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Добірка наукової літератури з теми "Embedding techniques"

Автор: Grafiati

Опубліковано: 17 січня 2024

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Статті в журналах з теми "Embedding techniques"

Duong, Chi Thang, Trung Dung Hoang, Hongzhi Yin, Matthias Weidlich, Quoc Viet Hung Nguyen, and Karl Aberer. "Scalable robust graph embedding with Spark." Proceedings of the VLDB Endowment 15, no. 4 (December 2021): 914–22. http://dx.doi.org/10.14778/3503585.3503599.

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Graph embedding aims at learning a vector-based representation of vertices that incorporates the structure of the graph. This representation then enables inference of graph properties. Existing graph embedding techniques, however, do not scale well to large graphs. While several techniques to scale graph embedding using compute clusters have been proposed, they require continuous communication between the compute nodes and cannot handle node failure. We therefore propose a framework for scalable and robust graph embedding based on the MapReduce model, which can distribute any existing embedding technique. Our method splits a graph into subgraphs to learn their embeddings in isolation and subsequently reconciles the embedding spaces derived for the subgraphs. We realize this idea through a novel distributed graph decomposition algorithm. In addition, we show how to implement our framework in Spark to enable efficient learning of effective embeddings. Experimental results illustrate that our approach scales well, while largely maintaining the embedding quality.

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Li, Pandeng, Yan Li, Hongtao Xie, and Lei Zhang. "Neighborhood-Adaptive Structure Augmented Metric Learning." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 2 (June 28, 2022): 1367–75. http://dx.doi.org/10.1609/aaai.v36i2.20025.

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Most metric learning techniques typically focus on sample embedding learning, while implicitly assume a homogeneous local neighborhood around each sample, based on the metrics used in training ( e.g., hypersphere for Euclidean distance or unit hyperspherical crown for cosine distance). As real-world data often lies on a low-dimensional manifold curved in a high-dimensional space, it is unlikely that everywhere of the manifold shares the same local structures in the input space. Besides, considering the non-linearity of neural networks, the local structure in the output embedding space may not be homogeneous as assumed. Therefore, representing each sample simply with its embedding while ignoring its individual neighborhood structure would have limitations in Embedding-Based Retrieval (EBR). By exploiting the heterogeneity of local structures in the embedding space, we propose a Neighborhood-Adaptive Structure Augmented metric learning framework (NASA), where the neighborhood structure is realized as a structure embedding, and learned along with the sample embedding in a self-supervised manner. In this way, without any modifications, most indexing techniques can be used to support large-scale EBR with NASA embeddings. Experiments on six standard benchmarks with two kinds of embeddings, i.e., binary embeddings and real-valued embeddings, show that our method significantly improves and outperforms the state-of-the-art methods.

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Mao, Yuqing, and Kin Wah Fung. "Use of word and graph embedding to measure semantic relatedness between Unified Medical Language System concepts." Journal of the American Medical Informatics Association 27, no. 10 (October 1, 2020): 1538–46. http://dx.doi.org/10.1093/jamia/ocaa136.

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Abstract Objective The study sought to explore the use of deep learning techniques to measure the semantic relatedness between Unified Medical Language System (UMLS) concepts. Materials and Methods Concept sentence embeddings were generated for UMLS concepts by applying the word embedding models BioWordVec and various flavors of BERT to concept sentences formed by concatenating UMLS terms. Graph embeddings were generated by the graph convolutional networks and 4 knowledge graph embedding models, using graphs built from UMLS hierarchical relations. Semantic relatedness was measured by the cosine between the concepts’ embedding vectors. Performance was compared with 2 traditional path-based (shortest path and Leacock-Chodorow) measurements and the publicly available concept embeddings, cui2vec, generated from large biomedical corpora. The concept sentence embeddings were also evaluated on a word sense disambiguation (WSD) task. Reference standards used included the semantic relatedness and semantic similarity datasets from the University of Minnesota, concept pairs generated from the Standardized MedDRA Queries and the MeSH (Medical Subject Headings) WSD corpus. Results Sentence embeddings generated by BioWordVec outperformed all other methods used individually in semantic relatedness measurements. Graph convolutional network graph embedding uniformly outperformed path-based measurements and was better than some word embeddings for the Standardized MedDRA Queries dataset. When used together, combined word and graph embedding achieved the best performance in all datasets. For WSD, the enhanced versions of BERT outperformed BioWordVec. Conclusions Word and graph embedding techniques can be used to harness terms and relations in the UMLS to measure semantic relatedness between concepts. Concept sentence embedding outperforms path-based measurements and cui2vec, and can be further enhanced by combining with graph embedding.

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SAMANTA, SAURAV. "NONCOMMUTATIVITY FROM EMBEDDING TECHNIQUES." Modern Physics Letters A 21, no. 08 (March 14, 2006): 675–89. http://dx.doi.org/10.1142/s0217732306019037.

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We apply the embedding method of Batalin–Tyutin for revealing noncommutative structures in the generalized Landau problem. Different types of noncommutativity follow from different gauge choices. This establishes a duality among the distinct algebras. An alternative approach is discussed which yields equivalent results as the embedding method. We also discuss the consequences in the Landau problem for a non-constant magnetic field.

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Tan, Eugene, Shannon Algar, Débora Corrêa, Michael Small, Thomas Stemler, and David Walker. "Selecting embedding delays: An overview of embedding techniques and a new method using persistent homology." Chaos: An Interdisciplinary Journal of Nonlinear Science 33, no. 3 (March 2023): 032101. http://dx.doi.org/10.1063/5.0137223.

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Delay embedding methods are a staple tool in the field of time series analysis and prediction. However, the selection of embedding parameters can have a big impact on the resulting analysis. This has led to the creation of a large number of methods to optimize the selection of parameters such as embedding lag. This paper aims to provide a comprehensive overview of the fundamentals of embedding theory for readers who are new to the subject. We outline a collection of existing methods for selecting embedding lag in both uniform and non-uniform delay embedding cases. Highlighting the poor dynamical explainability of existing methods of selecting non-uniform lags, we provide an alternative method of selecting embedding lags that includes a mixture of both dynamical and topological arguments. The proposed method, Significant Times on Persistent Strands (SToPS), uses persistent homology to construct a characteristic time spectrum that quantifies the relative dynamical significance of each time lag. We test our method on periodic, chaotic, and fast-slow time series and find that our method performs similar to existing automated non-uniform embedding methods. Additionally, [Formula: see text]-step predictors trained on embeddings constructed with SToPS were found to outperform other embedding methods when predicting fast-slow time series.

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Liang, Jiongqian, Saket Gurukar, and Srinivasan Parthasarathy. "MILE: A Multi-Level Framework for Scalable Graph Embedding." Proceedings of the International AAAI Conference on Web and Social Media 15 (May 22, 2021): 361–72. http://dx.doi.org/10.1609/icwsm.v15i1.18067.

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Recently there has been a surge of interest in designing graph embedding methods. Few, if any, can scale to a large-sized graph with millions of nodes due to both computational complexity and memory requirements. In this paper, we relax this limitation by introducing the MultI-Level Embedding (MILE) framework – a generic methodology allowing contemporary graph embedding methods to scale to large graphs. MILE repeatedly coarsens the graph into smaller ones using a hybrid matching technique to maintain the backbone structure of the graph. It then applies existing embedding methods on the coarsest graph and refines the embeddings to the original graph through a graph convolution neural network that it learns. The proposed MILE framework is agnostic to the underlying graph embedding techniques and can be applied to many existing graph embedding methods without modifying them. We employ our framework on several popular graph embedding techniques and conduct embedding for real-world graphs. Experimental results on five large-scale datasets demonstrate that MILE significantly boosts the speed (order of magnitude) of graph embedding while generating embeddings of better quality, for the task of node classification. MILE can comfortably scale to a graph with 9 million nodes and 40 million edges, on which existing methods run out of memory or take too long to compute on a modern workstation. Our code and data are publicly available with detailed instructions for adding new base embedding methods: https://github.com/jiongqian/MILE.

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Moudhich, Ihab, and Abdelhadi Fennan. "Evaluating sentiment analysis and word embedding techniques on Brexit." IAES International Journal of Artificial Intelligence (IJ-AI) 13, no. 1 (March 1, 2024): 695. http://dx.doi.org/10.11591/ijai.v13.i1.pp695-702.

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<p>In this study, we investigate the effectiveness of pre-trained word embeddings for sentiment analysis on a real-world topic, namely Brexit. We compare the performance of several popular word embedding models such global vectors for word representation (GloVe), FastText, word to vec (word2vec), and embeddings from language models (ELMo) on a dataset of tweets related to Brexit and evaluate their ability to classify the sentiment of the tweets as positive, negative, or neutral. We find that pre-trained word embeddings provide useful features for sentiment analysis and can significantly improve the performance of machine learning models. We also discuss the challenges and limitations of applying these models to complex, real-world texts such as those related to Brexit.</p><p> </p>

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Zhou, Jingya, Ling Liu, Wenqi Wei, and Jianxi Fan. "Network Representation Learning: From Preprocessing, Feature Extraction to Node Embedding." ACM Computing Surveys 55, no. 2 (March 31, 2023): 1–35. http://dx.doi.org/10.1145/3491206.

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Network representation learning (NRL) advances the conventional graph mining of social networks, knowledge graphs, and complex biomedical and physics information networks. Dozens of NRL algorithms have been reported in the literature. Most of them focus on learning node embeddings for homogeneous networks, but they differ in the specific encoding schemes and specific types of node semantics captured and used for learning node embedding. This article reviews the design principles and the different node embedding techniques for NRL over homogeneous networks. To facilitate the comparison of different node embedding algorithms, we introduce a unified reference framework to divide and generalize the node embedding learning process on a given network into preprocessing steps, node feature extraction steps, and node embedding model training for an NRL task such as link prediction and node clustering. With this unifying reference framework, we highlight the representative methods, models, and techniques used at different stages of the node embedding model learning process. This survey not only helps researchers and practitioners gain an in-depth understanding of different NRL techniques but also provides practical guidelines for designing and developing the next generation of NRL algorithms and systems.

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Goel, Mukta, and Rohit Goel. "Comparative Analysis of Hybrid Transform Domain Image Steganography Embedding Techniques." International Journal of Scientific Research 2, no. 2 (June 1, 2012): 388–90. http://dx.doi.org/10.15373/22778179/feb2013/131.

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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 (May 31, 2018): 393. http://dx.doi.org/10.14419/ijet.v7i2.32.15721.

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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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Дисертації з теми "Embedding techniques"

Thanh, Trung Huynh. "On leverage embedding techniques for network alignment." Thesis, Griffith University, 2022. http://hdl.handle.net/10072/416055.

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Networks are natural but powerful structures that capture relationships between different entities in many domains, such as social networks, citation networks, bioinformatic networks. In many applications that require multiple networks analysis, network alignment, the task of recognizing node correspondence across different networks, plays an important role. A wellknown application of network alignment is to identify which accounts in different social networks belong to the same person. Given the appeal of network alignment, there is a rich body of researches that aims to tackle this problem. However, many research challenges still exist, such as enhancing the accuracy and improving the scalability due to the information explosion. With such motivation, in scope of our PhD work, we address the three crucial challenges in network alignment literature, namely (i) enhancing scalability of network alignment on large-scale graphs, (ii) enhancing the robustness of network alignment to adversarial conditions and (iii) multi-modal information integration for network aligners. To do so, we focus on proposing aligner frameworks for different types of input attributed networks from simple to complex. Each framework attempts to answer simultaneously all three research questions by leveraging embedding techniques, where the input networks are embedded into insightful, low-dimensional vector spaces. This helps to enrich the nodes’ individual context with multi-modal information, thus facilitates the distinction between nodes. The learnt embeddings also enables faster alignment retrieval by direct vector comparison. Our proposed techniques improve upon the state-of-the-art for different types of attributed networks and cover a large range of applications.
Thesis (PhD Doctorate)
Doctor of Philosophy (PhD)
School of Info & Comm Tech
Institute of Integrated and Intelligent Systems
Science, Environment, Engineering and Technology
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Chochlidakis, Georgios. "Mobility-aware virtual network embedding techniques for next-generation mobile networks." Thesis, King's College London (University of London), 2018. https://kclpure.kcl.ac.uk/portal/en/theses/mobilityaware-virtual-network-embedding-techniques-for-nextgeneration-mobile-networks(174e714f-2a4a-447a-bcd5-d526170377fd).html.

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Network virtualisation has become one of the most prominent solutions for sus-tainability towards the dramatic increase of data demand in next-generation mobile networks. In addition, apart from increasing the overall infrastructure utilisation, it also greatly improves the manageability, the scalability and the robustness of the network. In order to allow multiple virtual networks to coexist in the same substrate network, the need for efficient network sharing techniques is imperative. The main purpose of this work is to provide a holistic optimization framework for vir-tual network embedding solutions, where the actual user mobility effect is explicitly considered. First, the main focus is given on the study of the mobility effect and the impact of the mobility management techniques on the end-to-end communication of the mobile user. A hybrid-distributed mobility management scheme is proposed and compared against the latest mobility management schemes. Then, an optimisation framework for efficient mobility-aware virtual network embedding is proposed and evaluated by comparison with other works from the literature. Moving deeper in the area of virtual network embedding, the focus is given on minimizing the end-to-end delay and providing service differentiation, allowing in this way delay sensitive services to use the formed virtual networks with the minimum possible delay, as op-posed to other more elastic services that use the same substrate network. The last part of this work is the study and the analysis of the stochastic nature of the virtual network embedding parameters and the proposal of an optimisation framework for adjustable-robustness virtual network embedding. Driven by the benefits from virtualising the network and its functions, research as well as industry are expected to exploit in a greater degree than today the merits of this concept. The co-existence of multiple tenants not only will greatly change the network industry from a business perspective, but also will emphasise the need for more efficient and flexible network sharing techniques. This work belongs to the initial efforts to embrace and adopt the virtualisation concept in the next-generation wireless networks.

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Obidallah, Waeal. "Multi-Layer Web Services Discovery using Word Embedding and Clustering Techniques." Thesis, Université d'Ottawa / University of Ottawa, 2021. http://hdl.handle.net/10393/41840.

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Web services discovery is the process of finding the right Web services that best match the end-users’ functional and non-functional requirements. Artificial intelligence, natural language processing, data mining, and text mining techniques have been applied by researchers in Web services discovery to facilitate the process of matchmaking. This thesis contributes to the area of Web services discovery and recommendation, adopting the Design Science Research Methodology to guide the development of useful knowledge, including design theory and artifacts. The lack of a comprehensive review of Web services discovery and recommendation in the literature motivated us to conduct a systematic literature review. Our main purpose in conducting the systematic literature review was to identify and systematically compare current clustering and association rules techniques for Web services discovery and recommendation by providing answers to various research questions, investigating the prior knowledge, and identifying gaps in the related literature. We then propose a conceptual model and a typology of Web services discovery systems. The conceptual model provides a high-level representation of Web services discovery systems, including their various elements, tasks, and relationships. The proposed typology of Web services discovery systems is composed of five groups of characteristics: storage and location characteristics, formalization characteristics, matchmaking characteristics, automation characteristics, and selection characteristics. We reference the typology to compare Web services discovery methods and architectures from the extant literature by linking them to the five proposed characteristics. We employ the proposed conceptual model with its specified characteristics to design and develop the multi-layer data mining architecture for Web services discovery using word embedding and clustering techniques. The proposed architecture consists of five layers: Web services description and data preprocessing; word embedding and representation; syntactic similarity; semantic similarity; and clustering. In the first layer, we identify the steps to parse and preprocess the Web services documents. Bag of Words with Term Frequency–Inverse Document Frequency and three word-embedding models are employed for Web services representation in the second layer. Then in the third layer, four distance measures, including Cosine, Euclidean, Minkowski, and Word Mover, are studied to find the similarities between Web services documents. In layer four, WordNet and Normalized Google Distance are employed to represent and find the similarity between Web services documents. Finally, in the fifth layer, three clustering algorithms, including affinity propagation, K-means, and hierarchical agglomerative clustering, are investigated to cluster Web services based on the observed documents’ similarities. We demonstrate how each component of the five layers is employed in the process of Web services clustering using random-ly selected Web services documents. We conduct experimental analysis to cluster Web services using a collected dataset of Web services documents and evaluating their clustering performances. Using a ground truth for evaluation purposes, we observe that clusters built based on the word embedding models performed better compared to those built using the Bag of Words with Term Frequency–Inverse Document Frequency model. Among the three word embedding models, the pre-trained Word2Vec’s skip-gram model reported higher performance in clustering Web services. Among the three semantic similarity measures, path-based WordNet similarity reported higher clustering performance. By considering the different words representations models and syntactic and semantic similarity measures, the affinity propagation clustering technique performed better in discovering similarities among Web services.

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Ojike, Uzoma. "Combining tools and techniques for embedding an ecosystem approach in spatial planning." Thesis, Imperial College London, 2013. http://hdl.handle.net/10044/1/11196.

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Despite the attention garnered by sustainability in the last three decades and the advances in its tools and techniques, we are no closer to attaining sustainability now than we were at the start. This elusiveness has been attributed to the lack of a clearly defined global method for evaluating sustainability and poor integration into sector, national and international policies and decision-making, amongst others. A clear limitation observed in most concepts/methods is their inability to integrate effectively ecological, economic and social sustainability during assessment. Rather, there is a tendency to assess them separately and integrate them after the assessment. This process often leaves loopholes in sustainability assessment as there are trade-offs created that often favour economic sustainability but more rarely favour environmental, or even social, sustainability. In order to address this limitation, the Millennium Ecosystem Assessment (MEA) in 2005 recognized that the complex interactions between these ecological, economic and social processes have to be understood and established a universal valuation concept known as ecosystem services which can be used in sustainability assessment and Spatial Planning. Ecosystem services are the benefits or services created by the ecosystem which are essential for the daily functioning of humans and economies. This research explores how best to achieve integration of the Ecosystem Approach within environmental/sustainability assessment. It adopts a mixed method approach that combines the use of existing qualitative techniques, Network Analysis and stakeholder engagement, and quantitative techniques, Geographical Information Systems, within a regeneration case study at local level (Dartford in North Kent, United Kingdom). The thesis makes recommendations for better integration of an Ecosystem Approach in Spatial Planning and decision making and the ways in which assessment tools and techniques can be best combined.

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Alain, Martin. "A compact video representation format based on spatio-temporal linear embedding and epitome." Thesis, Rennes 1, 2016. http://www.theses.fr/2016REN1S001/document.

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L'efficacité des services de compression vidéo est de nos jours un enjeu essentiel, et est appelé à le devenir d'autant plus dans le futur, comme l'indique la croissance constante du trafic vidéo et la production de nouveaux formats tels que la vidéo à haute résolution, à gamme de couleur ou dynamique étendues, ou encore à fréquence d'images augmentée. Le standard MPEG HEVC est aujourd'hui un des schémas de compression les plus efficaces, toutefois, il devient nécessaire de proposer de nouvelles méthodes originales pour faire face aux nouveaux besoins de compression. En effet, les principes de bases des codecs modernes ont été conçu il y a plus de 30 ans : la réduction des redondances spatiales et temporelles du signal en utilisant des outils de prédiction, l'utilisation d'une transformée afin de diminuer d'avantage les corrélations du signal, une quantification afin de réduire l'information non perceptible, et enfin un codage entropique pour prendre en compte les redondances statistiques du signal. Dans cette thèse, nous explorons de nouvelles méthodes ayant pour but d'exploiter d'avantage les redondances du signal vidéo, notamment à travers des techniques multi-patchs. Dans un premier temps, nous présentons des méthodes multi-patchs basées LLE pour améliorer la prédiction Inter, qui sont ensuite combinées pour la prédiction Intra et Inter. Nous montrons leur efficacité comparé à H.264. La seconde contribution de cette thèse est un schéma d'amélioration en dehors de la boucle de codage, basé sur des méthodes de débruitage avec épitome. Des épitomes de bonne qualité sont transmis au décodeur en plus de la vidéo encodée, et nous pouvons alors utiliser coté décodeur des méthodes de débruitage multi-patchs qui s'appuient sur les patchs de bonne qualité contenu dans les épitomes, afin d'améliorer la qualité de la vidéo décodée. Nous montrons que le schéma est efficace en comparaison de SHVC. Enfin, nous proposons un autre schéma d'amélioration en dehors de la boucle de codage, qui s'appuie sur un partitionnement des patchs symétrique à l'encodeur et au décodeur. Coté encodeur, on peut alors apprendre des projections linéaires pour chaque partition entre les patchs codés/décodés et les patchs sources. Les projections linéaires sont alors envoyés au décodeur et appliquées aux patchs décodés afin d'en améliorer la qualité. Le schéma proposé est efficace comparé à HEVC, et prometteur pour des schémas scalables comme SHVC
Efficient video compression is nowadays a critical issue, and is expected to be more and more crucial in the future, with the ever increasing video traffic and the production of new digital video formats with high resolution, wide color gamut, high dynamic range, or high frame rate. The MPEG standard HEVC is currently one of the most efficient video compression scheme, however, addressing the future needs calls for novel and disruptive methods. In fact, the main principles of modern video compression standards rely on concepts designed more than 30 years ago: the reduction of spatial and temporal redundancies, through prediction tools, the use of a transform to further reduce the inner correlations of the signal, followed by quantization to remove non-perceptive information, and entropy coding to remove the remaining statistical redundancies. In this thesis, we explore novel methods which aims at further exploiting the natural redundancies occurring in video signals, notably through the use of multi-patches techniques. First, we introduce LLE-based multi-patches methods in order to improve Inter prediction, which are then combined for both Intra and Inter predictions, and are proven efficient over H.264. We then propose epitome-based de-noising methods to improve the performances of existing codecs in a out-of-the-loop scheme. High quality epitomes are transmitted to the decoder in addition to the coded sequence, and we can then use at the decoder side multi-patches de-noising methods relying on the high quality patches from the epitomes, in order to improve the quality of the decoded sequence. This scheme is shown efficient compared to SHVC. Finally, we proposed another out-of-the-loop scheme relying on a symmetric clustering of the patches performed at both encoder and decoder sides. At the encoder side, linear mappings are learned for each cluster between the coded/decoded patches and the corresponding source patches. The linear mappings are then sent to the decoder and applied to the decoded patches in order to improve the quality of the decoded sequence. The proposed scheme improves the performances of HEVC, and is shown promising for scalable schemes such as SHVC

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Raahemi, Mohammad. "Intelligent Prediction of Stock Market Using Text and Data Mining Techniques." Thesis, Université d'Ottawa / University of Ottawa, 2020. http://hdl.handle.net/10393/40934.

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The stock market undergoes many fluctuations on a daily basis. These changes can be challenging to anticipate. Understanding such volatility are beneficial to investors as it empowers them to make inform decisions to avoid losses and invest when opportunities are predicted to earn funds. The objective of this research is to use text mining and data mining techniques to discover the relationship between news articles and stock prices fluctuations. There are a variety of sources for news articles, including Bloomberg, Google Finance, Yahoo Finance, Factiva, Thompson Routers, and Twitter. In our research, we use Factive and Intrinio news databases. These databases provide daily analytical articles about the general stock market, as well as daily changes in stock prices. The focus of this research is on understanding the news articles which influence stock prices. We believe that different types of stocks in the market behave differently, and news articles could provide indications on different stock price movements. The goal of this research is to create a framework that uses text mining and data mining algorithms to correlate different types of news articles with stock fluctuations to predict whether to “Buy”, “Sell”, or “Hold” a specific stock. We train Doc2Vec models on 1GB of financial news from Factiva to convert news articles into vectors of 100 dimensions. After preprocessing the data, including labeling and balancing the data, we build five predictive models, namely Neural Networks, SVM, Decision Tree, KNN, and Random Forest to predict stock movements (Buy, Sell, or Hold). We evaluate the performances of the predictive models in terms of accuracy and area under the ROC. We conclude that SVM provides the best performance among the five models to predict the stock movement.

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Senjean, Bruno. "Development of new embedding techniques for strongly correlated electrons : from in-principle-exact formulations to practical approximations." Thesis, Strasbourg, 2018. http://www.theses.fr/2018STRAF035/document.

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Cette thèse traite du développement et de l’implémentation de nouvelles méthodes visant à décrire la corrélation électronique forte dans les molécules et les solides. Après avoir introduit l’état de l’art des méthodes utilisées en chimie quantique et en physique de la matière condensée, une nouvelle méthode hybride combinant théorie de la fonction d’onde et théorie de la fonctionnelle de la densité (DFT) est présentée et s’intitule “site-occupation embedding theory” (SOET). Celle-ci est appliquée au modèle de Hubbard à une dimension. Ensuite, le problème du gap fondamental est revisité en DFT pour les ensembles, où la dérivée discontinue est réécrite comme une fonctionnelle de la densité de l'état fondamental. Enfin, une extension à la chimie quantique est proposée, basée sur une fonction d’onde de séniorité zéro complémentée par une fonctionnelle de la matrice densité, et exprimée dans la base des orbitales naturelles
The thesis deals with the development and implementation of new methods for the description of strong electron correlation effects in molecules and solids. After introducing the state of the art in quantum chemistry and in condensed matter physics, a new hybrid method so-called ``site-occupation embedding theory'' (SOET) is presented and is based on the merging of wavefunction theory and density functional theory (DFT). Different formulations of this theory are described and applied to the one-dimensional Hubbard model. In addition, a novel ensemble density functional theory approach has been derived to extract the fundamental gap exactly. In the latter approach, the infamous derivative discontinuity is reformulated as a derivative of a weight-dependent exchange-correlation functional. Finally, a quantum chemical extension of SOET is proposed and based on a seniority-zero wavefunction, completed by a functional of the density matrix and expressed in the natural orbital basis

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Paoli, Roberto. "Cell culture interfaces for different organ-on-chip applications: from photolithography to rapid-prototyping techniques with sensor embedding." Doctoral thesis, Universitat de Barcelona, 2019. http://hdl.handle.net/10803/668376.

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Despite the last 60 years have seen major advances in many scientific and technological inputs of drug Research and Development, the number of new molecules hitting the market per billion US dollars of R&D spending has been declined steadily during the same period. The current scenario highlights the need for new research tools to enable reduce costly animal and clinical trials while providing a better prediction about drug efficacy and security in humans A recent emerging approach to improve the current models is emerging from the field of microfluidics, which studies systems that process or manipulate tiny amounts of fluids using channels with dimensions of tens to hundreds of micrometers. Combining microfluidics with cell culture, scientists gave rise to a new field named “Organ-on-chip” (OOC). Microfluidic OOCs are advanced platforms designed to mimic physiological structures and continuous flow conditions, thus allowing the culture of cells in a friendlier microenvironment. This thesis, titled “Cell culture interfaces for different organ-on-chip applications: from photolithography to rapid-prototyping techniques with sensor embedding”, aims to design, simulate and test new OOC devices to reproduce cell culture interface under flow conditions. The work has a focus on the exploration of novel fabrication techniques which enable rapid prototyping of OOC devices, reducing costs, time and human labor associated to the fabrication process. The final objective is to demonstrate the viability of the devices as research tools for biological problems, applying them to the tubular kidney and the blood brain barrier (BBB). To achieve the objective, at least three device version have been developed: 1) OOCv1, fabricated by multilayer PDMS soft lithography; 2) OOCv2, fabricated in thermoplastic by layered object manufacturing using both a vinyl cutter and a laser cutter, integrating standard fluidic connectors alone (OOCv2.1) or together with embedded electrodes (OOCv2.2); 3) OOCv3 using a mixed technique of laser cut and 3D printing by stereolithography. All devices are fabricated using biocompatible materials with high optical quality and an embedded commercial membrane. The biological experiments with renal tubular epithelial cells, realized on OOCv1 and OOCv2.1 devices, demonstrated the viability of the device for culturing cells under flow conditions. The study realized on fatty acid oxidation and accumulation in cells exposed to physiological and diabetogenic oscillating levels of glucose suggest a possible positive role of shear stress in activation of fatty acid metabolism. The studies were performed using a compact experimental unit with embedded flow control which reduce significatively the complexity and cost of the fluidic experimental setup. The biological experiments on the BBB confirmed viability of OOCv2.1 and OOCv2.2 for compartmentalized co-culturing of endothelial cells and pericytes. The formation and recovery of the barrier after disruptive treatment has been assessed using different techniques, including immunostaining, fluorescence and live phase contrast imaging, and electrical impedance spectroscopy. The repeatability of measurements using electrodes was verified. A model to classify measurements from different timepoints has been developed, resulting in accuracy of 100% in learning and 90% in testing case. Results are confirmed by imaging data, which also suggest a critical role of pericytes in the development, maintenance, and regulation of BBB, in accordance with the literature.
En los últimos años está emergiendo una nueva propuesta para mejorar los modelos actuales en el estudio de nuevos fármacos. Mediante la fusión de cultivos celulares y microfluídica ha nacido un nuevo campo de aplicación denominado “Órgano-en-un-chip” (OOC), donde se recrea un entorno fisiológico capaz de reproducir unidades funcionales mínimas de diversos órganos del cuerpo humano. Un elemento importante para el desarrollo de dispositivos OOC es la reproducción de zonas de interacción entre varios tejidos formados por diferentes tipos celulares. Esta tesis, titulada “Interfaces de cultivo celular para diferentes aplicaciones de OOC: desde fotolitografía a técnicas de prototipado rápido con inclusión de sensores”, tiene como objetivo el diseño, simulación y evaluación de dispositivos OOC capaces de reproducir superficies de contacto de tejidos contiguos expuestos a flujo. El trabajo está enfocado a la exploración de nuevas técnicas de fabricación que permitan el prototipado rápido de dispositivos OOC, reduciendo costes, tiempo y mano de obra asociada a dicha fabricación. El objetivo final es demostrar la utilidad de los dispositivos como herramientas de investigación para problemas biológicos, aplicándolos en esta tesis al estudio del túbulo renal y de la barrera hematoencefálica. Para ello se han fabricado tres versiones de dispositivos: 1) OOCv1 fabricado por litografía suave en múltiples capas de PDMS; 2) OOCv2 fabricado con cortadora de vinilo y cortadora láser en múltiples capas de materiales termoplásticos y con electrodos integrados en la versión OOCv2.2; 3) OOCv3 fabricado mediante impresión 3D por esterolitografía. Todos los dispositivos están hechos de materiales biocompatibles de alta calidad óptica, con conectores fluídicos y una membrana comercial integrada. Los experimentos biológicos sobre túbulo renal, realizados en los dispositivos OOCv1 y OOCv2, han demostrado la viabilidad de los dispositivos, integrados con un sistema de flujo, para estudios de la metabolización de ácidos grasos en el riñón relacionados con condiciones diabetogénicas. Los experimentos biológicos sobre la barrera hematoencefálica han confirmado la viabilidad de OOCv2 para el cocultivo compartimentado de células endoteliales de cerebro y pericitos. La integración de electrodos en el OOCv2.2 ha demostrado ser una técnica fiable para la medición de la integridad de barreras biológicas de modo no-invasivo, libre de etiqueta (“label-free”), y a tiempo real gracias a la espectroscopía de impedancia.

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Antici, Francesco. "Advanced techniques for cross-language annotation projection in legal texts." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2021. http://amslaurea.unibo.it/23884/.

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Nowadays, the majority of the services we benefit from, are provided online and their use is regulated by the acceptance to the terms of service by the users. All our data are handled accordingly with the clauses of such document and all our behaviours must comply with it. Given so, it would be very useful to find automated techniques to ensure fairness of the document or inform the users about possible threats. The focus of this work, is to create resources aimed to the development of such tools in languages other than English, which may lack in linguistic resources and annotated corpus. The enormous breakthroughs of the last years in Natural Language Processing techniques made it possible the creation of such tools through automated and unsupervised process. One of the means to achieve that is through the annotation projection between two parallel corpora. The difficulties and costs of creating ad hoc resource for every language has brought the need to find another way for achieving the goal.\\ This work investigates the cross language annotation projection technique based on sentence embedding and similarity metrics to find matches between sentences. Several combination of methods and algorithms are compared, among which there are monolingual and multilingual embedding neural models. The experiments are conducted on two datasets, where the reference language is always English and the projection are evaluated on Italian, German and Polish. The results obtained provide a robust and reliable technique for the task and a good starting point to build multilingual tools.

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Sakarya, Hatice. "A Contribution To Modern Data Reduction Techniques And Their Applications By Applied Mathematics And Statistical Learning." Master's thesis, METU, 2010. http://etd.lib.metu.edu.tr/upload/12612819/index.pdf.

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High-dimensional data take place from digital image processing, gene expression micro arrays, neuronal population activities to financial time series. Dimensionality Reduction - extracting low dimensional structure from high dimension - is a key problem in many areas like information processing, machine learning, data mining, information retrieval and pattern recognition, where we find some data reduction techniques. In this thesis we will give a survey about modern data reduction techniques, representing the state-of-the-art of theory, methods and application, by introducing the language of mathematics there. This needs a special care concerning the questions of, e.g., how to understand discrete structures as manifolds, to identify their structure, preparing the dimension reduction, and to face complexity in the algorithmically methods. A special emphasis will be paid to Principal Component Analysis, Locally Linear Embedding and Isomap Algorithms. These algorithms are studied by a research group from Vilnius, Lithuania and Zeev Volkovich, from Software Engineering Department, ORT Braude College of Engineering, Karmiel, and others. The main purpose of this study is to compare the results of the three of the algorithms. While the comparison is beeing made we will focus the results and duration.

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Книги з теми "Embedding techniques"

Center, Langley Research, ed. Graph embedding techniques for bounding condition numbers of incomplete factor preconditioners. Hampton, Va: National Aeronautics and Space Administration, Langley Research Center, 1997.

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Guattery, Stephen. Graph embedding techniques for bounding condition numbers of incomplete factor preconditioners. Hampton, Va: Institute for Computer Applications in Science and Engineering, NASA Langley Research Center, 1997.

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Lin, Yan. Novel Techniques in Recovering, Embedding, and Enforcing Policies for Control-Flow Integrity. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-73141-0.

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Formalin-fixed paraffin-embedded tissues: Methods and protocols. New York: Humana Press, 2011.

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Kuixiong, Gao, ed. Polyethylene glycol as an embedment for microscopy and histochemistry. Boca Raton: CRC Press, 1993.

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Newman, G. R. Resin microscopy and on-section immunocytochemistry. Berlin: Springer-Verlag, 1993.

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Embedding Formative Assessment: Practical Techniques for K-12 Classrooms. Learning Sciences International, 2015.

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Behrens, Stefan, Boldizsar Kalmar, Min Hoon Kim, Mark Powell, and Arunima Ray, eds. The Disc Embedding Theorem. Oxford University Press, 2021. http://dx.doi.org/10.1093/oso/9780198841319.001.0001.

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The disc embedding theorem provides a detailed proof of the eponymous theorem in 4-manifold topology. The theorem, due to Michael Freedman, underpins virtually all of our understanding of 4-manifolds in the topological category. Most famously, this includes the 4-dimensional topological Poincaré conjecture. Combined with the concurrent work of Simon Donaldson, the theorem reveals a remarkable disparity between the topological and smooth categories for 4-manifolds. A thorough exposition of Freedman’s proof of the disc embedding theorem is given, with many new details. A self-contained account of decomposition space theory, a beautiful but outmoded branch of topology that produces non-differentiable homeomorphisms between manifolds, is provided. Techniques from decomposition space theory are used to show that an object produced by an infinite, iterative process, which we call a skyscraper, is homeomorphic to a thickened disc, relative to its boundary. A stand-alone interlude explains the disc embedding theorem’s key role in smoothing theory, the existence of exotic smooth structures on Euclidean space, and all known homeomorphism classifications of 4-manifolds via surgery theory and the s-cobordism theorem. The book is written to be accessible to graduate students working on 4-manifolds, as well as researchers in related areas. It contains over a hundred professionally rendered figures.

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Lin, Yan. Novel Techniques in Recovering, Embedding, and Enforcing Policies for Control-Flow Integrity. Springer International Publishing AG, 2021.

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(Editor), Stefan Katzenbeisser, and Fabien, A.P. Petitcolas (Editor), eds. Information Hiding Techniques for Steganography and Digital Watermarking. Artech House Publishers, 2000.

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Частини книг з теми "Embedding techniques"

Armbruster, Barbara L., and Edward Kellenberger. "Low-Temperature Embedding." In Ultrastructure Techniques for Microorganisms, 267–95. Boston, MA: Springer US, 1986. http://dx.doi.org/10.1007/978-1-4684-5119-1_10.

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Maddalena, L., I. Manipur, M. Manzo, and M. R. Guarracino. "On Whole-Graph Embedding Techniques." In Trends in Biomathematics: Chaos and Control in Epidemics, Ecosystems, and Cells, 115–31. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-73241-7_8.

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Hayat, M. A. "Rinsing, Dehydration and Embedding." In Principles and Techniques of Electron Microscopy, 79–137. London: Macmillan Education UK, 1989. http://dx.doi.org/10.1007/978-1-349-09857-6_2.

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Aldrich, H. C., and H. H. Mollenhauer. "Secrets of Successful Embedding, Sectioning, and Imaging." In Ultrastructure Techniques for Microorganisms, 101–32. Boston, MA: Springer US, 1986. http://dx.doi.org/10.1007/978-1-4684-5119-1_4.

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Puel, Laurence. "Embedding with patterns and associated recursive path ordering." In Rewriting Techniques and Applications, 371–87. Berlin, Heidelberg: Springer Berlin Heidelberg, 1989. http://dx.doi.org/10.1007/3-540-51081-8_120.

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Bartal, Yair, Douglas E. Carroll, Adam Meyerson, and Ofer Neiman. "Bandwidth and Low Dimensional Embedding." In Approximation, Randomization, and Combinatorial Optimization. Algorithms and Techniques, 50–61. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-22935-0_5.

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Dey, Pranab. "Embedding of Tissue in Histopathology." In Basic and Advanced Laboratory Techniques in Histopathology and Cytology, 29–33. Singapore: Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-10-8252-8_3.

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Dey, Pranab. "Embedding of Tissue in Histopathology." In Basic and Advanced Laboratory Techniques in Histopathology and Cytology, 29–34. Singapore: Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-6616-3_3.

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Bockmayr, Alexander. "Embedding OR Techniques in Constraint Logic Programming." In Operations Research ’92, 252–54. Heidelberg: Physica-Verlag HD, 1993. http://dx.doi.org/10.1007/978-3-662-12629-5_74.

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Paul, Sunhera, and Mark Stamp. "Word Embedding Techniques for Malware Evolution Detection." In Malware Analysis Using Artificial Intelligence and Deep Learning, 321–43. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-62582-5_12.

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Тези доповідей конференцій з теми "Embedding techniques"

Zhang, Yizhou, Guojie Song, Lun Du, Shuwen Yang, and Yilun Jin. "DANE: Domain Adaptive Network Embedding." In Twenty-Eighth International Joint Conference on Artificial Intelligence {IJCAI-19}. California: International Joint Conferences on Artificial Intelligence Organization, 2019. http://dx.doi.org/10.24963/ijcai.2019/606.

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Recent works reveal that network embedding techniques enable many machine learning models to handle diverse downstream tasks on graph structured data. However, as previous methods usually focus on learning embeddings for a single network, they can not learn representations transferable on multiple networks. Hence, it is important to design a network embedding algorithm that supports downstream model transferring on different networks, known as domain adaptation. In this paper, we propose a novel Domain Adaptive Network Embedding framework, which applies graph convolutional network to learn transferable embeddings. In DANE, nodes from multiple networks are encoded to vectors via a shared set of learnable parameters so that the vectors share an aligned embedding space. The distribution of embeddings on different networks are further aligned by adversarial learning regularization. In addition, DANE's advantage in learning transferable network embedding can be guaranteed theoretically. Extensive experiments reflect that the proposed framework outperforms other state-of-the-art network embedding baselines in cross-network domain adaptation tasks.

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Yang, Li. "Data embedding techniques and applications." In the 2nd international workshop. New York, New York, USA: ACM Press, 2005. http://dx.doi.org/10.1145/1160939.1160948.

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Pranathi, Karedla Sai, and C. P. Prathibhamol. "Node Classification through Graph Embedding Techniques." In 2021 4th Biennial International Conference on Nascent Technologies in Engineering (ICNTE). IEEE, 2021. http://dx.doi.org/10.1109/icnte51185.2021.9487668.

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Cheng, Weiyu, Yanyan Shen, Yanmin Zhu, and Linpeng Huang. "DELF: A Dual-Embedding based Deep Latent Factor Model for Recommendation." In Twenty-Seventh International Joint Conference on Artificial Intelligence {IJCAI-18}. California: International Joint Conferences on Artificial Intelligence Organization, 2018. http://dx.doi.org/10.24963/ijcai.2018/462.

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Among various recommendation methods, latent factor models are usually considered to be state-of-the-art techniques, which aim to learn user and item embeddings for predicting user-item preferences. When applying latent factor models to recommendation with implicit feedback, the quality of embeddings always suffers from inadequate positive feedback and noisy negative feedback. Inspired by the idea of NSVD that represents users based on their interacted items, this paper proposes a dual-embedding based deep latent factor model named DELF for recommendation with implicit feedback. In addition to learning a single embedding for a user (resp. item), we represent each user (resp. item) with an additional embedding from the perspective of the interacted items (resp. users). We employ an attentive neural method to discriminate the importance of interacted users/items for dual-embedding learning. We further introduce a neural network architecture to incorporate dual embeddings for recommendation. A novel attempt of DELF is to model each user-item interaction with four deep representations that are subtly fused for preference prediction. We conducted extensive experiments on real-world datasets. The results verify the effectiveness of user/item dual embeddings and the superior performance of DELF on item recommendation.

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Hao, Jianzhong, Guangxi Peng, Chin-Li Liaw, Wee Chye Ho, Lihao Chen, Shiro Takahashi, Jun Hong Ng, Xiaoqun Zhou, and Cjao Lu. "Impact of embedding techniques in FBG sensors." In 2004 9th IEEE Singapore International Conference on Communication Systems (ICCS). IEEE, 2004. http://dx.doi.org/10.1109/iccs.2004.1359438.

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Okasaki, Chris. "Techniques for embedding postfix languages in Haskell." In the ACM SIGPLAN workshop. New York, New York, USA: ACM Press, 2002. http://dx.doi.org/10.1145/581690.581699.

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Brunk, Hugh. "Host-aware spread spectrum watermark embedding techniques." In Electronic Imaging 2003, edited by Edward J. Delp III and Ping W. Wong. SPIE, 2003. http://dx.doi.org/10.1117/12.477326.

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Neelima, A., and Shashi Mehrotra. "A Comprehensive Review on Word Embedding Techniques." In 2023 International Conference on Intelligent Systems for Communication, IoT and Security (ICISCoIS). IEEE, 2023. http://dx.doi.org/10.1109/iciscois56541.2023.10100347.

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S, Lovelyn Rose. "Comparative Study on Different Word Embedding Techniques." In Proceedings of the First International Conference on Combinatorial and Optimization, ICCAP 2021, December 7-8 2021, Chennai, India. EAI, 2021. http://dx.doi.org/10.4108/eai.7-12-2021.2314494.

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Yazdizadeh, Tina, and Wei Shi. "Performance Evaluation for the use of ELMo Word Embedding in Cyberbullying Detection." In 3rd International Conference on Data Science and Machine Learning (DSML 2022). Academy and Industry Research Collaboration Center (AIRCC), 2022. http://dx.doi.org/10.5121/csit.2022.121511.

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Communication using modern internet technologies has revolutionized the ways humans exchange information.. Despite the numerous advantages offered by such technology, its applicability is still limited due to problems stemming from personal attacks and pseudoattacks. On social media platforms, these toxic contents may take the form of texts (e.g., online chats, emails), speech, and even images and movie clips. Because the cyberbullying of an individual via the use of such toxic digital content may have severe consequences, it is essential to design and implement, among others, various techniques to automatically detect, using machine learning approaches, cyberbullying on social media. It is important to use word embedding techniques to represent words for text analysis, typically in the form of a real-valued vector that encodes the meaning of words. The extracted embeddings are used to decide if a digital input contains cyberbullying contents. Supplying strong word representations to classification methods is a key facet of such detection approaches. In this paper, we evaluate the ELMo word embedding against three other word embeddings, namely, TF-IDF, Word2Vec, and BERT, using three basic machine learning models and four deep learning models. The results show that the ELMo word embeddings have the best results when combined with neural network-based machine learning models.

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Звіти організацій з теми "Embedding techniques"

Datta, Bithin, Jean Beegle, M. Kavvas, and Gerald Orlob. Development of an expert-system embedding pattern-recognition techniques for pollution-source identification. Report for 30 September 1987-29 November 1989. Office of Scientific and Technical Information (OSTI), December 1989. http://dx.doi.org/10.2172/6855981.

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Buurma, Andrew W., and Roberto G. Rojas. Aerospace Sensor Component and Subsystem Investigation And Innovation-2 Component Exploration and Development (ASCII-2 CED). Delivery Order 0002: Volume 3 - Reconfigurable Aperture Antenna Virtual Prototyping (General Techniques for De-Embedding RF Passive Devices). Fort Belvoir, VA: Defense Technical Information Center, September 2005. http://dx.doi.org/10.21236/ada445437.

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