Podcasts about smultron

Blaubeeren, Moltebeeren, Lingon ... Wer in den Sommer- und frühen Herbstmonaten durch Schwedens Wälder streift, wird viele Beeren finden. Sie zu pflücken, kann eine fast schon meditative Tätigkeit mitten in der Natur sein - Mückenstiche inklusive. Für viele Schweden gehört das Beerenpflücken ganz fest zum Schwedensommer. Für viele Saisonarbeiter auch, bei ihnen aber mit sehr unterschiedlichen Erfahrungen. So erschütterte im letzten Jahr ein Skandal Schweden. In dieser Episode geht es rund ums Beerenpflücken in Schweden: Welche Beeren können gepflückt werden? Was war der Lingon-Rausch? Und was hat es mit dem Skandal auf sich? Hör gerne rein! Du willst Elchkuss unterstützen? Dann besuche uns bei Steady: https://steadyhq.com/de/elchkuss-schweden-entdecken/about Falls bei dir die Shownotes nicht angezeigt werden, dann findest du sie auf jeden Fall bei Podigee: https://elchkuss.podigee.io/

Strap yourself in for the sixth episode of The Sewing Club Podcast where we bring you the fantastic Smultron Dress from Paradise Patterns. It's a stylish, easy breezy dress that screams holiday fun—perfect for beating the summer heat! Kylie: https://www.kylieandthemachine.com/Gem: https://www.sewinggem.com.au/Sewing Club Podcast Community FACEBOOK GROUP https://www.facebook.com/groups/3678270342453518Paradise Patterns : https://paradisepatterns.com/Paradise Patterns Smultron Dress: https://paradisepatterns.com/products/smultron-dress-pdf-sewing-pattern-sizes-00-30-summer-resort-wear-half-circle-bias-cut-dress-with-spaghetti-straps?_pos=1&_psq=smultr&_ss=e&_v=1.020% off with code SEWINGCLUBPODCASTParadise Patterns You Tube: https://www.youtube.com/channel/UChD6y_bjhGx1kJY9BjKkQcQSmultron Dress Tutorial: https://www.youtube.com/watch?v=QeW4Ri6xAFA&t=660sLoop Turner: https://www.sewinggem.com.au/products/prym-loop-turnerBanrol: https://www.sewinggem.com.au/products/banrol-interfacing-for-waistband-and-belting KATM Labels - Comfy https://www.kylieandthemachine.com/products/comfy-sew-in-labels?_pos=1&_sid=8b140cee5&_ss=rKATM Labels - Sweary Sewist #4 https://www.kylieandthemachine.com/products/the-sweary-sewist-4-label-collection-9-sew-in-labels?_pos=2&_sid=89256936e&_ss=rGemma Blog Post: https://www.sewinggem.com.au/blogs/sg-blog/ep-6-smultron-dress-by-paradise-patternsKylie Blog Post: https://www.kylieandthemachine.com/blogs/news/sewing-club-podcast-ep-6-smultron-dressNext Month's Pattern - Pekka Jacket: https://readytosew.fr/en/women-pdf/38-pekka-jacket.html Hosted on Acast. See acast.com/privacy for more information.

Mia funderar över smultron, minnen och upplevelser som är värda att spara på.Support till showen http://supporter.acast.com/arvet. See acast.com/privacy for privacy and opt-out information.

Whenever you bring a punnet of strawberries to a party, chances are you will be asked if they are Swedish. The Swedes are obsessed with these Earth's rubies that a shortage of them is seen as a major catastrophe. The Swedish strawberries are the "celebrity" fruit during summer as you may have noticed the midsommartårtor or gräddtårtor med jordgubbar in every social gathering at the recent midsummer celebration. They are the key ingredients in many Swedish desserts, too. What makes them so special? They are bite-size; easy to pop them in your mouth, sweet, divine and flavourful, unllike those that I have eaten before. Smultron is a wild strawberry that you can only find them in the forest if you have bionic eyes because they are tinier than the Swedish strawberries. All these talk about strawberries is making me hungry. Enjoy listening to today's podcast while I'll go and pick some smultron before they all run out!

Hargan, Smultron och Dwyrydd Coley slåss i Svärdsspelens första omgång. Manuari satsar alla sina pengar. Även Ryana kliver in i arenan för sin första kamp. Skymningsväktaren överväger legenden om den utvalde riddaren.Det här är en podd av Dan Hörning. Följ Dan Hörning här:Twitter: @danhorningInstagram: https://www.instagram.com/dan_horning/?hl=enFacebook: https://www.facebook.com/danhorningofficiell/Youtube: https://www.youtube.com/channel/UCV2Qb7SmL9mejE5RCv1chwgDe här romanerna utspelar sig i samma värld som Neogames rollspel Eon. See acast.com/privacy for privacy and opt-out information.

Z przyjemnością spotykamy się z Wami po raz dziesiąty! W szóstym kompocie rozprawialiśmy się z kalkulatorami i arkuszami kalkulacyjnymi, dziś mierzymy się z najciekawszymi edytorami i procesorami tekstu zarówno w wersji dla Maca, jak i urządzenia z iOS'em. Na przestrzeni dziejów, od czasów prehistorycznych aż po obecne, pisanie – jako sposób komunikacji oraz zachowania informacji dla potomnych, zmieniło się znacząco. A dzięki wynalazkom takim jak papier, druk i maszyna do pisania, tworzenie trwałych dokumentów stało się szybsze, łatwiejsze, tańsze i bardziej powtarzalne. Rozwój komputerów w dużym stopniu ukształtował rynek publikacji dając możliwości wcześniej nieosiągalne. Dziś możemy być nie tylko autorami, ale również korektorami, składaczami, drukarzami i wydawcami. W podkaście wspominamy ewolucję edytorów tekstu oraz przybliżamy oprogramowanie, z którym mieliśmy i/lub nadal mamy do czynienia. Edytory dla programistów i web developerów, edycja markdown: Smultron – 47,99 zł – macOS Textastic – 37,99 zł – macOS / 47,99 zł – iOS Espresso – 79 $ – macOS Coda– 99 $ – macOS BBEdit  – 49,99 $ – macOS TextWrangler – darmowy, nie rozwijany – macOS (nie działa pod 10.13) iA Writer – 94,99 zł – macOS / 23,99 zł – iOS Byword – 52,99 zł – macOS / 27,99 zł – iOS Ulysses – subskrypcja 17,99 zł miesięcznie – macOS / 27,99 zł – iOS Bear – subskrypcja 4,99 zł miesięcznie – macOS / 27,99 zł – iOS Edytory i procesory tekstu, notatniki: TextEdit – wbudowany Notatki – wbudowany Pages – darmowy – macOS / iOS  / online w witrynie iCloud Microsoft Word 2016 dla Mac – 579,99 zł – macOS Microsoft Word – darmowy – iOS Microsoft Office 2016 dla Użytkowników Domowych i Uczniów dla komputerów Mac standalone  – 639,99 zł – macOS Microsoft Office 365 Personal  – subskrypcja 299,99 zł rocznie – macOS Microsoft Office 365 dla Użytkowników Domowych  – subskrypcja 429,99 zł rocznie – macOS Microsoft Office Online  – darmowy – macOS / iOS Microsoft OneNote – darmowy – iOS Dokumenty Google – darmowy – macOS / iOS LibreOffice Writer – darmowy – macOS OpenOffice Writer  darmowy – macOS NeoOffice – macOS – 15 $ Professional / 139,99 zł Kingsoft WPS Office – iOS - darmowy Nissus Writer – 79 $ – macOS Pro / 20 $ – macOS Express Mellel – od 230,48 zł – macOS / 94,99 zł – iOS Mariner Write – 29,95 $ – macOS Paper Pro by 53 – 23,99 zł – iOS Drafts – 23,99 zł – iOS Edytory do zdań specjalnych: Final Draft – 249,99 $ – macOS Scrivener – 45 $ – macOS / 94,99 zł – iOS Scrivo Pro – 37,99 zł – iOS iBooks Author – darmowy – macOS LaTeX / MacTeX – darmowy – macOS CodeRunner – 69,99 zł – macOS QuarkXPress – 829 € – macOS - 79 $ – wersja edukacyjna / Quark Design Pad – 47,99 zł – iOS Adobe InDesign – subskrypcja od 24,59 € miesięcznie – macOS MyScript Nebo – 13,99 zł – iOS MyScript Memo – darmowy / 8,99 zł za eksport jako tekst – iOS Rewelacyjne zestawienie edytorów dla iOS: http://brettterpstra.com/ios-text-editors/ Nasz podcast znajdziecie w iTunes (link), możecie też dodać do swojego ulubionego czytnika RSS (link) lub przesłuchać bezpośrednio w przeglądarce (link). Zapraszamy do kontaktu na Twitterze: Remek Rychlewski @RZoG. Marek Telecki @mantis30. Natomiast całe przedsięwzięcie firmuje konto @ApplejuicePl. Jesteśmy również dostępni dla Was pod adresem e-mail kompot[at]applejuice.pl

I säsongens första avsnitt river vi av lite sommarlovsjuridik (med betoning på lite, rent juridikmässigt). Vi diskuterar humor och juridik. Är det ok att thefatjewish (på Instagram) inte är upphovsman till sina skämt? Är det ok att sno andras skämt på Twitter? Dessutom har HD avgjort frågan om husrannsakan hos Aftonbladet (hovrättens avgörande diskuterades i S05E09). Och så har Stefan Holm högtflygande skadeståndsplaner – han har stämt en bensinmack.  Det här är säsong 6 – välkomna tillbaka! Och tack sponsorer: Delphi, Familjens Jurist, Lindahl och G&D!  

Lilla podden följer upp med epic megastyle-podd. Dvs. vi dissekerar nya Avengers: Age of Ultron och the fail that is Black Widow’s rollfigur. Melody vidhåller att hon fortfarande älskar Marvel och Joss Whedon trots rätt så massiva felsteg i AOU, Nikoo är mest skeptisk till allt. Så det mesta är i vanlig ordning. Utöver dunderfilm […]

We present methods for improved handling of morphologically rich languages (MRLS) where we define MRLS as languages that are morphologically more complex than English. Standard algorithms for language modeling, tagging and parsing have problems with the productive nature of such languages. Consider for example the possible forms of a typical English verb like work that generally has four four different forms: work, works, working and worked. Its Spanish counterpart trabajar has 6 different forms in present tense: trabajo, trabajas, trabaja, trabajamos, trabajáis and trabajan and more than 50 different forms when including the different tenses, moods (indicative, subjunctive and imperative) and participles. Such a high number of forms leads to sparsity issues: In a recent Wikipedia dump of more than 400 million tokens we find that 20 of these forms occur only twice or less and that 10 forms do not occur at all. This means that even if we only need unlabeled data to estimate a model and even when looking at a relatively common and frequent verb, we do not have enough data to make reasonable estimates for some of its forms. However, if we decompose an unseen form such as trabajaréis `you will work', we find that it is trabajar in future tense and second person plural. This allows us to make the predictions that are needed to decide on the grammaticality (language modeling) or syntax (tagging and parsing) of a sentence. In the first part of this thesis, we develop a morphological language model. A language model estimates the grammaticality and coherence of a sentence. Most language models used today are word-based n-gram models, which means that they estimate the transitional probability of a word following a history, the sequence of the (n - 1) preceding words. The probabilities are estimated from the frequencies of the history and the history followed by the target word in a huge text corpus. If either of the sequences is unseen, the length of the history has to be reduced. This leads to a less accurate estimate as less context is taken into account. Our morphological language model estimates an additional probability from the morphological classes of the words. These classes are built automatically by extracting morphological features from the word forms. To this end, we use unsupervised segmentation algorithms to find the suffixes of word forms. Such an algorithm might for example segment trabajaréis into trabaja and réis and we can then estimate the properties of trabajaréis from other word forms with the same or similar morphological properties. The data-driven nature of the segmentation algorithms allows them to not only find inflectional suffixes (such as -réis), but also more derivational phenomena such as the head nouns of compounds or even endings such as -tec, which identify technology oriented companies such as Vortec, Memotec and Portec and would not be regarded as a morphological suffix by traditional linguistics. Additionally, we extract shape features such as if a form contains digits or capital characters. This is important because many rare or unseen forms are proper names or numbers and often do not have meaningful suffixes. Our class-based morphological model is then interpolated with a word-based model to combine the generalization capabilities of the first and the high accuracy in case of sufficient data of the second. We evaluate our model across 21 European languages and find improvements between 3% and 11% in perplexity, a standard language modeling evaluation measure. Improvements are highest for languages with more productive and complex morphology such as Finnish and Estonian, but also visible for languages with a relatively simple morphology such as English and Dutch. We conclude that a morphological component yields consistent improvements for all the tested languages and argue that it should be part of every language model. Dependency trees represent the syntactic structure of a sentence by attaching each word to its syntactic head, the word it is directly modifying. Dependency parsing is usually tackled using heavily lexicalized (word-based) models and a thorough morphological preprocessing is important for optimal performance, especially for MRLS. We investigate if the lack of morphological features can be compensated by features induced using hidden Markov models with latent annotations (HMM-LAs) and find this to be the case for German. HMM-LAs were proposed as a method to increase part-of-speech tagging accuracy. The model splits the observed part-of-speech tags (such as verb and noun) into subtags. An expectation maximization algorithm is then used to fit the subtags to different roles. A verb tag for example might be split into an auxiliary verb and a full verb subtag. Such a split is usually beneficial because these two verb classes have different contexts. That is, a full verb might follow an auxiliary verb, but usually not another full verb. For German and English, we find that our model leads to consistent improvements over a parser not using subtag features. Looking at the labeled attachment score (LAS), the number of words correctly attached to their head, we observe an improvement from 90.34 to 90.75 for English and from 87.92 to 88.24 for German. For German, we additionally find that our model achieves almost the same performance (88.24) as a model using tags annotated by a supervised morphological tagger (LAS of 88.35). We also find that the German latent tags correlate with morphology. Articles for example are split by their grammatical case. We also investigate the part-of-speech tagging accuracies of models using the traditional treebank tagset and models using induced tagsets of the same size and find that the latter outperform the former, but are in turn outperformed by a discriminative tagger. Furthermore, we present a method for fast and accurate morphological tagging. While part-of-speech tagging annotates tokens in context with their respective word categories, morphological tagging produces a complete annotation containing all the relevant inflectional features such as case, gender and tense. A complete reading is represented as a single tag. As a reading might consist of several morphological features the resulting tagset usually contains hundreds or even thousands of tags. This is an issue for many decoding algorithms such as Viterbi which have runtimes depending quadratically on the number of tags. In the case of morphological tagging, the problem can be avoided by using a morphological analyzer. A morphological analyzer is a manually created finite-state transducer that produces the possible morphological readings of a word form. This analyzer can be used to prune the tagging lattice and to allow for the application of standard sequence labeling algorithms. The downside of this approach is that such an analyzer is not available for every language or might not have the coverage required for the task. Additionally, the output tags of some analyzers are not compatible with the annotations of the treebanks, which might require some manual mapping of the different annotations or even to reduce the complexity of the annotation. To avoid this problem we propose to use the posterior probabilities of a conditional random field (CRF) lattice to prune the space of possible taggings. At the zero-order level the posterior probabilities of a token can be calculated independently from the other tokens of a sentence. The necessary computations can thus be performed in linear time. The features available to the model at this time are similar to the features used by a morphological analyzer (essentially the word form and features based on it), but also include the immediate lexical context. As the ambiguity of word types varies substantially, we just fix the average number of readings after pruning by dynamically estimating a probability threshold. Once we obtain the pruned lattice, we can add tag transitions and convert it into a first-order lattice. The quadratic forward-backward computations are now executed on the remaining plausible readings and thus efficient. We can now continue pruning and extending the lattice order at a relatively low additional runtime cost (depending on the pruning thresholds). The training of the model can be implemented efficiently by applying stochastic gradient descent (SGD). The CRF gradient can be calculated from a lattice of any order as long as the correct reading is still in the lattice. During training, we thus run the lattice pruning until we either reach the maximal order or until the correct reading is pruned. If the reading is pruned we perform the gradient update with the highest order lattice still containing the reading. This approach is similar to early updating in the structured perceptron literature and forces the model to learn how to keep the correct readings in the lower order lattices. In practice, we observe a high number of lower updates during the first training epoch and almost exclusively higher order updates during later epochs. We evaluate our CRF tagger on six languages with different morphological properties. We find that for languages with a high word form ambiguity such as German, the pruning results in a moderate drop in tagging accuracy while for languages with less ambiguity such as Spanish and Hungarian the loss due to pruning is negligible. However, our pruning strategy allows us to train higher order models (order > 1), which give substantial improvements for all languages and also outperform unpruned first-order models. That is, the model might lose some of the correct readings during pruning, but is also able to solve more of the harder cases that require more context. We also find our model to substantially and significantly outperform a number of frequently used taggers such as Morfette and SVMTool. Based on our morphological tagger we develop a simple method to increase the performance of a state-of-the-art constituency parser. A constituency tree describes the syntactic properties of a sentence by assigning spans of text to a hierarchical bracket structure. developed a language-independent approach for the automatic annotation of accurate and compact grammars. Their implementation -- known as the Berkeley parser -- gives state-of-the-art results for many languages such as English and German. For some MRLS such as Basque and Korean, however, the parser gives unsatisfactory results because of its simple unknown word model. This model maps unknown words to a small number of signatures (similar to our morphological classes). These signatures do not seem expressive enough for many of the subtle distinctions made during parsing. We propose to replace rare words by the morphological reading generated by our tagger instead. The motivation is twofold. First, our tagger has access to a number of lexical and sublexical features not available during parsing. Second, we expect the morphological readings to contain most of the information required to make the correct parsing decision even though we know that things such as the correct attachment of prepositional phrases might require some notion of lexical semantics. In experiments on the SPMRL 2013 dataset of nine MRLS we find our method to give improvements for all languages except French for which we observe a minor drop in the Parseval score of 0.06. For Hebrew, Hungarian and Basque we find substantial absolute improvements of 5.65, 11.87 and 15.16, respectively. We also performed an extensive evaluation on the utility of word representations for morphological tagging. Our goal was to reduce the drop in performance that is caused when a model trained on a specific domain is applied to some other domain. This problem is usually addressed by domain adaption (DA). DA adapts a model towards a specific domain using a small amount of labeled or a huge amount of unlabeled data from that domain. However, this procedure requires us to train a model for every target domain. Instead we are trying to build a robust system that is trained on domain-specific labeled and domain-independent or general unlabeled data. We believe word representations to be key in the development of such models because they allow us to leverage unlabeled data efficiently. We compare data-driven representations to manually created morphological analyzers. We understand data-driven representations as models that cluster word forms or map them to a vectorial representation. Examples heavily used in the literature include Brown clusters, Singular Value Decompositions of count vectors and neural-network-based embeddings. We create a test suite of six languages consisting of in-domain and out-of-domain test sets. To this end we converted annotations for Spanish and Czech and annotated the German part of the Smultron treebank with a morphological layer. In our experiments on these data sets we find Brown clusters to outperform the other data-driven representations. Regarding the comparison with morphological analyzers, we find Brown clusters to give slightly better performance in part-of-speech tagging, but to be substantially outperformed in morphological tagging.

I avsnitt 18 gstas vi av Smultron och fr reda p hur det r att plugga spel och funderar kring vad egentligen som gr en bra art direction samt presenterar vra nske-mash-ups!

"Aufregende Produkte" sollen noch dieses Jahr erscheinen; Neue iPhone Werbespots in Europa; Microsofts Anti-Apple Werbung findet keinen Anklang; Gericht verbietet Handel mit gebrauchten Software-Lizenzen; A2DP Adapter für das iPhone; Invoice 3; Smultron 3.5