Relevant bibliographies by topics / Sequential labeling

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers

Academic literature on the topic 'Sequential labeling'

Author: Grafiati
Published: 6 September 2023

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Journal articles on the topic "Sequential labeling"

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Sumathi, P., and G. Geetha Ramani. "Arithmetic Sequential Graceful Labeling on Star Related Graphs." Indian Journal Of Science And Technology 15, no. 44 (November 28, 2022): 2356–62. http://dx.doi.org/10.17485/ijst/v15i44.1863.

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Kantabutra, Sanpawat. "Fast Sequential and Parallel Vertex Relabelings of Km,m." International Journal of Foundations of Computer Science 26, no. 01 (January 2015): 33–50. http://dx.doi.org/10.1142/s0129054115500021.

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Given an undirected, connected, simple graph G = (V,E), two vertex labelings LV and L'V of the vertices of G, and a label flip operation that interchanges a pair of labels on adjacent vertices, the Vertex Relabeling Problem is to transform G from LV into L'V using the flip operation. Agnarsson et al. showed solving the Vertex Relabeling Problem on arbitrary graphs can be done in θ(n2), where n is the number of vertices in G. In this article we study the Vertex Relabeling Problem on graphs Km,m and introduce the concept of parity and precise labelings. We show that, when we consider the parity labeling, the problem on graphs Km,m can be solved quickly in O(log m) time using m processors on an EREW PRAM. Additionally, we also show that the number of processors can be further reduced to [Formula: see text] in this case while the time complexity does not change. When the labeling is precise, the parallel time complexity increases by a factor of log m while the processor complexities remain m and [Formula: see text]. We also show that, when graphs are restricted to Km,m, this problem can be solved optimally in O(m) time when the labeling is parity, and can be solved in O(m log m) time when the labeling is precise, thereby improving the result in Agnarsson et al. for this specific case. Moreover, we generalize the result in the case of precise labeling to the cases when LV and L'V can be any configuration. In the end we give a conclusion and a list of some interesting open problems.
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Wu, Xian, Wei Fan, and Yong Yu. "Sembler: Ensembling Crowd Sequential Labeling for Improved Quality." Proceedings of the AAAI Conference on Artificial Intelligence 26, no. 1 (September 20, 2021): 1713–19. http://dx.doi.org/10.1609/aaai.v26i1.8351.

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Many natural language processing tasks, such as named entity recognition (NER), part of speech (POS) tagging, word segmentation, and etc., can be formulated as sequential data labeling problems. Building a sound labeler requires very large number of correctly labeled training examples, which may not always be possible. On the other hand, crowdsourcing provides an inexpensive yet efficient alternative to collect manual sequential labeling from non-experts. However the quality of crowd labeling cannot be guaranteed, and three kinds of errors are typical: (1) incorrect annotations due to lack of expertise (e.g., labeling gene names from plain text requires corresponding domain knowledge); (2) ignored or omitted annotations due to carelessness or low confidence; (3) noisy annotations due to cheating or vandalism. To correct these mistakes, we present Sembler, a statistical model for ensembling crowd sequential labelings. Sembler considers three types of statistical information: (1) the majority agreement that proves the correctness of an annotation; (2) correct annotation that improves the credibility of the corresponding annotator; (3) correct annotation that enhances the correctness of other annotations which share similar linguistic or contextual features. We evaluate the proposed model on a real Twitter and a synthetical biological data set, and find that Sembler is particularly accurate when more than half of annotators make mistakes.
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Wang, Xiuying, Bo Xu, Changliang Li, and Wendong Ge. "Labeling Sequential Data Based on Word Representations and Conditional Random Fields." International Journal of Machine Learning and Computing 5, no. 6 (December 2015): 439–44. http://dx.doi.org/10.18178/ijmlc.2015.5.6.548.

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Kang, Qiyu, and Wee Peng Tay. "Sequential Multi-Class Labeling in Crowdsourcing." IEEE Transactions on Knowledge and Data Engineering 31, no. 11 (November 1, 2019): 2190–99. http://dx.doi.org/10.1109/tkde.2018.2874003.

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Cserép, Gergely B., András Herner, Otto S. Wolfbeis, and Péter Kele. "Tyrosine specific sequential labeling of proteins." Bioorganic & Medicinal Chemistry Letters 23, no. 21 (November 2013): 5776–78. http://dx.doi.org/10.1016/j.bmcl.2013.09.002.

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A. Manonmani and R. Savithiri. "Double quadrilateral snakes on k-odd sequential harmonious labeling of graphs." Malaya Journal of Matematik 3, no. 04 (October 1, 2015): 607–11. http://dx.doi.org/10.26637/mjm304/019.

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The objective of this paper is to investigate some $k$-odd sequential harmonious labeling of graphs. In particular, we show that $k$-odd sequential harmonious labeling of double quadrilateral snakes $\left(2 Q_x\right.$-snakes) for each $x \geq 1$. We also prove that, $2 m Q_x$-snakes are $k$-odd sequential harmonious labeling of graphs for each $m, x \geq 1$. Finally, we present some examples and verified to illustrate proposed theories.
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Seoud, M. A., M. El-Zekey, and E. F. El-Gazar. "Mean, Odd Sequential and Triangular Sum Graphs." Circulation in Computer Science 2, no. 4 (May 20, 2017): 40–52. http://dx.doi.org/10.22632/ccs-2017-252-08.

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In this paper, we prove that all odd sequential graphs are mean graphs, but not all mean graphs are an odd sequential graph. We show that some new families generated by some graph operations on some standard graphs are admitting mean labeling and odd sequential labeling. Finally, we conclude some new results in triangular sum graphs.
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Qin, Jie, Li Liu, Zhaoxiang Zhang, Yunhong Wang, and Ling Shao. "Compressive Sequential Learning for Action Similarity Labeling." IEEE Transactions on Image Processing 25, no. 2 (February 2016): 756–69. http://dx.doi.org/10.1109/tip.2015.2508600.

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Maoying Qiao, Wei Bian, Richard Yi Da Xu, and Dacheng Tao. "Diversified Hidden Markov Models for Sequential Labeling." IEEE Transactions on Knowledge and Data Engineering 27, no. 11 (November 1, 2015): 2947–60. http://dx.doi.org/10.1109/tkde.2015.2433262.

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Dissertations / Theses on the topic "Sequential labeling"

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Tener, Greg. "ATTACKS ON DIFFICULT INSTANCES OF GRAPH ISOMORPHISM: SEQUENTIAL AND PARALLEL ALGORITHMS." Doctoral diss., University of Central Florida, 2009. http://digital.library.ucf.edu/cdm/ref/collection/ETD/id/2631.

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The graph isomorphism problem has received a great deal of attention on both theoretical and practical fronts. However, a polynomial algorithm for the problem has yet to be found. Even so, the best of the existing algorithms perform well in practice; so well that it is challenging to find hard instances for them. The most efficient algorithms, for determining if a pair of graphs are isomorphic, are based on the individualization-refinement paradigm, pioneered by Brendan McKay in 1981 with his algorithm nauty. Nauty and various improved descendants of nauty, such as bliss and saucy, solve the graph isomorphism problem by determining a canonical representative for each of the graphs. The graphs are isomorphic if and only if their canonical representatives are identical. These algorithms also detect the symmetries in a graph which are used to speed up the search for the canonical representative--an approach that performs well in practice. Yet, several families of graphs have been shown to exist which are hard for nauty-like algorithms. This dissertation investigates why these graph families pose difficulty for individualization-refinement algorithms and proposes several techniques for circumventing these limitations. The first technique we propose addresses a fundamental problem pointed out by Miyazaki in 1993. He constructed a family of colored graphs which require exponential time for nauty (and nauty's improved descendants). We analyze Miyazaki's construction to determine the source of difficulty and identify a solution. We modify the base individualization-refinement algorithm by exploiting the symmetries discovered in a graph to guide the search for its canonical representative. This is accomplished with the help of a novel data structure called a guide tree. As a consequence, colored Miyazaki graphs are processed in polynomial time--thus obviating the only known exponential upper-bound on individualization-refinement algorithms (which has stood for the last 16 years). The preceding technique can only help if a graph has enough symmetry to exploit. It cannot be used for another family of hard graphs that have a high degree of regularity, but possess few actual symmetries. To handle these instances, we introduce an adaptive refinement method which utilizes the guide-tree data structure of the preceding technique to use a stronger vertex-invariant, but only when needed. We show that adaptive refinement is very effective, and it can result in dramatic speedups. We then present a third technique ideally suited for large graphs with a preponderance of sparse symmetries. A method was devised by Darga et al. for dealing with these large and highly symmetric graphs, which can reduce runtime by an order of magnitude. We explain the method and show how to incorporate it into our algorithm. Finally, we develop and implement a parallel algorithm for detecting the symmetries in, and finding a canonical representative of a graph. Our novel parallel algorithm divides the search for the symmetries and canonical representative among each processor, allowing for a high degree of scalability. The parallel algorithm is benchmarked on the hardest problem instances, and shown to be effective in subdividing the search space.
Ph.D.
School of Electrical Engineering and Computer Science
Engineering and Computer Science
Computer Science PhD
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Carr, Michael John. "Estimating parameters of a stochastic cell invasion model with Fluorescent cell cycle labelling using approximate Bayesian computation." Thesis, Queensland University of Technology, 2021. https://eprints.qut.edu.au/226946/1/Michael_Carr_Thesis.pdf.

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Understanding the underlying mechanisms of melanoma cell behaviour is crucial to developing better drug treatment methods. In this thesis, we use advanced mathematical modelling and statistical inference techniques to obtain, for the first time, accurate estimates of the rates at which cells multiply and spread at multiple stages of the cell cycle. The mathematical model is fitted to data that uses fluorescent cell cycle labelling technology to visualise different phases of the cell cycle in real time. The accurately calibrated mathematical model enables a deeper understanding of cell behaviour and has potential for more precisely assessing treatment efficacy.
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Cheung, Anthony Hing-lam. "Design and implementation of an Arabic optical character recognition system." Thesis, Queensland University of Technology, 1998. https://eprints.qut.edu.au/36073/1/36073_Cheung_1998.pdf.

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Character recognition is not a difficult task for humans who repeat the process thousands of times every day as they read papers or books. However, after more than 40 years of intensive investigation, there is still no machine that can recognize alphabetic characters as well as humans. Optical Character Recognition (OCR) is the process of converting a raster image representation of a document into a format that a computer can process. It involves many sub-disciplines of computer science including digital image processing, pattern recognition, natural language processing, artificial intelligence, and database systems. Applications of OCR systems are broad and include postal code recognition in postal departments, automatic document entries in companies and government departments, cheque sorting in banks, machine translation, etc. The objective of this thesis is to design an optical character recognition system which can recognize Arabic script. This system has to be: 1) accurate: with a recognition accuracy of 953; 2) robust: able to recognize two different Arabic fonts; and 3) efficient: it should be a real time system. This proposed system is composed of five image processing processes: 1) Image Acquisition; 2) Preprocessing; 3) Segmentation; 4) Feature Extraction; and 5) Classification. The recognized results are presented to users via a window-based user-interface. Thus, they can control the system, recognize and edit documents by a click on the mouse button. A thinning algorithm, a word segmentation algorithm and a recognition based character segmentation algorithm for Arabic script have been proposed to increase the recognition accuracy of the system. The Arabic word segmentation algorithm successfully segments the horizontally overlapped Arabic words, whereas the recognition-based character segmentation algorithm replaces the classical character segmentation method and raises the level of accuracy of recognition of the proposed system. These blocks have been integrated. Results to test the requirements of accuracy, robustness and efficiency are presented. Finally, some extensions to the system have also been proposed.
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Huang, Chih Kai, and 黃致凱. "Named Entity Recognition in Difangzhi Using Sequential Labeling Techniques." Thesis, 2016. http://ndltd.ncl.edu.tw/handle/k77974.

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碩士
國立政治大學
資訊科學學系
104
Difangzhi is the local gazetteers compiled by local government of China. Its content is plenty and extensive. It’s including many undetected information, like biographical information, geographical information, and officer record information and so on. Because of the difference between Difangzhi corpus and modern Chinese language, we should not use current natural language processing tools directly. In order to extract biographical information, we construct our model to recognize the named entity and use the noun list to assist our annotation method in Difangzhi corpus. In this study, we use supervised learning to construct our model. At first, we need to generate our training data. According to the personal information list with manual verification and noun lists, we have reliable information to annotate words in Difangzhi corpus. However, they still have some noise in those lists. As a result, we must do the preprocessing to those lists for cleaning. After, the ambiguity problem will happen when we trying to annotate our corpus. Here we provide three methods to annotate our corpus with disambiguation. Using the annotated corpus to generate training data and built the condition random fields models. In our experiment, we use our models generated by three different annotate methods to predict the character label in testing Difangzhi corpus. According to the labeled result, we extract the person name and address name to evaluate. The result shows the precision of person name recognition is over 80%, and precision of address name recognition is about 86%. Because of the training corpus and test corpus is quite similar, the performances of our model is pretty well. Therefore, we use labeled result to find correlation of person name and address name. Using a simple way to connect person name and address name and sampling the result to evaluate. The sample result shows we could connect person name and address name correctly in some specific grammars. In order to analyze more deeply, we attempt to split clauses in Difangzhi corpus. Use finite state machine model to recognize the beginning of clauses. Although the result shows we could find some beginning of clauses, but our method still lose many beginning of clauses. In the future work, we attempt to add more information to annotate Difangzhi corpus and modify our disambiguated methods to make the recognition result better. In order to get more information about the person in the corpus, we will try to split paragraphs or sentences more precisely. Besides, we also try to analyze grammar in the corpus. Finding useful pattern to connect person name and other entities, like address name, officer name and so on. Generating the information about people appears in the corpus automatically.
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SECCI, ERICA. "New strategy of protein expression in mammalian cells for in-cell NMR." Doctoral thesis, 2015. http://hdl.handle.net/2158/1015496.

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The research activities of my doctorate have been devoted to the application of in-cell NMR for the characterization of proteins directly inside human cells. Within this context, I have also addressed the development of a methodology to perform sequential labeling for in-cell NMR in human cells. The in-cell NMR approach, which consists in observing one or more selectively labeled protein(s) or nucleic acid in living cells through high resolution NMR experiments, is a powerful tool to obtain structural and functional information in situ, thus overcoming the limitation of studying macromolecules isolated from the other cellular components. Protein folding, posttranslational modifications and interactions with specific partners or cellular elements can be studied in the physiological environment in which the protein of interest operates. My research work also addressed the in vitro characterization of the phosphorylation pattern of the non-structural protein 5A domain 3 of Hepatitis C virus by casein kinase 2.
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Books on the topic "Sequential labeling"

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Donkin, Chris, Babette Rae, Andrew Heathcote, and Scott D. Brown. Why Is Accurately Labeling Simple Magnitudes So Hard? A Past, Present, and Future Look at Simple Perceptual Judgment. Edited by Jerome R. Busemeyer, Zheng Wang, James T. Townsend, and Ami Eidels. Oxford University Press, 2015. http://dx.doi.org/10.1093/oxfordhb/9780199957996.013.6.

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Absolute identification is a deceptively simple task that has been the focus of empirical investigation and theoretical speculation for more than half a century. Since Miller’s (1956) seminal paper the puzzle of why people are severely limited in their capacity to accurately perform absolute identification has endured. Despite the apparent simplicity of absolute identification, many complicated and robust effects are observed in both response latency and accuracy, including capacity limitations, strong sequential effects and effects of the position of a stimulus within the set. Constructing a comprehensive theoretical account of these benchmark effects has proven difficult, and existing accounts all have shortcomings. We review classical empirical findings, as well as some newer findings that challenge existing theories. We then discuss a variety of theories, with a focus on the most recent proposals, make some broad conclusions about general classes of models, and discuss the challenges ahead for each class.
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Book chapters on the topic "Sequential labeling"

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Zhao, Zhongtang, Li Liu, Lingling Li, and Qian Ma. "SLOSELM: Self Labeling Online Sequential Extreme Learning Machine." In Internet and Distributed Computing Systems, 179–89. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-45940-0_16.

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Chen, Gang, Ran Xu, and Sargur N. Srihari. "Sequential Labeling with Online Deep Learning: Exploring Model Initialization." In Machine Learning and Knowledge Discovery in Databases, 772–88. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-46227-1_48.

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Zhang, Guopeng, and Massimo Piccardi. "Sequential Labeling with Structural SVM Under an Average Precision Loss." In Lecture Notes in Computer Science, 344–54. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-49055-7_31.

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Wu, Yu-Chieh, Yue-Shi Lee, Jie-Chi Yang, and Show-Jane Yen. "An Integrated Deterministic and Nondeterministic Inference Algorithm for Sequential Labeling." In Information Retrieval Technology, 221–30. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-17187-1_21.

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Zhou, Guodong, Lingpeng Yang, Jian Su, and Donghong Ji. "Mutual Information Independence Model Using Kernel Density Estimation for Segmenting and Labeling Sequential Data." In Computational Linguistics and Intelligent Text Processing, 155–66. Berlin, Heidelberg: Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/978-3-540-30586-6_15.

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Van Bockstaele, Elisabeth J., Janet L. Kravets, Xin-Mei Wen, and Beverly A. S. Reyes. "Using Sequential Dual-Immunogold-Silver Labeling and Electron Microscopy to Determine the Fate of Internalized G-Protein-Coupled Receptors Following Agonist Treatment." In Neuromethods, 139–66. New York, NY: Springer New York, 2015. http://dx.doi.org/10.1007/7657_2015_77.

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Parameswari, R. "Total Magic Cordial and Total Sequential Cordial Labeling of Path Related Graph." In Innovations in Science and Technology Vol. 5, 58–68. Book Publisher International (a part of SCIENCEDOMAIN International), 2022. http://dx.doi.org/10.9734/bpi/ist/v5/1862a.

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Lin, Yunan, Yanqing Chen, Wei Fang, and Yongsheng Cao. "Design of Automated Warehouse Storage Scheme in Crop Genebank." In Advances in Transdisciplinary Engineering. IOS Press, 2022. http://dx.doi.org/10.3233/atde221194.

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The contributions of crop germplasm resources to food security depend on their conservation and accessibility for use. The automated warehouse has begun to be applied to the ex situ preservation of crop germplasm resources in the crop genebank. Identifying the proper storage scheme for potentially hundreds of thousands of seeds is a primary task that faces the crop genebank manager during the design of a new automated crop genebank. There are mainly three areas to focus on, hardware and software, seeds storage assignment policy and seeds labelling technology. This paper aims to propose automated crop genebank storage schemes for two kinds of crop genebank (the long-term crop genebank and the middle-term crop genebank), which supports managers in determining the technologies that can be applied in the automated crop genebank. Firstly, the selection of hardware and software should be based on the functional orientation of the long-term crop genebank and the middle-term crop genebank. Secondly, for the seed storage assignment policy, the sequential storage assignment is designed for the long-term genebank while the cache storage assignment is developed for the middle-term genebank. Finally, a QR code labelling technology based on image recognition is designed for both the long-term crop genebank and the middle-term crop genebank.
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Bensimon, David, Vincent Croquette, Jean-François Allemand, Xavier Michalet, and Terence Strick. "DNA and RNA Polymerases." In Single-Molecule Studies of Nucleic Acids and Their Proteins, 121–54. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198530923.003.0007.

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This chapter discusses the application of single-molecule approaches in the study of DNA and RNA polymerases. After an introduction to DNA replication and the structure of DNA polymerases, it reviews experiments on DNA polymerization on stretched ssDNA, moving on to DNA polymerization at a stretched DNA fork (mimicking the replication fork). Next it looks at single-molecule sequencing approaches based on DNA polymerization with sequential incorporation of fluorescently labelled nucleotides, comparing with nanopore sequencing. It outlines the use of fluorescent approaches in the study of replication dynamics in vivo in single cells, then discussing transcription by RNA polymerases, the stages of transcription (open-complex, abortive initiation, transcription elongation, termination), and the general structure of RNA polymerases. It describes single-molecule experiments (using manipulation/fluorescent approaches) of the transcription stages and ends with a discussion of experiments studying the dynamics of transcription in vivo at a single locus in a eukaryotic cell with fluorescent labelling.
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Gao, Qilu, Gengbiao Zhang, Hongkun Liu, Hongyi Zheng, Li Zhang, Jinghua Wu, and Wenbin Zheng. "Meridian Sinew Therapy for Cerebral Blood Flow and Brain Function in Sub-Healthy Individuals: A Study of ASL and rsfMRI." In Computer Methods in Medicine and Health Care. IOS Press, 2022. http://dx.doi.org/10.3233/atde220538.

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Finding new ways to prevent and reduce the incidence of dementia is a serious world problem. This study aimed to perform imaging comparisons between pre- and post-meridian sinew therapy using arterial spin labeling (ASL) and resting-state functional MRI (rs-fMRI). Meanwhile, the results were studied to provide imaging evidence to support the effect of this meridian sinew therapy to slow down the brain aging and to reveal the related neurological mechanisms. Eighteen sub-healthy volunteers were selected as subjects. Three treatment strategies were adopted, acupuncture (group A), myofascial release (group B), and the integrated acupuncture and myofascial release (group C). The subjects were assigned to receive the three treatment modalities sequentially. 3T MRI examinations were provided before and after each treatment, including routine brain MRI plain scan, ASL and rs-fMRI scan. Compared with the results before and after treatment, the number of brain regions with increased cerebral blood flow (CBF) values in the group A, group B, and group C were respectively 1, 15 and 10 brain regions, all including the right cingulate gyrus. And rs-fMRI showed that multiple brain regions was activated, mainly temporal lobe and frontal lobe. The independent component analysis showed that the right intraorbital superior frontal gyrus and the occipital region was activated. Meridian sinew therapy can increase CBF and enhance neuronal activity in brain regions significantly associated with cognitive and memory functions, which may be the main targets where it actions on to achieve “Xingshen Yizhi (waking up the spirit and reinforcing thinking activity)” effect. The combination of ASL and rs-fMRI may be an effective imaging modality for future quantitative monitoring of the preventive and therapeutic effects of the meridian sinew therapy.
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Conference papers on the topic "Sequential labeling"

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Wang, Yiran, Hiroyuki Shindo, Yuji Matsumoto, and Taro Watanabe. "Structured Refinement for Sequential Labeling." In Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021. Stroudsburg, PA, USA: Association for Computational Linguistics, 2021. http://dx.doi.org/10.18653/v1/2021.findings-acl.164.

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Sun, Xu, and Jun'ichi Tsujii. "Sequential labeling with latent variables." In the 12th Conference of the European Chapter of the Association for Computational Linguistics. Morristown, NJ, USA: Association for Computational Linguistics, 2009. http://dx.doi.org/10.3115/1609067.1609153.

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Kang, Qiyu, and Wee Peng Tay. "Sequential multi-class labeling in crowdsourcing." In WI '17: International Conference on Web Intelligence 2017. New York, NY, USA: ACM, 2017. http://dx.doi.org/10.1145/3106426.3106446.

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Màrquez, Lluís, Pere Comas, Jesús Giménez, and Neus Català. "Semantic role labeling as sequential tagging." In the Ninth Conference. Morristown, NJ, USA: Association for Computational Linguistics, 2005. http://dx.doi.org/10.3115/1706543.1706579.

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Chen, Sheng, Alan Fern, and Sinisa Todorovic. "Multi-object Tracking via Constrained Sequential Labeling." In 2014 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2014. http://dx.doi.org/10.1109/cvpr.2014.148.

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Qiao, Maoying, Wei Bian, Richard Yi Da Xu, and Dacheng Tao. "Diversified hidden Markov models for sequential labeling." In 2016 IEEE 32nd International Conference on Data Engineering (ICDE). IEEE, 2016. http://dx.doi.org/10.1109/icde.2016.7498400.

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Kim, Seokhwan, and Rafael E. Banchs. "Sequential Labeling for Tracking Dynamic Dialog States." In Proceedings of the 15th Annual Meeting of the Special Interest Group on Discourse and Dialogue (SIGDIAL). Stroudsburg, PA, USA: Association for Computational Linguistics, 2014. http://dx.doi.org/10.3115/v1/w14-4345.

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Ono, Satoshi, Haruki Matsuyama, Ken-ichi Fukui, and Shigeki Hosoda. "Error detection of oceanic observation data using sequential labeling." In 2015 IEEE International Conference on Data Science and Advanced Analytics (DSAA). IEEE, 2015. http://dx.doi.org/10.1109/dsaa.2015.7344896.

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Zhang, Guopeng, and Massimo Piccardi. "Sequential labeling with structural SVM under the F1 loss." In 2014 IEEE International Conference on Image Processing (ICIP). IEEE, 2014. http://dx.doi.org/10.1109/icip.2014.7026067.

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Lin, Lu, Zheng Luo, Dezhi Hong, and Hongning Wang. "Sequential Learning with Active Partial Labeling for Building Metadata." In BuildSys '19: The 6th ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation. New York, NY, USA: ACM, 2019. http://dx.doi.org/10.1145/3360322.3360866.

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