we attempted to do so but it would be too large of a document if we kept expanding the branches. We tried to experiment with software such as Balsamiq, Axure, and Adobe XD. Nothing currently on the market seemed to fulfill our needs so we ended up putting everything on a Google spreadsheet. We were able to cut them down to about 300 pages which is a significant trim.
This was the most challenging stage in the process for me because not only did I have to decide which pages to keep, but I also had to figure out how to rearrange each page in relation to one another. The navigation and content had to be organized in a clear and consistent way. Dealing with content is messy, complicated, and painful. But the web is comprised of content, and deserves our time and attention.
This was not originally a deliverable for the client. But after completing the Information Architecture, we thought it was necessary to create a document that actually describes the specific goals, content, and functionality for each webpage. We didn’t do this for every single page but only focused on the sections related to the 4 User Flows. This document was more of a resource for our internal team, and even more so to the developers. Not only do the developers have to know that the “Join Now” page would live under the “Membership” tab in the navigation which is outlined in the IA, they also have know the features and functionalities that need to be built for that page.
Now onto my favorite part! We created desktop and mobile wireframes in Photoshop to visually outline how each webpage would look.  For example, a Childcare Resource Service landing page could structurally use the same template as the Youth and Family Services landing page. Although those two have different content, the template could still fulfill both of their needs. Before I moved to Photoshop, I like to sketch everything with pen, paper, and whiteboard first. This way, I was able to discuss my thoughts with other designers before “committing” to Photoshop. Also, I conducted a Competitive Analysis for my own benefit. This wasn’t a required deliverable but I felt it was necessary to see what other YMCA websites were doing. Why reinvent the wheel? It also helps give me inspiration when I look to other websites and examine what I liked and disliked.
I was actually quite surprised to find out that we were using this method. Creating wireframes in Photoshop takes a lot of work although it does save a lot of time during the design phase. In design, all we had to do was replace each gray box with an actual image or change the font/colors. I still prefer Rapid Prototyping in which one would use software such as Balsamiq, Axure, or UXPin because it allows for someone to create wireframes quickly and make more iterations. I believe that this makes it easier to do user research because many software have interactive wire framing so one can easily observe how users interact with your wireframes.
Fortunately, we did transfer these Photoshop files to Invision so at least they had the option of being interactive. I also would’ve liked to incorporate more formal User Research for this project and presented this option to the team, but due to budgetary constraints we were unable to do so. It makes me uneasy whenever I design without user data because I don’t like to assume how users will interact with a website. All I could really do was follow best website practices (namely Nielson’s 10 Heuristics) and bug my friends for their feedback.
The final step in our UX Approach were the designs. YMCA already has a brand guideline which outlined the acceptable fonts, colors, amongst other things. So designs were created with those in mind. Our Creative Director stepped in and played a major role during this process because photography was a major element to the designs. Quite frankly, their current Photography selections weren’t that great. So our Creative Director led our team in figuring out which stock photos to buy and which we would need to do in a Photoshoot. I volunteered to be a part of their photoshoot as a model and it was a lot of fun! I had to do zumba, barre, and yoga while smiling the entire time and trying not to sweat too much. It’s harder than it looks!
This is still a work in Progress. Website and Mobile App are set to launch in Early 2017.


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ChiPy had its first sprint today. We worked on PyPI. There were some larger goals, but in the end we didn't get a whole lot done. I think this is actually typical for sprints -- they are a chance to get started on something, and become comfortable with a new project, but they aren't exceptionally productive.
We started out the day by getting a working environment going. This proved to be rather difficult. Every one of us happened to have a Mac laptop, but installing SQLite (PyPI's backend database) and pysqlite turned out to be rather difficult, along with making sure Apache was up, CGI was being served, and HTTP authentication was getting through (something Apache makes difficult). Personally I got stuck on pysqlite, because apparently my Python installation is all messed up. The whole thing was just poor judgement on our parts -- we ended up all working on the chipy.org server, where Chris had a decent working environment for a web application. In the future I'd plan on working on a single server from the start, or else making sure everyone got a working environment set up before they come.
Another issue (which we kind of expected) is that none of us had worked on PyPI before, so we weren't familiar with the code or what was involved. But the code is actually pretty straight forward -- it's really just a normal database-driven web application, so it wasn't too hard.
We split up a bit. I worked on creating ZPT templates, and removing the HTML from the main class/module. Brian worked on documenting the SQLite database, and getting stuff in place to make it easier to update the schema and add new fields, while retaining information from the old database. He's also interested in moving it to PostgreSQL. Everyone else worked on adding XMLRPC support. I had thought the server already supported XMLRPC for the distutil register command, but apparently that command just submits a normal POST form. They are really interested in making a command-line client to PyPI, so XMLRPC (using xmlrpclib) seemed like the right way to do it. In the end they mostly spent time learning about XMLRPC and getting exceptions working sanely and whatnot; they didn't have a chance to actually implement useful functions. Luckily that's actually pretty easy with xmlrpclib, and I think they did a lot of the hard stuff in the sprint -- it's a good chance to work on things you aren't familiar with.
The people who worked together used screen, which worked pretty well. There was an editor conflict; I use Emacs, and a couple other people use vi, and Brant was trying to decide which one he'd use, so he wasn't familiar with either. But besides that screen was a good way to collaborate, even though I felt a need to develop a set of CB-like signals to indicate who's driving and when you're done.
We set stuff up in a Subversion repository to share our work; and I think we'll all be continuing what we started so the repository will continue to be useful. (I still have to make a better, more technical summary for Catalog-SIG)
I have to say that screen is one of the most useful applications around. I've been using daily it since my college days, though I can't say I've ever used it in group share mode. That's something I'm going to have to try out. I believe someone wrote a collaborative editor in Python for these types of sessions, but I honestly wouldn't find a reason to use it when screen+${EDITOR} works just as well. Perhaps using IRC or Jabber could help with your CB messages. ;-)
Well, since we were all in the same room, we actually just had to say "ok, I'm done", or "let me edit that." It's just easy to forget and then have more than one person typing (or everyone staring at the screen waiting for someone to do something).
To do it in multi-user mode we all logged in as the same user, and used screen -x. And then hit some magic keys (maybe just ^A-#) -- I haven't used screen much before so I'm fuzzy on the details.
There's an entire suite of access control list commands to use for multi-user sessions. You can apparently create user-specific ACL's or add entire groups to the session, and it allows fairly finite control over windows and commands available. It's a pretty advanced piece of software, most of the features of which are never used by the average end-user. One of my favorite .screenrc lines is the following:
# Always add the status bar # ------------------------------------------ # "%-w%n %t%+w %= %H(%l) %Y/%m/%d %0c" # ------------------------------------------ # %-w All windows up to the current one # %n %t Number and name of current window # %+w All windows after the current one # %= Pad spaces # %H Host name # %l System Load # %m/%d %c Month/Day TIME # ------------------------------------------ caption always "%{+b}%{.gR}%-w%{.rB}%n %t%{-}%+w %=%{..B} %H(%l) %{..R}%Y/%m/%d %0c"


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The APFELgrid plugin is a tool designed to accelerate the computation of physical observables at hadron colliders, by means of combining the interpolated weight-grids provided by the APPLgrid project with the evolution of parton densities provided by APFEL. The tool is aimed at PDF fitting groups, as it allows for the extremely rapid recalculation of cross-sections when varying the input PDF.
APFELgrid generates FastKernel (or FK) tables which reduce the computation of NLO pQCD predictions to a particuarly compact form in terms of PDFs at a single initial scale.
These tables are used for fits in the NNPDF series of PDF determinations.
This package consists of two major parts
The APFELgrid code is installed directly as a plugin to APFEL. Its use is demonstrated in the example example_gen.cc.
The FastKernel driver is supplied as a single C++ header which may be dropped into users projects where convenient. Included in the driver are routines for the basic and SIMD accelerated convolution with externally provided PDFs. An example of how to use the included driver is given in example_conv.cc. The FK table format itselfis a simple plaintext format designed such that writing custom interfaces to it should be simple.
Your APPLgrid installation must be modified such that all headers are installed (simply copy all headers from /src/ to /appl_grid/ before installation of APPLgrid)
A bootstrap script is provided which downloads and installs the relevant APPLgrid headers, detects the path of APFEL and configures and compiles the package for installation there.
To use this script and install to the default directory type
./setup.sh make install
Alternatively, the APPLgrid headers may be installed manually as specified above, and the installation performed in the typical autotools fashion
(autoreconf -i) ./configure make make install
A few tests can be performed upon the package with
make check
which is automatically executed by the boostrap script.
For usage examples, see
Documentation is generated with docco. ./docco_generate.sh
For comments or questions please get in contact via github.


Review of Mighty Aphrodite (1995) Written and Directed by: Woody Allen Rating: *** out of ****
Just leave it to Woody Allen to compare the life of a neurotic New Yorker to Greek Tragedy - after all, no one else is going to do it. Allen is probably the only American director that can get away with making a film which intercuts a traditional narrative with scenes of a Greek Chorus commenting on the story. Heck, he's the only director who can actually pitch this sort of idea and not be laughed at by the studio executives.
The story follows Lenny (Allen), a sports writer who is persuaded by his wife Amanda (Helena Bonham Carter) to adopt a baby. As the child grows older, Lenny becomes obsessed with finding the parents of the child, and embarks on a minor odyssey to meet them, despite the warning of the Greek Chorus. Eventually he manages to track down the biological mother of his child, Linda Ash (Mira Sorvino), who lacks all of the qualities which Lenny's son seems to posses.  Needless to say, this causes much grief for Lenny and he tries to change her into a wholesome lady so that he can romantically pair her with Kevin (Michael Rapaport), an equally dim boxer.
The Greek Chorus is certainly the most innovative features of this film, and serves as the premise for many of the jokes in the film, such as the Chorus appealing to Zeus for guidance but only getting his answering machine. Unfortunately, the film does suffer a bit from the fact that a few too many of the jokes fall flat, especially considering that this is coming from Woody Allen and not just any other writer-director.
Mira Sorvino won the Academy Award for best actress, and it is a worthy performance. Sorvino manages to portray the na´ve character with a sense of balance; she plays dumb but without diving to absurd depths where the authenticity of the character is lost.
Mighty Aphrodite certainly isn't Allen's greatest film, but for fans of his work (like myself), it's still worth seeing how the auteur creates yet another bizarre situation for himself and laughing along as he tries to work it all out.
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King’s College London Health Science DTC
Data Hackathon Autumn Session 2019
Everyone is invited to a third instalment of King’s College London HSDTC Hackathon sessions. The event is intended for everyone with interest in computational and quantitative methods in biology and how such methods can be used to tackle unique challenges of interest to our community.
Our idea is to bring researchers from various backgrounds for an evening of computational biology problem solving.
We aim to provide a relaxed environment for people to engage, discuss and exchange programming knowledge.
Theme of the evening
The Hackathon session will be focusing on Data Science approaches, such as Data Processing and Machine Learning, that can be applied to tackle biological problems.
Our task will consists of investigating a publicly available dataset, selecting statistical models and setting up an analysis pipeline.
Coding language of the evening
This session will primarily focus on Python-specific approaches for solving Hackathon challenges. However, you are welcome to use any language of your choice. We are always looking to improving ourselves!
For whom is this Hackathon intended?
The Hackathon is suitable for anyone at King’s, including undergraduate and postgraduate students, Post-Docs and PIs, who regularly use or wish to regularly use coding for their research, regardless of their level of expertise with programming.
Hackathon format
We will divide ourselves in groups of 4-5 people and attempt in solving the given evening challenge.
Hackathon challenges
The Hackathon organisers will provide you with a dataset and a set of tasks to tackle during the evening. There will be a group of experienced coders and bioinformaticians to help you out with the challenge.
What do you need to bring
- Laptops, power supplies and an inquisitive spirit.
For Python
- Working Python environment (3.x) that could be provided by downloading Anaconda.
- Working Jupyter Notebook that is already shipped using Anaconda.
- Code editor of your liking, for instance PyCharm or spyder.
For R
- If you decide to use R during the evening, make sure you have a working R environment and a good code editor, such as RStudio.
When and Where?
- Date: 11th of November, 2019
- Where: G552, Waterloo Bridge Wing, Waterloo Campus
- When: 5PM - 9PM
The doors are open at 5PM and everyone is welcome to come and network with fellow coders! The Hackathon session will start at 5:30PM and during the evening we will have food and drinks provided by HSDTC.
Registration
To register for the Data Hackathon, please follow to King’s website on Skills Forge.
Want to brush up your Python skills?
There are numerous online Python tutorials, but you can start by taking a look at the following tutorials:
Data Science-specific Python tutorials: