The latest in the Labelwriter range, this beast prints up to 100mm labels!
DYMO now offers 100mm (4") wide label printing from the DYMO LabelWriter 4XL. This is Dymo's first wide format label printer. The easy and cost effective way to print a massive range of professional looking quality labels in size not previously available from LabelWriters.
The 4XL has 300dpi print quality: fast print speeds and a slick modern: compact design. The Dymo 4XL label printer gives fantastic results at a great price point. The economic direct thermal print technology means the only supplies you'll need are the Dymo labels. No ink. No toner: No wasted time.
The DYMO LabelWriter 4XL label printer prints on the range of Dymo LabelWriter labels and can be used for many applications from office to industrial environments including shipping labels: stock labels: pallet labels. With the DYMO LabelWriter 4XL label printer: you get high quality printing: fast print speeds and a compact design - all at a great price.
Using Dymo's Version 8 software (on Mac or PC) you can print name badges: SmartStamps: address labels: shipping: file & folder labels: barcodes: and durable general purpose labels. The following bar codes are supported: Code 39/128: Codabar: UPC A/E: EAN 8/13/128: ITF-14 and Interleaved 2 of 5. Address labels can be selectively printed from Outlook Contacts or MacAddress: one at a time using the QuickPrint widget: or up to 71 per minute when merging. Alternatively you can print text directly from applications like Word: Excel and CardScan. Further: all the above benefits may be enjoyed by colleagues: using Windows/Mac printer sharing.
USB Connected - Just use the included USB cable.
CLICK HERE to buy labels for this printer.
Can you still format your labels with the company logo etc and does it work just as well with smaller labels for addressing etc. Thank you.
You can do everything you can do on a standard Labelwriter printer but on up to 100mm wide media when using the Labelwriter 4XL printer. You can import logos barcodes and add text on all sizes of labels.
Depending on the size of the round labels you need it may be worth considering this Brother QL printers as there are two standard sizes of labels 24mm 12mm diameter which can be used with the QL printers.
No Dymo Labelwriters are all wired.
Do you sell any wireless label printers?
You can design your label to print both horizontal and vertical using the Dymo Labelwriter range of printers.
The labels supplied for the 4XL are paper and are not waterproof.
We want to be able to print product box labels with our company logo on. We want each label to print a sequential serial number. Do you have any software that offer this facility?
The software package supplied with your printer will enable you to print your company logo and a sequential serial number.
Hi - I want to use these labels on items that will be stored in the fridge. Will they be affected by condensation (i.e. once the items is out of the fridge)?
All the Dymo LabelWriter printers use direct thermal paper so it is not waterproof and the labels will deteriorate and be affected by the moisture. Depending on how big you want the labels and the budget we have a number of other printers that will print onto waterproof labels. If you can email your contact number to sales@labelzone.co.uk someone will give you a call to run through the products that would be suitable or feel free to email a bit more information to us and we can reply back via email. Many thanks.
We are looking to purchase to a label printer to replace our existing one. Currently we have a Kroy printer that prints 750mm by 500mm labels for product labeling. This printer looks to be ideal am I right in thinking this printer would be suitable for our specifications?
The Dymo Labelwriter range of printers only print in black this is due to the Direct Thermal printing technology. The printhead heats up and marks the Direct Thermal label media therefore you are not able to get a colour Direct Thermal label printer.
What about the life of use? This printer needs only labels? I mean does not need ink or cartridge?
The Dymo Labelwriter range do not use ink as they print direct thermally. The only consumable for this printer are the labels.
Can this also print the normal sized labels i.e. labels used in the Dymo 450 model.
Yes the Dymo XL can use all the LabelWriter labels in the range.


Anyone who lives in Auburn, Washington or who regularly drives through the area knows that drivers often have to share the road with large trucks. Whether you are driving on in-town roads in Auburn or are traveling at higher speeds on I-5 or I-18, a collision with a semi-truck can result in debilitating injuries. Many truck accidents involving smaller passenger vehicles result in fatal injuries. According to the Insurance Institute for Highway Safety (IIHS) and the Highway Loss Data Institute (HLDI), "most deaths in large truck crashes are passenger vehicle occupants." Since large trucks can weigh as much as 20 to 30 times more than a car or SUV and have a much higher ground clearance, occupants of those smaller vehicles are at great risk of life-threatening injury if they are involved in a wreck with a big rig.
Large truck crashes typically happen because of another party's negligence or wrongdoing. Victims of truck accidents often are able to file a claim against more than one party in order to be eligible for compensation for their losses. An aggressive Auburn, WA truck accident lawyer can discuss your case with you today and can help you to determine your options for filing a claim.
How Often Do Truck Accidents Happen in Auburn, Washington?
Seat belts can help but are not as effective in preventing truck accident injuries as 44 percent of fatal accident victims were wearing seatbelts at the time of the crash.
There are many different causes of truck crashes in Washington State and throughout the country. An experienced Auburn trucking accident lawyer can examine the facts of your case and can help you to determine whether you have a strong claim after a truck crash.
Who Can I Sue in an Auburn, Washington Trucking Collision Case?
Property owner where the accident occurred.
Depending upon the cause of the accident, you could end up filing a negligence claim against a truck driver and a trucking company, or you could end up filing a product liability lawsuit against a tire manufacturer, for instance.
Many truck crashes in Washington State are devastating. Passenger vehicle occupants and motorcyclists frequently suffer fatal injuries or injuries that leave them permanently disabled. If you or someone you love got hurt in a truck collision in Washington, you should learn more about filing a claim by speaking with an Auburn truck accident attorney. An advocate at our firm can discuss your case with you today. Contact Khan Law Firm PLLC to learn more about the services we provide to clients in the larger Seattle and Tacoma areas.


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Of course we have a new contest this month. Join the Community and submit your picture about a MALE MUSICIAN of your choice. That's our topic this time. You can create any famous musician you want, no matter if he is a singer, composer, DJ or drummer. Eben fictional characters count, if they are well known. Submit your unedited SP-Studio picture till October 15th and it will appear in the vote. Voting end at the end of this month. The five winners get their own gallery to show their best SP-Studio art and who manages to make the 1st place even has a free wish for a SP-Studio item of his choice. So show what you can do :).


Our new article is going to tell you about HTML5 file upload. Yes, I explained basics of html5 file upload in the past (in one of our previous articles), but today I would like to give you another example, the better one. Now, you can just drag and drop your images (multiple images) in order to start uploading. Plus, the script displays overall progress (in percentage, plus – files left) and server response (without actual uploading). I am going to display progress at CANVAS element in realtime. And, at the last – our script can upload files not only into own server, but to another too (we can say, that this is cross-site uploader).
Ok, download the sources and lets begin !
As you can see, it consists of several main elements: 'Drop area' at the left and 'Info block' at the right. When we drag and drop image files on our dropArea, in the Info block we will get response from server. Pay attention, that there is 'Destination url' element. Right now it connected to our server. But you can change it to your url anytime.
Most of code is already commented. I hope that you can understand all this code. Anyway – some explanation how it works: in the beginning, we have linked two handlers to our DropArea: 'drop' and 'dropover'. When we keep our dragged files over our Drop area, we can apply custom styles to our drop area. Then, when we drop our files, our script starts executing 'processFiles' function (it pushs all the dropped files into array, and starts uploading them step by step). In the result, we send data through XMLHttpRequest object to custom recipient server. During sending the files, we also display overall progress at our canvas element.
Its server-side file. It doesn't upload files of course. But it returns some information about our files (which we will display at our receiver (client) side).
Hope this helped to you! Welcome back to read our new articles about HTML5. Good luck!
This is awesome! Quick question, what's the largest file size someone can upload with this?
It depends only on limits of your server.
how can i upload to the file to the server.?
Hello, you can perform actual upload in upload.php file.
This script is amazing, thanks !
Script is working fine in Chrome, but not with IE9.
Didn't work for me. I got a message which said progress 100% and an index listing of files in the folder specified in the destination URL. The image file I tested with wasn't listed.
Our script doesn't save files at server, it has all functional to send and receive, but I didn't add actual save to server's disk. In the result of upload you should get short info about received file. Where exactly it didn't work? At our demo page or at your computer?
Hi, is it possible to have two of these on the same page? There seems to be issues when doing this even when using different ids as the handles.
Do you mean – two drop areas, or two drop areas + 2 results areas? Anyway, everything is possible. Yes, you can add few more drop areas, and – in this case you will need just to add more 'addEventListener' for your new objects.
You should implement any kind of login system. And after – check, if a member is logged in or not every time you perform upload or even display upload form.
How to replace destination url with 'upload.php' ?
thank you so much for you'r example.
Correction: Test your demo on Opera on Windows 8 and it works fine. Odd though, but obviously a Opera/Mac issue. (it works in Safari on Mac too).
As for me, I already tested in most of popular browsers (FF, Chrome, Safari). This is more than 80-90% of all browsers nowadays.
you can use several ways, as example – jQuery.
Thank you for the demo and your effort in helping the community. I have been successful in using your scripts to DnD files and save them on my local environment(mac bookpro, apache, mysql, php). My problem is with my production environment (redhat, apache, mysql, php). My $_FILES is empty. Can you shed some light on why? I even loaded your demo on the redhat box and got the same results $_FILES is empty. Great site, I look forward to exploring the rest of your demo.
It is very strange in your case, because on our environment (apache/php/mysql) it works good, and it should, because we use the ordinary XMLHttpRequest. There is no any visible reason why it could not work for you, maybe, you faced with a file size limit?
Great work. Good clean code – easy to follow and does the job well. I'm not sure about the canvas part – sure I get that you're demoing HTML 5 but this is a drag and drop file upload demo no? Anyway – awesome job.
Is there any way to customize this code for video uploading.?
Yes, of course, this is not restricted.
? I dont get it, because I dont see a div class f in the client. Can you please explain?
how do i get the temp file name in the php script?
Hi. Great tutorial. Sorry for my English. I'm testing your example and I would like to know where and how to set the url of the server where the files would be saved.
Great code and thanks for sharing.
I'm trying to use this to upload files but I can't actually see the files after I receive the Result: your file has successfully been received.
Can you please let me know where the files are stored.
Guess you could remove my comment, as perhaps it might be that people should work it for themselves???


text:
Edith Breckwoldt, geb. Burmester (* 1937 in Lauenburg; † 22. August 2013 in Hamburg) war eine deutsche Bildhauerin aus Norddeutschland. Bekannt ist sie für ihre groß- und kleinformatigen Skulpturen im Hamburger Raum, die um die Themen Frieden, Gemeinsamkeit und Symbiose kreisen.

Leben
Edith Burmester war eine von drei Töchtern des Lauenburger Binnentankschiffgroßreeders, genannt "Onassis der Oberelbe", Christoph Burmester († 1988, siehe auch Hitzler Werft). 1959 zog sie nach Hamburg und heiratete Frank Breckwoldt, einen Enkel des Parfüm- und Feinseifenfabrikanten Georg Dralle, der heute Mitinhaber und Geschäftsführer der Ryf Coiffeur GmbH ist. Das Paar hat eine gemeinsame Tochter, Birke, benannt nach dem Birkenwasser Birkin von Dralle. Ende der achtziger Jahre vertrieb Edith Breckwoldt, die "anerkannte Visagistin mit internationaler Berufserfahrung", über die Ryf-Coiffeur-Salons Schminkgrundkurse auf VHS-Videokassetten.
Gemeinsam mit ihrem Ehemann gründete sie 2012 die Breckwoldt-Stiftung, die zukünftig Projekte und Künstler der Bildhauerei und werthaltige Unternehmensführung fördern soll.

Bildhauerei
1990 fand Breckwoldt ihren Weg zur Bildhauerei. Die Technik ließ sie sich von Fritz Fleer vermitteln, einem Meisterschüler von Edwin Scharff. Früh distanzierte sie sich von ihrem Lehrer, um ihren eigenen Stil der 'Gefühlten-Formen' zu entwickeln, eine persönliche Stilrichtung, die sich durch die Betonung eines emotionalen Schöpfungsprozesses auszeichnet. Ihre Skulpturen schuf sie als Reaktion auf ein dominantes inneres Gefühl ohne Skizze, rein intuitiv. Auf diese Weise entstand ein Œuvre mit abstrakten und figurativen Formen. Eine Dauerausstellung mit ihren Bronzeplastiken fand bis April 2017 in der privaten Galerie mit Kunstgarten in der Alten Landstraße in Hamburg-Poppenbüttel statt.

Edith Breckwoldt starb wenige Tage nach ihrem 76. Geburtstag am 22. August 2013 auf Schloss Hohenlinden in Hamburg-Poppenbüttel. Im April 2017 fand Breckwoldts Gesamtwerk eine neue Heimat in Lauenburg (Elbe). Hier soll es als "Kunstpfad Lauenburg" der Öffentlichkeit zugänglich gemacht werden.

Werke im Mahnmal St. Nikolai, Hamburg
Zwei der drei Bronzeplastiken – Friedensgebet und Erdenengel – von Edith Breckwoldt im Hamburger Mahnmal Ehemalige Hauptkirche St. Nikolai sind Schenkungen der Künstlerin an den Förderkreis "Rettet die Nikolaikirche" e.V., dessen 2. Vorsitzender ihr Mann Frank Breckwoldt ist.
 Friedensgebet ist der Titel einer Figur aus dem Jahr 2001. Sie stellt eine kniende, betende Frau dar, die von einem Kind umarmt wird. Sie befindet sich inmitten des sogenannten "Garten der Kontemplation", einem abgegrenzten Bereich im ehemaligen nördlichen Seitenschiff, das mit Rhododendren bepflanzt und mit verschiedenfarbigen Kieselsteinen ausgelegt ist. Die Figur soll einen Bogen von der erschreckenden Vergangenheit zu einer hoffnungsvollen Zukunft schlagen.
 Das Friedensgebet ist in die Geburtsstadt der Künstlerin, nach Lauenburg an der Elbe, umgezogen und wurde am 1. Dezember 2017 an der Maria-Magdalenen-Kirche feierlich enthüllt.

 Erdenengel ist der Titel einer zentralen, sechs Meter hohe Bronzeplastik aus dem Jahr 2003. Am Sockel ist in acht Sprachen der Titel und Untertitel der Plastik angebracht. Die Botschaft der Künstlerin lautet Nimm meine Hand, und ich führe Dich zu Dir zurück und soll ausdrücken, dass alle Erkenntnis im Menschen selber ruht: Wenn er zu sich selbst zurückfindet, so findet er auch Frieden; dies ist wiederum Voraussetzung für Friedlichkeit zwischen den Menschen.

 Prüfung, eine 2004 von Edith Breckwoldt geschaffene Bronzefigur, ist in der Apsis des ehemaligen südlichen Seitenschiffs aufgestellt und der Gedenkstätte des ehemaligen Stammlagers Sandbostel gewidmet, das, 60 km westlich von Hamburg gelegen, von 1939 bis 1945 eines der größten deutschen Kriegsgefangenenlager war. Mehr als 50.000 Menschen fanden dort den Tod, darunter etwa 10.000 Häftlinge aus dem KZ Neuengamme. Der Sockel der Skulptur ist aus Backsteinen der Barackenfundamente aufgeschichtet, die auf dem Lagergelände von Schülern aus Sandbostel gesammelt wurden. Die Künstlerin beschriftete eine bronzene Tafel mit einem Dietrich Bonhoeffer zugeschriebenen Zitat:

Weitere Werke im öffentlichen Raum
 Ich bin, Bronzeplastik, 1995, in Lauenburg – ihrer Geburtsstadt – vor dem Rathaus
 Friedensgebet, Bronzeplastik, in Lauenburg/ Elbe, Maria-Magdalenen-Kirche

Ausstellungen
 Gefühlte Formen, 2004, 7. Februar – 9. März, unter dem Mahnmal St. Nikolai, Hamburg
 Gefühlte Formen, 2007, Galerie Lonnes, Bremen
 Kunstpfad Lauenburg, 2017, Eröffnung am 25. Juni, Kreissparkasse Herzogtum Lauenburg, Lauenburg/ Elbe

Weblinks

 http://www.breckwoldt-stiftung.de/
 http://www.edith-breckwoldt.de/#/sculptures-perceived-shapes/
 http://www.edith-breckwoldt.de/#/press-reports/
 http://www.edith-breckwoldt.de/#/edith-breckwoldt-stipendium/
 http://www.ln-online.de/Lokales/Lauenburg/Breckwoldt-Nachlass-wird-Kunstprojekt-fuer-jedermann
 http://www.ln-online.de/Lokales/Lauenburg/Lauenburg-hofft-auf-fuenf-Millionen-Euro
 http://kulturportal-herzogtum.de/2017/06/23/ein-erster-blick-auf-breckwoldts-werk/
 http://www.alstertal-magazin.de/flash/atm1312/blaetterkatalog/html/magazin_diesem_kalten.html
 http://www.artslant.com/ew/events/show/401149-you-never-know-when-you-meet-an-angel
 https://www.youtube.com/watch?time_continue=1&v=oo-9R1sDRYs

Quellen

Bildhauer (Deutschland)
Person (Lauenburg/Elbe)
Deutscher
Geboren 1937
Gestorben 2013
Frau

redpajama_set_name: RedPajamaWikipedia


text:
\section{Introduction}
\label{sec:introduction}
As one of the fundamental tasks in natural language processing, machine reading comprehension is fueled by avid neural modeling investigations in recent years. Given a certain textual content, the goal is to answer a series of questions based on semantic understanding. Many studies have focused on monological documents like Wikipedia \cite{rajpurkar2016squad_1} and news articles \cite{Hermann2015cnndata}, and some recent work are focusing on dialogue comprehension \cite{liu2019healthData,ma2018dialogueQA,2019Dream}. Different from passages, human-to-human dialogues are a dynamic and interactive flow of information exchange \cite{sacks1978simplest}, which are often informal, verbose, and repetitive, and this brings unique challenges for adopting document-oriented approaches on the conversational samples.

Recently, there is growing interest in automated extraction of relevant information from clinical dialogues \cite{du2019ext-sym,liu2019turn-by-turn,liu2019healthData}, in the form of dialogue comprehension. However, neural models via supervised learning usually require a certain amount of training data, and it is difficult to collect and construct large annotated resources for clinical-related tasks. Fine-tuning large-scale pre-trained language models has become a data-efficient learning paradigm, and achieves substantial improvement on various downstream tasks and applications \cite{devlin2019bert,liu2019roberta}. However, these general-purpose language backbones are trained on the online crawled data with universal objectives. Although this can provide feature-rich contextualized representations, it also limits their capability in specific domains. On the other hand, while some recent studies have proposed methods for pre-training on dialogue samples \cite{gao2020dialogueFeed,wu2020tod,zhang2020dialogpt,zhong2022-diaLM,zou2021-abs-pre}, they are focusing more on the generation of daily conversations (e.g., open-domain social chat). When adopting such generic language backbones on more targeted scenarios such as clinical-related tasks, their performance becomes sub-optimal due to the significant domain difference \cite{krishna2021soap}.

To address such challenges, in this work, we propose a domain-specific language pre-training, and adopt it to improve spoken dialogue comprehension performance on the clinical inquiry-answering conversations.
While the common methods for constructing pre-training samples (e.g., the token masking used in BERT \cite{devlin2019bert}, the infilling and deletion used in BART \cite{lewis2020bart}) proved effective for contextualized modeling, text manipulation designed for specific resources/tasks can bring further improvement \cite{gururangan2020dont-stop}. Therefore, considering the nature of human conversations \cite{sacks1978simplest} and the interactive flow of inquiry-answering dialogues, we propose a set of sample manipulations for conversational pre-training. At the token level, we introduce a masking strategy especially on speaker tokens, and we propose a permuting operation for better speaker role modeling. At the utterance level, we propose the utterance masking and intra-topic permutation scheme. More specifically, given a dialogue, we randomly select one utterance, and mask it or exchange it with another span in the same topic, and the language model is guided to reconstruct the coherent conversation flow based on the context. Moreover, we add an additional token at the beginning of each utterance to explicitly present the utterance boundary information.

Our experiments are conducted on a multi-topic symptom checking conversation dataset, where nurses inquire and discuss symptom information with patients. We empirically show that the proposed approach brings significant improvement on the dialogue comprehension task, especially in the low resource scenario.

\section{Domain-specific Language Pre-training}
\label{sec:methodology}
In this section, we elaborate on the six types of pre-training sample generation. We then describe the reconstruction-based learning process of language modeling to fuse domain-specific conversational features.

\subsection{Conversation-based Sample Construction}
Human-to-human spoken conversations are an interactive process of information exchange. Compared with the monological passages, a language backbone for multi-party dialogues is required to infuse the conversational linguistic features \cite{gu2020speaker}, such as speaker roles and utterance boundary information \cite{liu2019turn-by-turn}, as well as the underlying dialogue discourse structures \cite{sacks1978simplest}.

To guide language backbones to model the characteristics of conversations, we adopt the following six types of pre-training sample, as shown in Table \ref{table:pretrain-types}.\\
\textbf{Token Masking} Following the common pre-training scheme proposed in \cite{devlin2019bert}, for tokens in the sequence, 5\% of them are randomly sampled and replaced with a $<$mask$>$ token.\\
\textbf{Token Infilling} Following the denoising learning scheme proposed in \cite{lewis2020bart}, 5\% of input tokens are randomly sampled and replaced with random tokens extracted from the vocabulary.\\
\textbf{Speaker Masking} To encourage the model to grasp the speaker information \cite{gu2020speaker} in the interactive flow. We randomly sampled 10\% of the utterances, and replaced their speaker tokens with a $<$mask$>$ token.\\
\textbf{Speaker Permutation} Previous work shows that the text infilling scheme is helpful to tackle the training-testing inconsistency issue of masking-based methods \cite{lewis2020bart}. Thus, aside from the speaker masking, we randomly sampled 10\% of the utterances, and exchange its speaker with another one in the conversation.\\
\textbf{Utterance Masking} To encourage the model to grasp more contextual information at the utterance level, we further adopt a span masking scheme. More specifically, we randomly sampled 10\% of the utterances and mask the whole span, and the model will try to recover it based on the context understanding.\\
\textbf{Intra-Topic Utterance Permutation} While human conversations are often less structured than well-organized documents, they are inherently organized around the dialogue topics in a coarse-grained structure \cite{sacks1978simplest}. Therefore, we propose an intra-topic utterance permutation strategy, in which we randomly sampled 5\% of the utterances, and exchange them with another one in the same topic. This operation injects more noise into the conversation flow, and the model can only restore the original order by leveraging the underlying dialogue discourse information.\\
Moreover, we add a special $<$u$>$ token at the start position of each utterance, which can convey the utterance-level boundary information \cite{zhang2020dialogpt}, and is similar to the sentence-level $<$s$>$ token used in document-based approaches \cite{liu2019roberta}.

\begin{table*}[t!]
\caption{Pre-training data processing for dialogue samples. Here the raw utterances are extracted from the synthetic clinical inquiry-answering conversations. $<$mask$>$ denotes places replaced with the mask token. $<$random$>$ denotes replacing with a random token from a vocabulary. The permutation is to change the order of two randomly selected tokens/utterances.}
\begin{center}
\resizebox{1.0\linewidth}{!}{
\begin{tabular}{p{6.5cm}p{6.5cm}}
\toprule
     \textbf{Raw Utterances} & \textbf{Processed Utterances} \\
     \midrule
     \textbf{Token Masking} & \\
     Nurse: Do you have any headache at night? & Nurse: Do you have any headache at \textbf{$<$mask$>$}? \\
     Patient: No no headache, just a bit cough... & Patient: No no headache, just a \textbf{$<$mask$>$} cough.. \\
     Nurse: Cough? you mean cough at every night? & Nurse: Cough? you \textbf{$<$mask$>$} cough at every night? \\

    \midrule
    \textbf{Token Infilling} & \\
    Nurse: Do you have any headache at night? & Nurse: Do you have any \textbf{$<$random$>$} at night? \\
    Patient: No no headache, just a bit cough. & Patient: No \textbf{$<$random$>$} headache, just a bit cough.. \\
    Nurse: Cough? you mean cough at every night? & Nurse: Cough? you mean cough at every night? \\

    \midrule
     \textbf{Speaker Masking} & \\
     Nurse: Do you have any headache at night? & Nurse: Do you have any headache at night? \\
     Patient: No no headache, just a bit cough.. & \textbf{$<$mask$>$}: No no headache, just a bit cough.. \\
     Nurse: Cough? you mean cough at every night? & \textbf{$<$mask$>$}: Cough? you mean cough at every night? \\

    \midrule
     \textbf{Speaker Permutation} & \\
     Nurse: Do you have any headache at night? & Do you have any headache at night?  \\
     Patient: No no headache, just a bit cough.. & \textbf{Nurse}: No no headache, just a bit cough.. \\
     Nurse: Cough? you mean cough at every night? & Nurse: Cough? you mean cough at every night? \\

    \midrule
     \textbf{Utterance Masking} & \\
     Nurse: Do you have any headache at night? &  Nurse: Do you have any headache at night? \\
     Patient: No no headache, just a bit cough.. & Patient: \textbf{$<$mask$>$ $<$mask$>$ $<$mask$>$ $<$mask$>$}.. \\
     Nurse: Cough? you mean cough at every night? & Nurse: Cough? you mean cough at every night? \\

    \midrule
     \textbf{Intra-Topic Utterance Permutation} & \\
     Nurse: Do you have any headache at night? & Nurse: Do you have any headache at night?  \\
     Patient: No no headache, just a bit cough.. & \textbf{Nurse: Cough? you mean cough at every night?}  \\
     Nurse: Cough? you mean cough at every night? & \textbf{Patient: No no headache, just a bit cough..}\\

\bottomrule
\end{tabular}}
\end{center}
\label{table:pretrain-types}
\end{table*}

\subsection{Experiment Setup of Pre-training}
With the conversation samples built on the aforementioned strategies, we conduct the language backbone pre-training, and a Transformer-based neural architecture is used \cite{vaswani2017transformer}. To leverage the generic prior language knowledge, we select  \textit{`RoBERTa-base'} model to initialize the Transformer model, and conduct the \textbf{reconstruction-based} learning process \cite{liu2019roberta}. More specifically, as shown in Figure \ref{fig-pretrain}, the input sequence is the dialogue content after text manipulation, and the target sequence consists of the tokens from the original utterances.

In our experiment, the pre-training data was the combination of the SAMSum corpus \cite{gliwa2019samsum}, a subset of OpenSubtitles \cite{lison2016opensubtitles2016}, and a set of synthetic clinical symptom checking conversations (40k samples). SAMSum is a social chat corpus consisting of 16k dialogues. OpenSubtitles is compiled from a large collection of TV and movie scripts across many languages, and we randomly selected 50k samples from the English part. The mixed conversational data contains a certain amount of dialogues with multiple participants, speaker role information, and conversational structures. During training, we fixed the max input length to 512 tokens. When constructing the pre-training samples, we first conducted text manipulations on tokens and speaker entities. Then the utterance-level operations were performed. We trained the language backbone with 5,000 warm-up steps. Batch size was set to 64 via applying gradient accumulation, and the initial learning rate was set at 1e-5. Cross-entropy was used as the loss function, and we selected the checkpoints at the knee point of loss decrease.

\begin{figure}[t!]
    \begin{center}
    \includegraphics[width=0.75\textwidth]{images/PT_Pretrain.png}
    \end{center}
     \caption{Overview of the language model pre-training. Here we adopt the bi-directional mask-recovering training as in \cite{liu2019roberta}. Superscript values denote utterance ID.}
    \label{fig-pretrain}
\end{figure}

\section{Dialogue Comprehension on Clinical Inquiry-Answering Conversations}

\subsection{Task Definition}
\label{ssec:task_definition}
One example of the dialogue comprehension task on clinical inquiry-answering conversations is shown in Table \ref{table:qa_example}. The input consists of a multi-turn symptom checking dialogue $D$ and a question $Q$ specifying a \textit{symptom} with one of its \textit{attributes}; the output is the extracted answer $A$ from the given dialogue. A training or test sample is defined as $S=\{D, Q, A\}$. Five attributes, specifying certain details of clinical significance, are defined to characterize the answer types of $A$: (1) \textit{time} the patient has been experiencing the symptom, (2) \textit{activities} that trigger the symptom (to occur or worsen), (3) \textit{extent} of seriousness, (4) \textit{frequency} occurrence of the symptom, and (5) \textit{location} of symptom. For each symptom/attribute, it can take on different linguistic expressions, defined as \textit{entities}.

\begin{table}[ht!]
\small
\caption{\label{table:qa_example} One example of the reading comprehension task on clinical inquiry-answering conversations. The synthetic dialogue is used for demonstration.}
\begin{center}
\begin{tabular}{p{9cm}}
\toprule
\textbf{Conversation Example (Truncated)} \\
\midrule
Nurse: Hi Mr.[Name], you were discharged on [date]. There are some questions I'd like to check with you.\\
Patient: Ok, Ok ... I think I feel better ...\\
Nurse: Is your left leg still {\color{blue}swollen}? You said so the last time I call you?\\
Patient: Yes, {\color{blue}only a bit} when I drink too much water ...\\
\midrule
\textbf{Question:} What is the extent of the swollen? \\
\midrule
\textbf{Reference Answer Span:} only a bit \\
\bottomrule
\end{tabular}
\end{center}
\end{table}

\begin{figure}[ht!]
    \begin{center}
    \includegraphics[width=0.67\textwidth]{images/PT_QA_model.png}
    \end{center}
     \caption{Overview of the machine comprehension model on a question-answering task. $Q$ presents the question sequence (in green), and it is concatenated with the conversation sequence as input. Following \cite{devlin2019bert,liu2019healthData}, the answer span is extracted from the conversation content by predicting the start and end positions (in purple).}
    \label{fig-qa-model}
\end{figure}

\subsection{Clinical Dialogue Corpus}
\label{ssec:corpus}
The reading comprehension task is conducted on the data of nurse-to-patient symptom monitoring conversations.
The corpus was inspired by real dialogues in the clinical setting where nurses inquire about symptoms of patients \cite{liu2019healthData}. Linguistic structures at the semantic, syntactic, discourse, and pragmatic levels were abstracted from these conversations to construct templates for simulating multi-turn dialogues (40k samples in our settings). The informal styles of expressions, including incomplete sentences, incorrect grammar, and diffuse flow of topics were preserved.

A team of linguistically trained personnel refined, substantiated, and corrected the automatically simulated dialogues by enriching verbal expressions through different English speaking populations in Asia, Europe, and the U.S., validating logical correctness through checking if the conversations were natural, reasonable, and not disobeying common sense, and verifying the clinical content by consulting certified and registered nurses.
These conversations cover 9 topics/symptoms (e.g. headache, cough).
For each conversation, the average word number is 255 and the average turn number is 15.5. For the comprehension task, questions were raised to query different attributes of a specified symptom; e.g., \textit{How frequently did you experience headaches?} Answer spans in the dialogues were labeled with start and end indices, following the annotation scheme as in \cite{rajpurkar2016squad_1}. Note that if the queried symptom or attribute is not mentioned in the dialogue, the ground-truth output is \textit{``No Answer''}, as the same definition in \cite{liu2019healthData}.

\subsection{Baseline Models}
We further fine-tuned the Transformer-based model on the dialogue comprehension task, and compared it with several baselines, including \textbf{Pointer LSTM} \cite{wang2016matchLSTM}, Bi-Directional Attention Flow network (\textbf{Bi-DAF}) \cite{seo2016bidaf}, and \textbf{R-Net} \cite{wang2017rnet}.
To evaluate the effectiveness of our domain-specific language pre-training, we use the \textbf{Vanilla Transformer} and the original \textbf{RoBERTa-base} model as control, and our proposed model is
\textbf{RoBERTa-base w/ Domain-specific Pre-training}. As shown in Figure \ref{fig-qa-model}, we formulate the comprehension task as an answer extraction process. With the feature-rich contextualized representation, the answer span is generated by predicting its start/end position in the sequence, by adding a linear layer on the last layer hidden states from the language modeling \cite{devlin2019bert,liu2019roberta}.

\subsection{Training Configuration}
All models were implemented with Pytorch and Hugging Face Transformers \cite{paszke2019pytorch}.
For models without pre-trained language backbones (e.g. Bi-DAF, R-Net), Glove embedding \cite{pennington2014glove} was utilized, and out-of-vocabulary words were replaced with the \textit{$<$unk$>$} token. Hidden size and embedding dimension were 300, and those of Transformer-based models were 768. We used Adam \cite{kingma2014adam} with batch size 32, and gradient accumulation was applied. The initial learning rates were set at 2e-5, and dropout rate \cite{srivastava2014dropout} was set to 0.2. During training, the validation-based early stop strategy was applied. During prediction, we selected answer spans using the maximum product of $p_{start}$ and $p_{end}$.

\subsection{Evaluation: Comparison with Baselines}
\label{ssec:basic_result}
We conduct the evaluation on the synthetic clinical dialogue corpus, where the training, validation, and test size were 40k, 3k, and 3k, respectively. We adopted Exact Match (EM) and F1 score as metrics as the SQuAD benchmark \cite{rajpurkar2016squad_1}.

As shown in Table \ref{result-baseline}, the vanilla Transformer model obtains a slightly lower performance than the non-Transformer strong baselines (i.e. Bi-DAF and R-Net), and \textit{`RoBERTa-base'} is on par with them. This demonstrates that the prior knowledge from general language pre-training is beneficial for the downstream tasks. With the conversational pre-training, our proposed mode obtains substantial gains and achieve the best EM and F1 scores, showing that the domain-specific feature fusion is effective.

\begin{table}[t!]
\small
\caption{\label{result-baseline}
Evaluation result of the baseline models and our approach on the test set. \textit{Domain PT} denotes the proposed domain-specific pre-training.}
\begin{center}
\begin{tabular}{p{5.5cm}cc}
\toprule
\textbf{Model} & \textbf{\ \ EM Score\ \ } & \textbf{\ \ F1 Score\ \ } \\
\midrule
Pointer LSTM & 77.81 & 82.71 \\
Bi-Attention Flow (Bi-DAF) & 87.31 & 88.57 \\
R-Net (Our Implementation) & 88.22 & 90.13 \\
\midrule
Vanilla Transformer & 85.38 & 85.92 \\
RoBERTa-base w/o Domain PT & 88.37 & 90.15 \\
RoBERTa-base w/ Domain PT & \textbf{92.31} & \textbf{93.69} \\
\bottomrule
\end{tabular}
\end{center}
\end{table}

\subsection{Evaluation in Low-Resource Scenarios}
\label{ssec:low_res_result}
The limited amount of training data is a major pain point for clinical-related language tasks, as it is time-consuming and labor-intensive to collect and annotate the corpus at a large scale. Following the observation in previous work \cite{gururangan2020dont-stop}, we expect the domain-specific language modeling can result in more efficient learning on downstream tasks. To simulate the low-resource training scenario, we conducted experiments on a range of smaller training sizes (from 3k to 40k) with a fixed-size test set (3k samples). As shown in Figure \ref{fig-low-resource}, the proposed approach outperforms all other models significantly, especially when the training size is smaller than 20k.

\begin{figure}[t!]
    \begin{center}
    \includegraphics[width=0.7\textwidth]{images/PT_lowRes.png}
    \end{center}
     \caption{\label{fig-low-resource} Experimental result on the low-resource training. X axis is the sample size, Y axis is the evaluation metrics, including exact match (EM) and F1 score.}
\end{figure}

\subsection{Evaluation: Pre-training Scheme Comparison}
\label{ssec:pretrain_type_exp}
To evaluate the effectiveness of the aforementioned strategies of pre-training sample construction. We conduct an experiment by adding different text manipulations to train the general-purpose language backbone. As shown in Table \ref{result-pt-table}, we observed that the token-level infilling can bring certain improvements, and introducing the conversation-related manipulations (i.e., speaker and utterance masking and permutation) are helpful for the final dialogue comprehension performance.

\begin{table}[t!]
\small
\begin{center}
\begin{tabular}{p{6.2cm}cc}
\toprule
\textbf{Model} & \textbf{ EM Score } & \textbf{ F1 Score } \\
\midrule
Pre-training on the \textit{RoBERTa-base} backbone \\
+ Token-level Masking & 88.79 & 90.30 \\
+ Token-level Infilling & 90.73 & 92.12 \\
+ Speaker Mask \& Permutation & 91.01 & 92.41 \\
+ Utterance Mask \& Permutation & \textbf{92.31} & \textbf{93.69} \\
\bottomrule
\end{tabular}
\end{center}
\caption{\label{result-pt-table}Performance comparison on pre-training schemes. The text manipulations are added from token level to utterance level.}
\end{table}

\section{Conclusions}
In this paper, we introduced a domain-specific language pre-training approach, and adopted it to improve   performance on downstream tasks such as question answering. Based on the linguistic characteristics of spoken dialogues, we proposed a combination of six strategies to build samples for conversational language pre-training, and conducted reading comprehension experiments on a multi-topic inquiry-answering conversation data. The experimental results showed that the proposed approach can boost performance and achieve more efficient learning outcomes. Future work include extending conversational pre-training to other clinical tasks \cite{Kurisinkel2021report,liu2019conv_summ} and resources.

\begin{acknowledgement}
Research efforts were supported by funding and infrastructure from A*STAR, Singapore (Grant No. IAF H19/01/a0/023). We gratefully acknowledge valuable inputs from Angela Ng, Hong Choon Oh, Sharon Ong, Sheldon Lee, Weiliang Huang, and Ying Zi Oh at the Department of Cardiology, Health Management Unit, and Department of Health Services Research, Changi General Hospital, Singapore. We thank the anonymous reviewers for their precious feedback to help improve and extend this piece of work.
\end{acknowledgement}

\input{references}
\end{document}

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text: Computer Open Course

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text:
Main » 2013 » March » 28 » Newcastle eyeing transfer targets
Newcastle eyeing transfer targets
Newcastle managing director Derek Llambias has promised the club will support Alan Pardew in the transfer market in the off-season.
Newcastle were in danger of being dragged into a relegation fight earlier this season, but have turned things around since the January arrivals of, among others, Moussa Sissoko and Mathieu Debuchy.
And, despite the significant investment seen earlier this year, Llambias has revealed that chief scout Graham Carr is already identifying new targets.
"There is still money available. There are several positions we are looking at," he told reporters.
"Next year, we will have the TV money. There is a budget to spend. But our transfer policy remains the same - no trophy buys. Basically, we will continue our model as it is. It works, so we won't break it.
"We are actively looking at players, and have been since October. All over Europe - it's not just France. That's been a good market for us but there are only so many you can get from there.
"I think Graham Carr has more company when he is out there. He's not wearing a disguise just yet. If he started wearing glasses, he would start looking like his son (comedian Alan Carr)."
Newcastle announced profits of 1.4 million pounds for the last financial year on Wednesday, and Llambias has hailed the work done behind the scenes at the club.
He added: "Consider where we were in 2008/09, and where we are now. It is a huge difference and we have increased the size of the squad in the meantime.
"We are definitely going in the right direction. We had a 17 million-pound operating loss in our first year."

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New Delhi [India], Feb 18 (ANI): Star Indian striker Sunil Chhetri was awarded the first-ever 'Football Ratna' by Football Delhi, the State Association governing the sport in the national capital.
Delhi Football president of Shaji Prabhakaran presented the honour at a function which was held at the Jawaharlal Nehru Stadium.
Chhetri, who was recently named as the 6th Indian Footballer in the elite Padma Shri list expressed delight after receiving the award and assured all kind of support from his side for development of football in the capital city.
"I am honoured by the kind gesture of Football Delhi for recognising me with the Football Ratna award," AIFF quoted Chhetri, as saying.
"My support will always be there for the development of football in Delhi. I am sure the current management is working hard to develop football and make Delhi a model football state in India. My best wishes to everyone associated with the sport in Delhi," he added.
Chhetri who stays second in the list of goal scorers among active players in World Football at the moment, had begun his football career in Delhi.
Prabhakaran mentioned that "Football Delhi is inspired by Sunil's spectacular achievements".
"It is a great honour for us to confer first ever 'Football Ratna' to Sunil Chhetri, and we express our gratitude and thanks to Sunil for his time and inspiration to develop football in Delhi," he stated.
"We at Football Delhi are inspired by the spectacular achievements of Sunil Chhetri. His professionalism, dedication, discipline and passion, on and off the field is an example not only for the Indian football fraternity but also for every Indian to dream big and achieve something spectacular in life," he added.


text:
Home » Gaming » Street Fighter 6 full launch roster confirmed
Street Fighter 6 full launch roster confirmed
Street Fighter 6 will launch with 18 fighters, Capcom said today, in a video revealing the final seven characters to make up the fighting-game flagship's launch roster.
For the record, the seven names that had yet to be confirmed were Manon, Marisa, Lily, JP, Dee Jay, Cammy, and Zangief. The other 11 were previously announced (four of them on Thursday), but we'll recap anyway: Luke, Jamie, Kimberly, Juri, Ryu, E. Honda, Blanka, Guile, Ken, Chun-Li, and Dhalsim.
That's the core eight fighters from Street Fighter 2's original lineup; mainstays Cammy and Dee Jay; and fresh faces Juri (debuting in Super Street Fighter 4) and Luke (a DLC fighter first appearing in Street Fighter 5: Champion Edition). The other six (Jamie, JP, Kimberly, Lily, Manon, and Marisa, who together sound like the cast of a direct-to-DVD Saved by the Bell) are all brand new.
But wait, Street Fighter fans might be saying, what about that leak back in June? There were 22 names on it. Indeed. But Friday's announcement from Tokyo Game Show 2022 made no mention of Rashid, Ed, A.K.I., or Akuma. Are they the first four DLC fighters? It's anyone's guess for now.
Street Fighter 6 does not have a specific release date, but it's slated for a 2023 launch. When it does arrive, it will be available on PlayStation 4, PlayStation 5, Windows PC, and Xbox Series X.
Tekken director impressed by Masahiro Sakurai's knowledge of games
Resident Evil Showcase set to air October 2022


In Part 1 of this article, I discussed using a Java application as a Web service client and a creative way to secure that client using JAAS and SAML. Part 2 is a consequence of the same application, but I examine how to attach a SAML token to a SOAP message from within a Java application to invoke a Web service that is secured using WS-Security SAML policy file. Here I focus on the mechanism needed to invoke a secure Web service.
I don't discuss configuring SAML on an application server, generating Web service client stubs, annotating a Web service to use SAML as a WS-Security token or configuring a SAML provider/authority. Those types of things are implementation-specific, but important when creating an end-to-end SOA security architecture.
Using an IDE or Ant script, it is relatively easy to design and create Web services (and to compile the bindings) that invoke the service from a remote client application. Adding security to the application, on the other hand, is a horse of a different color. However, if security is not seriously considered, then you should face the fact that you are building what I call a "Pet Store" application.
Figure 1, a high-level view of the deployment architecture for the target Java application, depicts the security model workflow. In the model, the Java application makes an HTTP(S) call to the SAML authority inside the firewall using a .NET service that integrates with AFDS (Active Directory Federation Service). The return parameter is a signed, SAML token that is generated based on the user's credentials (i.e., a Kerberos ticket). This token is injected into a SOAP header from within a Java application, which can then invoke a secure Web service that is protected using WS-Security.
Figure 1: Java application acquires a SAML token to invoke a secure Web service requests.
SAMLAuthenticationHandler extends GenericHandler, which is part of the javax.xml.rpc.handler package, and responsible for marshaling the SAML token into the SOAP header.
Authenticator is an application object used to retrieve the SAML token from a third-party authority. This implementation is based on the SAML provider used in your target architecture and thus, not part of a Java package.
Stub is part of the javax.xml.rpc.Stub package used for binding with a remote Web service. It provides SOAP RPC functionality.
Service_Impl is a generated Web service object that is a local representation of the remote Web service object.
HandlerInfo is part of the javax.xml.rpc.handler package, this class has a callback method that sets the SAMLAuthenticationHandler.class.
HandlerRegistry is part of the javax.xml.rpc.handler package, which is responsible for registering the callback on the service object (Service_Impl).
Properties is a part of the java.util package used to contain the application URLs, namespaces, SOAP ports, etc. Binding information for remote services will change overtime, if not contained in a dynamic Web service registry. It is recommended that these end-points be stored outside of the application in a properties file, if not stored in a service registry.


text:
Q: Using library installed in one virtual environment in another virtual environment Can we use library installed in one virtual environment from another virtual environment?

For eg:

Library installed in venv from another virtual environment.

A: Short answer: No
Virtual environment's purpose is to isolate different python instalation, including their libraries. If you ever find a way to do this, it must be considered a hack and should be reported to virtual environment as a bug.

A: It should be possible, you just have to import the module by giving the full path to the module present in the source virtualenv. How to import a module given the full path?
But it'll NOT WORK as dependencies to the module will not be present in your target virtualenv. You can try importing all dependencies by using full path it'll work that way.

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In a large bowl mix flour, baking powder and salt together with a whisk then set aside.
In a separate bowl, cream shortening and sugar together until well blended.
Add one egg at a time to the shortening mixture, beat on medium speed until fluffy.
Add vanilla and anise extract.
Add flour mixture to the shortening mixture one large spoonful at a time until the entire amount is blended.
If mixture seems very dry, add 2 to 4 Tbs of milk.
Let the batter set for a while to make it easier to handle.
Once batter has rested, make balls from it roughly the size of a walnut and place them on a parchment lined cookie sheet.
Bake at 350 degrees for 10 to 15 minutes or until the top just starts to crack and they are lightly browned on the bottom. Cookies should be pale and even colored on the top.
In a small bowl, mix powdered sugar and milk together to form a thick, slow moving glaze.
Dip each cookie into the glaze.
Hold glazed cookie over another bowl and sprinkle the top with candy ball decorations in Christmas colors.
Place on a rack to let the glaze dry and harden.


text:
Q: Integrating Isotope and Vue I just started using vue about a month ago and love it so far. Currently I'm running into some issues using it along side isotope.js, a slick filtering/sorting/layout library.
The problem is isotope wants to control adding and removing elements in the layout (currently done with jquery). If I add an item with jquery, vue isn't aware of any functionality on the item (looking for events and the like) and if I add with view, isotope doesn't know to arrange the element.
I've illustrated the issue in this fiddle. Note that clicking removes an element from the container and clicking to remove from isotope also removes the element from the vue container, but not the other other way around. Clicking also tries logging to the console, which of course only works on the elements put into the DOM with v-for.
I've found a temporary work around that manually keeping the two in sync, but it is far from ideal.
What is the idiomatic way to solve for this? Obviously I'd love to be able to use v-for to append and remove items. Is it time for a custom directive? Where do I get started?
Adding elements this way is an anti-pattern, but I don't know how else to make isotope aware of them.
add: function(thing) {

  // add to isotope - vue is unaware of the test() fn when it enters the DOM
  this.iso.isotope('insert', $('' + thing.name + ''))

  // add to vue
  this.things.push({
    name: thing.name
  })
  this.itemName = ""

},

Thanks!

A: I created a directive to use isotope with vue.js. It expose all the option available in isotope and make filtering and sorting reactive to property changes. It is used in a similar way as a v-for directive:

{{element.name}}

Example:

Example on fidlle:
fiddle 1, fiddle 2, fiddle 3
Available on github: Vue.Isotope