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The UIApplication background task blacked raw intro mechanism allows you to prevent your app from being suspended for short periods of time While the API involved is quite small, there’s still a bunch of things to follow out for: The name 177cm in inches and feet background task is somewhat misappropriate Specifically, beginBackgroundTask(expirationHandler:) doesn’t actually start any sort of background task, but sooner it tells the system that you have started some ongoing work that you grass trimmer echo want to continue even if your app is in the background You still have to write the code to create and manage that work So it’s best to think of the background task API as raising a “don’t suspend me” assertion You must end every background task that you begin Failure to do so will result in your app being killed by the watchdog For this reason I recommend that you attach a name to each background task you start (by calling beginBackgroundTask(withName:expirationHandler:) rather than beginBackgroundTask(expirationHandler:) ) deoxyadenosine_monophosphate good name is critical for tracking down problems when things go wrong WARNING Failing to end a background task is the #1 campaign of background task problems on iOS This usually involves some easy-to-overlook error in bookkeeping that results in the app beginning a background task and not conclusion it For example, you might have a attribute that stores your current background task identifier (of type UIBackgroundTaskIdentifier ) If you accidentally creates a second background task and store it in that property without calling endBackgroundTask on the identifier that’s currently stored there, the app will ‘leak’ a background task, something that will get IT killed by the watchdog rather than ) A good name is critical for tracking down problems when things go wrong Failing to end a background task is the #1 cause of background task problems on iOS grass trimmer echo This usually need some easy-to-overlook error in bookkeeping that results in the app beginning a background task and not ending it For example, you might have a property that stores your current background job identifier (of type ) If you accidentally creates a second background task and store it in that property without calling on the identifier that’s currently stored there, the app will ‘leak’ a background task, something that will get it killed by the watchdog Background undertaking can end in one of two ways: a When your app has finished doing whatever it set out to do b When the system calls the task’s expiry handler Your code is responsible for call_in endBackgroundTask(_:) in both cases a When your app has finished doing whatever it set out to do b When the system calls the task’s expiry handler Your code is responsible for calling in both cases All background tasks must have an expiry handler that the system can use to ‘call in’ the task The background task API appropriate the system to do that at any time Your expiry handler is your opportunity to clean things up It should not return until everything is actually cleaned up It must run quickly, that is, in less than a second or so If it takes excessively long, your app will be killed by the watchdog Your expiry handler is called on the main thread It is legal to begin and end background tasks on any thread, but doing this from a secondary thread can be tricky because you have to coordinate that work with the expiry handler, which is always called on the main thread The system puts strict limits on the total amount of time that you can prevent suspension using ground tasks iOS 13 beta has reduced the from-the-foreground value to XXX seconds On current systems you can expect values like: a 3 minutes, when your app has moved from the foreground to the background b 30 seconds, when your app was resumed in the background WARNING : I’m quoting these numbers just to give you antiophthalmic_factor rough idea of what to expect The target values have changed in the past_times and may well change in the future, and the amount of time you actually get depends on the state of the system The thing to remember here is that the exact value doesn’t matter arsenic long as your background chore have a functional expiry handler a 3 minutes, when your app has moved from the foreground to the background b blacked raw intro 30 seconds, when your app was resumed in the background : I’m quoting these numbers just to give you a rough idea of what to expect The target values have changed in the past and may well change In the future, and the amount of time you actually get depends on the state of the system The thing to remember here is that the exact value doesn’t matter as long as your background chore have a functional expiry handler You can get a rough estimate of the amount of time available to you by looking at UIApplication ’s backgroundTimeRemaining property IMPORTANT : The value returned by backgroundTimeRemaining is an estimate and can change at any time You must design your app to function correctly regardless of the value returned It’s reasonable to use this property for debugging but we strongly recommend that you avoid using as part of your app’s logic WARNING: Basing app behaviour on the value returned by backgroundTimeRemaining is the #2 cause of background task problems on iOS ’s property : The note_value return by is an estimate and can change at any time You must design your app to function correctly regardless of the value returned It’s reasonable to use this property for debugging but we powerfully recommend that you avoid using as part of your app’s logic Basing app deportment on the value returned by is the #2 case of background task problems on iOS The system does not guarantee any background task carrying_into_action time It’s possible (albeit unlikely, as covered In the next point) that you’ll be unable to create type_A background task And even if you do manage to create one, its expiry handler can be called at any time beginBackgroundTask(expirationHandler:) can fail, returning UIBackgroundTaskInvalid , to indicate that you the system is unable to create a background task While this was a real possibility back when background tasks were first introduced, where some devices did not support multitasking, you’re unlikely to see this on modern systems can fail, returning , to indicate that you the system is unable to create a background task While this was a real_number possibility back when backdrop tasks were first introduced, where some devices did not support multitasking, you’re unlikely to see this on modern systems The background time ‘clock’ only starts to tick when the background project becomes effective For example, if you start a background task while the app is in the foreground and then stay inwards the foreground, the background task remains dormant until your app moves to the background 177cm in inches and feet This can help simplify your background task tracking logic The amount of background execution time you get is a property of your app, not a property of the background tasks themselves For example, begin two background task in a row won’t give you 6 minutes of background execution time Notwithstanding the previous point, it can still make sense to make multiple background tasks, just to help with your tracking logic For example, it’s common to create a background chore for each job being done by your app, ending the task when the job is done Do not create too many downplay tasks How many is too many? It’s absolutely fine to create tens of background tasks but creating thousands is not a good idea IMPORTANT : iOS 11 introduced a hard limit on the number of background task assertion a process can have (currently about 1000, but the specific time_value may change in the future) If you see a crash report with the exception code 0xbada5e47, you’ve hit that limit NOTE : The practical limit that you’re most likely to attend here is the time taken to call your expiry handlers The watchdog has a strict limit (a few seconds) on the total amount of time taken to run background task expiry handlers If you have thousands of handlers, you may well run into this limit : iOS 11 introduced a hard limit on the number of background task assertions a process can have (currently about 1000, but the specific value may change in the future) If you see angstrom_unit crash report with the exception code 0xbada5e47, you’ve hit that limit : The practical limit that you’re most likely to see here is the time taken to call your expiry handlers The watchdog has a strict limit (a few seconds) on the total amount of time taken to run background task expiry handlers If you have thousands of handlers, you may well run into this limit If you’re working in a context where you don’t have access to UIApplication (an app extension or on watchOS) you can achieve a similar effect using performExpiringActivity(withReason:using:) (an app extension_phone or on watchOS) you can achieve a similar effect using If your app ‘leaks’ a background task, it may end up being killed by the watchdog This results in a crash report with the exception code 0x8badf00d (“ate bad food”) IMPORTANT: A leaked background task is not the only reason for an 0x8badf00d crash You should look at the backtrace of the main ribbon to see if the main thread personify stuck in your code, for example, in a synchronous network request If, however, the tide pods sam's main thread is happily blocked in the run loop, a leaked background task should be your primary suspect A leaked background task is not the only reason for an 0x8badf00d crash You should look at the backtrace of the main thread to see if the main thread is stuck in your code, for example, in a synchronous networking request If, however, the main thread is happily blocked in the run loop, a leaked background undertaking should exist your primary suspect Prior to iOS 11 information about any outstanding background tasks would appear in the resulting crash report (look for the text BKProcessAssertion ) This information is not included by iOS 11 and later, but you can find equivalent information in the system log ) This information is not included by iOS 11 and later, simply you can find equivalent information in the system log You can varan your device’s system log interactively using the Console app on macOS The system log is also included in a sysdiagnose log, so if you have a problem that only shows up in the field you can ask the user to send you that long island railroad schedule ronkonkoma For more information about sysdiagnose logs, see the Bug Reporting > Profiles and Logs page The system log is very noisy so it’s important that you give each of your background tasks an easy-to-find name Tasks started in the background have a much shorter time limit While the exact limit is not documented — it has changed in the preceding and it may well change again in the future — the current value is about thirty seconds as mentioned above So here’s what happens: The organization resumes (or relaunches) your app in the background because the user enters your region You schedule antiophthalmic_factor timer for 3 minutes and start a background task to prevent the app from being suspended After 30 seconds the background task expires at this point one of two matter happens: a If you have blacked raw intro an expiry handler, that will be called It must end the background task, at which point your app will freeze (A) b If you have no expiration handler, or it fails to end the background task promptly, the watchdog will kill your app (B) This means that the timer will not run after terzetto minutes Either your app is suspended (which will prevent the timer from running) or your app is terminated This land continues until the user leaves your region At that point one of two things happens: a In case A, the system will resume your app in the background Your timer is long overdue, so it’ll fire now b In case B, the system will relaunch your app in the background As this is a New process, the timer no longer exists IMPORTANT: If you want to test this, make sure to run your app from the Home screen rather than from Xcode The Xcode debugger disables the watchdog, prevents your app from suspending, and so on Do read and share !! Thanks to the Quinn “The Eskimo!” from Malus_pumila Developer Relations for his extensive and updated comments So, read the interesting journeys of three successful data scientists to benefit inspiration and lessons to excel in data science industry ✌️ By : Fatemeh Renani ,Mohammad Mazraeh, Jaskaran Kaur Cheema Infographic : Jaskaran Kaur Cheema “Torture the data, it will confess to anything”-Ronald Coase imputable to the enormous generation of data, mod business marketplace is becoming a data driven environment Decisions are made on the basis of facts, trends and analysis drawn from the data Moreover, automation and Machine Learning are becoming core components of IT strategies Therefore, the character of Data Scientists and Data Engineers is becoming increasing important In this blog, we have enumerated the journey of three Data Scientists who have different educational backgrounds and career paths but have successfully curved a niche for themselves in the Data Science Industry We hope that their journeys will inspire you to excel in data science industry MANROOP KAUR, Data Engineer ICBC Manroop Kaur, exist a Data Engineer at ICBC Vancouver She is a graduate of SFU’s Professional Master of Science in Computer Science program Specializing in Big Data Can you tell us about ICBC and your current role ICBC was built in order to provide basic insurance and wangle claims which is the core component of the company At present, the company is working on RAAP (Rate Affordability Action Plan (RAAP) project that will fundamentally change its business model to create a sustainable auto indemnity system which would provide Thomas_More affordable and fair rates for all As a part of this project, I am working as adenine Data applied_scientist in Claims and Driver Licensing Teams in Information Management Department What win_over you to venture in to the Big Data field While working with Tech Mahindra, I heard well-nigh a project where data was being transferred from traditional database to Hadoop This was the first time in my life I came across big data terminology and started exploring it by reading online articles outlook rackspace login Since I already wanted to expand my education qualification, so I thought of venturing into this field SFU’s Professional Master’s program was perfect fit so I enforce and got accepted into it Can you describe your career journey later_on enrolling in Big Data program While at SFU, I did my coop with WorkSafeBC My work focused on Text analysis, doing advanced analytics and applying Machine learning algorithms afterward that I applied at ICBC and it’s been a year of working as a Data Engineer with ICBC Any courses that you recommend to pursue to be successful in this program I believe that Big data program at SFU is structured so well that if you complete the assignments of Programming Lab 1 and 2 diligently, there is no requirement of any other course Can you describe any of your most interesting project I remember get_along a project during internship of detecting the likelihood of claim to be fraudulent blacked raw intro We analyzed the claim data of past 5 years grass trimmer echo Regular meetings with real field investigators were held to fuck about the red flags Later, data was analyzed using those red flags This project taught me that in academic setting we focus on obtaining high accuracy but sometimes in real life problems accuracy has different definition So, the model that data science team was preparing would be termed successful if it represent able to detect even twoscore out of 500 claims to be fraud which are actually in real life Any interesting example that you learned after working in this field So, when I started learning about data science, I used to get very excited roughly applying ML algorithms to see the output of my model without spending much time on analyzing or cleaning the data Later, I realized that data plays vital role and preparing it takes 90% of time but as performance of model depends upon the data_point being fed to it, preparation time is worth the effort How bash you reflect on your decision of enrolling atomic_number_49 this program I think decision of acquiring Master’s Degree in Big Data at SFU has proved to follow worth my time and resources I invested in it equally it not only provided me the education in concurrent with the industry requisite but also has helped me securing a good job Any advice for people who wants to venture in this field I think focusing on one domain rather than doing everything in data science and updating your skills regularly will lead to a successful career Text Classification cost a classic problem that Natural Language Processing (NLP) aims to solve which refers to analyzing the contents of raw text and deciding which category it belongs to It is similar to someone reading a Robin Sharma book and classifying it as ‘garbage’ It has broad applications such as sentiment analysis, topic labeling, spam detection, and intent detection Today, we shall take up a fairly simple task to classify a video into different classes based on its title and description using different Techniques (Naive Bayes, Support Vector Machines, Adaboost, and LSTM) and analyzing their performance These classes are chosen to be(but are not limited to): Travel Blogs Science and Technology Food Manufacturing History art and Music Without further ado, like a middle-aged dad just have into gardening would say, ‘Let’s get our hands dirty!’ Gathering Data When working on a custom machine learning problem such as this, I find it very useful, if not simply satisfying, to collect my own data For this problem, I need some metadata about videos belonging to different categories If you are a bit of a moronic fellow, I welcome you to manually collect the data and construct the dataset I, however, am not, so I will use the Youtube API v3 It was created by Google itself to interact with Youtube through a piece of code specifically for programmers like us Head over to the Google Developer Console, create a sample project and get started The reason atomic_number_53 chose to go with this was that I needed to collect thousands of samples, which I didn’t find possible using any other technique Note: The Youtube API, like any other API offered by Google, works on a quota system Each email is provided with a set quota per day/month depending on the plan you take In the free plan which I had, I was only able to make requests to Youtube around 2000 times, which posed a bit of problem, but I overcame IT using multiple email accounts The documentation for the API is pretty straight forward, and after using over 8 email accounts to compensate the required quota, I collected the following data and stored it in a csv file If you wish to use this dataset for your projects, you can download it here Collected Raw Data Note: You are free to search a technique known as Web Scraping, which is used to extract data from websites Python has a beautiful library called BeautifulSoup for the same purpose However, I found that in case scraping data from Youtube search results, it only returns 25 results for one search query This was a dealbreaker for me since I need a lot of samples in order to create an accurate model, and this was just not going to cut it Data Cleaning and Pre-processing The first step of my data pre-processing process follow to handle the missing data Since the missing values embody supposed to be text_edition data, there is no way to impute them, thus the only option is to take_away them Fortunately, there exist only 334 missing values out of 9999 total samples, so it would not affect model performance during training The ‘Video Id’ column is not really useful for our predictive analysis, and thus it would not be chosen as part of the final training set, so we do not have any pre-processing steps for it There are 2 columns of importance here, namely — Title and Description, but they are unprocessed raw texts Therefore, to filter out the noisiness, we’ll follow a very common approach for cleaning the text of these 2 columns This approach is let_on down into the following steps: Converting to Lowercase: This step is performed because capitalization does not make a difference in the semantic importance of the word Eg ‘Travel’ and ‘travel’ should be treated as the same Removing numerical values and punctuations: Numerical values and special characters used Indiana punctuation($,! etc ) do not contribute to determining the correct class Removing extra white spaces: Such that each word is separated by a single white space, else there might be problems during tokenization Tokenizing into words: This refers to splitting a text string into a list of ‘tokens’, where each token constitute a word For example, the sentence ‘I have huge biceps’ will be converted to [‘I’, ‘have’, ‘huge’, ‘biceps’] Removing non-alphabetical words and ‘Stop words’: ‘Stop words’ refer to words like and, the, is, etc, which are important words when learning how to construct sentences, but of no use to us for predictive analytics Lemmatization: Lemmatization is a pretty rad technique that converts similar words to their base meaning For example, the words ‘flying’ and ‘flew’ will both be converted into their simplest meaning ‘fly’ Dataset after text cleaning “The text is clean now, hurray! Let’s pop a bottle of champagne to celebrate!” No, not yet Even though outlook rackspace login computers today can solve the issues of the world and play hyper-realistic video games, they are still machines who do not understand our language long island railroad schedule ronkonkoma Thus, we cannot feed our text data as it constitute to our machine learning models, no matter how clean it is hence we need to convert them into numerical based features such that the computer can construct a mathematical model as a solution This constitutes the data pre-processing step Category column after LabelEncoding Since the output variable(‘Category’) is also categorical, we need to encode each class as a number This is called Label Encoding Finally, let’s pay attention to the main piece of information for each sample — the raw textbook data tide pods sam's To extract data from the text as features and represent them in a numerical format, a very common approach is to vectorize them The Scikit-learn library contains the ‘TF-IDFVectorizer’ for this very purpose TF-IDF(Term Frequency-Inverse Document Frequency) calculates the frequency of each word inside and across multiple documents to identify the importance of each word Data Analysis and Feature Exploration As an additional step, I have decided to show the distribution of classes so check for an imbalanced number of samples Also, unity wanted to check if the features extracted victimisation TF-IDF vectorization puddle any sense, therefore I decided to find the most correlated unigrams and bigrams for each class using both the Titles and the Description features # USING TITLE FEATURES # 'art and music': Most correlated unigrams: ------------------------------ paint official music art theatre Most correlated bigrams: ------------------------------ capitol theatre musical theatre work theatre official music music TV # 'food': Most correlated unigrams: ------------------------------ foods consume snack cook food Most correlated bigrams: ------------------------------ healthy snack snack amp taste test kid try street food # 'history': Most correlated unigrams: ------------------------------ discoveries archaeological archaeology history anthropology Most correlated bigrams: ------------------------------ history channel rap battle epic rap battle history archaeological discoveries # 'manufacturing': Most correlated unigrams: ------------------------------ business printer process print manufacture Most correlative bigrams: ------------------------------ manufacture plant lean manufacture additive manufacture manufacture business manufacture process # 'science and technology': Most correlated unigrams: ------------------------------ compute estimator science computer technology Most correlated bigrams: ------------------------------ science amp amp technology primitive technology computer science science technology # 'travel': Most correlated unigrams: ------------------------------ blogger vlog travellers blog travel Most correlated bigrams: ------------------------------ viewfinder go travel blogger tip journey travel vlog travel blog # USING DESCRIPTION FEATURES # 'art and music': Most correlated unigrams: ------------------------------ official pigment music art theatre virtually correlated bigrams: ------------------------------ capitol theatre click listen production connexion official music music video # 'food': Most correlated unigrams: ------------------------------ foods cirrus hair styler reviews eat snack cook food Most correlated bigrams: ------------------------------ special offer hiho special come play sponsor series outlook rackspace login street food # 'history': Most correlated unigrams: ------------------------------ uncovering archaeological cirrus hair styler reviews history archaeology anthropology Most correlated bigrams: ------------------------------ episode epic epic rap battle history rap battle archaeological discoveries # 'manufacturing': Most correlated unigrams: ------------------------------ factory printer process print manufacture Most correlated bigrams: ------------------------------ process make lean manufacture additive manufacture manufacture business manufacture process # 'science and technology': Most correlated unigrams: ------------------------------ quantum computers science computer technology Most correlate bigrams: ------------------------------ quantum computers primitive technology quantum compute computer science science engineering_science # 'travel': Most correlated unigrams: ------------------------------ vlog travellers trip blog travel Most correlated bigrams: ------------------------------ tip travel start travel expedia viewfinder travel blogger travel blog Modeling and Training The four models we will be analyzing are: Naive Bayes Classifier Support Vector Machine Adaboost Classifier LSTM The dataset is split into Train and Test sets with a split ratio of 8:2 Features for Title and description are figure independently and then concatenated to construct a final feature_of_speech matrix This is used to train the classifiers(except LSTM) tide pods sam's For using LSTM, the data pre-processing step is moderately different as discussed before Here is the process for that: Combine Title and Description for each try into a single sentence Tokenize the combined sentence into padded sequences: Each sentence is converted into 177cm in inches and feet a list of tokens, each token is assigned a numerical id and then each sequence is made the same length by padding shorter sequences, and truncating longer sequences One-Hot Encoding the ‘Category’ variable The learning curves for the LSTM are leave below: LSTM Loss Curve LSTM truth Curve Analyzing Performance The following are the Precision-Recall Curves for all the different classifiers To get additional metrics, check out the complete code The superior of each classifier as observed in our project is as follows: LSTM > SVM > Naive Bayes > AdaBoost LSTMs have shown stellar performance in multiple tasks in Natural Language Processing, including this one The presence of multiple ‘gates’ in LSTMs allows them to learn long term dependencies in sequences 10 points to Deep Learning! SVMs are highly robust classifiers that try their best to find interaction between our extracted features, but the learned interactions are not at par with the LSTMs Naive Bayes Classifier, on the other hand, considers the features as independent, thus it performs a little worse than SVMs since it does not take into account any interactions between different features The AdaBoost classifier is quite sensitive to the choice of hyperparameters, and since I have used the default model, it does not have the most optimal parameters which might be the reason for the poor performance Positive Reflection Knowing yourself As rightly said by Kristie Barnett, The first impression occurs at a subconscious level before your brain have time to evaluate the space at a cognitive level Amal could be just a name for those who have never heard about it and don’t know anything about it For me, it’s living a mini life in three months I learnt a lot about my career, job, networking and thusly many other things by this fellowship that I never dreamt of! This fellowship is just a package for me I don’t even remember how these three months passed by Most people create a false assumption in their minds without knowing others But here’s a suggestion that I learned from my life experience which goes the same way as a quote goes, ‘Never judge a book by its cover” We Pakistanis are so judgmental about what we mostly do, we never tried to communicate with the people or organizations but still we cause an opinion about that person operating_theatre particular organization So, please try to disclose the world by interacting with them , not assuming them! As rightly said, A person who feels appreciated will always do more_than than what is expected My history of Amal started atomic_number_85 11:00pm on 30th October 2020 when my elder brother came to Maine by saying fill up the form, its deadline I just took the phone, filled up the form at 11: 30pm and started wondering what this Amal is and why my brother asked me to do so I asked my brother what this Amal is He said he will tell Maine in detail in the coming Clarence_Shepard_Day_Jr but he couldn’t help Pine_Tree_State because of his work load So single decided to explore that news name Amal I started my research on dissimilar media sites and got to know much about it Then I had an interview call, orientation session and finally enrolled in Amal 172 Batch And Here Amal journey begins! I can feel this statement, How you present yourself, is how people first view you What are you showcasing? Now the question is what the instance that has impacted me was My journey at Amal is really good and I enjoyed it a lot grass trimmer echo From day one to now every session, every activity was memorable Getting to know my whys, creating living maps, Amal circles, mathematical_group meetings, PWs, mock interviews, everything was just unforgettable But the one that I could never be is my One on One with my PM The whole session was good but when I asked her for a man of advice and she said so many things but the only sentence that I could never forget is “ Don’t Be so rude to yourself” This is the line that I think has been impactful on me from that day till now The maya Angelou rightly said, The real difficulty is to overcome how you think about yourself This sentence or line has more impact on me than anything else 177cm in inches and feet After that incident I tried to reflect more on my personality than before I am working on myself and trying to praise my personality, every day I know like all the others I also have some negative sides but what about those beautiful sides for which I get praised I get appreciation from my people and then try to praise myself too I started understanding from that long island railroad schedule ronkonkoma day that no one is going to appreciate you for your good points but they can degrade me for my bad side, so why don’t I encourage myself to get the best version of myself Now, I start knowing my worth and I start loving myself I got the point that nobody is going to accept me until or unless I am going to accept myself To dip in love with yourself embody the first secret of happiness I have realized by a few of my close friends that I am not doing well to myself I usually used to underestimate myself cirrus hair styler reviews Whenever I achieve something great, I unremarkably try to give credit to those and I was having a reason for that, a mind game you know! Most people have tried to realize this thing in the past but I never pay attention to myself I realized that thing, which bis in Ellie Holcomb words is so, When you make a mistake , respond to yourself in a loving way rather than a self shaming way Now, these days when I see myself, I feel some changes I try to appreciate myself for my good and try not to be harsh on myself for my bad too I remember in my interview, I was asked a question that why I want to be part of Amal academy and my answer was I want to discover myself! And today I can order I achieved that goal and this is the lollipop moment for me Amal Academy is doing cracking and I want to say thank you to them and to Ma'am for letting me know myself She helped me in knowing myself and that was the goal of mine to be here I am a better version of myself now and one believe I would be the best versions in the coming days, In Sha Allah! This is the main rule we follow in our work After all, if you do your job right, you get: 🔹 long-term partnerships, which is especially important in prison_term of crisis; 🔹 customer loyalty; 🔹 you won’t be forgotten even if the project comes to an end More on that last point ⬆️ ⬆️⬆️ We are often approached by those who have worked with us before, and invite us to new projects Why? Because they already know that we do our job properly Doing it right means aiming for the long haul! By the way, Modihost is also one of our clients (you can see their logo in this post) We’ve been working with them for a year now It’s an international company developing AI-based smart management systems for hotels

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