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{"Orem": {"Alfred William Harper House": "The Alfred William Harper House is a historic house located at 125 West 400 North in Lindon, Utah.\n\n\n== Description and history ==\nIt is a two-story, soft rock vernacular house that was built in 1877 and extended in 1889.\nIt was listed on the National Register of Historic Places on June 9, 1987."}}
{ "cve": { "data_type": "CVE", "data_format": "MITRE", "data_version": "4.0", "CVE_data_meta": { "ID": "CVE-2012-4447", "ASSIGNER": "secalert@redhat.com" }, "problemtype": { "problemtype_data": [ { "description": [ { "lang": "en", "value": "CWE-119" } ] } ] }, "references": { "reference_data": [ { "url": "http:\/\/www.openwall.com\/lists\/oss-security\/2012\/09\/25\/14", "name": "[oss-security] 20120925 Re: CVE Request: libtiff: Heap-buffer overflow when processing a TIFF image with PixarLog Compression", "refsource": "MLIST", "tags": [] }, { "url": "http:\/\/secunia.com\/advisories\/49938", "name": "49938", "refsource": "SECUNIA", "tags": [ "Vendor Advisory" ] }, { "url": "https:\/\/bugzilla.redhat.com\/show_bug.cgi?id=860198", "name": "https:\/\/bugzilla.redhat.com\/show_bug.cgi?id=860198", "refsource": "MISC", "tags": [] }, { "url": "http:\/\/www.debian.org\/security\/2012\/dsa-2561", "name": "DSA-2561", "refsource": "DEBIAN", "tags": [] }, { "url": 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Anu Joshy<delimit>0<delimit>15<delimit>0<delimit>380<delimit>178<delimit>51<delimit>https://www.quora.com/profile/Anu-Joshy<delimit>NA<delimit>NA<delimit>http://www.facebook.com/100000077227845<delimit>NA<delimit>Paradoxical Thinker<delimit>Chennai, Tamil Nadu, IndiaSuggest Bio<delimit>NA<delimit>Indian Institute of Technology, Madras (IITM)Suggest Bio<delimit>PoetrySuggest Bio<delimit>5<delimit>Dating and RelationshipsSuggest Bio<delimit>2<delimit>IndiaSuggest Bio<delimit>2
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{"cacheTime": 1595096219, "baseUrl": "https://kater.me/d/77563", "title": "K吃掉r", "content": [{"author": "🐲🐱‍🚀🦃🎃🥙🌮🥐🥟我不是_MANO教主🐲🐱‍🚀🦃🎃🥙🌮🥐🥟", "body": "\n<p>\n K ate r\n </p>\n"}, {"author": "可可蘿的頭號大粉絲", "body": "\n<p>\n k以前吃掉R才對\n </p>\n"}, {"author": "🐲🐱‍🚀🦃🎃🥙🌮🥐🥟我不是_MANO教主🐲🐱‍🚀🦃🎃🥙🌮🥐🥟", "body": "\n<p>\n<a class=\"PostMention\" data-id=\"801780\" href=\"https://kater.me/d/77563/2\">\n 可可蘿的頭號大粉絲\n </a>\n 度 度的度度\n </p>\n"}, {"author": "沒一個好的,駁回", "body": "\n<p>\n 很好笑\n </p>\n"}]}
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The ambitious goal of this work is to develop a cross-lingual name tagging and linking framework for 282 languages that exist in Wikipedia. All the data sets, resources and systems for 282 languages are made publicly available as a new benchmark 1. We achieve this goal by performing a series of new KB mining methods: generating “silver-standard” annotations by transferring annotations from English to other languages through crosslingual links and KB properties, refining annotations through self-training and topic selection, deriving language-specific morphology features from anchor links, and mining word translation pairs from crosslingual links. Given a document in any of these languages, our framework is able to identify name mentions, assign a coarse-grained or fine-grained type to each mention, and link it to an English Knowledge Base (KB) if it is linkable. Both name tagging and linking results for 282 languages are promising on Wikipedia data and on-Wikipedia data. Creating “Silver-standard” through crosslingual entity transfer. Based on the observation that Wikipedia titles within any language tend to follow a consistent style and format, we propose an effective method to derive word translation pairs from these titles based on automatic alignment (Section 3.2). For entity linking, we don’t have ground truth for unlinkable mentions, so we only compute linking accuracy for linkable name mentions. (Mao Zedong (December 26, 1893 - September 9, 1976) is a Chinese revolutionary and politician. FA8750-13-2-0041 and FA8750-13-20045, and NSF CAREER No. We leverage these markups for develop- ✤Wikipedia Article: Mao Zedong (d. 26 Aralık 1893 - ö. We can see that our approach advances state-of-the-art languageindependent methods (Zhang et al., 2016a; Tsai et al., 2016) on the same data sets for most languages, and achieves 6.5% - 17.6% lower F-scores than the models trained from manually annotated gold-standard documents that include thousands of name mentions. The training instances for various entity types are quite imbalanced for some languages. Only a small percentage of names can be directly translated by matching against cross-lingual Wikipedia title pairs. Then we apply the initial tagger to the first unlabeled cluster, select the automatically labeled sentences with high confidence, add them back into the training data, and then re-train the tagger. We can see that except Romanian, our approach outperforms or achieves comparable accuracy as their method on all languages, without using any additional resources or tools such as name transliteration. In the future, we will explore the topological structure of related languages and exploit cross-lingual knowledge transfer to enhance the quality of extraction and linking. Efforts to save annotation cost for name tagging: Some previous work including (Ji and Grishman, 2006; Richman and Schone, 2008; Althobaiti et al., 2013) exploited semi-supervised methods to save annotation cost. For example, language barrier was one of the main difficulties faced by humanitarian workers responding to the Ebola crisis in 2014. For fair comparison, we use the same learning method and feature set as described in Section 2.3 to train the models using gold-standard data. Using this data set for training and testing, we achieved 96.0% F-score on this initial step, slightly better than their results (94.6% F-score). Wikipedia as background features for IE: Wikipedia pages have been used as additional features to improve various Information Extraction (IE) tasks, including name tagging (Kazama and Torisawa, 2007), coreference resolution (Paolo Ponzetto and Strube, 2006), relation extraction (Chan and Roth, 2010) and event extraction (Hogue et al., 2014). Table 4 shows the data statistics. 8http://www.darpa.mil/program/low-resource-languagesfor-emergent-incidents For each name mention, we generate all possible combinations of continuous tokens. The linking accuracy is higher than 80% for most languages. The first step is to classify English Wikipedia entries into certain entity types and then propagate these labels to other languages. The founder of the Chinese Communist Party (CCP) and the People's Republic of China.) ogy features contributed 11.1% and 7.1% absolute name tagging F-score gains to Turkish and Uzbek LORELEI data sets respectively. We adopt an idea from (Guo et al., 2014) which computes Normalized Pointwise Mutual Information (NPMI) (Bouma, 2009) between a tag and a token: 4For languages that don’t have word segmentation, we consider each character as a token, and use character embeddings only. The name tagging results on LORELEI data set are presented in Table 5. Based on the linked Wikipedia pages, we rank the frequency of Wikipedia categories and select the top 1% categories (4,035 in total) for our experiments. Mine word translations from cross-lingual links. Multi-lingual name tagging: Some recent research (Zhang et al., 2016a; Littell et al., 2016; Tsai et al., 2016) under the DARPA LORELEI program focused on developing name tagging techniques for low-resource languages. For example, the Turkish name tagger trained from the entire data set without topic selection yields 49.7% Fscore on LORELEI data set, and the performance is improved to 51.5% after topic selection. Other automatic name annotation generation methods have been proposed, including KB driven distant supervision (An et al., 2003; Mintz et al., 2009; Ren et al., 2015) and cross-lingual projection (Li et al., 2012; Kim et al., 2012; Che et al., 2013; Wang et al., 2013; Wang and Manning, 2014; Zhang et al., 2016b). Impact of Self-Training Using Turkish as a case study, the learning curves of self-training on Wikipedia and non-Wikipedia test sets are shown in Figure 6. (2013) manually annotated 4,853 English Wikipedia pages with 6 coarsegrained types (Person, Organization, Location, Other, Non-Entity, Disambiguation Page). For each pair of AMR entity type ta and YAGO entity type ty, we compute the Pointwise Mutual Information (PMI) (Ward Church and Hanks, 1990) of mapping ta to ty across all mentions in the AMR corpus. The only difference is that we construct knowledge networks (KNs) g(ti) for T based on their co-occurrence within a context window 9 instead of their AMR relations, because AMR parsing is not available for foreign languages. [[9 Eylül]] [[1976]]), Çinli devrimci ve siyasetçi. They exploited Wikipedia links to train a supervised linker. Multi-lingual entity linking: NIST TAC-KBP Tri-lingual entity linking (Ji et al., 2016) focused on three languages: English, Chinese and Spanish. We release the following resources for each of these 282 languages: “silver-standard” name tagging and linking annotations with multiple levels of granularity, morphology analyzer if it’s a morphologically-rich language, and an endto-end name tagging and linking system. Without using any manual annotations, our name taggers outperform previous methods on the same data sets for many languages. [[Akdeniz]]den means “from Mediterranean Sea”. For example, the gazetteers we used are different from those in (Tsai et al., 2016), and we did not use brown clusters as additional features. Figure 4 and Figure 5 summarize the performance, with some example languages marked for various ranges of data size. Derive morphology analysis from Wikipedia markups. Previous methods relied on either high-cost supervised learning (Roth et al., 2008; Mahmoudi et al., 2013; Ahlberg et al., 2015), or low-quality unsupervised learning (Grönroos et al., 2014; Ruokolainen et al., 2016). In some emergent settings such as natural disasters it’s important to train a system rapidly. 3.2 Name Translation The cross-lingual Wikipedia title pairs, generated through crowd-sourcing, generally follow a consistent style and format in each language. 1 Introduction Information provided in languages which people can understand saves lives in crises. Figure 1 shows an example of Wikipedia markups and KB properties. Çin Komünist Partisinin (ÇKP) ve Çin Halk Cumhuriyetinin kurucusu. 7D = 30,000 in our experiment. In this way, our framework produces three levels of entity typing schemas with different granularity: 4 main types (Person (PER), Organization (ORG), Geo-political Entity (GPE), Location (LOC)), 139 types in AMR, and 9,154 types in YAGO. As a result, the performance of ORG is the lowest, while GPE and LOC achieve higher than 75% F-scores for most languages. 6 Conclusions and Future Work We developed a simple yet effective framework that can extract names from 282 languages and link them to an English KB. The 9In our experiments, we use the previous four and next four name mentions as a context window. Not surprisingly, name tagging performs better for languages with more training mentions. 3.3 Entity Linking Given a set of tagged name mentions M = {m1,m2, ...,mn}, we first obtain their English translations T = {t1, t2, ..., tn} using the approach described above. For example, an entity with a birth date is likely to be a person, while an entity with a population property is likely to be a geo-political entity. Many fine-grained entity typing approaches (Fleischman and Hovy, 2002; Giuliano, 2009; Ekbal et al., 2010; Ling and Weld, 2012; Yosef et al., 2012; Nakashole et al., 2013; Gillick et al., 2014; Yogatama et al., 2015; Del Corro et al., 2015) also created annotations based on Wikipedia anchor links. program 8. Mayfield et al. 2 Name Tagging 2.1 Overview Our first step is to generate “silver-standard” name annotations from Wikipedia markups and train a universal name tagger. Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics, pages 1946–1958 Vancouver, Canada, July 30 - August 4, 2017. c©2017 Association for Computational Linguistics https://doi.org/10.18653/v1/P17-1178 The ambitious goal of this work is to develop a cross-lingual name tagging and linking framework for 282 languages that exist in Wikipedia. The views and conclusions contained in this document are those of the authors and should not be interpreted as representing the official policies, either expressed or implied, of the U.S. Government. The documents are from news sources and discussion fora. This procedure is repeated n times until we scan through all unlabeled data. Both name tagging and linking results for 282 languages are promising on Wikipedia data and on-Wikipedia data. Then we construct KNs g(ej) for each entity candidate ej in ti’s entity candidate list E(ti). For example, no Wikipedia titles contain the Turkish name “Pekin Teknoloji Enstitüsü (Beijing Institute of Technology)”. Given a document in any of these languages, our framework is able to identify name mentions, assign a coarse-grained or fine-grained type to each mention, and link it to an English Knowledge Base (KB) if it is linkable. (McNamee et al., 2011) extended it to 21 languages. 2.5 Final Training Data Selection for Populous Languages For some populous languages that have many millions of pages in Wikipedia, we obtain many sentences from self-training. 2.4 Self-Training to Enrich and Refine Labels The name annotations acquired from the above procedure are far from complete to compete with manually labeled gold-standard data. 4.3 Analysis Impact of KB-derived Morphological Features We measured the impact of our affix lists derived from Wikipedia markups on two morphologicallyrich languages: Turkish and Uzbek. Most of these previous methods manually classified many English Wikipedia entries into pre-defined entity types. 4 Experiments 4.1 Performance on Wikipedia Data We first conduct an evaluation using Wikipedia data as “silver-standard”. For example, Latin data includes 11% PER names, 84% GPE/LOC names and 5% ORG names. (2) there is no space between words, including Chinese, Thai and Japanese; (3) they are not written in latin script, such as the Dravidian group (Tamil, Telugu, Kannada, Malayalam). Some top-ranked topic labels include “International medical and health organizations”, “Human rights organizations”, “International development agencies”, “Western Asian countries”, “Southeast African countries”and “People in public health”. ✤Wikipedia Markup: [[Mao Zedong]] (d. [[26 Aralık]] [[1893]] - ö. IIS-1523198. In this work, we treat all languages independently when training their corresponding name taggers. Each entry in Wikipedia is also mapped to external knowledge bases such as DBpedia3, YAGO (Mahdisoltani et al., 2015) and Freebase (Bollacker et al., 2008) that contain rich properties. The U.S. Government is authorized to reproduce and distribute reprints for Government purposes notwithstanding any copyright notation here on. We run an English name tagger (Manning et al., 2014) and entity linker (Pan et al., 2015) on the Leidos corpus released by the DARPA LORELEI 5τ = 0 in our experiment. If no surface form can be matched then we determine the mention as unlinkable. In contrast, our approach doesn’t need any manual annotations or language-specific features, while generates both coarse-grained and fine-grained types. For all Wikipedia articles in a language, we cluster the unlabeled sentences into n clusters 6 by collecting sentences with low cross-entropy into the same cluster. Name mentions in Wikipedia are often labeled as anchor links to their corresponding referent pages. The initial annotations obtained from above are too incomplete and inconsistent. (2011) constructed a crosslingual entity linking collection for 21 languages, which covers ground truth for the largest number of languages to date. For example, from the Wikipedia markups of the following Turkish sentence: “Kıta Fransası, güneyde [[Akdeniz]]den kuzeyde [[Manş Denizi]] ve [[Kuzey Denizi]]ne, doğuda [[Ren Nehri]]nden batıda [[Atlas Okyanusu]]na kadar yayılan topraklarda yer alır. To fill in this gap, we would need to exploit more linguistic resources. The entity linking results on non-NIL mentions are presented in Table 6. We first initialize the name frequency threshold S to 40. Finally we run GIZA++ (Josef Och and Ney, 2003) to extract word for word translations from these title pairs, as shown in Table 2. For the first time, we propose to customize name annotations for specific downstream applications. We achieve this goal by performing a series of new KB mining methods: generating “silver-standard” annotations by transferring annotations from English to other languages through crosslingual links and KB properties, refining annotations through self-training and topic selection, deriving language-specific morphology features from anchor links, and mining word translation pairs from crosslingual links. Then we apply an unsupervised collective inference approach to link T to the KB, similar to our previous work (Pan et al., 2015). Using all DBpedia entity properties as features (60,231 in total), we train Maximum Entropy models to assign types with three levels of granularity to all English Wikipedia pages. We can see that self-training provides significant improvement for both Wikipedia (6% absolute gain) and non-Wikipedia test data (12% absolute gain). We developed a new method to derive generalizable affixes for morphologically rich language based on Wikipedia markups. Refine annotations through self-training. We evaluated our framework on both Wikipedia articles and external formal and informal texts and obtained promising results. Our previous work on event extraction (Li et al., 2011) found that by carefully select 1/3 topically related training documents for a test set, we can achieve the same performance as a model trained from the entire training set. From Table 2 we can see that the order of modifier and head word keeps consistent in Turkish and English titles. In the following we will describe how we acquire linguistic features. We generate the following 6 combinations: “Pekin”, “Teknoloji”, “Enstitüsü”, “Pekin Teknoloji”, “Teknoloji Enstitüsü” and “Pekin Teknoloji Enstitüsü”, and then extract all cross-lingual Wikipedia title pairs containing each combination. As expected the learning curve on Wikipedia data is more smooth and converges more slowly than that of non-Wikipedia data. Another unique challenge for morphologically rich languages is to segment each token into its stemming form and affixes. Wikipedia is a massively multi-lingual resource that currently hosts 295 languages and contains naturally annotated markups 2 and rich informational structures through crowd-sourcing for 35 million articles in 3 billion words. Next, we propagate the label of each English Wikipedia page to all entity mentions in all languages in the entire Wikipedia through monolingual redirect links and cross-lingual links. Again, we use a cross-lingual knowledge transfer strategy to leverage the widely available English corpora to choose entities with specific Wikipedia topic categories (Section 2.5). 10The mapping to language names can be found at http://nlp.cs.rpi.edu/wikiann/mapping 4.2 Performance on Non-Wikipedia Data In order to have more direct comparison with state-of-the-art name taggers trained from human annotated gold-standard data, we conduct experiments on non-Wikipedia data in 9 languages for which we have human annotated ground truths from the DARPA LORELEI program. We apply self-training to propagate and refine the labels. Customize annotations through cross-lingual topic transfer. Nothman et al. 9 Eylül 1976), Çinli devrimci ve siyasetçi. Therefore we compare our approach with theirs that uses a supervised name transliteration model (McNamee et al., 2011). We observed that self-training can provide further gains when the training data contains certain amount of noise. NPMI(tag, token) = ln p(tag,token)p(tag)p(token) − ln p(tag, token) (1) Then we select the sentences in which all annotations satisfy NPMI(tag, token) > τ as seeds 5. Therefore the results of our models trained from gold-standard data are slightly different from some previous work such as (Tsai et al., 2016), mainly due to different learning algorithms and different features sets. Previous work used name string match to propagate labels. Also note that since we don’t have perfect annotations on Wikipedia data for any language, these results can be used to estimate how predictable our “silver-standard” data is, but they are not directly comparable to traditional name tagging results measured against goldstandard data annotated by human. 2.3 Learning Model and KB Derived Features We use a typical neural network architecture that consists of Bi-directional Long Short-Term Memory and Conditional Random Fields (CRFs) network (Lample et al., 2016) as our underlying learning model for the name tagger for each language. 5 Related Work Wikipedia markup based silver standard generation: Our work was mainly inspired from previous work that leveraged Wikipedia markups to train name taggers (Nothman et al., 2008; Dakka and Cucerzan, 2008; Mika et al., 2008; Ringland et al., 2009; Alotaibi and Lee, 2012; Nothman et al., 2013; Althobaiti et al., 2014). HR0011-15-C-0115, ARL/ARO MURI W911NF-10-1-0533, DARPA DEFT No. Figure 2 shows our overall procedure and the following subsections will elaborate each component. All the data sets, resources and systems for 282 languages are made publicly available as a new benchmark 1. (Metropolitan France extends from the Mediterranean Sea to the English Channel and the North Sea, and from the Rhine to the Atlantic Ocean. We utilize the AMR corpus where each entity name mention is manually labeled as one of 139 types and linked to Wikipedia if it’s linkable. We iteratively run the selection algorithm until the size of the training set reaches D for a certain S. Topical Relatedness: Various criteria should be adopted for different scenarios. In total we obtained 10 million English pages labeled as entities of interest. 6n = 20 in our experiment. The languages with low name tagging performance can be categorized into three types: (1) the number of mentions is less than 2K, such as AtlanticCongo (Wolof), Berber (Kabyle), Chadic (Hausa), Oceanic (Fijian), Hellenic (Greek), Igboid (Igbo), Mande (Bambara), Kartvelian (Georgian, Mingrelian), Timor-Babar (Tetum), Tupian (Guarani) and Iroquoian (Cherokee) language groups; Precision is generally higher than recall for most of these languages, because the small number of linked mentions is not enough to cover a wide variety of entities. Impact of Topical Relatedness We also found that the topical relatedness measure proposed in Section 2.5 not only significantly reduces the size of training data and thus speeds up the training process for many languages, but also consistently improves the quality. We mine reliable word translations from cross-lingual Wikipedia titles, which enables us to adopt unsupervised English entity linking techniques such as (Pan et al., 2015) to directly link translated English name mentions to English KB. For example, if a name mention appears multiple times in a Wikipedia article, only the first mention is labeled with an anchor link. We propose to break language barriers by extracting information (e.g., entities) from a massive variety of languages and ground the information into an existing knowledge base which is accessible to a user in his/her own 1http://nlp.cs.rpi.edu/wikiann language (e.g., a reporter from the World Health Organization who speaks English only). We exploit the English Abstract Meaning Representation (AMR) corpus (Banarescu et al., 2013) which includes both name tagging and linking annotations for fine-grained entity types to train an automatic classifier. This framework follows a fully automatic training and testing pipeline, without the needs of any manual annotations or knowledge from native speakers. These approaches require English annotations for projection (Tsai et al., 2016), some input from a native speaker, either through manual annotations (Littell et al., 2016), or a linguistic survey (Zhang et al., 2016a). To the best of our knowledge, our multilingual name tagging and linking framework is applied to the largest number of languages. We first train an initial name tagger using seeds selected from the labeled data. If the number of the sentences is more than a desired size D for training 7, we set the threshold S = S + 5, otherwise S = S − 5. Name translation is a crucial step to generate candidate entities in cross-lingual entity linking. [[Çin Komünist Partisi]]nin (ÇKP) ve [[Çin Halk Cumhuriyeti]]nin kurucusu. Furthermore, we exploit each entry’s properties in DBpedia as features and thus eliminate the need of language-specific features and resources such as part-of-speech tagging as in previous work (Section 2.2). 3 Cross-lingual Entity Linking 3.1 Overview After we extract names from test documents in a source language, we translate them into English by automatically mined word translation pairs (Section 3.2), and then link translated English mentions to an external English KB (Section 3.3). We exploit Wikipedia markups to automatically learn affixes as language-specific features (Section 2.3). We share the same goal as (Sil and Florian, 2016) to extend cross-lingual entity linking to all languages in Wikipedia. For each language, we use 70% of the selected sentences for training and 30% for testing. The Leidos corpus consists of documents related to various disaster topics. The general idea of deriving noisy annotations from KB properties can also be extended to other IE tasks such as relation extraction. Note that this approach can only perform morphology analysis for words whose stem forms and affixes are directly concatenated. Therefore we develop the following effective methods to rank and select high-quality annotated sentences. Using an emergent disaster setting as a use case, we prefer sentences that include entities related to disaster related topics. But their methods required labeled data and name transliteration. We rank names by their frequency and dynamically set the frequency threshold to select a list of common names. Our framework performs both name identification and typing and takes advantage of richer structures in the KBs. Table 3 presents the overall performance for three coarse-grained entity types: PER, ORG and GPE/LOC, sorted by the number of name mentions. )”, we can learn the following suffixes: “den”, “ne”, “nden” and “na”. Commonness: we prefer sentences that include common entities appearing frequently in Wikipedia. In contrast, we apply self-training to label other mentions without links in Wikipedia articles even if they have different surface forms from the linked mentions (Section 2.4). Then we select the annotated sentences including names (e.g., “World Health Organization”) in all languages labeled with these topic labels to train the final model. We use such affix lists to perform basic word stemming, and use them as additional features to determine name boundary and type. The morphol- 11McNamee et al. (2011) did not develop a model for Chinese even though Chinese data set was included in the collection. For example, “den” is a noun suffix which indicates ablative case in Turkish. In total we obtain 2,756 entity mentions, along with their AMR entity types, Wikipedia titles, YAGO entity types and DBpedia properties. This indicates that when the training data is incomplete and noisy, the model can benefit from self-training through iterative label correction and propagation. F-score is generally higher than 80% when there are more than 10K mentions, and it significantly drops when there are less than 250 mentions. Then we leverage an entity’s properties in DBpedia as features for assigning types. The overall linking process is illustrated in Figure 3. For each translated name mention ti, an initial list of candidate entities E(ti) = {e1, e2, ..., ek} is generated based on a surface form dictionary mined from KB properties (e.g., redirects, names, aliases). When a Wikipedia user tries to link an entity mention in a sentence to an existing page, she/he will mark the title (the entity’s canonical form, without affixes) within the mention using brackets “[[]]”, from which we can naturally derive a word’s stem and affixes for free. Acknowledgments This work was supported by the U.S. DARPA LORELEI Program No. Therefore, each name mention is also assigned a list of YAGO entity types, ranked by their PMI scores with AMR types. 2.2 Initial Annotation Generation We start by assigning an entity type or “other” to each English Wikipedia entry. We compute the similarity between g(ti) and g(ej) based on three measures: salience, similarity and coherence, and select the candidate entity with the highest score. The major challenges and our new solutions are summarized as follows. ing a language universal framework to automatically extract name mentions from documents in 2https://en.wikipedia.org/wiki/Help:Wiki markup 3http://wiki.dbpedia.org 1946 282 languages, and link them to an English KB (Wikipedia in this work). Previous work on Arabic name tagging (Althobaiti et al., 2014) extracted entity titles as a gazetteer for stemming, and thus it cannot handle unknown names.
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{ "actions": [ { "acted_at": "2001-06-26", "references": [ { "reference": "CR S6930", "type": null } ], "text": "Sponsor introductory remarks on measure.", "type": "action" }, { "acted_at": "2001-06-26", "committee": "Committee on Governmental Affairs", "references": [ { "reference": "CR S6930", "type": "text of measure as introduced" } ], "status": "REFERRED", "text": "Referred to the Committee on Governmental Affairs.", "type": "referral" }, { "acted_at": "2001-10-16", "in_committee": "Committee on Governmental Affairs", "references": [], "subcommittee": "International Security, Proliferation and Federal Services", "text": "Committee on Governmental Affairs referred to Subcommittee on International Security, Proliferation and Federal Services.", "type": "referral" } ], "amendments": [], "bill_id": "sconres56-107", "bill_type": "sconres", "committees": [ { "activity": [ "referral", "in committee" ], "committee": "Senate Governmental Affairs", "committee_id": "SSGA" }, { "activity": [ "referral" ], "committee": "Senate Governmental Affairs", "committee_id": "SSGA", "subcommittee": "Subcommittee on International Security, Proliferation and Federal Services", "subcommittee_id": "07" } ], "congress": "107", "cosponsors": [ { "district": null, "name": "Clinton, Hillary Rodham", "sponsored_at": "2002-02-12", "state": "NY", "thomas_id": "01631", "title": "Sen", "withdrawn_at": null }, { "district": null, "name": "Landrieu, Mary L.", "sponsored_at": "2001-08-02", "state": "LA", "thomas_id": "01546", "title": "Sen", "withdrawn_at": null } ], "enacted_as": null, "history": { "awaiting_signature": false, "enacted": false, "vetoed": false }, "introduced_at": "2001-06-26", "number": "56", "official_title": "A concurrent resolution expressing the sense of Congress that a commemorative postage stamp should be issued by the United States Postal Service honoring the members of the Armed Forces who have been awarded the Purple Heart.", "popular_title": null, "related_bills": [], "short_title": null, "sponsor": { "district": null, "name": "Snowe, Olympia J.", "state": "ME", "thomas_id": "01085", "title": "Sen", "type": "person" }, "status": "REFERRED", "status_at": "2001-06-26", "subjects": [ "Armed forces and national security", "Commemorations", "Government operations and politics", "Military medals, decorations, etc.", "Philately", "Postage stamps", "War casualties" ], "subjects_top_term": "Commemorations", "summary": { "as": "Introduced", "date": "2001-06-26", "text": "Expresses the sense of Congress that: (1) a commemorative postage stamp should be issued by the Postal Service to honor the members of the armed forces who have been awarded the Purple Heart; and (2) the Citizens' Stamp Advisory Committee should recommend to the Postmaster General that such a stamp be issued within one year after the adoption of this resolution." }, "titles": [ { "as": "introduced", "title": "A concurrent resolution expressing the sense of Congress that a commemorative postage stamp should be issued by the United States Postal Service honoring the members of the Armed Forces who have been awarded the Purple Heart.", "type": "official" } ], "updated_at": "2013-02-02T20:51:10-05:00" }
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{"author":"akMassey","questions":[{"type":"quiz","question":"The legally binding formal agreement between an employee and employer is known as a","time":20000,"points":true,"pointsMultiplier":1,"choices":[{"answer":"job description","correct":false},{"answer":"employment contract","correct":true},{"answer":"employment statement","correct":false},{"answer":"job offer","correct":false}],"image":"","video":{"id":"","startTime":0,"endTime":0,"service":"youtube"},"questionFormat":0},{"type":"quiz","question":"What is the correct order for the job employment process","time":20000,"points":true,"pointsMultiplier":1,"choices":[{"answer":"advertise, apply, interview, offer","correct":true},{"answer":"advertise, interview, apply, offer","correct":false},{"answer":"applications, interviews, accept, offer","correct":false},{"answer":"application, advertise, interview, 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Accounting+ includes: Sexual Themes, Violence, Crude Humor, Strong Language, Blood, and Drug References!!"}}}}
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{"first_name":"Ali","last_name":"Asaria","permalink":"ali-asaria","crunchbase_url":"http://www.crunchbase.com/person/ali-asaria","homepage_url":null,"birthplace":null,"twitter_username":null,"blog_url":null,"blog_feed_url":null,"affiliation_name":"Well.ca","born_year":null,"born_month":null,"born_day":null,"tag_list":"","alias_list":null,"created_at":"Tue Feb 03 05:03:04 UTC 2009","updated_at":"Thu Jul 30 20:52:56 UTC 2009","overview":"<p>Ali Asaria is the CEO and founder of Well.ca, a Canadian health and beauty store. Ali is a technologist who is passionate about making Well.ca have the most innovative software possible while simultaneously building the most friendly, compassionate company culture possible.</p>\n\n<p>Prior to founding Well.ca, Ali worked at Research In Motion (RIM) where he independently developed BrickBreaker, the most popular Blackberry game that is now on more than 15 million devices worldwide. In 2005, Ali was RIM’s youngest patent recipient. Prior to RIM, Ali worked in Microsoft’s Redmond office on the Office 2007 User Interface Team.</p>","image":null,"degrees":[],"relationships":[{"is_past":false,"title":"CEO, Founder","firm":{"name":"Well.ca","permalink":"well-ca","type_of_entity":"company","image":{"available_sizes":[[[150,60],"assets/images/resized/0003/5522/35522v4-max-150x150.jpg"],[[250,100],"assets/images/resized/0003/5522/35522v4-max-250x250.jpg"],[[328,132],"assets/images/resized/0003/5522/35522v4-max-450x450.jpg"]],"attribution":null}}}],"investments":[],"milestones":[],"video_embeds":[],"external_links":[],"web_presences":[]}
{"userName": "JensOttoLange", "bio": "Design Thinking Coach fr digitale Innovation Berlin University of the Arts Industrial Design Berlin University of the Arts Communications", "fullName": "Jens Otto Lange", "externalUrl": ["http://de.linkedin.com/in/jensottolange", "http://www.xing.com/profile/JensOtto_Lange", "http://www.twitter.de/jensottolange", "http://www.facebook.com/jensottolange", "https://www.youtube.com/c/JensOttoLange"], "bigrams": ["Je", "en", "ns", "sO", "Ot", "tt", "to", "oL", "La", "an", "ng", "ge", "Je", "en", "ns", "s", "O", "Ot", "tt", "to", "o", "L", "La", "an", "ng", "ge"]}
{ "title": "HDFC Bank to mull relying on Indian market in $2.3 bn share sale", "link": "https://www.business-standard.com/article/finance/hdfc-bank-to-mull-relying-on-indian-market-in-2-3-bn-share-sale-118061800931_1.html", "pubDate": "Mon, 18 Jun 2018 19:32:00 +0530", "content": "HDFC Bank said in December its board had approved a potential equity offering of as much as Rs 240 billion", "contentSnippet": "HDFC Bank said in December its board had approved a potential equity offering of as much as Rs 240 billion", "guid": "https://www.business-standard.com/article/finance/hdfc-bank-to-mull-relying-on-indian-market-in-2-3-bn-share-sale-118061800931_1.html", "isoDate": "2018-06-18T14:02:00.000Z", "sentiment": { "score": 1, "comparative": 0.07692307692307693, "tokens": [ "hdfc", "bank", "to", "mull", "relying", "on", "indian", "market", "in", "23", "bn", "share", "sale" ], "words": [ "share" ], "positive": [ "share" ], "negative": [] }, "article": {}, "adjectives": [ "Indian", "approved", "potential", "billion" ], "verbs": [ "market", "share", "Bank", "board", "mull" ] }
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{"session": "Comisi\u00f3n Sexta Senado", "text": "Muy buenos d\u00edas, es una feliz oportunidad de estar en este escenario.\nAgradecemos a la doctora Martha Villalba, a la, ministra de Educaci\u00f3n\nGina Parody quien ha estado muy pendiente de estos desarrollos\nacad\u00e9micos, a nuestra viceministra de Educaci\u00f3n Superior Natalia\nAriza, a la doctora Gabriela Delgado de Colciencias, en este escenario\nacad\u00e9mico es preciso ahondar en aquellas situaciones que en los 17\na\u00f1os de estar funcionando el Sue Caribe nos hemos planteado.\nIndudablemente el Sue Caribe es una red que se fundamenta en unas\nsinergias desde el principio de corresponsabilidad y de unas l\u00edneas\nestrat\u00e9gicas de auto evaluaci\u00f3n, auto regulaci\u00f3n y trabajo conjunto y\ntenemos productos y en esos productos acad\u00e9micos tenemos las tres\nmaestr\u00edas, dos doctorados y tres cuatro programas que vienen en\nmarcha de doctorados y maestr\u00edas; los esfuerzos que se han hecho\nson unos esfuerzos que han tenido la gran dificultad de seguir\npotenciando los desarrollos en el horizonte de la acreditaci\u00f3n\ninstitucional de nuestras universidades, el a\u00f1o pasado la, ministra y\nViceministra nos convoc\u00f3 a preocuparnos por lograr que nuestras\ninstituciones lo repens\u00e1ramos en el horizonte de la acreditaci\u00f3n\ninternacional, y es as\u00ed como se cre\u00f3 la declaraci\u00f3n de Sincelejo\nmencionada por los compa\u00f1eros y firmada por los rectores de las\nuniversidades y la se\u00f1ora, ministra y la Universidad de Cartagena.\nComo l\u00edder acad\u00e9mico para dinamizar procesos de mejoras en todas y\ncada una de las universidades, este programa cre\u00f3 un hito hist\u00f3rico en\nel proyecto y en el plan estrat\u00e9gico acad\u00e9mico que tra\u00eda el Sue Caribe\ndonde ven\u00edamos trabajando en la formaci\u00f3n de nuestros profesores,\ncreando ofertas en el nivel de formaci\u00f3n avanzada, el programa paso\na paso por la excelencia en el caribe colombiano nos llevo a mirar la\nexcelencia en los programas de pregrado, que en los 17 a\u00f1os no\nhab\u00edamos hecho un trabajo con los programas de pregrado, estos son\naquellos que nos est\u00e1n mostrando una realidad que hace evidente la\nnecesidad que tenemos de apropiar recursos para la formaci\u00f3n de\nnuestros docentes, de becas, nosotros tenemos la oferta de maestr\u00edas\ny doctorados que no la hab\u00eda en la regi\u00f3n caribe, para casi todas las\nuniversidades las primeras maestr\u00edas y primeros doctorados han sido\nestos programas programas conjuntos hechos con gran esfuerzo con\nla voluntad siempre de los rectores, que han podido cambiar rectores\npero la esencia del Sue Caribe de la corresponsabilidad ha estado all\u00ed,\nde la autoevaluaci\u00f3n, de la autorregulaci\u00f3n y nosotros crear desde la\nautogesti\u00f3n productos acad\u00e9micos, tenemos tambi\u00e9n una red de\ninvestigadores del caribe, es una red internacional creada por la\nUniversidad de Wesindies y es una red que apunta a trabajar\nproblemas comunes del caribe colombiano con el gran caribe, y all\u00ed no\nhemos podido lograr Colciencias apoyos, se nos prometi\u00f3 una\nconvocatoria especial para apoyar este tipo de proyectos que son\nproyectos virtuales que tienen que ver con el agro, ejemplo tenemos\nproyectos de cacao, de \u00f1ame, de yuca, para el caso de San Andr\u00e9s,\nde la fruta pan, proyectos del mar, del manejo energ\u00e9tico, proyectos\nde educaci\u00f3n de buscar sobre todo en proyectos de creaci\u00f3n de unos\npuentes entre esas pol\u00edticas y buenas pr\u00e1cticas con la infancia, de\nbiling\u00fcismo all\u00ed necesitamos que se nos colabore con algunas\nconvocatorias que son especiales para proyectos que tienen en su\nesencia no solo el desarrollo de un conocimiento que va a impactar la\nvocaci\u00f3n de nuestras regiones del gran caribe, sino que estamos\ntrabajando con la internacionalizaci\u00f3n de nuestros profesores,\nestudiantes, con el biling\u00fcismo, o sea que convergen en esta\nestrategia e iniciativa del Sue Caribe voluntades. A todos aquellos\nfactores que van a ser de nuestra investigaci\u00f3n, desarrollo social y\ntecnol\u00f3gico la regionalizaci\u00f3n ha sido otro de los puntos que el consejo\nde rectores en su plan estrat\u00e9gico hemos trabajado, tenemos ofertas\nen regiones de dif\u00edcil acceso, caso curioso en los procesos de\nautoevaluaci\u00f3n la opini\u00f3n y la percepci\u00f3n de esos estudiantes de las\nregiones tienen unos indicadores que superan en su nivel de\nsatisfacci\u00f3n a los nuestros en la presencial; sin embargo el alto\ngobierno y los actores en las universidades su gran preocupaci\u00f3n es\nla educaci\u00f3n a distancia, por todos aquellos costos que tiene; tambi\u00e9n\nla conectividad porque tenemos un gran impase en poder nosotros\nimplementar mediaciones pedag\u00f3gicas desde la virtualidad y este\ntrabajo tambi\u00e9n aunado l\u00f3gico a los gobiernos locales por efectos\ntambi\u00e9n que contribuyen como la energ\u00eda en que se puedan tener\nestos avances de mediaciones virtuales que nos permitir\u00edan que estos\nprogramas que apuntan a la regionalizaci\u00f3n tuvieran mejores\ncondiciones de calidad, en relaci\u00f3n a nuestras nuevas ofertas estamos\nfortaleciendo las ofertas que tenemos en especial aquellas que\ntenemos con los ciclos proped\u00e9uticos, y hoy d\u00eda con las novedades\nque est\u00e1 proponiendo la nueva ley con la articulaci\u00f3n del sistema\neducativo desde la media por reconociendo y homologaci\u00f3n de cr\u00e9dito\ndesde all\u00ed estamos trabajando como esas din\u00e1micas de articular el\nsistema educativo colombiano, llaman a nuestras universidades a\nprever tambi\u00e9n una atenci\u00f3n especial en estos programas a distancia,\ny programas por ciclos proped\u00e9uticos.\nEn relaci\u00f3n a la cobertura las universidades han hecho un esfuerzo\npor ampliar la cobertura, las estad\u00edsticas lo demuestran pero el gran\nreto que tenemos es que la cobertura es con calidad, y la calidad\ncuesta, cuando nosotros hacemos un balance desde los planes de\nmejora que desde la autoevaluaci\u00f3n hemos venido trabajando en\nnuestras universidades, nos encontramos que esos planes construidos\ndesde las recomendaciones de los pares acad\u00e9micos tanto para\nrenovar registro calificado para llegar a la acreditaci\u00f3n de programas y\nla renovaci\u00f3n de acreditaciones de programas son unos planes de\nmejoras que financieramente nuestras universidades tienen\ndificultades y estamos haciendo grandes esfuerzos porque estos\nprogramas no puedan estar en niveles deseables que los pares\nacad\u00e9micos nos est\u00e1n recomendando.\nEn este programa paso a paso por la excelencia universitaria en el\ncaribe colombiano que lindara la Universidad de Cartagena y que\nvenimos desarrollando de una forma de codirecci\u00f3n con las dem\u00e1s\nuniversidades, tenemos como meta que en diciembre nosotros\npodamos tener por lo menos dos universidades con acreditaci\u00f3n\ninstitucional que ser\u00edan Cartagena y Magdalena; que podamos tener\ndos universidades en condiciones iniciales para acreditaci\u00f3n\ninstitucional que serian Atl\u00e1ntico y C\u00f3rdoba, y que las Universidades\nPopular del Cesar, y la Universidad de la Guajira puedan tener por lo\nmenos dos programas acreditados cada uno, y las universidades de\nSucre esta ad portas tambi\u00e9n de solicitar sus condiciones iniciales; o\nsea que el programa paso a paso con la excelencia universitaria en el\ncaribe es un programa que cambio los puntos focales del plan\nestrat\u00e9gico del Sue Caribe, donde est\u00e1bamos concentrados en la\nformaci\u00f3n del profesorado que lo seguimos estando pero en la\ncreaci\u00f3n de ofertas de maestr\u00edas y doctorados que fomentan la\ninvestigaci\u00f3n, la internacionalizaci\u00f3n.\nPero hoy d\u00eda se incrementa la dedicaci\u00f3n a las ofertas de pregrado\ncon el horizonte de la acreditaci\u00f3n internacional de la Universidad de\nCartagena y de la Universidad del Magdalena, este escenario\nacad\u00e9mico tiene resultados ya, pero tenemos grandes dificultades\npara poder nosotros cumplir con aquellos lineamientos de calidad de\ntodas y cada una de las universidades, son como una expectativa la\nhoja de ruta con la cual estamos caminando pero que si se requiere\nque tengamos un apoyo en los recursos para que puedan hacer viable\ntodas aquellas condiciones de calidad.\nAgradezco mucho a los representantes que nos han dado esta\noportunidad de escucharnos, a la presidenta, a la, ministra y\nviceministra, a la, directora de fomento de Colciencias, y estoy segura\nque de este dialogo vamos a construir nuevas rutas, vamos a tener\nmayores esperanzas como lo dec\u00eda el presidente del Sue, que siempre\nla esperanza es lo que en estos 17 a\u00f1os ha sido el faro y es lo que ha\npermitido la corresponsabilidad de nuestra red de Sue Caribe haya\ntenido los frutos que hoy podemos mostrar y que igualmente haya\npodido construir expectativas con el Gobierno Nacional con los apoyos\nrequeridos.", "external_id": "#dra-diana-lago", "party": null, "presidents": [], "title": "Representante", "time": "2015-05-20T09:03:20-05:00", "transcript": "10816-acta-025-de-2015", "order": 20, "name": "Dra. Diana Lago"}
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"", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", ""], ["Hungary", "HUN", "Public and publicly guaranteed debt service (% of exports of goods, services and primary income)", "DT.TDS.DPPG.XP.ZS", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", ""], ["Hungary", "HUN", "Public and publicly guaranteed debt service (% of GNI)", "DT.TDS.DPPG.GN.ZS", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "9", "11", "13"], ["Hungary", "HUN", "Public credit registry coverage (% of adults)", "IC.CRD.PUBL.ZS", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", ""], ["Hungary", "HUN", "Public spending on education, total (% of GDP)", "SE.XPD.TOTL.GD.ZS", "", "", "", "", "", "", "", "", "", "", "3", "3", "3", "3", "3", "3", "3", "3", "3", "3", "4", "4", "4", "5", "4"], ["Hungary", "HUN", "Public spending on education, total (% of government expenditure)", 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"IQ.WEF.PORT.XQ", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", ""], ["Hungary", "HUN", "Quasi money (current LCU)", "FM.LBL.QMNY.CN", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "230,290,000,000", "235,220,000,000", "248,390,000,000"], ["Hungary", "HUN", "Quasi-liquid liabilities (% of GDP)", "FS.LBL.QLIQ.GD.ZS", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", ""], ["Hungary", "HUN", "Rail lines (total route-km)", "IS.RRS.TOTL.KM", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "7,613", "7,617", "7,611", "7,612", "7,616"], ["Hungary", "HUN", "Railways, goods transported (million ton-km)", "IS.RRS.GOOD.MT.K6", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "24,041", "23,951", "20,164", "22,256", "21,968"], ["Hungary", "HUN", "Railways, passengers carried (million passenger-km)", "IS.RRS.PASG.KM", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "13,550", "13,389", "12,913", "10,981", "11,155"], ["Hungary", "HUN", "Ratio of female to male labor force participation rate (%)", "SL.TLF.CACT.FM.ZS", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", ""], ["Hungary", "HUN", "Ratio of female to male primary enrollment (%)", "SE.ENR.PRIM.FM.ZS", "", "", "", "", "", "", "", "", "", "", "", "97", "98", "99", "99", "100", "100", "100", "100", "100", "99", "99", "99", "99", "99"], ["Hungary", "HUN", "Ratio of female to male secondary enrollment (%)", "SE.ENR.SECO.FM.ZS", "", "", "", "", "", "", "", "", "", "", "", "89", "90", "93", "92", "93", "94", "94", "95", "96", "96", "97", "99", "99", "99"], ["Hungary", "HUN", "Ratio of female to male tertiary enrollment (%)", "SE.ENR.TERT.FM.ZS", "", "", "", "", "", "", "", "", "", "", "", "77", "82", "87", "89", "93", "97", "103", "106", "105", "104", 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"\n我默默地看着你\n黑夜的框拥住:一双\n330万划素的瞳孔\n在回忆的脑海里被迫靠岸\n白幡飘扬。折起的莲花座\n我看见水百合哭泣\n而梦的羽衣太轻\n我默默地看着你\n白天的光射进:一张\n扬起45度的嘴唇\n在故事的章节里划上句号\n阳光飘落。沉重的紫檀香\n我听见雨在风中挣扎\n而路的颜色太湿\n我默默地看着你\n黄蔴带丝丝绕:一朵\n菊花芬芳扭转成结\n别一针细鐡铜勾刺上心头\n思念飘渺。寂寞的桧木棺\n我感觉观世音的慈悲\n带着你的笑越走\n越远"
{"artist": "Blank & Jones", "timestamp": "2011-08-05 16:53:51.146119", "similars": [], "tags": [["trance", "100"], ["progressive trance", "100"]], "track_id": "TRIZUIX12903CA7ACB", "title": "Menue (Intro)"}
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{ "Utterance": "Wow, alright. Well, thanks. And, uh, you- you have a good one.", "StartTime": 223.22, "EndTime": 227.52, "Speaker": "Female", "Emotion": "undecided", "SessionID": "Ses04" }
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Just a minor title comment.", "markup": "markdown", "html": "<p>Code looks good to me. Just a minor title comment.</p>", "type": "rendered"}, "created_on": "2017-04-28T22:10:15.353170+00:00", "user": {"display_name": "Patricio Reyna Almandos", "uuid": "{8d07164d-cee3-4eb0-8816-97f42c297bc3}", "links": {"self": {"href": "https://api.bitbucket.org/2.0/users/%7B8d07164d-cee3-4eb0-8816-97f42c297bc3%7D"}, "html": {"href": "https://bitbucket.org/%7B8d07164d-cee3-4eb0-8816-97f42c297bc3%7D/"}, "avatar": {"href": "https://avatar-management--avatars.us-west-2.prod.public.atl-paas.net/557058:2f4f0911-c46a-4f3c-92af-650544415846/235d39e4-77b1-46b7-ac0f-900424378fef/128"}}, "nickname": "Patricio Reyna Almandos", "type": "user", "account_id": "557058:2f4f0911-c46a-4f3c-92af-650544415846"}, "updated_on": "2017-04-28T22:10:15.355461+00:00", "type": "pullrequest_comment", "id": 35949780}, {"links": {"self": {"href": "data/repositories/osrf/cloudsim-widgets/pullrequests/153/comments/35949981.json"}, "html": {"href": "#!/osrf/cloudsim-widgets/pull-requests/153/_/diff#comment-35949981"}}, "deleted": false, "pullrequest": {"type": "pullrequest", "id": 153, "links": {"self": {"href": "data/repositories/osrf/cloudsim-widgets/pullrequests/153.json"}, "html": {"href": "#!/osrf/cloudsim-widgets/pull-requests/153"}}, "title": "SRC: Link area and copy to clipboard"}, "content": {"raw": "It works. I tested in my local env. \nMinor suggestion: add a notification to users saying \"copied to clipboard!\" or smth similar ?", "markup": "markdown", "html": "<p>It works. I tested in my local env. \nMinor suggestion: add a notification to users saying \"copied to clipboard!\" or smth similar ?</p>", "type": "rendered"}, "created_on": "2017-04-28T22:17:40.511626+00:00", "user": {"display_name": "Patricio Reyna Almandos", "uuid": "{8d07164d-cee3-4eb0-8816-97f42c297bc3}", "links": {"self": {"href": "https://api.bitbucket.org/2.0/users/%7B8d07164d-cee3-4eb0-8816-97f42c297bc3%7D"}, "html": {"href": "https://bitbucket.org/%7B8d07164d-cee3-4eb0-8816-97f42c297bc3%7D/"}, "avatar": {"href": "https://avatar-management--avatars.us-west-2.prod.public.atl-paas.net/557058:2f4f0911-c46a-4f3c-92af-650544415846/235d39e4-77b1-46b7-ac0f-900424378fef/128"}}, "nickname": "Patricio Reyna Almandos", "type": "user", "account_id": "557058:2f4f0911-c46a-4f3c-92af-650544415846"}, "updated_on": "2017-04-28T22:17:40.513972+00:00", "type": "pullrequest_comment", "id": 35949981}, {"links": {"self": {"href": "data/repositories/osrf/cloudsim-widgets/pullrequests/153/comments/35950055.json"}, "html": {"href": "#!/osrf/cloudsim-widgets/pull-requests/153/_/diff#comment-35950055"}}, "parent": {"id": 35949981, "links": {"self": {"href": "data/repositories/osrf/cloudsim-widgets/pullrequests/153/comments/35949981.json"}, "html": {"href": "#!/osrf/cloudsim-widgets/pull-requests/153/_/diff#comment-35949981"}}, "depth": 1}, "deleted": false, "pullrequest": {"type": "pullrequest", "id": 153, "links": {"self": {"href": "data/repositories/osrf/cloudsim-widgets/pullrequests/153.json"}, "html": {"href": "#!/osrf/cloudsim-widgets/pull-requests/153"}}, "title": "SRC: Link area and copy to clipboard"}, "content": {"raw": "Nice, done! facd4c5", "markup": "markdown", "html": "<p>Nice, done! <a href=\"#!/osrf/cloudsim-widgets/commits/facd4c5\" rel=\"nofollow\" class=\"ap-connect-link\">facd4c5</a></p>", "type": "rendered"}, "created_on": "2017-04-28T22:20:05.051352+00:00", "user": {"display_name": "Louise Poubel", "uuid": "{5cfa2075-477b-4ded-bdb9-8d2479544ec4}", "links": {"self": {"href": "https://api.bitbucket.org/2.0/users/%7B5cfa2075-477b-4ded-bdb9-8d2479544ec4%7D"}, "html": {"href": "https://bitbucket.org/%7B5cfa2075-477b-4ded-bdb9-8d2479544ec4%7D/"}, "avatar": {"href": "https://avatar-management--avatars.us-west-2.prod.public.atl-paas.net/557058:6ff86fcb-b7ab-44a5-b8a6-f6d9cae8b6e8/7d903d90-c5ea-4182-b7ef-0d467e9e1c74/128"}}, "nickname": "chapulina", "type": "user", "account_id": "557058:6ff86fcb-b7ab-44a5-b8a6-f6d9cae8b6e8"}, "updated_on": "2017-04-28T22:20:05.053697+00:00", "type": "pullrequest_comment", "id": 35950055}], "page": 1, "size": 5}
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{ "alterego_mod:MasochisticConstitution": { "NAME": "Masochistic Constitution", "FLAVOR": "Direct all of their hatred towards yourself.", "DESCRIPTIONS": [ "Gain 1 Max HP after every battle in which you took unblocked damage." ] }, "alterego_mod:FleetingLove": { "NAME": "Fleeting Love", "FLAVOR": "Wronged by the one you loved, you close your heart off from others.", "DESCRIPTIONS": [ "You can take a maximum of 6 damage on the first turn of combat." ] }, "alterego_mod:SomeoneToFightFor": { "NAME": "Someone to Fight For", "FLAVOR": "Thinking about your lover waiting for you is really motivating!", "DESCRIPTIONS": [ "Start combat in a random Alterego #yStance." ] }, "alterego_mod:SistersRibbon": { "NAME": "Sister's Ribbon", "FLAVOR": "If only you could reach into the past, and talk to her before all of this.", "DESCRIPTIONS": [ " NL Usable once per combat. NL Right click: Move a card from your exhaust pile to your hand." ] } }
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{"name":"UnHub","permalink":"unhub","crunchbase_url":"http://www.crunchbase.com/company/unhub","homepage_url":"http://UnHub.com","blog_url":"","blog_feed_url":"","twitter_username":null,"category_code":"web","number_of_employees":2,"founded_year":2009,"founded_month":3,"founded_day":2,"deadpooled_year":null,"deadpooled_month":null,"deadpooled_day":null,"deadpooled_url":null,"tag_list":"socialnetworks, socialnetworking, social, sharing, socialmedia, identity, skittles","alias_list":"","email_address":"info@unhub.com","phone_number":"","description":"Aggregated Social Network","created_at":"Mon Mar 09 04:09:01 UTC 2009","updated_at":"Mon Apr 20 18:46:32 UTC 2009","overview":"<p>UnHub provides simple ways to showcase the online you.</p>\n\n<p>Personal Profile Bar</p>\n\n<p>Three days after being inspired by the new Skittles website, we released the UnHub Profile Bar which allows anyone to create a similar site for free. Your UnHub Profile URL directs others to your profiles, photostreams, channels, etc. with a persistent bar at the top of the screen. Instead of having to share numerous links to your various profiles, you can simply share your UnHub URL.</p>\n\n<p>Your UnHub URL is perfect for email signatures or your Twitter profile&#8217;s &#8220;Web&#8221; entry. It&#8217;s for people and businesses. For example, see &lt;a href=&quot;http://unhub.com/BarackObama&quot; target=&quot;<em>blank&quot;&gt;Barack Obama&#8217;s&lt;/a&gt; and &lt;a href=&quot;http://unhub.com/josies&quot; target=&quot;</em>blank&quot;&gt;Josie&#8217;s Restaurant&#8217;s&lt;/a&gt;.</p>\n\n<p>Personal Link Shortener</p>\n\n<p>UnHub&#8217;s newest service is a personal link shortener. Like others, we turn long URLs into a short ones. Your shortened links also include a bar at the top with a link to your UnHub Profile Bar (or any other website you choose), so people can see who shared the link, even if it gets forwarded along or retweeted.</p>\n\n<p>To get started, just type unhub.com/ in front of any URL in your browser&#8217;s address bar and hit enter. The UnHub Link Bar also displays value added info about the page itself, such as number of Diggs, bookmarks on Delicious and clicks on bit.ly.</p>","image":{"available_sizes":[[[150,50],"assets/images/resized/0003/7564/37564v2-max-150x150.png"],[[225,75],"assets/images/resized/0003/7564/37564v2-max-250x250.png"],[[225,75],"assets/images/resized/0003/7564/37564v2-max-450x450.png"]],"attribution":null},"products":[{"name":"Unhub Personal Profile Bar","permalink":"personal-profile-bar","image":null}],"relationships":[{"is_past":false,"title":"Co-Founder","person":{"first_name":"Vinicius","last_name":"Vacanti","permalink":"vin-vacanti","image":{"available_sizes":[[[150,150],"assets/images/resized/0003/4958/34958v4-max-150x150.jpg"],[[250,250],"assets/images/resized/0003/4958/34958v4-max-250x250.jpg"],[[450,450],"assets/images/resized/0003/4958/34958v4-max-450x450.jpg"]],"attribution":""}}},{"is_past":false,"title":"Co-Founder","person":{"first_name":"Jim","last_name":"Moran","permalink":"jim-moran","image":{"available_sizes":[[[131,150],"assets/images/resized/0003/9346/39346v2-max-150x150.jpg"],[[146,167],"assets/images/resized/0003/9346/39346v2-max-250x250.jpg"],[[146,167],"assets/images/resized/0003/9346/39346v2-max-450x450.jpg"]],"attribution":""}}}],"competitions":[],"providerships":[],"total_money_raised":"$0","funding_rounds":[],"investments":[],"acquisition":null,"acquisitions":[],"offices":[],"milestones":[],"ipo":null,"video_embeds":[{"embed_code":"<object width=\"400\" height=\"260\"><param name=\"allowfullscreen\" value=\"true\" /><param name=\"allowscriptaccess\" value=\"always\" /><param name=\"movie\" value=\"http://vimeo.com/moogaloop.swf?clip_id=3547985&amp;server=vimeo.com&amp;show_title=1&amp;show_byline=1&amp;show_portrait=0&amp;color=&amp;fullscreen=1\" /><embed src=\"http://vimeo.com/moogaloop.swf?clip_id=3547985&amp;server=vimeo.com&amp;show_title=1&amp;show_byline=1&amp;show_portrait=0&amp;color=&amp;fullscreen=1\" type=\"application/x-shockwave-flash\" allowfullscreen=\"true\" allowscriptaccess=\"always\" width=\"400\" height=\"260\"></embed></object><br /><a href=\"http://vimeo.com/3547985\">UnHub Demo</a> from <a href=\"http://vimeo.com/user1211317\">Vinicius Vacanti</a> on <a href=\"http://vimeo.com\">Vimeo</a>.","description":"<p>UnHub Demo Page</p>"},{"embed_code":"<object width=\"400\" height=\"300\"><param name=\"allowfullscreen\" value=\"true\" /><param name=\"allowscriptaccess\" value=\"always\" /><param name=\"movie\" value=\"http://vimeo.com/moogaloop.swf?clip_id=4091462&amp;server=vimeo.com&amp;show_title=1&amp;show_byline=1&amp;show_portrait=0&amp;color=&amp;fullscreen=1\" /><embed src=\"http://vimeo.com/moogaloop.swf?clip_id=4091462&amp;server=vimeo.com&amp;show_title=1&amp;show_byline=1&amp;show_portrait=0&amp;color=&amp;fullscreen=1\" type=\"application/x-shockwave-flash\" allowfullscreen=\"true\" allowscriptaccess=\"always\" width=\"400\" height=\"300\"></embed></object><br /><a href=\"http://vimeo.com/4091462\">UnHub Preview</a> from <a href=\"http://vimeo.com/user1211317\">Vinicius Vacanti</a> on <a href=\"http://vimeo.com\">Vimeo</a>","description":"<p>Unhub Personal Link Shortener</p>"}],"screenshots":[{"available_sizes":[[[150,135],"assets/images/resized/0003/7562/37562v2-max-150x150.png"],[[250,225],"assets/images/resized/0003/7562/37562v2-max-250x250.png"],[[450,405],"assets/images/resized/0003/7562/37562v2-max-450x450.png"]],"attribution":null},{"available_sizes":[[[150,82],"assets/images/resized/0003/7563/37563v2-max-150x150.png"],[[250,137],"assets/images/resized/0003/7563/37563v2-max-250x250.png"],[[450,247],"assets/images/resized/0003/7563/37563v2-max-450x450.png"]],"attribution":null}],"external_links":[]}
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{"New_Orleans": {"Florida Projects": "The Florida Project is a 2017 American drama film directed by Sean Baker and written by Baker and Chris Bergoch. It stars Willem Dafoe, Brooklynn Prince, Bria Vinaite, Valeria Cotto, Christopher Rivera, and Caleb Landry Jones. The plot follows a six-year-old girl living in a motel with her rebellious mother in Greater Orlando as they try to stay out of trouble and make ends meet."}}
[{"text": "【十问华为鸿蒙操作系统:跟安卓有何差异?其他终端厂商怎么看?】“鸿蒙”初开,万众瞩目。这款华为开发的操作系统,到底有何特别之处?华为怎么处理鸿蒙和安卓系统的关系?在手机操作系统上,鸿蒙短期内仍然是华为的备用选择。近日,华为创始人任正非在英国天空电视台采访时就表态,“如果美国封锁,不让谷歌提供安卓系统给我们的话,世界上出现第三种系统,对美国称霸世界也是不利的。”鸿蒙和安卓等操作系统比还存在哪些差距?华为表示:鸿蒙和安卓等操作系统相比,最大的差距显然还是生态。其他终端厂商对鸿蒙是什么态度?余承东称,有跟其他厂商交流过,他们都比较感兴趣,“他们有一点点担忧,鸿蒙是华为开发的。我们成立了开源基金会,其他手机公司也参加了,我们会让大家看到这对大家是公平的,都可以用。”O十问华为鸿蒙操作系统:跟安卓有何差异?其他终端厂商怎么看?", "author": "凤凰网财经", "time": "凤凰网财经2019年08月19日08:02 来自专业版微博", "transmit": 31, "comment": 95, "zan": 266, "sentiment": [{"positive_prob": 0.00315848, "confidence": 0.992981, "negative_prob": 0.996841, "sentiment": 0}]}, {"text": "【不止“鸿蒙”,华为将再推竞争利器】俄罗斯卫星通讯社:华为近日宣布,将在今年10月推出自己的地图服务,取名地图套件(MapKit)。MapKit不是直接供消费者使用,而是旨在鼓励软件开发人员根据其映射功能提出应用程序。该公司表示,华为地图套件将连接到约150个国家和地区的第三方移动应用程序和本地地图服务提供商。O网页链接", "author": "参考消息", "time": "参考消息2019年08月19日11:30 来自微博weibo.com", "transmit": 74, "comment": 78, "zan": 653, "sentiment": [{"positive_prob": 0.733796, "confidence": 0.408435, "negative_prob": 0.266204, "sentiment": 2}]}, {"text": "朋友圈一堆华为的人发鸿蒙OS1.0​", "author": "科技有意思", "time": "科技有意思2019年08月19日16:14 来自热搜第一推荐", "transmit": 10, "comment": 77, "zan": 462, "sentiment": [{"positive_prob": 0.906488, "confidence": 0.792196, "negative_prob": 0.0935118, "sentiment": 2}]}, {"text": "不止“鸿蒙”华为将再推竞争利器O不止“鸿蒙”华为将再推竞争利器​", "author": "但斌", "time": "但斌2019年08月19日10:11 来自新浪新闻中心", "transmit": 39, "comment": 17, "zan": 67, "sentiment": [{"positive_prob": 0.06946, "confidence": 0.845644, "negative_prob": 0.93054, "sentiment": 0}]}, {"text": "十问华为鸿蒙操作系统:跟安卓有何差异,其他终端厂商怎么看。(澎湃)​", "author": "财联社APP", "time": "财联社APP2019年08月19日10:03 来自微博weibo.com", "transmit": 26, "comment": 27, "zan": 83, "sentiment": [{"positive_prob": 0.728211, "confidence": 0.396025, "negative_prob": 0.271789, "sentiment": 2}]}, {"text": "鸿蒙操作系统是个啥,牛不牛?O鸿蒙操作系统是个啥,牛不牛?|余兴镐这是一个对鸿蒙的深度解读,对华为强调的“分布式OS”特性解释得不错。值得注意的一个演示,是拿大疆无人机当摄像头外设。由于公布的细节不多,鸿蒙受到了较大质疑。华为的朋友说,很快就有大量信息公布出来。​", "author": "风云学会陈经", "time": "风云学会陈经2019年08月19日08:46 来自小米9", "transmit": 61, "comment": 36, "zan": 175, "sentiment": [{"positive_prob": 0.664435, "confidence": 0.254301, "negative_prob": 0.335565, "sentiment": 2}]}, {"text": "看到这个截图,表示很羡慕#华为鸿蒙##鸿蒙OS#​", "author": "菊厂搞机", "time": "菊厂搞机2019年08月19日16:04 来自HUAWEIP30Pro", "transmit": 10, "comment": 26, "zan": 127, "sentiment": [{"positive_prob": 0.999662, "confidence": 0.99925, "negative_prob": 0.000337613, "sentiment": 2}]}, {"text": "荣耀智慧屏在华为荣耀生态里面承担了什么定位,实际表现又怎样?戳文章了解详情发布了头条文章:《鸿蒙OS首款产品:荣耀智慧屏和智能电视对比评测,差别在哪里?》O鸿蒙OS首款产品:荣耀智慧屏和智能电视对比评测,差别在哪里?​", "author": "科技氪", "time": "科技氪2019年08月19日19:18 来自微博weibo.com", "transmit": 19, "comment": 54, "zan": 65, "sentiment": [{"positive_prob": 0.46456, "confidence": 0.291196, "negative_prob": 0.53544, "sentiment": 1}]}, {"text": "华为说,只要美国允许用安卓,就在手机上接着用安卓。这个大度其实是没有必要的。本来就说鸿蒙的生态还不行,那就更要尽早使用,这样生态才能发展起来,不用岂不是老也起不来么?​", "author": "李北方同学", "time": "李北方同学2019年08月19日22:24 来自搜狗高速浏览器", "transmit": 22, "comment": 40, "zan": 99, "sentiment": [{"positive_prob": 0.000788147, "confidence": 0.998249, "negative_prob": 0.999212, "sentiment": 0}]}]
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{"text": "It is easy to reduce the word problem for TMs to this - the rules encode legal configuration transitions of the TM , and the input and output words represent the initial and final configuration of the TM . -Shaull Feb 23 ' 13 at 9:39 Nederhof , M J 2010 , ' Transforming lexica as trees ' .", "label": "", "metadata": {}, "score": "36.664185"} {"text": "And here , in particular , the constitutive transformation that we discussed just a bit earlier .So we conclude then that the updated Lagrangian terms are identically equal to the total Lagrangian terms , provided we follow the transformation rules that I have summarized .", "label": "", "metadata": {}, "score": "39.459366"} {"text": "The ' ' nastier ' ' the grammar is for computational reasons the better it is for us .( We would prefer a grammar which has been written without any regard for its computational properties . )We are looking for an appropriate grammar for a long time now .", "label": "", "metadata": {}, "score": "42.5854"} {"text": "3 ) Same question but for a context - sensitive grammar .In this case , the decision problem of whether there exists a derivation at all is PSPACE - complete .I am not sure about complexity of finding a shortest derivation .", "label": "", "metadata": {}, "score": "43.194046"} {"text": "Special Issue on Efficient .Processing with HPSG : Methods , Systems , Evaluation , forthcoming .Mark - Jan Nederhof .Efficient generation of random sentences .Natural .Language Engineering 2(1 ) , 1996 .Stephan Oepen , Dan P. Flickinger .", "label": "", "metadata": {}, "score": "43.252296"} {"text": "The parameters and features are not important to us , nor is the language generated by the grammar or the declarative \" meaning \" .Our purpose is to measure the size of different ( variants of ) LR parsers for natural language grammars .", "label": "", "metadata": {}, "score": "43.554"} {"text": "Of course , the kinematic relations for the total and updated Lagrangian formulations , we discussed in an earlier lecture already .Let 's look at this question here , now , in quite gentle terms .And I have rephrased it here once more , so as to really make very clear what I mean .", "label": "", "metadata": {}, "score": "44.171963"} {"text": "You can use these material relations directly in a large displacement , large rotation analysis , provided we use the total Lagrangian formulation .And I explained that , I spent a bit of time on that , in the previous lecture .", "label": "", "metadata": {}, "score": "44.296585"} {"text": "Does anyone still use transformational grammars , a la COMIT ? -John ] -- Mark - Jan Nederhof , Giorgio Satta , and Stuart M. Shieber .Partially ordered multiset context - free grammars and ID / LP parsing .In Proceedings of the Eighth International Workshop on Parsing Technologies , pages 171 - 182 , Nancy , France , April 2003 .", "label": "", "metadata": {}, "score": "44.495625"} {"text": "In the meanwhile I post a preliminary list of projects and URLs relevant for the issue of grammar debugging ( list extracted from the responses received and through browsing on Internet ) .RELEVANT EUROPEAN PROJECTS .OTHER RELEVANT PROJECTS .-", "label": "", "metadata": {}, "score": "44.544617"} {"text": "Let us look at the second term .Here , we have , on the left hand side , the term of the total Lagrangian formulation .And we ask whether this is equal to the term that I showed you on the right hand side , corresponding to the updated Lagrangian formulation .", "label": "", "metadata": {}, "score": "46.065384"} {"text": "And that explains the difference in the response calculated .But the TL and UL formulations , using the eight node isoparametric plane , the strain elements , we obtain basically the same response .The formations are very large for this frame .", "label": "", "metadata": {}, "score": "46.39605"} {"text": "Here , again , is a total external virtual work .And here , a constitutive relation enters , corresponding to this formulation .And here we have the Cauchy stress that is being used in the updated Lagrangian formulation .The terms used in the formulations are summarized on this view graph .", "label": "", "metadata": {}, "score": "46.418846"} {"text": "Across So there 's a plus here for the updated Lagrangian formulation .However , if the stress - strain law is available in terms of the second Piola - Kirchhoff stress , then it is most natural to use a total Lagrangian formulations .", "label": "", "metadata": {}, "score": "47.401833"} {"text": "It seems reminiscent of PPAD , except that a path is not guaranteed to exist ... - usul Feb 23 ' 13 at 7:52 .Just to clarify .It is not restricted to just a and b. The input string can have many characters .", "label": "", "metadata": {}, "score": "47.54386"} {"text": "As noted by usul , my previous answer is too broad , so these are some additional notes with personal ideas ( so it is now more like an extended comment , than an answer ) : .You can easily find a grammar that simulates a NTM ; as pointed out by Jeffe Markov algorithms are Turing complete , so given a TM and a string w you can build a grammar and a source string that generate a target string iff TM halts on w. So finding the minimum is undecidable , too .", "label": "", "metadata": {}, "score": "47.677185"} {"text": "In fact , we used four node isoparametric beam elements .We have not discussed these yet .We will discuss the formulation of these elements in a later lecture .And you can see that the beam elements give a slightly different response solution .", "label": "", "metadata": {}, "score": "47.850403"} {"text": "Are they context - free ? - usul Feb 22 ' 13 at 22:48 .What do you mean by context - free rules ?All I am given are those rules .From a particular string it converts to another string .", "label": "", "metadata": {}, "score": "48.30967"} {"text": "And we do not make any transformation , therefore , on the material relationships .We simply use the total Lagrangian formulation with E and nu given , these two constants plugged in .And these constants are the same throughout the TL solution .", "label": "", "metadata": {}, "score": "48.583942"} {"text": "If you would like to ' ' encode ' ' the grammar first such that it can no longer be used for language recognition ( e.g. by deleting the parameters / features or giving the nonterminals dummy names ) then it would be fine with us .", "label": "", "metadata": {}, "score": "48.711765"} {"text": "Lexica , lexicon schemas , and lexicon transformations are all seen as particular kinds of trees .This is the author version of the conference contribution . meta - sexp is a META parser generator using LL(1 ) grammars with s - expressions .", "label": "", "metadata": {}, "score": "48.832756"} {"text": "Now , remember our laboratory physical experimental results have given us the considered affiliations for the towards Lagrangian formulation .However , we now have a program that only is operating on the updated Lagrangian formulation .In other words , how can we obtain , with this Program 2 , identically the same results as are obtained using Program 1 .", "label": "", "metadata": {}, "score": "49.147556"} {"text": "So this is the relationship , a very basic relationship , that we want to use in our derivation .And it is obtained by simply looking at the Green - Lagrange strain , the way it 's defined , and the way we have defined it in an earlier lecture , and premultiplying by d0x transposed , and postmultiplying by d0x .", "label": "", "metadata": {}, "score": "49.291782"} {"text": "In the end we demonstrate that , in the paraxial regime , given a defocused transverse pattern in the Debye approximation , it is possible to find a similar pattern but magnified and situated at another plane within the Kirchhoff theory .", "label": "", "metadata": {}, "score": "49.556263"} {"text": "And then , afterwards , we do the UL solution , again , simply putting E and nu into the analysis and keeping it constant throughout that analysis .For large displacement , large rotation , but small strain conditions , the TL and UL formulation will give similar results , similar because we do n't make these transformations that I discussed earlier in the lecture in order to obtain exactly the same results .", "label": "", "metadata": {}, "score": "49.781597"} {"text": "Then you can surely show that Program 1 with the total Lagrangian formulation will give identically the same results , provided these transformations are performed in the program .And we have to transform this tensor here , as shown here , to this tensor .", "label": "", "metadata": {}, "score": "50.048515"} {"text": "The program results for a particular physical problem might look systematically as shown here .Of course , these two relationships here have been obtained from a set of physical laboratory experiments .This is how Program 1 works .Now we have also , say , a Program 2 .", "label": "", "metadata": {}, "score": "50.708324"} {"text": "Profiling .Test Suite Technology Ten Years After .Journal of .Computer Speech and Language 12(4):411 - 435 .Special Issue on .Evaluation ( June 1998 ) .Shuly Wintner and Nissim Francez .Efficient Implementation of .Unification - Based Grammars .", "label": "", "metadata": {}, "score": "50.8939"} {"text": "Held at ACL 2010 .Curran Associates , Inc. , Sweden , pp .37 - 45 .Abstract : .We investigate the problem of structurally changing lexica , while preserving the information .We present a type of lexicon transformation that is complete on an interesting class of lexica .", "label": "", "metadata": {}, "score": "51.94847"} {"text": "If we were to use this relationship in the total Lagrangian formulation and that relationship in the updated Lagrangian formulation for large strains , then we would get very different results .Let us look at the simple problem that we already solved earlier ones , or that we considered earlier ones in the previous lecture , using the total Lagrangian formulation .", "label": "", "metadata": {}, "score": "52.141438"} {"text": "Now we can compare the updated and total Lagrangian formulations .And the question that we ask is under what condition is this left hand side , which we use in the total Lagrangian formulation equal to this term here , which we use in the updated Lagrangian formulation .", "label": "", "metadata": {}, "score": "52.469658"} {"text": "There 's a relationship between these increments , as shown here .And this relationship I will focus our attention upon further on the next view graph , because I 'd like you to clearly understand how we obtain this relationship .", "label": "", "metadata": {}, "score": "52.48095"} {"text": "And we are asking now only how can we use Program 2 , which only contains the updated Lagrangian formulation .So we know how St0Smn is defined .And if we know this quantity , we have to make this transformation to calculate the Cauchy stress in the Program 2 .", "label": "", "metadata": {}, "score": "52.570263"} {"text": "And on the left hand side , you have what I showed you earlier .Let me go back one view graph , just to make sure that we understand each other .On the right hand side , you immediately obtain this term minus that term .", "label": "", "metadata": {}, "score": "52.805115"} {"text": "We reduce ID / LP grammars to poms - grammars , thereby getting finer - grained bounds on the parsing complexity of ID / LP grammars .We argue that in practice , the width of attested ID / LP grammars is small , yielding effectively polynomial time complexity for ID / LP grammar parsing .", "label": "", "metadata": {}, "score": "53.383957"} {"text": "We multiply out , and immediately , we obtain the right hand side .Notice ij , of course , are dummy indices , which can be substituted by m and n. .It 's interesting to upset here that this transformation is a kinematic transformation , which is actually buried in the proper use of the updated Lagrangian formulation .", "label": "", "metadata": {}, "score": "53.771873"} {"text": "( See the second example in the Quick Introduction . )Introducing New Transformations .Every transformation process issued by meta - sexp is controlled by TRANSFORM - GRAMMAR methods .( defgeneric transform - grammar ( ctx in - meta directive & optional args ) ( : documentation \" META grammar transformation methods . \" ) )", "label": "", "metadata": {}, "score": "54.30459"} {"text": "And we now use this term here , or this equation , linking up dtx to d0x and expand these two terms to obtain this equation .Now , notice that this equation has to hold for any material fiber .And surely , this relationship here must then hold .", "label": "", "metadata": {}, "score": "54.824646"} {"text": "Yes , it is possible to let this problem get solved by the programmer , e.g. via global variables .But this approach yields to another problem : thread safety .Anyway , that was the best that I can come up with ; if you have any other ideas , I 'd be happy to hear them .", "label": "", "metadata": {}, "score": "55.23219"} {"text": "Also pay attention how meta - sexp handles unrecognized transformation directives : .( defmethod transform - grammar ( ctx in - meta directive & optional args ) \" The most unspecific transformation method . \" With similar patterns , you can introduce new transformation directives to meta - sexp .", "label": "", "metadata": {}, "score": "55.50972"} {"text": "The variations on these linear strain increments and nonlinear strain increments of the total Lagrangian formulation and the updated Lagrangian formulation are also related via the deformation gradient .In fact , twice the deformation gradient enters here .Let 's look at how we derive this kinematic relationships .", "label": "", "metadata": {}, "score": "55.799316"} {"text": "For instance , consider how : AND and : NOT directive transformations are implemented : .Also pay attention how meta - sexp handles unrecognized transformation directives : .( defmethod transform - grammar ( ctx in - meta directive & optional args ) \" The most unspecific transformation method . \" With similar patterns , you can introduce new transformation directives to meta - sexp .", "label": "", "metadata": {}, "score": "56.175278"} {"text": "It is tested on SBCL but should be portable to other implementations as well . meta - sexp is implemented using sevaral transformation methods .Therefore , besides builtin grammar transformators coming with meta - sexp by default , you are allowed to add your own transformation methods too .", "label": "", "metadata": {}, "score": "56.50097"} {"text": "It is tested on SBCL but should be portable to other implementations as well . meta - sexp is implemented using sevaral transformation methods .Therefore , besides builtin grammar transformators coming with meta - sexp by default , you are allowed to add your own transformation methods too .", "label": "", "metadata": {}, "score": "56.50097"} {"text": "On aligning trees .In Proceedings of the Second Conference .on Empirical Methods in Natural Language Processing , Brown University , .August 1 - 2 1997 .Jo Calder .How to build a ( quite general ) linguistic diagram . editor .", "label": "", "metadata": {}, "score": "56.811188"} {"text": "For example : .There can be N rules .The characters for the input and output string do n't have to be just a and b , they can be any characters .How could I do an algorithm that would find the minimum number of transformations that would need to be applied in the input string to get the target string .", "label": "", "metadata": {}, "score": "56.982174"} {"text": "And we also use that the incremental , the tangent material relationship , is the same as the total stress - strain relationship in both cases .I 'd like to just demonstrate to you with a very simple problem analysis what I mean here .", "label": "", "metadata": {}, "score": "57.0849"} {"text": "So we get a totally different description of the material response using these two formulation when the material is subjected to large strains .Let us now also look at one example that demonstrates this feature , the features that I just discussed a bit more .", "label": "", "metadata": {}, "score": "57.720005"} {"text": "Yes , it is possible to let this problem get solved by the programmer via global variables .But this approach yields to another problem : thread safety .Anyway , that was the best that I can come up with ; if you have any other ideas , I 'd be happy to hear them .", "label": "", "metadata": {}, "score": "57.96512"} {"text": "In the updated Lagrangian formulation , we 're integrating over the current volume , time t. And there is this transformation , where the mass density 's at time t , and the original mass density , enters .Notice here are listed the linear and the nonlinear increment in the Green - Lagrange strain .", "label": "", "metadata": {}, "score": "58.632286"} {"text": "And this shows , then , directly that the 0 eij must be equal to twice a deformation gradient times ters .The reason being that this term here is a function of the incremental displacement only .Linear functional incremental displacement , so is this .", "label": "", "metadata": {}, "score": "58.88652"} {"text": "Similarly here , and this then , completes the derivation of all the kinematic relationships that I had in the earlier table and that we want to use now .In addition , we have , also , the important relationship between the second Piola - Kirchhoff stress and the Cauchy stress .", "label": "", "metadata": {}, "score": "59.364395"} {"text": "Therefore we can really conclude that 's the choice of whether to use a total Lagrangian formulation or the updated Lagrangian formulation is based only merely on how effective computationally one formulation is over the other .And then we note now that the B matrix , the strain displacement matrix in the U.L. formulation contains less entries than the strain displacement matrix in the T.L. formulation .", "label": "", "metadata": {}, "score": "59.76747"} {"text": "Of course , there 's a relationship between the new components and the original components of the material fiber .And here , goes into the deformation gradient .Now the equation that I just referred to really relates this length and that length .", "label": "", "metadata": {}, "score": "59.85902"} {"text": "However , certain discrepancies arise when the Fresnel number , as viewed from focus , is close to unity .In that case , we should use the Kirchhoff formulation to evaluate accurately the three - dimensional amplitude distribution of the field in the focal region .", "label": "", "metadata": {}, "score": "60.35578"} {"text": "And I 'm putting that on the back .Notice I 'm using a transposed here and a transposed there .The basic relation of the Green - Lagrange strain , once again , is this being equal to that part with out the d0x transposed in front , and without the d0x on the back .", "label": "", "metadata": {}, "score": "60.749313"} {"text": "We know this tensor .And what I 'm saying now is that we have to transform this tensor , as shown here , to obtain this tensor .And it is this one that would go then into all our Program 2 computations .", "label": "", "metadata": {}, "score": "60.76255"} {"text": "Most of the time , you 'll need to define your own parsers using CREATE - PARSER - CONTEXT methods and DEFRULE , DEFRENDERER macros .In a rule or renderer body , if supplied , ATTACHMENT argument will get bound to ATTACHMENT keyword given to CREATE - PARSER - CONTEXT .", "label": "", "metadata": {}, "score": "61.003296"} {"text": "Hommer Smith Feb 22 ' 13 at 22:54 .Well , for instance , the decision problem of whether a string can be parsed by a given context - sensitive grammar is PSPACE - complete .That seems to be a special case of your problem unless you have further restrictions ( although I guess from your example that it 's not context - free ) .", "label": "", "metadata": {}, "score": "61.29841"} {"text": "Now we want to ask the question what happens in the elastic analysis when we subject this structure , in other words , to the load r and we used once a TL and once a UL formulation , but with the same material constants .", "label": "", "metadata": {}, "score": "61.3264"} {"text": "If you know a Green - Lagrange strain at a point , then we can pick any material fiber with these components .This is a very basic equation that we want to study a bit more on the next view graph .", "label": "", "metadata": {}, "score": "61.383293"} {"text": "Unlike the unbouded Kolmogorov complexity , finding the smallest program that computes a given string is decidable , but we are obviously nearby intractability .Here it is a simple introduction and a very nice paper on the power of random strings .", "label": "", "metadata": {}, "score": "61.4969"} {"text": "Of course , I have to do the same thing here .d0x , transposed , goes in front of the bracket .And d0x goes into the back of the bracket .Let me make clear what I 'm doing here .", "label": "", "metadata": {}, "score": "61.552425"} {"text": "( You can use MAKE - LIST - ACCUM and MAKE - CHAR - ACCUM functions to initialize new accumulators .Moreover , you 'll probably need EMPTY - LIST - ACCUM - P and EMPTY - CHAR - ACCUM - P predicates too . )", "label": "", "metadata": {}, "score": "61.675346"} {"text": "And this tangent material relation , you find by this tensor , which we know , then Program 2 will give identically the same results as Program 1 .The kinematic relations that we talked earlier about are buried in the proper implementation of the updated Lagrangian formulation in Program 2 .", "label": "", "metadata": {}, "score": "61.7232"} {"text": "The increment in the second Piola - Kirchhoff stress , refer to time t , is given by the right hand side here , where this increment in Green - Lagrange strain is refer to time t. .We also have this relationship between the stress increments .", "label": "", "metadata": {}, "score": "61.93078"} {"text": "This can be a character .This can be a string .( Will get expanded into an AND ed character list with an outermost : CHECKPOINT . )Treat as a custom form .( Will get evaluated as is . )", "label": "", "metadata": {}, "score": "62.024902"} {"text": "Computer science .Issue Date : .Nederhof , M J & Satta , G 2008 , ' Computing partition functions of PCFGs ' Research on Language and Computation , vol 6 , no . 2 , pp .139 - 162 .", "label": "", "metadata": {}, "score": "62.252605"} {"text": "If the number of transformations is fixed , I think that the the problem is NP complete ( I 've a fuzzy idea of a quick reduction from monotone NAE-3SAT ) .I leave the previous answer below .The problem of finding the smallest CFG grammar that generates a given string ( also known as Minimum Grammar Compression or MGC ) is NP - complete ( J. Storer , \" NP - completeness results concerning data compression \" , 1977 ) .", "label": "", "metadata": {}, "score": "62.418686"} {"text": "To answer this question , we have to go back to some derivations that we discussed in an earlier lecture .And there , we discussed that this is here the governing continuum mechanics equation , corresponding to the total Lagrangian formulation .", "label": "", "metadata": {}, "score": "62.55056"} {"text": "And if you create all the nonlinear parts , we find that 0ei , e to ij , is equal to twice the deformation gradient times te to rs .And those two equations are given down here .If we now take variations on the left hand side and on the right hand side , and if you recognize that 's a variation , is respect to the configuration at time t plus delta t , it is clear that the deformation gradient terms do n't change .", "label": "", "metadata": {}, "score": "63.460392"} {"text": "Visualization and Intermedia Representations , 1998 .Jean - Luc Cochard .Un environnement d'analyse linguistique . robuste .CPD version 1.7 .IDIAP , Martigny , Switzerland , Octobre 1992 .Flickinger , D. , Oepen , S. , Uszkoreit , H. , and Tsujii J. ( Eds . )", "label": "", "metadata": {}, "score": "64.04343"} {"text": "Shuly Wintner , Nissim Francez , and Evgeniy Gabrilovich .AMALIA - a .unified platform for parsing and generation .In Ruslan Mitkov , .Nicolas Nicolov , and Nikolai Nikolov , editors , Recent Advances in .Natural Language Processing , pages 135 - 142 , Tzigov Chark , Bulgaria , .", "label": "", "metadata": {}, "score": "64.21054"} {"text": "If you look at the maximum deformations once more , a little bit closer , we find that the vertical tip displacement in the TL formulation for this problem is given as 15.289 meters , and in the UL formulation , as 15.282 meters .", "label": "", "metadata": {}, "score": "64.252754"} {"text": "It 's given by a fourth order tensor transformation , which I 'd like to now discuss with you further .This is an important relationship that we want to spend a bit off time on .The relationship is derived as follows-- the increment in the second Piola - Kirchhoff stress is clearly given by the tangent material relation times increment in the Green - Lagrange strain .", "label": "", "metadata": {}, "score": "64.27592"} {"text": "Notice this differentiation here of tU1 with respect to the coordinate 1 at time t. It 's nothing else than tL minus 0L over tL. Here we have that same quantity squared .Of course , there 's a minus 1/2 in front .", "label": "", "metadata": {}, "score": "64.311356"} {"text": "Calls specified RENDERER ( that is defined with DEFRENDERER ) with supplied arguments .FORM FORM ... ) .Sequentially evaluates supplied FORM s within an AND scope and regardless of the return value of AND ed FORM s , block returns T .", "label": "", "metadata": {}, "score": "64.375656"} {"text": "Notice that h / L is 1/50 .We modeled this structural using 51 two - dimensional eight node elements , plane strain elements .Notice here is a typical element shown , an eight node element .There are 25 elements along here .", "label": "", "metadata": {}, "score": "64.7365"} {"text": "Let us now look at a very special case , namely , the case of elasticity .Now if we use this equation in the relation that we just established , or that we talked about , namely giving us of a Cauchy stress , in terms of the second Piola - Kirchhoff stress , we obtain this equation here .", "label": "", "metadata": {}, "score": "64.99488"} {"text": "The Almansi strain 1 , 1 component , of course , in this particular case , has this relationship here .Notice t [ ? sigma ? ] over tL , versus 0L in the Green - Lagrange strain .And a minus sign here , whereas we have a plus sign there .", "label": "", "metadata": {}, "score": "65.138626"} {"text": "We ca n't see any difference to the accuracy that we have plotted the response .Notice that in these TL and UL solutions , we used exactly the same Young 's modulus and Poisson ratio .This is , of course , here is a polar decomposition of the deformation gradient , that for this particular case , large displacement , large rotation , but small strains , this deformation gradient is simply almost equal to the rotation matrix only .", "label": "", "metadata": {}, "score": "65.185814"} {"text": "Cauchy stress given in terms of the Almansi strain , and this E curl , which we used already before in the last lecture in this total Lagrangian formulation .Notice this E curl here is given on the right hand side here in terms of Young 's modulus and Poisson 's ratio .", "label": "", "metadata": {}, "score": "65.26279"} {"text": "Introducing New Transformations .( defgeneric transform - grammar ( ctx in - meta directive & optional args ) ( : documentation \" META grammar transformation methods . \" ) )To introduce a new transformation directive , just create a new TRANSFORM - GRAMMAR method with related lambda list specializers .", "label": "", "metadata": {}, "score": "65.72327"} {"text": "-SamM Feb 23 ' 13 at 7:23 .@SamM I think if we are given the transformation rules encoded as pairs as I assumed above , then I think it is polynomial since the number of vertices in the graph is $ 2n+2 $ for $ n$ pairs .", "label": "", "metadata": {}, "score": "65.85078"} {"text": "As a result , we give a geometrical interpretation of the focal - shift effect and present a brief comment on the problem of the best - focus location .References .You do not have subscription access to this journal .", "label": "", "metadata": {}, "score": "66.14608"} {"text": "Here we have a simple four node element that is being pulled out into the red configuration .Notice the original length is 0L. The pullout is t delta .Of course , t delta could also be negative , in which case we are pushing in here .", "label": "", "metadata": {}, "score": "66.34435"} {"text": "There is a length-$k$ transformation of $ s$ to $ t$ if and only if there is a path of length $ k$ between them in the graph . 2 )We are given an input string , output string , and context - free grammar , and asked if the grammar generates the output string from the input string in a parse tree of $ k$ or fewer nodes .", "label": "", "metadata": {}, "score": "66.47621"} {"text": "In this lecture , I would like to continue with our discussion of the use of constitutive relations used in nonlinear analysis .In the previous lecture , we already considered the modeling of elastic and hyper elastic materials , subjected to large displacements , large rotations , and large strains .", "label": "", "metadata": {}, "score": "66.615486"} {"text": "I am pretty much lost in strategies on doing this .It comes to my mind creating an automata but I am not sure how would I apply in this situation .I think is an interesting problem and I have been researching online , but all I can find is transformations given rules , but not how to ensure it 's a minimum .", "label": "", "metadata": {}, "score": "66.65048"} {"text": "This is the tangent material tensor to be used in the updated Lagrangian formulation , referred to time t. .The tangent material relation is once more summarized here .And this is what we need to use in the updated Lagrangian formulation .", "label": "", "metadata": {}, "score": "66.75771"} {"text": "Abstract : .We investigate the problem of computing the partition function of a probabilistic context - free grammar , and consider a number of applicable methods .We report experiments involving the Wall Street Journal corpus .README.md .Overview . meta - sexp is a META parser generator using LL(1 ) grammars with s - expressions .", "label": "", "metadata": {}, "score": "66.98724"} {"text": "Directory .Dear colleagues , at the end of September I posted a query about references to the problem of debugging computational grammars ( LINGUIST List volume 10.1395 ) .Unfortunately , I have not yet been able to assemble a structured and coherent summary of the responses I received .", "label": "", "metadata": {}, "score": "67.32404"} {"text": "Soc .Am .A 17 , 1185 - 1191 ( 2000 ) .Abstract .The Debye formulation of focused fields has been systematically used to evaluate , for example , the point - spread function of an optical imaging system .", "label": "", "metadata": {}, "score": "67.334885"} {"text": "Pleuk Grammar Development Shell : Edinburgh .OTHER RELEVANT LINKS . - ACL / EACL97 Workshop on Computational Environments for Grammar .PAPERS AND BOOKS .Miriam Butt , Tracy Holloway King , Maria - Eugenia Nino , Frederique .Segond .", "label": "", "metadata": {}, "score": "67.34204"} {"text": "Of course , we introduced that one in an earlier lecture .Finally , we have a relationship between the tangent material tensor , corresponding to the total Lagrangian formulation and corresponding to the updated Lagrangian formulation .Notice zero here , t there .", "label": "", "metadata": {}, "score": "67.60009"} {"text": "And the constitutive relations that we are employing in that total Lagrangian formulation are defined as shown here .So total stress at time , t , of course , second Piola - Kirchhoff stress , is given as a function of displacements .", "label": "", "metadata": {}, "score": "68.170845"} {"text": "Here , now , we have the Cauchy stress in terms of the Almansi strain tensor , multiplying a new constitutive tensor .And that new constitutive tensor is given down here .Notice the right hand side here contains the constitutive tensor of the total Lagrangian formulation , t0 here .", "label": "", "metadata": {}, "score": "68.3373"} {"text": "Now up to 2 % , you certainly would consider it a small strain problem .So here we are just at the limit of going over into a lot strain region near the base of the column .This is all I wanted to present to you , discuss with you , in this lecture .", "label": "", "metadata": {}, "score": "68.374954"} {"text": "See atoms.lisp for DEFATOM examples .Here is a tiny example : .( defrule integer ?( & aux ( sign 1 ) d ( num 0 ) ) ( ) (: ?( : or ( : and \" - \" ( : assign sign -1 ) ) \" + \") )", "label": "", "metadata": {}, "score": "69.14873"} {"text": "Notice this is a tangent material tensor .Here we have the second Piola - Kirchhoff stress , and here , as well .And of course , here on the right hand side , we have to total external loads entering the total external virtual work .", "label": "", "metadata": {}, "score": "69.71517"} {"text": "Now notice that when we compare these two right hand sides , we see the deformation gradient entering twice here , whereas four times here .We would like to recast , now , this equation on the Cauchy stress , so as to have the deformation gradient here entering in the same form as we have it entering here .", "label": "", "metadata": {}, "score": "69.93518"} {"text": "Well , so what we are doing , then , is to follow a material fiber , original length 0 ds , through it 's motion to t ds and to t plus delta t ds .And that 's expressed by the three formulas that are on the previous view graph .", "label": "", "metadata": {}, "score": "70.29233"} {"text": "The displacements and rotations have certainly been very large in the analysis of this frame , but the strains are still quite small .If you look at the strain at the base of the frame , maybe we have the maximum strain , maximal moments there , we find that the moment is approximately as shown here .", "label": "", "metadata": {}, "score": "71.406296"} {"text": "Of course , we have shown that already , by the solution that I showed you on the slide , the analysis of the arch subjected to an apex load .But we will see that now again here in this example .", "label": "", "metadata": {}, "score": "72.62622"} {"text": "The same definition is also given here , but in matrix form .And if we substitute for the inverse deformation gradient , in terms of displacements , and multiply components out , we obtain directly this relationship here for the Almansi strain tensor , where , notice our notation is this t and that t are those two t 's here .", "label": "", "metadata": {}, "score": "72.922424"} {"text": "You see here , we have-- oops .I should have not to use that delta t , because I 'm looking at time t. This is the equation for the Green - Lagrange strain , from here to there .", "label": "", "metadata": {}, "score": "72.96355"} {"text": "And we also can apply incrementally .Notice this a Green - Lagrange strain increment , from time t , to time t plus delta t. But refer to time t. That 's why we have the little t down here .", "label": "", "metadata": {}, "score": "73.491554"} {"text": "If you recognize that this is dtx , that is dtx , you immediately get on the right hand side , one half , bracket open , d-- well , I should say t ds , squared , minus 0ds squared .", "label": "", "metadata": {}, "score": "73.55713"} {"text": "Stanford , CA : CSLI Lecture .Notes 95 , CSLI Publications , 1999 .Bran Boguraev , John Carroll , Ted Briscoe , Claire Grover .Software .support for practical grammar development .In Proceedings of .COLING-88 , 1988 .", "label": "", "metadata": {}, "score": "73.56482"} {"text": "In other words , Poisson ratio and Young 's modulus , being identically the same , entering here .More specifically , you would put in here a Young 's modulus , say , of 30 million pounds per square inch and the Poisson ratio of 0.3 .", "label": "", "metadata": {}, "score": "73.84619"} {"text": "What does that mean ?It means that the second Piola - Kirchhoff stresses of course are given by the relationships that were determined by physical laboratory results .Remember , we know how old the second Piola - Kirchhoff stress is defined as a function of deformations , of the deformations in the material .", "label": "", "metadata": {}, "score": "74.1718"} {"text": "And that 's important .There 's also a minus here , which is a bit of a surprise , because in the Green - Lagrange strain definition , of course , we have a plus here , but different terms , of course , different terms .", "label": "", "metadata": {}, "score": "74.182495"} {"text": "Sequentially evaluates supplied FORM s within an AND scope until it returns NIL .Regardless of the return value of AND ed FORM s , block returns T .( : + FORM FORM ... ) .Sequentially evaluates supplied FORM s within an AND scope , and repeats this process till FORM s return NIL .", "label": "", "metadata": {}, "score": "74.28296"} {"text": "\" What 's the role of ATTACHMENT slot given to CREATE - PARSER - CONTEXT ( or specified as a keyword while making an instance of PARSER - CONTEXT class ) ?Think it as a state storage unit between passes to defined rules and renderers .", "label": "", "metadata": {}, "score": "74.31471"} {"text": "\" What 's the role of ATTACHMENT slot given to CREATE - PARSER - CONTEXT ( or specified as a keyword while making an instance of PARSER - CONTEXT class ) ?Think it as a state storage unit between passes to defined rules and renderers .", "label": "", "metadata": {}, "score": "74.31471"} {"text": "Well , let 's look now at the results that we obtained for this problem .Here we have plotted the force vertically , meganewtons , and the vertical displacement of the tip in terms of meter .The 2-D elements , TL and UL formulations , give this result here , the black curve , solid curve .", "label": "", "metadata": {}, "score": "74.84485"} {"text": "And that has a length of 0 ds .The components of this vector , the vector that is shown here , of length 0 ds , the components are shown here .And that vector we call a d 0 x. This material fiber moves from time 0 to time t , to this configuration .", "label": "", "metadata": {}, "score": "75.50273"} {"text": "A is the tip load .The frame has a thickness h , L length here , L length there , thickness h here , as well .The width of the frame structure is b. And the geometric data are given here .", "label": "", "metadata": {}, "score": "75.921844"} {"text": "Notice , again , the kinematic transformation here and the stress transformation here .Finally , we also have to look at this term , the term in which the material tensor enters .We ask again if this left hand side equal to the right hand side using the proper transformations .", "label": "", "metadata": {}, "score": "76.142105"} {"text": "( apply # ' callback ( meta - sexp::parser - context - data , ctx ) ( meta - sexp::parser - context - cursor , ctx ) , @args ) ) ( defrule integer - debug ?( ) ( ) ( : or ( : rule integer ? )", "label": "", "metadata": {}, "score": "76.31522"} {"text": "And at this point here , when delta over L0 is one , we have 3/2 , verified by that formula .The engineering strain , of course , is just a straight line .The Almansi strain looks as shown here by the red curve .", "label": "", "metadata": {}, "score": "76.57601"} {"text": "Let 's look at this relationship here .What does it really mean physically ?Well , it means the following-- here we have our stationary coordinate frame , x1 , x2 , x3 .And at time zero , we have a material fiber here , any material fiber .", "label": "", "metadata": {}, "score": "76.91802"} {"text": "Now , using these four equations , we directly obtain by substitution , that this left hand side , substituted for d0 Sij , must be equal to this right hand side , where we have substituted this relationship for d0 epsilon rs .", "label": "", "metadata": {}, "score": "76.9543"} {"text": "( : TYPE TYPE - CHECKER )( : TYPE ( OR TYPE - CHECKER TYPE - CHECKER ... ) ) .Checks type of the atom at the current position through supplied function(s ) .( : RULE RULE ARG ARG ... )", "label": "", "metadata": {}, "score": "77.02955"} {"text": "This is exactly what we try to achieve .And we achieve it by using the Almansi strain tensor , which is defined as follows .Here we have the definition of the Almansi strain tensor , in terms of the Green - Lagrange strain tensor .", "label": "", "metadata": {}, "score": "77.04474"} {"text": "Notice here we have the Lame constants , lambda and mu , which we already used in the previous lecture , and the chronica deltas .The t 0 Cijrs tensor is given as here on the right hand side .Again , Lame constants and chronica delta entries .", "label": "", "metadata": {}, "score": "78.69492"} {"text": "That is very easy to see that this is the Green - Lagrange strain .Notice here we have 1/2 minus 1/2 .That minus 1/2 comes from this relationship .This is minus one at this point .And that is plus 1/2 .", "label": "", "metadata": {}, "score": "79.284676"} {"text": "( : CHAR - RESET CHAR - ACCUM ) .Resets supplied accumulators .( : EOF ) .( : DEBUG )( : DEBUG VAR ) .Prints current character and its position in the input data .If VAR is specified , prints the value of the VAR .", "label": "", "metadata": {}, "score": "79.46332"} {"text": "( : + ( : not \" ] ] \" )( : char - push text ) ) ) \" ] ] \" ( : render internal - link ! ref text ) ) ( defrule wiki - markup ?( : and ( : assign c ( : read - atom ) ) ( write - char c attachment ) ) ) ) ( get - output - stream - string attachment ) ) ( wiki - markup ?", "label": "", "metadata": {}, "score": "79.53891"} {"text": "( : + ( : not \" ] ] \" )( : char - push text ) ) ) \" ] ] \" ( : render internal - link ! ref text ) ) ( defrule wiki - markup ?( : and ( : assign c ( : read - atom ) ) ( write - char c attachment ) ) ) ) ( get - output - stream - string attachment ) ) ( wiki - markup ?", "label": "", "metadata": {}, "score": "79.53891"} {"text": "( Args : ( : RULE : INTEGER - DEBUG ? ) ) ( create - parser - context \" +123 \" ) ) ; 123 .Identical to ( NOT FORM ) .( FORM is encapsulated within a : CHECKPOINT before getting evaluated . )", "label": "", "metadata": {}, "score": "80.33473"} {"text": "Here you now see the slide that shows the arch .It is subjected to a point load at it 's apex .And we measure the displacement at the apex .The displacement is w0 , half of the arch was modeled with 12 eight - node elements .", "label": "", "metadata": {}, "score": "80.69012"} {"text": "Here is another example demonstrating the usage of META symbol .( defrule in - wonderland ?( ) ( ) \" META - SEXP \" ( progn ( format t \" META - SEXP in Wonderland ! \" ) ( meta ( : type space ? ) \" in Wonderland ! \" ) )", "label": "", "metadata": {}, "score": "81.279465"} {"text": "( defrenderer internal - link !( & aux ( ref ( make - char - accum ) ) ( text ( make - char - accum ) ) ) ( ) \" [ [ \" ( : + ( : not ( : or \" ] ] \" ( : type ( or white - space ? newline ? ) ) ) )", "label": "", "metadata": {}, "score": "81.312164"} {"text": "( defrenderer internal - link !( & aux ( ref ( make - char - accum ) ) ( text ( make - char - accum ) ) ) ( ) \" [ [ \" ( : + ( : not ( : or \" ] ] \" ( : type ( or white - space ? newline ? ) ) ) )", "label": "", "metadata": {}, "score": "81.312164"} {"text": "Tests input in the current cursor position using specified type / form .If any , supplied arguments will get passed to rule .( : ASSIGN VAR FORM )( : ASSIGN ( VAR1 VAR2 ... ) FORM ) .Assigns returned value of FORM to VAR , and returns assigned value .", "label": "", "metadata": {}, "score": "81.780334"} {"text": "The Almansi strain tensor is really , overall , not a very useful measure , strain measure .But we wanted to introduce it here briefly .And it is quite useful in the one particular case , namely , in the analysis of isotropic materials undergoing large displacements and large rotations , but generally small strains .", "label": "", "metadata": {}, "score": "83.34417"} {"text": "There are no other restrictions than the ones I have written .I have been looking around the web to see any similar problems so I can have a base start , but no luck :( - Hommer Smith Feb 22 ' 13 at 23:16 .", "label": "", "metadata": {}, "score": "83.778946"} {"text": "There 's the load .We are pressing down here .These are the deformations at a load of one meganewton .And this is how the frame looks at full load , five meganewton-- very large deformations .In fact , the deformation are so large for the frame , that in an actual practical problem , probably the frame , of course , would have undergone inelastic deformations .", "label": "", "metadata": {}, "score": "83.91954"} {"text": "The Cauchy stress is directly obtained in terms of the physical force applied to the bar divided by the cross sectional area , which is constant .If we use that current length is given in terms of the original length plus the extension and this material relationship here , we obtain directly this force displacement response .", "label": "", "metadata": {}, "score": "84.406746"} {"text": "Because we would do : . 1 - Apply rule 1 ( abba ) 2 - Apply rule 1 again ( bbaba ) 3 - Apply rule 2 ( bababa ) .It could happen that given an input and a target , there is no solution and that should be noticed too .", "label": "", "metadata": {}, "score": "84.45486"} {"text": "If : CHAR - PUSH is called with only one argument , current character gets read and pushed into supplied accumulator .( You can use MAKE - LIST - ACCUM and MAKE - CHAR - ACCUM functions to initialize new accumulators .", "label": "", "metadata": {}, "score": "86.59526"} {"text": "( : LIST - PUSH ITEM - VAR LIST - ACCUM )( : CHAR - PUSH CHAR - VAR CHAR - ACCUM )( : CHAR - PUSH CHAR - ACCUM ) .Pushes supplied ITEM - VAR / CHAR - VAR into specified LIST - ACCUM / CHAR - ACCUM .", "label": "", "metadata": {}, "score": "88.36037"} {"text": "And this tensor is very useful in the way I just described .The Almansi strain tensor is a symmetric tensor .The ij components are equal to the ji components .The components are not invariant under rigid body rotation of the material .", "label": "", "metadata": {}, "score": "89.178406"} {"text": "And we will pull this bar out and also compress it in and look at the force required to pull the bar out or compress it .And we want to now look at this problem using this material relationship here , Cauchy stress in terms of Almansi strain with E curl constant .", "label": "", "metadata": {}, "score": "90.627335"} {"text": "You may subscribe either as an OSA member , or as an authorized user of your institution .", "label": "", "metadata": {}, "score": "91.87885"} {"text": "( & aux ( sign 1 ) d ( num 0 ) ) ( ) (: ?( : or ( : and \" - \" ( : assign sign -1 ) ) \" + \") )( : + ( : assign d ( : type digit ? ) ) ( ) ( ) \" META - SEXP \" ( progn ( format t \" META - SEXP in Wonderland ! \" ) ( meta ( : type space ? ) \" in Wonderland ! \" ) )", "label": "", "metadata": {}, "score": "93.44273"} {"text": "-Hommer Smith Feb 23 ' 13 at 8:41 .Also , it is important to notice that I am not given any ' k ' .I need to find the shortest . -Hommer Smith Feb 23 ' 13 at 9:23 .", "label": "", "metadata": {}, "score": "97.45645"} {"text": "Tools .Our Corporate Supporters .About MIT OpenCourseWare .MIT OpenCourseWare makes the materials used in the teaching of almost all of MIT 's subjects available on the Web , free of charge .With more than 2,200 courses available , OCW is delivering on the promise of open sharing of knowledge .", "label": "", "metadata": {}, "score": "99.19664"} {"text": "( create - parser - context \" META - SEXP in Wonderland ! \" ) )META - SEXP in Wonderland !( create - parser - context \" META - SEXP in Fooland ! \" ) )META - SEXP in Wonderland !", "label": "", "metadata": {}, "score": "100.748566"} {"text": "( create - parser - context : data \" META - SEXP in Wonderland ! \" ) )META - SEXP in Wonderland !( create - parser - context : data \" META - SEXP in Fooland ! \" ) )", "label": "", "metadata": {}, "score": "100.99635"} {"text": "The following content is provided under a Creative Commons license .Your support will help MIT OpenCourseWare continue to offer high quality educational resources for free .To make a donation or view additional materials from hundreds of MIT courses , visit MIT OpenCourseWare at ocw.mit.edu .", "label": "", "metadata": {}, "score": "102.9633"}
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{"first_name":"Abe","last_name":"Thomas","permalink":"abe-thomas","crunchbase_url":"http://www.crunchbase.com/person/abe-thomas","homepage_url":"","birthplace":"","twitter_username":null,"blog_url":"","blog_feed_url":"","affiliation_name":"Unaffiliated","born_year":null,"born_month":null,"born_day":null,"tag_list":"","alias_list":null,"created_at":"Wed Jul 30 09:20:54 UTC 2008","updated_at":"Wed Jul 30 09:23:19 UTC 2008","overview":"<p>Abe Thomas serves as Vice President of Online Marketing for MySpace, the world&#8217;s premier social network. In this role Abe is responsible for developing the overall strategy for acquiring new customers through online advertising, affiliates and search marketing. Working closely with the company&#8217;s creative group, database engineers and product marketing teams Abe spearheads companywide initiatives geared towards driving loyalty from existing customers through email, direct mail and on the website, using effective segmentation and promotional strategies and tactics.</p>\n\n<p>In 2006, Abe spent a year in Mumbai, India leading eBay India&#8217;s Internet Marketing team. With Google still in its infancy in India, successful optimization of eBay&#8217;s Search program resulted in a high representation of eBay pages in Google&#8217;s Paid Search program and Google&#8217;s Natural Search results.</p>\n\n<p>In Jan 2007, Abe moved to PayPal Merchant Services where he was focused on marketing PayPal to our largest merchants.</p>\n\n<p>Prior to joining eBay, Abe held multiple business development, product marketing and consulting positions at AltaVista, Palm, IBM Consulting and Motorola.</p>\n\n<p>Abe received a B.S. in Electrical Engineering from Purdue University, a MSEE from USC and then an MBA from the University of Chicago. He lives in San Jose with his wife and two girls aged 3 and 6. He loves playing Basketball, Cricket, cooking and spending time with his kids.</p>","image":{"available_sizes":[[[150,99],"assets/images/resized/0002/3012/23012v1-max-150x150.jpg"],[[170,113],"assets/images/resized/0002/3012/23012v1-max-250x250.jpg"],[[170,113],"assets/images/resized/0002/3012/23012v1-max-450x450.jpg"]],"attribution":""},"degrees":[],"relationships":[{"is_past":false,"title":"Vice President Online Marketing","firm":{"name":"MySpace","permalink":"myspace","type_of_entity":"company","image":{"available_sizes":[[[150,31],"assets/images/resized/0000/4414/4414v80-max-150x150.jpg"],[[250,52],"assets/images/resized/0000/4414/4414v80-max-250x250.jpg"],[[450,94],"assets/images/resized/0000/4414/4414v80-max-450x450.jpg"]],"attribution":null}}},{"is_past":true,"title":"","firm":{"name":"eBay","permalink":"ebay","type_of_entity":"company","image":{"available_sizes":[[[150,62],"assets/images/resized/0000/3625/3625v1-max-150x150.png"],[[210,87],"assets/images/resized/0000/3625/3625v1-max-250x250.png"],[[210,87],"assets/images/resized/0000/3625/3625v1-max-450x450.png"]],"attribution":null}}},{"is_past":true,"title":"","firm":{"name":"Palm","permalink":"palm","type_of_entity":"company","image":{"available_sizes":[[[150,150],"assets/images/resized/0002/3874/23874v2-max-150x150.png"],[[193,193],"assets/images/resized/0002/3874/23874v2-max-250x250.png"],[[193,193],"assets/images/resized/0002/3874/23874v2-max-450x450.png"]],"attribution":null}}},{"is_past":true,"title":"","firm":{"name":"Motorola Solutions","permalink":"motorola-solutions","type_of_entity":"company","image":{"available_sizes":[[[150,18],"assets/images/resized/0001/0857/10857v4-max-150x150.png"],[[250,30],"assets/images/resized/0001/0857/10857v4-max-250x250.png"],[[450,55],"assets/images/resized/0001/0857/10857v4-max-450x450.png"]],"attribution":null}}},{"is_past":true,"title":"","firm":{"name":"AltaVista","permalink":"altavista","type_of_entity":"company","image":{"available_sizes":[[[150,58],"assets/images/resized/0002/1995/21995v1-max-150x150.png"],[[199,77],"assets/images/resized/0002/1995/21995v1-max-250x250.png"],[[199,77],"assets/images/resized/0002/1995/21995v1-max-450x450.png"]],"attribution":null}}},{"is_past":true,"title":"","firm":{"name":"IBM","permalink":"ibm","type_of_entity":"company","image":{"available_sizes":[[[150,60],"assets/images/resized/0002/1370/21370v1-max-150x150.png"],[[170,68],"assets/images/resized/0002/1370/21370v1-max-250x250.png"],[[170,68],"assets/images/resized/0002/1370/21370v1-max-450x450.png"]],"attribution":null}}}],"investments":[],"milestones":[],"video_embeds":[],"external_links":[],"web_presences":[{"external_url":"http://www.myspace.com/abethomas08","title":"MySpace Page"}]}
{"Q_ID": "1316310010", "S_NAME": "Methods", "Q_NAME": "Calculate total number of Leap year between two years", "Q_DESC": "Write a java program to Calculate total number of Leap year between two years", "TESTCASE_1": "1990\n2012\n###---###SEPERATOR---###---\nThe Total number of year is : 6", "TESTCASE_2": "0\n###---###SEPERATOR---###---\n0", "TESTCASE_3": "0\n###---###SEPERATOR---###---\n0", "TESTCASE_4": "0\n###---###SEPERATOR---###---\n0", "TESTCASE_5": "0\n###---###SEPERATOR---###---\n0"}