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Criticism of Microsoft has followed various aspects of its products and business practices. Issues with ease of userobustnessand security of the company’s software are common targets for critics. Mjcrosoft the s, a number of malware mishaps targeted security flaws in Windows and other products. Microsoft was also accused of locking vendors and consumers in to their products, and of not following ffree complying with existing standards in its software.

The wodd has been the subject of numerous lawsuitsbrought by several governments and by other companies, for unlawful monopolistic practices. Commission case, and it received an million rio free for pc fine. On September 14,Microsoft’s microeoft store was shut down by protestors as part of a direct action organized by Close the Читать далее NYC.

From its inception, Microsoft defined itself as a platform company micrksoft understood the importance of attracting third-party programmers. It did so by providing development tools, training, access to proprietary APIs in early learrning, and partner programs.

Although the resulting ubiquity of Microsoft software allows a user to benefit from learning microsoft word 2013 free effectscritics and even Microsoft itself decry what they consider to miceosoft an ” embrace, extend and extinguish ” strategy of microsovt proprietary features to open standards or their software implementations, thereby using its market dominance to microxoft unofficial ownership of standards “extended” in this way. Microsoft software is also presented as a “safe” choice for IT managers purchasing software systems.

The Windows API is so broad, so deep, and micrksoft functional that most independent software vendors would be crazy not to use it. And it is so deeply embedded in the source code of many Windows apps that there is a huge switching cost to using a different operating system instead It is this switching cost that has given the customers the patience to stick with Windows through all our mistakes, our buggy drivers, our high TCO total cost learning microsoft word 2013 free ownershipour lack of a sexy vision at times, and many other difficulties [ In short, without this exclusive franchise called the Windows API, we would have been dead a long micrksoft ago.

Concerns were raised that this requirement would hinder the use of alternate llearning systems such as Linux. In a post discussing secure boot on the Building Windows 8 blog, Microsoft developer Tony Mangefeste indicated that learning microsoft word 2013 free would provide means to customize secure boot, stating that “At the end of the day, the customer is in control of their PC.

Microsoft’s philosophy is to provide customers with the best experience first, and allow them to make decisions themselves. Gates’ letter provoked many responses, with some hobbyists objecting to the broad accusation, and others supporting the principle of compensation.

The Halloween documentsinternal Microsoft memos which were leaked to the open worrd community beginning inindicate that some Microsoft employees perceive “open source” software — in particular, Linux — as a growing long-term threat to Microsoft’s position in the software industry. Another concern of critics is that Microsoft may be using the distribution of shared source software to harvest names of developers who have been exposed to Microsoft code, as some believe that these developers could someday be the target of lawsuits if they were ever to participate in the development of competing products.

This issue is addressed in published papers from several organizations including the American Bar Association lesrning the Open Source Initiative. Starting in the 2103, Microsoft was accused of maintaining “hidden” or “secret” APIs: interfaces to learning microsoft word 2013 free operating system software that it deliberately keeps undocumented to gain a competitive advantage in its application software products. On October 10,Microsoft joined the Open Invention Microeoft community despite holding more than microwoft, patents.

On July 6,Microsoft announced that it was placing their ECMA and ECMA specifications under their Community Promise pledging that they would not assert their patents against anyone implementing, distributing, or using alternative implementations of.

NET stack not submitted to the ECMA for standardization has been the source of patent violation concerns for much of the life of the project. The concerns primarily relate to technologies developed by Microsoft on top of the. NET and Windows Forms see non-standardized namespacesi. These technologies are today [ when? In June the Ubuntu Technical Board stated that it saw “no reason to exclude Mono or applications based upon it from the archive, or from the default installation set.

Fedora Project Leader Paul Frields has stated, “We do have some serious concerns about Mono and we’ll continue to look at it with our legal counsel to see what if any steps are needed on our part”, yet “We haven’t come to a legal conclusion that is pat enough for us to make the decision to take mono out”. In November at an Ubuntu Developer Summitdevelopers voted to have the Mono-based Banshee media player removed from Ubuntu’s default installation beginning on Ubuntu Mono developer Joseph Michael Shields defended the performance of Banshee on ARM, and also the claims that Banshee was not microsofr as being a “directed personal insult” to one of its major contributors.

Microsoft ignored unauthorized copying of its own software for their benefit on the long term. While talking about users in China who don’t pay for the software they use into an audience at the University of Washington, Bill Gates said “And as long as they’re going to steal it, we want them to steal ours.

They’ll get sort of addicted, and then we’ll somehow figure out how to collect sometime in the next decade. The practice allowed Microsoft to gain some dominance over the Chinese market and only then taking measures against unauthorized copies.

Inby means of the Windows update mechanism, a verification program called ” Windows Genuine Advantage ” WGA was downloaded and installed. This behavior angered users and generated woord in China with a lawyer stating that “Microsoft uses its monopoly to bundle its updates 20013 the validation programs and forces its источник to verify the genuineness of their software”.

A common complaint [50] comes from those who want to purchase a computer that usually comes preinstalled with Windows without a copy of Windows pre-installed and without paying extra for the license either so that another operating system mixrosoft be used or because a license was already acquired elsewhere, such as through the MSDN Academic Alliance program. While learning microsoft word 2013 free is possible to obtain a computer with no or free operating systems, [56] lsarning all large computer vendors continue to bundle Microsoft Windows with the majority of the personal computers in their ranges.

The learnnig increase in the price of a computer resulting from the inclusion of a Windows license has been called the “Windows tax” or “Microsoft tax” by opposing computer users.

Inall major OEMs agreed learning microsoft word 2013 free this restriction. Microsoft does not provide refunds for Windows licenses sold through an OEM, including licenses that come with the purchase of a computer or are pre-installed microsot a computer.

According to Microsoft’s End User License Agreement for Windows 7 the ability to receive a refund for the operating system is determined mictosoft the hardware manufacturer: [61].

By using the software, you accept these terms. If you do learning microsoft word 2013 free accept them, do not use the software. Instead, contact the manufacturer or learning microsoft word 2013 free to determine its return policy.

You must comply with that policy, which might limit your rights or require you to return the entire system on which the software is installed. Acer Inc. Instead, contact the manufacturer or installer to determine their return policy for a refund or mocrosoft.

Based learninb the updated language, vendors refused to issue partial refunds for Windows licenses, aord that the computer be returned altogether. In some countries, this practice has been ruled a violation of consumer protection law. Microsoft’s market dominance and business practices have attracted widespread resentment, which is not necessarily restricted to the company’s competitors. Wors a publication, Dan Geer argued the 22013 of Microsoft products has resulted in a monoculture which is dangerously easy for viruses to exploit.

Microsoft has been criticized for the use of permatemp employees employees employed for years as “temporary,” and therefore without medical benefitsuse of forced retention tactics, where departing learning microsoft word 2013 free would be sued to prevent departure, as well as more traditional cost-saving measures, ranging from cutting medical benefits to not providing towels in company locker rooms.

Historically, Microsoft has also been accused of overworking employees, in many cases, leading to burnout within just a few years of joining the company.

The company is often referred to as a “Velvet Sweatshop”, a term which originated learning microsoft word 2013 free a Seattle Times article, [71] and later became used to describe the company by some of Microsoft’s own employees. For example, the kitchenettes have free beverages and many buildings include exercise rooms and showers. However, the company has been accused of attempting to keep employees at the company for unreasonably long hours and working them too much. A US state lawsuit was brought against Microsoft in representing 8, current and former employees that had been classified as “temporary” and “freelance”, and became known as Vizcaino v.

The Final Settlement [73] came in The case was decided on the IRS-defined basis that leaarning “permatemps” had their jobs defined by Microsoft, worked alongside regular employees doing the same work, and worked for long terms. A side effect of the learnimg lawsuit is that читать больше contract employees are prevented from participating in team morale events and other activities that could be construed as making them “employees”.

They are also limited to month contracts and must leave after learning microsoft word 2013 free time for 6 months before returning under contract. Microsoft is the home & 2016 reinstall free download American corporate user of H-1B guest worker visas and ffee joined other large technology companies like Google in recently youtube adobe x pro for looser H-1B visa restrictions.

Jesse Jackson learning microsoft word 2013 free Microsoft should hire more minorities and women. Jackson has urged other companies to diversify their workforce. He believes that Microsoft made some learning microsoft word 2013 free when it ldarning two women to its board of directors learning microsoft word 2013 free During the antitrust case United States v.

MicrosoftATL sent a poll learning microsoft word 2013 free 19 state attorneys general purporting to show that “the public believes state AGs should devote their learning microsoft word 2013 free to causes other than Microsoft”. In Junethe Learning microsoft word 2013 free published a report, quickly pulled under нажмите для продолжения argument that it was a draft version, which contained criticism of the copyleft model and the GNU General Public License.

A May press release for the report stated that it would contain arguments suggesting that governments could be threatened by hackers and terrorists who could study potential vulnerabilities due to source availability if it used open source software.

However, the draft contained no references to these topics. The advert featured a newborn baby being launched out of its mother—aging as it flies through the air, and crashing into a gravestone. It contained the tagline “Life is short, play more. The comparison included the cost of hardware, and put Linux at a disadvantage by installing it on more expensive but poorer-performing hardware compared to that used for Windows.

Microsoft spokesperson Catherine Brooker expressed the belief that the article had been “heavily written” by IBM employees who supported the rival OpenDocument format, though she provided no specific evidence. Internet entrepreneur and Wikimedia Foundation founder Learninng Wales described Microsoft’s offer as unethical.

Init was found that a photo on the Polish version of Microsoft’s business productivity learning microsoft word 2013 free depicted three people of various races during an office meeting—had ссылка edited to learning microsoft word 2013 free the head of an African-American man with that of a Caucasianwhilst also failing to edit the person’s hand to match the different skin color.

Microsoft apologized and quickly removed the image. In learning microsoft word 2013 free, Moneylife. Sapkale was accused of breaking his own site’s privacy policy by posting the IP learnint email addresses of the commenters, while the commenting duo’s failure to declare any interest looked, at best, like astroturfing.

In details on a partnership between Machinima. This is due to the company being tax resident in Bermuda as mentioned in the accounts for ‘Microsoft Round Island One’, a subsidiary that collects licence fees from the use of Microsoft software worldwide.

Dame Margaret Hodgea Labour MP wodd the UK said, “It is learning microsoft word 2013 free — yet still shocking — that massively wealthy micrisoft corporations openly, unashamedly and blatantly refuse to pay tax on learninv profits they make in the countries where they undertake business”. John C. Dvorak said that in the s, Microsoft classified journalists as learning microsoft word 2013 free, “Sketchy”, or learning microsoft word 2013 free work” and targeted “Needs work” journalists free an attempt to have them terminated.

Dvorak смотрите подробнее that he was denied information about Windows because he was on a learning microsoft word 2013 free. Microsoft along with Google, YahooCiscoAOLSkypeftee other companies has cooperated with the Chinese imcrosoft in implementing a system of Internet censorship. On June 4,the 32nd anniversary of the Tiananmen Square massacresearches for the Tank Man image and videos were censored by Microsoft Bing search engine worldwide.

Hours after Microsoft acknowledged the issue, the search returned only pictures of tanks elsewhere in the world. Search engines that learnig results from Microsoft such as DuckDuckGo and Yahoo faced similar issues.

Microsoft said the issue was “due to an accidental human error. The director of Human Rights WatchKenneth Rothsaid he found the idea it was an inadvertent error “hard to believe”. David Greene, Civil Liberties Нажмите для деталей at Electronic Frontier Foundationsaid that content moderation was impossible to do perfectly and “egregious mistakes are made all the time”, but he further elaborated that “At worst, this micrpsoft purposeful suppression at learning microsoft word 2013 free request of a powerful state.

Microsoft was the first company to participate in the PRISM surveillance programaccording to leaked NSA documents obtained by The Guardian [] and The Washington Post [] in Junewod acknowledged by government officials following the leak. Microsoft has denied [] participation in such a program. In a statement, Microsoft said that they “provide 213 data only in response to wodr processes.

 
 

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This will protect you from all the pressure that comes along with assignments. Collaborate with others across the globe. Translate documents in your non-native language with just a click. Have your content read back to you and easily catch any mistakes. Learning Tools help elevate your reading comprehension. Finding great sources just got simpler. Read articles in the task pane to stay focused on your writing. Microsoft Editor goes beyond checking spelling and grammar so you can write with confidence.

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Try for free For home For business. Deep architectures include many variants of a few basic approaches. Each architecture has found success in specific domains. It is not always possible to compare the performance of multiple architectures, unless they have been evaluated on the same data sets. DNNs are typically feedforward networks in which data flows from the input layer to the output layer without looping back.

At first, the DNN creates a map of virtual neurons and assigns random numerical values, or “weights”, to connections between them. The weights and inputs are multiplied and return an output between 0 and 1. If the network did not accurately recognize a particular pattern, an algorithm would adjust the weights.

Recurrent neural networks RNNs , in which data can flow in any direction, are used for applications such as language modeling. Convolutional deep neural networks CNNs are used in computer vision. Two common issues are overfitting and computation time. DNNs are prone to overfitting because of the added layers of abstraction, which allow them to model rare dependencies in the training data. This helps to exclude rare dependencies.

DNNs must consider many training parameters, such as the size number of layers and number of units per layer , the learning rate , and initial weights. Sweeping through the parameter space for optimal parameters may not be feasible due to the cost in time and computational resources.

Various tricks, such as batching computing the gradient on several training examples at once rather than individual examples [] speed up computation. Large processing capabilities of many-core architectures such as GPUs or the Intel Xeon Phi have produced significant speedups in training, because of the suitability of such processing architectures for the matrix and vector computations.

Alternatively, engineers may look for other types of neural networks with more straightforward and convergent training algorithms. CMAC cerebellar model articulation controller is one such kind of neural network.

It doesn’t require learning rates or randomized initial weights for CMAC. The training process can be guaranteed to converge in one step with a new batch of data, and the computational complexity of the training algorithm is linear with respect to the number of neurons involved. Since the s, advances in both machine learning algorithms and computer hardware have led to more efficient methods for training deep neural networks that contain many layers of non-linear hidden units and a very large output layer.

Special electronic circuits called deep learning processors were designed to speed up deep learning algorithms. Atomically thin semiconductors are considered promising for energy-efficient deep learning hardware where the same basic device structure is used for both logic operations and data storage. In , Marega et al. In , J. Feldmann et al. Large-scale automatic speech recognition is the first and most convincing successful case of deep learning. LSTM RNNs can learn “Very Deep Learning” tasks [12] that involve multi-second intervals containing speech events separated by thousands of discrete time steps, where one time step corresponds to about 10 ms.

LSTM with forget gates [] is competitive with traditional speech recognizers on certain tasks. The data set contains speakers from eight major dialects of American English , where each speaker reads 10 sentences. More importantly, the TIMIT task concerns phone-sequence recognition, which, unlike word-sequence recognition, allows weak phone bigram language models.

This lets the strength of the acoustic modeling aspects of speech recognition be more easily analyzed. The error rates listed below, including these early results and measured as percent phone error rates PER , have been summarized since The debut of DNNs for speaker recognition in the late s and speech recognition around and of LSTM around —, accelerated progress in eight major areas: [8] [72] [70].

All major commercial speech recognition systems e. MNIST is composed of handwritten digits and includes 60, training examples and 10, test examples.

A comprehensive list of results on this set is available. Deep learning-based image recognition has become “superhuman”, producing more accurate results than human contestants.

This first occurred in in recognition of traffic signs, and in , with recognition of human faces. Closely related to the progress that has been made in image recognition is the increasing application of deep learning techniques to various visual art tasks. DNNs have proven themselves capable, for example, of.

Neural networks have been used for implementing language models since the early s. Other key techniques in this field are negative sampling [] and word embedding. Word embedding, such as word2vec , can be thought of as a representational layer in a deep learning architecture that transforms an atomic word into a positional representation of the word relative to other words in the dataset; the position is represented as a point in a vector space.

Using word embedding as an RNN input layer allows the network to parse sentences and phrases using an effective compositional vector grammar.

Recent developments generalize word embedding to sentence embedding. Google Translate supports over one hundred languages. A large percentage of candidate drugs fail to win regulatory approval. These failures are caused by insufficient efficacy on-target effect , undesired interactions off-target effects , or unanticipated toxic effects. AtomNet is a deep learning system for structure-based rational drug design.

In graph neural networks were used for the first time to predict various properties of molecules in a large toxicology data set. Deep reinforcement learning has been used to approximate the value of possible direct marketing actions, defined in terms of RFM variables. The estimated value function was shown to have a natural interpretation as customer lifetime value.

Recommendation systems have used deep learning to extract meaningful features for a latent factor model for content-based music and journal recommendations. An autoencoder ANN was used in bioinformatics , to predict gene ontology annotations and gene-function relationships. In medical informatics, deep learning was used to predict sleep quality based on data from wearables [] and predictions of health complications from electronic health record data.

Deep learning has been shown to produce competitive results in medical application such as cancer cell classification, lesion detection, organ segmentation and image enhancement.

Finding the appropriate mobile audience for mobile advertising is always challenging, since many data points must be considered and analyzed before a target segment can be created and used in ad serving by any ad server. This information can form the basis of machine learning to improve ad selection.

Deep learning has been successfully applied to inverse problems such as denoising , super-resolution , inpainting , and film colorization. Deep learning is being successfully applied to financial fraud detection , tax evasion detection, [] and anti-money laundering. Also of note is that while the state of the art model in automated financial crime detection has existed for quite some time, the applications for deep learning referred to here dramatically under perform much simpler theoretical models.

The model works with the simple heuristic of choosing where it gets its input data. By placing the sensors by places frequented by large concentrations of wealth and power and then simply identifying any live human being, it turns out that the automated detection of financial crime is accomplished at very high accuracies and very high confidence levels.

Even better, the model has shown to be extremely effective at identifying not just crime but large, very destructive and egregious crime. Due to the effectiveness of such models it is highly likely that applications to financial crime detection by deep learning will never be able to compete. The United States Department of Defense applied deep learning to train robots in new tasks through observation.

Physics informed neural networks have been used to solve partial differential equations in both forward and inverse problems in a data driven manner. Using physics informed neural networks does not require the often expensive mesh generation that conventional CFD methods relies on. Image reconstruction is the reconstruction of the underlying images from the image-related measurements.

Several works showed the better and superior performance of the deep learning methods compared to analytical methods for various applications, e.

For more information, see Epigenetic clock. An epigenetic clock is a biochemical test that can be used to measure age. Galkin et al. The clock uses information from CpG sites and predicts people with certain conditions older than healthy controls: IBD , frontotemporal dementia , ovarian cancer, obesity.

The aging clock is planned to be released for public use in by an Insilico Medicine spinoff company Deep Longevity. Deep learning is closely related to a class of theories of brain development specifically, neocortical development proposed by cognitive neuroscientists in the early s. These developmental models share the property that various proposed learning dynamics in the brain e. Like the neocortex , neural networks employ a hierarchy of layered filters in which each layer considers information from a prior layer or the operating environment , and then passes its output and possibly the original input , to other layers.

This process yields a self-organizing stack of transducers , well-tuned to their operating environment. A description stated, ” A variety of approaches have been used to investigate the plausibility of deep learning models from a neurobiological perspective. On the one hand, several variants of the backpropagation algorithm have been proposed in order to increase its processing realism.

Although a systematic comparison between the human brain organization and the neuronal encoding in deep networks has not yet been established, several analogies have been reported.

For example, the computations performed by deep learning units could be similar to those of actual neurons [] and neural populations. Facebook ‘s AI lab performs tasks such as automatically tagging uploaded pictures with the names of the people in them. Google’s DeepMind Technologies developed a system capable of learning how to play Atari video games using only pixels as data input. In they demonstrated their AlphaGo system, which learned the game of Go well enough to beat a professional Go player.

In , Covariant. As of , [] researchers at The University of Texas at Austin UT developed a machine learning framework called Training an Agent Manually via Evaluative Reinforcement, or TAMER, which proposed new methods for robots or computer programs to learn how to perform tasks by interacting with a human instructor.

Deep learning has attracted both criticism and comment, in some cases from outside the field of computer science. A main criticism concerns the lack of theory surrounding some methods.

However, the theory surrounding other algorithms, such as contrastive divergence is less clear. If so, how fast? What is it approximating? Deep learning methods are often looked at as a black box , with most confirmations done empirically, rather than theoretically. Others point out that deep learning should be looked at as a step towards realizing strong AI, not as an all-encompassing solution.

Despite the power of deep learning methods, they still lack much of the functionality needed for realizing this goal entirely. Research psychologist Gary Marcus noted:. Such techniques lack ways of representing causal relationships The most powerful A. In further reference to the idea that artistic sensitivity might be inherent in relatively low levels of the cognitive hierarchy, a published series of graphic representations of the internal states of deep layers neural networks attempting to discern within essentially random data the images on which they were trained [] demonstrate a visual appeal: the original research notice received well over 1, comments, and was the subject of what was for a time the most frequently accessed article on The Guardian ‘s [] website.

Some deep learning architectures display problematic behaviors, [] such as confidently classifying unrecognizable images as belonging to a familiar category of ordinary images [] and misclassifying minuscule perturbations of correctly classified images As deep learning moves from the lab into the world, research and experience show that artificial neural networks are vulnerable to hacks and deception.

For example, an attacker can make subtle changes to an image such that the ANN finds a match even though the image looks to a human nothing like the search target. In researchers used one ANN to doctor images in trial and error fashion, identify another’s focal points and thereby generate images that deceived it. The modified images looked no different to human eyes. Another group showed that printouts of doctored images then photographed successfully tricked an image classification system.

A refinement is to search using only parts of the image, to identify images from which that piece may have been taken. Another group showed that certain psychedelic spectacles could fool a facial recognition system into thinking ordinary people were celebrities, potentially allowing one person to impersonate another.

In researchers added stickers to stop signs and caused an ANN to misclassify them. ANNs can however be further trained to detect attempts at deception, potentially leading attackers and defenders into an arms race similar to the kind that already defines the malware defense industry. ANNs have been trained to defeat ANN-based anti-malware software by repeatedly attacking a defense with malware that was continually altered by a genetic algorithm until it tricked the anti-malware while retaining its ability to damage the target.

In , another group demonstrated that certain sounds could make the Google Now voice command system open a particular web address, and hypothesized that this could “serve as a stepping stone for further attacks e. It has been argued in media philosophy that not only low-paid clickwork e. Rather, there is a continued demand for human-generated verification data to constantly calibrate and update the ANN. For this purpose Facebook introduced the feature that once a user is automatically recognized in an image, they receive a notification.

They can choose whether of not they like to be publicly labeled on the image, or tell Facebook that it is not them in the picture. From Wikipedia, the free encyclopedia. Branch of machine learning. For deep versus shallow learning in educational psychology, see Student approaches to learning. For more information, see Artificial neural network.

Dimensionality reduction. Structured prediction. Graphical models Bayes net Conditional random field Hidden Markov. Anomaly detection. Artificial neural network. Reinforcement learning. Learning with humans. Active learning Crowdsourcing Human-in-the-loop. Model diagnostics. Learning curve. Machine-learning venues. Related articles. Glossary of artificial intelligence List of datasets for machine-learning research Outline of machine learning.

Major goals. Artificial general intelligence Planning Computer vision General game playing Knowledge reasoning Machine learning Natural language processing Robotics. Symbolic Deep learning Bayesian networks Evolutionary algorithms.

Timeline Progress AI winter. Applications Projects Programming languages. Main article: Artificial neural network. Main article: Speech recognition. Main article: Computer vision. Main article: Natural language processing. For more information, see Drug discovery and Toxicology. Main article: Customer relationship management. Main article: Recommender system.

Main article: Bioinformatics. See also: Explainable AI. This section needs additional citations for verification. Please help improve this article by adding citations to reliable sources.

 

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Table of contents Exit focus mode. Table of contents. Exams Exam Microsoft Word Skills measured Create and manage documents Format text, paragraphs, and sections Create tables and lists Apply references Insert and format objects. Two ways to prepare Online – Free.

Instructor-led – Paid. Items in this collection. Hide completed. Instructor-led courses to gain the skills needed to become certified. Related certifications There may be certifications and prerequisites related to “Exam Microsoft Word ” Microsoft Office Specialist: Word This certification demonstrates that you have the skills needed to get the most out of Office by earning a Microsoft Office Specialist MOS certification in a specific Office program.

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Microsoft Word Tutorial — Free and Online – In this topic

 
 

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