Hierarchical Data Structure Research Articles

Hierarchical Information-Extreme Machine Learning of Hand Prosthesis Control System Based on Decursive Data Structure

The article considers the machine learning method for a hand prosthesis control system that recognizes electromyographic signals with a non-invasive recording system. The method was developed within the information-extreme intelligent data analysis technology framework to maximize the system’s information capacity during machine learning. The method is based on adapting the input information description to maximize the probability of correct classification decisions, similar to artificial neural networks. However, unlike neural-like structures, the proposed method was developed within a functional approach to modeling cognitive processes of natural intelligence formation and decision-making. This approach allowed the recognition system to adapt to arbitrary initial conditions of electromyogram formation and flexibility when retraining the system by expanding the alphabet of recognition classes. The decision rules formed by the results of information-extreme machine learning were characterized by high efficiency as an essential indicator of an intelligent prosthesis. The distinctiveness of the developed method from known machine learning methods was in applying a hierarchical data structure as a decursive binary tree, which allowed for transitioning from multi-class machine learning to two-class learning for each stratum of the decursive tree. The modified Kullback–Leibler information measure was the optimization criterion for machine learning parameters. The proposed hierarchical information-extreme machine learning method was implemented using electromyographic biosignals of cognitive commands for six finger and hand movements as an example.

Random hierarchical model for estimation of wheat yield in the North China Plain at different spatial scales

Accurate and large-scale wheat yield prediction in the North China Plain (NCP) can provide necessary information for agricultural policies and agricultural trade. Many studies have presented wheat yield estimation methods by using existing machine learning methods and remote sensing (RS) or environmental data. However, these methods only blindly input multi-source data into the model and do not consider the hierarchical relationships and interaction between different data types. In addition, there has been scant attention paid to the consistency of yield estimation models across varying spatial scales. To address these problems, a novel dynamic yield estimation model, known as random hierarchical model (RHM), which takes into account the hierarchical relationship of multi-source data, is constructed to estimate the wheat yield in the NCP. First, the time interval of wheat growth period is refined by using the 24 solar terms in China, and a time series multi-source dataset of climate, soil, and RS is constructed. Second, the hierarchical linear model is used to layer multi-source data and randomly select environmental and RS features with multiple time intervals. Multiple hierarchical models are constructed and optimized for integration, and the interrelationships between data collected at different levels are fully utilized, which can improve the accuracy of the yield estimation models in interannual and large-scale applications. Finally, the RHM at different spatial scales is cross-verified by using the measured and statistical data of the NCP for 4 years. The results indicated that the error of the RHM estimation is smaller than that of widely used machine learning models at different spatial scales of field-level measurement data (R2 = 0.52, nRMSE = 16.43%), county-level measurement integration data (R2 = 0.62, nRMSE = 12.83%), and county-level official statistics (R2 = 0.68, nRMSE = 11.41%). Our proposed RHM that considers the hierarchical structure of multi-source data is a reliable and a promising method for improving yield estimation. In addition, heterogeneity in the hierarchical relationships is observed between the different types of data in the RHM at different spatial scales, resulting in differences in the optimal lead time for estimating yield and the importance of key driving factors in the model, indicating that the cross-spatial scale applications of the model are not allowed. This study provides insights for large-scale wheat yield estimation and yield response to different environments and provided evidence and explanation for the prohibition of generalization of models at different spatial scales.

Hierarchical Representation of Measurement Data, Metrological Uncertainty and Metadata for Calibrated Battery Tests

AbstractWe present an interoperable hierarchical data representation for battery tests, leading to improved scalability of data transmission and enhanced data accessibility and comprehensibility for both human interpretation and machine processing. The hierarchical data format includes the raw trace electrical measurement data, the metrological calibration and uncertainty data, the metadata such as experimental settings, instruments and software versions, as well as post‐processed data such as electrochemical model fit parameters. This data representation allows repetition of the battery test under the exact same conditions such that identical results are achieved within defined error bounds. This is in line with the general F. A. I. R. data approach and provides repeatability and traceability in the battery value chain. As an application of the hierarchical data representation, we show the classification of cells as pass/fail being performed with quantitative confidence levels. We demonstrate the complete workflow of establishing the hierarchical data structure for electrochemical impedance spectroscopy (EIS), starting from metrological traceability of the calibration and uncertainty analysis towards the storage of the structured data as a single integrated file that preserves the hierarchical data format. The structured data file is provided in JSON format in the Zenodo repository, as well as the program scripts to generate and read the JSON EIS files.

Neural nonnegative matrix factorization for hierarchical multilayer topic modeling

We introduce a new method based on nonnegative matrix factorization, Neural NMF, for detecting latent hierarchical structure in data. Datasets with hierarchical structure arise in a wide variety of fields, such as document classification, image processing, and bioinformatics. Neural NMF recursively applies NMF in layers to discover overarching topics encompassing the lower-level features. We derive a backpropagation optimization scheme that allows us to frame hierarchical NMF as a neural network. We test Neural NMF on a synthetic hierarchical dataset, the 20 Newsgroups dataset, and the MyLymeData symptoms dataset. Numerical results demonstrate that Neural NMF outperforms other hierarchical NMF methods on these data sets and offers better learned hierarchical structure and interpretability of topics.

Semi-Supervised Classification of Malware Families Under Extreme Class Imbalance via Hierarchical Non-Negative Matrix Factorization with Automatic Model Selection

Identification of the family to which a malware specimen belongs is essential in understanding the behavior of the malware and developing mitigation strategies. Solutions proposed by prior work, however, are often not practicable due to the lack of realistic evaluation factors. These factors include learning under class imbalance, the ability to identify new malware, and the cost of production-quality labeled data. In practice, deployed models face prominent, rare, and new malware families. At the same time, obtaining a large quantity of up-to-date labeled malware for training a model can be expensive. In this article, we address these problems and propose a novel hierarchical semi-supervised algorithm, which we call the HNMFk Classifier , that can be used in the early stages of the malware family labeling process. Our method is based on non-negative matrix factorization with automatic model selection, that is, with an estimation of the number of clusters. With HNMFk Classifier , we exploit the hierarchical structure of the malware data together with a semi-supervised setup, which enables us to classify malware families under conditions of extreme class imbalance. Our solution can perform abstaining predictions, or rejection option, which yields promising results in the identification of novel malware families and helps with maintaining the performance of the model when a low quantity of labeled data is used. We perform bulk classification of nearly 2,900 both rare and prominent malware families, through static analysis, using nearly 388,000 samples from the EMBER-2018 corpus. In our experiments, we surpass both supervised and semi-supervised baseline models with an F1 score of 0.80.

Cellsig plug-in enhances CIBERSORTx signature selection for multidataset transcriptomes with sparse multilevel modelling.

The precise characterization of cell-type transcriptomes is pivotal to understanding cellular lineages, deconvolution of bulk transcriptomes, and clinical applications. Single-cell RNA sequencing resources like the Human Cell Atlas have revolutionised cell-type profiling. However, challenges persist due to data heterogeneity and discrepancies across different studies. One limitation of prevailing tools such as CIBERSORTx is their inability to address hierarchical data structures and handle nonoverlapping gene sets across samples, relying on filtering or imputation. Here, we present cellsig, a Bayesian sparse multilevel model designed to improve signature estimation by adjusting data for multilevel effects and modelling for gene-set sparsity. Our model is tailored to large-scale, heterogeneous pseudobulk and bulk RNA sequencing data collections with nonoverlapping gene sets. We tested the performances of cellsig on a novel curated Human Bulk Cell-type Catalogue, which harmonizes 1435 samples across 58 datasets. We show that cellsig significantly enhances cell-type marker gene ranking performance. This approach is valuable for cell-type signature selection, with implications for marker gene validation, single-cell annotation, and deconvolution benchmarks. Codes and the interactive app are available at https://github.com/stemangiola/cellsig; and the database is available at https://doi.org/10.5281/zenodo.7582421.

Cover Trees Revisited: Exploiting Unused Distance and Direction Information

The cover tree (CT) and its improved version are hierarchical data structures that simplified navigating nets while maintaining good runtime guarantees. They can perform nearest neighbor search in logarithmic time and provide efficient computation in practice. In this paper, we revisit cover trees for nearest neighbor search, and propose a more competitive method. The central idea of our method is to fully exploit the unused distance and direction information. More specially, our method introduces three novel concepts/techniques: (I) range list, (II) quadrant information, and (III) vectorial angle cosine. These techniques are seamlessly integrated into our suggested data structure and search algorithms. As an extra bonus, we explore approximate nearest neighbor and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$k$</tex-math></inline-formula> nearest neighbor based on the proposed techniques, and present algorithms for handling updates. Extensive experimental results, based on both real and synthetic datasets, consistently demonstrate that our method is attractive and competitive, compared against existing cover tree structures for nearest neighbor search and its variants.

A Tensorized Hierarchical Graph Attention Network for Stock Market Forecasting

Abstract: Stock market forecasting is a tough mission because of its complex and dynamic nature. Deep learning models have recently been proven to be successful at stock market predictions. Traditional deep learning models, however, frequently disregard the hierarchical structure and temporal relationships of the stock market. Here, we introduce the StockTensor, a brand-new tensorized hierarchical graph attention network for stock market forecasting. StockTensor models the hierarchical structure of the stock market data by constructing a hierarchical graph of stocks. At each level of the hierarchy, StockTensor uses a graph attention network to learn the relationships between stocks and aggregate the information from neighboring stocks. StockTensor also models the temporal dependencies of the stock market data by using a recurrent neural network. The recurrent neural network learns to predict future stock prices based on the current stock prices and the historical stock prices. We evaluate StockTensor on two real-world stock market datasets. The results show that StockTensor outperforms several stateof-the-art stock market forecasting models.

Signal detection statistics of adverse drug events in hierarchical structure for matched case-control data.

The tree-based scan statistic is a data mining method used to identify signals of adverse drug reactions in a database of spontaneous reporting systems. It is particularly beneficial when dealing with hierarchical data structures. One may use a retrospective case-control study design from spontaneous reporting systems (SRS) to investigate whether a specific adverse event of interest is associated with certain drugs. However, the existing Bernoulli model of the tree-based scan statistic may not be suitable as it fails to adequately account for dependencies within matched pairs. In this article, we propose signal detection statistics for matched case-control data based on McNemar's test, Wald test for conditional logistic regression, and the likelihood ratio test for a multinomial distribution. Through simulation studies, we demonstrate that our proposed methods outperform the existing approach in terms of the type I error rate, power, sensitivity, and false detection rate. To illustrate our proposed approach, we applied the three methods and the existing method to detect drug signals for dizziness-related adverse events related to antihypertensive drugs using the database of the Korea Adverse Event Reporting System.

Analyzing sales of the Korean restaurant franchise during the COVID-19 pandemic with the mixed-effects model approach.

Using point-of-sales (POS) data, the sales trends of 48 member stores of a Korean restaurant franchise during the COVID-19 pandemic were analyzed. As daily sales are nested in each member store of a franchise, the hierarchical structure of POS data was fully and effectively utilized by employing a mixed-effects model. The results showed that although sales volumes in all member stores were negatively affected by the pandemic, the level of impact varied according to store location: sales at some stores were drastically reduced, while a few others even achieved a slight increase in sales during the pandemic. These findings suggest that the government support policy for small business owners should be designed in a locally optimized way, to take account of neighborhood characteristics and the degree of sales loss for individual business owners.

CLICK: Integrating Causal Inference and Commonsense Knowledge Incorporation for Counterfactual Story Generation

Counterfactual reasoning explores what could have happened if the circumstances were different from what actually occurred. As a crucial subtask, counterfactual story generation integrates counterfactual reasoning into the generative narrative chain, which requires the model to preserve minimal edits and ensure narrative consistency. Previous work prioritizes conflict detection as a first step, and then replaces conflicting content with appropriate words. However, these methods mainly face two challenging issues: (a) the causal relationship between story event sequences is not fully utilized in the conflict detection stage, leading to inaccurate conflict detection, and (b) the absence of proper planning in the content rewriting stage results in a lack of narrative consistency in the generated story ending. In this paper, we propose a novel counterfactual generation framework called CLICK based on causal inference in event sequences and commonsense knowledge incorporation. To address the first issue, we utilize the correlation between adjacent events in the story ending to iteratively calculate the contents from the original ending affected by the condition. The content with the original condition is then effectively prevented from carrying over into the new story ending, thereby avoiding causal conflict with the counterfactual conditions. Considering the second issue, we incorporate structural commonsense knowledge about counterfactual conditions, equipping the framework with comprehensive background information on the potential occurrence of counterfactual conditional events. Through leveraging a rich hierarchical data structure, CLICK gains the ability to establish a more coherent and plausible narrative trajectory for subsequent storytelling. Experimental results show that our model outperforms previous unsupervised state-of-the-art methods and achieves gains of 2.65 in BLEU, 4.42 in ENTScore, and 3.84 in HMean on the TIMETRAVEL dataset.

혼합효과 랜덤 포레스트를 적용한 청소년기 진로성숙도 발달 예측 변인 탐색

The purpose of this study is to explore predictive variables of career maturity during middle and high school. To do this, the mixed effects random forest that considers the hierarchical data structure of longitudinal data was applied to GEPS(gyeonggi education panel study) data from the 4th to 9th wave (7th-12th grades). To derive the major predictive variables which are more stable, the analysis through multiple imputation was repeated five times to identify the top 20 variables with a high SHAP (shapley additive expansion) importance index. As a result, 17 major predictive variables were derived by using SHAP value: ‘self-understanding as a learner’, ‘proactive initiative’, sub factors of self-directed learning ability, friend relationships, teacher relationships, and ‘career search during vacation’ were selected as key variables that predict development of career maturity. In addition, variables related to relationships between parent-child such as ‘parent parenting behavior’ and ‘attachment’, as well as students affective attitudes such as ‘self-esteem’, ‘self-efficacy’, and ‘learning motivation’ were selected as key predictive variables, too. The relationship between major predictive variables and career maturity was examined through the SHAP dependence plot, and educational implications for career development of adolescents were discussed.

A multilevel analysis of the triple burden of malnutrition in Indonesia: trends and determinants from repeated cross-sectional surveys

BackgroundAlthough child malnutrition has been reducing, the coexistence in mothers and children of various forms of malnutrition has continued to rise around the world. In the Indonesian context, a knowledge gap exists on the coexistence of multiple malnutrition burdens. This study examines trends in the coexistence of the triple burden of malnutrition (TBM) among mother–child pairs living in the same house and explores multilevel (individual, household, and community) factors associated with TBM in Indonesia.MethodsWe used data from the 2013 and 2018 Indonesia Basic Health Research, the nationally representative survey of the Indonesian population, as repeated cross-sectional surveys. Study samples were mothers and children (0–59 months old), who resided in the same household and indicated by the same identifier number. The anthropometric measurements of the mothers and children, and the hemoglobin levels of the children were collected. We employed a multilevel mixed-effects model to consider the hierarchical data structure. The model captured the role of cluster, district, provincial differences, and the individual, household, community-level, and TBM status characteristics.ResultsOf 3,891 mother–child pairs analyzed, 24.9% experienced TBM. Girls had 63% higher odds than boys of TBM (aOR: 1.63; 95% CI: 1.30 to 2.03). Significantly lower odds were found in children of mothers who had a gestational age lower than 37 weeks (aOR: 0.72; 95% CI: 0.55 to 0.94). At the household level, children with a father who had a high-school, primary-school, or no school education had significantly higher odds of TBM than children of fathers who had graduated from academy. Children of mothers who visited Antenatal Care (ANC) no more than 6 times had significantly lower odds (aOR: 0.65; 95% CI: 0.47 to 0.88). Children of mothers who consumed Iron and Folic Acid (IFA) supplements had significantly lower odds.ConclusionTBM is related to characteristics at not just the individual level but also the family and community levels. To achieve significant outcomes, integrated nutrition interventions in Indonesia should also consider family and community factors.

Predictors of Treatment Response in Patients with Treatment-Resistant Depression: Outcomes of a Randomized Trial

Objective: This report aims to compare the effectiveness between electroconvulsive therapy (ECT) and transcranial direct stimulation (tDCS) among patients with treatment-resistant depression (TRD) and the associated factors. Methods: This was a secondary data analysis of a randomized, controlled, open-label trial conducted from 1 July 2018, to 31 December 2018. The dataset of 90 patients included in the study was retrieved from Mendeley Data. Patients with TRD were randomized 1:1 into either receiving ECT or tDCS. A good treatment response was determined as an improvement from the Hamilton Depression (HAM-D) baseline score at a rate of more than or equal to 50% at the end of a 2-week intervention. A mixed-effect logistic regression was performed to explain the hierarchical data structure of predictors to successful treatment outcome. Results: The largest magnitude of change was consistently observed in the ECT treatment arm across both HAM-D score and the clinical global impression severity scale (CGI-S) scale (p &lt; 0.001). Lower baseline HAM-D scores (OR: 0.72, 95% CI: 0.58, 0.92), lower baseline CGI-S scale (OR: 0.30, 95% CI: 0.17, 0.55), and ECT as the choice of treatment modality (OR: 14.0, 95% CI: 5.08, 38.58) independently predicted successful therapy among TRD patients, while modelling with multiple logistic regression determined that low socio-economic status (aOR: 20.01, 95% CI: 1.89, 211.47), ECT (aOR: 31.7, 95% CI: 6.32, 159.0) and a lower baseline CGI-S scale (aOR: 0.18, 95% CI: 0.06, 0.57) were significantly predictive of a positive treatment outcome among patients with TRD. Conclusions: ECT was more effective in alleviating depressive symptoms in TRD as compared to tDCS.

SHEPHARD: a modular and extensible software architecture for analyzing and annotating large protein datasets.

The emergence of high-throughput experiments and high-resolution computational predictions has led to an explosion in the quality and volume of protein sequence annotations at proteomic scales. Unfortunately, sanity checking, integrating, and analyzing complex sequence annotations remains logistically challenging and introduces a major barrier to entry for even superficial integrative bioinformatics. To address this technical burden, we have developed SHEPHARD, a Python framework that trivializes large-scale integrative protein bioinformatics. SHEPHARD combines an object-oriented hierarchical data structure with database-like features, enabling programmatic annotation, integration, and analysis of complex datatypes. Importantly SHEPHARD is easy to use and enables a Pythonic interrogation of largescale protein datasets with millions of unique annotations. We use SHEPHARD to examine three orthogonal proteome-wide questions relating protein sequence to molecular function, illustrating its ability to uncover novel biology. We provided SHEPHARD as both a stand-alone software package (https://github.com/holehouse-lab/shephard), and as a Google Colab notebook with a collection of precomputed proteome-wide annotations (https://github.com/holehouse-lab/shephard-colab). Supplementary data are available at Bioinformatics online.

Learn global and optimize local: A data-driven methodology for last-mile routing

In last-mile routing, the task of finding a route is often framed as a Traveling Salesman Problem to minimize travel time and associated cost. However, solutions stemming from this approach do not match the realized paths as drivers deviate due to navigational considerations and preferences. To prescribe routes that incorporate this tacit knowledge, a data-driven model is proposed that aligns well with the hierarchical structure of delivery data wherein each stop belongs to a zone — a geographical area. First, on the global level, a zone sequence is established as a result of a minimization over a cost matrix which is a weighted combination of historical information and distances (travel times) between zones. Subsequently, within zones, sequences of stops are determined, such that, integrated with the predetermined zone sequence, a full solution is obtained.The methodology is particularly promising as it propels itself within the top-tier of submissions to the Last-Mile Routing Research Challenge while maintaining an elegant decomposition that ensures a feasible implementation into practice. The concurrence between prescribed and realized routes underpins the adequateness of a hierarchical breakdown of the problem, and the fact that drivers make a series of locally optimal decisions when navigating. Furthermore, experimenting with the balance between historical information and distance exposes that historic information is pivotal in deciding a starting zone of a route. The experiments also reveal that at the end of a route, historical information can best be discarded, making the time it takes to return to the station the primary concern.

Пример алгоритма для расчёта специального лекарственного севооборота

The article presents a description of the algorithm in Word Excel format for calculating the structure of the small-scale field experiments areas as part of special medicinal crop rotation on the example of the VILAR experimental field (Moscow region) according to data for the period 2011-2021. The programming of the algorithm is implemented on the basis of conditional and logical operators. In the comments to the algorithm is given: a description of the hierarchical structure, criteria, types and relationships of data, the scheme of the algorithm, data features. The illustrations show the screen view of the algorithm, input and output data, the structure and location of data in the calculation table at different stages of using the algorithm. This algorithm is applicable for a system most similar to a crop rotation system with separate working areas within a crop rotation field of variable size. The results are relevant in the field of applied information technologies and scientific applied research for use in agricultural production in order to improve the efficiency of land use. Keywords: APPLIED INFORMATION TECHNOLOGIES, BRANCHING ALGORITHM, STRUCTURE OF CROP ROTATION AREAS, FIELD EXPERIENCE

The impact of firm leverage on investment decisions: The new approach of hierarchical method

This paper investigates the impact of firm leverage on its investment activities. Especially, the research is conducted in the context of the Vietnamese emerging market, an incomplete market in South East Asia with the existence of inefficient market problems such as information asymmetry and agency conflicts which are the root cause of the relationship between corporate leverage and investment. Regarding methodology, we build the two main types of econometric models: traditional multiple regression and multilevel model (also called hierarchical, mixed, or nested data model). The purpose of employing the multilevel model is to observe the hierarchical structure of data and the effect of each data level in the hierarchy on firm investment that the traditional regression model may fail to achieve. We construct three-level predictors (three levels of leverage) for investment: observation unit, firm level, and industry level. We find that debt used in a firm can harm or reduce its investment activities. All three hierarchical levels of leverage show negative and significant effects on investment. Especially, the impact of leverage at the first level of data clustering on investment gets stronger under the moderation of industry leverage. In this case, the multilevel model is a more appropriate estimation method than the traditional regression.

Data mining from a hierarchical dataset for mechanical metamaterials composed of curved-sides triangles

Mechanical metamaterials composed of curvilinear elements (MM-CEs) offer better programmability and functionality than their rectilinear counterparts. They thus hold promising potential for diverse applications, such as flexible electronics, impact protection, and deformation control. However, existing research on MM-CEs has primarily focused on specific curve shapes, neglecting the vast potential of the wider curve design space. Based on data-driven methods, this work builds a dataset with a hierarchical data structure, which transforms the infinite and difficult-to-conceptualize curve data into a finite number of potentially researchable curve sub-types. Moreover, methods are developed based on data mining to better understand the broad design space, excavate hidden connections of different curve sub-types and discover new rules for designing MM-CEs. The outcomes of this study offer novel strategies to investigate other structures and metamaterials with complex geometries as well.

Representing Hierarchical Structured Data Using Cone Embedding

Extracting hierarchical structure in graph data is becoming an important problem in fields such as natural language processing and developmental biology. Hierarchical structures can be extracted by embedding methods in non-Euclidean spaces, such as Poincaré embedding and Lorentz embedding, and it is now possible to learn efficient embedding by taking advantage of the structure of these spaces. In this study, we propose embedding into another type of metric space called a metric cone by learning an only one-dimensional coordinate variable added to the original vector space or a pre-trained embedding space. This allows for the extraction of hierarchical information while maintaining the properties of the pre-trained embedding. The metric cone is a one-dimensional extension of the original metric space and has the advantage that the curvature of the space can be easily adjusted by a parameter even when the coordinates of the original space are fixed. Through an extensive empirical evaluation we have corroborated the effectiveness of the proposed cone embedding model. In the case of randomly generated trees, cone embedding demonstrated superior performance in extracting hierarchical structures compared to existing techniques, particularly in high-dimensional settings. For WordNet embeddings, cone embedding exhibited a noteworthy correlation between the extracted hierarchical structures and human evaluation outcomes.