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Journal articles on the topic 'Joint clustering with alignment'

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Author: Grafiati

Published: 20 April 2024

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1

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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2

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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5

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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6

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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7

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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8

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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10

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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11

Bigvand, Anahita Mansouri, Te Bu, and Anoop Sarkar. "Joint Prediction of Word Alignment with Alignment Types." Transactions of the Association for Computational Linguistics 5 (December 2017): 501–14. http://dx.doi.org/10.1162/tacl_a_00076.

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Current word alignment models do not distinguish between different types of alignment links. In this paper, we provide a new probabilistic model for word alignment where word alignments are associated with linguistically motivated alignment types. We propose a novel task of joint prediction of word alignment and alignment types and propose novel semi-supervised learning algorithms for this task. We also solve a sub-task of predicting the alignment type given an aligned word pair. In our experimental results, the generative models we introduce to model alignment types significantly outperform the models without alignment types.

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12

Corpet, F. "Multiple sequence alignment with hierarchical clustering." Nucleic Acids Research 16, no. 22 (November 25, 1988): 10881–90. http://dx.doi.org/10.1093/nar/16.22.10881.

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13

Feng, Zijin, Miao Qiao, and Hong Cheng. "Modularity-based Hypergraph Clustering: Random Hypergraph Model, Hyperedge-cluster Relation, and Computation." Proceedings of the ACM on Management of Data 1, no. 3 (November 13, 2023): 1–25. http://dx.doi.org/10.1145/3617335.

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A graph models the connections among objects. One important graph analytical task is clustering which partitions a data graph into clusters with dense innercluster connections. A line of clustering maximizes a function called modularity. Modularity-based clustering is widely adopted on dyadic graphs due to its scalability and clustering quality which depends highly on its selection of a random graph model. The random graph model decides not only which clustering is preferred - modularity measures the quality of a clustering based on its alignment to the edges of a random graph, but also the cost of computing such an alignment. Existing random hypergraph models either measure the hyperedge-cluster alignment in an All-Or-Nothing (AON) manner, losing important group-wise information, or introduce expensive alignment computation, refraining the clustering from scaling up. This paper proposes a new random hypergraph model called Hyperedge Expansion Model (HEM), a non-AON hypergraph modularity function called Partial Innerclusteredge modularity (PI) based on HEM, a clustering algorithm called Partial Innerclusteredge Clustering (PIC) that optimizes PI, and novel computation optimizations. PIC is a scalable modularity-based hypergraph clustering that can effectively capture the non-AON hyperedge-cluster relation. Our experiments show that PIC outperforms eight state-of-the-art methods on real-world hypergraphs in terms of both clustering quality and scalability and is up to five orders of magnitude faster than the baseline methods.

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14

Zhang, Gang, Lijia Pan, Jiansheng Chen, Yanmin Gong, and Fuyuan Liu. "Joint Alignment of Image Faces." IEEE Access 8 (2020): 114884–91. http://dx.doi.org/10.1109/access.2020.3003332.

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15

Yoshimoto, Kensei, Masahiko Noguchi, Akifumi Yamada, and Yuki Nasu. "Compensatory Function of the Subtalar Joint for Lower Extremity Malalignment." Advances in Orthopedics 2019 (February 24, 2019): 1–8. http://dx.doi.org/10.1155/2019/7656878.

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It is important to evaluate the subtalar joint and hip-knee-ankle alignment to understand lower extremity alignment. In this review, we focused on the compensatory changes in the subtalar joint alignment for the deformity of the knee and ankle joint, reviewing previous research. The subtalar joint alignment was compensatory valgus in patients with varus knee and ankle deformity, whereas it was uncertain whether the subtalar joint alignment was compensatory varus in patients with valgus knee and ankle deformity. The subtalar joint valgus alignment improved after total knee arthroplasty or high tibial osteotomy for varus knee deformity, even if the deformity was severe. In contrast, whether the subtalar joint alignment changed after the surgery for ankle or valgus knee deformity has not been considered. Further research on the compensatory function of the subtalar joint is needed.

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Niu, Jianwei, Zhizhong Li, and Gavriel Salvendy. "Alignment Influence on 3D Anthropometric Data Clustering." Ergonomics Open Journal 1, no. 1 (November 17, 2008): 62–66. http://dx.doi.org/10.2174/1875934300801010062.

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17

Zemla, A., B. Geisbrecht, J. Smith, M. Lam, B. Kirkpatrick, M. Wagner, T. Slezak, and C. E. Zhou. "STRALCP structure alignment-based clustering of proteins." Nucleic Acids Research 35, no. 22 (November 26, 2007): e150-e150. http://dx.doi.org/10.1093/nar/gkm1049.

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18

Traynor, Christopher, and James Jastifer. "First-Tarsometatarsal Joint Alignment After First-Metatarsophalangeal Joint Arthrodesis for Hallux Valgus." Foot & Ankle Orthopaedics 6, no. 2 (January 1, 2021): 247301142110085. http://dx.doi.org/10.1177/24730114211008514.

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Background: Instability of the first-tarsometatarsal (TMT) joint has been proposed as a cause of hallux valgus. Although there is literature demonstrating how first-TMT arthrodesis affects hallux valgus, there is little published on how correction of hallux valgus affects the first-TMT joint alignment. The purpose of this study was to determine if correction of hallux valgus impacts the first-TMT alignment and congruency. Improvement in alignment would provide evidence that hallux valgus contributes to first-TMT instability. Our hypothesis was that correcting hallux valgus angle (HVA) would have no effect on the first-TMT alignment and congruency. Methods: Radiographs of patients who underwent first-MTP joint arthrodesis for hallux valgus were retrospectively reviewed. The HVA, 1-2 intermetatarsal angle (IMA), first metatarsal–medial cuneiform angle (1MCA), medial cuneiform–first metatarsal angle (MC1A), relative cuneiform slope (RCS), and distal medial cuneiform angle (DMCA) were measured and recorded for all patients preoperatively and postoperatively. Results: Of the 76 feet that met inclusion criteria, radiographic improvements were noted in HVA (23.6 degrees, P < .0001), 1-2 IMA (6.2 degrees, P < .0001), 1MCA (6.4 degrees, P < .0001), MC1A (6.5 degrees, P < .0001), and RCS (3.3 degrees, P = .001) comparing preoperative and postoperative radiographs. There was no difference noted with DMCA measurements (0.5 degrees, P = .53). Conclusion: Our findings indicate that the radiographic alignment and subluxation of the first-TMT joint will reduce with isolated treatment of the first-MTP joint. Evidence suggests that change in the HVA can affect radiographic alignment and subluxation of the first-TMT joint. Level of Evidence: Level IV, retrospective case series.

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Rahman, Faiz Aulia, Utriweni Mukhaiyar, and Sparisoma Virdi. "Analysis of Boid Algorithm Weights using Alignment Clustering Index." BIO Web of Conferences 92 (2024): 01016. http://dx.doi.org/10.1051/bioconf/20249201016.

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Fish that engage in a school often exhibit unique patterns. Schooling behaviour itself has multiple benefits, among them increasing their likelihood of obtaining food and reducing the risk of predator threats. One of the methods used to simulate this behaviour is boid, proposed by Reynolds. There are three main rules that govern how each boid moves in a group: cohesion, alignment, and separation. Each rule is controlled by a weight parameter that could be changed to encourage or discourage certain rules. This study is aimed at analysing the boid weight parameters, specifically cohesion and alignment, utilizing the Alignment Clustering Index (ACI). The findings indicate that as the weight of alignment increases and the weight of cohesion decreases, boids tend to exhibit more pronounced flocking behaviour. On the contrary, as the weight of cohesion increases and alignment weight decreases, boids move as smaller subgroups comprising about 2-3 members each.

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Krähenbühl, Nicola, Lukas Zwicky, Manja Deforth, Beat Hintermann, and Markus Knupp. "Subtalar Joint Alignment in Ankle Osteoarthritis." Foot & Ankle Orthopaedics 2, no. 3 (September 1, 2017): 2473011417S0002. http://dx.doi.org/10.1177/2473011417s000249.

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Category: Ankle Arthritis, Hindfoot Introduction/Purpose: The influence of the subtalar joint on the evolution of ankle joint osteoarthritis is still a matter of debate. Although subtalar joint compensation of deformities above the ankle joint was proposed until mid-stage of ankle osteoarthritis, the evidence of this assumption is weak. In this study, we investigated the subtalar joint alignment in different stages of ankle joint osteoarthritis using weightbearing CT scans. The influence of the tibio-talar tilt and presence of subtalar joint osteoarthritis was additionally assessed. We hypothesized, that the subtalar joint compensates for deformities above the ankle joint in early- to mid-stage of ankle osteoarthritis. We also hypothesized, that subtalar joint compensation increases with a pronounced tibio-talar tilt and decreases with the presence of subtalar joint osteoarthritis. Methods: We included patients with ankle joint osteoarthritis treated in our institution from January 2013 to April 2016. A control group of 28 patients was additionally assessed. Varus and valgus ankles were subdivided according to the modified Takakura classification, the tilt of the talus in the ankle mortise and stage of subtalar joint osteoarthritis. The type of ankle osteoarthritis was diagnosed on a plain weightbearing anterior to posterior radiograph of the ankle. The medial distal tibial angle (TAS) and the angle between the tibial shaft and the surface of the talar dome (TTS) were measured. The subtalar joint alignment was assessed using weightbearing CT scans. Two angles were assessed: The subtalar inclination angle (SIA) was measured to investigate the subtalar compensation. For assessment of the morphology of the talus, the inftal-subtal angle (ISA) was determined. Results: This analysis showed significant differences of the subtalar inclination between varus feet and the controls (SIA, P=.001). Regarding the talar morphology, significant differences were found between varus/ valgus feet and the controls (ISA, P=.001 and .036, respectively). No significant differences of the subtalar joint inclination and talar morphology could be identified comparing different stages of ankle joint osteoarthritis inside the varus or valgus group. No relationship between the tilt of the talus in the ankle joint mortise and the subtalar joint inclination or talar morphology was identified. Neither presence nor absence of subtalar joint osteoarthritis influenced the subtalar joint inclination and talar morphology. Conclusion: Varus ankles compensate in the subtalar joint for deformities above the ankle joint. Compensation had no influence on the stage of ankle osteoarthritis, extent of the tibio-talar tilt and stage of subtalar joint osteoarthritis. Consequently, the progression of ankle joint osteoarthritis is more depended on the supramalleolar alignment and integrity of the periarticular structures (i.e. ligaments and tendons) than on the osseous alignment of the subtalar joint.

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Zhang, Hongmei, Yubo Zou, Will Terry, Wilfried Karmaus, and Hasan Arshad. "Joint Clustering With Correlated Variables." American Statistician 73, no. 3 (July 9, 2018): 296–306. http://dx.doi.org/10.1080/00031305.2018.1424033.

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Jeong, Bi O., Jong Hun Baek, and Wookjae Song. "Changes in the Ankle Joint and Hindfoot Alignment Following Varus Deformity Correction of the Knee with Total Knee Arthroplasty." Foot & Ankle Orthopaedics 2, no. 3 (September 1, 2017): 2473011417S0000. http://dx.doi.org/10.1177/2473011417s000051.

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Category: Ankle, Hindfoot Introduction/Purpose: Compensatory changes occur in the ankle joint and the subtalar joint of the hindfoot to maintain neutral alignment in coronal plane when varus or valgus deformity is present in the knee joint. The purpose of this study is (1) to analyze how the ankle and hindfoot joint compensate the changes in mechanical axis that occur with varus deformity of the knee, (2) to analyze the changes in ankle and hindfoot joint alignment when mechanical axis alignment is corrected by correction of varus deformity of the knee with total knee arthroplasty (TKA), and (3) to radiographically evaluate the degree of changes in ankle and hindfoot joint alignment according to the degree of varus deformity correction. Methods: 375 knees that underwent TKA with varus deformity were prospectively studied. The degree of varus deformity of the knee joint was measured in standing long leg anteroposterior views taken before and 6 months after TKA. The angle between the ground surface and the superior dome of the talus, and the talar tilt were measured in standing ankle joint anteroposterior views taken before and 6 months after TKA. Preoperative tibial anterior surface angle and tibial lateral surface angle were measured. Hindfoot alignment was evaluated in hindfoot alignment views taken before and 6 months after operation by measuring the heel ratio, the heel angle, and the heel distance. These measurements were used to analyze the change in ankle joint and hindfoot alignment between before and 6 months after varus deformity correction. The relationship between the degree of varus knee correction and the change in ankle joint and hindfoot alignment was also analyzed. Results: The mean mechanical angle of the knee changed from varus 10.6±5.1° before operation to varus 0.1±3.2° after operation, which was statistically significant (P<0.001). The mean correction angle was 10.6±4.6°. Before operation, the mean tibial anterior surface angle was 89.9±3.1° and the mean tibial lateral surface angle was 81.5±2.9°. Talar tilt changed significantly from 0.3±2.0° to 0.0±1.6°, implying a varus change of the ankle joint (P=0.002). All of the evaluated hindfoot alignment parameters demonstrated a significant varus change (P<0.001), as the heel ratio changed from 0.2±0.2 preoperatively to 0.3±0.2 postoperatively (P<0.001), the heel angle changed from 11.4±7.0° preoperatively to 5.0±7.4°postoperatively (P<0.001), and the heel distance changed from 10.5±6.6 mm preoperatively to 6.4±6.4 mm postoperatively (P<0.000). Conclusion: Varus knee deformity tended to be associated with valgus deformity of the ankle joint and hindfoot alignment. Alignment in the ankle joint and the hindfoot both changed to a varus trend after correction of varus deformity of the knee. Such changes in ankle joint and hindfoot alignment are considered to be due to the compensatory changes following alterations in lower limb alignment. Therefore, it is necessary to perform a meticulous examination of the ankle joint as well as the knee joint and consider subsequent alteration of ankle joint and hindfoot alignment before surgical correction of knee joint alignment.

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Valente de Oliveira, José, Alexandre Szabo, and Leandro Nunes de Castro. "Particle Swarm Clustering in clustering ensembles: Exploiting pruning and alignment free consensus." Applied Soft Computing 55 (June 2017): 141–53. http://dx.doi.org/10.1016/j.asoc.2017.01.035.

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Rust, P. A., E. T. H. Ek, and S. K. Y. Tham. "Assessment of normal trapeziometacarpal joint alignment." Journal of Hand Surgery (European Volume) 42, no. 6 (February 14, 2017): 605–9. http://dx.doi.org/10.1177/1753193417690473.

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The purpose of this study was to determine the alignment of the normal trapeziometacarpal joint and any changes in its alignment with age. Radial, dorsal and dorsoradial subluxation were measured on computerized tomographic scans in 50 joints of 50 adults aged 18 to 62. There were statistically significant correlations between increasing age and dorsoradial subluxation but no significant correlation with dorsal and radial subluxation. Significant dorsoradial subluxation occurs after 46 years of age in the normal trapeziometacarpal joint. A mean dorsoradial subluxation of 21% (range 14%–30%) can be considered normal in this age group.

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Garrod, Simon, and Martin J. Pickering. "Joint Action, Interactive Alignment, and Dialog." Topics in Cognitive Science 1, no. 2 (April 2009): 292–304. http://dx.doi.org/10.1111/j.1756-8765.2009.01020.x.

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Krähenbühl, Nicola, Lena Siegler, Manja Deforth, Lukas Zwicky, Beat Hintermann, and Markus Knupp. "Subtalar joint alignment in ankle osteoarthritis." Foot and Ankle Surgery 25, no. 2 (April 2019): 143–49. http://dx.doi.org/10.1016/j.fas.2017.10.004.

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Rachar, Matthew. "Alignment and commitment in joint action." Philosophical Psychology 31, no. 6 (March 21, 2018): 831–49. http://dx.doi.org/10.1080/09515089.2018.1448377.

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Diego, Ferran, Joan Serrat, and Antonio M. Lopez. "Joint Spatio-Temporal Alignment of Sequences." IEEE Transactions on Multimedia 15, no. 6 (October 2013): 1377–87. http://dx.doi.org/10.1109/tmm.2013.2247390.

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Lebsir, Rabah, Abdesslem Layeb, and Tahi Fariza. "A Greedy Clustering Algorithm for Multiple Sequence Alignment." International Journal of Cognitive Informatics and Natural Intelligence 15, no. 4 (October 2021): 1–17. http://dx.doi.org/10.4018/ijcini.20211001.oa41.

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This paper presents a strategy to tackle the Multiple Sequence Alignment (MSA) problem, which is one of the most important tasks in the biological sequence analysis. Its role is to align the sequences in their entirety to derive relationships and common characteristics between a set of protein or nucleotide sequences. The MSA problem was proved to be an NP-Hard problem. The proposed strategy incorporates a new idea based on the well-known divide and conquer paradigm. This paper presents a novel method of clustering sequences as a preliminary step to improve the final alignment; this decomposition can be used as an optimization procedure with any MSA aligner to explore promising alignments of the search space. In their solution, authors proposed to align the clusters in a parallel and distributed way in order to benefit from parallel architectures. The strategy was tested using classical benchmarks like BAliBASE, Sabre, Prefab4 and Oxm, and the experimental results show that it gives good results by comparing to the other aligners.

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Middleton, Sarah A., and Junhyong Kim. "NoFold: RNA structure clustering without folding or alignment." RNA 20, no. 11 (September 18, 2014): 1671–83. http://dx.doi.org/10.1261/rna.041913.113.

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Krissinel, Eugene. "Protein structure alignment using efficient small-fragment clustering." Acta Crystallographica Section A Foundations of Crystallography 66, a1 (August 29, 2010): s314. http://dx.doi.org/10.1107/s0108767310092780.

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Andreatta, Massimo, Bruno Alvarez, and Morten Nielsen. "GibbsCluster: unsupervised clustering and alignment of peptide sequences." Nucleic Acids Research 45, W1 (April 12, 2017): W458—W463. http://dx.doi.org/10.1093/nar/gkx248.

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Liao, Mengmeng, and Xiaodong Gu. "Subspace clustering based on alignment and graph embedding." Knowledge-Based Systems 188 (January 2020): 105029. http://dx.doi.org/10.1016/j.knosys.2019.105029.

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Yu, Jun, Richang Hong, Meng Wang, and Jane You. "Image clustering based on sparse patch alignment framework." Pattern Recognition 47, no. 11 (November 2014): 3512–19. http://dx.doi.org/10.1016/j.patcog.2014.05.002.

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Lu, Yanting, Liantao Wang, Jianfeng Lu, Jingyu Yang, and Chunhua Shen. "Multiple kernel clustering based on centered kernel alignment." Pattern Recognition 47, no. 11 (November 2014): 3656–64. http://dx.doi.org/10.1016/j.patcog.2014.05.005.

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Orabi, Baraa, Emre Erhan, Brian McConeghy, Stanislav V. Volik, Stephane Le Bihan, Robert Bell, Colin C. Collins, Cedric Chauve, and Faraz Hach. "Alignment-free clustering of UMI tagged DNA molecules." Bioinformatics 35, no. 11 (October 23, 2018): 1829–36. http://dx.doi.org/10.1093/bioinformatics/bty888.

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Torarinsson, E., J. H. Havgaard, and J. Gorodkin. "Multiple structural alignment and clustering of RNA sequences." Bioinformatics 23, no. 8 (February 25, 2007): 926–32. http://dx.doi.org/10.1093/bioinformatics/btm049.

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Chen, Sujie, and Roger S. Cheng. "Clustering for Interference Alignment in Multiuser Interference Network." IEEE Transactions on Vehicular Technology 63, no. 6 (July 2014): 2613–24. http://dx.doi.org/10.1109/tvt.2013.2292897.

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Nielsen, Fiona G. G., Kasper Galschiøt Markus, Rune Møllegaard Friborg, Lene Monrad Favrholdt, Hendrik G. Stunnenberg, and Martijn Huynen. "CATCHprofiles: Clustering and Alignment Tool for ChIP Profiles." PLoS ONE 7, no. 1 (January 4, 2012): e28272. http://dx.doi.org/10.1371/journal.pone.0028272.

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Qiu, Liping, Qin Zhang, Xiaojun Chen, and Shaotian Cai. "Multi-Level Cross-Modal Alignment for Image Clustering." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 13 (March 24, 2024): 14695–703. http://dx.doi.org/10.1609/aaai.v38i13.29387.

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Recently, the cross-modal pretraining model has been employed to produce meaningful pseudo-labels to supervise the training of an image clustering model. However, numerous erroneous alignments in a cross-modal pretraining model could produce poor-quality pseudo labels and degrade clustering performance. To solve the aforementioned issue, we propose a novel Multi-level Cross-modal Alignment method to improve the alignments in a cross-modal pretraining model for downstream tasks, by building a smaller but better semantic space and aligning the images and texts in three levels, i.e., instance-level, prototype-level, and semantic-level. Theoretical results show that our proposed method converges, and suggests effective means to reduce the expected clustering risk of our method. Experimental results on five benchmark datasets clearly show the superiority of our new method.

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Bi, Shuyuan, and Wei Liu. "Clustering Analysis of Online Teaching Cases and Evaluation of Teaching Results." International Journal of Emerging Technologies in Learning (iJET) 18, no. 03 (February 15, 2023): 128–42. http://dx.doi.org/10.3991/ijet.v18i03.38055.

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To facilitate front-line teachers to provide more efficient cases for classroom teaching activities in limited teaching time, it is necessary to conduct clustering analysis of online teaching cases. The existing clustering study on text-based teaching resources focuses on the representation and fusion of different views of texts and the discovery of consistent clustering allocation, and ignores the high-dimensional sparsity of text data. To this end, this paper studies clustering analysis of online teaching cases and evaluation of teaching results. This paper gives the design flow of online teaching with case teaching as the core and proposes a text multi-view clustering algorithm based on case subject alignment, and presents a clustering model structure based on subject alignment. Based on the grey system theory, this paper compares online classroom teaching results before and after teachers use clustering algorithm to process teaching cases. Finally, the experimental setup is given, and the effectiveness of the proposed algorithm is verified by experiment.

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DENG, YONGGANG, SHANKAR KUMAR, and WILLIAM BYRNE. "Segmentation and alignment of parallel text for statistical machine translation." Natural Language Engineering 13, no. 3 (July 6, 2006): 235–60. http://dx.doi.org/10.1017/s1351324906004293.

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We address the problem of extracting bilingual chunk pairs from parallel text to create training sets for statistical machine translation. We formulate the problem in terms of a stochastic generative process over text translation pairs, and derive two different alignment procedures based on the underlying alignment model. The first procedure is a now-standard dynamic programming alignment model which we use to generate an initial coarse alignment of the parallel text. The second procedure is a divisive clustering parallel text alignment procedure which we use to refine the first-pass alignments. This latter procedure is novel in that it permits the segmentation of the parallel text into sub-sentence units which are allowed to be reordered to improve the chunk alignment. The quality of chunk pairs are measured by the performance of machine translation systems trained from them. We show practical benefits of divisive clustering as well as how system performance can be improved by exploiting portions of the parallel text that otherwise would have to be discarded. We also show that chunk alignment as a first step in word alignment can significantly reduce word alignment error rate.

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Rakhel Artania, Corry, Yusuf Nasirudin, and Ari Sudarsono. "HUBUNGAN ALIGNMENT KNEE JOINT TERHADAP AGILITY PADA PEMAIN SEPAK BOLA AMATIR SEKOLAH SEPAK BOLA (SSB) USIA 11-14 TAHUN." JURNAL PROFESIONAL FISIOTERAPI 2, no. 1 (January 17, 2023): 5–13. http://dx.doi.org/10.24127/fisioterapi.v2i1.3289.

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Latar Belakang: Alignment knee joint (bentuk sendi lutut) mempunyai hubungan yang negatif dengan tingkat kelincahan. Yang artinya semakin tinggi jarak bentuk sendi lutut maka akan semakin rendah skor dari kemampuan kelincahan. Dan kemampuan kelincahan merupakan salah satu keterampilan paling penting yang harus dimiliki oleh seorang pemain sepak bola sedari usia dini. Metode Penelitian: Penelitian ini merupakan penelitian “cross sectional” yang dilakukan sekali dalam satu waktu untuk mencari hubungan alignment knee joint terhadap agility pada pemain sepakbola amatir sekolah sepak bola (ssb) usia 11-14 tahun yang berlokasi di jakarta dan depok.Hasil : uji hipotesis dilakukan dengan menggunakan uji spearman dengan hasil nilai sig(2-tailed) untuk hubungan antara alignment knee joint dengan agility T test P=0,044 untuk hubungan antara alignment knee joint dengan pengukuran shuttle run test terdapat nilai sig(2-tailed) sebesar P=0,035. Simpulan : Terdapat hubungan yang signifikan antara alignment knee joint terhadap agility pada pemain sepakbola amatir sekolah sepak bola (ssb) usia 11-14 tahun.

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Itikap, Muhammad, and Burlian Mughnie. "KNEE ALIGNMENT IN ADOLESCENT FOOTBALL TRAINEES." Journal of Prosthetics Orthotics and Science Technology 1, no. 1 (September 20, 2022): 45–49. http://dx.doi.org/10.36082/jpost.v1i1.654.

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Genu varum and valgum can trigger for osteoarthritis and happened between 16-18 years old. The study aimed to find out the relationship between knee joint alignment and footballers activities. This was a cross-sectional study, with 64 participants from two groups and only participants whom were meet all inclusion criteria were joined. It showed the knee joint angle in comparison to the norm was very significant with p- value 0.000 and there was a connection to practicing football against knee joint alignment, more specifically varum. In conclusion, there is a significant relationship between practicing football and the knee joint as measured by degree og genu varum and vagum as differences were found in year and duration of training.

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Burssens, Arne, Peter Kvarda, Caspar S. Steiner, Roman Susdorf, Ursina Peterhans, Nicola Krahenbuhl, Alexej Barg, Roxa Ruiz, and Beat Hintermann. "Correction of the Hindfoot Alignment after Supramalleolar Osteomy in Ankle Varus Deformity - A Three-Dimensional Analysis Using Weightbearing CT." Foot & Ankle Orthopaedics 7, no. 1 (January 2022): 2473011421S0000. http://dx.doi.org/10.1177/2473011421s00009.

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Category: Ankle; Ankle Arthritis; Hindfoot Introduction/Purpose: While correction of varus alignment at the level of the ankle joint has been investigated extensively after supramalleolar osteotomy (SMOT), the effect on the hindfoot alignment remains unclear. This can be attributed to the limitations of former 2-dimensional radiographic measurements used to quantify the complex 3-dimensional subtalar joint alignment. Therefore, we aimed to determine both the ankle and subtalar joint alignment before and after SMOT using a weightbearing CT and autogenerated 3-dimensional measurements. Methods: Twenty-seven patients with a mean age of 53 years (SD=10.1; range=25-73) were retrospectively analyzed in a pre- post study design using weightbearing CT images. Inclusion criteria were correction of ankle varus deformity by either an opening wedge (N=19) or dome osteotomy (N=8). Exclusion criteria consisted of an additional inframalleolar bony correction, i.e. calcaneal osteotomy or subtalar arthrodesis. Corresponding three-dimensional bone models were reconstructed to compute the autogenerated measurements: tibial anterior surface (TAS) -, tibiotalar surface (TTS)-, talar tilt (TT) - and talocalcaneal (TC) angle. Results: The pre-operative (TAS=86.9°, SD=4.9; TTS=79.8°, SD=5.6; TT=8.8°, SD=4.3) radiographic parameters of the ankle joint alignment improved significantly compared to the post-operative parameters (TAS=92.4°, SD=4.9; TTS=87.1°, SD=6.3; TT=5.1°, SD=2.7; P<0.05). (Fig. 1A) Radiographic parameters to assess the subtalar joint alignment improved significantly from preoperatively (TCax =42.8°, SD=9.3; TCsag=42.3°,SD=10.9; TCcor =29.5°,SD=11.8) to post-operatively (TCax =37.8°, SD=8.8; TCsag=39.1°, SD=10.6; TCcor=24.6°,SD=9.1; P<0.05). (Fig. 1B) Conclusion: A supramalleolar osteotomy is able to correct both the ankle and subtalar joint alignment. However, correction at the level of the subtalar joint accounted for only 3 to 4 degrees, which was less than found for the ankle joint alignment. For cases where a higher correction at the subtalar joint is necessary, we thus suggest adding a calcaneal osteotomy or subtalar arthrodesis to the SMOT.

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Liu, Tong, Gaven Martin, YongXin Zhu, Lin Peng, and Li Li. "Joint Robust Multi-view Spectral Clustering." Neural Processing Letters 52, no. 3 (May 1, 2020): 1843–62. http://dx.doi.org/10.1007/s11063-020-10257-0.

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Gao, Fan, William Carlton, and Susan Kapp. "Effects of joint alignment and type on mechanical properties of thermoplastic articulated ankle-foot orthosis." Prosthetics and Orthotics International 35, no. 2 (June 2011): 181–89. http://dx.doi.org/10.1177/0309364611409617.

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Background: Articulated or hinged ankle-foot orthosis (AFO) allow more range of motion. However, quantitative investigation on articulated AFO is still sparse.Objective: The objective of the study was to quantitatively investigate effects of alignment and joint types on mechanical properties of the thermoplastic articulated AFO.Study design: Tamarack dorsiflexion assist flexure joints with three durometers (75, 85 and 95) and free motion joint were tested. The AFO joint was aligned with the center of the motor shaft (surrogate ankle joint), 10 mm superior, inferior, anterior and posterior with respect to the motor shaft center.Methods: The AFO was passively moved from 20° plantar flexion to 15° dorsiflexion at a speed of 10°/s using a motorized device. Mechanical properties including index of hysteresis, passive resistance torque and quasi-static stiffness (at neutral, 5°, 10° and 15° in plantar flexion) were quantified.Results: Significant effects of joint types and joint alignment on the mechanical properties of an articulated thermoplastic AFO were revealed. Specifically, center alignment showed minimum resistance and stiffness while anterior and posterior alignment showed significantly higher resistance and stiffness. The dorsiflexion assist torques at neutral position ranged from 0.69 ± 0.09 to 1.88 ± 0.10 Nm.Conclusions: Anterior and posterior alignment should be avoided as much as possible.Clinical relevanceThe current study suggested that anterior and posterior alignment be avoided as much as possible in clinical practice due to potential skin irritation and increase in stress around the ankle joint.

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Redelings, Benjamin D., and Marc A. Suchard. "Joint Bayesian Estimation of Alignment and Phylogeny." Systematic Biology 54, no. 3 (June 1, 2005): 401–18. http://dx.doi.org/10.1080/10635150590947041.

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Ge, Yongxin, Cheng Peng, Mingjian Hong, Sheng Huang, and Dan Yang. "Joint Local Regressors Learning for Face Alignment." Neurocomputing 208 (October 2016): 262–68. http://dx.doi.org/10.1016/j.neucom.2015.11.116.

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Schmitt, Holger, Hannes Kappel, Michael T. Moser, Eloy Cardenas-Montemayor, Karoly Engelleiter, Benita Kuni, and Michael Clarius. "Determining knee joint alignment using digital photographs." Knee Surgery, Sports Traumatology, Arthroscopy 16, no. 8 (June 13, 2008): 776–80. http://dx.doi.org/10.1007/s00167-008-0570-6.

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