Multi-layer Tree Research Articles

Eigenstate calculation in the state-averaged (multi-layer) multi-configurational time-dependent Hartree approach.

A new approach for the calculation of eigenstates with the state-averaged (multi-layer) multi-configurational time-dependent Hartree (MCTDH) approach is presented. The approach is inspired by the recent work of Larsson [J. Chem. Phys. 151, 204102 (2019)]. It employs local optimization of the basis sets at each node of the multi-layer MCTDH tree and successive downward and upward sweeps to obtain a globally converged result. At the top node, the Hamiltonian represented in the basis of the single-particle functions (SPFs) of the first layer is diagonalized. Here p wavefunctions corresponding to the p lowest eigenvalues are computed by a block Lanczos approach. At all other nodes, a non-linear operator consisting of the respective mean-field Hamiltonian matrix and a projector onto the space spanned by the respective SPFs is considered. Here, the eigenstate corresponding to the lowest eigenvalue is computed using a short iterative Lanczos scheme. Two different examples are studied to illustrate the new approach: the calculation of the vibrational states of methyl and acetonitrile. The calculations for methyl employ the single-layer MCTDH approach, a general potential energy surface, and the correlation discrete variable representation. A five-layer MCTDH representation and a sum of product-type Hamiltonian are used in the acetonitrile calculations. Very fast convergence and order of magnitude reductions in the numerical effort compared to the previously used block relaxation scheme are found. Furthermore, a detailed comparison with the results of Avila and Carrington [J. Chem. Phys. 134, 054126 (2011)] for acetonitrile highlights the potential problems of convergence tests for high-dimensional systems.

A multilayered urban tree dataset of point clouds, quantitative structure and graph models

The significance of urban trees in promoting human health and well-being has been amplified by urbanization and the climate change effects. Simultaneously, advancements in remote sensing techniques have enhanced the opportunities for studying urban trees. The TreeML-Data has been compiled to support these efforts. It consists of labelled point clouds of 40 scanning projects of streets in Munich, 3,755 leaf-off (scans in winter) point clouds of individual trees, quantitative structure models (QSM), tree structure measurements, and tree graph structure models of these trees. The dataset offers valuable data for generating and evaluating models in various scientific disciplines, which include remote sensing, computer vision, machine learning, urban forestry, urban ecosystem, green architecture, and graph analysis. To ensure its quality, the tree structure measurements and QSM have been crosschecked. For instance, the tree diameter at breast height (DBH) in the sample dataset exhibits a deviation of approximately 1.5 cm (4.3%) when compared to manual measurements. In conclusion, the quality checks confirm its reliability for subsequent studies when compared to manual measurements.

An applied study of multi-layer decision tree optimization algorithms in machine learning

Abstract This paper investigates the multi-layer decision tree optimization algorithm in machine learning and explores its efficiency and accuracy in applying complex data classification. The focus is on the optimization strategy of the algorithm when dealing with large datasets with multiple types of records. The research adopts methods such as data set Discretization, fuzzification processing and hierarchical fuzzy decision tree construction algorithm. Through experimental verification, our constructed multilevel fuzzy decision tree performs well in two application scenarios: rainfall prediction and fingerprint identification. In the rainfall prediction experiment, using 5200 sample data, the algorithm achieves a classification accuracy of 61%. In the fingerprint recognition experiment, from 3,000 fingerprint images, a 100% recognition rate was performed, and the rejection rate was 25.2%. The results show that the multi-layer decision tree optimization algorithm can effectively handle large data sets of multiple types and significantly improve the accuracy and efficiency of classification. The algorithm offers high adaptability and accuracy when dealing with continuous type attributes. This study provides a new perspective and methodology for the application of multi-layer decision trees in machine learning, which is of great significance for the future development of data processing and classification techniques.

Belief rule-base expert system with multilayer tree structure for complex problems modeling

Belief rule-base (BRB) expert system is one of recognized and fast-growing approaches in the areas of complex problems modeling. However, the conventional BRB has to suffer from the combinatorial explosion problem since the number of rules in BRB expands exponentially with the number of attributes in complex problems, although many alternative techniques have been looked at with the purpose of downsizing BRB. Motivated by this challenge, in this paper, multilayer tree structure (MTS) is introduced for the first time to define hierarchical BRB, also known as MTS-BRB. MTS-BRB is able to overcome the combinatorial explosion problem of the conventional BRB. Thereafter, the additional modeling, inferencing, and learning procedures are proposed to create a self-organized MTS-BRB expert system. To demonstrate the development process and benefits of the MTS-BRB expert system, case studies including benchmark classification datasets and research and development (R&D) project risk assessment have been done. The comparative results showed that, in terms of modelling effectiveness and/or prediction accuracy, MTS-BRB expert system surpasses various existing, as well as traditional fuzzy system-related and machine learning-related methodologies.

PhastSim: Efficient simulation of sequence evolution for pandemic-scale datasets.

Sequence simulators are fundamental tools in bioinformatics, as they allow us to test data processing and inference tools, and are an essential component of some inference methods. The ongoing surge in available sequence data is however testing the limits of our bioinformatics software. One example is the large number of SARS-CoV-2 genomes available, which are beyond the processing power of many methods, and simulating such large datasets is also proving difficult. Here, we present a new algorithm and software for efficiently simulating sequence evolution along extremely large trees (e.g. > 100, 000 tips) when the branches of the tree are short, as is typical in genomic epidemiology. Our algorithm is based on the Gillespie approach, and it implements an efficient multi-layered search tree structure that provides high computational efficiency by taking advantage of the fact that only a small proportion of the genome is likely to mutate at each branch of the considered phylogeny. Our open source software allows easy integration with other Python packages as well as a variety of evolutionary models, including indel models and new hypermutability models that we developed to more realistically represent SARS-CoV-2 genome evolution.

The Improved Nonparametric Regression Model for the IoT Link Load Balancing Control Algorithm

In order to improve the load balance control effect of the Internet of Things link, this paper combines the nonparametric regression model to improve the load balancing algorithm of the Internet of Things link. Moreover, this paper proposes a load balancing research strategy based on data plane data flow, which is aimed at improving the load balancing problem of data flow in the link. The data center network uses a multilayer fat tree topology to store information in the flow table corresponding to the switch for data flow processing and forwarding operations. In addition, this paper constructs a load balancing model for intelligent Internet of Things link and verifies the model in this paper through experimental research. The research results show that the load balancing control algorithm for Internet of Things link based on the nonparametric regression model proposed in this paper can effectively improve the internal scheduling of the Internet of Things link system and promote the load balancing effect.

Analisis Performa Algoritma Machine Learning pada Prediksi Penyakit Cerebrovascular Accidents

Cerebrovascular Accidents (stroke) are a disease that threatens and causes death and disability and disability in the world, in Indonesia the number of people affected by stroke is increasing every year. Stroke can be prevented by adopting a healthy lifestyle, eating nutritious food, and doing physical activity. The purpose of this study is to create an effective stroke prediction model, the system uses parameters from lifestyle factors, controllable factors such as medical risk factors, and uncontrollable factors. Four classification algorithms are proposed, namely multi-layer perceptron, KNN, Decision Tree, and Random Forest. The results show that the classification algorithm can work effectively by producing a perfect score of 99.99% accuracy at the 10K-Fold Validation level of validation.

Memory effect and phase transition in a hierarchical trap model for spin glasses.

We introduce an efficient dynamical tree method that enables us to explicitly demonstrate the thermoremanent magnetization memory effect in a hierarchical energy landscape. Our simulation nicely reproduces the nontrivial waiting-time and waiting-temperature dependences in this nonequilibrium phenomenon. We further investigate the condensation effect, in which a small set of microstates dominates the thermodynamic behavior in the multilayer trap model. Importantly, a structural phase transition of the multilayer tree model is shown to coincide with the onset of the condensation phenomenon. Our results underscore the importance of hierarchical structure and demonstrate the intimate relation between the glassy behavior and structure of barrier trees.

SERAPAN KARBON PADA SISTEM AGROFORESTRY BERBASIS GAHARU (Gyrinops versteegii) DI SRAGEN DAN KARANGANYAR, JAWA TENGAH

Agroforestry, the practice of growing trees and crops in interacting combinations, has a pivotal role to reduce the CO2 concentration in the atmosphere, and it subsequently sequesters the carbon in part of stems, branches, roots, and crowns of the trees composing their vegetation communities. The objective of the research is to know the carbon sequestration in the three agroforestry practices; those are multilayer tree garden, taungya, and home garden. The research was carried out from April 2017 to March 2018 at Sragen and Karanganyar regency central Java Province. Plot method, with the plot size of 20m x20m for trees, 10m x10 m for pole, and 5m x5m for sapling, was used in the sampling. The carbon sequestration was measured by Chave's allometric equation. The carbon sequestration of above ground the trees planted at taungya was 265.18 kg C ha-1 y-1, in home garden was 236.29 kg C ha-1 y-1, and in multi layer tree garden was 290.14 kg C ha-1 y-1. Seasonal fluctuation of the higest carbon sequestration in home garden occurs on October-December; in multilayer tree garden occurs on January-March; and in taungya occurs on July-September. 
 Key words: Agroforestry practices, G.versteegii, seasonal fluctuation, Carbon sequestration

Time series prediction using machine learning: a case of Bitcoin returns

PurposeThe purpose of this study is to compare five data-driven-based ML techniques to predict the time series data of Bitcoin returns, namely, alternating model tree, random forest (RF), multiple linear regression, multi-layer perceptron regression and M5 Tree algorithms.Design/methodology/approachThe data used to forecast time series data of Bitcoin returns ranges from 8 July 2010 to 30 Aug 2020. This study used several predictors to predict bitcoin returns including economic policy uncertainty, equity market volatility index, S&P returns, USD/EURO exchange rates, oil and gold prices, volatilities and returns. Five statistical indexes, namely, correlation coefficient, mean absolute error, root mean square error, relative absolute error and root relative squared error are determined. The results of these metrices are used to develop colour intensity ranking.FindingsAmong the machine learning (ML) techniques used in this study, RF models has shown superior predictive ability for estimating the Bitcoin returns.Originality/valueThis study is first of its kind to use and compare ML models in the prediction of Bitcoins. More studies can be carried out by using further cryptocurrencies and other ML data-driven models in future.

Novel Static Multi-Layer Forest Approach and Its Applications

The existing multi-layer tree is of dynamic linked list type which has many limitations and is complicated due to the pointer-node structure. Static array representation gives more flexibility in programming of algorithms and operations like insertion, deletion, and search. It also reduces the storage space. This paper presents a new method for representing multi-layer forest data structure in array format. It also explains various tree operations, unique data compression algorithm and migration algorithm between traditional approach and the proposed data structure. Most of the fundamental algorithms like those from artificial intelligence that employ decision trees are based on trees/forest data structure. The current paper brings a completely new idea in the representation of these data structures without employing recursion and targeting memory optimizations with reduced code complexities. The applications of forest data structures are many and span over various interdisciplinary areas of Engineering, Medicine, Aviation, Locomotive, Marine, etc. The proposed novel approach not just introduces a new method to look at the tree data structure but also provides the flexibility to adapt to the existing methods as per the user needs. A few such applications in Simulink debugging and the Forest visualisation have been discussed in detail in this paper.

Co-citation and Co-authorship Networks of Statisticians

We collected and cleaned a large data set on publications in statistics. The data set consists of the coauthor relationships and citation relationships of 83, 331 papers published in 36 representative journals in statistics, probability, and machine learning, spanning 41 years. The data set allows us to construct many different networks, and motivates a number of research problems about the research patterns and trends, research impacts, and network topology of the statistics community. In this paper we focus on (i) using the citation relationships to estimate the research interests of authors, and (ii) using the coauthor relationships to study the network topology. Using co-citation networks we constructed, we discover a “statistics triangle”, reminiscent of the statistical philosophy triangle (Efron, 1998). We propose new approaches to constructing the “research map” of statisticians, as well as the “research trajectory” for a given author to visualize his/her research interest evolvement. Using co-authorship networks we constructed, we discover a multi-layer community tree and produce a Sankey diagram to visualize the author migrations in different sub-areas. We also propose several new metrics for research diversity of individual authors. We find that “Bayes”, “Biostatistics”, and “Nonparametric” are three primary areas in statistics. We also identify 15 sub-areas, each of which can be viewed as a weighted average of the primary areas, and identify several underlying reasons for the formation of co-authorship communities. We also find that the research interests of statisticians have evolved significantly in the 41-year time window we studied: some areas (e.g., biostatistics, high-dimensional data analysis, etc.) have become increasingly more popular. The research diversity of statisticians may be lower than we might have expected. For example, for the personalized networks of most authors, the p-values of the proposed significance tests are relatively large.

Artificial Intelligence-Based Joint Movement Estimation Method for Football Players in Sports Training

Football is a product in the process of human socialization; it can strengthen the body and enhance the ability of teamwork. The introduction of artificial intelligence into football training is an inevitable trend; this trend must be bound to intensify, but how to apply artificial intelligence to solve the problem of the joint movement estimation method for football players in sports training is still the main difficulty now. The basic principle of football training action pattern recognition is to determine the type of football player’s action by processing and analyzing the movement information obtained by the sensor. Due to the complex movements towards football players and the changeable external environment, there are still many problems with action recognition. Focusing on the detailed classification of different sports modes, this article conducts research on the recognition of the joint movement estimation method for football players in sports training. This paper uses the recognition algorithm based on the multilayer decision tree recognizer to identify the joint movement; the experiment shows that the method used in this paper accurately identified joint movement for football players in sports training.

Multi-layer tree liquid state machine recurrent auto encoder for thyroid detection

The proposed work presents thyroid detection strategy by addressing the various challenges faced when raw data is applied to complex neural network like structure. The authors present a Multi-Layer Tree Liquid State Machine Recurrent Auto encoder for the detection of the thyroid nodules. The tree based architecture prevents the loss of original information from dataset when applied to machine learning models like neural network. Liquid State Machine (LSM) prevents the loss of temporal feature of the data from the dataset. The multi layered architecture of the proposed system helps to classify the thyroid stage accurately. The classification rate of the proposed strategy increased when compared to other techniques where the aspect of dataset is not considered.

SDABS: A Flexible and Efficient Multi-Authority Hybrid Attribute-Based Signature Scheme in Edge Environment

The explosive growth of the Internet of Things and modern networking technologies lay the foundation for the development of intelligent transportation systems and smart cities. To analyzing massive data under the required time for transportation issues, the edge computing paradigm is applied, which pre-processing large amounts of data at the network edge to save bandwidth and improve response time. However, data reliability and security are still facing many challenges in the edge environment. In this article, we propose a multi-authority hybrid attribute-based signature scheme (SDABS). It is composed of four phases: system initialization, signature generation, signature verification, and attribute revocation phases. To better describe frequently changing features in the transportation systems like location, the dynamic attribute is introduced in building the signature. The multi-layer policy tree is applied to support flexible and various access policies, which also naturally form user groups and help data searching. Besides, the multi-authority structure is more suitable for the distributed edge environment. We evaluate SDABS from both theoretical analysis and practical analysis. Compared with two classical signature schemes (MABS and ODMA-ABS), experimental results demonstrate that the proposed SDABS can achieve better performance at an acceptable cost in the terms of attributes and attribute authorities.

Multi-layered tree crown extraction from LiDAR data using graph-based segmentation

With the development of Light Detection and Ranging (LiDAR) and Unmanned Aerial Vehicle (UAV) technology, extracting tree crowns from LiDAR data and infering their geometrical features are becoming more available for everyone. Although the forest has a significant stratification phenomenon in the vertical direction, the existing individual tree detection methods generally aimed at solving overstory trees detection. As a result, the understory trees cannot be effectively extracted. To effectively detect individual tree from multi-layer forests, a multi-layered tree extraction method using a graph-based segmentation algorithm was proposed in this study. First, using the graph-based segmentation algorithm delineates the canopy of the overstory tree on the canopy height model (CHM) generated by LiDAR data. Then, using the sliding window detection method extracts the LiDAR data of understory trees. Finally, the information of understory trees is extracted by the graph-based segmentation algorithm. To verify the performance of the proposed method, this study selected six experimental plots from two research areas. According to the result of our method, the highest matching score and average score for overstory trees reached 91.3 and 86.3; the highest matching score and average score for understory trees reached 78.1 and 63.2. Compared with other methods, our method has better detection results. The experimental results show that the proposed method can extract the understory and overstory trees effectively, thus improving the accuracy of individual tree extraction in multi-layered forests.

Carbon stock potential at several agarwood-based agroforestry practices in Sragen and Karanganyar, Central Java

Agroforestry practices have a pivotal role to reduce the CO2 concentration in the atmosphere and they subsequently store the carbon in part of stems, branches, roots, and crowns of the trees composing their vegetation communities. The objective of the research is to determine the carbon stocks in the three agroforestry practices. These are Multilayer Tree Garden (MTG), Taungya (TGY), and Home Garden (HMG). The research was carried out from May 2017 to November 2017 at Sragen and Karanganyar Regency, Central Java. A plot size of 20 m x 100 m for big trees, 5 m x 40 m for small trees, and 1 m x 1 m for understory vegetation was used in the sampling. The carbon stock was measured by Chave’s allometric equation. The carbon stock in the multilayer tree garden is the highest (44.47 Mg C/ha) compared with the taungya (38.81 Mg C/ha) and home garden (31.41 Mg C/ha). Meanwhile, the above-ground carbon stock in the home garden practice is the biggest (14.97 Mg C/ha or 48 %) among the multilayer tree garden (9.67 Mg C/ha or 23%) and taungya (6.11 Mg C/ha or 16%). A 14-year-old G. versteegii in the taungya practice has the biggest carbon stocks (2.46 Mg C/ha) compared to the multilayer tree garden (1.73 Mg C/ha) and home garden (0.56 Mg C/ha).

Simulation of multi-platform LiDAR for assessing total leaf area in tree crowns

LiDAR (Light Detection and Ranging) technology has been increasingly implemented to assess the biophysical attributes of forest canopies. However, LiDAR-based estimation of tree biophysical attributes remains difficult mainly due to the occlusion of vegetative elements in multi-layered tree crowns. In this study, we developed a new algorithm along with a multiple-scan methodology to analyse the impact of occlusion on LiDAR-based estimates of tree leaf area. We reconstructed five virtual tree models using a computer graphic-based approach based on in situ measurements from multiple tree crowns, for which the position, size, orientation and area of all leaves were measured. Multi-platform LiDAR simulations were performed on these 3D tree models through a point-line intersection algorithm. An approach based on the Delaunay triangulation algorithm with automatic adaptive threshold selection was proposed to construct the scanned leaf surface from the simulated discrete LiDAR point clouds. In addition, the leaf area covered by laser beams in each layer was assessed in combination with the ratio and number of the scanned points. Quantitative comparisons of LiDAR scanning for the occlusion effects among various scanning approaches, including fixed-position scanning, multiple terrestrial LiDAR scanning and airborne-terrestrial LiDAR cross-scanning, were assessed on different target trees. The results showed that one simulated terrestrial LiDAR scan alongside the model tree captured only 25–38% of the leaf area of the tree crown. When scanned data were acquired from three simulated terrestrial LiDAR scans around one tree, the accuracy of the leaf area recovery rate reached 60–73% depending on the leaf area index, tree crown volume and leaf area density. When a supplementary airborne LiDAR scanning was included, occlusion was reduced and the leaf area recovery rate increased to 72–90%. Our study provides an approach for the measurement of total leaf area in tree crowns from simulated multi-platform LiDAR data and enables a quantitative assessment of occlusion metrics for various tree crown attributes under different scanning strategies.

Thermal Comfort and Longwave Radiation over Time in Urban Residential Complexes

Large cities with a high concentration of high-rise buildings are shaded by urban canyon. This brings a cooling effect compared to the space exposed to the sun, but is not always cool due to the longwave radiation emitted from buildings and the built environment. We tested the micro-scale effects of major external spatial factors, trees, and buildings, under shade on longwave radiation shifts to understand the effects of large shaded areas in megacities. Incoming and outgoing longwave radiations (ILR and OLR, respectively) were found to decrease the overall observation by time zone. Longwave radiation on a micro-scale was also inversely proportional to the tree volume. From mean radiant temperature (MRT) analysis, we found that about a 10% decrease in MRT could be achieved by increasing tree volume by around 50%. Larger tree volumes corresponded to greater blocking effects on longwave radiation. Considering the tree volume, a multilayer urban tree canopy composition can more favorably improve the thermal environment and energy sustainability of a city compared to a single-layer canopy. Larger trees planted with harmonious shrubs are the most effective in reducing longwave radiation.

Tree registration to counter elite capture of forestry benefits in Ghana’s Ashanti and Brong Ahafo regions

The regularization of rights to commercial tree species, or “tree registration,” is being promoted by the State (Forestry Commission of Ghana) and non-governmental organizations in Ghana to help minimize elite capture of forestry benefits. Our study examines smallholder farmers’ response to tree registration in selected Ashanti and Brong Ahafo Region communities. Responses are considered in light of persistent constraints farmers endure, including crop damage from tree felling and non-remission (to communities from timber concessionaires) of social responsibility agreement (SRA) benefits. Nearly one-half of farmers said trees had been removed from their farms resulting in crop damage, but virtually no one could verify SRA benefits. Most farmers were unaware of tree titling, and no one had completed the registration process. The multilayered landownership and tree tenure structures in Ghana make registering difficult, if not impossible for smallholder farmers, many of whom are without the requisite social or financial capital, to follow through on instructions given to complete registration. Respondents cite a number of benefits gained by registering such as reductions in non-consensual chainsaw timber felling. However, filial and community piety (the obligation to respect and honor one’s family and extended relations) may undermine farmers’ use of statutory empowerment (gained through registration) to minimize elite capture.