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Fundamentals Of Internet Applications By Anshuman Sharma
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Sabina Gream
Dec 1, 2023, 3:46:45 AM12/1/23
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Cloud computing is a software technology built on applications that save data on remote servers that can be accessed through the internet or in a simple words Cloud computing is the delivery of various resources over the Internet. Tools and applications such as data storage, servers, databases, networking, and software are examples of these resources. A user can use a web browser to access data stored in the cloud. You just need to ensure that your device is connected to the internet in order to upload and view your files.
fundamentals of internet applications by anshuman sharma
DOWNLOAD https://larenrafes.blogspot.com/?jo=2wHfPx
Internet of Things (IoT) is presently a hot technology worldwide. Government, academia, and industry are involved in different aspects of research, implementation, and business with IoT. IoT cuts across different application domain verticals ranging from civilian to defence sectors. These domains include agriculture, space, healthcare, manufacturing, construction, water, and mining, which are presently transitioning their legacy infrastructure to support IoT. Today it is possible to envision pervasive connectivity, storage, and computation, which, in turn, gives rise to building different IoT solutions. IoT-based applications such as innovative shopping system, infrastructure management in both urban and rural areas, remote health monitoring and emergency notification systems, and transportation systems, are gradually relying on IoT based systems. Therefore, it is very important to learn the fundamentals of this emerging technology.
On the world wide web, toxic content detectors are a crucial line of defense against potentially hateful and offensive messages. As such, building highly effective classifiers that enable a safer internet is an important research area. Moreover, the web is a highly multilingual, cross-cultural community that develops its own lingo over time. As such, it is crucial to develop models that are effective across a diverse range of languages, usages, and styles. In this paper, we present the fundamentals behind the next version of the Perspective API from Google Jigsaw. At the heart of the approach is a single multilingual token-free Charformer model that is applicable across a range of languages, domains, and tasks. We demonstrate that by forgoing static vocabularies, we gain flexibility across a variety of settings. We additionally outline the techniques employed to make such a byte-level model efficient and feasible for productionization. Through extensive experiments on multilingual toxic comment classification benchmarks derived from real API traffic and evaluation on an array of code-switching, covert toxicity, emoji-based hate, human-readable obfuscation, distribution shift, and bias evaluation settings, we show that our proposed approach outperforms strong baselines. Finally, we present our findings from deploying this system in production.
In this tutorial, we introduce recent advances in pretrained text representations, as well as their applications to a wide range of text mining tasks. We focus on minimally-supervised approaches that do not require massive human annotations, including (1) self-supervised text embeddings and pretrained language models that serve as the fundamentals for downstream tasks, (2) unsupervised and distantly-supervised methods for fundamental text mining applications, (3) unsupervised and seed-guided methods for topic discovery from massive text corpora and (4) weakly-supervised methods for text classification and advanced text mining tasks.
Counterfactual estimators enable the use of existing log data to estimate how some new target policy would have performed, if it had been used instead of the policy that logged the data. We say that those estimators work "off-policy", since the policy that logged the data is different from the target policy. In this way, counterfactual estimators enable Off-policy Evaluation (OPE) akin to an unbiased offline A/B test, as well as learning new decision-making policies through Off-policy Learning (OPL). The goal of this tutorial is to summarize Foundations, Implementations, and Recent Advances of OPE and OPL (OPE/OPL), with applications in recommendation, search, and an ever growing range of interactive systems. Specifically, we will introduce the fundamentals of OPE/OPL and provide theoretical and empirical comparisons of conventional methods. Then, we will cover emerging practical challenges such as how to handle large action spaces, distributional shift, and hyper-parameter tuning. We will then present Open Bandit Pipeline, an open-source Python software for OPE/OPL to better enable new research and applications. We will conclude the tutorial with future directions.
Assessment:Item 1: 10% Coursework 1Item 2: 10% Coursework 2Item 3: 80% Examination (2 hours 30 mins)Level: 7
Physics and AstronomyStatistical Data AnalysisPhysical and Chemical SciencesSPA6328Semester 16YesStatistical Data AnalysisCredits: 15.0
Contact: Dr Ulla Blumenschein
Description: Statistical Data Analysis teaches the fundamentals of probability and statistics, data analysis, and machine learning, as applied to discovering, classifying, and measuring new phenomena. It draws on examples from a wide range of applications, within physics and far beyond. Students will learn to perform statistical calculations, to understand statistical usage in scientific research papers, and to apply practical programming techniques for more advanced analyses.
Cardiovascular magnetic resonance imaging (CVMRI) is of proven clinical value in the non-invasive imaging of cardiovascular diseases. CVMRI requires rapid image acquisition, but acquisition speed is fundamentally limited in conventional MRI. Parallel imaging provides a means for increasing acquisition speed and efficiency. However, signal-to-noise (SNR) limitations and the limited number of receiver channels available on most MR systems have in the past imposed practical constraints, which dictated the use of moderate accelerations in CVMRI. High levels of acceleration, which were unattainable previously, have become possible with many-receiver MR systems and many-element, cardiac-optimized RF-coil arrays. The resulting imaging speed improvements can be exploited in a number of ways, ranging from enhancement of spatial and temporal resolution to efficient whole heart coverage to streamlining of CVMRI work flow. In this review, examples of these strategies are provided, following an outline of the fundamentals of the highly accelerated imaging approaches employed in CVMRI. Topics discussed include basic principles of parallel imaging; key requirements for MR systems and RF-coil design; practical considerations of SNR management, supported by multi-dimensional accelerations, 3D noise averaging and high field imaging; highly accelerated clinical state-of-the art cardiovascular imaging applications spanning the range from SNR-rich to SNR-limited; and current trends and future directions. PMID:17562047
eebf2c3492
fundamentals of internet applications by anshuman sharma
DOWNLOAD https://larenrafes.blogspot.com/?jo=2wHfPx
Internet of Things (IoT) is presently a hot technology worldwide. Government, academia, and industry are involved in different aspects of research, implementation, and business with IoT. IoT cuts across different application domain verticals ranging from civilian to defence sectors. These domains include agriculture, space, healthcare, manufacturing, construction, water, and mining, which are presently transitioning their legacy infrastructure to support IoT. Today it is possible to envision pervasive connectivity, storage, and computation, which, in turn, gives rise to building different IoT solutions. IoT-based applications such as innovative shopping system, infrastructure management in both urban and rural areas, remote health monitoring and emergency notification systems, and transportation systems, are gradually relying on IoT based systems. Therefore, it is very important to learn the fundamentals of this emerging technology.
On the world wide web, toxic content detectors are a crucial line of defense against potentially hateful and offensive messages. As such, building highly effective classifiers that enable a safer internet is an important research area. Moreover, the web is a highly multilingual, cross-cultural community that develops its own lingo over time. As such, it is crucial to develop models that are effective across a diverse range of languages, usages, and styles. In this paper, we present the fundamentals behind the next version of the Perspective API from Google Jigsaw. At the heart of the approach is a single multilingual token-free Charformer model that is applicable across a range of languages, domains, and tasks. We demonstrate that by forgoing static vocabularies, we gain flexibility across a variety of settings. We additionally outline the techniques employed to make such a byte-level model efficient and feasible for productionization. Through extensive experiments on multilingual toxic comment classification benchmarks derived from real API traffic and evaluation on an array of code-switching, covert toxicity, emoji-based hate, human-readable obfuscation, distribution shift, and bias evaluation settings, we show that our proposed approach outperforms strong baselines. Finally, we present our findings from deploying this system in production.
In this tutorial, we introduce recent advances in pretrained text representations, as well as their applications to a wide range of text mining tasks. We focus on minimally-supervised approaches that do not require massive human annotations, including (1) self-supervised text embeddings and pretrained language models that serve as the fundamentals for downstream tasks, (2) unsupervised and distantly-supervised methods for fundamental text mining applications, (3) unsupervised and seed-guided methods for topic discovery from massive text corpora and (4) weakly-supervised methods for text classification and advanced text mining tasks.
Counterfactual estimators enable the use of existing log data to estimate how some new target policy would have performed, if it had been used instead of the policy that logged the data. We say that those estimators work "off-policy", since the policy that logged the data is different from the target policy. In this way, counterfactual estimators enable Off-policy Evaluation (OPE) akin to an unbiased offline A/B test, as well as learning new decision-making policies through Off-policy Learning (OPL). The goal of this tutorial is to summarize Foundations, Implementations, and Recent Advances of OPE and OPL (OPE/OPL), with applications in recommendation, search, and an ever growing range of interactive systems. Specifically, we will introduce the fundamentals of OPE/OPL and provide theoretical and empirical comparisons of conventional methods. Then, we will cover emerging practical challenges such as how to handle large action spaces, distributional shift, and hyper-parameter tuning. We will then present Open Bandit Pipeline, an open-source Python software for OPE/OPL to better enable new research and applications. We will conclude the tutorial with future directions.
Assessment:Item 1: 10% Coursework 1Item 2: 10% Coursework 2Item 3: 80% Examination (2 hours 30 mins)Level: 7
Physics and AstronomyStatistical Data AnalysisPhysical and Chemical SciencesSPA6328Semester 16YesStatistical Data AnalysisCredits: 15.0
Contact: Dr Ulla Blumenschein
Description: Statistical Data Analysis teaches the fundamentals of probability and statistics, data analysis, and machine learning, as applied to discovering, classifying, and measuring new phenomena. It draws on examples from a wide range of applications, within physics and far beyond. Students will learn to perform statistical calculations, to understand statistical usage in scientific research papers, and to apply practical programming techniques for more advanced analyses.
Cardiovascular magnetic resonance imaging (CVMRI) is of proven clinical value in the non-invasive imaging of cardiovascular diseases. CVMRI requires rapid image acquisition, but acquisition speed is fundamentally limited in conventional MRI. Parallel imaging provides a means for increasing acquisition speed and efficiency. However, signal-to-noise (SNR) limitations and the limited number of receiver channels available on most MR systems have in the past imposed practical constraints, which dictated the use of moderate accelerations in CVMRI. High levels of acceleration, which were unattainable previously, have become possible with many-receiver MR systems and many-element, cardiac-optimized RF-coil arrays. The resulting imaging speed improvements can be exploited in a number of ways, ranging from enhancement of spatial and temporal resolution to efficient whole heart coverage to streamlining of CVMRI work flow. In this review, examples of these strategies are provided, following an outline of the fundamentals of the highly accelerated imaging approaches employed in CVMRI. Topics discussed include basic principles of parallel imaging; key requirements for MR systems and RF-coil design; practical considerations of SNR management, supported by multi-dimensional accelerations, 3D noise averaging and high field imaging; highly accelerated clinical state-of-the art cardiovascular imaging applications spanning the range from SNR-rich to SNR-limited; and current trends and future directions. PMID:17562047
eebf2c3492
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