Alignment Method Research Articles

Comparative analysis of ocular biometrics using spectral domain optical coherence tomography with Purkinje image and optic nerve head alignments in mice

BackgroundMice are an emerging model for experimental myopia. Due to their small eye size, non-invasive optical coherence tomography is essential for evaluating ocular biometrics. There is currently no universally accepted protocol for those measurements. This study aims to compare ocular biometric measurements using two methods: Purkinje image-based alignment and optic nerve head alignment, utilizing spectral domain optical coherence tomography. Gaining an understanding of the implications of these methods in determining axial elongation in the normal growing eyes of wild-type C57BL/6J mice would offer valuable insight into their relevance for the experimental myopia model.MethodsOcular dimensions and refractive development were measured on postnatal days P21 (n = 10), P28 (n = 15), and P35 (n = 8). The Purkinje image-based alignment (P1) was determined using a photorefractor and aligned perpendicular to the corneal apex using SD-OCT. In comparison, due to the absence of a fovea in the mouse retina, the optic nerve head (ONH) alignment was used. Variance analysis, regression analysis, and Bland–Altman analysis were performed to compare the differences between alignment methods as well as the replication by another operator.ResultsMice developed hyperopic ametropia under normal visual conditions. The photorefractor measured a technical variation of 3.9 D (95% CI, n = 170, triplicates). Bland–Altman analysis revealed a shorter (mean ± SD) axial length (− 26.4 ± 18.1 μm) and vitreous chamber depth (− 39.9 ± 25.4 μm) in the Purkinje image-based alignment. There was a significant difference in the relative growth trend in VCD (linear regression, p = 0.02), which was relatively stable and showed shortening when measured with ONH alignment from postnatal age 21 to 35 days.ConclusionsSD-OCT allowed precise in-vivo measurement and segmentation of ocular dimensions, regardless of the methods adopted. P1 alignment consistently resulted in significantly shorter VCD and AL compared to ONH alignment at most time points. When considering temporal changes from P21 to P35, both methods showed similar results, with significant elongation of ACD, LT, and AL as expected. However, our findings revealed a significant shortening of VCD over time with the adoption of ONH alignment, while the change in P1 alignment was relatively stable. Therefore, AL provides a better measure for evaluating ocular growth in mice using optical coherence tomography than VCD for myopia research.

Assessment of varus thrust using inertial measurement units.

Varus thrust is common in those with knee osteoarthritis. Varus thrust is traditionally identified with visual analysis or motion capture, methods that are either dichotomous or limited to the laboratory setting. Inertial measurement unit data has been found to correlate with motion capture measures of varus thrust in those with severe knee osteoarthritis, allowing for a quantitative and accessible way of measuring varus thrust. However, such measures have not been examined across a wider range of cartilage health. The goal of this study was to compare motion capture and inertial measurement unit estimates of varus thrust in adults who were asymptomatic or who had knee osteoarthritis. Adults with (n=17) and without (n=10) knee osteoarthritis walked over-ground while motion capture and inertial measurement unit data were collected. We tested the correlations between motion capture variables (peak external knee adduction moment and knee adduction angular velocity during the first half of stance) and inertial measurement unit variables (peak frontal axis shank, thigh, and knee angular velocity during the first half of stance). No significant relationships were found between the inertial measurement unit and motion capture variables. Between-study differences in cohorts or sensor-to-segment alignment methods may explain the conflicting findings. Our findings suggest that assessing varus thrust across the spectrum of knee health (including those with and without knee osteoarthritis) may not be feasible using these inertial measurement unit measures. We should explore additional inertial measurement unit measures to enable accurate detection or monitoring of individuals with knee osteoarthritis.

Method of precise optical crystal alignment by tilting the diamond anvil cell

A new, accurate method for fast and precise optical alignment of crystals in a diamond anvil cell (DAC) on a diffractometer has been developed. It enables highly accurate crystal alignment within instruments with a Boehler–Almax DAC design, achieving precision better than 0.02 mm easily. Other advantages of this method are simplicity, speed and instant visual feedback when aligning the crystal. This method employs Snell's law, which relates the angles of incidence and refraction of light passing through different media to estimate the crystal position within the DAC by measuring the apparent transverse displacement of the crystal image at various viewing angles after rotating the DAC by 180°. This information allows for fine-tuning of the crystal alignment within the DAC, ensuring optimal conditions for high-pressure diffraction experiments.

Sample-Cohesive Pose-Aware Contrastive Facial Representation Learning

Abstract Self-supervised facial representation learning (SFRL) methods, especially contrastive learning (CL) methods, have been increasingly popular due to their ability to perform face understanding without heavily relying on large-scale well-annotated datasets. However, analytically, current CL-based SFRL methods still perform unsatisfactorily in learning facial representations due to their tendency to learn pose-insensitive features, resulting in the loss of some useful pose details. This could be due to the inappropriate positive/negative pair selection within CL. To conquer this challenge, we propose a Pose-disentangled Contrastive Facial Representation Learning (PCFRL) framework to enhance pose awareness for SFRL. We achieve this by explicitly disentangling the pose-aware features from non-pose face-aware features and introducing appropriate sample calibration schemes for better CL with the disentangled features. In PCFRL, we first devise a pose-disentangled decoder with a delicately designed orthogonalizing regulation to perform the disentanglement; therefore, the learning on the pose-aware and non-pose face-aware features would not affect each other. Then, we introduce a false-negative pair calibration module to overcome the issue that the two types of disentangled features may not share the same negative pairs for CL. Our calibration employs a novel neighborhood-cohesive pair alignment method to identify pose and face false-negative pairs, respectively, and further help calibrate them to appropriate positive pairs. Lastly, we devise two calibrated CL losses, namely calibrated pose-aware and face-aware CL losses, for adaptively learning the calibrated pairs more effectively, ultimately enhancing the learning with the disentangled features and providing robust facial representations for various downstream tasks. In the experiments, we perform linear evaluations on four challenging downstream facial tasks with SFRL using our method, including facial expression recognition, face recognition, facial action unit detection, and head pose estimation. Experimental results show that PCFRL outperforms existing state-of-the-art methods by a substantial margin, demonstrating the importance of improving pose awareness for SFRL. Our evaluation code and model will be available at https://github.com/fulaoze/CV/tree/main.

Fusing LiDAR and Photogrammetry for Accurate 3D Data: A Hybrid Approach

The fusion of LiDAR and photogrammetry point clouds is a necessary advancement in 3D-modeling, enabling more comprehensive and accurate representations of physical environments. The main contribution of this paper is the development of an innovative fusion system that combines classical algorithms, such as Structure from Motion (SfM), with advanced machine learning techniques, like Coherent Point Drift (CPD) and Feature-Metric Registration (FMR), to improve point cloud alignment and fusion. Experimental results, using a custom dataset of real-world scenes, demonstrate that the hybrid fusion method achieves an average error of less than 5% in the measurements of small reconstructed objects, with large objects showing less than 2% deviation from real sizes. The fusion process significantly improved structural continuity, reducing artifacts like edge misalignments. The k-nearest neighbors (kNN) analysis showed high reconstruction accuracy for the hybrid approach, demonstrating that the hybrid fusion system, particularly when combining machine learning-based refinement with traditional alignment methods, provides a notable advancement in both geometric accuracy and computational efficiency for real-time 3D-modeling applications.

Research on Transfer Alignment Algorithms Based on SE(3) in ECEF Frame

The initial attitude error is challenging to satisfy the requirements of the linear model due to the complex nature of the ocean environment. This presents a challenge in the transfer alignment of the ship. In order to enhance the precision and velocity of ship transfer alignment, as well as to streamline the alignment processes, this paper proposes a transfer alignment methodology based on the Earth-Centered Earth-Fixed (ECEF) frame special Euclidean group (SE(3)) matrix Lie group. After introducing the two navigation states, velocity and attitude, from the ECEF frame into SE(3), the nonlinear inertial navigation system error state model and its corresponding measurement equations are derived based on the mapping relationship between the Lie groups and Lie algebra. The method effectively solves the error problem due to linear approximation in the traditional transfer alignment method, and applies to misalignment angles of arbitrary scale. The simulation results verify the effectiveness and rapidity of the proposed alignment method in the case of arbitrary misalignment angles.

OrthoBrowser: Gene Family Analysis and Visualization

Abstract Motivation The analysis of gene families across diverse species is pivotal in elucidating evolutionary dynamics and functional genomic landscapes. Typical analysis approaches often require significant computational expertise and user time. Results We introduce OrthoBrowser, a static site generator that will index and serve phylogeny, gene trees, multiple sequence alignments, and novel multiple synteny alignments. This greatly enhances the usability of tools like OrthoFinder by making the detailed results much more visually accessible. This interface can scale reasonably up to hundreds of genomes, allows a user to filter this large dataset to a subset of samples they are interested in at that particular moment in time, or “zoom in” to only a subtree of the orthogroup. The multiple synteny alignment method uses a progressive hierarchical alignment approach in the protein space using orthogroup membership to establish orthology. Orthobrowser makes it easy for users to identify, interact with, explore, and share key information about their gene families of interest. Availability and implementation OrthoBrowser is pip installable and is available under MIT license at: https://gitlab.com/salk-tm/orthobrowser. Complete example OrthoBrowser results are available at: https://orthobrowserexamples.netlify.app/.

Alignment Method for Marine Propulsion Systems with Single Stern Tube Bearing Based on Fine-Tuning a Pre-Trained Model

This paper addresses the issue of insufficient accuracy and efficiency in existing methods for the alignment of marine propulsion systems with single stern tube bearing, caused by uncertainties in the actual parameters of propulsion systems and the scarcity of data, and a new alignment method based on fine-tuning a pre-trained model is proposed. First, a characterization method for the attitude of the main engine is proposed, taking into account the specific alignment requirements of marine propulsion systems with single stern tube bearing. Next, a pre-trained model is constructed based on large-scale samples from the design propulsion system and the fine-tuning of the pre-trained model is performed using small samples from the actual propulsion system’s alignment process to obtain the target model, which guides the practical alignment. Finally, the effectiveness and superiority of the proposed method are validated by applying actual measured data and by applying finite element simulations; in eight alignments, all verification parameter errors are much smaller than the maximum allowable error. The results show that the proposed method significantly improves the accuracy and efficiency of alignment of this type of propulsion system and provides a technical approach to the small-sample modeling problem in the alignment of propulsion systems.

Improvements in the vertical-laser alignment method for joule balance

A vacuum-compatible vertical-laser alignment method based on an oil mirror and an air-spaced lens is used to measure and compensate for the vertical deviation angle of the laser beams in a joule balance. This paper reports improvements in this alignment method regarding the focal-plane positioning error, off-axis error of the lens, tradeoff of oil mirror parameters, and measurement resolution. Dual spot coincidence is used to locate the lens focal plane, and the optimal collimation region in the Gaussian beam of the laser source is located, which is regarded as a parallel light source. An observation mechanism is designed to realize the online detection of the lens off-axis distance, and the optimal area for balancing the influence of the angular error of the corner cube and the lens off-axis distance is preliminarily obtained. The performance parameters of silicone oil are balanced, and the measurement resolution is improved from three perspectives. The sealing performance of the chamber for containing silicone oil is also verified experimentally. Furthermore, through an experiment, the measurement accuracy of the vertical-laser alignment method is preliminarily verified.

A Knowledge Graph Summarization Model Integrating Attention Alignment and Momentum Distillation

The integrated knowledge graph summarization model improves summary performance by combining text features and entity features. However, the model still has the following shortcomings: the knowledge graph data used introduce data noise that deviates from the original text semantics; and the text and knowledge graph entity features cannot be fully integrated. To address these issues, a knowledge graph summarization model integrating attention alignment and momentum distillation (KGS-AAMD) is proposed. The pseudo-targets generated by the momentum distillation model serve as additional supervision signals during training to overcome data noise. The attention-based alignment method lays the foundation for the subsequent full integration of text and entity features by aligning them. Experimental results on two public datasets, namely CNN / Daily Mail and XSum, show that KGS-AAMD surpasses multiple baseline models and ChatGPT in terms of the quality of summary generation, exhibiting significant performance advantages.

A reproducible representation of healthy tibiofemoral kinematics during stair descent using REFRAME – part I: REFRAME foundations and validation

In clinical movement biomechanics, kinematic measurements are collected to characterise the motion of articulating joints and investigate how different factors influence movement patterns. Representative time-series signals are calculated to encapsulate (complex and multidimensional) kinematic datasets succinctly. Exacerbated by numerous difficulties to consistently define joint coordinate frames, the influence of local frame orientation and position on the characteristics of the resultant kinematic signals has been previously proven to be a major limitation. Consequently, for consistent interpretation of joint motion (especially direct comparison) to be possible, differences in local frame position and orientation must first be addressed. Here, building on previous work that introduced a frame orientation optimisation method and demonstrated its potential to induce convergence towards a consistent kinematic signal, we present the REference FRame Alignment MEthod (REFRAME) that addresses both rotational and translational kinematics, is validated here for a healthy tibiofemoral joint, and allows flexible selection of optimisation criteria to fittingly address specific research questions. While not claiming to improve the accuracy of joint kinematics or reference frame axes, REFRAME does enable a representation of knee kinematic signals that accounts for differences in local frames (regardless of how these differences were introduced, e.g. anatomical heterogeneity, use of different data capture modalities or joint axis approaches, intra- and inter-rater reliability, etc.), as evidenced by peak root-mean-square errors of 0.24° ± 0.17° and 0.03 mm ± 0.01 mm after its implementation. By using a self-contained optimisation approach to systematically re-align the position and orientation of reference frames, REFRAME allows researchers to better assess whether two kinematic signals represent fundamentally similar or different underlying knee motion. The openly available implementation of REFRAME could therefore allow the consistent interpretation and comparison of knee kinematic signals across trials, subjects, examiners, or even research institutes.

Adaptive Month Matching: A Phenological Alignment Method for Transfer Learning in Cropland Segmentation

The increasing demand for food and rapid population growth have made advanced crop monitoring essential for sustainable agriculture. Deep learning models leveraging multispectral satellite imagery, like Sentinel-2, provide valuable solutions. However, transferring these models to diverse regions is challenging due to phenological differences in crop growth stages between training and target areas. This study proposes the Adaptive Month Matching (AMM) method to align the phenological stages of crops between training and target areas for enhanced transfer learning in cropland segmentation. In the AMM method, an optimal Sentinel-2 monthly time series is identified in the training area based on deep learning model performance for major crops common to both areas. A month-matching process then selects the optimal Sentinel-2 time series for the target area by aligning the phenological stages between the training and target areas. In this study, the training area covered part of the Mississippi River Delta, while the target areas included diverse regions across the US and Canada. The evaluation focused on major crops, including corn, soybeans, rice, and double-cropped winter wheat/soybeans. The trained deep learning model was transferred to the target areas, and accuracy metrics were compared across different time series chosen by various phenological alignment methods. The AMM method consistently demonstrated strong performance, particularly in transferring to rice-growing regions, achieving an overall accuracy of 98%. It often matched or exceeded other phenological matching techniques in corn segmentation, with an average overall accuracy across all target areas exceeding 79% for cropland segmentation.

An Infrared and Visible Image Alignment Method Based on Gradient Distribution Properties and Scale-Invariant Features in Electric Power Scenes.

In grid intelligent inspection systems, automatic registration of infrared and visible light images in power scenes is a crucial research technology. Since there are obvious differences in key attributes between visible and infrared images, direct alignment is often difficult to achieve the expected results. To overcome the high difficulty of aligning infrared and visible light images, an image alignment method is proposed in this paper. First, we use the Sobel operator to extract the edge information of the image pair. Second, the feature points in the edges are recognised by a curvature scale space (CSS) corner detector. Third, the Histogram of Orientation Gradients (HOG) is extracted as the gradient distribution characteristics of the feature points, which are normalised with the Scale Invariant Feature Transform (SIFT) algorithm to form feature descriptors. Finally, initial matching and accurate matching are achieved by the improved fast approximate nearest-neighbour matching method and adaptive thresholding, respectively. Experiments show that this method can robustly match the feature points of image pairs under rotation, scale, and viewpoint differences, and achieves excellent matching results.

Frontal Plane Knee Kinematics and Kinetics During Gait in Children and Youth with Achondroplasia-Correspondence with Static X-Ray Images and Relevance to Symptoms.

Background: Frontal knee malalignments are hallmarks of Achondroplasia (ACH), along with disproportional short stature. Typically, X-rays are used to assess them, but 3D gait analysis (3DGA) may additionally be used to evaluate dynamic knee function. The research questions were as follows: (1) What is the relationship between X-rays and 3DGA in ACH? (2) Do children with ACH have abnormal frontal knee kinematics and kinetics? (3) Are there aspects of 3DGA that relate to knee symptoms? Methods: A total of 62 knees of 31 children with ACH (age: 11.1 ± 4.3 years, 34 symptomatic knees) underwent 3DGA and X-ray as part of their standard clinical care. X-rays were analyzed for mechanical tibiofemoral angle (mTFA). Relationships between X-rays and 3DGA were determined. Sixty-two knees of 31 age-matched typically developing (TD) children served as references for 3DGA. Frontal knee kinematics (including thrust RoM) and adduction moments (KAMs) were compared. Multiple regression was performed for measurements associated with KAM, and ANOVA was used to compare TD and ACH knees with and without pain. Results: There was a high correlation between static frontal knee angles and mTFA (r = 0.93, p < 0.001, mean difference = -2.9°). ACH knees with a regular mTFA also showed significantly increased KAM. Multiple regression analysis showed that mTFA was the most relevant predictor of KAM (R2 = 0.41-0.75). Symptomatic knees (n = 34/62) experienced significantly more knee RoM in early stance than asymptomatic knees. Conclusions: Three-dimensional gait analysis may be an objective screening method for dynamic knee alignment and stability and may complement radiography in monitoring ACH. Symptoms may depend on knee thrust, while the impact of altered KAM needs further study.

BetaAlign: a deep learning approach for multiple sequence alignment.

Multiple sequence alignments are extensively used in biology, from phylogenetic reconstruction to structure and function prediction. Here, we suggest an out-of-the-box approach for the inference of multiple sequence alignments, which relies on algorithms developed for processing natural languages. We show that our AI-based methodology can be trained to align sequences by processing alignments that are generated via simulations, and thus different aligners can be easily generated for datasets with specific evolutionary dynamics attributes. We expect that natural-language processing solutions will replace or augment classic solutions for computing alignments, and more generally, challenging inference tasks in phylogenomics. The multiple sequence alignment (MSA) problem is a fundamental pillar in bioinformatics, comparative genomics, and phylogenetics. Here we characterize and improve BetaAlign, the first deep learning aligner, which substantially deviates from conventional algorithms of alignment computation. BetaAlign draws on natural language processing (NLP) techniques and trains transformers to map a set of unaligned biological sequences to an MSA. We show that our approach is highly accurate, comparable and sometimes better than state-of-the-art alignment tools. We characterize the performance of BetaAlign and the effect of various aspects on accuracy; for example, the size of the training data, the effect of different transformer architectures, and the effect of learning on a subspace of indel-model parameters (subspace learning). We also introduce a new technique that leads to improved performance compared to our previous approach. Our findings further uncover the potential of NLP-based methods for sequence alignment, highlighting that AI-based algorithms can substantially challenge classic approaches in phylogenomics and bioinformatics. Datasets used in this work are available on HuggingFace (Wolf et al., 2020) at: https://huggingface.co/dotan1111. Source code is available at: https://github.com/idotan286/SimulateAlignments. Supplementary data are available at Bioinformatics online.

Active alignment control system for thin disk regenerative amplifier.

We present an active alignment and stabilization control system for laser setups based on a thin-disk regenerative amplifier. This method eliminates power and pointing instability during the warm-up period and improves long-term stability throughout the entire operation. The alignment method is based on a four-mirror control system consisting of two motorized mirrors placed within the regenerative amplifier cavity, two additional motorized mirrors external to the amplifier cavity, and four camera detectors. The implemented stabilization system achieves significant performance improvements, increasing the power stability from 1.87% to 0.79% RMS and the peak-to-peak stability from 7.43% to 3.88%. Furthermore, the system significantly enhances beam positional stability, achieving up to a sixfold improvement in certain sensor measurements. The advantage of this method is the removal of long-term pointing instability by adding a second controlled motorized mirror to the cavity, in addition to using only one cavity end mirror for optimizing the overlap with the pump spot on the cavity medium. To achieve higher precision in pointing, the nonlinear hysteresis effect of the piezoelectric actuator is mitigated. Although the power and pointing stability of the cavity are secured, pointing instability at the input of the compressor occurs. This issue is resolved by two additional controlled motorized mirrors external to the cavity.

A novel sensor-independent convolutional neural network for structural damage detection：illustrated by a case study on gantry crane

Structural damage detection is a critical technology for ensuring the safety of engineering structures. In recent years, intelligent structural damage detection algorithms based on machine learning have garnered widespread attention globally, thanks to their efficient and reliable detection performance. However, existing damage detection methods largely depend on the use of sensors, which not only increases operational and maintenance costs but also limits the applicability of these methods—especially in parts of structures where sensors are difficult to install. To overcome this barrier, this paper introduces a novel sensor-independent convolutional neural network (CNN) approach for predicting the maximum response of structures under seismic excitation. This method utilizes IDA method to collect a large number of samples (these samples have been uploaded to Mendeley Data for reference). It employs innovative data processing and grouping methods for feature alignment as well as the division of the test and training sets. By automatically extracting the signal characteristics of seismic excitation and optimizing feature combinations through multi-layer fusion, this method achieves high-precision and robust prediction without relying on sensors. To validate the effectiveness of this method, this study conducted a performance evaluation on a typical gantry crane metal structure. The results show that the proposed sensor-independent CNN algorithm achieves high-precision structural response prediction, with over 99% accuracy for peak responses, demonstrating the robustness and reliability of the model. It also performs excellently in key metrics such as root mean square error, error bias, and learning rate progression, making it a reliable and cost-effective solution for real-time seismic damage detection.

R-C Telescope Alignment Method Based on Astigmatism Correction of Symmetrical Field of View Misalignment

R-C Telescope Alignment Method Based on Astigmatism Correction of Symmetrical Field of View Misalignment

Leveraging neighborhood distance awareness for entity alignment in temporal knowledge graphs.

Entity alignment (EA) is a typical strategy for knowledge graph integration, aiming to identify and align different entity pairs representing the same real object from different knowledge graphs. Temporal Knowledge Graph (TKG) extends the static knowledge graph by introducing timestamps. However, since temporal knowledge graphs are constructed based on their own data sources, this usually leads to problems such as missing or redundant entity information in the temporal knowledge graph. Therefore, temporal knowledge graph entity alignment is a meaningful task to avoid the above problems. However, most existing methods tend to ignore the impact of direct and indirect neighborhood information on EA. Therefore, we propose a temporal knowledge graph entity alignment method with neighborhood distance awareness, namely Tem-DA. Tem-DA models direct and indirect neighbors separately, capturing directly connected neighbors with related entities and indirectly connected neighbors with unrelated entities through a distance detection module. Furthermore, we propose a gating mechanism weight the elements in the embedding matrix and through cross-entropy loss function with regularization terms to address adaptive fusion. This mechanism dynamically adjusts the weights of different feature embeddings, thus providing a more flexible and adaptive feature fusion strategy compared with traditional linear weight adjustment methods. Tem-DA effectively captures the temporal information of entities and take advantage of overlapping properties that may have multiple identical time intervals between different entities to encode the temporal information. For some entities that may lack sufficient temporal information, Tem-DA estimates the temporal characteristics of adjacent information and generates temporal embedding vectors. Experimental results on two monolingual TKG datasets show that Tem-DA outperforms popular baseline methods.

Optical camera/SAR consistent imaging alignment method based on SAR imaging compensation under airborne co-aperture system

Optical camera/SAR consistent imaging alignment method based on SAR imaging compensation under airborne co-aperture system