Similarity Measure Function Research Articles

STNMDA: A Novel Model for Predicting Potential Microbe-Drug Associations with Structure-Aware Transformer

Introduction: Microbes are intimately involved in the physiological and pathological processes of numerous diseases. There is a critical need for new drugs to combat microbe-induced diseases in clinical settings. Predicting potential microbe-drug associations is, therefore, essential for both disease treatment and novel drug discovery. However, it is costly and time-consuming to verify these relationships through traditional wet lab approaches. Methods: We proposed an efficient computational model, STNMDA, that integrated a StructureAware Transformer (SAT) with a Deep Neural Network (DNN) classifier to infer latent microbedrug associations. The STNMDA began with a “random walk with a restart” approach to construct a heterogeneous network using Gaussian kernel similarity and functional similarity measures for microorganisms and drugs. This heterogeneous network was then fed into the SAT to extract attribute features and graph structures for each drug and microbe node. Finally, the DNN classifier calculated the probability of associations between microbes and drugs. Results: Extensive experimental results showed that STNMDA surpassed existing state-of-the-art models in performance on the MDAD and aBiofilm databases. In addition, the feasibility of STNMDA in confirming associations between microbes and drugs was demonstrated through case validations. Conclusion: Hence, STNMDA showed promise as a valuable tool for future prediction of microbedrug associations.

Interpretable Representation and Customizable Retrieval of Traffic Congestion Patterns Using Causal Graph-Based Feature Associations

The substantial increase in traffic data offers new opportunities to inspect traffic congestion dynamics from different perspectives. This paper presents a novel framework for the interpretable representation and customizable retrieval of traffic congestion patterns using causal relation graphs, which harnesses many of these opportunities. By integrating domain knowledge with innovative data management techniques, we address the challenges of effectively handling and retrieving the growing volume of traffic data for diverse analytical purposes. The framework leverages causal graphs to encode traffic congestion patterns, capturing fundamental phenomena and their spatiotemporal relationships, thus facilitating an interpretable high-level view of traffic dynamics. Moreover, a customizable similarity measurement function is introduced based on inexact graph matching, allowing users to tailor the retrieval process to specific requirements. This framework’s capability to retrieve customizable and interpretable congestion patterns is demonstrated through extensive experiments with real-world highway traffic data in the Netherlands, highlighting its value in supporting diverse data-driven studies and applications.

MSO‐DETR: Metric space optimization for few‐shot object detection

AbstractIn the metric‐based meta‐learning detection model, the distribution of training samples in the metric space has great influence on the detection performance, and this influence is usually ignored by traditional meta‐detectors. In addition, the design of metric space might be interfered with by the background noise of training samples. To tackle these issues, we propose a metric space optimisation method based on hyperbolic geometry attention and class‐agnostic activation maps. First, the geometric properties of hyperbolic spaces to establish a structured metric space are used. A variety of feature samples of different classes are embedded into the hyperbolic space with extremely low distortion. This metric space is more suitable for representing tree‐like structures between categories for image scene analysis. Meanwhile, a novel similarity measure function based on Poincaré distance is proposed to evaluate the distance of various types of objects in the feature space. In addition, the class‐agnostic activation maps (CCAMs) are employed to re‐calibrate the weight of foreground feature information and suppress background information. Finally, the decoder processes the high‐level feature information as the decoding of the query object and detects objects by predicting their locations and corresponding task encodings. Experimental evaluation is conducted on Pascal VOC and MS COCO datasets. The experiment results show that the effectiveness of the authors’ method surpasses the performance baseline of the excellent few‐shot detection models.

Analyzing the Impacts of Land Use and Network Features on Passenger Flow Distribution at Urban Rail Stations from a Classification Perspective

This study employed big data analytics to investigate the impacts of land use and network features on passenger flow distribution at urban rail stations. The aim was to provide decision support for differentiated operational management strategies for various types of rail stations, thereby achieving refined operation and the sustainable development of urban rail systems. First, this study compared clustering results using different similarity measurement functions within the K-means algorithm framework, selecting the optimal similarity measurement function to construct clustering models. Second, factors influencing passenger flow distribution were selected from land use and network features, forming a feature set that when combined with clustering model results, served as input for the XGBoost model to analyze the relationship between various features and the station passenger flow distribution. The case study showed that (1) the clustering results using a dynamic time-warping distance as the similarity measurement function was optimal; (2) the results of the XGBoost model highlighted commercial services and closeness centrality as the most important factors that affected rail station passenger flow distribution; (3) urban rail stations in Nanjing could be categorized into four types: “strong traffic attraction stations”, “balanced traffic attraction stations”, “suburban strong traffic occurrence stations”, and “distant suburban strong traffic occurrence stations”. Differentiated operational and management strategies were developed for these station types. This paper offers a novel approach for enhancing the operational management of urban rail transit, which not only boosts operational efficiency but also aligns with the goals of sustainable development by promoting resource-efficient transportation solutions.

Binocular Visual Measurement Method Based on Feature Matching.

To address the issues of low measurement accuracy and unstable results when using binocular cameras to detect objects with sparse surface textures, weak surface textures, occluded surfaces, low-contrast surfaces, and surfaces with intense lighting variations, a three-dimensional measurement method based on an improved feature matching algorithm is proposed. Initially, features are extracted from the left and right images obtained by the binocular camera. The extracted feature points serve as seed points, and a one-dimensional search space is established accurately based on the disparity continuity and epipolar constraints. The optimal search range and seed point quantity are obtained using the particle swarm optimization algorithm. The zero-mean normalized cross-correlation coefficient is employed as a similarity measure function for region growing. Subsequently, the left and right images are matched based on the grayscale information of the feature regions, and seed point matching is performed within each matching region. Finally, the obtained matching pairs are used to calculate the three-dimensional information of the target object using the triangulation formula. The proposed algorithm significantly enhances matching accuracy while reducing algorithm complexity. Experimental results on the Middlebury dataset show an average relative error of 0.75% and an average measurement time of 0.82 s. The error matching rate of the proposed image matching algorithm is 2.02%, and the PSNR is 34 dB. The algorithm improves the measurement accuracy for objects with sparse or weak textures, demonstrating robustness against brightness variations and noise interference.

Адаптивная мера сходства геополей, основанная на обобщении бинарных мер сходства

The authors discuss the task of geofield similarity measurement. The local and global approaches to it are reviewed. The rationale of inapplicability of measures, originally developed for images, for general case of geofields is given. The adaptive function family based on generalization of binary similarity measures is proposed. It assumes adaptation to specific scales, tasks and subject domain. The software implementation of this functions family is discussed. Its applicability for detection of similarities and difference of several geofield special cases is considered. The computational performance of the proposed mechanism different use is studied. It is shown that MT-IoU one (Multi-threshold Intersection-over-Union) is flexible and performant framework for building specialized geofield similarity measure functions

Research on textile-vamp punching based on punching point gradient phase and edge significance

A visual technology to identify the punching points of a textile vamp to replace manual punching by machinery is proposed in this paper. This could solve several problems relating to the manual punching of textile vamp, such as high manual-punching strength, low efficiency, and poor punching accuracy. Unsharp mask guided filtering was adopted, to enhance the details of the textile-vamp punching points, considering the edge point position, gradient phase, and edge significance of the punching points, in building the template matching similarity measure function. A partial Hausdorff distance was proposed to sum and average values, to improve the degree of matching of punching point shape defects. A local search area of punching points to improve identification efficiency and punching evaluation criteria was established to evaluate the punching effects. The results showed that the matching similarity of the complete boundary was above 0.9. Positioning accuracy was 0.43 mm in the x-direction, 0.38 mm in the y-direction, and repeat positioning accuracy was 0.09 mm. The center average relative error was 5.03% and the relative error of the radius was 7.81%. Identification timeliness increased with the rotation angle, template size, and the number of punching points. When the rotation angle was between –180° and 180° and the number of punching points was 24, identification timeliness was 830 ms, which met productivity requirements. Textile-vamp punching grades A to D qualified, however, grades E and F were unqualified for punching.

Incipient fault feature extraction of rolling element bearings based on SOSO boosting technique and improved energy operator

The incipient fault features of rolling element bearings (REBs) are easily overwhelmed by environmental noise and vibration interference. Therefore, this paper proposes a novel fault feature extraction method for REBs based on a SOSO (Strengthen-Operate denoising-Subtract-Strengthen) boosting technique. Firstly, an improved fast non-local mean filtering (IFNLM) algorithm is proposed by improving the similarity measure and kernel function while reducing the amount of weight calculation based on distance symmetry. Secondly, a SOSO_IFNLM boosting filtering structure is constructed to reduce the noise of the original vibration signal and enhance the early faint fault pulse. Finally, a k-value improved symmetric higher-order frequency-weighted energy operator (k-SHFWEO) is proposed to detect the bearing fault features from denoised signals. The effectiveness and feasibility of the proposed SOSO_IFNLM-k-SHFWEO method are numerically and experimentally investigated. The results demonstrate that the proposed method has better fault feature extraction capability for early weak faults of REBs and higher efficiency compared to other popular methods.

Nonlinearity evaluation and damage evolution law of simply supported RC beams based on the FRF similarity measure

Reinforced concrete (RC) beam structures are subject to a certain degree of nonlinearity owing to their material properties and other factors. However, the present evaluation methods for their degree of nonlinearity are only applicable to specific types of excitation. In this study, a nonlinear degree evaluation method suitable for impulse excitation is proposed. The nonlinearity degree (ND) index was defined based on the frequency response function similarity measure, which was used to acquire the structural state and compare multiple states. The effectiveness and noise robustness of the proposed method were verified using numerical examples. Additionally, this method was applied to study the nonlinear damage relationship characteristics of RC beam structures. The normalized value given by the ND index accurately determines the relative nonlinear degree and evolution law of the damage state of simply supported RC beams and further clarifies the nonlinear damage relationship. Finally, the control mechanism of the nonlinear factors was revealed by comparing the nonlinear damage relationships of RC beams with different structural properties. This study provides a simple and effective new method for assessing the degree of nonlinearity in engineering structures and further improves the empirical theoretical system of nonlinear damage relationships for RC beam structures.

A Novel Similarity Measure and Score Function of Pythagorean Fuzzy Sets and Their Application in Assignment Problem

A Novel Similarity Measure and Score Function of Pythagorean Fuzzy Sets and Their Application in Assignment Problem

A binary chaotic horse herd optimization algorithm for feature selection

One of the most challenging and common problems in machine learning is the Feature Selection (FS) process, which reduces the dataset size by finding optimal subsets of features. The Horse Herd Optimization Algorithm (HOA) is a new metaheuristic algorithm created by modeling the herd behavior of horses and developed for large scale optimization problems. This paper proposes the binary version of the HOA as a wrapper FS method to solve the FS problem. The proposed algorithm is a binary chaotic horse herd optimization algorithm for feature selection (BCHOAFS). The proposed BCHOAFS is applied to select the optimal feature combination that maximizes classification accuracy while minimizing the number of selected features. Classifier algorithms from machine learning algorithms were used to test the accuracy of the reduced subsets. The proposed method was named binary horse herd optimization for feature selection (BHOAFS) before adding chaotic maps; the k-nearest neighbor (k-NN) and Support Vector Machine (SVM) were tested as separate classifiers. It has been seen that k-NN classification accuracy gives better results than SVM. The BHOAFS-kNN method using the k-NN classification was combined with five chaotic maps and named as BCHOAFS-Logistics, BCHOAFS-Piecewise, BCHOAFS-Singer, BCHOAFS-Sinusoidal, BCHOAFS-Tent. The BCHOAFS versions were run on datasets consisting of 18 different sizes and quality datasets (i.e., low scale, medium scale, and large scale) taken from the UCI repository and compared with state-of-the-art algorithms in previous studies. The results prove that the proposed version, especially with the BCHOAFS-Piecewise and the BCHOAFS-Singer chaotic map outperforms or competes with well-known methods. The proof of the proposed approach’s statistical significance has been validated using the Friedman Signed Rank test and post hoc Wilcoxon test. The novelty of BCHOAFS is that HOA, which is an optimization algorithm specially designed for large scale data, is the first binary chaotic-based algorithm developed for feature selection problems. It also proposes a new local search strategy called Similarity Measurement Function (SMF). As a result, versions of the proposed algorithm BCHOAFS can be used for the FS problem.

GOGCN: Graph Convolutional Network on Gene Ontology for Functional Similarity Analysis of Genes.

The measurement of gene functional similarity plays a critical role in numerous biological applications, such as gene clustering, the construction of gene similarity networks. However, most existing approaches still rely heavily on traditional computational strategies, which are not guaranteed to achieve satisfactory performance. In this study, we propose a novel computational approach called GOGCN to measure gene functional similarity by modeling the Gene Ontology (GO) through Graph Convolutional Network (GCN). GOGCN is a graph-based approach that performs sufficient representation learning for terms and relations in the GO graph. First, GOGCN employs the GCN-based knowledge graph embedding (KGE) model to learn vector representations (i.e., embeddings) for all entities (i.e., terms). Second, GOGCN calculates the semantic similarity between two terms based on their corresponding vector representations. Finally, GOGCN estimates gene functional similarity by making use of the pair-wise strategy. During the representation learning period, GOGCN promotes semantic interaction between terms through GCN, thereby capturing the rich structural information of the GO graph. Further experimental results on various datasets suggest that GOGCN is superior to the other state-of-the-art approaches, which shows its reliability and effectiveness.

Study of Different Similarity Measure Functions with Reference to Cluster Analysis

Study of Different Similarity Measure Functions with Reference to Cluster Analysis

Enhancing the depth perception of DSA images with 2D-3D registration.

Today, cerebrovascular disease has become an important health hazard. Therefore, it is necessary to perform a more accurate and less time-consuming registration of preoperative three-dimensional (3D) images and intraoperative two-dimensional (2D) projection images which is very important for conducting cerebrovascular disease interventions. The 2D-3D registration method proposed in this study is designed to solve the problems of long registration time and large registration errors in 3D computed tomography angiography (CTA) images and 2D digital subtraction angiography (DSA) images. To make a more comprehensive and active diagnosis, treatment and surgery plan for patients with cerebrovascular diseases, we propose a weighted similarity measure function, the normalized mutual information-gradient difference (NMG), which can evaluate the 2D-3D registration results. Then, using a multi-resolution fusion optimization strategy, the multi-resolution fused regular step gradient descent optimization (MR-RSGD) method is presented to attain the optimal value of the registration results in the process of the optimization algorithm. In this study, we adopt two datasets of the brain vessels to validate and obtain similarity metric values which are 0.0037 and 0.0003, respectively. Using the registration method proposed in this study, the time taken for the experiment was calculated to be 56.55s and 50.8070s, respectively, for the two sets of data. The results show that the registration methods proposed in this study are both better than the Normalized Mutual (NM) and Normalized Mutual Information (NMI). The experimental results in this study show that in the 2D-3D registration process, to evaluate the registration results more accurately, we can use the similarity metric function containing the image gray information and spatial information. To improve the efficiency of the registration process, we can choose the algorithm with gradient optimization strategy. Our method has great potential to be applied in practical interventional treatment for intuitive 3D navigation.

A pairwise functional connectivity similarity measure method based on few-shot learning for early MCI detection.

Alzheimer's disease is an irreversible neurological disease, therefore prompt diagnosis during its early stage, i.e., early mild cognitive impairment (MCI), is crucial for effective treatment. In this paper, we propose an automatic diagnosis method, a few-shot learning-based pairwise functional connectivity (FC) similarity measure method, to detect early MCI. We first employ a sliding window strategy to generate a dynamic functional connectivity network (FCN) using each subject's rs-fMRI data. Then, normal controls (NCs) and early MCI patients are distinguished by measuring the similarity between the dynamic FC series of corresponding brain regions of interest (ROIs) pairs in different subjects. However, previous studies have shown that FC patterns in different ROI-pairs contribute differently to disease classification. To enable the FCs of different ROI-pairs to make corresponding contributions to disease classification, we adopt a self-attention mechanism to weight the FC features. We evaluated the suggested strategy using rs-fMRI data obtained from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database, and the results point to the viability of our approach for detecting MCI at an early stage.

Evidence for functional connectivity pattern changes in Frontotemporal dementia patients using connectome gradient mapping

AbstractBackgroundFrontotemporal dementia (FTD) is a neurodegenerative syndrome with a broad range of clinical manifestations making its early detection and progression monitoring difficult. As we enter the first clinical trials, useful biomarkers are needed. It is believed that functional connectivity underlies cognitive function which makes it a potential meaningful biomarker of disease progression. Studies of functional connectivity in FTD have provided a range of findings suggesting changes in resting‐state networks. However, many of the analysis methods used are either far from the biological processes or restricted to a priori defined networks. Recent work has revealed the emergence of a brain network hierarchy during neurodevelopment, separating immediate environment processing from transmodal and integrative cortices. It is assumed that this organization supports higher order cognition.MethodWe included 36 behavioural FTD patients (bvFTD) and 50 controls. We applied a novel whole‐brain analysis approach, connectome gradient mapping, to resting‐state fMRI data. This approach extracts gradients representing measures of functional similarity. Results are presented as brain parcellations within connectivity space, where the distance between parcels describes the extent of segregation between networks (Fig A).ResultsbvFTD patients showed a disruption in macroscale hierarchy affecting integration and segregation of unimodal and transmodal networks. Specifically, in bvFTD patients, we observed a significant shift of cognitive/behavioural networks, such as the Limbic and Default‐mode, which are less segregated from the primary function networks, such as visual and auditory (Fig B).ConclusionUsing this novel method, we found that the Default‐mode and Limbic networks seem particularly vulnerable in FTD whereby they lose their evolutionarily derived characteristics of segregation. These networks underlie the complex cognitive and behavioural phenotypes observed in FTD. In the near future, we will evaluate whether this model could explain the main symptoms of patients, such as cognitive disinhibition (Stroop/Hayling tests), alterations in social cognition (miniSEA) and worse general cognitive function (MMSE). Overall, this method applied to early and/or presymptomatic stages will be useful for early disease identification and predicting treatment outcomes, making it of interest in future therapeutic studies for neurodegenerative diseases.

System-Wide Trust: The Impact of an Error in a Multi-component System

Given the rise of automation adoption in a variety of industries, it is essential we understand how individuals perceive these systems. Previous studies have found that a failure of one component in a system leads to decreased trust across a whole system, but few, if any, studies have considered how users define the confines of a system. To address this gap, this study replicated and extended Mehta et al. (2019) by incorporating measures of trust, similarity, and functional relatedness between six human crew members and six automated components. We noted that our functional relatedness and similarity measures were predictors of the magnitude of trust difference between conditions, with a high degree of shared variance. Our lack of a within subjects design means that future studies should further test whether measures approximating perceptions of ‘system’ (e.g., perceived functional relatedness and similarity) predict the size of contagion effects.

Antiocclusion Visual Tracking Algorithm Combining Fully Convolutional Siamese Network and Correlation Filtering.

Machine learning only uses single-channel grayscale features to model the target, and the filter solution process is relatively simple. When the target has a large change relative to the initial frame, the tracking effect is poor. When there is the same kind of target interference in the target search area, the tracking results will be poor. The tracking algorithm based on the fully convolutional Siamese network can solve these problems. By learning the similarity measurement function, the similarity between the template and the target search area is evaluated, and the target area is found according to the similarity. It adopts offline pre-training and does not update online for tracking, which has a faster tracking speed. According to this study, (1) considering the accuracy and speed, the target tracking algorithm based on correlation filtering performs well. A sample adaptive update model is introduced to eliminate unreliable samples, which effectively enhances the reliability of training samples. With simultaneous changes in illumination and scale, fast motion and in-plane rotation IPR can still be maintained. (2) Determined by calculating the Hessian matrix, in the Struck function, Bike3 parameter adjustment can achieve fast tracking, and Boat5 ensures that the system stability is maintained in the presence of interference factors. The position of the highest scoring point in the fine similarity score map of the same size as the search image is obtained by bicubic interpolation as the target position. (3) The parallax discontinuity caused by the object boundary cannot be directly processed as a smooth continuous parallax. The MeanShift vector obtained by calculating the target template feature and the feature to be searched can increase the accuracy by 53.1%, reduce the robustness by 31.8%, and reduce the error by 28.6% in the SiamVGG algorithm.

Adversarial learning for semi-supervised pediatric sleep staging with single-EEG channel.

Despite the progress recently made towards automatic sleep staging for adults, children have complicated sleep structures that require attention to the pediatric sleep staging. Semi-supervised learning, i.e., training networks with both labeled and unlabeled data, greatly reduces the burden of epoch-by-epoch annotation for physicians. However, the inherent class-imbalance problem in sleep staging task undermines the effectiveness of semi-supervised methods such as pseudo-labeling. In this paper, we propose a Bi-Stream Adversarial Learning network (BiSALnet) to generate pseudo-labels with higher confidence for network optimization. Adversarial learning strategy is adopted in Student and Teacher branches of the two-stream networks. The similarity measurement function minimizes the divergence between the outputs of the Student and Teacher branches, and the discriminator continuously enhances its discriminative ability. In addition, we employ a powerful symmetric positive definite (SPD) manifold structure in the Student branch to capture the desired feature distribution properties. The joint discriminative power of convolutional features and nonlinear complex information aggregated by SPD matrices is combined by the attention feature fusion module to improve the sleep stage classification performance. The BiSALnet is tested on pediatric dataset collected from local hospital. Experimental results show that our method yields the overall classification accuracy of 0.80, kappa of 0.73 and F1-score of 0.76. We also examine the generality of our method on a well-known public dataset Sleep-EDF. Our BiSALnet exhibits noticeable performance with accuracy of 0.91, kappa of 0.85 and F1-score of 0.77. Remarkably, we have obtained comparable performance with state-of-the-art supervised approaches with fairly limited labeled data.

Uniformidade e comparabilidade: uma análise da relevância para o mercado de capitais brasileiro

ABSTRACT This research aimed to analyze the relationship between the characteristics of uniformity and comparability and the fundamental qualitative characteristic of relevance of financial reports for the Brazilian capital market, discussing the possibility of a trade-off between comparability and relevance, the importance of considering both comparability and relevance to maximize the utility of financial statements, and the gaps regarding the “effectiveness” of uniformity and achieving comparability to the detriment of relevance. It is important to investigate the “effectiveness” of uniformity in terms of the utility of the decision due to the confusion that exists between the concepts of uniformity and comparability. The results will encourage regulatory bodies to reflect on the confusion that exists between uniformity and comparability and on fulfilling the characteristics of relevance and comparability, indicating what the relationship is between these from the viewpoint of the capital market. Uniformity is identified by means of the earnings covariance measure and comparability via the accounting function similarity measure. The relevance models are assessed using panel data regressions covering the period from 2013 to 2018. The sample includes companies from the B3S.A. - Brasil, Bolsa, Balcão belonging to sectors classified in the Level 2 North American Classification System (NAICS) that comprise at least two companies. The results indicated that uniformity is not relevant for the Brazilian capital market, even when it contributes to comparability. These can be explained by the possible production of superficial comparability. They also revealed that comparability is relevant and positively related to the share price. However, the inclusion of this variable led to a decrease in explanatory power, which may indicate the achievement of comparability to the detriment of relevance. Finally, it is revealed that the Brazilian capital market distinguishes between the concepts of uniformity and comparability when evaluating companies.