Similar Relationship Research Articles

A graph-based approach for traffic prediction using similarity and causal relations between nodes

Accurate traffic prediction is crucial for the development of intelligent transportation systems (ITS) and various smart applications. To achieve this, effectively capturing the complex interplay between spatial and temporal dependencies across traffic nodes is essential. However, existing research often focuses solely on fixed spatial dependencies between neighboring nodes, neglecting the potential influence of distant nodes. This paper addresses these limitations by exploring two key types of relationships among traffic nodes. First, we examine indirect relationships based on similarity, where nodes exhibit similar traffic patterns irrespective of their physical proximity. Second, we investigate direct causal relationships, where traffic conditions at one node are directly influenced by other nodes. Based on these findings, we propose a novel approach named TPSC (Traffic Prediction using nodes Similarity and Causal relations). TPSC incorporates a classifier that utilizes the K-Prototype algorithm to group nodes based on their traffic similarity and nearby points of interest (POIs). Separate models are then trained for each cluster. Our model leverages a spatial module that employs graph convolutional network representation learning alongside transfer entropy to capture causal relationships and dynamic spatial dependencies. Additionally, a temporal module is introduced to capture periodic temporal dependencies across three components: recent patterns, daily patterns, and weekly patterns. Extensive experiments are conducted to evaluate proposed model on four large-scale traffic datasets for multiple traffic characteristics including speed, flow, and travel time. The experimental results demonstrate that TPSC surpasses the performance of the compared baselines, highlighting its superior predictive capabilities in traffic prediction.

Navigating the Manifold of Translucent Appearance

AbstractWe present a perceptually‐motivated manifold for translucent appearance, designed for intuitive editing of translucent materials by navigating through the manifold. Classic tools for editing translucent appearance, based on the use of sliders to tune a number of parameters, are challenging for non‐expert users: These parameters have a highly non‐linear effect on appearance, and exhibit complex interplay and similarity relations between them. Instead, we pose editing as a navigation task in a low‐dimensional space of appearances, which abstracts the user from the underlying optical parameters. To achieve this, we build a low‐dimensional continuous manifold of translucent appearance that correlates with how humans perceive this type of materials. We first analyze the correlation of different distance metrics in image space with human perception. We select the best‐performing metric to build a low‐dimensional manifold, which can be used to navigate the space of translucent appearance. To evaluate the validity of our proposed manifold within its intended application scenario, we build an editing interface that leverages the manifold, and relies on image navigation plus a fine‐tuning step to edit appearance. We compare our intuitive interface to a traditional, slider‐based one in a user study, demonstrating its effectiveness and superior performance when editing translucent objects.

A novel intuitionistic fuzzy rough instance selection and attribute reduction with kernelized intuitionistic fuzzy C-means clustering to handle imbalanced datasets

Due to advancement of internet and lab based technologies, large volume of high dimensional data are generated every day. These data usually consisted of several issues such as class imbalance, noise, later uncertainty, irrelevant and/or redundant features, and redundancy in size. These issues degrade the overall performance measures of the various machine learning algorithms. An efficient method to cope with such issues for a large sized datasets is to apply efficient data reduction techniques. In the recent years, numerous data reduction techniques have been presented based on fuzzy rough set theory to tackle these obstacles. However, building such a method that can handle all the above mentioned issues simultaneously is still a challenging task. In this study, we handle all these obstacles simultaneously by introducing a novel approach to obtain the reduced dataset by combining kernelized intuitionistic fuzzy C-means with intuitionistic fuzzy rough set model. Intuitionistic fuzzy rough set handles the vagueness and uncertainty in a better way than fuzzy set aided models as it takes use of membership, non-membership as well as hesitancy to capture the uncertainty of real-valued datasets. To generate the membership and non-membership grades, kernelized intuitionistic fuzzy C- means based notion is introduced. Further, an intuitionistic fuzzy rough set model is established by addressing lower and upper approximations based on a novel similarity relation. Moreover, all the necessary conditions are justified with the relevant mathematical theorems. Next, this model is employed for dimensionality reduction based on the concept of discernibility matrix by using the idea of different classes’ ratios to avoid the noise. Thereafter, positive region is defined with the help of lower approximation. The positive region information is applied to tackle problematic instances available in both minority and majority class for the imbalanced dataset after generation of artificial samples by synthetic minority oversampling technique (SMOTE). A comprehensive experimental study is added to show the effectiveness of the proposed technique. Finally, a framework is established to improve the prediction of animal toxin peptides.

Decoupled Contrastive Learning for Long-Tailed Recognition

Supervised Contrastive Loss (SCL) is popular in visual representation learning. Given an anchor image, SCL pulls two types of positive samples, i.e., its augmentation and other images from the same class together, while pushes negative images apart to optimize the learned embedding. In the scenario of long-tailed recognition, where the number of samples in each class is imbalanced, treating two types of positive samples equally leads to the biased optimization for intra-category distance. In addition, similarity relationship among negative samples, that are ignored by SCL, also presents meaningful semantic cues. To improve the performance on long-tailed recognition, this paper addresses those two issues of SCL by decoupling the training objective. Specifically, it decouples two types of positives in SCL and optimizes their relations toward different objectives to alleviate the influence of the imbalanced dataset. We further propose a patch-based self distillation to transfer knowledge from head to tail classes to relieve the under-representation of tail classes. It uses patch-based features to mine shared visual patterns among different instances and leverages a self distillation procedure to transfer such knowledge. Experiments on different long-tailed classification benchmarks demonstrate the superiority of our method. For instance, it achieves the 57.7% top-1 accuracy on the ImageNet-LT dataset. Combined with the ensemble-based method, the performance can be further boosted to 59.7%, which substantially outperforms many recent works. Our code will be released.

Enhancing Ensemble Clustering with Adaptive High-Order Topological Weights

Ensemble clustering learns more accurate consensus results from a set of weak base clustering results. This technique is more challenging than other clustering algorithms due to the base clustering result set's randomness and the inaccessibility of data features. Existing ensemble clustering methods rely on the Co-association (CA) matrix quality but lack the capability to handle missing connections in base clustering. Inspired by the neighborhood high-order and topological similarity theories, this paper proposes a topological ensemble model based on high-order information. Specifically, this paper compensates for missing connections by mining neighborhood high-order connection information in the CA matrix and learning optimal connections with adaptive weights. Afterward, the learned excellent connections are embedded into topology learning to capture the topology of the base clustering. Finally, we incorporate adaptive high-order connection representation and topology learning into a unified learning framework. To our knowledge, this is the first ensemble clustering work based on topological similarity and high-order connectivity relations. Extensive experiments on multiple datasets demonstrate the effectiveness of the proposed method. The source code of the proposed approach is available at https://github.com/ltyong/awec.

Analisis Strategi Jual Beli Topi Online Melalui Facebook dengan Sistem Lelang Ditinjau Dari Perspektif Islam

The presence of social media brings fresh air to business actors to always develop their business. By utilizing the social media platform, business actors can conduct mu'amalah transactions with anyone and at any time without being limited by space and time. Facebook social media is social media that is used as a forum by its users to buy and sell online with an auction 
 This study uses a descriptive qualitative approach that focuses on the actual description of the condition of one of the cases. Descriptive qualitative research aims to describe existing phenomena, both scientifically and man-made, in the form of changes in activity, characteristics, changes in the relationship of similarities and differences between one another.
 The results of this study indicate that buying and selling online with an auction system on Facebook can be used as a reference as a strategy for marketing products in an online buying and selling system. The strategy of buying and selling online with an auction system from an Islamic perspective makes economic development for business actors buying and selling online with an auction system.

Selection of HBV key reactivation factors based on maximum information coefficient combined with cosine similarity.

Hepatitis B Virus (HBV) reactivation is the most common complication for patients with primary liver cancer (PLC) after radiotherapy. How to reduce the reactivation of HBV has been a hot topic in the study of postoperative radiotherapy for liver cancer. To find out the inducement of HBV reactivation, a feature selection algorithm (MIC-CS) using maximum information coefficient (MIC) combined with cosine similarity (CS) was proposed to screen the risk factors that may affect HBV reactivation. Firstly, different factors were coded and MIC between patients was calculated to acquire the association between different factors and HBV reactivation. Secondly, a cosine similarity algorithm was constructed to calculate the similarity relationship between different factors, thus removing redundant information. Finally, combined with the weight of the two, the potential risk factors were sorted and the key factors leading to HBV reactivation were selected. The results indicated that HBV baseline, external boundary, TNM, KPS score, VD, AFP, and Child-Pugh could lead to HBV reactivation after radiotherapy. The classification model was constructed for the above factors, with the highest classification accuracy of 84% and the AUC value of 0.71. Comparing multiple feature selection methods, the results showed that the effect of the MIC-CS was significantly better than MIM, CMIM, and mRMR, so it has a very broad application prospect.

Hybrid similarity relation based mutual information for feature selection in intuitionistic fuzzy rough framework and its applications

Fuzzy rough entropy established in the notion of fuzzy rough set theory, which has been effectively and efficiently applied for feature selection to handle the uncertainty in real-valued datasets. Further, Fuzzy rough mutual information has been presented by integrating information entropy with fuzzy rough set to measure the importance of features. However, none of the methods till date can handle noise, uncertainty and vagueness simultaneously due to both judgement and identification, which lead to degrade the overall performances of the learning algorithms with the increment in the number of mixed valued conditional features. In the current study, these issues are tackled by presenting a novel intuitionistic fuzzy (IF) assisted mutual information concept along with IF granular structure. Initially, a hybrid IF similarity relation is introduced. Based on this relation, an IF granular structure is introduced. Then, IF rough conditional and joint entropies are established. Further, mutual information based on these concepts are discussed. Next, mathematical theorems are proved to demonstrate the validity of the given notions. Thereafter, significance of the features subset is computed by using this mutual information, and corresponding feature selection is suggested to delete the irrelevant and redundant features. The current approach effectively handles noise and subsequent uncertainty in both nominal and mixed data (including both nominal and category variables). Moreover, comprehensive experimental performances are evaluated on real-valued benchmark datasets to demonstrate the practical validation and effectiveness of the addressed technique. Finally, an application of the proposed method is exhibited to improve the prediction of phospholipidosis positive molecules. RF(h2o) produces the most effective results till date based on our proposed methodology with sensitivity, accuracy, specificity, MCC, and AUC of 86.7%, 90.1%, 93.0% , 0.808, and 0.922 respectively.

SFFNet: Staged Feature Fusion Network of Connecting Convolutional Neural Networks and Graph Convolutional Neural Networks for Hyperspectral Image Classification

The immense representation power of deep learning frameworks has kept them in the spotlight in hyperspectral image (HSI) classification. Graph Convolutional Neural Networks (GCNs) can be used to compensate for the lack of spatial information in Convolutional Neural Networks (CNNs). However, most GCNs construct graph data structures based on pixel points, which requires the construction of neighborhood matrices on all data. Meanwhile, the setting of GCNs to construct similarity relations based on spatial structure is not fully applicable to HSIs. To make the network more compatible with HSIs, we propose a staged feature fusion model called SFFNet, a neural network framework connecting CNN and GCN models. The CNN performs the first stage of feature extraction, assisted by adding neighboring features and overcoming the defects of local convolution; then, the GCN performs the second stage for classification, and the graph data structure is constructed based on spectral similarity, optimizing the original connectivity relationships. In addition, the framework enables the batch training of the GCN by using the extracted spectral features as nodes, which greatly reduces the hardware requirements. The experimental results on three publicly available benchmark hyperspectral datasets show that our proposed framework outperforms other relevant deep learning models, with an overall classification accuracy of over 97%.

Fundamental absorption bandwidth to thickness limit for transparent homogeneous layers

Abstract Past work has considered the analytic properties of the reflection coefficient for a metal-backed slab. The primary result established a fundamental relationship for the minimal layer thickness to bandwidth ratio achievable for an absorber. There has yet to be establishment of a similar relationship for non-metal-backed layers, and here we present the universal result based on the Kramers–Kronig relations. Our theory is validated with transfer matrix calculations of homogeneous materials, and full-wave numerical simulations of electromagnetic metamaterials. Our results place more general fundamental limits on absorbers and thus will be important for both fundamental and applied studies.

Impact Analysis of Inflation and Unemployment on Economic Growth in Malaysia: A Study from 2000 to 2020

This paper analyses the relationship between inflation, unemployment, and economic growth in Malaysia: 2000-2020. Advanced econometric models are used to explore how these macroeconomic variables interact and influence each other within the Malaysian context. The study employs One mixture method approach, combining quantitative and qualitative analyses to provide a comprehensive understanding. The study presents insights into the Malaysian economy, highlighting the relationship between inflation, unemployment, and economic growth. The findings are essential for policymakers and economists, providing evidence-based guidance for effective economic strategies. The study contributes to the knowledge of Malaysian economics and can serve as a model for analyzing similar relationships in other developing economies.

Longitudinal relationship between grip strength and cognitive function in a European population older than 50 years: A cross-lagged panel model

ObjectivesThe aim of this study was to investigate the dynamic longitudinal relationship between grip strength and cognitive function. Methods6175 participants aged ≥50 years were included in the study using three waves of follow-up data from the Survey of Health, Ageing, and Retirement in Europe in 2015 (T1), 2017 (T2) and 2019 (T3). Cognitive function was assessed using numeracy, verbal fluency, immediate recall, delayed recall and total. The cross-lagged panel model was used for analysis. ResultsThere was a correlation between grip strength and cognitive function. Standardized path coefficient from numeracy T1 to grip strength T2 was 0.017 (p = 0.003), and from numeracy T2 to grip strength T3 was 0.014 (p = 0.012). Standardized path coefficient from grip strength T1 to numeracy T2 was 0.096 (p < 0.001), and from grip strength T2 to numeracy T3 was 0.113 (p < 0.001). Other indicators of cognitive function had similar relationships with grip strength. ConclusionsThe study found a statistically significant longitudinal and bidirectional relationship between grip strength and cognitive function in a sample of people aged ≥50 years from several European countries.

Time since first naturalization is key to explaining non‐native plant invasions on islands

AbstractAimInvestigating the extent of insular invasions by non‐native species (i.e., the number of islands they occupy) is central to island conservation. However, interrelationships among plant life history traits, naturalization histories, and island characteristics in determining island occupancy by non‐native plant species are poorly understood. We investigated whether island occupancy by different non‐native plant species declines in relation to their year of first naturalization and whether periods of first naturalization differ among growth forms, dispersal modes, and biogeographic origins. Then, we asked if non‐native plants that naturalized more recently occur more frequently on islands that are large, less isolated, and close to urban areas. We contrasted trends across growth forms, dispersal modes, and biogeographic origins.Location264 offshore islands in northern Aotearoa New Zealand.TaxaVascular plant species.MethodsWe combined field surveys and published data for 767 non‐native plant species on the islands. We categorized each species according to its growth form (n = 3), dispersal mode (n = 4) and biogeographic origin (n = 5) and identified its year of first naturalization in Aotearoa New Zealand. We tested our hypotheses using ANCOVA and generalized linear models (GLMs).ResultsThere were similar declines in island occupancy in relation to the year of first naturalization in Aotearoa New Zealand across all trait and biogeographic origin categories. First naturalization times of herbaceous species, those with unspecialized dispersal modes, and those originating from Eurasia and the Mediterranean basin were disproportionately earlier than other categories. Non‐native plants with more recent first naturalization occur more frequently on large islands close to urban areas, but not on less isolated ones. Relationships with island characteristics did not differ among trait and biogeographic origin categories.Main ConclusionsOverall, time of first naturalization was more important than trait and biogeographic origin categories in explaining non‐native plant invasion patterns on islands. Since there were similar relationships between island occupancy and the year of first naturalization in Aotearoa New Zealand for all categories, management bodies should focus on non‐native plant species of trait and biogeographic origin categories that have naturalized recently (e.g., woody species from other regions within Oceania), and on large islands close to urban areas. Introduction and naturalization histories provide essential context for interpreting the role of plant traits and biogeographic origin in understanding plant invasions on islands.

Origin, evolution, and maintenance of gene-strand bias in bacteria.

Gene-strand bias is a characteristic feature of bacterial genome organization wherein genes are preferentially encoded on the leading strand of replication, promoting co-orientation of replication and transcription. This co-orientation bias has evolved to protect gene essentiality, expression, and genomic stability from the harmful effects of head-on replication-transcription collisions. However, the origin, variation, and maintenance of gene-strand bias remain elusive. Here, we reveal that the frequency of inversions that alter gene orientation exhibits large variation across bacterial populations and negatively correlates with gene-strand bias. The density, distance, and distribution of inverted repeats show a similar negative relationship with gene-strand bias explaining the heterogeneity in inversions. Importantly, these observations are broadly evident across the entire bacterial kingdom uncovering inversions and inverted repeats as primary factors underlying the variation in gene-strand bias and its maintenance. The distinct catalytic subunits of replicative DNA polymerase have co-evolved with gene-strand bias, suggesting a close link between replication and the origin of gene-strand bias. Congruently, inversion frequencies and inverted repeats vary among bacteria with different DNA polymerases. In summary, we propose that the nature of replication determines the fitness cost of replication-transcription collisions, establishing a selection gradient on gene-strand bias by fine-tuning DNA sequence repeats and, thereby, gene inversions.

Genome-wide identification and comparative analysis of the Amino Acid Transporter (AAT) gene family and their roles during Phaseolus vulgaris symbioses

Amino acid transporters (AATs) are essential integral membrane proteins that serve multiple roles, such as facilitating the transport of amino acids across cell membranes. They play a crucial role in the growth and development of plants. Phaseolus vulgaris, a significant legume crop, serves as a valuable model for studying root symbiosis. In this study, we have conducted an exploration of the AAT gene family in P. vulgaris. In this research, we identified 84 AAT genes within the P. vulgaris genome sequence and categorized them into 12 subfamilies based on their similarity and phylogenetic relationships with AATs found in Arabidopsis and rice. Interestingly, these AAT genes were not evenly distributed across the chromosomes of P. vulgaris . Instead, there was an unusual concentration of these genes located toward the outer edges of chromosomal arms. Upon conducting motif analysis and gene structural analysis, we observed a consistent presence of similar motifs and an intron-exon distribution pattern among the subfamilies. When we analyzed the expression profiles of PvAAT genes, we noted tissue-specific expression patterns. Furthermore, our investigation into AAT gene expression under rhizobial and mycorrhizal symbiotic conditions revealed that certain genes exhibited high levels of expression. Specifically, ATLa5 and LHT2 was notably upregulated under both symbiotic conditions. These findings point towards a potential role of AATs in the context of rhizobial and mycorrhizal symbiosis in P. vulgaris, in addition to their well-established regulatory functions.

Surgery Within 24 Hours Reduces Mortality and General Complication Rates in Patients Who Have Periprosthetic Femoral Fractures at the Hip

BackgroundIn patients who have hip fractures, treatment within 24 hours reduces mortality and complication rates. A similar relationship can be assumed for patients who have hip periprosthetic femoral fractures (PPFs) owing to the similar baseline characteristics of the patient populations. This monocentric retrospective study aimed to compare the complication and mortality rates in patients who had hip PPF treated within and after 24 hours. MethodsIn total, 350 consecutive patients who had hip PPF in a maximum-care arthroplasty and trauma center between 2006 and 2020 were retrospectively evaluated. The cases were divided into 2 groups using a time to surgery (TTS) of 24 hours as the cutoff value. The primary outcome variables were operative and general complications as well as mortalities within 1 year. ResultsOverall, the mean TTS was 1.4 days, and the 1-year mortality was 14.6%. The TTS ≤ 24 hours (n = 166) and TTS > 24 hours (n = 184) groups were comparable in terms of baseline characteristics and comorbidities. Surgical complications were equally frequent in the 2 groups (16.3 versus 15.2%, P = .883). General complications occurred significantly more often in the late patient care group (11.4 versus 28.3%, P < .001). In addition, the 30-day mortality (0.6 versus 5.5%, P = .012), and 1-year mortality (8.3 versus 20.5%, P = .003) rates significantly increased in patients who had TTS > 24 hours. Cox regression analysis yielded a hazard ratio of 4.385 (P < .001) for the TTS > 24 hours group. ConclusionsPrompt treatment is required for patients who have hip PPF to reduce mortality and overall complications.

CGDD: Multiview Graph Clustering via Cross-Graph Diversity Detection.

Multiview graph clustering has emerged as an important yet challenging technique due to the difficulty of exploiting the similarity relationships among multiple views. Typically, the similarity graph for each view learned by these methods is easily corrupted because of the unavoidable noise or diversity among views. To recover a clean graph, existing methods mainly focus on the diverse part within each graph yet overlook the diversity across multiple graphs. In this article, instead of merely considering the sparsity of diversity within a graph as previous methods do, we incline to a more suitable consideration that the diversity should be sparse across graphs. It is intuitive that the divergent parts are supposed to be inconsistent with each other, otherwise it would contradict the definition of diversity. By simultaneously and explicitly detecting the multiview consistency and cross-graph diversity, a pure graph for each view can be expected. The multiple pure graphs are further fused to the structured consensus graph with exactly r connected components where r is the number of clusters. Once the consensus graph is obtained, the cluster label to each instance can be directly allocated as each connected component precisely corresponds to an individual cluster. An alternating iterative algorithm is designed to optimize the subtasks of learning the similarity graphs adaptively, detecting the consistency as well as cross-graph diversity, fusing the multiple pure graphs, and assigning cluster label to each instance in a mutual reinforcement manner. Extensive experimental results on several benchmark multiview datasets demonstrate the effectiveness of our model, in comparison to several state-of-the-art algorithms.

Biomass Change Estimated by TanDEM-X Interferometry and GEDI in a Tanzanian Forest

Mapping and quantification of forest biomass change are key for forest management and for forests’ contribution to the global carbon budget. We explored the potential of covering this with repeated acquisitions with TanDEM-X. We used an eight-year period in a Tanzanian miombo woodland as a test case, having repeated TanDEM-X elevation data for this period and repeated field inventory data. We also investigated the use of GEDI space–LiDAR footprint AGB estimates as an alternative to field inventory. The map of TanDEM-X elevation change appeared to be an accurate representation of the geography of forest biomass change. The relationship between TanDEM-X phase height and above-ground biomass (AGB) could be represented as a straight line passing through the origin, and this relationship was the same at both the beginning and end of the period. We obtained a similar relationship when we replaced field plot data with the GEDI data. In conclusion, temporal change in miombo woodland biomass is closely related to change in InSAR elevation, and this enabled both an accurate mapping and quantification wall to wall within 5–10% error margins. The combination of TanDEM-X and GEDI may have a near-global potential for estimation of temporal change in forest biomass.

Fault Diagnosis Method for Rolling Bearings Based on Grey Relation Degree.

Aiming at the difficult problem of extracting fault characteristics and the low accuracy of fault diagnosis throughout the full life cycle of rolling bearings, a fault diagnosis method for rolling bearings based on grey relation degree is proposed in this paper. Firstly, the subtraction-average-based optimizer is used to optimize the parameters of the variational mode decomposition algorithm. Secondly, the vibration signals of bearings are decomposed by using the optimized results, and the feature vector of the intrinsic mode function component corresponding to the minimum envelope entropy is extracted. Finally, the grey proximity and similarity relation degree based on standard distance entropy are weighted to calculate the grey comprehensive relation degree between the feature vector of vibration signals and each standard state. By comparing the results, the diagnosis of different fault states and degrees of rolling bearings is realized. The XJTU-SY dataset was used for experimentation, and the results show that the proposed method achieves a diagnostic accuracy of 95.24% and has better diagnosis performance compared to various algorithms. It provides a reference for the fault diagnosis of rolling bearings throughout the full life cycle.

The Colley Method is an Extension of the Borda Count

Summary The Colley and Massey methods are two well-known methods to rate sports teams. We prove that when the Colley method is applied to election data, then it returns the same outcome as the Borda count, a well-known election procedure. Hence, the Colley method is an extension of the Borda count to partial orderings of candidates. A similar relationship holds between the Massey method and range voting. Real data from Major League Baseball is used.