Similar Measurements Research Articles

Similarity network aggregation for the analysis of glacier ecosystems

AbstractThe synthesis of information deriving from complex networks is a topic receiving increasing relevance in ecology and environmental sciences. In particular, the aggregation of multilayer networks, that is, network structures formed by multiple interacting networks (the layers), constitutes a fast‐growing field. In several environmental applications, the layers of a multilayer network are modeled as a collection of similarity matrices describing how similar pairs of biological entities are, based on different types of features (e.g., biological traits). The present paper first discusses two main techniques for combining the multi‐layered information into a single network (the so‐called monoplex), that is, similarity network fusion and similarity matrix average (SMA). Then, the effectiveness of the two methods is tested on a real‐world dataset of the relative abundance of microbial species in the ecosystems of nine glaciers (four glaciers in the Alps and five in the Andes). A preliminary clustering analysis on the monoplexes obtained with different methods shows the emergence of a tightly connected community formed by species that are typical of cryoconite holes worldwide. Moreover, the weights assigned to different layers by the SMA algorithm suggest that two large South American glaciers (Exploradores and Perito Moreno) are structurally different from the smaller glaciers in both Europe and South America. Overall, these results highlight the importance of integration methods in the discovery of the underlying organizational structure of biological entities in multilayer ecological networks.

Family environment and self-esteem development in adolescence: A replication and extension

A study by Krauss et al. (2020) suggested that the family environment (e.g., parental warmth, economic conditions of family) plays an important role for self-esteem development in adolescence. The present research sought to closely replicate and extend the study, using 4-wave longitudinal data from the Iowa Youth and Families Project, including 451 families. To replicate the prior study, we conducted the same set of analyses with similar measures and multi-informant assessments of mothers, fathers, and children from the same families. To extend the previous study, we tested novel aspects (i.e., controlling for prior exposure and testing the effect of the quality of sibling relationships). Overall, the findings provide no evidence for prospective effects between family environment and self-esteem in adolescence.

Community Detection Method Based on GNN and Improved K-means

As the scale of networks continues to expand, their structures become increasingly complex, and the diversity of node information within these networks intensifies. Traditional community detection methods are no longer capable of meeting the growing demands. Thus, the design of a powerful community detection method with low complexity and cost is an urgent issue to be addressed. This paper proposes a community detection method based on Graph Neural Networks (GNN) and an improved K-means algorithm. Initially, the original adjacency matrix is reconstructed based on the importance of nodes, resulting in a new node similarity matrix. Subsequently, a graph autoencoder is employed to extract structural information from the network graph. The decoder part utilizes the form of vector inner product to restore the similarity matrix using the structural information from the hidden layer. The features extracted by the graph autoencoder are then utilized by the improved K-means algorithm to ultimately achieve community division. Finally, this paper summarizes the shortcomings of community detection and provides a prospect for future research directions.

Advances in assessing Ca, K, and Mn translocation in oak tree stems (Quercus spp.)

As a part of the biogeochemical cycle, nutrient translocation plays an important role in enhancing the capacity of perennial plants to grow in nutrient-poor soils. Although leaf translocation has been extensively studied, nutrient translocation between wood rings has received considerably less attention, primarily because of methodological constraints. This study aimed to (i) evaluate the effects of different drying techniques on Ca, K, and Mn concentrations, (ii) calibrate a semi-quantitative method for obtaining ring-to-ring nutrient concentrations along wood cores, and (iii) develop a complete calculation chain for nutrient translocation. Three pairs of cores per tree were extracted from nine oaks, and three drying methods—103 °C, 65 °C, and freeze-drying—were applied to each core pair. For each core pair, the first core was analyzed using ITRAX. The second core was analyzed using ICP-OES following the mineralization of a 20 mg wood sample. Ca, K, and Mn concentrations and wood density were not affected by the drying methods (p > 0.05 for Ca, K, and Mn). After upscaling at the stand level, the total translocation was 10.8 ± 5.5 kg ha−1, 14.8 ± 11.4 kg ha−1, and 2.6 ± 0.9 kg ha−1 for Ca, K, and Mn, respectively, after 45 growing years. The total Ca, K, and Mn translocation showed a strong tree effect, partly explained by tree diameter. The study findings suggest that similar measurements can be performed on all wood cores sampled in previous studies and stored after air-drying. These results provide a reference for future analyses of Ca, K, and Mn translocations in different species from wide geographic areas.

Music time signature detection using ResNet18

Time signature detection is a fundamental task in music information retrieval, aiding in music organization. In recent years, the demand for robust and efficient methods in music analysis has amplified, underscoring the significance of advancements in time signature detection. In this study, we explored the effectiveness of residual networks for time signature detection. Additionally, we compared the performance of the residual network (ResNet18) to already existing models such as audio similarity matrix (ASM) and beat similarity matrix (BSM). We also juxtaposed with traditional algorithms such as support vector machine (SVM), random forest, K-nearest neighbor (KNN), naive Bayes, and that of deep learning models, such as convolutional neural network (CNN) and convolutional recurrent neural network (CRNN). The evaluation is conducted using Mel-frequency cepstral coefficients (MFCCs) as feature representations on the Meter2800 dataset. Our results indicate that ResNet18 outperforms all other models thereby showing the potential of deep learning models for accurate time signature detection.

Effect of using the Simple Erosion Narrowing Score or Sharp/van der Heijde score on power of rheumatoid arthritis clinical trials to detect differences in radiographic progression.

The Simple Erosion Narrowing Score (SENS) is a simplification of the Sharp/van der Heijde score (SHS). Previous studies found SENS and SHS to have very similar measurement properties, but suggest that SENS has a lower discriminative ability that may result in reduced power. Therefore, we aimed to quantify the effect of using SENS rather than SHS on the power to show between-group differences in radiographic progression. Using data from two clinical trials in rheumatoid arthritis (DRESS and BeSt), SENS was derived from the SHS. Criterion validity of the SENS in relation to the SHS was assessed by calculating the Spearman correlation. The power of both scores to show a difference between groups was compared using bootstrapping to generate 10.000 replications of each study. Then, the number of replications with a significant difference in progression (using ANCOVA adjusted for baseline scores) were compared. Correlations between SENS and SHS were all >0.9, indicating high criterion validity of SENS compared with SHS as a reference standard. There was one exception, the DRESS study showed a somewhat lower correlation for the change score at 18 months (0.787). The loss in power of SENS over SHS was limited to at most 19% (BeSt year 5). In addition, the difference in power between SENS and SHS is smaller at higher levels of power. SENS appears to be a reasonable alternative to SHS, with only a limited loss of power to show between-group differences in radiographic progression.

Molecular and agro-morphological characterization of new barley genotypes in arid environments

BackgroundGenetic diversity, population structure, agro-morphological traits, and molecular characteristics, are crucial for either preserving genetic resources or developing new cultivars. Due to climate change, water availability for agricultural use is progressively diminishing. This study used 100 molecular markers (25 TRAP, 22 SRAP, 23 ISTR, and 30 SSR). Additionally, 15 morphological characteristics were utilized to evaluate the optimal agronomic traits of 12 different barley genotypes under arid conditions.ResultsSubstantial variations, ranging from significant to highly significant, were observed in the 15 agromorphological parameters evaluated among the 12 genotypes. The KSU-B101 barley genotype demonstrated superior performance in five specific traits: spike number per plant, 100-grain weight, spike number per square meter, harvest index, and grain yield. These results indicate its potential for achieving high yields in arid regions.The Sahrawy barley genotype exhibited the highest values across five parameters, namely leaf area, spike weight per plant, spike length, spike weight per square meter, and biological yield, making it a promising candidate for animal feed. The KSU-B105 genotype exhibited early maturity and a high grain count per spike, which reflects its early maturity and ability to produce a high number of grains per spike. This suggests its suitability for both animal feed and human food in arid areas. Based on marker data, the molecular study found that the similarity coefficients between the barley genotypes ranged from 0.48 to 0.80, with an average of 0.64. The dendrogram constructed from these data revealed three distinct clusters with a similarity coefficient of 0.80. Notably, the correlation between the dendrogram and its similarity matrix was high (0.903), indicating its accuracy in depicting the genetic relationships. The combined analysis revealed a moderate correlation between the morphological and molecular analysis, suggesting alignment between the two characterization methods.ConclusionsThe morphological and molecular analyses of the 12 barley genotypes in this study effectively revealed the varied genetic characteristics of their agro-performance in arid conditions. KSU-B101, Sahrawy, and KSU-B105 have emerged as promising candidates for different agricultural applications in arid regions. Further research on these genotypes could reveal their full potential for breeding programs.

Self-supervised spectral clustering with spectral embedding for hyperspectral image classification

ABSTRACT Spectral clustering, as an algorithm based on graph theory and spectral theory, has shown excellent performance in the classification tasks of hyperspectral images in recent years. Although better results have been achieved, some challenges still exist. The inclusion of a priori information can increase the performance of spectral clustering algorithms; however, in practice, it is often unable to meet the demand for a priori information; at the same time, spectral clustering for the quality of the similarity matrix is demanding, and some of the current algorithms are to improve the similarity matrix from the aspect of data planning, and ultimately for the label feature matrices are diluted. In response to the above problems, this paper proposes a self-supervised spectral clustering with spectral embedding (SESSC). The algorithm obtains a low-dimensional representation of the data through spectral embedding, which can simplify clustering while preserving feature information; then uses the similarity matrix about the data and the guidance of the sample constraint information to obtain a new similarity matrix in order to further refine the structural graph, and the result can feed back and optimize the label feature matrix and the low-dimensional representation of the data. Additionally, we introduce a fractional theory in the update of the sample variable matrix, which assures the integrity and validity of the information in the update. Experimental results show that the proposed algorithm has better performance in hyperspectral image classification than existing spectral clustering algorithms.

Automatic adjustment of the parameters of the temperature measurement algorithm in a wide range

Existing methods and devices for measuring temperature are described, and it is noted that resistance thermometers are most widely used as temperature sensors in systems for collecting and monitoring environmental parameters. It is shown that the main source of temperature measurement errors when connecting sensors remotely is the resistance of the connecting lines, determined by their length. The main methods for reducing the measurement error caused by the influence of the resistance of the vehicle connection line on the temperature measurement results are considered. The advantages of two-wire circuits in comparison with three- and four-wire circuits are formulated. As an analogue, a two-wire method of measuring temperature with an integrating converter is considered in detail, implemented on the basis of a device with a built-in microcontroller, and allowing to obtain such advantages as reducing measurement time, detuning from interference and quantization noise and, as a result, reducing the temperature measurement error in comparison with similar two-wire measurement methods. An alternative two-wire method for measuring temperature with automatic adjustment of the parameters of the measurement algorithm is proposed, aimed at increasing the measurement accuracy by weakening the influence of the time constant. The search for the optimal time of the first integration step was carried out. Experimental studies and evaluation calculations were carried out to confirm the effectiveness of the proposed solution. The coefficient of variation when the resistance of the vehicle changes in the range of 1–4 kOhm lies in the range, and the range of change in the relative error of resistance measurement when using auto-tuning within the specified range has decreased by more than 4 times. The results of the experiment allow us to count on the possibility of using the method in control and regulation systems with remote connection of temperature sensors.

Seasonal dynamics and punctuated carbon sink reduction suggest photosynthetic capacity of boreal silver birch is reduced by the accumulation of hexose.

The 'assimilates inhibition hypothesis' posits that accumulation of nonstructural carbohydrates (NSCs) in leaves reduces leaf net photosynthetic rate, thus internally regulating photosynthesis. Experimental work provides equivocal support mostly under controlled conditions without identifying a particular NSC as involved in the regulation. We combined 3-yr in situ leaf gas exchange observations (natural dynamics) in the upper crown of mature Betula pendula simultaneously with measurements of concentrations of sucrose, hexoses (glucose and fructose), and starch, and similar measurements during several one-day shoot girdling (perturbation dynamics). Leaf water potential and water and nitrogen content were measured to account for their possible contribution to photosynthesis regulation. Leaf photosynthetic capacity (A/Ci) was temporally negatively correlated with NSC accumulation under both natural and perturbation states. For developed leaves, leaf hexose concentration explained A/Ci variation better than environmental variables (temperature history and daylength); the opposite was observed for developing leaves. The weaker correlations between NSCs and A/Ci in developing leaves may reflect their strong internal sink strength for carbohydrates. By contrast, the strong decline in photosynthetic capacity with NSCs accumulation in mature leaves, observed most clearly with hexose, and even more tightly with its constituents, provides support for the role of assimilates in regulating photosynthesis under natural conditions.

Structure preserved fast dimensionality reduction

Many graph-based unsupervised dimensionality reduction techniques have raised concerns about their high accuracy. However, there is an urgent need to address the enormous time consumption problem in large-scale data scenarios. Therefore, we present a novel approach named Structure Preserved Fast Dimensionality Reduction (SPFDR). Firstly, the parameter-insensitive, sparse, and scalable bipartite graph is constructed to build the similarity matrix. Then, employing alternating iterative optimization, the linear dimensionality reduction matrix and the optimal similarity matrix preserved cluster structure are learned. The computational complexity of the conventional graph-based dimension reduction method costs O(n2d+d3), yet the proposed approach is O(ndm+nm2), wherein n, m, and d are the number of instances, anchors, and features, respectively. Eventually, experiments conducted with multiple open datasets will provide convincing evidence for how effective and efficient the proposed method is.

The role of Criminal Justice in dealing with past atrocities in the Spanish and Argentine transitions: common grounds, but different pathways

ABSTRACT The comparative study of relevant Argentine and Spanish legislation and jurisprudence offers a perfect picture of the debate surrounding the role and limits of Criminal Law in transitional contexts. An analysis of these two experiences, and of the different choices they have taken, allows to point out three controversial aspects: firstly, the clash between legislative choices (often implying amnesties or similar measures) and judicial decisions that may somehow attempt to ‘correct’ the former. Secondly, the concerns raised by this creative judicial activity, in terms of its compatibility with the principles of legality and non-retroactivity and the prohibition of arbitrariness. Thirdly, the extent of the duty to prosecute and punish gross human rights violations vis-à-vis the limits of Criminal Justice. The article suggests reminding that, in extraordinary (transitional) contexts, Criminal Justice can and should be combined with other complementary mechanisms aimed at granting truth, justice, reparation and the building of a shared memory.

Apportionable matrices and gracefully labelled graphs

To apportion a complex matrix means to apply a similarity so that all entries of the resulting matrix have the same magnitude. We initiate the study of apportionment, both by unitary matrix similarity and general matrix similarity. There are connections between apportionment and classical graph decomposition problems, including graceful labellings of graphs, Hadamard matrices, and equiangular lines, and potential applications to instantaneous uniform mixing in quantum walks. The connection between apportionment and graceful labellings allows the construction of apportionable matrices from trees. A generalization of the well-known Eigenvalue Interlacing Inequalities using graceful labellings is also presented. It is shown that every rank one matrix can be apportioned by a unitary similarity, but there are 2×2 matrices that cannot be apportioned. A necessary condition for a matrix to be apportioned by unitary matrix is established. This condition is used to construct a set of matrices with nonzero Lebesgue measure that are not apportionable by a unitary matrix.

Enhancing Medical Image Retrieval with UMLS-Integrated CNN-Based Text Indexing.

In recent years, Convolutional Neural Network (CNN) models have demonstrated notable advancements in various domains such as image classification and Natural Language Processing (NLP). Despite their success in image classification tasks, their potential impact on medical image retrieval, particularly in text-based medical image retrieval (TBMIR) tasks, has not yet been fully realized. This could be attributed to the complexity of the ranking process, as there is ambiguity in treating TBMIR as an image retrieval task rather than a traditional information retrieval or NLP task. To address this gap, our paper proposes a novel approach to re-ranking medical images using a Deep Matching Model (DMM) and Medical-Dependent Features (MDF). These features incorporate categorical attributes such as medical terminologies and imaging modalities. Specifically, our DMM aims to generate effective representations for query and image metadata using a personalized CNN, facilitating matching between these representations. By using MDF, a semantic similarity matrix based on Unified Medical Language System (UMLS) meta-thesaurus, and a set of personalized filters taking into account some ranking features, our deep matching model can effectively consider the TBMIR task as an image retrieval task, as previously mentioned. To evaluate our approach, we performed experiments on the medical ImageCLEF datasets from 2009 to 2012. The experimental results show that the proposed model significantly enhances image retrieval performance compared to the baseline and state-of-the-art approaches.

Efficient Optimization of Sparse User Encoder Recommenders

Embedding representations are a popular approach for modeling users and items in recommender systems, e.g., matrix factorization, two-tower models, or autoencoders, in which items and users are embedded in a low-dimensional, dense embedding space. On the other hand, there are methods that model high-dimensional relationships between items, most notably item-based collaborative filtering (CF), which is based on an item-to-item similarity matrix. Item-based CF was proposed over two decades ago and gained new interest through new learning methods in the form of SLIM [ 18 ] and, most recently, EASE [ 25 ]. In this work, we rephrase traditional item-based CF as sparse user encoders in which the user encoder is an (arbitrary) function and the item representation is learned. Item-based CF is a special case in which the sparse user encoding is the one-hot encoding of a user’s history. In contrast to typical dense user/item encoder models, this work targets high-dimensional and sparse user encoders. The core contribution is an efficient closed-form learning algorithm that can solve arbitrary sparse user encoders. Several applications of this algorithm are presented, including higher-order encoders, hashed encoders, and feature-based encoders.

Sparse multi-view image clustering with complete similarity information

Multi-view image clustering aims to efficiently divide the collection of images by studying the characteristics of different views. Many studies performed Laplacian dimensionality reduction on the original image to avoid noise interference in constructing the similarity matrix. However, they ignored the problem that local similarity information and isolated image information are lost due to dimensionality reduction. These problems will result in the similarity matrix being insufficient to accurately describe the similarity between images, which in turn affects the clustering accuracy. Therefore, we propose a sparse multi-view spectral clustering model with complete similarity information between images (SMSC-CSI). The model combines the original image space and the low-dimensional spectral embedding space based on the adaptive neighbors method to learn the initial similarity matrices of each view jointly. On the one hand, the original space can retain all the similarity information between images so that the initial similarity matrix can accurately describe the similarity between images, which is conducive to accurate clustering. On the other hand, the low-dimensional space can avoid noise interference and retain the main structure of high-dimensional data, improving the robustness of the initial similarity matrix. Meanwhile, to ensure consistency among the views, the model minimizes the difference between the initial similarity matrix and the central fusion matrix by alternately updating to obtain the optimal weights and central fusion matrix for each view. Finally, we can obtain ideal clustering results directly with low model complexity and without post-processing steps such as K-means by adding non-negative Laplace rank constraints and ℓ0-norm constraints to the model objective function. Experimental results on different real image datasets show that SMSC-CSI can outperform some traditional clustering models and recent models on multi-view image clustering.

A Fusion Learning Model Based on Deep Learning for Single-Cell RNA Sequencing Data Clustering.

Single-cell RNA sequencing (scRNA-seq) technology provides a means for studying biology from a cellular perspective. The fundamental goal of scRNA-seq data analysis is to discriminate single-cell types using unsupervised clustering. Few single-cell clustering algorithms have taken into account both deep and surface information, despite the recent slew of suggestions. Consequently, this article constructs a fusion learning framework based on deep learning, namely scGASI. For learning a clustering similarity matrix, scGASI integrates data affinity recovery and deep feature embedding in a unified scheme based on various top feature sets. Next, scGASI learns the low-dimensional latent representation underlying the data using a graph autoencoder to mine the hidden information residing in the data. To efficiently merge the surface information from raw area and the deeper potential information from underlying area, we then construct a fusion learning model based on self-expression. scGASI uses this fusion learning model to learn the similarity matrix of an individual feature set as well as the clustering similarity matrix of all feature sets. Lastly, gene marker identification, visualization, and clustering are accomplished using the clustering similarity matrix. Extensive verification on actual data sets demonstrates that scGASI outperforms many widely used clustering techniques in terms of clustering accuracy.

Clustering algorithm based on DINNSM and its application in gene expression data analysis.

Selecting an appropriate similarity measurement method is crucial for obtaining biologically meaningful clustering modules. Commonly used measurement methods are insufficient in capturing the complexity of biological systems and fail to accurately represent their intricate interactions. This study aimed to obtain biologically meaningful gene modules by using the clustering algorithm based on a similarity measurement method. A new algorithm called the Dual-Index Nearest Neighbor Similarity Measure (DINNSM) was proposed. This algorithm calculated the similarity matrix between genes using Pearson's or Spearman's correlation. It was then used to construct a nearest-neighbor table based on the similarity matrix. The final similarity matrix was reconstructed using the positions of shared genes in the nearest neighbor table and the number of shared genes. Experiments were conducted on five different gene expression datasets and compared with five widely used similarity measurement techniques for gene expression data. The findings demonstrate that when utilizing DINNSM as the similarity measure, the clustering results performed better than using alternative measurement techniques. DINNSM provided more accurate insights into the intricate biological connections among genes, facilitating the identification of more accurate and biological gene co-expression modules.

Robust tensor ring-based graph completion for incomplete multi-view clustering

Incomplete multi-view clustering (IMVC) aims to enhance clustering performance by leveraging complementary information from multi-view data, even in the presence of missing instances. This is challenging due to the interference caused by these missing data points. Current IMVC algorithms generally adopt two main strategies: either disregarding the missing instances and focusing on observable data for clustering, or complementing the missing instances within each view to improve the clustering results. However, effectively leveraging the latent information within these missing instances remains a challenge. In response, we propose a novel IMVC framework that complements the tensor consisting of similarity matrices learned from the available instances to enhance the relationships among samples from different views. In addition, we integrate tensor ring completion and M-estimator-based methods into the IMVC approach. This promotes the integration of inter-view information and mitigates errors introduced by missing instances in the model, respectively. Furthermore, we develop two efficient half-quadratic (HQ) based iterative algorithms with soft/hard threshold or multivariate generalization of minimax-concave (GMC) penalty to regularize the low-rank property. Comprehensive evaluations on seven benchmark datasets demonstrate that our method outperforms state-of-the-art IMVC approaches across various metrics.

IFKMHC: Implicit Fuzzy K-Means Model for High-Dimensional Data Clustering.

The graph-information-based fuzzy clustering has shown promising results in various datasets. However, its performance is hindered when dealing with high-dimensional data due to challenges related to redundant information and sensitivity to the similarity matrix design. To address these limitations, this article proposes an implicit fuzzy k-means (FKMs) model that enhances graph-based fuzzy clustering for high-dimensional data. Instead of explicitly designing a similarity matrix, our approach leverages the fuzzy partition result obtained from the implicit FKMs model to generate an effective similarity matrix. We employ a projection-based technique to handle redundant information, eliminating the need for specific feature extraction methods. By formulating the fuzzy clustering model solely based on the similarity matrix derived from the membership matrix, we mitigate issues, such as dependence on initial values and random fluctuations in clustering results. This innovative approach significantly improves the competitiveness of graph-enhanced fuzzy clustering for high-dimensional data. We present an efficient iterative optimization algorithm for our model and demonstrate its effectiveness through theoretical analysis and experimental comparisons with other state-of-the-art methods, showcasing its superior performance.