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Michigan's Jim Harbaugh (somehow, but not really) deals with NFL coaching rumors
NFL coaching rumors are flying after a busy Black Monday, and Jim Harbaugh has found his name at the center of many conversations.
Harbaugh became a hot coach again after bringing Michigan to an overall record of 12-2 and a College Football Playoff in 2021. Several reports indicated that the 58-year-old was interested in jumping into the NFL and would at least enjoy the offers, but Harbaugh he did not express speculation directly – until Wednesday.
MORE: NFL Head Coach vacancies evaluation
During a Zoom call with reporters ahead of the Paul "Bear" Bryant Coach of the Year awards ceremony, Harbaugh, who coached the 49ers in 2011-14, was asked if his name spread in rumors.
"It's a little nicer this year compared to last year's rumors," Harbaugh said.
Well, that makes everything clear!
Harbaugh is definitely right, because he had a lot of Michigan fans who demanded a new coach after the Wolverines finished 2: 4 in 2020 in the middle of the COVID-19 pandemic. Harbaugh finally managed to get over the hump in 2021 and won his first victory against the state of Ohio and the Big Ten Championship.
MORE: Bears? Raiders? The NFL rating is good for Harbaugh
Now that there are six NFL openings (seven unless the Raiders give Rich Bisacci a steady job) and Harbaugh's stock at an all-time high, will he really leave his alma mater? In a separate interview with Sports Radio 610 in HoustonHarbaugh did not do much to silence the rumors, only adding that he would spend some time with his family before the recruitment season started at full speed.
"Maybe tomorrow we'll find some family-related things. We just have to enjoy these days with our family, because we're going to recruit in the next few days," Harbaugh said. "It's been a bit of the last few days spent with my family and I couldn't have imagined a better place."
It certainly sounds like he will enjoy this process a little longer.
Stetson Bennett scored in the decisive match Georgia vs. Alabama?
What is NVP? The full list of Nickelodeon fans' awards, which is played in Cowboys vs. 49ers
Claressa Shields rejects what's in leaked emails, doesn't want any involvement in Jake Paul's cards ...
Jermain Defoe seeks Sunderland, Ipswich Town, Charlton Athletic and Oxford United after Rangers leave | News from the Transfer Center
Next year as a potential starting point for Eileendover Racing news
A teenager with rare leukemia has an emotional party in California to uncover bone marrow donors [video]
Bruno Fernandes reacts to transfer links from Barcelona, Man United star tears "bad journalism"
The Ashes: England needs to make personnel changes after the "expedition from hell," says Michael Atherton Cricket news
A caregiver who stole over £ 7,000 from a vulnerable woman caught by her husband
Anthony Martial: Manchester United striker denies Ralf Rangnick's claim that he refused to play | football news


The CT Colonography Screening for Colorectal Cancer Act, a bi-partisan bill introduced in Congress, would provide Medicare coverage for virtual colonoscopies, which would benefit groups who are less likely to get screened for colorectal cancer.
The bill was introduced in the U.S. House of Representatives by Reps. Danny Davis, D-Ill., and Brad Wenstrup, R-Ohio.
"Colorectal cancer is largely preventable thanks to effective screening tools. Ensuring access to proven screening exams through Medicare is especially important for America's vulnerable populations," Mr. Davis said in a statement.
Virtual colonoscopies are a less invasive option for colorectal cancer screening that uses X-ray equipment to scan the large intestine for polyps, which can become cancerous. Studies have shown the availability of virtual colonoscopies raises CRC screening rates and lowers costs.
The U.S. Census Bureau National Interview Survey shows a lack of Medicare coverage means virtual colonoscopy is utilized less than traditional colonoscopies. However, virtual colonoscopies are more prevalent among Latinos and African Americans, the American College of Radiology said. Both groups are less likely overall to get screened for CRC, and cancers in these groups are more likely to be found later than in whites.
Currently, 40 states require insurance companies cover virtual colonoscopies. However, once privately insured patients turn 65, they could lose coverage, said Anne Carlson, Colon Cancer Coalition president.
According to research cited by the American College of Radiology, virtual colonoscopies are as accurate as a traditional colonoscopy for patients who are 65 and older.


How did jazz happen to you?
I was 17 when I first heard jazz. At the time I was studying Math and Literature, and music was nothing more than a hobby. But listening to jazz changed my relationship with music, which came alive. I began to see it as a space to express myself creatively. It took me four more years to realize that I could do nothing else. Though it was a huge leap of faith, I made the switch to music. Eight years later, I have a full-fledged career as a jazz pianist based in New York City.


When setting up database mirroring, I am getting error that the database cannot be opened. It is in recovering state. I followed the steps to setup mirroring which says to restore database with no recovery. If I restore database with recovery, I get another error saying that the server does not exist or cannot be reached. How to solve this issue.
I am executing the mirroring setup as a domain user account. which is also the logon for sql service on both the primary and mirror server. dbname is the database name. Do I need to assign any permission to the domain user account. If so how and what permission?
1. Make sure you have granted "CONNECT" permission to the service account on the endpoints you're using in your mirroring configuration.
2. Make sure you're invoking the mirroring setup from the principal server.
3. Use an older version of SSMS. Per this thread, it seems this issue occurs in the newer SSMS version.
4. Use T-SQL to set up mirroring if #3 is not something you like.
My thought is yours is most likely the ssms version case wherein #3 above should help. Give it a try.
Can you bring the exact error message/s?
In the meantime, have you tried to set it up via T-SQL commands? See this guide. Also youtube is plenty of tutorials for seeting up mirroring.
That said, I would recommend you to go with AlwaysOn Avalibility Groups, that is the evolution of the old mirroring.
When setting up database mirroring, I am getting error that the database cannot be opened. It is in recovering state.
That's just the way to do it. I mean, when you configure Mirroring, you'd have to restore a full backup of the principal DB on to the Mirror server (with NORECOVERY) followed by a transaction log backup restore (unless the DB is new), again with NO RECOVERY. You then right-click the DB and go to Mirroring page to configure Mirroring by following the wizard. Post the complete error message that you're seeing.
Followed the same thing. Still I am getting this error.


text:
After Misery
Quick Reviews: 'The Card Counter' | 'Escape Room: Tournament Of Champions'
Hope you did all your math homework 'cause we're doing a bunch of counting today. Old-timer director and writer Paul Schrader sets the table with The Card Counter, a neat and tidy crime thriller that struggles to get a winning hand, no matter how weird the camera lenses get as it goes on. Oscar Isaac stars as William Tell, current day gambler circling east coast casinos, and former day military man who served a prison sentence for his part in torturing detainees at Abu Ghraib in Iraq, following instructions of superior Gordo (Willem Dafoe) who wasn't caught. William gets approached at a casino by a young man named Cirk (Tye Sheridan) and the two start touring together after learning that Cirk's life was also impacted by Gordo. They are then joined by La Linda (Tiffany Haddish), a gambler-manager-of-sorts who is able to get financial backers for William's games.
You probably wouldn't be too far off from the truth if you thought that the film exists just for cool locations to shoot in with action that is provided by poker and other gambling games. Acting is terribly fine all things considered, though Haddish brings just the right amount of grace and confidence to otherwise middling character shapes that are the main trio. There are cool shots of casino tables and inviting narration by Isaac but it does feel like Schrader is running on empty, especially what seems to have happened in post-production. Him and editor Benjamin Rodriguez Jr. are stuck in the mud which keeps sequences going on and on with no end in sight. Things don't improve with the score led by Robert Levon Been and vocals and instrumentation by others as the music seems to be in constant shootout with the editing, leaving only one survivor each time. Even smaller things like editing of ADR sounded really wrong for the environments, noticeably in a scene at a garden with elaborate lighting designs. It's almost like as the main plot about revenge got less exciting, so did cinematic nature of the movie.
Smileys: Tiffany Haddish
Frowneys: Pacing, score, sound editing, editing
[Note: Both the theatrical cut and alternate extended cut were watched, this review is mainly for the alternate cut as that was deemed to be better.]
It was rather obvious to notice what kind of franchise potential 2019's 'Escape Room' had considering its low-risk, high box office approach as PG-13 horror. Naturally, sequel Escape Room: Tournament Of Champions doesn't try to change the foundation too much other than having two different cuts of the film that build completely different worlds outside the main characters Zoey (Taylor Russell) and Ben (Logan Miller). No big deal, right? Theatrical cut basically sacrifices all the stakes and effects just to bring back a character for no reason so we're just gonna move on and talk about the alternate cut. After a prologue which finally tells you something about the big bad company Minos, Zoey and Logan eventually are tricked into another series of escape rooms with a new group: Nathan (Thomas Cocquerel), Brianna (Indya Moore), Rachel (Holland Roden) and Theo (Carlito Olivero), all also winners of their previous escape room groups.
Least surprising thing about the sequel perhaps is that once again production design is the best part about it, courtesy of Edward Thomas who returns from the first one. With what seems like larger budget this time around, there's a lot more VFX and SFX work combined with all set pieces which makes the situations feel slightly more threatening. Russell and Miller both do a solid job leading the way but unfortunately for director Adam Robitel, the script (which credits four people, plus another two for story) isn't doing him any favours as everything is just about pointing things out and explaining every single one of those things. You'd hope at this point that the filmmakers would realise that part of the fun is for the audience also figure things out, sometimes before or along with the characters. Now you're just listening to a lecture, really.
That world-building in this alternate cut that was mentioned earlier features solid ideas and they're well-shot and directed too, instead of what feels like fan service with new scenes in the theatrical version. Time will tell if the franchise wants to rehash the formula or does it want to shake things up by introducing new players to the game.
Smileys: Production design
Frowneys: Screenplay, dialogue
escape room tournament of champions
oscar isaac
taylor russell
2021 Films Summary
2021 Television Summary

redpajama_set_name: RedPajamaCommonCrawl


While San Diego embraces its newfound love for elevated Mexican cuisine, Baja Norte has been upping its culinary game for nearly a decade. It's time you took a weekend, or three, and ate your way through a Mexican vacation. Here's your hit list.
Like a permanent food truck party. Must-trys: bacon sausage topped with bacon candy at Humo; kimchi fried rice at Don Ramen.
Every city has its soul-soothing dive bar, and this is Tijuana's. Order the jerky in spicy gravy.
Taco culture meets the mind of a chef. Try the Kraken (grilled octopus in Mexican pesto) or the Mala Costra (chili stuffed with marlin, cheese, and grasshoppers).
A culinary high point, from chefs Jair Téllez and Martin Vargas. Get Korean barbecue tacos with carne asada and kimchi.
Chef Javier Plascencia is a star of Baja cuisine. Misión 19 is his modern showroom atop a LEED-certified high-rise.
Let other gringos stop at Puerto Nuevo. This is the fishing village you want, with giant spider crabs in garlic sauce, sea urchins, oysters, you name it.
Doña Banderas has been serving tostadas with fresh local seafood (octopus, Pismo clam, sea urchin) since 1960. Try the peanut-almond salsa.
Chef Benito Molina is a legend in Ensenada. At this waterfront hang, the house ceviche (shrimp, fish, octopus, clams) will blow your mind.
This is Molina's flagship, often cited as the birthplace of modern Baja cuisine (where he cooks with his wife, Solange Muris).
The eco-friendly Alximia winery looks like a spaceship, and pioneering chef Martin San Roman serves "Baja Provençal" on their terrace.
Esthela is a local legend who turned her home into a restaurant, serving Sinaloa-style breakfasts like machaca and eggs. Buy her bottled salsa.
Baja fuses with high-end Italian fare. The Dal Bon family of chefs are heroes in Todos Santos, and their new Valle spot features spit-roasted meats and house-made pastas.
Michelin-starred chef Drew Deckman cooks under pine trees and the Baja stars. Everything here is wood-smoked and delicious.
In a gorgeous wood-and-glass building, chef Diego Hernández has become world-renowned for turning out stunning prix fixe masterpieces.
Javier Plascencia, again. This is his outdoor wine country kitchen, with oysters and lamb roasting on the grill station and wine aplenty. Sit under his giant oak tree for days.


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Automatic summarization is the process of shortening a set of data computationally, to create a subset (a summary) that represents the most important or relevant information within the original content. Artificial intelligence algorithms are commonly developed and employed to achieve this, specialized for different types of data.

Text summarization is usually implemented by natural language processing methods, designed to locate the most informative sentences in a given document. On the other hand, visual content can be summarized using computer vision algorithms. Image summarization is the subject of ongoing research; existing approaches typically attempt to display the most representative images from a given image collection, or generate a video that only includes the most important content from the entire collection. Video summarization algorithms identify and extract from the original video content the most important frames (key-frames), and/or the most important video segments (key-shots), normally in a temporally ordered fashion. Video summaries simply retain a carefully selected subset of the original video frames and, therefore, are not identical to the output of video synopsis algorithms, where new video frames are being synthesized based on the original video content.

Commercial products
In 2022 Google Docs released an automatic summarization feature.

Approaches

There are two general approaches to automatic summarization: extraction and abstraction.

Extraction-based summarization

Here, content is extracted from the original data, but the extracted content is not modified in any way. Examples of extracted content include key-phrases that can be used to "tag" or index a text document, or key sentences (including headings) that collectively comprise an abstract, and representative images or video segments, as stated above. For text, extraction is analogous to the process of skimming, where the summary (if available), headings and subheadings, figures, the first and last paragraphs of a section, and optionally the first and last sentences in a paragraph are read before one chooses to read the entire document in detail. Other examples of extraction that include key sequences of text in terms of clinical relevance (including patient/problem, intervention, and outcome).

Abstractive-based summarization

Abstractive summarization methods generate new text that did not exist in the original text. This has been applied mainly for text. Abstractive methods build an internal semantic representation of the original content (often called a language model), and then use this representation to create a summary that is closer to what a human might express. Abstraction may transform the extracted content by paraphrasing sections of the source document, to condense a text more strongly than extraction. Such transformation, however, is computationally much more challenging than extraction, involving both natural language processing and often a deep understanding of the domain of the original text in cases where the original document relates to a special field of knowledge.
"Paraphrasing" is even more difficult to apply to image and video, which is why most summarization systems are extractive.

Aided summarization

Approaches aimed at higher summarization quality rely on combined software and human effort. In Machine Aided Human Summarization, extractive techniques highlight candidate passages for inclusion (to which the human adds or removes text). In Human Aided Machine Summarization, a human post-processes software output, in the same way that one edits the output of automatic translation by Google Translate.

Applications and systems for summarization

There are broadly two types of extractive summarization tasks depending on what the summarization program focuses on. The first is generic summarization, which focuses on obtaining a generic summary or abstract of the collection (whether documents, or sets of images, or videos, news stories etc.). The second is  query relevant summarization, sometimes called query-based summarization, which summarizes objects specific to a query. Summarization systems are able to create both query relevant text summaries and generic machine-generated summaries depending on what the user needs.

An example of a summarization problem is document summarization, which attempts to automatically produce an abstract from a given document. Sometimes one might be interested in generating a summary from a single source document, while others can use multiple source documents (for example, a cluster of articles on the same topic). This problem is called multi-document summarization. A related application is summarizing news articles. Imagine a system, which automatically pulls together news articles on a given topic (from the web), and concisely represents the latest news as a summary.

Image collection summarization is another application example of automatic summarization. It consists in selecting a representative set of images from a larger set of images. A summary in this context is useful to show the most representative images of results in an image collection exploration system. Video summarization is a related domain, where the system automatically creates a trailer of a long video. This also has applications in consumer or personal videos, where one might want to skip the boring or repetitive actions. Similarly, in surveillance videos, one would want to extract important and suspicious activity, while ignoring all the boring and redundant frames captured.

At a very high level, summarization algorithms try to find subsets of objects (like set of sentences, or a set of images), which cover information of the entire set. This is also called the core-set. These algorithms model notions like diversity, coverage, information and representativeness of the summary. Query based summarization techniques, additionally model for relevance of the summary with the query. Some techniques and algorithms which naturally model summarization problems are TextRank and PageRank, Submodular set function, Determinantal point process, maximal marginal relevance (MMR) etc.

Keyphrase extraction
The task is the following. You are given a piece of text, such as a journal article, and you must produce a list of keywords or key[phrase]s that capture the primary topics discussed in the text. In the case of research articles, many authors provide manually assigned keywords, but most text lacks pre-existing keyphrases. For example, news articles rarely have keyphrases attached, but it would be useful to be able to automatically do so for a number of applications discussed below.
Consider the example text from a news article:

"The Army Corps of Engineers, rushing to meet President Bush's promise to protect New Orleans by the start of the 2006 hurricane season, installed defective flood-control pumps last year despite warnings from its own expert that the equipment would fail during a storm, according to documents obtained by The Associated Press".

A keyphrase extractor might select "Army Corps of Engineers", "President Bush", "New Orleans", and "defective flood-control pumps" as keyphrases. These are pulled directly from the text. In contrast, an abstractive keyphrase system would somehow internalize the content and generate keyphrases that do not appear in the text, but more closely resemble what a human might produce, such as "political negligence" or "inadequate protection from floods". Abstraction requires a deep understanding of the text, which makes it difficult for a computer system.
Keyphrases have many applications. They can enable document browsing by providing a short summary, improve information retrieval (if documents have keyphrases assigned, a user could search by keyphrase to produce more reliable hits than a full-text search), and be employed in generating index entries for a large text corpus.

Depending on the different literature and the definition of key terms, words or phrases, keyword extraction is a highly related theme.

Supervised learning approaches
Beginning with the work of Turney, many researchers have approached keyphrase extraction as a supervised machine learning problem.
Given a document, we construct an example for each unigram, bigram, and trigram found in the text (though other text units are also possible, as discussed below). We then compute various features describing each example (e.g., does the phrase begin with an upper-case letter?). We assume there are known keyphrases available for a set of training documents. Using the known keyphrases, we can assign positive or negative labels to the examples. Then we learn a classifier that can discriminate between positive and negative examples as a function of the features. Some classifiers make a binary classification for a test example, while others assign a probability of being a keyphrase. For instance, in the above text, we might learn a rule that says phrases with initial capital letters are likely to be keyphrases.
After training a learner, we can select keyphrases for test documents in the following manner. We apply the same example-generation strategy to the test documents, then run each example through the learner. We can determine the keyphrases by looking at binary classification decisions or probabilities returned from our learned model. If probabilities are given, a threshold is used to select the keyphrases.
Keyphrase extractors are generally evaluated using precision and recall. Precision measures how
many of the proposed keyphrases are actually correct. Recall measures how many of the true
keyphrases your system proposed. The two measures can be combined in an F-score, which is the
harmonic mean of the two (F = 2PR/(P + R) ). Matches between the proposed keyphrases and the known keyphrases can be checked after stemming or applying some other text normalization.

Designing a supervised keyphrase extraction system involves deciding on several choices (some of these apply to unsupervised, too). The first choice is exactly how to generate examples. Turney and others have used all possible unigrams, bigrams, and trigrams without intervening punctuation and after removing stopwords. Hulth showed that you can get some improvement by selecting examples to be sequences of tokens that match certain patterns of part-of-speech tags. Ideally, the mechanism for generating examples produces all the known labeled keyphrases as candidates, though this is often not the case. For example, if we use only unigrams, bigrams, and trigrams, then we will never be able to extract a known keyphrase containing four words. Thus, recall may suffer. However, generating too many examples can also lead to low precision.

We also need to create features that describe the examples and are informative enough to allow a learning algorithm to discriminate keyphrases from non- keyphrases. Typically features involve various term frequencies (how many times a phrase appears in the current text or in a larger corpus), the length of the example, relative position of the first occurrence, various boolean syntactic features (e.g., contains all caps), etc. The Turney paper used about 12 such features. Hulth uses a reduced set of features, which were found most successful in the KEA (Keyphrase Extraction Algorithm) work derived from Turney's seminal paper.

In the end, the system will need to return a list of keyphrases for a test document, so we need to have a way to limit the number. Ensemble methods (i.e., using votes from several classifiers) have been used to produce numeric scores that can be thresholded to provide a user-provided number of keyphrases. This is the technique used by Turney with C4.5 decision trees. Hulth used a single binary classifier so the learning algorithm implicitly determines the appropriate number.

Once examples and features are created, we need a way to learn to predict keyphrases. Virtually any supervised learning algorithm could be used, such as decision trees, Naive Bayes, and rule induction. In the case of Turney's GenEx algorithm, a genetic algorithm is used to learn parameters for a domain-specific keyphrase extraction algorithm. The extractor follows a series of heuristics to identify keyphrases. The genetic algorithm optimizes parameters for these heuristics with respect to performance on training documents with known key phrases.

Unsupervised approach: TextRank
Another keyphrase extraction algorithm is TextRank. While supervised methods have some nice properties, like being able to produce interpretable rules for what features characterize a keyphrase, they also require a large amount of training data. Many documents with known keyphrases are needed. Furthermore, training on a specific domain tends to customize the extraction process to that domain, so the resulting classifier is not necessarily portable, as some of Turney's results demonstrate.
Unsupervised keyphrase extraction removes the need for training data. It approaches the problem from a different angle. Instead of trying to learn explicit features that characterize keyphrases, the TextRank algorithm exploits the structure of the text itself to determine keyphrases that appear "central" to the text in the same way that PageRank selects important Web pages. Recall this is based on the notion of "prestige" or "recommendation" from social networks. In this way, TextRank does not rely on any previous training data at all, but rather can be run on any arbitrary piece of text, and it can produce output simply based on the text's intrinsic properties. Thus the algorithm is easily portable to new domains and languages.

TextRank is a general purpose graph-based ranking algorithm for NLP. Essentially, it runs PageRank on a graph specially designed for a particular NLP task. For keyphrase extraction, it builds a graph using some set of text units as vertices. Edges are based on some measure of semantic or lexical similarity between the text unit vertices. Unlike PageRank, the edges are typically undirected and can be weighted to reflect a degree of similarity. Once the graph is constructed, it is used to form a stochastic matrix, combined with a damping factor (as in the "random surfer model"), and the ranking over vertices is obtained by finding the eigenvector corresponding to eigenvalue 1 (i.e., the stationary distribution of the random walk on the graph).

The vertices should correspond to what we want to rank. Potentially, we could do something similar to the supervised methods and create a vertex for each unigram, bigram, trigram, etc. However, to keep the graph small, the authors decide to rank individual unigrams in a first step, and then include a second step that merges highly ranked adjacent unigrams to form multi-word phrases. This has a nice side effect of allowing us to produce keyphrases of arbitrary length. For example, if we rank unigrams and find that "advanced", "natural", "language", and "processing" all get high ranks, then we would look at the original text and see that these words appear consecutively and create a final keyphrase using all four together. Note that the unigrams placed in the graph can be filtered by part of speech. The authors found that adjectives and nouns were the best to include. Thus, some linguistic knowledge comes into play in this step.

Edges are created based on word co-occurrence in this application of TextRank. Two vertices are connected by an edge if the unigrams appear within a window of size N in the original text. N is typically around 2–10. Thus, "natural" and "language" might be linked in a text about NLP. "Natural" and "processing" would also be linked because they would both appear in the same string of N words. These edges build on the notion of "text cohesion" and the idea that words that appear near each other are likely related in a meaningful way and "recommend" each other to the reader.

Since this method simply ranks the individual vertices, we need a way to threshold or produce a limited number of keyphrases. The technique chosen is to set a count T to be a user-specified fraction of the total number of vertices in the graph. Then the top T vertices/unigrams are selected based on their stationary probabilities. A post- processing step is then applied to merge adjacent instances of these T unigrams. As a result, potentially more or less than T final keyphrases will be produced, but the number should be roughly proportional to the length of the original text.

It is not initially clear why applying PageRank to a co-occurrence graph would produce useful keyphrases. One way to think about it is the following. A word that appears multiple times throughout a text may have many different co-occurring neighbors. For example, in a text about machine learning, the unigram "learning" might co-occur with "machine", "supervised", "un-supervised", and "semi-supervised" in four different sentences. Thus, the "learning" vertex would be a central "hub" that connects to these other modifying words. Running PageRank/TextRank on the graph is likely to rank "learning" highly. Similarly, if the text contains the phrase "supervised classification", then there would be an edge between "supervised" and "classification". If "classification" appears several other places and thus has many neighbors, its importance would contribute to the importance of "supervised". If it ends up with a high rank, it will be selected as one of the top T unigrams, along with "learning" and probably "classification". In the final post-processing step, we would then end up with keyphrases "supervised learning" and "supervised classification".

In short, the co-occurrence graph will contain densely connected regions for terms that appear often and in different contexts. A random walk on this graph will have a stationary distribution that assigns large probabilities to the terms in the centers of the clusters. This is similar to densely connected Web pages getting ranked highly by PageRank. This approach has also been used in document summarization, considered below.

Document summarization
Like keyphrase extraction, document summarization aims to identify the essence of a text. The only real difference is that now we are dealing with larger text units—whole sentences instead of words and phrases.

Supervised learning approaches
Supervised text summarization is very much like supervised keyphrase extraction. Basically, if you have a collection of documents and human-generated summaries for them, you can learn features of sentences that make them good candidates for inclusion in the summary. Features might include the position in the document (i.e., the first few sentences are probably important), the number of words in the sentence, etc. The main difficulty in supervised extractive summarization is that the known summaries must be manually created by extracting sentences so the sentences in an original training document can be labeled as "in summary" or "not in summary". This is not typically how people create summaries, so simply using journal abstracts or existing summaries is usually not sufficient. The sentences in these summaries do not necessarily match up with sentences in the original text, so it would be difficult to assign labels to examples for training. Note, however, that these natural summaries can still be used for evaluation purposes, since ROUGE-1 evaluation only considers unigrams.

Maximum entropy-based summarization
During the DUC 2001 and 2002 evaluation workshops, TNO developed a sentence extraction system for multi-document summarization in the news domain. The system was based on a hybrid system using a Naive Bayes classifier and statistical language models for modeling salience. Although the system exhibited good results, the researchers wanted to explore the effectiveness of a maximum entropy (ME) classifier for the meeting summarization task, as ME is known to be robust against feature dependencies. Maximum entropy has also been applied successfully for summarization in the broadcast news domain.

Adaptive summarization
A promising approach is adaptive document/text summarization. It involves first recognizing the text genre and then applying summarization algorithms optimized for this genre. Such software has been created.

TextRank and LexRank
The unsupervised approach to summarization is also quite similar in spirit to unsupervised keyphrase extraction and gets around the issue of costly training data. Some unsupervised summarization approaches are based on finding a "centroid" sentence, which is the mean word vector of all the sentences in the document. Then the sentences can be ranked with regard to their similarity to this centroid sentence.

A more principled way to estimate sentence importance is using random walks and eigenvector centrality. LexRank is an algorithm essentially identical to TextRank, and both use this approach for document summarization. The two methods were developed by different groups at the same time, and LexRank simply focused on summarization, but could just as easily be used for keyphrase extraction or any other NLP ranking task.

In both LexRank and TextRank, a graph is constructed by creating a vertex for each sentence in the document.

The edges between sentences are based on some form of semantic similarity or content overlap. While LexRank uses cosine similarity of TF-IDF vectors, TextRank uses a very similar measure based on the number of words two sentences have in common (normalized by the sentences' lengths). The LexRank paper explored using unweighted edges after applying a threshold to the cosine values, but also experimented with using edges with weights equal to the similarity score. TextRank uses continuous similarity scores as weights.

In both algorithms, the sentences are ranked by applying PageRank to the resulting graph. A summary is formed by combining the top ranking sentences, using a threshold or length cutoff to limit the size of the summary.

It is worth noting that TextRank was applied to summarization exactly as described here, while LexRank was used as part of a larger summarization system (MEAD) that combines the LexRank score (stationary probability) with other features like sentence position and length using a linear combination with either user-specified or automatically tuned weights. In this case, some training documents might be needed, though the TextRank results show the additional features are not absolutely necessary.

Unlike TextRank, LexRank has been applied to multi-document summarization.

Multi-document summarization

Multi-document summarization is an automatic procedure aimed at extraction of information from multiple texts written about the same topic. Resulting summary report allows individual users, such as professional information consumers, to quickly familiarize themselves with information contained in a large cluster of documents. In such a way, multi-document summarization systems are complementing the news aggregators performing the next step down the road of coping with information overload. Multi-document summarization may also be done in response to a question.

Multi-document summarization creates information reports that are both concise and comprehensive. With different opinions being put together and outlined, every topic is described from multiple perspectives within a single document. While the goal of a brief summary is to simplify information search and cut the time by pointing to the most relevant source documents, comprehensive multi-document summary should itself contain the required information, hence limiting the need for accessing original files to cases when refinement is required. Automatic summaries present information extracted from multiple sources algorithmically, without any editorial touch or subjective human intervention, thus making it completely unbiased.

Diversity
Multi-document extractive summarization faces a problem of redundancy. Ideally, we want to extract sentences that are both "central" (i.e., contain the main ideas) and "diverse" (i.e., they differ from one another). For example, in a set of news articles about some event, each article is likely to have many similar sentences. To address this issue, LexRank applies a heuristic post-processing step that adds sentences in rank order, but discards sentences that are too similar to ones already in the summary. This method is called Cross-Sentence Information Subsumption (CSIS). These methods work based on the idea that sentences "recommend" other similar sentences to the reader. Thus, if one sentence is very similar to many others, it will likely be a sentence of great importance. Its importance also stems from the importance of the sentences "recommending" it. Thus, to get ranked highly and placed in a summary, a sentence must be similar to many sentences that are in turn also similar to many other sentences. This makes intuitive sense and allows the algorithms to be applied to an arbitrary new text. The methods are domain-independent and easily portable. One could imagine the features indicating important sentences in the news domain might vary considerably from the biomedical domain. However, the unsupervised "recommendation"-based approach applies to any domain.

A related method is Maximal Marginal Relevance (MMR), which uses a general-purpose graph-based ranking algorithm like Page/Lex/TextRank that handles both "centrality" and "diversity" in a unified mathematical framework based on absorbing Markov chain random walks (a random walk where certain states end the walk). The algorithm is called GRASSHOPPER. In addition to explicitly promoting diversity during the ranking process, GRASSHOPPER incorporates a prior ranking (based on sentence position in the case of summarization).

The state of the art results for multi-document summarization are obtained using mixtures of submodular functions. These methods have achieved the state of the art results for Document Summarization Corpora, DUC 04 - 07. Similar results were achieved with the use of determinantal point processes (which are a special case of submodular functions) for DUC-04.

A new method for multi-lingual multi-document summarization that avoids redundancy generates ideograms to represent the meaning of each sentence in each document, then evaluates similarity by comparing ideogram shape and position. It does not use word frequency, training or preprocessing. It uses two user-supplied parameters: equivalence (when are two sentences to be considered equivalent?) and relevance (how long is the desired summary?).

Submodular functions as generic tools for summarization
The idea of a submodular set function has recently emerged as a powerful modeling tool for various summarization problems. Submodular functions naturally model notions of coverage, information, representation and diversity. Moreover, several important combinatorial optimization problems occur as special instances of submodular optimization. For example, the set cover problem is a special case of submodular optimization, since the set cover function is submodular. The set cover function attempts to find a subset of objects which cover a given set of concepts. For example, in document summarization, one would like the summary to cover all important and relevant concepts in the document. This is an instance of set cover. Similarly, the facility location problem is a special case of submodular functions. The Facility Location function also naturally models coverage and diversity. Another example of a submodular optimization problem is using a determinantal point process to model diversity. Similarly, the Maximum-Marginal-Relevance procedure can also be seen as an instance of submodular optimization. All these important models encouraging coverage, diversity and information are all submodular. Moreover, submodular functions can be efficiently combined, and the resulting function is still submodular. Hence, one could combine one submodular function which models diversity, another one which models coverage and use human supervision to learn a right model of a submodular function for the problem.

While submodular functions are fitting problems for summarization, they also admit very efficient algorithms for optimization. For example, a simple greedy algorithm admits a constant factor guarantee. Moreover, the greedy algorithm is extremely simple to implement and can scale to large datasets, which is very important for summarization problems.

Submodular functions have achieved state-of-the-art for almost all summarization problems. For example, work by Lin and Bilmes, 2012 shows that submodular functions achieve the best results to date on DUC-04, DUC-05, DUC-06 and DUC-07 systems for document summarization. Similarly, work by Lin and Bilmes, 2011, shows that many existing systems for automatic summarization are instances of submodular functions. This was a breakthrough result establishing submodular functions as the right models for summarization problems.

Submodular Functions have also been used for other summarization tasks. Tschiatschek et al., 2014 show that mixtures of submodular functions achieve state-of-the-art results for image collection summarization. Similarly, Bairi et al., 2015 show the utility of submodular functions for summarizing multi-document topic hierarchies. Submodular Functions have also successfully been used for summarizing machine learning datasets.

Applications

Specific applications of automatic summarization include:
 The Reddit bot "autotldr", created in 2011 summarizes news articles in the comment-section of reddit posts. It was found to be very useful by the reddit community which upvoted its summaries hundreds of thousands of times. The name is reference to TL;DR − Internet slang for "too long; didn't read".
 Adversarial stylometry may make use of summaries, if the detail lost is not major and the summary is sufficiently stylistically different to the input.

Evaluation

The most common way to evaluate the informativeness of automatic summaries is to compare them with human-made model summaries.

Evaluation can be intrinsic or extrinsic, and inter-textual or intra-textual.

Intrinsic versus extrinsic
Intrinsic evaluation assesses the summaries directly, while extrinsic evaluation evaluates how the summarization system affects the completion of some other task. Intrinsic evaluations have assessed mainly the coherence and informativeness of summaries. Extrinsic evaluations, on the other hand, have tested the impact of summarization on tasks like relevance assessment, reading comprehension, etc.

Inter-textual versus intra-textual
Intra-textual evaluation assess the output of a specific summarization system, while inter-textual evaluation focuses on contrastive analysis of outputs of several summarization systems.

Human judgement often varies greatly in what it considers a "good" summary, so creating an automatic evaluation process is particularly difficult. Manual evaluation can be used, but this is both time and labor-intensive, as it requires humans to read not only the summaries but also the source documents. Other issues are those concerning coherence and coverage.

The most common way to evaluate summaries is ROUGE (Recall-Oriented Understudy for Gisting Evaluation). It is very common for summarization and translation systems in NIST's Document Understanding Conferences. ROUGE is a recall-based measure of how well a summary covers the content of human-generated summaries known as references. It calculates n-gram overlaps between automatically generated summaries and previously written human summaries. It is recall-based to encourage inclusion of all important topics in summaries. Recall can be computed with respect to unigram, bigram, trigram, or 4-gram matching. For example, ROUGE-1 is the fraction of unigrams that appear in both the reference summary and the automatic summary out of all unigrams in the reference summary. If there are multiple reference summaries, their scores are averaged. A high level of overlap should indicate a high degree of shared concepts between the two summaries.

ROUGE cannot determine if the result is coherent, that is if sentences flow together in a sensibly. High-order n-gram ROUGE measures help to some degree.

Another unsolved problem is Anaphor resolution. Similarly, for image summarization, Tschiatschek et al., developed a Visual-ROUGE score which judges the performance of algorithms for image summarization.

Domain-specific versus domain-independent summarization
Domain-independent summarization techniques apply sets of general features to identify information-rich text segments. Recent research focuses on domain-specific summarization using knowledge specific to the text's domain, such as medical knowledge and ontologies for summarizing medical texts.

Qualitative
The main drawback of the evaluation systems so far is that we need a reference summary (for some methods, more than one), to compare automatic summaries with models. This is a hard and expensive task. Much effort has to be made to create corpora of texts and their corresponding summaries. Furthermore, some methods require manual annotation of the summaries (e.g. SCU in the Pyramid Method). Moreover, they all perform a quantitative evaluation with regard to different similarity metrics.

History
The first publication in the area dates back to 1957  (Hans Peter Luhn), starting with a statistical technique. Research increased significantly in 2015. Term frequency–inverse document frequency had been used by 2016. Pattern-based summarization was the most powerful option for multi-document summarization found by 2016. In the following year it was surpassed by latent semantic analysis (LSA) combined with non-negative matrix factorization (NMF). Although they did not replace other approaches and are often combined with them, by 2019 machine learning methods dominated the extractive summarization of single documents, which was considered to be nearing maturity. By 2020, the field was still very active and research is shifting towards abstractive summation and real-time summarization.

Recent approaches
Recently the rise of transformer models replacing more traditional RNN (LSTM) have provided a flexibility in the mapping of text sequences to text sequences of a different type, which is well suited to automatic summarization. This includes models such as T5 and Pegasus.

See also
 Sentence extraction
 Text mining
 Multi-document summarization

References

Works cited

Further reading

, Conceptual artwork using automatic summarization software in Microsoft Word 2008.
, Published in Proceeding RIAO'10 Adaptivity, Personalization and Fusion of Heterogeneous Information, CID Paris, France
, The GRASSHOPPER algorithm
, Conceptual Structures for STEM Research and Education.

Computational linguistics
Natural language processing
Tasks of natural language processing
Data mining

redpajama_set_name: RedPajamaWikipedia


The Quest offers a fresh, contemporary and comprehensive presentation of spiritual principles. Learn to incorporate spiritual principles in everyday living through exercises and activities suggested in the activity guidebook Adventures on the Quest.
In every human heart there exists the hope of connecting with "something more." Tucked away deep within us, a part of us has always sought--yearned for--that connection. The Quest opens the way for that connection. It presents eternal truths in a contemporary and very personal way. Whether you are just beginning your own individual quest or desire a deeper spiritual understanding, this guidebook and the accompanying activity book, Adventures on the Quest, will lead you with warmth and practicality through the pilgrimage of your soul. What you hold in your hand is not a book in the usual sense. The Quest is a process--a journey--which can transform your life far beyond anything that you may now envision.
Mary-Alice and Richard Jafolla founded Spirit of Life, a nonprofit organization dedicated to all aspects of wellness. They are former directors of Silent Unity®, the prayer ministry of Unity, serving people of all faiths with 24/7 prayer support and authors of The Simple Truth, The Quest for Prayer and The Lazarus Blueprint.


text:
Kalophrynus minusculus is a species of frog in the family Microhylidae. It is endemic to Indonesia and occurs on extreme western Java and on the southeastern tip of Sumatra. It is a relatively small frog, as suggested by its specific name minusculus and vernacular names small sticky frog and dwarf sticky frog.

Description
Adult males grow to  and adult females to (based on a single specimen)  in snout–vent length. The head is wider than it is long. The eyes are moderately large. The tympana are visible and smaller than the eyes. The dorsum is brownish black with darker bands and stripes.

Females lay 30–50 eggs that hatch into non-feeding tadpoles.

Habitat and conservation
Kalophrynus minusculus occurs in lowland forests, including degraded, patchy forests with canopy cover. It can be found in large numbers during the reproductive season when it breeds in temporary pools on the forest floor. However, at other times, it is difficult to find because it is well camouflaged with the forest substrate.

It is threatened by habitat loss caused by logging and agricultural encroachment, especially on Java. It is present in the Ujung Kulon National Park (Java) and Bukit Barisan Selatan National Park (Sumatra).

References

minusculus
Frogs of Asia
Amphibians of Indonesia
Endemic fauna of Indonesia
Fauna of Java
Fauna of Sumatra
Amphibians described in 1998
Taxonomy articles created by Polbot


text:
Q: linking entityframework to a new Sql DataType I am using ASP.NET MVC5 and MS SQL 2008 and EntityFramework 6, in my application i have a class Experiences that will allow the clients to add details of their experiences from period till period:
public class Experience
{
    public int Id { get; set; }
    public string Title { get; set; }
    public string Company { get; set; }
    public string FromDate { get; set; }
    public string ToDate { get; set; }
    public string Description { get; set; }
}

the FromDate format will be MMM yyyy, example Jan 2009, Oct 2010 , ...etc
the ToDate format will be either MMM yyyy or Present and should be greater than or equal the FromDate value for example:

*

*From = Jan 2010, To= Oct 2014 ==> Accepted

*From = Jan 2010, To= Present ==> Accepted

*From = Oct 2014, To = Jul 2014  ==> Rejected the value to To Date should be greater than the From date
I made the class Period as follow
public class Period: IComparable
{
    public Period()
    {
         this.Month="Present";
    }

    public Period(string Month,int Year)
    {
        this.Month=Month;
        this.Year=Year;
    }

    public int CompareTo(object obj)
    {
        if(obj==null)
            return 1;
        Period period = obj as Period;
        if (period != null)
        {
            if(this.Month=="Present" && period.Month=="Present")
                return 0;
            else if(period.Month=="Present")
                return -1;
            else if(this.Month=="Present")
                return 1;
            else
            {
                DateTime date;
                DateTime periodDate;
                if(!DateTime.TryParse(string.Format("01 {0} {1}",Month,Year),out date))
                    throw new ArgumentException("Instance is not a valid Period!");
                if(!DateTime.TryParse(string.Format("01 {0} {1}",period.Month,period.Year),out periodDate))
                    throw new ArgumentException("Object is not a valid Period!");
                return date.Date.CompareTo(periodDate.Date);
            }
        }
           else
              throw new ArgumentException("Object is not a Period");
    }

    public override int GetHashCode()
    {
        if(this==null)
            return 0;
        else
        {
            DateTime date;
            if(!DateTime.TryParse(string.Format("01 {0} {1}",Month,Year),out date))
                throw new ArgumentException("Instance is not a valid Period!");
            return date.GetHashCode();
        }
    }

    public override bool Equals(object obj)
    {
        return this.CompareTo(obj)==0;
    }

    public static bool operator==(Period left, Period right)
    {
        return left.Equals(right);
    }

    public static bool operator!=(Period left, Period right)
    {
        return !(left==right);
    }

    public static bool operator<(Period left, Period right)
    {
        return left.CompareTo(right)<0;
    }

    public static bool operator>(Period left, Period right)
    {
        return left.CompareTo(right)>0;
    }

    public override string ToString()
    {
        if(string.IsNullOrWhiteSpace(Month))
            return string.Empty;
        else if(Month=="Present")
            return Month;
        else
            return string.Format("{0} {1}",Month,Year);
    }

    public string Month{get; set;}
    public int Year{get; set;}
}

Sample DEMO
in sql i created the following datatype
create type Period from nvarchar(8) not null;

Questions:

*

*How can i define Period in sql?

*How can i tell EntityFramework to link period to the data type period in sql?

any idea will be appreciated

A: here a workaround about how to do it
your experience class will be like this
public class Experience
{
    public int Id { get; set; }
    public string Title { get; set; }
    public string Company { get; set; }
    public Period FromDate { get; set; }
    public Period ToDate { get; set; }
    public string Description { get; set; }
}

change the following in your period class which will be a [ComplexType]
// change the folllowing
[NotMapped]
public string Month{get; set;}
[NotMapped]
public int Year{get; set;}

// add this property to use it for EntityFramework mapping
public string Date
{
    get
    {
        return ToString();
    }
    set
    {
        if (!string.IsNullOrEmpty(Date))
            {
                if (Date == "Present")
                    Month = "Present";
                else
                {
                    var split = Date.Split(' ');
                    Month = split[0];
                    Year = Convert.ToInt32(split[1]);
                }
            }
        }
    }

in your Mapping you do the following
 modelBuilder.Entity<Experience>()
                .Property(t => t.FromDate.Date)
                .IsRequired()
                .HasMaxLength(8);

 modelBuilder.Entity<Experience>()
            .Property(t => t.ToDate.Date)
            .IsRequired()
            .HasMaxLength(8);

to let the entity framework to make the fields as Period datatype, you can do this
1- enable-migration
2- add-migration first // this will create a file dbmigration that has the database and table construction
3- in the function Up at end you write the following using Sql(@"");, here i will put the sql statements for you
create function CheckPeriod(@period nvarchar(8)) -- this function will make sure that Period is valid
returns bit
as
begin
    declare @month nvarchar(3)
    if ( @period is null )
        return 0
    if( @period = 'Present')
        return 1;
    set @month=substring(@period,0,3)
    if(@month in ('Jan','Feb','Mar','May','Jun','Jul','Aug','Sep','Oct','Nov','Dec'))
        return 1;
    return isdate(@period);
end

create function CheckValidity(@right as nvarchar(8),@left as nvarchar(8)) -- this function will compare FromDate to ToDate and return valid if ToDate>FromDate
returns bit
as
begin
    if(@right='Present')
        return 0;
    if(@left='Present' and cast(@right as date)>=getdate())
        return 0;
    if(@left='Present')
        return 1;
    if(cast(@left as date)>cast(@right as date))
        return 1;
    return 0;
end

exec sp_addtype 'Period','nvarchar(8)','NOT NULL'

alter table Experiences alter column FromDate Period
alter table Experiences alter column ToDate Period

alter table Experiences
add constraint FromDateIsPeriod check ( dbo.CheckPeriod(FromDate)=1)

alter table Experiences
add constraint ToDateIsPeriod check ( dbo.CheckPeriod(ToDate)=1)

alter table Experiences
add constraint PeriodValidity check ( dbo.CheckValidity(FromDate,ToDate)=1)

and in the Down function if you want you write the code to drop the functions and to alter the table fields to return them back as nvarchar(8) and finally to drop the datatype Period
hope this will help you

redpajama_set_name: RedPajamaStackExchange


Carl Barks (27 Mairch 1901, in Merrill, Oregon – 25 August 2000, in Grants Pass, Oregon) wis a weel-kent American Disney comic airtist. He first wrote stories wi his ain chairacters, but stairtit daein Donald Duck comics. Carl Barks has creatit loads o chairacters fir Duckburg. The maist weel-kent o thaim's Scrooge McDuck, Scots millionair.
Wikimedia Commons haes media relatit tae Carl Barks.
This Disney-relatit airticle is a stub. Ye can help Wikipaedia bi expandin it.
This page wis last eeditit on 8 Apryle 2017, at 01:59.


6th day of Week 19 and the 34th day of the cycle of Tiferet (Balance/Beauty), Yesod shebeHod shebeTiferet, Foundation within Glory/Acknowledgement within Balance/Beauty.
the desire of the wicked will be lost.
I yearned for Your salvation, O Lord, and Your Torah is my occupation.


All Your Music Needs In One Place.
Celemony Melodyne 4 studio Update, Update from Celemony Melodyne Studio 3 (Download), Pitch and timing correction software and plugin, Largely free mono- and polyphonic audio material. Melodyne Studio 3.1.2.0 Full Version Published : 08.27 Author : Radius heaven .
Melodyne 4.1 Serial Numbers. Convert Melodyne 4.1 trail version to full software.. Celemony Melodyne Studio 4 v4.1.0.001. . Soft / Audio Soft 25-11-2016. . Furthermore, Melodyne 4 studio now also runs directly as a plug-in within your DAW .
. keygen #Tags:celemony,melodyne,studio,edition Celemony Melodyne Studio Edition v3.2.2.2 keygen RatingRelated DownloadsDownloads Celemony Melodyne studio . 11 .


Kyle, along with his brother Kenny, are the award-winning Producers, Directors, and Founders of Saylors Brothers Entertainment, LTD. During the course of their years of industry success, they have had their feet in practically every medium the industry has to offer, from National and International Television programming, to hit music videos that have topped the charts on Mtv and BET worldwide, to award-winning, critically-acclaimed documentaries and feature films. Their work has been featured on, and in, Fox News, Newsweek, BBC, Time Magazine, NY Times, LA Times, Variety, Hollywood Reporter, Forbes, Huffington Post, Indie Wire, Charisma, Christianity Today, and others.
Their current projects include the Dance Network Original Series "The Breakdown: The Science of Dance", the Andrae' Crouch Tribute with major artists involved including Stevie Wonder, CeCe Winans, Yolanda Adams, and many more. The latest feature film project helmed by the Saylors Brothers is the gripping film "Veil of Tears" which was released in theaters to rave reviews from critics and audiences alike. Kyle was DP of the critically-acclaimed film Sound of Redemption at LA Film Fest with Executive Producer and New York Times #1 Best-Selling author Michael Connelly (Lincoln Lawyer), and Paris-based director N.C. Hiekien.
Past projects created, produced and/or directed by the Brothers include; the innovative Prime-Time FOX Sports & Red Bull Television Series "The Gamebreaker", starring NFL stars such as Dez Bryant, Reggie Bush, DeMarcus Ware, and Jason Witten among others. The Award-winning, critically-acclaimed film "Kimjongilia" (co-produced with Academy Award nominated Mike Figgis - "Leaving Las Vegas"), which began it's run as a Finalist at the Sundance Film Festival, where the film held its World Premier and was nominated for the coveted Grand Jury Prize. The daring film "My Finish Line" was an exclusive inside look at the Indy 500, with special guests Mario Andretti and Eric Close ("Without a Trace"), and was granted unprecedented access to the "World's Greatest Race" on Race Day.
The Saylors Brothers have taken a long road from a small Texas town where they grew up, to be able to pursue their lifelong passion which has taken them around the globe. The Saylors' passion for creating and continual push-forward mindset advances their innovative ideas and skills. This allows them to develop unique projects that are high quality while maintaining redemptive value, for both 'faith' and 'family', as well as the mainstream marketplace. With the Saylors, it's more than just entertaining, it's entertainment with purpose.


text:
It's feeling like summer out there
Today will be hot and muggy with highs in the upper 80s, rain not expected
It's feeling like summer out there Today will be hot and muggy with highs in the upper 80s, rain not expected Check out this story on news-leader.com: https://www.news-leader.com/story/news/local/ozarks/2014/06/16/feeling-like-summer/10570551/
News-Leader Published 7:17 a.m. CT June 16, 2014 | Updated 11:34 a.m. CT June 17, 2014
Weather(Photo: NOAA)
Today is expected to be warm and humid with highs in the upper 80s, according to the National Weather Service.
MORE: WEATHER
The dew point — the temperature at which water in the air condenses on surfaces like cars or grass — will be in the low 70s, indicating there is a lot of water in the air. But rain isn't expected for the next few days, according to the National Weather Service.
"It's going to feel muggy," said meteorologist Jeff Raberding.
Read or Share this story: https://www.news-leader.com/story/news/local/ozarks/2014/06/16/feeling-like-summer/10570551/


text:
Q: ActionMailer with Rails: uninitialized constant UserMailer Getting this error in console when I try UserMailer.welcome.deliver_now
(irb):6:in `': uninitialized constant UserMailer (NameError)

UserMailer.welcome.deliver_now
^^^^^^^^^^
Did you mean?  UserMailerPreview

And before I was getting this NameError, the console was throwing the error that the method welcome was undefined.
enter image description here
This is my file structure.
app/mailers/application_mailer.rb:
class ApplicationMailer < ActionMailer::Base
  default from: "from@example.com"
  layout "mailer"
end

app/mailers/user_mailer.rb:
class UserMailer < ApplicationMailer
  def welcome
    @greeting = "Hi"
    mail to: "to@example.org"
  end
end

app/user_mailer/welcome_email.html.erb:

<%= @greeting %>,

Example Email Body

app/user_mailer/welcome_email.text.erb:
<%= @greeting %>,

Example Email Body

A: You say:
app/mailers/application_mailer.rb:
class ApplicationMailer < ActionMailer::Base
  default from: "from@example.com"
  layout "mailer"
end

app/mailers/user_mailer.rb:
class UserMailer < ApplicationMailer
  def welcome
    @greeting = "Hi"
    mail to: "to@example.org"
  end
end

But that isn't true. Those files are in app/views/mailers according to your screenshot. Rails isn't able to autoload them properly. Move them to the correct location and try again.


You cannot do ethics until you know the facts.
Johannes JM van Delden (1960) is full professor of medical ethics at the Julius Center for Health Sciences and Primary Care of the medical school of Utrecht University. Ever since working as a house office at an intensive care ward he is highly interested in medical ethics. He wrote a thesis on the medical and ethical aspects of Do Not Resuscitate orders. Also, he was one of the principal researchers of the study of medical decisions concerning the end of life for the Remmelink committee. He was secretary of the International Association of Bioethics. After his education as a nursing home physician he has worked in several nursing homes for 15 years (until May 2011). His special fields of interest are: research ethics, moral problems at the end of life and moral problems in the care for the elderly.
Research ethics: our focus is on the conditions under which a responsible innovation in drug development will be possible and on issues of justice in research. End of life ethics: our focus is on understanding the choices of practitioners and the development of moral frameworks for practices such as palliative sedation. Moral problems in the care for the elderly. The focus will be on the ethical issues of concrete practices such as vaccination against influenza of health care personnel and diagnostic choices of nursing home physicians. Teaching: an important development in the medical curriculum of the UMC Utrecht is the introduction of the 8 weeks course on medical humanities. This is an interdisciplinary field in which medical ethics, health law, philosophy and history of medicine, medical sociology and the arts come together.
Kars MC, Grypdonck MHF, de Korte-Verhoef MC, Kamps WA, Meijer-van den Bergh MM, Verkerk MA, Delden JJM van. Support Care Cancer, 2011;1:27-35.
Opinions of health care professionals and the public after eight years of euthanasia legislation in the Netherlands: a mixed method approach.
Trends in end-of-life practices before and after the enactment of the euthanasia law in the Netherlands from 1990 to 2010: a repeated cross-sectional survey.
Onwuteaka-Philipsen BD, Brinkman-Stoppelenburg A, Penning C, de Jong-Krul GJ, van Delden JJ, van der Heide A. Lancet, 2012;380:908-15.
Adaptive trials in clinical research: scientific and ethical issues to consider.
Graaf R van der, Roes KC, van Delden JJ. JAMA, 2012;307:2379-2380.
Disclosure of individuel genetic data to research participants: the debate reconsidered.
Bredenoord AL, Kroes HY, Cuppen E, Parker M, Delden JJM van. Trends Genet, 2011;27(2):41-7.


Penn Yan/Elegy is a steel poem that I used to partially encase a derelict 1958 Penn Yan runabout boat. I then burned the entire structure in a public performance in Hamilton, NY. The physical dimensions of the wooden boat functioned as a mold for the steel poem, whose final shape has been determined by the boat's destruction.
Penn Yan/Elegy embodies the attempt to use language as a container for the experience of concurrent loss and awe. In this piece I engage the paradox that while language often fails to hold the weight of profound emotions, it is ultimately the primary attempt to bridge understanding. In this way I have tried to conflate the notions of form and elegy, while visually engaging the relationship between language and memory, artifact and memorial.


Business Travel Account is a global, market-leading centralised billing product, which is a key differentiator for American Express for our corporate clients, including some of the world's most profitable and influential companies. You will join our emerging set of scrum teams building new capabilities to bolster the already market-leading offering.
We value ideas, opinions and great communicators. We're proud that a culture of respect and teamwork exists among colleagues.
Existing team of 25 technical staff. Small, co-located Agile teams of 7-8 people each including Engineers, Scrum Masters, Quality Engineers plus Product Owners. We operate in a Scrum and Scaled Agile Framework environment (SAFe).
Most team members arrive in the office between 8.30-10am, when we hold our stand-ups. We use tools such as Slack, Confluence, JIRA, and BitBucket to collaborate. We enjoy the luxury of avoiding a long commute to London and place an emphasis on technically interesting work. We have an outstanding work/life balance.
We are beginning our journey of transforming the technical landscape in our teams to use a microservice architecture and are looking to bolster our technical talent by acquiring full-stack developers, using technologies such as Java, Spring, and React.
As a developer, you will be responsible for end-to-end delivery of your software from inception to production, working on a mixture of green- and brown-field projects, learning from and contributing to your peer group.
Good knowledge of core Java (up to 1.8), including Spring, JUnit, (including Mockito & Hamcrest), TDD, JQuery, AngularJS and Angular 6, React, Oracle DB SQL, Cucumber. Mainframe experience a plus. Agile experience a plus.
Excellent spoken and written English, clear oral communication skills.
Must be willing and able to express new ideas, concerns and opinions directly to team.
Must be proactive, enthusiastic, flexible, and results driven.


text:
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Reflected Poppies
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Large Rectangle Wedding Favor Box
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Peaceful and sweet, this Wedding Favor Box is part of our Reflected Poppies design suite. Vintage illustrations of poppy blossoms pop in and out of this design suite, which are accented by script lettering throughout. This vintage design suite would beautifully suit a spring or summer wedding.
Personalized on all six sides
One-piece box with tuck top
Folding assembly required View Instructions
We recommend using liners before placing
any unwrapped food inside Favor Boxes.
Related Wedding Favors
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Layout Number
WFB-12484 3.875" x 2.75" x 1.75"
Try this design in 3 other styles!
Additional Coordinating Items
Table Number Cards
Tea-length Menu Cards
Wedding Menu Cards
Tea-length Programs
Half-fold Programs


Minutes having been read and confirmed, further correspondence between Messrs Frere and Co., Lord Zetland's solicitors, and the Clerk to the Board with reference to the sewer laid down at the expense of the late Earl, was read. In reply to the first letter the Clerk stated that by the sanitary acts the silver in question was vested in the local authorities, who were not empowered to make any payment for its use, and denied that part of the Coatham drainage pass through it. In a second letter Messrs Frere and Co. said they held a contrary opinion, and repeated their former allegation with respect to the Coatham drainage. It was resolved that the Clerk brigade communicate with Messrs Frere and Vo., Denying that any part of the Coatham drainage, either now or at any other time, had passed into the Redcar sewers.
Offer from the Gas Company for the lighting of the town during the ensuing winter, was accepted, and ordered to be entered on the minutes.
Mr. Thos. Watson having received noticed that the houses 2 and 3, Albert Street, were unfit for human habitation, a letter was read from him enquiring at what meeting of the Board the resolution that he should receive the notice in question had been passed, as he had not seen it reported in the newspapers. The Clerk was instructed to inform Mr. Watson that the notice was given in pursuance of a general order of the Board made after a sanitary inspection of the town by the sanitary committee, the medical officer, and the surveyor.
The Surveyor reported that Mr. Scott, butcher, had erected a wooden shed in the yard at the back of his premises, without having first submitted plans for the approval of the Board. The Klerk was ordered to give notice for the removal of the same within seven days.
The Medical Officer reported that the town was at present in a good summitry state, there being an entire absence of zymotic diseases. The death rate for the last quarter was 23 per 1,000. This was unusually high, but was attributable to the five deaths by drowning, and the excessive mortality amongst children, 10 having died under two years of age from malnourishment and diarrhoea; the other 8 deaths were from chronic and non–preventable causes.
A letter was read by the Chairman, from Mr. Rowed, in which the writer complainant of the use and efficiency of some of the drivers employed by the bus proprietors, and instancing an accident with a trolley which he had witnessed. The matter was referred to the police.
It was ordered that the securities for two loans of £400 each from the Yorkshire Insurance Company be signed and sealed.
REDCAR – Re-numbering of Streets.
COATHAM – Miss Turner's School, Coatham, examinations.


text:
Quon: "Independent" – a self-assured presence on the mic
Published on November 24, 2018 in Reviews by staff
Quon, the American rapper hailing from Amarillo, TX, has been impacting the industry with his singles: "Im It" "Hunnid Miles n Runnin" and "Get It Wit Me". Now the artist takes another gigantic step forward with his brand new single and video release: "Independent". This is the most urgent and catchy a Quon project has sounded in some time, taking no prisoners with its constant barrage of rhymes and renewed energy. The rapper has a penchant for writing pervasive hooks and one liners, delivered in his matter-of-fact deadpan that lodges in your brain.
Easily digestible, often tongue in cheek, Quon has also perfected the art of knocking out four bar choruses that pull you in. He legitimately sounds like he enjoys performing, his vocals betraying something akin to a smirk; as if relishing in the ability to release songs like "Independent".
The fact is few could follow Quon in the niche he is carving out for himself. It's a tough stomping ground, bangers and party anthems with southern trap underpinnings, which are glued together by Quon's charisma. With an easy delivery and a great voice, there's a reason why his braggadocio sounds appealing rather than intimidating.
By sticking to his guns on "Independent", Quon proves he is a threat to the up-and-comers as well as the veterans. He's not wanting to be too innovative, because that's not necessary, he's safe in his habitat, making music his way, and audiences are the better for it. "Independent" distills the sonic extremes of contemporary hip-hop into a potent hybrid of radio-friendly sheen and hard-knocking club rhythm.
Basically, Quon has the instruction manual on how to create a good banger, and he uses it on "Independent". The energy is through the roof, with the main participant cocky and confident, while the funky, malicious production and rhymes are irresistible. This track is indeed hot as hell, both in its vibe and the performance.
The hook is simple, but gets stuck in your head the instant you hear it. The piano loop is hypnotizing, while the bass and percussion goes down hard. This is a perfect example of a banger that's simply overpowering because it's just that good. "Independent" confirms that Quon can succeed in turning underground clout into mainstream appeal, if he wants to.
All-in-all, Quon displays dynamism, lyrical acrobatics and an understanding of the female psyche, as well as dealing out a very in-depth analysis of gender relations. If you are inquisitive about what this dude has to say about the "Independent" lifestyle or would like to get your feet wet in an incredible rap tour de force, look no further than this track.
Quon knows how to play to his strengths while staying relevant in the context of hip-hop as a whole, and ultimately, this leads to another successful project. This this makes for both a devastatingly self-assured presence on the mic and a brazen sonic sensibility. When Quon is in his zone, he's unstoppable, with a knack for converting throwaway slogans into positively transcendent pop hooks.
OFFICIAL LINKS: YOUTUBE – SOUNDCLOUD – TWITTER – FACEBOOK – INSTAGRAM – SPOTIFY
Tags: AmarilloHip HopIndependentQuonRapTX
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speculator (2)
merchant or trader (4)
[[missing key: search-facet.tree.open-section]] colonial administration (4)
colonial official (1)
revolutionary leader (American revolution) (2)
signer of the declaration of independence (1)
banking and finance x
colonial administration x
Duer, William (1743-1799), businessman and politician
Jonathan J. Bean
Duer, William (18 March 1743–07 May 1799), businessman and politician, was born in Devonshire, England, the son of John Duer, a wealthy squire, and Frances Frye. After graduating from Eton, Duer joined the British army and traveled to India, where he later served as secretary to Lord Clive, governor-general of the East India Company. Duer soon fell ill, however, and returned to England. His father's subsequent death left him in possession of a large inheritance, which included an estate in the West Indies. By 1768 Duer was actively managing his estate and trading with prominent businessmen in New York, including ...
Johnson, Sir William (1715-1774), merchant, land speculator, and royal official among the Iroquois Indians
Francis Jennings
Johnson, Sir William (1715–11 July 1774), merchant, land speculator, and royal official among the Iroquois Indians, was born in County Meath, Ireland, the son of Christopher Johnson and Anne Warren, members of the gentry. William's mother provided the family connections that started her son on the way to fortune. Her brother, Vice Admiral ...
Morris, Robert (1735-1806), preeminent merchant and revolutionary financier
Clarence L. Ver Steeg
Morris, Robert (20 January 1735–08 May 1806), preeminent merchant and revolutionary financier, was born in Liverpool, England, the son of Robert Morris, Sr., an ironmonger and later a tobacco agent in Maryland, and Elizabeth Murphet. Shortly after Morris joined his father in Maryland in 1747, his father placed him in the care of Robert Greenway of Philadelphia, who obtained an apprenticeship for Robert in the established Philadelphia mercantile house of Charles Willing. Morris quickly displayed exceptional talent and resourcefulness in commerce, sometimes serving as supercargo on the firm's vessels. He also became a lifetime friend of Charles Willing's son ...
Willing, Thomas (1731-1821), merchant, political leader, and banker
William Weisberger
Willing, Thomas (19 December 1731–19 January 1821), merchant, political leader, and banker, was born in Philadelphia, Pennsylvania, the son of Charles Willing, a successful merchant and, later, mayor of Philadelphia, and Anne Shippen, granddaughter of Edward Shippen, the first mayor of Philadelphia. The Willings in 1740 sent young Thomas to England to be educated. He first went to school at Bath between 1740 and 1743 and then attended Robert Wheeler's school at Wells, Somersetshire. Willing went to London in September 1748 and for six months took courses in business at the Watt's Academy. The same year he began to read law at the Inner Temple....