Academic literature on the topic 'Embedding Analysis'
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Journal articles on the topic "Embedding Analysis"
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Lainscsek, Claudia, and Terrence J. Sejnowski. "Delay Differential Analysis of Time Series." Neural Computation 27, no. 3 (2015): 594–614. http://dx.doi.org/10.1162/neco_a_00706.
Full textAbstract:
Nonlinear dynamical system analysis based on embedding theory has been used for modeling and prediction, but it also has applications to signal detection and classification of time series. An embedding creates a multidimensional geometrical object from a single time series. Traditionally either delay or derivative embeddings have been used. The delay embedding is composed of delayed versions of the signal, and the derivative embedding is composed of successive derivatives of the signal. The delay embedding has been extended to nonuniform embeddings to take multiple timescales into account. Both embeddings provide information on the underlying dynamical system without having direct access to all the system variables. Delay differential analysis is based on functional embeddings, a combination of the derivative embedding with nonuniform delay embeddings. Small delay differential equation (DDE) models that best represent relevant dynamic features of time series data are selected from a pool of candidate models for detection or classification. We show that the properties of DDEs support spectral analysis in the time domain where nonlinear correlation functions are used to detect frequencies, frequency and phase couplings, and bispectra. These can be efficiently computed with short time windows and are robust to noise. For frequency analysis, this framework is a multivariate extension of discrete Fourier transform (DFT), and for higher-order spectra, it is a linear and multivariate alternative to multidimensional fast Fourier transform of multidimensional correlations. This method can be applied to short or sparse time series and can be extended to cross-trial and cross-channel spectra if multiple short data segments of the same experiment are available. Together, this time-domain toolbox provides higher temporal resolution, increased frequency and phase coupling information, and it allows an easy and straightforward implementation of higher-order spectra across time compared with frequency-based methods such as the DFT and cross-spectral analysis.
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Samko, Natasha. "Embeddings of weighted generalized Morrey spaces into Lebesgue spaces on fractal sets." Fractional Calculus and Applied Analysis 22, no. 5 (2019): 1203–24. http://dx.doi.org/10.1515/fca-2019-0064.
Full textAbstract:
Abstract We study embeddings of weighted local and consequently global generalized Morrey spaces defined on a quasi-metric measure set (X, d, μ) of general nature which may be unbounded, into Lebesgue spaces Ls(X), 1 ≤ s ≤ p < ∞. The main motivation for obtaining such an embedding is to have an embedding of non-separable Morrey space into a separable space. In the general setting of quasi-metric measure spaces and arbitrary weights we give a sufficient condition for such an embedding. In the case of radial weights related to the center of local Morrey space, we obtain an effective sufficient condition in terms of (fractional in general) upper Ahlfors dimensions of the set X. In the case of radial weights we also obtain necessary conditions for such embeddings of local and global Morrey spaces, with the use of (fractional in general) lower and upper Ahlfors dimensions. In the case of power-logarithmic-type weights we obtain a criterion for such embeddings when these dimensions coincide.
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Sabbeh, Sahar F., and Heba A. Fasihuddin. "A Comparative Analysis of Word Embedding and Deep Learning for Arabic Sentiment Classification." Electronics 12, no. 6 (2023): 1425. http://dx.doi.org/10.3390/electronics12061425.
Full textAbstract:
Sentiment analysis on social media platforms (i.e., Twitter or Facebook) has become an important tool to learn about users’ opinions and preferences. However, the accuracy of sentiment analysis is disrupted by the challenges of natural language processing (NLP). Recently, deep learning models have proved superior performance over statistical- and lexical-based approaches in NLP-related tasks. Word embedding is an important layer of deep learning models to generate input features. Many word embedding models have been presented for text representation of both classic and context-based word embeddings. In this paper, we present a comparative analysis to evaluate both classic and contextualized word embeddings for sentiment analysis. The four most frequently used word embedding techniques were used in their trained and pre-trained versions. The selected embedding represents classical and contextualized techniques. Classical word embedding includes algorithms such as GloVe, Word2vec, and FastText. By contrast, ARBERT is used as a contextualized embedding model. Since word embedding is more typically employed as the input layer in deep networks, we used deep learning architectures BiLSTM and CNN for sentiment classification. To achieve these goals, the experiments were applied to a series of benchmark datasets: HARD, Khooli, AJGT, ArSAS, and ASTD. Finally, a comparative analysis was conducted on the results obtained for the experimented models. Our outcomes indicate that, generally, generated embedding by one technique achieves higher performance than its pretrained version for the same technique by around 0.28 to 1.8% accuracy, 0.33 to 2.17% precision, and 0.44 to 2% recall. Moreover, the contextualized transformer-based embedding model BERT achieved the highest performance in its pretrained and trained versions. Additionally, the results indicate that BiLSTM outperforms CNN by approximately 2% in 3 datasets, HARD, Khooli, and ArSAS, while CNN achieved around 2% higher performance in the smaller datasets, AJGT and ASTD.
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He, Hongliang, Junlei Zhang, Zhenzhong Lan, and Yue Zhang. "Instance Smoothed Contrastive Learning for Unsupervised Sentence Embedding." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 11 (2023): 12863–71. http://dx.doi.org/10.1609/aaai.v37i11.26512.
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Contrastive learning-based methods, such as unsup-SimCSE, have achieved state-of-the-art (SOTA) performances in learning unsupervised sentence embeddings. However, in previous studies, each embedding used for contrastive learning only derived from one sentence instance, and we call these embeddings instance-level embeddings. In other words, each embedding is regarded as a unique class of its own, which may hurt the generalization performance. In this study, we propose IS-CSE (instance smoothing contrastive sentence embedding) to smooth the boundaries of embeddings in the feature space. Specifically, we retrieve embeddings from a dynamic memory buffer according to the semantic similarity to get a positive embedding group. Then embeddings in the group are aggregated by a self-attention operation to produce a smoothed instance embedding for further analysis. We evaluate our method on standard semantic text similarity (STS) tasks and achieve an average of 78.30%, 79.47%, 77.73%, and 79.42% Spearman’s correlation on the base of BERT-base, BERT-large, RoBERTa-base, and RoBERTa-large respectively, a 2.05%, 1.06%, 1.16% and 0.52% improvement compared to unsup-SimCSE.
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Srinidhi, K., T. L.S Tejaswi, CH Rama Rupesh Kumar, and I. Sai Siva Charan. "An Advanced Sentiment Embeddings with Applications to Sentiment Based Result Analysis." International Journal of Engineering & Technology 7, no. 2.32 (2018): 393. http://dx.doi.org/10.14419/ijet.v7i2.32.15721.
Full textAbstract:
We propose an advanced well-trained sentiment analysis based adoptive analysis “word specific embedding’s, dubbed sentiment embedding’s”. Using available word and phrase embedded learning and trained algorithms mainly make use of contexts of terms but ignore the sentiment of texts and analyzing the process of word and text classifications. sentimental analysis on unlike words conveying same meaning matched to corresponding word vector. This problem is bridged by combining encoding opinion carrying text with sentiment embeddings words. But performing sentimental analysis on e-commerce, social networking sites we developed neural network based algorithms along with tailoring and loss function which carry feelings. This research apply embedding’s to word-level, sentence-level sentimental analysis and classification, constructing sentiment oriented lexicons. Experimental analysis and results addresses that sentiment embedding techniques outperform the context-based embedding’s on many distributed data sets. This work provides familiarity about neural networks techniques for learning word embedding’s in other NLP tasks.
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Ruskanda, Fariska Zakhralativa, Stefanus Stanley Yoga Setiawan, Nadya Aditama, and Masayu Leylia Khodra. "Sentiment Analysis of Sentence-Level using Dependency Embedding and Pre-trained BERT Model." PIKSEL : Penelitian Ilmu Komputer Sistem Embedded and Logic 11, no. 1 (2023): 171–80. http://dx.doi.org/10.33558/piksel.v11i1.6938.
Full textAbstract:
Sentiment analysis is a valuable field of research in NLP with many applications. Dependency tree is one of the language features that can be utilized in this field. Dependency embedding, as one of the semantic representations of a sentence, has shown to provide more significant results compared to other embeddings, which makes it a potential way to improve the performance of sentiment analysis tasks. This study aimed to investigate the effect of dependency embedding on sentence-level sentiment analysis through experimental research. The study replaced the Vocabulary Graph embedding in the VGCN-BERT sentiment classification system architecture with several dependency embedding representations, including word vector, context vector, average of word and context vectors, weighting on word and context vectors, and merging of word and context vectors. The experiments were conducted on two datasets, SST-2 and CoLA, with more than 19 thousand labeled sentiment sentences. The results indicated that dependency embedding can enhance the performance of sentiment analysis at the sentence level.
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Truică, Ciprian-Octavian, Elena-Simona Apostol, Maria-Luiza Șerban, and Adrian Paschke. "Topic-Based Document-Level Sentiment Analysis Using Contextual Cues." Mathematics 9, no. 21 (2021): 2722. http://dx.doi.org/10.3390/math9212722.
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Document-level Sentiment Analysis is a complex task that implies the analysis of large textual content that can incorporate multiple contradictory polarities at the phrase and word levels. Most of the current approaches either represent textual data using pre-trained word embeddings without considering the local context that can be extracted from the dataset, or they detect the overall topic polarity without considering both the local and global context. In this paper, we propose a novel document-topic embedding model, DocTopic2Vec, for document-level polarity detection in large texts by employing general and specific contextual cues obtained through the use of document embeddings (Doc2Vec) and Topic Modeling. In our approach, (1) we use a large dataset with game reviews to create different word embeddings by applying Word2Vec, FastText, and GloVe, (2) we create Doc2Vecs enriched with the local context given by the word embeddings for each review, (3) we construct topic embeddings Topic2Vec using three Topic Modeling algorithms, i.e., LDA, NMF, and LSI, to enhance the global context of the Sentiment Analysis task, (4) for each document and its dominant topic, we build the new DocTopic2Vec by concatenating the Doc2Vec with the Topic2Vec created with the same word embedding. We also design six new Convolutional-based (Bidirectional) Recurrent Deep Neural Network Architectures that show promising results for this task. The proposed DocTopic2Vecs are used to benchmark multiple Machine and Deep Learning models, i.e., a Logistic Regression model, used as a baseline, and 18 Deep Neural Networks Architectures. The experimental results show that the new embedding and the new Deep Neural Network Architectures achieve better results than the baseline, i.e., Logistic Regression and Doc2Vec.
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Li, Qizhi, Xianyong Li, Yajun Du, Yongquan Fan, and Xiaoliang Chen. "A New Sentiment-Enhanced Word Embedding Method for Sentiment Analysis." Applied Sciences 12, no. 20 (2022): 10236. http://dx.doi.org/10.3390/app122010236.
Full textAbstract:
Since some sentiment words have similar syntactic and semantic features in the corpus, existing pre-trained word embeddings always perform poorly in sentiment analysis tasks. This paper proposes a new sentiment-enhanced word embedding (S-EWE) method to improve the effectiveness of sentence-level sentiment classification. This sentiment enhancement method takes full advantage of the mapping relationship between word embeddings and their corresponding sentiment orientations. This method first converts words to word embeddings and assigns sentiment mapping vectors to all word embeddings. Then, word embeddings and their corresponding sentiment mapping vectors are fused to S-EWEs. After reducing the dimensions of S-EWEs through a fully connected layer, the predicted sentiment orientations are obtained. The S-EWE method adopts the cross-entropy function to calculate the loss between predicted and true sentiment orientations, and backpropagates the loss to train the sentiment mapping vectors. Experiments show that the accuracy and macro-F1 values of six sentiment classification models using Word2Vec and GloVe with the S-EWEs are on average 1.07% and 1.58% higher than those without the S-EWEs on the SemEval-2013 dataset, and on average 1.23% and 1.26% higher than those without the S-EWEs on the SST-2 dataset. In all baseline models with S-EWEs, the convergence time of the attention-based bidirectional CNN-RNN deep model (ABCDM) with S-EWEs was significantly decreased by 51.21% of ABCDM on the SemEval-2013 dataset. The convergence time of CNN-LSTM with S-EWEs was vastly reduced by 41.34% of CNN-LSTM on the SST-2 dataset. In addition, the S-EWE method is not valid for contextualized word embedding models. The main reasons are that the S-EWE method only enhances the embedding layer of the models and has no effect on the models themselves.
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Beckner, William. "Estimates on Moser Embedding." Potential Analysis 20, no. 4 (2004): 345–59. http://dx.doi.org/10.1023/b:pota.0000009813.38619.47.
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Górka, Przemysław, Tomasz Kostrzewa, and Enrique G. Reyes. "Sobolev Spaces on Locally Compact Abelian Groups: Compact Embeddings and Local Spaces." Journal of Function Spaces 2014 (2014): 1–6. http://dx.doi.org/10.1155/2014/404738.
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We continue our research on Sobolev spaces on locally compact abelian (LCA) groups motivated by our work on equations with infinitely many derivatives of interest for string theory and cosmology. In this paper, we focus on compact embedding results and we prove an analog for LCA groups of the classical Rellich lemma and of the Rellich-Kondrachov compactness theorem. Furthermore, we introduce Sobolev spaces on subsets of LCA groups and study its main properties, including the existence of compact embeddings intoLp-spaces.
Dissertations / Theses on the topic "Embedding Analysis"
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Abdikalikova, Zamira. "Embedding theorems for spaces with multiweighted derivatives." Licentiate thesis, Luleå tekniska universitet, Matematiska vetenskaper, 2007. http://urn.kb.se/resolve?urn=urn:nbn:se:ltu:diva-26092.
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This Licentiate Thesis consists of four chapters, which deal with a new Sobolev type function space called the space with multiweighted derivatives. This space is a generalization of the usual one dimensional Sobolev space. Chapter 1 is an introduction, where, in particular, the importance to study function spaces with weights is discussed and motivated. In Chapter 2 we consider and analyze some results of L. D. Kudryavtsev, where he investigated one dimensional Sobolev spaces. Moreover, in this chapter we present and prove analogous results by B. L. Baidel'dinov for generalized Sobolev spaces. These results are crucially for the proofs of the main results of this Licentiate Thesis. In Chapter 3 we prove some embedding theorems for these new generalized Sobolev spaces. The main results of Kudryavtsev and Baidel'dinov about characterization of the behavior of functions at a singularity take place in weak degeneration of spaces. However, with the help of our new embedding theorems we can extend these results to the case of strong degeneration. In Chapter 4 we prove some new estimates for each function in a Tchebychev system. In order to be able to study also compactness of the embeddings from Chapter 3 such estimates are crucial. I plan to study this question in detail in my further PhD studies.<br><p>Godkänd; 2007; 20071107 (ysko)</p>
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JOHANSSON, SIMON, and EMIL LENNGREN. "Steganographic embedding and steganalysisevaluation : An evaluation of common methods for steganographic embedding and analysis indigital images." Thesis, KTH, Skolan för datavetenskap och kommunikation (CSC), 2014. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-157499.
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Steganography is the technique that hides secret messages in seemingly inoccuous data. Steganalysis is the technique that is used to discover the use of steganography. Usuallyit is difficult to extract the exact hidden message because itis usually encrypted. Though often it is enough to merely uncover the use of steganography to reveal important information.In this report we discuss some common methods for steganography in images, as well as some methods of analysis,both general and targeted towards specific methods. We test how well the methods fare against each other and what one should consider when making use of steganography. According to our results, many steganographic methods need targeted analysis attacks to be discovered. Our advice is to avoid using the full potential capacity when embeddingdata. The hidden data should also be distributed randomly in the cover file.
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Rossiter, Darien Elizabeth. "Embedding e-learning in universities : analysis and conceptualisation of change processes." Thesis, Queensland University of Technology, 2006. https://eprints.qut.edu.au/16223/1/Darien_Rossiter_Thesis.pdf.
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E-learning has acquired the status of a "radical innovation" in higher education over the past decade. This claim is contestable, but certainly as the latest educational innovation, it can be attributed with introducing significant disruption into many facets of university life, reaching well beyond the traditional activities associated with the classroom pedagogies. In Australian universities, there are many now who simply take e-learning for granted as accepted teaching and learning practice (Oliver, 2004). Conversely, there are others who forecast its demise, claiming that, like previous educational technological innovations, it is another passing fad (Noble, 1998b). This thesis does not primarily engage this debate. Instead the purpose of this thesis is to gain insight into how universities can realise sustained benefits from the considerable investments to date that have been made in educational technological innovations. The inquiry seeks to understand better change within contemporary universities, in particular the process of embedding the e-learning innovation effectively. The intention is to produce an analysis useful to university executives, managers, teachers and researchers, as well as to make a more general contribution to knowledge about innovations in organisations.
The research literature on change and innovation in organisations is relevant but is reviewed and assessed as of limited value to the enquiry. This is because:
* the literature mainly focuses on the objective characteristics of an innovative product which cannot encompass the socially constructed value of e-learning
* it fails to differentiate between the concept of "embedding" and other change phases and constructs, mostly examining the precursory and innovation-producing processes
* the context of research into innovation has been primarily industrial, not university-based
* its variable analytic paradigm fails to produce holistic analyses which can be appreciated and enacted on by decision makers and practising managers.
For these reasons and because suitable research on innovation in universities is lacking, an introductory investigation based on grounded theory building was undertaken. To this end, four qualitative, descriptive case studies of contrasting Australian universities embedding e-learning were compiled. The four case universities (their identities protected through use of pseudonyms) assessed were:
* Gamma University - a multi-campus institution, geographically spread across urban and regional locations
* Lambda University - an established university, with the majority of students located at a single urban campus
* Epsilon University - a younger, multi-campus amalgamated university with a strong reputation for distance education
* Delta University - a relatively young multi-campus, urban university, although its parent bodies provide a longer history.
The cases were based on interviews and focus group sessions with 74 participants, and electronic resource and document analyses over two phases; the first conducted in 1998-1999 and the second in 2002-2003. These analyses provided holistic pragmatic accounts that encapsulate a number of issues. One issue was about the importance of creativity in the innovating process. A second set of issues centred on the theme of complexity and the multifarious nature of the e-learning innovation. Other themes included the significance of the innovation context, partnerships and collaborations, and the emerging polarisation of issues such as standardisation versus diversification. These issues provoked three major propositions about the process of embedding and prompted the development of two systems-based analytical frameworks; one focusing on the nature of system relationships and interactions and the second providing a longitudinal perspective of system change. The propositions are:
* the ability of a university to negotiate system intersections and transitions influences the degree to which e-learning can be embedded in that university
* complexity is an integral part of an innovation, therefore cannot be ignored or eliminated without destroying the kernel of the innovation itself, and its longterm viability
* the efficacy of the innovation is related, in some measure, to the ability to sustain partnerships and collaborations.
The analysis suggested that there are number of key influences which affect the embedding process and the ability of an organisation, such as a university, to manage the processes associated with the e-learning innovation. The key system influences which affect embedding include:
* the nature of the interactions and transactions occurring within the system, at the boundaries and between the phases of transition
* the importance of organisational context (cultural, technological, strategic, geographic)
* the pervasive impact of complexity on all dimensions of the research problem (the e-learning innovation, the change process and the university environment)
* the necessity for collaboration.
The implications of this study for university executives, managers and beyond are far reaching, and in some respects contradict accepted contemporary management practice. They include: seeking ways to maximise organisational tensions to achieve positive outcomes; enhancing decision making by allowing more flexibility and personal judgement into the process; developing greater tolerance for system fuzziness and uncertainty; and encouraging better utilisation of previous knowledge gained about innovation practices and processes.
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Rossiter, Darien Elizabeth. "Embedding e-learning in universities : analysis and conceptualisation of change processes." Queensland University of Technology, 2006. http://eprints.qut.edu.au/16223/.
Full textAbstract:
E-learning has acquired the status of a "radical innovation" in higher education over the past decade. This claim is contestable, but certainly as the latest educational innovation, it can be attributed with introducing significant disruption into many facets of university life, reaching well beyond the traditional activities associated with the classroom pedagogies. In Australian universities, there are many now who simply take e-learning for granted as accepted teaching and learning practice (Oliver, 2004). Conversely, there are others who forecast its demise, claiming that, like previous educational technological innovations, it is another passing fad (Noble, 1998b). This thesis does not primarily engage this debate. Instead the purpose of this thesis is to gain insight into how universities can realise sustained benefits from the considerable investments to date that have been made in educational technological innovations. The inquiry seeks to understand better change within contemporary universities, in particular the process of embedding the e-learning innovation effectively. The intention is to produce an analysis useful to university executives, managers, teachers and researchers, as well as to make a more general contribution to knowledge about innovations in organisations. The research literature on change and innovation in organisations is relevant but is reviewed and assessed as of limited value to the enquiry. This is because: * the literature mainly focuses on the objective characteristics of an innovative product which cannot encompass the socially constructed value of e-learning * it fails to differentiate between the concept of "embedding" and other change phases and constructs, mostly examining the precursory and innovation-producing processes * the context of research into innovation has been primarily industrial, not university-based * its variable analytic paradigm fails to produce holistic analyses which can be appreciated and enacted on by decision makers and practising managers. For these reasons and because suitable research on innovation in universities is lacking, an introductory investigation based on grounded theory building was undertaken. To this end, four qualitative, descriptive case studies of contrasting Australian universities embedding e-learning were compiled. The four case universities (their identities protected through use of pseudonyms) assessed were: * Gamma University - a multi-campus institution, geographically spread across urban and regional locations * Lambda University - an established university, with the majority of students located at a single urban campus * Epsilon University - a younger, multi-campus amalgamated university with a strong reputation for distance education * Delta University - a relatively young multi-campus, urban university, although its parent bodies provide a longer history. The cases were based on interviews and focus group sessions with 74 participants, and electronic resource and document analyses over two phases; the first conducted in 1998-1999 and the second in 2002-2003. These analyses provided holistic pragmatic accounts that encapsulate a number of issues. One issue was about the importance of creativity in the innovating process. A second set of issues centred on the theme of complexity and the multifarious nature of the e-learning innovation. Other themes included the significance of the innovation context, partnerships and collaborations, and the emerging polarisation of issues such as standardisation versus diversification. These issues provoked three major propositions about the process of embedding and prompted the development of two systems-based analytical frameworks; one focusing on the nature of system relationships and interactions and the second providing a longitudinal perspective of system change. The propositions are: * the ability of a university to negotiate system intersections and transitions influences the degree to which e-learning can be embedded in that university * complexity is an integral part of an innovation, therefore cannot be ignored or eliminated without destroying the kernel of the innovation itself, and its longterm viability * the efficacy of the innovation is related, in some measure, to the ability to sustain partnerships and collaborations. The analysis suggested that there are number of key influences which affect the embedding process and the ability of an organisation, such as a university, to manage the processes associated with the e-learning innovation. The key system influences which affect embedding include: * the nature of the interactions and transactions occurring within the system, at the boundaries and between the phases of transition * the importance of organisational context (cultural, technological, strategic, geographic) * the pervasive impact of complexity on all dimensions of the research problem (the e-learning innovation, the change process and the university environment) * the necessity for collaboration. The implications of this study for university executives, managers and beyond are far reaching, and in some respects contradict accepted contemporary management practice. They include: seeking ways to maximise organisational tensions to achieve positive outcomes; enhancing decision making by allowing more flexibility and personal judgement into the process; developing greater tolerance for system fuzziness and uncertainty; and encouraging better utilisation of previous knowledge gained about innovation practices and processes.
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Algervik, Robert. "Embedding Theorems for Mixed Norm Spaces and Applications." Doctoral thesis, Karlstads universitet, Avdelningen för matematik, 2010. http://urn.kb.se/resolve?urn=urn:nbn:se:kau:diva-5646.
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This thesis is devoted to the study of mixed norm spaces that arise in connection with embeddings of Sobolev and Besov type spaces. We study different structural, integrability, and smoothness properties of functions satisfying certain mixed norm conditions. Conditions of this type are determined by the behaviour of linear sections of functions. The work in this direction originates in a paper due to Gagliardo (1958), and was further developed by Fournier (1988), by Blei and Fournier (1989), and by Kolyada (2005). Here we continue these studies. We obtain some refinements of known embeddings for certain mixed norm spaces introduced by Gagliardo, and we study general properties of these spaces. In connection with these results, we consider a scale of intermediate mixed norm spaces, and prove intrinsic embeddings in this scale. We also consider more general, fully anisotropic, mixed norm spaces. Our main theorem states an embedding of these spaces to Lorentz spaces. Applying this result, we obtain sharp embedding theorems for anisotropic Sobolev-Besov spaces, and anisotropic fractional Sobolev spaces. The methods used are based on non-increasing rearrangements, and on estimates of sections of functions and sections of sets. We also study limiting relations between embeddings of spaces of different type. More exactly, mixed norm estimates enable us to get embedding constants with sharp asymptotic behaviour. This gives an extension of the results obtained for isotropic Besov spaces by Bourgain, Brezis, and Mironescu, and for anisotropic Besov spaces by Kolyada. We study also some basic properties (in particular the approximation properties) of special weak type spaces that play an important role in the construction of mixed norm spaces, and in the description of Sobolev type embeddings. In the last chapter, we study mixed norm spaces consisting of functions that have smooth sections. We prove embeddings of these spaces to Lorentz spaces. From this result, known properties of Sobolev-Liouville spaces follow.
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Chen, Brian 1972. "Design and analysis of digital watermarking, information embedding, and data hiding systems." Thesis, Massachusetts Institute of Technology, 2000. http://hdl.handle.net/1721.1/31086.
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Thesis (Ph.D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2000.<br>Includes bibliographical references (p. 139-142).<br>Digital watermarking, information embedding, and data hiding systems embed information, sometimes called a digital watermark, inside a host signal, which is typically an image, audio signal, or video signal. The host signal is not degraded unacceptably in the process, and one can recover the watermark even if the composite host and watermark signal undergo a variety of corruptions and attacks as long as these corruptions do not unacceptably degrade the host signal. These systems play an important role in meeting at least three major challenges that result from the widespread use of digital communication networks to disseminate multimedia content: (1) the relative ease with which one can generate perfect copies of digital signals creates a need for copyright protection mechanisms, (2) the relative ease with which one can alter digital signals creates a need for authentication and tamper-detection methods, and (3) the increase in sheer volume of transmitted data creates a demand for bandwidth-efficient methods to either backwards-compatibly increase capacities of existing legacy networks or deploy new networks backwards-compatibly with legacy networks. We introduce a framework within which to design and analyze digital watermarking and information embedding systems. In this framework performance is characterized by achievable rate-distortion-robustness trade-offs, and this framework leads quite naturally to a new class of embedding methods called quantization index modulation (QIM). These QIM methods, especially when combined with postprocessing called distortion compensation, achieve provably better rate-distortion-robustness performance than previously proposed classes of methods such as spread spectrum methods and generalized low-bit modulation methods in a number of different scenarios, which include both intentional and unintentional attacks. Indeed, we show that distortion-compensated QIM methods can achieve capacity, the information-theoretically best possible rate-distortion-robustness performance, against both additive Gaussian noise attacks and arbitrary squared error distortion-constrained attacks. These results also have implications for the problem of communicating over broadcast channels. We also present practical implementations of QIM methods called dither modulation and demonstrate their performance both analytically and through empirical simulations.<br>by Brian Chen.<br>Ph.D.
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Perinelli, Alessio. "A new approach to optimal embedding of time series." Doctoral thesis, Università degli studi di Trento, 2020. http://hdl.handle.net/11572/280754.
Full textAbstract:
The analysis of signals stemming from a physical system is crucial for the experimental investigation of the underlying dynamics that drives the system itself. The field of time series analysis comprises a wide variety of techniques developed with the purpose of characterizing signals and, ultimately, of providing insights on the phenomena that govern the temporal evolution of the generating system. A renowned example in this field is given by spectral analysis: the use of Fourier or Laplace transforms to bring time-domain signals into the more convenient frequency space allows to disclose the key features of linear systems. A more complex scenario turns up when nonlinearity intervenes within a system's dynamics. Nonlinear coupling between a system's degrees of freedom brings about interesting dynamical regimes, such as self-sustained periodic (though anharmonic) oscillations ("limit cycles"), or quasi-periodic evolutions that exhibit sharp spectral lines while lacking strict periodicity ("limit tori"). Among the consequences of nonlinearity, the onset of chaos is definitely the most fascinating one. Chaos is a dynamical regime characterized by unpredictability and lack of periodicity, despite being generated by deterministic laws. Signals generated by chaotic dynamical systems appear as irregular: the corresponding spectra are broad and flat, prediction of future values is challenging, and evolutions within the systems' state spaces converge to strange attractor sets with noninteger dimensionality. Because of these properties, chaotic signals can be mistakenly classified as noise if linear techniques such as spectral analysis are used. The identification of chaos and its characterization require the assessment of dynamical invariants that quantify the complex features of a chaotic system's evolution. For example, Lyapunov exponents provide a marker of unpredictability; the estimation of attractor dimensions, on the other hand, highlights the unconventional geometry of a chaotic system's state space. Nonlinear time series analysis techniques act directly within the state space of the system under investigation. However, experimentally, full access to a system's state space is not always available. Often, only a scalar signal stemming from the dynamical system can be recorded, thus providing, upon sampling, a scalar sequence. Nevertheless, by virtue of a fundamental theorem by Takens, it is possible to reconstruct a proxy of the original state space evolution out of a single, scalar sequence. This reconstruction is carried out by means of the so-called embedding procedure: m-dimensional vectors are built by picking successive elements of the scalar sequence delayed by a lag L. On the other hand, besides posing some necessary conditions on the integer embedding parameters m and L, Takens' theorem does not provide any clue on how to choose them correctly. Although many optimal embedding criteria were proposed, a general answer to the problem is still lacking. As a matter of fact, conventional methods for optimal embedding are flawed by several drawbacks, the most relevant being the need for a subjective evaluation of the outcomes of applied algorithms. Tackling the issue of optimally selecting embedding parameters makes up the core topic of this thesis work. In particular, I will discuss a novel approach that was pursued by our research group and that led to the development of a new method for the identification of suitable embedding parameters. Rather than most conventional approaches, which seek a single optimal value for m and L to embed an input sequence, our approach provides a set of embedding choices that are equivalently suitable to reconstruct the dynamics. The suitability of each embedding choice m, L is assessed by relying on statistical testing, thus providing a criterion that does not require a subjective evaluation of outcomes.
The starting point of our method are embedding-dependent correlation integrals, i.e. cumulative distributions of embedding vector distances, built out of an input scalar sequence. In the case of Gaussian white noise, an analytical expression for correlation integrals is available, and, by exploiting this expression, a gauge transformation of distances is introduced to provide a more convenient representation of correlation integrals. Under this new gauge, it is possible to test—in a computationally undemanding way—whether an input sequence is compatible with Gaussian white noise and, subsequently, whether the sequence is compatible with the hypothesis of an underlying chaotic system. These two statistical tests allow ruling out embedding choices that are unsuitable to reconstruct the dynamics. The estimation of correlation dimension, carried out by means of a newly devised estimator, makes up the third stage of the method: sets of embedding choices that provide uniform estimates of this dynamical invariant are deemed to be suitable to embed the sequence.The method was successfully applied to synthetic and experimental sequences, providing new insight into the longstanding issue of optimal embedding. For example, the relevance of the embedding window (m-1)L, i.e. the time span covered by each embedding vector, is naturally highlighted by our approach. In addition, our method provides some information on the adequacy of the sampling period used to record the input sequence.The method correctly distinguishes a chaotic sequence from surrogate ones generated out of it and having the same power spectrum. The technique of surrogate generation, which I also addressed during my Ph. D. work to develop new dedicated algorithms and to analyze brain signals, allows to estimate significance levels in situations where standard analytical algorithms are unapplicable. The novel embedding approach being able to tell apart an original sequence from surrogate ones shows its capability to distinguish signals beyond their spectral—or autocorrelation—similarities.One of the possible applications of the new approach concerns another longstanding issue, namely that of distinguishing noise from chaos. To this purpose, complementary information is provided by analyzing the asymptotic (long-time) behaviour of the so-called time-dependent divergence exponent. This embedding-dependent metric is commonly used to estimate—by processing its short-time linearly growing region—the maximum Lyapunov exponent out of a scalar sequence. However, insights on the kind of source generating the sequence can be extracted from the—usually overlooked—asymptotic behaviour of the divergence exponent. Moreover, in the case of chaotic sources, this analysis also provides a precise estimate of the system's correlation dimension. Besides describing the results concerning the discrimination of chaotic systems from noise sources, I will also discuss the possibility of using the related correlation dimension estimates to improve the third stage of the method introduced above for the identification of suitable embedding parameters. The discovery of chaos as a possible dynamical regime for nonlinear systems led to the search of chaotic behaviour in experimental recordings. In some fields, this search gave plenty of positive results: for example, chaotic dynamics was successfully identified and tamed in electronic circuits and laser-based optical setups. These two families of experimental chaotic systems eventually became versatile tools to study chaos and its possible applications. On the other hand, chaotic behaviour is also looked for in climate science, biology, neuroscience, and even economics. In these fields, nonlinearity is widespread: many smaller units interact nonlinearly, yielding a collective motion that can be described by means of few, nonlinearly coupled effective degrees of freedom. The corresponding recorded signals exhibit, in many cases, an irregular and complex evolution. A possible underlying chaotic evolution—as opposed to a stochastic one—would be of interest both to reveal the presence of determinism and to predict the system's future states. While some claims concerning the existence of chaos in these fields have been made, most results are debated or inconclusive. Nonstationarity, low signal-to-noise ratio, external perturbations and poor reproducibility are just few among the issues that hinder the search of chaos in natural systems. In the final part of this work, I will briefly discuss the problem of chasing chaos in experimental recordings by considering two example sequences, the first one generated by an electronic circuit and the second one corresponding to recordings of brain activity. The present thesis is organized as follows. The core concepts of time series analysis, including the key features of chaotic dynamics, are presented in Chapter 1. A brief review of the search for chaos in experimental systems is also provided; the difficulties concerning this quest in some research fields are also highlighted. Chapter 2 describes the embedding procedure and the issue of optimally choosing the related parameters. Thereupon, existing methods to carry out the embedding choice are reviewed and their limitations are pointed out. In addition, two embedding-dependent nonlinear techniques that are ordinarily used to characterize chaos, namely the estimation of correlation dimension by means of correlation integrals and the assessment of maximum Lyapunov exponent, are presented. The new approach for the identification of suitable embedding parameters, which makes up the core topic of the present thesis work, is the subject of Chapter 3 and 4. While Chapter 3 contains the theoretical outline of the approach, as well as its implementation details, Chapter 4 discusses the application of the approach to benchmark synthetic and experimental sequences, thus illustrating its perks and its limitations. The study of the asymptotic behaviour of the time-dependent divergent exponent is presented in Chapter 5. The alternative estimator of correlation dimension, which relies on this asymptotic metric, is discussed as a possible improvement to the approach described in Chapters 3, 4. The search for chaos out of experimental data is discussed in Chapter 6 by means of two examples of real-world recordings. Concluding remarks are finally drawn in Chapter 7.
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Perinelli, Alessio. "A new approach to optimal embedding of time series." Doctoral thesis, Università degli studi di Trento, 2020. http://hdl.handle.net/11572/280754.
Full textAbstract:
The analysis of signals stemming from a physical system is crucial for the experimental investigation of the underlying dynamics that drives the system itself. The field of time series analysis comprises a wide variety of techniques developed with the purpose of characterizing signals and, ultimately, of providing insights on the phenomena that govern the temporal evolution of the generating system. A renowned example in this field is given by spectral analysis: the use of Fourier or Laplace transforms to bring time-domain signals into the more convenient frequency space allows to disclose the key features of linear systems. A more complex scenario turns up when nonlinearity intervenes within a system's dynamics. Nonlinear coupling between a system's degrees of freedom brings about interesting dynamical regimes, such as self-sustained periodic (though anharmonic) oscillations ("limit cycles"), or quasi-periodic evolutions that exhibit sharp spectral lines while lacking strict periodicity ("limit tori"). Among the consequences of nonlinearity, the onset of chaos is definitely the most fascinating one. Chaos is a dynamical regime characterized by unpredictability and lack of periodicity, despite being generated by deterministic laws. Signals generated by chaotic dynamical systems appear as irregular: the corresponding spectra are broad and flat, prediction of future values is challenging, and evolutions within the systems' state spaces converge to strange attractor sets with noninteger dimensionality. Because of these properties, chaotic signals can be mistakenly classified as noise if linear techniques such as spectral analysis are used. The identification of chaos and its characterization require the assessment of dynamical invariants that quantify the complex features of a chaotic system's evolution. For example, Lyapunov exponents provide a marker of unpredictability; the estimation of attractor dimensions, on the other hand, highlights the unconventional geometry of a chaotic system's state space. Nonlinear time series analysis techniques act directly within the state space of the system under investigation. However, experimentally, full access to a system's state space is not always available. Often, only a scalar signal stemming from the dynamical system can be recorded, thus providing, upon sampling, a scalar sequence. Nevertheless, by virtue of a fundamental theorem by Takens, it is possible to reconstruct a proxy of the original state space evolution out of a single, scalar sequence. This reconstruction is carried out by means of the so-called embedding procedure: m-dimensional vectors are built by picking successive elements of the scalar sequence delayed by a lag L. On the other hand, besides posing some necessary conditions on the integer embedding parameters m and L, Takens' theorem does not provide any clue on how to choose them correctly. Although many optimal embedding criteria were proposed, a general answer to the problem is still lacking. As a matter of fact, conventional methods for optimal embedding are flawed by several drawbacks, the most relevant being the need for a subjective evaluation of the outcomes of applied algorithms. Tackling the issue of optimally selecting embedding parameters makes up the core topic of this thesis work. In particular, I will discuss a novel approach that was pursued by our research group and that led to the development of a new method for the identification of suitable embedding parameters. Rather than most conventional approaches, which seek a single optimal value for m and L to embed an input sequence, our approach provides a set of embedding choices that are equivalently suitable to reconstruct the dynamics. The suitability of each embedding choice m, L is assessed by relying on statistical testing, thus providing a criterion that does not require a subjective evaluation of outcomes.
The starting point of our method are embedding-dependent correlation integrals, i.e. cumulative distributions of embedding vector distances, built out of an input scalar sequence. In the case of Gaussian white noise, an analytical expression for correlation integrals is available, and, by exploiting this expression, a gauge transformation of distances is introduced to provide a more convenient representation of correlation integrals. Under this new gauge, it is possible to test—in a computationally undemanding way—whether an input sequence is compatible with Gaussian white noise and, subsequently, whether the sequence is compatible with the hypothesis of an underlying chaotic system. These two statistical tests allow ruling out embedding choices that are unsuitable to reconstruct the dynamics. The estimation of correlation dimension, carried out by means of a newly devised estimator, makes up the third stage of the method: sets of embedding choices that provide uniform estimates of this dynamical invariant are deemed to be suitable to embed the sequence.The method was successfully applied to synthetic and experimental sequences, providing new insight into the longstanding issue of optimal embedding. For example, the relevance of the embedding window (m-1)L, i.e. the time span covered by each embedding vector, is naturally highlighted by our approach. In addition, our method provides some information on the adequacy of the sampling period used to record the input sequence.The method correctly distinguishes a chaotic sequence from surrogate ones generated out of it and having the same power spectrum. The technique of surrogate generation, which I also addressed during my Ph. D. work to develop new dedicated algorithms and to analyze brain signals, allows to estimate significance levels in situations where standard analytical algorithms are unapplicable. The novel embedding approach being able to tell apart an original sequence from surrogate ones shows its capability to distinguish signals beyond their spectral—or autocorrelation—similarities.One of the possible applications of the new approach concerns another longstanding issue, namely that of distinguishing noise from chaos. To this purpose, complementary information is provided by analyzing the asymptotic (long-time) behaviour of the so-called time-dependent divergence exponent. This embedding-dependent metric is commonly used to estimate—by processing its short-time linearly growing region—the maximum Lyapunov exponent out of a scalar sequence. However, insights on the kind of source generating the sequence can be extracted from the—usually overlooked—asymptotic behaviour of the divergence exponent. Moreover, in the case of chaotic sources, this analysis also provides a precise estimate of the system's correlation dimension. Besides describing the results concerning the discrimination of chaotic systems from noise sources, I will also discuss the possibility of using the related correlation dimension estimates to improve the third stage of the method introduced above for the identification of suitable embedding parameters. The discovery of chaos as a possible dynamical regime for nonlinear systems led to the search of chaotic behaviour in experimental recordings. In some fields, this search gave plenty of positive results: for example, chaotic dynamics was successfully identified and tamed in electronic circuits and laser-based optical setups. These two families of experimental chaotic systems eventually became versatile tools to study chaos and its possible applications. On the other hand, chaotic behaviour is also looked for in climate science, biology, neuroscience, and even economics. In these fields, nonlinearity is widespread: many smaller units interact nonlinearly, yielding a collective motion that can be described by means of few, nonlinearly coupled effective degrees of freedom. The corresponding recorded signals exhibit, in many cases, an irregular and complex evolution. A possible underlying chaotic evolution—as opposed to a stochastic one—would be of interest both to reveal the presence of determinism and to predict the system's future states. While some claims concerning the existence of chaos in these fields have been made, most results are debated or inconclusive. Nonstationarity, low signal-to-noise ratio, external perturbations and poor reproducibility are just few among the issues that hinder the search of chaos in natural systems. In the final part of this work, I will briefly discuss the problem of chasing chaos in experimental recordings by considering two example sequences, the first one generated by an electronic circuit and the second one corresponding to recordings of brain activity. The present thesis is organized as follows. The core concepts of time series analysis, including the key features of chaotic dynamics, are presented in Chapter 1. A brief review of the search for chaos in experimental systems is also provided; the difficulties concerning this quest in some research fields are also highlighted. Chapter 2 describes the embedding procedure and the issue of optimally choosing the related parameters. Thereupon, existing methods to carry out the embedding choice are reviewed and their limitations are pointed out. In addition, two embedding-dependent nonlinear techniques that are ordinarily used to characterize chaos, namely the estimation of correlation dimension by means of correlation integrals and the assessment of maximum Lyapunov exponent, are presented. The new approach for the identification of suitable embedding parameters, which makes up the core topic of the present thesis work, is the subject of Chapter 3 and 4. While Chapter 3 contains the theoretical outline of the approach, as well as its implementation details, Chapter 4 discusses the application of the approach to benchmark synthetic and experimental sequences, thus illustrating its perks and its limitations. The study of the asymptotic behaviour of the time-dependent divergent exponent is presented in Chapter 5. The alternative estimator of correlation dimension, which relies on this asymptotic metric, is discussed as a possible improvement to the approach described in Chapters 3, 4. The search for chaos out of experimental data is discussed in Chapter 6 by means of two examples of real-world recordings. Concluding remarks are finally drawn in Chapter 7.
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Zha, Xiao. "Topological Data Analysis on Road Network Data." The Ohio State University, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=osu155563664988436.
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Liang, Qingqing. "Systematic Analysis and Optimization of Broadband Noise and Linearity in SiGe HBTs." Diss., Georgia Institute of Technology, 2005. http://hdl.handle.net/1853/6980.
Full textAbstract:
Noise and linearity are the two key concerns in RF transceiver systems. However, the impact of circuit topology and device technology on systems noise and linearity behaviors is poorly understood because of the complexity and diversity involved. There are two general questions that are addressed by the RF device and circuit designers: for a given device technology, how best to optimize the circuit topology; and for a given circuit topology, how best to optimize the device technology to improve the noise and linearity performance.
In this dissertation, a systematic noise and linearity calculation method is proposed. This approach offers simple and analytical solutions to optimize the noise and linearity characteristics of integrated circuits. Supported by this approach, the physics of state-of-the-art SiGe HBT technology devices can be decoupled and studied. The corresponding impact on noise and linearity is investigated. New optimization methodologies for noise and linearity at both the device and circuit level are presented.
In addition, this thesis demonstrates a technique that accurately extracts ac and noise parameters of devices/circuits in the millimeter-wave range. The extraction technique supports and verifies the device/circuit noise analysis from a measurement standpoint.
Books on the topic "Embedding Analysis"
1
Fu, Yun, and Yunqian Ma, eds. Graph Embedding for Pattern Analysis. Springer New York, 2013. http://dx.doi.org/10.1007/978-1-4614-4457-2.
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2
1975-, Parcet Javier, ed. Mixed-norm inequalities and operator space Lp embedding theory. American Mathematical Society, 2010.
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1951-, Sawyer E. T., ed. Embedding and multiplier theorems for H[superscript p](R[superscript n]). American Mathematical Society, 1985.
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4
K, Binienda Wieslaw, Kreider K. L, and Lewis Research Center, eds. Analysis of multiple cracks in an infinite functionally graded plate. National Aeronautics and Space Administration, Lewis Research Center, 1999.
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K, Binienda Wieslaw, Kreider K. L, and Lewis Research Center, eds. Analysis of multiple cracks in an infinite functionally graded plate. National Aeronautics and Space Administration, Lewis Research Center, 1999.
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Formalin-fixed paraffin-embedded tissues: Methods and protocols. Humana Press, 2011.
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7
Seitz, Evan Elliott. Analysis of Conformational Continuum and Free-energy Landscapes from Manifold Embedding of Single-particle Cryo-EM Ensembles of Biomolecules. [publisher not identified], 2022.
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Jakobson, Dmitry, Pierre Albin, and Frédéric Rochon. Geometric and spectral analysis. American Mathematical Society, 2014.
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Milman, Mario. Extrapolation and optimal decompositions withapplications to analysis. Springer-Verlag, 1994.
Find full textBook chapters on the topic "Embedding Analysis"
1
Sidorova, Natalia, and Martin Steffen. "Embedding Chaos." In Static Analysis. Springer Berlin Heidelberg, 2001. http://dx.doi.org/10.1007/3-540-47764-0_18.
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Berthelot, Jean-Michel. "6. The implications of ‘societal analysis’ for a programme of research." In Embedding Organizations. John Benjamins Publishing Company, 2000. http://dx.doi.org/10.1075/aios.4.09ber.
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Barrère-Maurisson, Marie-Agnes. "21. Societal analysis and social change in the family division of labour." In Embedding Organizations. John Benjamins Publishing Company, 2000. http://dx.doi.org/10.1075/aios.4.32bar.
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Sellier, François. "9. The contribution of societal analysis to the economic theory of the firm." In Embedding Organizations. John Benjamins Publishing Company, 2000. http://dx.doi.org/10.1075/aios.4.14sel.
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Maurice, Marc. "2. The paradoxes of societal analysis. A review of the past and prospects for the future." In Embedding Organizations. John Benjamins Publishing Company, 2000. http://dx.doi.org/10.1075/aios.4.05mau.
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Kouropteva, Olga, Oleg Okun, and Matti Pietikäinen. "Incremental Locally Linear Embedding Algorithm." In Image Analysis. Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11499145_53.
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Théret, Bruno. "7. Theoretical problems in international comparisons: Towards a reciprocal improvement of societal analysis and régulation theory by methodic structuralism." In Embedding Organizations. John Benjamins Publishing Company, 2000. http://dx.doi.org/10.1075/aios.4.10the.
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Ren, Peng, Furqan Aziz, Lin Han, Eliza Xu, Richard C. Wilson, and Edwin R. Hancock. "Geometricity and Embedding." In Similarity-Based Pattern Analysis and Recognition. Springer London, 2013. http://dx.doi.org/10.1007/978-1-4471-5628-4_6.
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Karam, Z. N., and W. M. Campbell. "Graph Embedding for Speaker Recognition." In Graph Embedding for Pattern Analysis. Springer New York, 2012. http://dx.doi.org/10.1007/978-1-4614-4457-2_10.
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Luo, Yong, Dacheng Tao, and Chao Xu. "Patch Alignment for Graph Embedding." In Graph Embedding for Pattern Analysis. Springer New York, 2012. http://dx.doi.org/10.1007/978-1-4614-4457-2_4.
Full textConference papers on the topic "Embedding Analysis"
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Fu, Yun, and Thomas S. Huang. "Correlation Embedding Analysis." In 2008 15th IEEE International Conference on Image Processing. IEEE, 2008. http://dx.doi.org/10.1109/icip.2008.4712100.
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Bollegala, Danushka. "Learning Meta Word Embeddings by Unsupervised Weighted Concatenation of Source Embeddings." In Thirty-First International Joint Conference on Artificial Intelligence {IJCAI-22}. International Joint Conferences on Artificial Intelligence Organization, 2022. http://dx.doi.org/10.24963/ijcai.2022/563.
Full textAbstract:
Given multiple source word embeddings learnt using diverse algorithms and lexical resources, meta word embedding learning methods attempt to learn more accurate and wide-coverage word embeddings. Prior work on meta-embedding has repeatedly discovered that simple vector concatenation of the source embeddings to be a competitive baseline. However, it remains unclear as to why and when simple vector concatenation can produce accurate meta-embeddings. We show that weighted concatenation can be seen as a spectrum matching operation between each source embedding and the meta-embedding, minimising the pairwise inner-product loss. Following this theoretical analysis, we propose two \emph{unsupervised} methods to learn the optimal concatenation weights for creating meta-embeddings from a given set of source embeddings. Experimental results on multiple benchmark datasets show that the proposed weighted concatenated meta-embedding methods outperform previously proposed meta-embedding learning methods.
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Hui Xue and Song-Can Chen. "Alternative robust local embedding." In 2007 International Conference on Wavelet Analysis and Pattern Recognition. IEEE, 2007. http://dx.doi.org/10.1109/icwapr.2007.4420738.
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Zheng, Q., and D. B. Skillicorn. "Spectral Embedding of Directed Networks." In ASONAM '15: Advances in Social Networks Analysis and Mining 2015. ACM, 2015. http://dx.doi.org/10.1145/2808797.2809310.
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Ghindici, Dorina, Gilles Grimaud, and Isabelle Simplot-Ryl. "Embedding verifiable information flow analysis." In the 2006 International Conference. ACM Press, 2006. http://dx.doi.org/10.1145/1501434.1501481.
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Oscar Deho, B., A. William Agangiba, L. Felix Aryeh, and A. Jeffery Ansah. "Sentiment Analysis with Word Embedding." In 2018 IEEE 7th International Conference on Adaptive Science & Technology (ICAST). IEEE, 2018. http://dx.doi.org/10.1109/icastech.2018.8506717.
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Makarov, Ilya, and Artem Oborevich. "Network Embedding for Cluster Analysis." In 2021 IEEE 21st International Symposium on Computational Intelligence and Informatics (CINTI). IEEE, 2021. http://dx.doi.org/10.1109/cinti53070.2021.9668468.
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Skillicorn, D. B., Q. Zheng, and C. Morselli. "Spectral embedding for dynamic social networks." In ASONAM '13: Advances in Social Networks Analysis and Mining 2013. ACM, 2013. http://dx.doi.org/10.1145/2492517.2492522.
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Yamazaki, Shinpei, Fumihiro Furukori, Qinzheng Zhao, Keiichiro Shirai, and Masayuki Okamoto. "Embedding a Mathematical OCR Module into OCRopus." In 2011 International Conference on Document Analysis and Recognition (ICDAR). IEEE, 2011. http://dx.doi.org/10.1109/icdar.2011.180.
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Weidong Yan, Zheng Tian, Jinhuan Wen, and Lulu Pan. "Point pattern matching based on manifold embedding." In 2010 International Conference on Image Analysis and Signal Processing. IEEE, 2010. http://dx.doi.org/10.1109/iasp.2010.5476067.
Full textReports on the topic "Embedding Analysis"
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Dunlavy, Daniel, Timothy Shead, Daniel Dunlavy, et al. Embedding Python for In-Situ Analysis. Office of Scientific and Technical Information (OSTI), 2018. http://dx.doi.org/10.2172/1734473.
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McGarrigle, M. Embedding Building Information Modelling into Construction Technology and Documentation Courses. Unitec ePress, 2014. http://dx.doi.org/10.34074/rsrp.005.
Full textAbstract:
The aim of this research is to generate a resource to assist construction lecturers in identifying opportunities where Building Information Modelling [BIM] could be employed to augment the delivery of subject content within individual courses on construction technology programmes. The methodology involved a detailed analysis of the learning objectives and underpinning knowledge of the course content by topic area, within the residential Construction Systems 1 course presently delivered at Unitec on the National Diplomas in Architectural Technology[NDAT], Construction Management [NDCM] and Quantity Surveying [NDQS]. The objective is to aid students’ understanding of specific aspects such as planning controls or sub-floor framing by using BIM models, and investigate how these could enhance delivery modes using image,animation and interactive student activity. A framework maps the BIM teaching opportunities against each topic area highlighting where these could be embedded into construction course delivery. This template also records software options and could be used in similar analyses of other courses within similar programmes to assist with embedding BIM in subject delivery.
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McGarrigle, M. Embedding Building Information Modelling into Construction Technology and Documentation Courses. Unitec ePress, 2014. http://dx.doi.org/10.34074/rsrp.005.
Full textAbstract:
The aim of this research is to generate a resource to assist construction lecturers in identifying opportunities where Building Information Modelling [BIM] could be employed to augment the delivery of subject content within individual courses on construction technology programmes. The methodology involved a detailed analysis of the learning objectives and underpinning knowledge of the course content by topic area, within the residential Construction Systems 1 course presently delivered at Unitec on the National Diplomas in Architectural Technology[NDAT], Construction Management [NDCM] and Quantity Surveying [NDQS]. The objective is to aid students’ understanding of specific aspects such as planning controls or sub-floor framing by using BIM models, and investigate how these could enhance delivery modes using image,animation and interactive student activity. A framework maps the BIM teaching opportunities against each topic area highlighting where these could be embedded into construction course delivery. This template also records software options and could be used in similar analyses of other courses within similar programmes to assist with embedding BIM in subject delivery.
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Romova, Zina, and Martin Andrew. Embedding Learning for Future and Imagined Communities in Portfolio Assessment. Unitec ePress, 2015. http://dx.doi.org/10.34074/rsrp.42015.
Full textAbstract:
In tertiary contexts where adults study writing for future academic purposes, teaching and learning via portfolio provides them with multiple opportunities to create and recreate texts characteristic of their future and imagined discourse communities. This paper discusses the value of portfolios as vehicles for rehearsing membership of what Benedict Anderson (1983) called “imagined communities”, a concept applied by such scholars as Yasuko Kanno and Bonny Norton (2003). Portfolios can achieve this process of apprenticeship to a specialist discourse through reproducing texts similar to the authentic artefacts of those discourse communities (Flowerdew, 2000; Hyland, 2003, 2004). We consider the value of multi-drafting, where learners reflect on the learning of a text type characteristic of the students’ future imagined community. We explore Hamp-Lyons and Condon’s belief (2000) that portfolios “critically engage students and teachers in continual discussion, analysis and evaluation of their processes and progress as writers, as reflected in multiple written products” (p.15). Introduced by a discussion of how theoretical perspectives on learning and assessing writing engage with portfolio production, the study presented here outlines a situated pedagogical approach, where students report on their improvement across three portfolio drafts and assess their learning reflectively. A multicultural group of 41 learners enrolled in the degree-level course Academic Writing [AW] at a tertiary institution in New Zealand took part in a study reflecting on this approach to building awareness of one’s own writing. Focus group interviews with a researcher at the final stage of the programme provided qualitative data, which was transcribed and analysed using textual analysis methods (Ryan and Bernard, 2003). Students identified a range of advantages of teaching and learning AW by portfolio. One of the identified benefits was that the selected text types within the programme were perceived as useful to the students’ immediate futures. This careful choice of target genre was reflected in the overall value of the programme for these learners.
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Xiao, Yao, Jun Xiong, Yuntao Zeng, and Ming Gong. Systematic evaluation and Meta analysis of the treatment of allergic rhinitis with needle-embedding therapy. INPLASY - International Platform of Registered Systematic Review and Meta-analysis Protocols, 2021. http://dx.doi.org/10.37766/inplasy2021.8.0029.
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Luo, Jing, Chen Zhang, Mengjie Xia, and Yuelian Chen. Acupoint catgut embedding reduces postoperative pain of mixed hemorrhoids: a meta-analysis of randomized controlled trials. INPLASY - International Platform of Registered Systematic Review and Meta-analysis Protocols, 2022. http://dx.doi.org/10.37766/inplasy2022.2.0021.
Full textAbstract:
Review question / Objective: Can acupoint catgut embedding alleviate postoperative pain of mixed hemorrhoids? Condition being studied: Mixed hemorrhoids. Information sources: Two authors (JL and CZ) will examine the publications independently and extract data according to predefined criteria. RCTs will be assessed for the methodology, study design, inclusion and exclusion criteria, and outcome measures. The methodological quality of each RCT will be recorded for method of randomization, blinding, protocol violation, and allocation concealment Any disagreement will be resolved by consensus discussions with the senior member of the review team (MJX and YLC). Data to collect includes intervention and control measures, measured outcomes and statistical significant difference with regards to chewing gum.
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Xue, Feng, Hao Huang, Yiwei Fu, Bojun Feng, Weizhong Yan, and Tianyi Wang. Deep Analysis Net with Causal Embedding for Coal-fired power plant Fault Detection and Diagnosis (DANCE4CFDD). Office of Scientific and Technical Information (OSTI), 2022. http://dx.doi.org/10.2172/1844966.
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Chen, Qiu. The effectiveness of acupoint catgut embedding in hyperlipidemia with obesity：protocol for a systematic review and meta-analysis. INPLASY - International Platform of Registered Systematic Review Protocols, 2020. http://dx.doi.org/10.37766/inplasy2020.4.0036.
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Yi, Xingqian, Li Wang, Qingying He, Rigun A, and Yimin Le. Acupoint catgut embedding for the treatment of peptic ulcers：A protocol for a systematic reviews and meta-analysis. INPLASY - International Platform of Registered Systematic Review and Meta-analysis Protocols, 2021. http://dx.doi.org/10.37766/inplasy2021.3.0097.
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Zhao, Hui Yan, Sungha Kim, ChangSop Yang, and Mi Ju Son. Comparing acupoint catgut embedding and acupuncture therapies in simple obesity: Protocol for a systematic review and meta-analysis. INPLASY - International Platform of Registered Systematic Review and Meta-analysis Protocols, 2021. http://dx.doi.org/10.37766/inplasy2021.10.0014.
Full textAbstract:
Review question / Objective: How effective are acupoint catgut embedding therapy sole or with other treatment? Condition being studied: Simple obesity. Information sources: We will search for trials from the following electronic databases: MEDLINE, EMBASE, the Cochrane Central Register of Controlled Trials (CENTRAL), and the Cumulative index to Nursing and Allied Health Literature (CINAHL). Trials will also be searched from three Korean medical databases (Oriental Medicine Advanced Searching Integrated System [OASIS], Science-On and KoreaMed), a Chinese database (Chinese National Knowledge Infrastructure [CNKI]) and a Japanese database (CiNii). Ongoing trials, trials will be searched on the Clinical Trials. gov (http://www.ClinicalTrials.gov), and the WHO International Clinical Trials Registry Platform (ICTRP) (http://apps.who.int/trialsearch/). We will also check the reference lists of reviews and the retrieved articles for additional studies. All bibliographic information and articles will be managed using EndNote (X8.2; Clarivate Analytics, Philadelphia). If the data of study are missing or insufficient, we will contact the corresponding authors by email.