![]() #r Replace follow # chars with, : csock, cursor on s, 3re ceeek Gqq format current line according to line-width Gq (in visual-mode) format selected text according to line-width function args)ĭf Delete until next occurence of (char) found (including ) ĭt Delete until next occurence of (char) found (without !!!) starting from char under cursorĭ^ Delete up unto the beginning of the lineĭib Delete contents in parenthesis '(' ')' block (e.g. (text is copied from link above) Editing x Delete char UNDER cursor Y`a yank text to unnamed buffer from cursor to position of mark a `a jump to position (line and column) of mark aĭ'a delete from current line to line of mark aĭ`a delete from current cursor position to position of mark aĬ'a change text from current line to line of mark a 'a jump to line of mark a (first non-blank character in line) see also ma set mark a at current cursor location Mark a position in a buffer and jump back to it. Unimpaired plugin ( ) provides the following mappings: [q see :cprev :cprev Jump to previous record/match in quickfix list :cnext Jump to next record/match in quickfix list Search in all files that are returns by the backtick command. :vimgrep //g **/*.cc Search in all *.cc files in every sub-directory (recursively) :vimgrep //g *.cc Search in all *.cc files current directory :vimgrep //g Search in the given files () :vimgrep /// % On the command line, / (that is: CTRL-R followed by /) see vimcasts#44 for introduction: :vimgrep //g % Search for with multiple occasions per line (g) [/ cursor to N previous start of a C commentīuilt-in grep, vimgrep uses vim's quickfix list. [I show all occurrences of word under cursor in current file [i show first declartion/use of the word under cursor Repeat previous f or F in same direction , Repeat previous f or F in opposite direction Gd jump to var declaration (see incsearch, hlsearch below)į Find char from current cursor position - forwardsį Find char from current cursor position - backwards Good to know E jump to end of words (no punctuation)ī jump backward by words (no punctuation)Ĭonsider consulting :help [ and :help g * search for word under cursor (forward) and highlight occurrence (see incsearch, hlsearch below) W jump by start of words (punctuation considered words)Į jump to end of words (punctuation considered words)ī jump backward by words (punctuation considered words) Z= Give Suggestions (prepent 1, use first suggestions automatically)Ĭ-f Move forward one full screen (page down)Ĭ-d Move forward 1/2 screen half page downĬ-u Move back (up) 1/2 screen half page up Exit Record mode with ESC q Start recording, everything will be recorded including movement Execute the recorded actions.Īssuming that you have the following in. Vim has 26 registers (a-z), select the one you want to record in, see below. S Erase the current letter under the cursor, set insert-modeĬc Delete the current line, set insert-mode O Begin a new line ABOVE the cursor and insert text O Begin a new line BELOW the cursor and insert text I Insert text before the first non-blank in the line ![]() Windows C-ws Split current window horizontally (alternative :split)Ĭ-wv Split current window vertically (alternative :vsplit)Ĭ-wARROW Jump to window left/right/top/bottom (arrow keys) to the currentĬ-w# Increase/resize current window to the right by # (default 1)Įntering insert mode a Append text after the cursor "p Paste yanked content in register (from a-z)Ĭ-z send vim to background (fg brings it to front again) "y Yank/copy marked region into register (register from a-z) V Enter visual mode for selection of LINESĬ-v Enter visual mode for selection of BLOCKS :q! force close if file has changed and not save changes
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If they do this, you will be unable to get results with your X-ray. Websites can decide not to index a page if they don’t want it to be findable by search engines. ![]() However, that’s only if the pages on these websites are indexed by search engines. Here’s an example, finding the word referral on You could do this for almost every website. For example, you could even X-ray our website if you’d like to. Almost every website can be searched with X-ray. You can simply do this by entering “site: nameofwebsite + your boolean search string” in a search engine. Or you can find even more candidates on a website you’re already searching on. As a recruiter, you can use this technique to find candidates on websites that are otherwise difficult to search. Actually, scientists and researchers have been using it for years to find relevant sources and research concerning their research topic on websites featuring other academic research.īasically, you enter Boolean search strings in a search engine to find results on a specific website. Online databases can be phone directories, job boards, social media channels such as LinkedIn or Facebook and every other website on the web. That sounds difficult, but it really isn’t. X-ray searching refers to the technique of using search engines such as Google or Bing to find information, usually candidates, in online databases. As if you can see through everything with your search, and frankly, you kind of do. X-ray search sounds awesome, right?! It sounds quite powerful. ![]() If you want to find out what the real power of Boolean search is, consider reading this blog post about Boolean search for recruitment.If you’re already familiar with Boolean search, continue reading to find out more about X-ray search. This way, you will only see profiles that match your search. If a keyword matches, it gives a ‘1’, if it doesn’t match it gives a ‘0’. This technique makes searching for good profiles easier, as it compares the search string to the profile it is searching. A “boolean” is simply one of the two binary digits: ‘0’ or ‘1’. One of the most popular ways to find great candidates is by using Boolean search strings. X-Ray Search: How you’ll get 10 times better at sourcing 29 April 2019 ![]() The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.Ĭompeting interests: The authors have declared that no competing interests exist.Īcute coronary syndrome (ACS), also known as a heart attack, is a leading cause of death and disability in the Asian region, with an in-hospital mortality rate of more than 5%. This work was supported by Kementerian Sains, Teknologi dan Inovasi, Malaysia (Grant No: TDF03211036). Data are however available from NHAM upon request using contact or email them at Any findings from the data need to be reported and permission needs to be obtained from the NHAM committee before publication.įunding: Funded studies, SK and SM received the fund. The data belongs to the individual ministry of health universities hospitals and private hospitals that require multiple institutional agreements for data release to third parties hence ethical approval is needed for analysis. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.ĭata Availability: The data that support the findings of this study are available from the National Heart Association of Malaysia (NHAM) but restrictions apply to the availability of these data, and so are not publicly available. Received: Accepted: NovemPublished: December 12, 2022Ĭopyright: © 2022 Kasim et al. PLoS ONE 17(12):Įditor: Seung-Hwa Lee, Samsung Medical Center, REPUBLIC OF KOREA (2022) In-hospital mortality risk stratification of Asian ACS patients with artificial intelligence algorithm. The final algorithm was converted into an online tool with a database for continuous data archiving for prospective validation.Ĭitation: Kasim S, Malek S, Song C, Wan Ahmad WA, Fong A, Ibrahim KS, et al. Age, heart rate, Killip class, cardiac catheterization, oral hypoglycemia use and antiarrhythmic agent were found to be common predictors of in-hospital mortality across all ML feature selection models in this study. TIMI risk score correctly identified 13.08% of the high-risk patient’s non-survival vs 24.7% for the DL model and 4.65% vs 19.7% of the high-risk patient’s non-survival for NSTEMI. When compared to the DL (SVM selected var) model, the TIMI score underestimates patients’ risk of mortality. There was no significant difference between DL (SVM selected var) algorithm and DL (RF selected var) algorithm (p = 0.5). In NSTEMI in-hospital prediction, DL (RF selected var) (AUC = 0.96, 95% CI: 0.95–0.96, reported slightly higher AUC compared to DL (SVM selected var) (AUC = 0.95, 95% CI: 0.94–0.95). ![]() The DL (SVM selected var) algorithm demonstrated the highest predictive performance with the least number of predictors (14 predictors) for in-hospital prediction of STEMI patients (AUC = 0.96, 95% CI: 0.95–0.96). ![]() The best model in this study is the combination of features selected from the SVM algorithm with a deep learning classifier. Deep learning models constructed using all features and selected features from machine learning resulted in higher performance than machine learning and TIMI risk score (p < 0.0001 for all). A total of 68528 patients were included in the analysis. ![]()
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Therefore, we can say that the capacity of Platform B is 1.5 times the capacity of Platform A.Īs a practical case, consider that our computer has 16 Gb RAM and our data set has 500,000 samples. The following figure illustrates the result of a data capacity test with two platforms.Īs we can see, Platform A can analyze up to 400,000 samples, while Platform B can analyze up to 600,000 samples. Note that the selection of a dataset suite is necessary. To compare the data capacity of machine learning platforms, we follow the next steps:Ĭhoose a reference computer (CPU, GPU, RAM.).Ĭhoose a reference benchmark (data set, neural network, training strategy).Ĭhoose a reference model (number of layers, number of neurons.).Ĭhoose a reference training strategy (loss index, optimization algorithm.).Ĭhoose a stopping criterion (loss goal, epochs number, maximum time.). The optimization algoritms it contains (SGD, Adam, LM.). The strategies used within the code for the efficient use of memory. The programming language in which it is written (C++, Java, Python.). ![]() We can measure data capacity as the number of samples that a machine learning platform can process for a given number of variables. In this way, the tool should perform all the essential tasks with that dataset. ![]() The data capacity of a machine learning platform can be defined as the biggest dataset that it can process. Therefore, tools capable of processing these volumes of data are necessary. However, machine learning platforms may crash due to memory problems when building models with big datasets. Nowadays, common datasets used in machine learning might contain thousands of variables and millions of samples. The business processes that explain why these firms are successful. In this way, they learn how well the targets perform and, more importantly, Usually to increase some aspect of performance. This allows organizations to develop plans on making improvements or adapting specific best practices, Key performance indicators typically measured here are data capacity, training speed, inference speed, and model precision.īenchmarking is used to measure performance using a specific indicator resulting in a metric that is then compared to others. Therefore, for machine learning tools to be efficient, they need to process large amounts of data in the shortest time possible. This post aims to identify the most critical key performance indicators (KPIs) and define a consistent measurement process.Īs we know, the volume, variety, and velocity of information stored in organizations are increasing significantly. However, comparing different machine learning platforms can be a difficult task due to the large number of factors involved in the performance of a tool. In machine learning, benchmarking is the practice of comparing tools to identify the best-performing technologies in the industry. How to benchmark the performance of machine learning platforms How to benchmark the performance of machine learning platforms:ĭata capacity, training speed, inference speed and model precision | Neural Designer |
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