Relevant bibliographies by topics / Joint clustering with alignment

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

Academic literature on the topic 'Joint clustering with alignment'

Author: Grafiati
Published: 20 April 2024

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Journal articles on the topic "Joint clustering with alignment"

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Deng, Wanxia, Qing Liao, Lingjun Zhao, Deke Guo, Gangyao Kuang, Dewen Hu, and Li Liu. "Joint Clustering and Discriminative Feature Alignment for Unsupervised Domain Adaptation." IEEE Transactions on Image Processing 30 (2021): 7842–55. http://dx.doi.org/10.1109/tip.2021.3109530.

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Samuroff, S., J. Blazek, M. A. Troxel, N. MacCrann, E. Krause, C. D. Leonard, J. Prat, et al. "Dark Energy Survey Year 1 results: constraints on intrinsic alignments and their colour dependence from galaxy clustering and weak lensing." Monthly Notices of the Royal Astronomical Society 489, no. 4 (August 16, 2019): 5453–82. http://dx.doi.org/10.1093/mnras/stz2197.

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Abstract We perform a joint analysis of intrinsic alignments and cosmology using tomographic weak lensing, galaxy clustering, and galaxy–galaxy lensing measurements from Year 1 (Y1) of the Dark Energy Survey. We define early- and late-type subsamples, which are found to pass a series of systematics tests, including for spurious photometric redshift error and point spread function correlations. We analyse these split data alongside the fiducial mixed Y1 sample using a range of intrinsic alignment models. In a fiducial non-linear alignment model analysis, assuming a flat Λ cold dark matter cosmology, we find a significant difference in intrinsic alignment amplitude, with early-type galaxies favouring $A_\mathrm{IA} = 2.38^{+0.32}_{-0.31}$ and late-type galaxies consistent with no intrinsic alignments at $0.05^{+0.10}_{-0.09}$. The analysis is repeated using a number of extended model spaces, including a physically motivated model that includes both tidal torquing and tidal alignment mechanisms. In multiprobe likelihood chains in which cosmology, intrinsic alignments in both galaxy samples and all other relevant systematics are varied simultaneously, we find the tidal alignment and tidal torquing parts of the intrinsic alignment signal have amplitudes $A_1 = 2.66 ^{+0.67}_{-0.66}$, $A_2=-2.94^{+1.94}_{-1.83}$, respectively, for early-type galaxies and $A_1 = 0.62 ^{+0.41}_{-0.41}$, $A_2 = -2.26^{+1.30}_{-1.16}$ for late-type galaxies. In the full (mixed) Y1 sample the best constraints are $A_1 = 0.70 ^{+0.41}_{-0.38}$, $A_2 = -1.36 ^{+1.08}_{-1.41}$. For all galaxy splits and IA models considered, we report cosmological parameter constraints consistent with the results of the main DES Y1 cosmic shear and multiprobe cosmology papers.
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Murillo-Vizuete, David, Raul Garcia-Bogalo, David Escobar-Anton, Lissette Horna-Castiñeiras, Juan Peralta-Molero, and Ricardo Larrainzar-Garijo. "Dynamic Alignment Analysis in the Osteoarthritic Knee Using Computer Navigation." Journal of Knee Surgery 30, no. 09 (February 13, 2017): 909–15. http://dx.doi.org/10.1055/s-0037-1598037.

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AbstractThe lower limb alignment is influenced by the geometry of the joint surfaces and surrounding soft tissue tension. The mechanical behavior changes in a normal, osteoarthritic, and postoperative knee. The purpose of this study is to determine the dynamic coronal femoral tibial mechanical angle (FTMA) in osteoarthritic knees using computer navigation. The authors hypothesize that there are different varus-valgus patterns between flexion and extension in the osteoarthritic knee. We conducted a transversal observational study and included patients with osteoarthritis who underwent primary navigation TKA (Orthopilot version 4.2; B. Braun Aesculap, Tuttlingen, Germany). In total, 98 consecutive patients with 100 osteoarthritic knee joints, on which total knee arthroplasty was performed in our institution from 2009 to 2010, were enrolled in this prospective study. The FTMA was measured with the patient supine with maximum knee extension possible (considering the value as 0), 30, 60, and 90 degrees. All FMTA data obtained were segmented by hierarchic cluster measuring method. Through the clustering system, five segments were generated for varus patients and three for valgus patients: expected varus, expected valgus, severe varus, severe valgus, structured varus, structured valgus, concave varus, mixed varus-valgus, and mixed valgus-varus. The findings of the present study have demonstrated that there is a well-defined dynamic alignment in osteoarthritic knees, resulting in a wide kinematic variation in the coronal FTMA between flexion and full extension. Further studies will be necessary to determine whether this dynamic approach to FTMA has clinical utility in the surgeon's decision-making process.
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Yu, Jixiang, Nanjun Chen, Ming Gao, Xiangtao Li, and Ka-Chun Wong. "Unsupervised Gene-Cell Collective Representation Learning with Optimal Transport." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 1 (March 24, 2024): 356–64. http://dx.doi.org/10.1609/aaai.v38i1.27789.

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Cell type identification plays a vital role in single-cell RNA sequencing (scRNA-seq) data analysis. Although many deep embedded methods to cluster scRNA-seq data have been proposed, they still fail in elucidating the intrinsic properties of cells and genes. Here, we present a novel end-to-end deep graph clustering model for single-cell transcriptomics data based on unsupervised Gene-Cell Collective representation learning and Optimal Transport (scGCOT) which integrates both cell and gene correlations. Specifically, scGCOT learns the latent embedding of cells and genes simultaneously and reconstructs the cell graph, the gene graph, and the gene expression count matrix. A zero-inflated negative binomial (ZINB) model is estimated via the reconstructed count matrix to capture the essential properties of scRNA-seq data. By leveraging the optimal transport-based joint representation alignment, scGCOT learns the clustering process and the latent representations through a mutually supervised self optimization strategy. Extensive experiments with 14 competing methods on 15 real scRNA-seq datasets demonstrate the competitive edges of scGCOT.
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El-Melegy, Moumen, Rasha Kamel, Mohamed Abou El-Ghar, Nora S. Alghamdi, and Ayman El-Baz. "Variational Approach for Joint Kidney Segmentation and Registration from DCE-MRI Using Fuzzy Clustering with Shape Priors." Biomedicines 11, no. 1 (December 21, 2022): 6. http://dx.doi.org/10.3390/biomedicines11010006.

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The dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) technique has great potential in the diagnosis, therapy, and follow-up of patients with chronic kidney disease (CKD). Towards that end, precise kidney segmentation from DCE-MRI data becomes a prerequisite processing step. Exploiting the useful information about the kidney’s shape in this step mandates a registration operation beforehand to relate the shape model coordinates to those of the image to be segmented. Imprecise alignment of the shape model induces errors in the segmentation results. In this paper, we propose a new variational formulation to jointly segment and register DCE-MRI kidney images based on fuzzy c-means clustering embedded within a level-set (LSet) method. The image pixels’ fuzzy memberships and the spatial registration parameters are simultaneously updated in each evolution step to direct the LSet contour toward the target kidney. Results on real medical datasets of 45 subjects demonstrate the superior performance of the proposed approach, reporting a Dice similarity coefficient of 0.94 ± 0.03, Intersection-over-Union of 0.89 ± 0.05, and 2.2 ± 2.3 in 95-percentile of Hausdorff distance. Extensive experiments show that our approach outperforms several state-of-the-art LSet-based methods as well as two UNet-based deep neural models trained for the same task in terms of accuracy and consistency.
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Huang, Weinan, Xiaowen Zhu, Haofeng Xia, and Kejian Wu. "Offshore Wind Energy Assessment with a Clustering Approach to Mixture Model Parameter Estimation." Journal of Marine Science and Engineering 11, no. 11 (October 28, 2023): 2060. http://dx.doi.org/10.3390/jmse11112060.

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In wind resource assessment research, mixture models are gaining importance due to the complex characteristics of wind data. The precision of parameter estimations for these models is paramount, as it directly affects the reliability of wind energy forecasts. Traditionally, the expectation–maximization (EM) algorithm has served as a primary tool for such estimations. However, challenges are often encountered with this method when handling complex probability distributions. Given these limitations, the objective of this study is to propose a new clustering algorithm, designed to transform mixture distribution models into simpler probability clusters. To validate its efficacy, a numerical experiment was conducted, and its outcomes were compared with those derived from the established EM algorithm. The results demonstrated a significant alignment between the new method and the traditional EM approach, indicating that comparable accuracy can be achieved without the need for solving complex nonlinear equations. Moreover, the new algorithm was utilized to examine the joint probabilistic structure of wind speed and air density in China’s coastal regions. Notably, the clustering algorithm demonstrated its robustness, with the root mean square error value being notably minimal and the coefficient of determination exceeding 0.9. The proposed approach is suggested as a compelling alternative for parameter estimation in mixture models, particularly when dealing with complex probability models.
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Eifler, Tim, Melanie Simet, Elisabeth Krause, Christopher Hirata, Hung-Jin Huang, Xiao Fang, Vivian Miranda, et al. "Cosmology with the Roman Space Telescope: synergies with the Rubin Observatory Legacy Survey of Space and Time." Monthly Notices of the Royal Astronomical Society 507, no. 1 (March 1, 2021): 1514–27. http://dx.doi.org/10.1093/mnras/stab533.

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ABSTRACT We explore synergies between the Nancy Grace Roman Space Telescope and the Vera Rubin Observatory’s Legacy Survey of Space and Time (LSST). Specifically, we consider scenarios where the currently envisioned survey strategy for the Roman Space Telescope’s High Latitude Survey (HLS reference), i.e. 2000 deg2 in four narrow photometric bands is altered in favour of a strategy of rapid coverage of the LSST area (to full LSST depth) in one band. We find that in only five months, a survey in the W-band can cover the full LSST survey area providing high-resolution imaging for >95 per cent of the LSST Year 10 gold galaxy sample. We explore a second, more ambitious scenario where the Roman Space Telescope spends 1.5 yr covering the LSST area. For this second scenario, we quantify the constraining power on dark energy equation-of-state parameters from a joint weak lensing and galaxy clustering analysis. Our survey simulations are based on the Roman Space Telescope exposure-time calculator and redshift distributions from the CANDELS catalogue. Our statistical uncertainties account for higher order correlations of the density field, and we include a wide range of systematic effects, such as uncertainties in shape and redshift measurements, and modelling uncertainties of astrophysical systematics, such as galaxy bias, intrinsic galaxy alignment, and baryonic physics. We find a significant increase in constraining power for the joint LSST + HLS wide survey compared to LSST Y10 (FoMHLSwide = 2.4 FoMLSST) and compared to LSST + HLS (FoMHLSwide = 5.5 FoMHLSref).
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Miao, Xia, Ziyao Yu, and Ming Liu. "Using Partial Differential Equation Face Recognition Model to Evaluate Students’ Attention in a College Chinese Classroom." Advances in Mathematical Physics 2021 (October 11, 2021): 1–10. http://dx.doi.org/10.1155/2021/3950445.

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The partial differential equation learning model is applied to another high-level visual-processing problem: face recognition. A novel feature selection method based on partial differential equation learning model is proposed. The extracted features are invariant to rotation and translation and more robust to illumination changes. In the evaluation of students’ concentration in class, this paper firstly uses the face detection algorithm in face recognition technology to detect the face and intercept the expression data, and calculates the rise rate. Then, the improved model of concentration analysis and evaluation of a college Chinese class is used to recognize facial expression, and the corresponding weight is given to calculate the expression score. Finally, the head-up rate calculated at the same time is multiplied by the expression score as the final concentration score. Through the experiment and analysis of the experimental results in the actual classroom, the corresponding conclusions are drawn and teaching suggestions are provided for teachers. For each face, a large neighborhood set is firstly selected by the k -nearest neighbor method, and then, the sparse representation of sample points in the neighborhood is obtained, which effectively combines the locality of k -nearest neighbor and the robustness of sparse representation. In the sparse preserving nonnegative block alignment algorithm, a discriminant partial optimization model is constructed by using sparse reconstruction coefficients to describe local geometry and weighted distance to describe class separability. The two algorithms obtain good clustering and recognition results in various cases of real and simulated occlusion, which shows the effectiveness and robustness of the algorithm. In order to verify the reliability of the model, this paper verified the model through in-class practice tests, teachers’ questions, and interviews with students and teachers. The results show that the proposed joint evaluation method based on expression and head-up rate has high accuracy and reliability.
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Nau, T., S. Cutts, and N. Naidoo. "DNA METHYLATION AND ITS INFLUENCE ON THE PATHOGENESIS OF OSTEOARTHRITIS: A SYSTEMATIC LITERATURE REVIEW." Orthopaedic Proceedings 105-B, SUPP_8 (April 11, 2023): 127. http://dx.doi.org/10.1302/1358-992x.2023.8.127.

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There is an evolving body of evidence that demonstrates the role of epigenetic mechanisms, such as DNA-methylation in the pathogenesis of OA. This systematic review aims to summarize the current evidence of DNA methylation and its influence on the pathogenesis of OA.A pre-defined protocol in alignment with the PRISMA guidelines was employed to systematically review eight bibliographic databases, to identify associations between DNA-methylation of articular chondrocytes and osteoarthritis. A search of Medline (Ovid), Embase, Web-of-Science, Scopus, PubMed, Cinahl (EBSCOhost), Cochrane Central and Google Scholar was performed between 1st January 2015 to 31st January 2021. Data extraction was performed by two independent reviewers.During the observation period, we identified 15 gene specific studies and 24 genome wide methylation analyses. The gene specific studies mostly focused on the expression of pro-inflammatory markers, such as IL8 and MMP13 which are overexpressed in OA chondrocytes. DNA hypomethylation in the promoter region resulted in overexpression, whereas hypermethylation was seen in non-OA chondrocytes. Others reported on the association between OA risk genes and the DNA methylation pattern close to RUNX2, which is an important OA signal. The genome wide methylation studies reported mostly on differentially methylated regions comparing OA chondrocytes and non-OA chondrocytes. Clustering of the regions identified genes that are involved in skeletal morphogenesis and development. Differentially methylated regions were seen in hip OA and knee OA chondrocytes, and even within different regions of an OA affected knee joint, differentially methylated regions were identified depending on the disease stage.This systematic review demonstrates the growing evidence of epigenetic mechanisms, such as DNA methylation, in the pathogenesis of OA. In recent years, there has been a focus on the interplay between OA risk genes and DNA methylation changes which revealed a reactivation of genes responsible for endochondral ossification during development. These are important findings and may help to identify eventual future therapeutic targets. However, the current body of literature is mostly showing the differences in DNA methylation of OA chondrocytes and non-OA chondrocytes, but a true longitudinal analysis demonstrating the DNA methylation changes actually happening is still not available.
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Sangalli, Laura M., Piercesare Secchi, Simone Vantini, and Valeria Vitelli. "-mean alignment for curve clustering." Computational Statistics & Data Analysis 54, no. 5 (May 2010): 1219–33. http://dx.doi.org/10.1016/j.csda.2009.12.008.

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Dissertations / Theses on the topic "Joint clustering with alignment"

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Arsenteva, Polina. "Statistical modeling and analysis of radio-induced adverse effects based on in vitro and in vivo data." Electronic Thesis or Diss., Bourgogne Franche-Comté, 2023. http://www.theses.fr/2023UBFCK074.

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Dans ce travail nous abordons le problème des effets indésirables induits par la radiothérapie sur les tissus sains. L'objectif est de proposer un cadre mathématique pour comparer les effets de différentes modalités d'irradiation, afin de pouvoir éventuellement choisir les traitements qui produisent le moins d'effets indésirables pour l’utilisation potentielle en clinique. Les effets secondaires sont étudiés dans le cadre de deux types de données : en termes de réponse omique in vitro des cellules endothéliales humaines, et en termes d'effets indésirables observés sur des souris dans le cadre d'expérimentations in vivo. Dans le cadre in vitro, nous rencontrons le problème de l'extraction d'informations clés à partir de données temporelles complexes qui ne peuvent pas être traitées avec les méthodes disponibles dans la littérature. Nous modélisons le fold change radio-induit, l'objet qui code la différence d'effet de deux conditions expérimentales, d’une manière qui permet de prendre en compte les incertitudes des mesures ainsi que les corrélations entre les entités observées. Nous construisons une distance, avec une généralisation ultérieure à une mesure de dissimilarité, permettant de comparer les fold changes en termes de toutes leurs propriétés statistiques importantes. Enfin, nous proposons un algorithme computationnellement efficace effectuant le clustering joint avec l'alignement temporel des fold changes. Les caractéristiques clés extraites de ces dernières sont visualisées à l'aide de deux types de représentations de réseau, dans le but de faciliter l'interprétation biologique. Dans le cadre in vivo, l’enjeu statistique est d’établir un lien prédictif entre des variables qui, en raison des spécificités du design expérimental, ne pourront jamais être observées sur les mêmes animaux. Dans le contexte de ne pas avoir accès aux lois jointes, nous exploitons les informations supplémentaires sur les groupes observés pour déduire le modèle de régression linéaire. Nous proposons deux estimateurs des paramètres de régression, l'un basé sur la méthode des moments et l'autre basé sur le transport optimal, ainsi que des estimateurs des intervalles de confiance basés sur le bootstrap stratifié
In this work we address the problem of adverse effects induced by radiotherapy on healthy tissues. The goal is to propose a mathematical framework to compare the effects of different irradiation modalities, to be able to ultimately choose those treatments that produce the minimal amounts of adverse effects for potential use in the clinical setting. The adverse effects are studied in the context of two types of data: in terms of the in vitro omic response of human endothelial cells, and in terms of the adverse effects observed on mice in the framework of in vivo experiments. In the in vitro setting, we encounter the problem of extracting key information from complex temporal data that cannot be treated with the methods available in literature. We model the radio-induced fold change, the object that encodes the difference in the effect of two experimental conditions, in the way that allows to take into account the uncertainties of measurements as well as the correlations between the observed entities. We construct a distance, with a further generalization to a dissimilarity measure, allowing to compare the fold changes in terms of all the important statistical properties. Finally, we propose a computationally efficient algorithm performing clustering jointly with temporal alignment of the fold changes. The key features extracted through the latter are visualized using two types of network representations, for the purpose of facilitating biological interpretation. In the in vivo setting, the statistical challenge is to establish a predictive link between variables that, due to the specificities of the experimental design, can never be observed on the same animals. In the context of not having access to joint distributions, we leverage the additional information on the observed groups to infer the linear regression model. We propose two estimators of the regression parameters, one based on the method of moments and the other based on optimal transport, as well as the estimators for the confidence intervals based on the stratified bootstrap procedure
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Gao, Zhiming. "Reducing the Search Space of Ontology Alignment Using Clustering Techniques." Thesis, Linköpings universitet, Databas och informationsteknik, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-141887.

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With the emerging amount of information available in the internet, how to make full use of this information becomes an urgent issue. One of the solutions is using ontology alignment to aggregate different sources of information in order to get comprehensive and complete information. Scalability is a problem regarding the ontology alignment and it can be settled down by reducing the search space of mapping suggestions. In this paper we propose an automated procedure mainly using different clustering techniques to prune the search space. The main focus of this paper is to evaluate different clustering related techniques to be applied in our system. K-means, Chameleon and Birch have been studied and evaluated, every parameter in these clustering algorithms is studied by doing experiments separately, in order to find the best clustering setting to the ontology clustering problem. Four different similarity assignment methods are researched and analyzed as well. Tfidf vectors and cosine similarity are used to identify the similar clusters in the two ontologies, experiments about threshold of cosine similarity are made to get the most suitable value. Our system successfully builds an automated procedure to generate reduced search space for ontology alignment, on one hand, the result shows that it reduces twenty to ninety times of comparisons that the ontology alignment was supposed to make, the precision goes up as well. On the other hand, it only needs one to two minutes of execution time, meanwhile the recall and f-score only drop down a little bit. The trade- off is acceptable for the ontology alignment system which will take tens of minutes to generate the ontology alignment of the same ontology set. As a result, the large scale ontology alignment becomes more computable and feasible.
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Aminu, M. (Mubarak). "Dynamic clustering for coordinated multipoint transmission with joint prosessing." Master's thesis, University of Oulu, 2016. http://urn.fi/URN:NBN:fi:oulu-201602111176.

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Coordinated Multipoint (CoMP) transmission has been identified as a promising concept to handle the substantial interference in the LTE-Advanced systems and it is one of the key technology components in the future 5G networks. CoMP transmission involves two coordination schemes: joint processing (JP) and coordinated beamforming (CB). The scope of this thesis is limited to JP. In the CoMP JP scheme, each user is coherently served by multiple base stations (BSs) and consequently, the user’s signal strength is enhanced and the interference is mitigated. The coherent joint processing requires sharing data and channel state information (CSI) of all the users among all the BSs, which leads to high backhaul capacity requirement and high signaling cost especially in large-scale networks. Grouping the BSs into smaller coordination clusters within which a user is served by only the BSs in the cluster will significantly reduce the signaling cost and the backhaul burden. In this thesis, optimal BS clustering and beamformer design for CoMP JP in the downlink of a multi-cell network is studied. The unique aspect of the study is that the BS clustering and the beamformer design are carried out jointly by iteratively solving a series of convex sub-problems. The BSs are dynamically grouped into small coordination clusters whereby each user is served by a few BSs that are in a coordination cluster. The joint BS clustering and beamformer design is performed to maximize a network utility function in the form of the weighted sum rate maximization (WSRM). The weighted sum rate maximization (WSRM) problem is formulated from the perspective of sparse optimization framework where sparsity is induced by penalizing the objective function with a power penalty. The WSRM problem is known to be non-convex and NP-hard. Therefore, it is difficult to solve directly. Two solutions are studied; in the first approach, the WSRM problem is solved via weighted minimum mean square error (WMMSE) minimization and the second approach involves approximation of the WSRM problem as a successive second order cone program (SSOCP). In both approaches, the objective function is penalized with a power penalty and the clusters can be adjusted by a single parameter in the problem. The performance evaluation of the proposed algorithms is carried out via simulation and it is shown that the serving sets in the network can be controlled according to the available backhaul capacity by properly selecting a single parameter in the problem. Finally, an algorithm for a fixed number of active links is proposed.
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Costigan, Patrick Allan. "Gait and lower limb alignment in patellofemoral joint pain syndrome." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1997. http://www.collectionscanada.ca/obj/s4/f2/dsk3/ftp05/nq22451.pdf.

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White, Derek A. "Factors affecting changes in joint alignment following knee osteotomy surgery." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 2001. http://www.collectionscanada.ca/obj/s4/f2/dsk3/ftp05/MQ63389.pdf.

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Nunes, Neuza Filipa Martins. "Algorithms for time series clustering applied to biomedical signals." Master's thesis, Faculdade de Ciências e Tecnologia, 2011. http://hdl.handle.net/10362/5666.

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Thesis submitted in the fulfillment of the requirements for the Degree of Master in Biomedical Engineering
The increasing number of biomedical systems and applications for human body understanding creates a need for information extraction tools to use in biosignals. It’s important to comprehend the changes in the biosignal’s morphology over time, as they often contain critical information on the condition of the subject or the status of the experiment. The creation of tools that automatically analyze and extract relevant attributes from biosignals, providing important information to the user, has a significant value in the biosignal’s processing field. The present dissertation introduces new algorithms for time series clustering, where we are able to separate and organize unlabeled data into different groups whose signals are similar to each other. Signal processing algorithms were developed for the detection of a meanwave, which represents the signal’s morphology and behavior. The algorithm designed computes the meanwave by separating and averaging all cycles of a cyclic continuous signal. To increase the quality of information given by the meanwave, a set of wave-alignment techniques was also developed and its relevance was evaluated in a real database. To evaluate our algorithm’s applicability in time series clustering, a distance metric created with the information of the automatic meanwave was designed and its measurements were given as input to a K-Means clustering algorithm. With that purpose, we collected a series of data with two different modes in it. The produced algorithm successfully separates two modes in the collected data with 99.3% of efficiency. The results of this clustering procedure were compared to a mechanism widely used in this area, which models the data and uses the distance between its cepstral coefficients to measure the similarity between the time series.The algorithms were also validated in different study projects. These projects show the variety of contexts in which our algorithms have high applicability and are suitable answers to overcome the problems of exhaustive signal analysis and expert intervention. The algorithms produced are signal-independent, and therefore can be applied to any type of signal providing it is a cyclic signal. The fact that this approach doesn’t require any prior information and the preliminary good performance make these algorithms powerful tools for biosignals analysis and classification.
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Tachibana, Kanta, Takeshi Furuhashi, Tomohiro Yoshikawa, Eckhard Hitzer, and MINH TUAN PHAM. "Clustering of Questionnaire Based on Feature Extracted by Geometric Algebra." 日本知能情報ファジィ学会, 2008. http://hdl.handle.net/2237/20676.

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Session ID: FR-G2-2
Joint 4th International Conference on Soft Computing and Intelligent Systems and 9th International Symposium on advanced Intelligent Systems, September 17-21, 2008, Nagoya University, Nagoya, Japan
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Hasnat, Md Abul. "Unsupervised 3D image clustering and extension to joint color and depth segmentation." Thesis, Saint-Etienne, 2014. http://www.theses.fr/2014STET4013/document.

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L'accès aux séquences d'images 3D s'est aujourd'hui démocratisé, grâce aux récentes avancées dans le développement des capteurs de profondeur ainsi que des méthodes permettant de manipuler des informations 3D à partir d'images 2D. De ce fait, il y a une attente importante de la part de la communauté scientifique de la vision par ordinateur dans l'intégration de l'information 3D. En effet, des travaux de recherche ont montré que les performances de certaines applications pouvaient être améliorées en intégrant l'information 3D. Cependant, il reste des problèmes à résoudre pour l'analyse et la segmentation de scènes intérieures comme (a) comment l'information 3D peut-elle être exploitée au mieux ? et (b) quelle est la meilleure manière de prendre en compte de manière conjointe les informations couleur et 3D ? Nous abordons ces deux questions dans cette thèse et nous proposons de nouvelles méthodes non supervisées pour la classification d'images 3D et la segmentation prenant en compte de manière conjointe les informations de couleur et de profondeur. A cet effet, nous formulons l'hypothèse que les normales aux surfaces dans les images 3D sont des éléments à prendre en compte pour leur analyse, et leurs distributions sont modélisables à l'aide de lois de mélange. Nous utilisons la méthode dite « Bregman Soft Clustering » afin d'être efficace d'un point de vue calculatoire. De plus, nous étudions plusieurs lois de probabilités permettant de modéliser les distributions de directions : la loi de von Mises-Fisher et la loi de Watson. Les méthodes de classification « basées modèles » proposées sont ensuite validées en utilisant des données de synthèse puis nous montrons leur intérêt pour l'analyse des images 3D (ou de profondeur). Une nouvelle méthode de segmentation d'images couleur et profondeur, appelées aussi images RGB-D, exploitant conjointement la couleur, la position 3D, et la normale locale est alors développée par extension des précédentes méthodes et en introduisant une méthode statistique de fusion de régions « planes » à l'aide d'un graphe. Les résultats montrent que la méthode proposée donne des résultats au moins comparables aux méthodes de l'état de l'art tout en demandant moins de temps de calcul. De plus, elle ouvre des perspectives nouvelles pour la fusion non supervisée des informations de couleur et de géométrie. Nous sommes convaincus que les méthodes proposées dans cette thèse pourront être utilisées pour la classification d'autres types de données comme la parole, les données d'expression en génétique, etc. Elles devraient aussi permettre la réalisation de tâches complexes comme l'analyse conjointe de données contenant des images et de la parole
Access to the 3D images at a reasonable frame rate is widespread now, thanks to the recent advances in low cost depth sensors as well as the efficient methods to compute 3D from 2D images. As a consequence, it is highly demanding to enhance the capability of existing computer vision applications by incorporating 3D information. Indeed, it has been demonstrated in numerous researches that the accuracy of different tasks increases by including 3D information as an additional feature. However, for the task of indoor scene analysis and segmentation, it remains several important issues, such as: (a) how the 3D information itself can be exploited? and (b) what is the best way to fuse color and 3D in an unsupervised manner? In this thesis, we address these issues and propose novel unsupervised methods for 3D image clustering and joint color and depth image segmentation. To this aim, we consider image normals as the prominent feature from 3D image and cluster them with methods based on finite statistical mixture models. We consider Bregman Soft Clustering method to ensure computationally efficient clustering. Moreover, we exploit several probability distributions from directional statistics, such as the von Mises-Fisher distribution and the Watson distribution. By combining these, we propose novel Model Based Clustering methods. We empirically validate these methods using synthetic data and then demonstrate their application for 3D/depth image analysis. Afterward, we extend these methods to segment synchronized 3D and color image, also called RGB-D image. To this aim, first we propose a statistical image generation model for RGB-D image. Then, we propose novel RGB-D segmentation method using a joint color-spatial-axial clustering and a statistical planar region merging method. Results show that, the proposed method is comparable with the state of the art methods and requires less computation time. Moreover, it opens interesting perspectives to fuse color and geometry in an unsupervised manner. We believe that the methods proposed in this thesis are equally applicable and extendable for clustering different types of data, such as speech, gene expressions, etc. Moreover, they can be used for complex tasks, such as joint image-speech data analysis
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Fahrni, Angela Petra [Verfasser], and Michael [Akademischer Betreuer] Strube. "Joint Discourse-aware Concept Disambiguation and Clustering / Angela Petra Fahrni ; Betreuer: Michael Strube." Heidelberg : Universitätsbibliothek Heidelberg, 2016. http://d-nb.info/1180614704/34.

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Coles, Lisa. "Functional kinematic study of knee replacement : the effect of implant design and alignment on the patellofemoral joint." Thesis, University of Bath, 2015. https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.642032.

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Total knee arthroplasty is a widely used and relatively successful procedure, designed to relieve pain and restore function to patients suffering from osteoarthritis. However, satisfaction following the procedure is low. One of the primary sources of pain and a cause of functional limitations following knee arthroplasty is the patellofemoral joint. Reasons for pain in the patellofemoral joint are not well understood but adverse patellofemoral biomechanics are thought to contribute. Many in vitro methods exist for the investigation of patellofemoral joint biomechanics but there is no consistent standard protocol. It is therefore difficult to draw any general conclusions regarding the effect of specific design or alignment factors on the biomechanics of the patellofemoral joint. The present study aimed to improve current understanding of factors contributing to patellofemoral complications. A knee simulator, which was based on the Oxford Knee Rig and included synthetic models for a number of soft tissue and bony structures, was developed. The simulator was demonstrated to provide a simplified but physiologically relevant model of the human knee, which allowed effective assessment of patellofemoral joint biomechanics under physiological loads. The system eliminated the need for cadaveric tissue and therefore demonstrated reduced variability, enabling the efficient assessment of a number of potentially influencing factors. A number of investigations were carried out using the simulator to assess the effect of patella component design and position, and femoral component alignment on patellofemoral biomechanics using the Scorpio NRG system. The results of these studies indicate the benefit of medialisation of the apex of the patella component and warn against excessive femoral component sagittal plane malalignment. However, in general they indicated the relatively forgiving nature of the Scorpio system to malalignment and highlighted that irrespective of alignment and patella component design, pressures in excess of material limits are frequently achieved in deep flexion.
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Books on the topic "Joint clustering with alignment"

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Physician integration & alignment: IPA, PHO, ACOS and beyond. Boca Raton: Taylor & Francis, 2013.

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Commission, Kenya Human Rights. Harmonization of decentralized development in Kenya: Towards alignment, citizen engagement, and enhanced accountability : a joint research report. 2nd ed. Nairobi]: KHRC, 2010.

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NCCER. 29110-09 Joint Fit-Up and Alignment. Pearson Education, Limited, 2009.

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NCCER. 29109-03 Joint Fit-Up and Alignment IG. Pearson Education, Limited, 2003.

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NCCER. 29109-03 Joint Fit-up and Alignment TG. Pearson Education, Limited, 2003.

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NCCER. 29110-14 Joint Fit-Up and Alignment Trainee Guide. Pearson Education, Limited, 2015.

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NCCER. ES29110-09 Joint Fit-Up and Alignment Trainee Guide in Spanish. Pearson, 2013.

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Todd, Maria K. Physician Integration and Alignment: IPA, PHO, ACOs, and Beyond. Productivity Press, 2012.

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Todd, Maria K. Physician Integration and Alignment: IPA, PHO, ACOs, and Beyond. Productivity Press, 2012.

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The effects of fixed and hinged ankle foot orthoses on gait myoelectric activity and standing joint alignment in children with cerebral palsy. 1990.

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Book chapters on the topic "Joint clustering with alignment"

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Sangalli, Laura M., Piercesare Secchi, Simone Vantini, and Valeria Vitelli. "Joint Clustering and Alignment of Functional Data: An Application to Vascular Geometries." In Advanced Statistical Methods for the Analysis of Large Data-Sets, 33–43. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-21037-2_4.

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Chatain, Thomas, Josep Carmona, and Boudewijn van Dongen. "Alignment-Based Trace Clustering." In Conceptual Modeling, 295–308. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-69904-2_24.

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Evermann, Joerg, Tom Thaler, and Peter Fettke. "Clustering Traces Using Sequence Alignment." In Business Process Management Workshops, 179–90. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-42887-1_15.

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Chen, Dong, Shaoqing Ren, Yichen Wei, Xudong Cao, and Jian Sun. "Joint Cascade Face Detection and Alignment." In Computer Vision – ECCV 2014, 109–22. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-10599-4_8.

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Du, Liang, and Yi-Dong Shen. "Joint Clustering and Feature Selection." In Web-Age Information Management, 241–52. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-38562-9_25.

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Mori, Yuichi, Masahiro Kuroda, and Naomichi Makino. "Joint Dimension Reduction and Clustering." In Nonlinear Principal Component Analysis and Its Applications, 57–64. Singapore: Springer Singapore, 2016. http://dx.doi.org/10.1007/978-981-10-0159-8_6.

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Hu, Tianming, Liping Liu, Chao Qu, and Sam Yuan Sung. "Joint Cluster Based Co-clustering for Clustering Ensembles." In Advanced Data Mining and Applications, 284–95. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11811305_32.

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Blakeney, William G., and Pascal-André Vendittoli. "Restricted Kinematic Alignment: The Ideal Compromise?" In Personalized Hip and Knee Joint Replacement, 197–206. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-24243-5_17.

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Ambra, Luiz Felipe, Andreas H. Gomoll, and Jack Farr. "Coronal and Axial Alignment: The Effects of Malalignment." In Joint Preservation of the Knee, 41–56. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-01491-9_3.

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Zafeiriou, Lazaros, Epameinondas Antonakos, Stefanos Zafeiriou, and Maja Pantic. "Joint Unsupervised Face Alignment and Behaviour Analysis." In Computer Vision – ECCV 2014, 167–83. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-10593-2_12.

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Conference papers on the topic "Joint clustering with alignment"

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Wang, Siwei, Xinwang Liu, En Zhu, Chang Tang, Jiyuan Liu, Jingtao Hu, Jingyuan Xia, and Jianping Yin. "Multi-view Clustering via Late Fusion Alignment Maximization." 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.2019/524.

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Multi-view clustering (MVC) optimally integrates complementary information from different views to improve clustering performance. Although demonstrating promising performance in many applications, we observe that most of existing methods directly combine multiple views to learn an optimal similarity for clustering. These methods would cause intensive computational complexity and over-complicated optimization. In this paper, we theoretically uncover the connection between existing k-means clustering and the alignment between base partitions and consensus partition. Based on this observation, we propose a simple but effective multi-view algorithm termed {Multi-view Clustering via Late Fusion Alignment Maximization (MVC-LFA)}. In specific, MVC-LFA proposes to maximally align the consensus partition with the weighted base partitions. Such a criterion is beneficial to significantly reduce the computational complexity and simplify the optimization procedure. Furthermore, we design a three-step iterative algorithm to solve the new resultant optimization problem with theoretically guaranteed convergence. Extensive experiments on five multi-view benchmark datasets demonstrate the effectiveness and efficiency of the proposed MVC-LFA.
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Liu, Teng L., Yu Zhang, and Jonathan H. Dennis. "Joint clustering and alignment for nucleosome occupancy analysis." In 2012 IEEE International Conference on Bioinformatics and Biomedicine Workshops (BIBMW). IEEE, 2012. http://dx.doi.org/10.1109/bibmw.2012.6470269.

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Li, Qi, Zhenan Sun, Ran He, and Tieniu Tan. "Joint Alignment and Clustering via Low-Rank Representation." In 2013 2nd IAPR Asian Conference on Pattern Recognition (ACPR). IEEE, 2013. http://dx.doi.org/10.1109/acpr.2013.66.

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Ben Halima, Slim, and Ahmed Saadani. "Joint clustering and interference alignment for overloaded femtocell networks." In 2012 IEEE Wireless Communications and Networking Conference (WCNC). IEEE, 2012. http://dx.doi.org/10.1109/wcnc.2012.6213965.

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Liu, Rui, Wei Cheng, Hanghang Tong, Wei Wang, and Xiang Zhang. "Robust Multi-Network Clustering via Joint Cross-Domain Cluster Alignment." In 2015 IEEE International Conference on Data Mining (ICDM). IEEE, 2015. http://dx.doi.org/10.1109/icdm.2015.13.

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Zeng, Xiangrui, Gregory Howe, and Min Xu. "End-to-end robust joint unsupervised image alignment and clustering." In 2021 IEEE/CVF International Conference on Computer Vision (ICCV). IEEE, 2021. http://dx.doi.org/10.1109/iccv48922.2021.00383.

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Lin, Fangfei, Bing Bai, Kun Bai, Yazhou Ren, Peng Zhao, and Zenglin Xu. "Contrastive Multi-view Hyperbolic Hierarchical Clustering." In Thirty-First International Joint Conference on Artificial Intelligence {IJCAI-22}. California: International Joint Conferences on Artificial Intelligence Organization, 2022. http://dx.doi.org/10.24963/ijcai.2022/451.

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Hierarchical clustering recursively partitions data at an increasingly finer granularity. In real-world applications, multi-view data have become increasingly important. This raises a less investigated problem, i.e., multi-view hierarchical clustering, to better understand the hierarchical structure of multi-view data. To this end, we propose a novel neural network-based model, namely Contrastive Multi-view Hyperbolic Hierarchical Clustering(CMHHC). It consists of three components, i.e., multi-view alignment learning, aligned feature similarity learning, and continuous hyperbolic hierarchical clustering. First, we align sample-level representations across multiple views in a contrastive way to capture the view-invariance information. Next, we utilize both the manifold and Euclidean similarities to improve the metric property. Then, we embed the representations into a hyperbolic space and optimize the hyperbolic embeddings via a continuous relaxation of hierarchical clustering loss. Finally, a binary clustering tree is decoded from optimized hyperbolic embeddings. Experimental results on five real-world datasets demonstrate the effectiveness of the proposed method and its components.
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Wang, Fang, Yongqiang Xie, Kai Zhang, and Rui Xia. "A Joint Model of Adaptive Clustering and Multi-kernel Learning for Entity Alignment." In BDSIC 2021: 2021 3rd International Conference on Big-data Service and Intelligent Computation. New York, NY, USA: ACM, 2021. http://dx.doi.org/10.1145/3502300.3502313.

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Hu, Menglei, and Songcan Chen. "Doubly Aligned Incomplete Multi-view Clustering." 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.2018/313.

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Nowadays, multi-view clustering has attracted more and more attention. To date, almost all the previous studies assume that views are complete. However, in reality, it is often the case that each view may contain some missing instances. Such incompleteness makes it impossible to directly use traditional multi-view clustering methods. In this paper, we propose a Doubly Aligned Incomplete Multi-view Clustering algorithm (DAIMC) based on weighted semi-nonnegative matrix factorization (semi-NMF). Specifically, on the one hand, DAIMC utilizes the given instance alignment information to learn a common latent feature matrix for all the views. On the other hand, DAIMC establishes a consensus basis matrix with the help of L2,1-Norm regularized regression for reducing the influence of missing instances. Consequently, compared with existing methods, besides inheriting the strength of semi-NMF with ability to handle negative entries, DAIMC has two unique advantages: 1) solving the incomplete view problem by introducing a respective weight matrix for each view, making it able to easily adapt to the case with more than two views; 2) reducing the influence of view incompleteness on clustering by enforcing the basis matrices of individual views being aligned with the help of regression. Experiments on four real-world datasets demonstrate its advantages.
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Zhai, Yuyao, Liang Chen, and Minghua Deng. "Realistic Cell Type Annotation and Discovery for Single-cell RNA-seq Data." In Thirty-Second International Joint Conference on Artificial Intelligence {IJCAI-23}. California: International Joint Conferences on Artificial Intelligence Organization, 2023. http://dx.doi.org/10.24963/ijcai.2023/552.

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The rapid development of single-cell RNA sequencing (scRNA-seq) technologies allows us to explore tissue heterogeneity at the cellular level. Cell type annotation plays an essential role in the substantial downstream analysis of scRNA-seq data. Existing methods usually classify the novel cell types in target data as an “unassigned” group and rarely discover the fine-grained cell type structure among them. Besides, these methods carry risks, such as susceptibility to batch effect between reference and target data, thus further compromising of inherent discrimination of target data. Considering these limitations, here we propose a new and practical task called realistic cell type annotation and discovery for scRNA-seq data. In this task, cells from seen cell types are given class labels, while cells from novel cell types are given cluster labels. To tackle this problem, we propose an end-to-end algorithm framework called scPOT from the perspective of optimal transport (OT). Specifically, we first design an OT-based prototypical representation learning paradigm to encourage both global discriminations of clusters and local consistency of cells to uncover the intrinsic structure of target data. Then we propose an unbalanced OT-based partial alignment strategy with statistical filling to detect the cells from the seen cell types across reference and target data. Notably, scPOT also introduces an easy yet effective solution to automatically estimate the overall cell type number in target data. Extensive results on our carefully designed evaluation benchmarks demonstrate the superiority of scPOT over various state-of-the-art clustering and annotation methods.
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Reports on the topic "Joint clustering with alignment"

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Shaver, Charles. Comparative Analysis of Tier 1 Joint Capability Area (JCA) Alignment with Joint Functions. Fort Belvoir, VA: Defense Technical Information Center, December 2010. http://dx.doi.org/10.21236/ada537302.

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Haigh, Susan, and Mary Lee Kennedy. Observations on Research Libraries’ Alignment with Institutional STEM Priorities / Observations quant à l’alignement des bibliothèques de recherche sur les priorités institutionnelles en STIM. Association of Research Libraries and Canadian Association of Research Libraries, May 2023. http://dx.doi.org/10.29242/report.stem2023.

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This report published by the Association of Research Libraries (ARL) and the Canadian Association of Research Libraries (CARL) synthesizes the two associations’ joint exploration of the need for, and nature of, alignment of research libraries with their universities’ STEM priorities. The report notes the challenges to be overcome, and provides examples of the ways libraries are already working to strengthen and support STEM at their institutions. The report includes a summary of common themes as well as observations of each institution visited.
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Bergsen, Pepijn, Carolina Caeiro, Harriet Moynihan, Marianne Schneider-Petsinger, and Isabella Wilkinson. Digital trade and digital technical standards. Royal Institute of International Affairs, January 2022. http://dx.doi.org/10.55317/9781784135133.

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There is increasing impetus for stronger cooperation between the US, EU and UK on digital technology governance. Drivers of this trend include the economic incentives arising from opportunities for digital trade; the ambition for digital technology governance to be underpinned by shared values, including support for a democratic, open and global internet; and the need to respond to geopolitical competition, especially from China. Two specific areas of governance in which there is concrete potential to collaborate, and in which policymakers have indicated significant ambitions to do so, are digital trade and digital technical standards. - To leverage strategic opportunities for digital trade, the US, EU and UK need to continue identifying and promoting principles based on shared values and agendas, and demonstrate joint leadership at the global level, including in the World Trade Organization (WTO) on e-commerce. - Policy actors in the US, EU and UK should work individually and collectively to build on the latest generation of digital trade agreements. This will help to promote closer alignment on digital rules and standards, and support the establishment of more up-to-date models for innovation and governance. - Collaborating on digital technical standards, particularly those underlying internet governance and emerging technologies, offers the US, EU and UK strategic opportunities to build a vision of digital technology governance rooted in multi-stakeholder participation and democratic values. This can provide a strong alternative to standards proposals such as China’s ‘New IP’ system. - Policy actors should seek to expand strategic cooperation on standards development among the US, EU and UK, among like-minded countries, and among states that are undecided on the direction of their technology governance, including in the Global South. They should also take practical steps to incorporate the views and expertise of the technology industry, the broader private sector, academia and civil society. By promoting best-practice governance models that are anticipatory, dynamic and flexible, transatlantic efforts for cooperation on digital regulation can better account for the rapid pace of technological change. Early evidence of this more forward-looking approach is emerging through the EU’s proposed regulation of digital services and artificial intelligence (AI), and in the UK’s proposed legislation to tackle online harms. The recently launched EU-US Trade and Technology Council is a particularly valuable platform for strengthening cooperation in this arena. But transatlantic efforts to promote a model of digital governance predicated on democratic values would stand an even greater chance of success if the council’s work were more connected to efforts by the UK and other leading democracies
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