Family Tree In Spanish Template: Fill & Download for Free

Because they are not.Spain is the top wine seller in the world, in the last year Italy edged over Spain for the first time in a long time and France is below both in third place.[1][1][1][1]Italy and France seem like the top wine sellers because a big chunk of the planet seems to rejoice in utter ignorance about Spain, and because they are actually top wine producers themselves (no denying that), just not the top 2. Spain is by far the country on the planet with the most vineyard soil, although being sunnier and drier usually our soil produces less wine per squared meter than the wet French countryside, but it doesn't matter because the advantage in soil is such that we still produce more wine than Italy and France. And it also doesn't matter because since our name is Spain no one will have a clue about it, there's no Spaniard in movies drinking wine with a beret dipping bread in mayonese next to a painting from Picasso.Vineyard in FranceVineyard in ItalyVineyard in SpainEven though Picasso was Spanish, mayonese is a sauce taken by Richelieu from the town of Mahón located in the Spanish island of Menorca, and berets are Basque-Navarran-Aragonese shepard hats (present in France to be fair, exactly on their border with Spain). C'est le Hollywood!Spanish kids (Navarran) in the Running of the Bulls, wearing the traditional red beret in Spanish “boina" that my grandpa and my great-grandpa wears and wore (in other colours)Despite this, or because of it, France and Italy are the ones that make the most money from the wine they sell. France goes from 3rd wine seller to being the top profitter with twice Italy's income and thrice Spain's. Italy also has a lot more income than Spain despite producing less too, Spain “only" comes third in income from wine sales in the world. The reason is obvious: marketing (not necessarily French marketing), marketing makes you associate wine with France and Italy, so the French can sell you their wine nicely priced and you will feel fancy drinking it. Again, ot the French fault, it's a combination of things, like those who go around saying French is a lot more complex and refined than Spanish, it's not the French's fault it's a combination of ideas floating around from several sources. Spanish things aren't refined, that's not an adjective that goes with it in most customers' minds and wine is supposed to be refined, my family drinks it daily in plastic water bottles, but beyond wine-countries it is fancy, and the Spanish brand isn't like the French and to a lesser extent the Italian brands are.Los Cigarrales, right outside Toledo in the background with vineyards all around itAnd it's not just with France, actually the same happens with olive oil, one out of every two drops of it on the planet are Spanish olive oil, and the other drop is the rest of the planet combined and a little of Spain's too. Yet your average Joe in most places thinks of Italy when he thinks of olive oil, can you imagine any product in the world in which a country controls over half the production? And yet that product is associated in pop culture to another place? The difference here is that the surplus in production is so stark that the higher price at which Italians can sell their olive oil due to the world thinking it's “their thing” doesn't get to outweigh that, like it does with wine.Olive fields in southern Spain, covering all there is to see in the horizonEither way the reason why these countries are the main producers of wine becomes quite obvious when you actually complete the list of top producers: Spain, Italy, France, Chile, Australia, the United States…What's in common? Mediterranean weather.This map that looks like the Roman Republic got coloniesThese are countries with large fields to grow wine located within warm-moderate weather zones. Spain and Italy are almost entirely covered by Mediterranean weather, France is largely covered but the uncovered part is still within warm-moderate weathers, Chile, Australia and the US happen to be in the limitted list of countries around the world to have sizeable Mediterranean weather areas, which happen to be the main producing areas within those countries.Beyond that the culture around wine built in Italy, Spain and France over the centuries makes those three considerably richer in varieties, partly also from the sheer larger production and larger number of vineyards, because the Mediterranean weather extends over the entire country instead of just a fraction of California and Valparaíso. There is a culture and tradition around wine in Spain, Italy and France that is just a lot more commercial among the other top producers. In fact, of the other top producers, the one with the most wine culture arguably is Chile, precisely because it's Hispanic, or in this case we could simply argue it's Latin since the list includes France and Italy.I once saw a video of a Chilean YouTuber in Spain saying “we have a lot of wine culture in Chile really, does Spain also have good wine", this sentence shows two of my point in one: the utter ignorance about Spain (asking if wine is a thing in Spain) and that Chileans have more of a culture around it.French president drinking wine in a wine fairMonument to wine makers in Jerez de la Frontera, a wine town in southern Spain home of Sherry WinePortugal is also hanging there among the top 10 producers in fact, only that the country is smaller and it is not the most wine-centred part of the Iberian peninsula either, the wine production tends to be along mid-course valleys of rivers in warm-template weathers, which all fall within Spain in Iberia.The Balkans don't have Mediterranean weather penetrating inland like Spain or Italy, only Greece in the southern tip is all Mediterranean weather, but it is too mountainous for large vineyard zones. They do have better olive oil production because those grow better in hot Mediterranean zones and they adapt better to hard terrain, which is a reason why olive trees—unlike vineyards—desert France and blossom in Spain in such numbers (vineyards are more in Spain too but they don't desert France) because olive trees like it warmer, they are not as tolerant of grey skies and rain as vineyards are. Both being typical of the Mediterranean landscape vineyards like humidity and olive trees the Sun.Footnotes[1] Global leading wine export countries based on volume, 2019 | Statista[1] Global leading wine export countries based on volume, 2019 | Statista[1] Global leading wine export countries based on volume, 2019 | Statista[1] Global leading wine export countries based on volume, 2019 | Statista

There is a native squirrel from tropical South América :Sciurus granatensis - Wikipedia, la enciclopedia libreThose little fellows are scarce due to loss of hábitat.Few years ago my family had a farm in the templated área close to La Mesa (69km from Bogotá 25 degrees average temperature) and in 17 years of travelling there I saw three of them: two Dead and one 30 mts above the floor on a tree.Seems that they became shy due to hunting and other human activities. They are clever but not as fértile as their North American grey cousin.In Cali probably you will see it inside the Farallones de Cali natural reserve. In Medellín probably at the South of the City on some forests close to Envigado where you will see native opposums (fara, zarigueya in spanish).Go far from big cities. Probably on the Región Cafetera are living on significant numbers. May God protect us from pervert people trafficking live animals and introducing non native species. Cold Altiplano Cundiboyacense( área surrounding Bogotá) would be a perfect hábitat for the gray squirrel that has caused a lot of damage on Europe. Please no one bring here animals of any type.

Everyone’s first introduction to this subject is almost invariably through RACTER, most famous of poetry engines, so it seems fitting to share from its ouevre:More than iron, more than lead, more than gold I need electricityI need it more than I need lamb or pork or lettuce or cucumberI need it for my dreams— as published in RACTER’s anthology The Policeman’s Beard is Half-Constructed in 1984[1][1][1][1]Computer-generated poetry has been around in some shape or fashion since the 1960s, but only in recent years has it become arresting. Over 60% of people, for example, cannot tell that the following was written by a machine[2][2][2][2]:youareinscribedin thelines on theceilingyouareinscribed inthe depthsofthestormwhile the following poem was actually accepted for publication under the impression it was written by a human:A home transformed by the lightningthe balanced alcoves smotherthis insatiable earth of a planet, Earth.They attacked it with mechanical hornsbecause they love you, love, in fire and wind.You say, what is the time waiting for in its spring?I tell you it is waiting for your branch that flows,because you are a sweet-smelling diamond architecturethat does not know why it grows.So these are the questions I’m going to address in a rambling work on one of my favourite topics of all time:What ingredient makes up state-of-the-art poetry generation engines today? How do they tick?How is such poetry generally received and judged by humans, the real arbiters of “good and original”?How do poetry generation engines work?The Naive ApproachThe naive approach towards generating poetry automatically is not hard to grasp — in fact, here is Roald Dahl summarizing it in his short story, The Great Automatic Grammatizer:Then suddenly, he was struck by a powerful but simple little truth, and it was this: that English grammar is governed by rules that are almost mathematical in their strictness! Given the words, and given the sense of what is to be said, then there is only one correct order in which those words can be arranged. No, he thought, that isn’t quite accurate. In many sentences there are several alternative positions for words and phrases, all of which may be grammatically correct. But what the hell. The theory itself is basically true. Therefore, it stands to reason that an engine built along the lines of the electric computer could be adjusted to arrange words (instead of numbers) in their right order according to the rules of grammar. Give it the verbs, the nouns, the adjectives, the pronouns, store them in the memory section as a vocabulary, and arrange for them to be extracted as required. Then feed it with plots and leave it to write the sentences.In a nutshell, a sentence can be represented by what linguists call a sentence diagram, a nice little tree outlining relationships between parts of speech:(D = Determiner, N = Noun, NP = Noun Phrase, S = Sentence, V = Verb, VP = Verb Phrase)[3][3][3][3]The important thing about such sentence diagrams is that they illustrate relationships: a sentence always consists of a noun phrase followed by a verb phrase, and so on and so forth.Dahl points out correctly that the exact noun phrase or verb phrase is irrelevant: to generate human-like sentences, one only needs to “fill in the blank”, so to speak. Thus, using such a template for a grammatical sentence, one can simply load up a dictionary of words and the syntactic category they fall into (noun, verb, adjective, etc.) and insert them into the template to generate a sentence. Constraints — rhyme, meter, and so on — could all be applied to the selection of words on the fly, as it were.Unfortunately, the earliest poetry templating engines (such as Theo Lutz’s Stochastic Texts) suffered from the problem of repetition: the underlying template structure becomes obvious once you ran the program several times, as exemplified in the following snippet:A HOUSE OF STEEL IN A COLD, WINDY CLIMATE USING ELECTRICITY INHABITED BY NEGROES WEARING ALL COLORSA HOUSE OF SAND IN SOUTHERN FRANCE USING ELECTRICITY INHABITED BY VEGETARIANSalthough some folks used shifting templates, altering the template itself over time to produce a pleasant effect, such as these two haiku snippets produced by John Morris[4][4][4][4]:Frogling, listen, watersInsatiable, listen,The still, scarecrow dusk.Listen: I dreamed, was slain.Up, battles! Echo these duskBattles! Glittering...The Mechanical ApproachForget grammar for a moment — a sentence is just a sequence of words.What if you could calculate the probability that one word would follow immediately after the other, given what you know of the sentence so far, and then insert the most probable word?So begat the Markov chain model of poetry generation, where programs are first given a corpus of text (an existing work, story, poem, etc.) to compute frequency distributions of words and characters over, and then urged to spit out the next N words using the probability of their occurrence. This was first pioneered by R. W. Gosper in the early 1970s at MIT, going by the cuter name of n-gram generation[5][5][5][5].A Markov state tree, outlining the probabilities of the next word in a sentence[6][6][6][6]Markov chains have been so well-studied that default implementations of them are now provided in standard natural language processing tools such as Python’s nltk[7][7][7][7]. One can even play with them in your browser — see Markomposition.Markov models can even be trained at the character, rather than word, level, resulting in such weird compositions as:book her sist be chin up seen a good deal uneasilent for coursationdropped, and thelitter on,The Queen was siliarly with them,the Footmance.The downsides of raw Markov models?They don’t guarantee replicating grammar. Thus, ungrammatical sentences are perfectly valid, which may or may not be part of the charm — after all, Dadaist sound poetry like Kurt Schwitter’s Ursonate is special for rejecting such strictures.The kinds of poetry they output are highly sensitive to the initial corpus they are trained over: a Markov poetry generator trained over The Rubaiyyat of Omar Khayyim will be very different than one trained over King James’ Bible.The Current State of the Art ApproachNaturally, the best we do today is a compromise: a hybrid mix of using a context-free grammar (in other words, a grammar that doesn’t care about the meaning of the words used) combined with a Markov chain model.In other words, we use a template where, once a word has been filled, the next word is chosen based on the likelihood it will appear thereafter per a reference text. As always, constraints based on measurable quantifiers are added in.Like anything in computer science, even this definition is blurry. Computer scientists and computational linguists may introduce additional ingredients on top of this basic model:The use of evolutionary algorithms on top of Markov chains, such as with MCGONAGALL[8], to solve the choice of which word to use next.The use of multiple different reference grammars, which are then tested against different desired properties like rhyme or meter, etc. This is done with WASP, which generates classical formal Spanish poetry[9].More modern experiments are trying to introduce neural networks into the process, which (at least to my mind) does not add anything conceptually innovative to Markov generation + reference grammars.The Cheating ApproachI’ve deliberately left out one important kind of poetry generation, namely case-based reasoning.In this approach, computers are given a series of input parameters, search over an existing library of human poems, and partly modify results from this library in their response. ASPERA, for example, does this to generate classic Spanish poetry.Since the question asks for original poetry, I’ve excluded it from consideration.What do humans think about computer poetry?Humans are inconsistent at evaluating computer poetry in general unless it falls on the extreme end of good or bad[10][10][10][10].The following is an excerpt from a paper that asked a panel of 7 graduate students in digital media to rate 30 poems, some computer-generated, some human:Looking at the data, some poems were rated very highly by nearly all judges, while others were rated very poorly, but a large mass of poems in the middle had inconsistent or inconclusive results. When the data is reduced to those poems with the highest and lowest average scores, intraclass correlation becomes very high.With the seven best and seven worst poems—nearly half of our original data set— the intraclass correlation is 0.73, and narrowing the number of poems raises that statistic still higher. It is intuitive that poems with the highest and lowest ratings would have relatively good agreement, while poems about which judges disagreed would have average scores closer to the middle. However, when we looked at only the seven best and seven worst in each of our 10,000 bootstrap samples, the mean and standard deviation for intraclass correlation did not rise.Therefore, the agreement on the best and worst poems is not merely a statistical artifact; our judges really were able to agree on these ends of the spectrum. It appears that our judges agree when selecting the best and worst poems in a group, but cannot reliably rank the group of poems as a whole.Beyond this, the meaningful literature peters out. I have been unable to find honest peer-reviewed assessments of what humans think about computer poetry in general.So, instead, as an amateur poet who deeply cherishes the craft of poetry, I can only offer my own opinions.For me, three big issues pop up:Computer poetry today doesn’t (and might never be able to) capture the full spectrum of figures of speech.In the examples above, we have seen poems that meet a pre-existing format (haikus, quatrains, sonnets, ghazals, etc.) and can even generate minor similes and metaphors.But poetic techniques don’t just stop there. How does one aptly generate synecdoche and zeugma? Or aposiopesis, paraleipsis, allusion, and irony?The absence of these elements presents a major obstacle towards classifying the results as “good”. We expect such depth from human poets routinely and regularly. Because these elements are not numerically quantifiable like rhyme and meter, it seems unlikely we shall see these elements emerge in computer poetry except by accident.To be absolutely fair to the field, attempts at introducing these “higher-order” figures of speech exist, so it is possible we will eventually catch up:A sarcasm-generator module exists for chatbots[11], although the creators explicitly declare it is not production-ready.Kidon and Brun were able to write a program that automatically detected opportune moments to say “That’s what she said”[12], although this effort relied only on noticing phrase correlations between setup and punchline.It’s unclear whether intent must be a prerequisite for good poetry.This question is a broader issue impacting AI literature in general.In literary theory, we often rely on various lenses to evaluate an author’s work.For example, as Roland Barthes criticizes in Death of The Author, the predominant mode of literary criticism in his lifetime relied on contextual understanding of the author’s life and times, to inform us what the intent of the author says.Computer poetry, however, demonstrates no intent and thus poses a challenge for some of these interpretation-generation techniques. A good resolution to this would likely have to have us go back to a theory of aesthetics and shake it from the ground up.Learning from human poetry requires solving some thorny language parsing issues.Reading and analyzing human poetry for intentional affect is likely the only way computer poetry will evolve in the future to produce richer poetry.But parsing natural language is really hard, and will pose a challenge whose resolution is currently unclear.One example of difficult-to-parse sentences that crop up in poetry, for example, is garden-path sentences — sentences that rely on the dual status of words to enable a different surprising meaning than originally intended:The old man the boat.The complex houses married and single soldiers and their familiesThe horse raced past the barn fellOverall, I think the challenge to original and good computer poetry is the ability to produce rich poetry, as opposed to superficially glib poetry.Rich poetry relies on higher-order methods of technique, narrative fervor and resonance with an audience. Almost all of these elements are hard to simulate using strictly numerical measures. Individual poems might find interpretative flavour, but talented individuals tasked to write using a specific higher-order figure of speech would likely be judged deeper most of the time.In this game of deep literary creativity, artifical intelligence has a long way to go, modern advances in processing speed and computer intelligence notwithstanding.# At all events my own essays and dissertations about love # and its endless pain and perpetual pleasure will be # known and understood by all of you who read this and # talk or sing or chant about it to your worried friends # or nervous enemies. Love is the question and the subject # of this essay. We will commence with a question: # does steak love lettuce? This question is implacably # hard and inevitably difficult to answer. Here is # a question: does an electron love a proton, # or does it love a neutron? Here is a question: does # a man love a woman or, to be specific and to be # precise, does Bill love Diane? The interesting # and critical response to this question is: no! He # is obsessed and infatuated with her. He is loony # and crazy about her. That is not the love of # steak and lettuce, of electron and proton and # neutron. This dissertation will show that the # love of a man and a woman is not the love of # steak and lettuce. Love is interesting to me # and fascinating to you but it is painful to # Bill and Diane. That is love!  - RACTER Footnotes[1] The Policeman’s Beard is Algorithmically Constructed -[1] The Policeman’s Beard is Algorithmically Constructed -[1] The Policeman’s Beard is Algorithmically Constructed -[1] The Policeman’s Beard is Algorithmically Constructed -[2] http://botpoet.com/leaderboard/[2] http://botpoet.com/leaderboard/[2] http://botpoet.com/leaderboard/[2] http://botpoet.com/leaderboard/[3] Sentence diagram | Wikiwand[3] Sentence diagram | Wikiwand[3] Sentence diagram | Wikiwand[3] Sentence diagram | Wikiwand[4] https://archive.bridgesmathart.org/2016/bridges2016-195.pdf[4] https://archive.bridgesmathart.org/2016/bridges2016-195.pdf[4] https://archive.bridgesmathart.org/2016/bridges2016-195.pdf[4] https://archive.bridgesmathart.org/2016/bridges2016-195.pdf[5] Language Technology Enables a Poetics of Interactive Generation[5] Language Technology Enables a Poetics of Interactive Generation[5] Language Technology Enables a Poetics of Interactive Generation[5] Language Technology Enables a Poetics of Interactive Generation[6] Building markov chains in golang[6] Building markov chains in golang[6] Building markov chains in golang[6] Building markov chains in golang[7] nltk.tag.hmm - NLTK 3.4.5 documentation[7] nltk.tag.hmm - NLTK 3.4.5 documentation[7] nltk.tag.hmm - NLTK 3.4.5 documentation[7] nltk.tag.hmm - NLTK 3.4.5 documentation[8] http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.9.5371&rep=rep1&type=pdf[9] https://pdfs.semanticscholar.org/8d7c/57868fb335441541f81ce0e4d37c1e162a02.pdf[10] http://www.computationalcreativity.net/iccc2016/wp-content/uploads/2016/01/Evaluating-digital-poetry.pdf[10] http://www.computationalcreativity.net/iccc2016/wp-content/uploads/2016/01/Evaluating-digital-poetry.pdf[10] http://www.computationalcreativity.net/iccc2016/wp-content/uploads/2016/01/Evaluating-digital-poetry.pdf[10] http://www.computationalcreativity.net/iccc2016/wp-content/uploads/2016/01/Evaluating-digital-poetry.pdf[11] https://sentic.net/wisdom2015joshi.pdf[12] https://www.aclweb.org/anthology/P11-2016.pdf