Leaf Nodes Research Articles

BP-Tree: Overcoming the Point-Range Operation Tradeoff for In-Memory B-Trees

B-trees are the go-to data structure for in-memory indexes in databases and storage systems. B-trees support both point operations (i.e., inserts and finds) and range operations (i.e., iterators and maps). However, there is an inherent tradeoff between point and range operations since the optimal node size for point operations is much smaller than the optimal node size for range operations. Existing implementations use a relatively small node size to achieve fast point operations at the cost of range operation throughput. We present the BP-tree , a variant of the B-tree, that overcomes the decades-old point-range operation tradeoff in traditional B-trees. In the BP-tree, the leaf nodes are much larger in size than the internal nodes to support faster range scans. To avoid any slowdown in point operations due to large leaf nodes, we introduce a new insert-optimized array called the buffered partitioned array (BPA) to efficiently organize data in leaf nodes. The BPA supports fast insertions by delaying ordering the keys in the array. This results in much faster range operations and faster point operations at the same time in the BP-tree. Our experiments show that on 48 hyperthreads, on workloads generated from the Yahoo! Cloud Serving Benchmark (YCSB), the BP-tree supports similar or faster point operation throughput (between .94×-1.2× faster) compared to Masstree and OpenBw-tree, two state-of-the-art in-memory key-value (KV) stores. On a YCSB workload with short scans, the BP-tree is about 7.4× faster than Masstree and 1.6× faster than OpenBw-tree. Furthermore, we extend the YCSB to add large range workloads, commonly found in database applications, and show that the BP-tree is 30× faster than Masstree and 2.5× faster than OpenBw-tree. We also provide a reference implementation for a concurrent B + -tree and find that the BP-tree supports faster (between 1.03×-1.2× faster) point operations when compared to the best-case configuration for B + -trees for point operations while supporting similar performance (about .95× as fast) on short range operations and faster (about 1.3× faster) long range operations.

Probabilistic Attribute Tree Structured Convolutional Neural Networks for Facial Expression Recognition in the Wild

Very recent work has demonstrated tremendous improvements in facial expression recognition (FER) using laboratory-controlled datasets. However, recognizing facial expressions under in-the-wild conditions still remains a challenge, especially on unseen subjects due to high inter-subject variations. In this paper, we propose a novel Probabilistic Attribute Tree Convolutional Neural Network (PAT-CNN) to explicitly deal with large intra-class variations caused by identity-related attributes, e.g., age, race, and gender. Specifically, a PAT module with an associated PAT loss is proposed to learn features in a hierarchical tree structure organized according to identity-related attributes, where the final features are less affected by the attributes. Then, expression-related features are extracted from leaf nodes. Samples are probabilistically assigned to tree nodes at different levels such that expression-related features can be learned from all samples weighted by probabilities. Furthermore, the proposed PAT-CNN can be learned from limited attribute-annotated samples to make the best use of available data. Experimental results on four spontaneous facial expression datasets, i.e., RAF-DB, SFEW, ExpW, and FER-2013, have demonstrated that the proposed PAT-CNN achieves the best performance when compared to state-of-the-art methods by explicitly modeling attributes. Impressively, a single model PAT-CNN achieves the best performance on the SFEW test dataset when compared to the state-of-the-art methods using an ensemble of hundreds of CNNs.

PM2VE: Power Metering Model for Virtualization Environments in Cloud Data Centers

Virtualization technologies provide solutions for cloud computing. Virtual resource scheduling is a crucial task in data centers, and the power consumption of virtual resources is a critical foundation of virtualization scheduling. Containers are the smallest unit of virtual resource scheduling and migration. Although many practical models for estimating the power consumption of virtual machines (VMs) have been proposed, few power estimation models of containers have been put forth. In this paper, we propose a fast-training piecewise regression model based on a decision tree for VM power metering and estimate the power of containers configured on the VM by treating the container as a group of processes on the VM. We select appropriate features from the collected metrics of VMs/containers to help our model fit the nonlinear relationship between power and features well. Besides, we optimize the leaf nodes of the regression tree, realizing the effective power metering of virtualization environments. We evaluate the proposed model on 13 tasks in PARSEC and compare it with several commonly used models in data centers. The experimental results prove the effectiveness of the proposed model, and the estimated power of containers is in line with expectations.

INDUCTION OF TOMATO POLYPHENOL OXIDASES IN RESPONSE TO COMMON CUTWORM (Spodoptera litura) AND JACK BEARDSLEY MEALYBUG (Pseudococcus jackbeardsleyi) INFESTATIONS

Our objectives were to evaluate the induction of polyphenol oxidases (PPOs) and expression patterns of PPO A, B, D, E, and F genes upon infestations by common cutworm (Spodoptera litura) and Jack Beardsley mealybug (Pseudococcus jackbeardsleyi). After common cutworm infestation at leaf node 4, PPO activities increased 10.2-fold locally, leading to systemic increase in PPO activities at upper leaf nodes 1-2 (3.0-fold) and lower leaf node 6 (1.7-fold). While infestation at leaf node 6 locally induced PPO activities 14.5-fold, and systemically induced PPO activities 3.0-fold at leaf node 4. By contrast, no significant induction of PPO was observed at any leaf nodes when either leaf node 4 or 6 was infested by mealybug. When common cutworm fed on PPO B::GUS fusion tomatoes, induced PPO B expression was observed locally particularly in mature leaves at veins, petioles, and abscission zones. However, PPO D::GUS fusion tomatoes showed locally induced expression especially in older leaves at veins and petioles. Systemically induced expression was also observed. Mealybug feeding also locally induced PPO B and PPO D expression in vascular tissues but at lower levels.

From trees to barcodes and back again II: Combinatorial and probabilistic aspects of a topological inverse problem

In this paper we consider two aspects of the inverse problem of how to construct merge trees realizing a given barcode. Much of our investigation exploits a recently discovered connection between the symmetric group and barcodes in general position, based on the simple observation that death order is a permutation of birth order. We show how to lift this combinatorial characterization of barcodes to an analogous combinatorialization of merge trees. As result of this study, we provide the first clear combinatorial distinction between the space of phylogenetic trees (as defined by Billera, Holmes and Vogtmann) and the space of merge trees: generic phylogenetic trees on n+1 leaf nodes fall into (2n−1)!! distinct equivalence classes, but the analogous number for merge trees is equal to the number of maximal chains in the lattice of partitions, i.e., (n+1)!n!2−n. The second aspect of our study is the derivation of precise formulas for the distribution of tree realization numbers (the number of merge trees realizing a given barcode) when we assume that barcodes are sampled using a uniform distribution on the symmetric group. We are able to characterize some of the higher moments of this distribution, thanks in part to a reformulation of our distribution in terms of Dirichlet convolution. This characterization provides a type of null hypothesis, apparently different from the distributions observed in real neuron data, which opens the door to doing more precise statistics and science.

Influence of Removing Leaf Node Neighbors on Network Controllability.

From the perspective of network attackers, finding attack sequences that can cause significant damage to network controllability is an important task, which also helps defenders improve robustness during network constructions. Therefore, developing effective attack strategies is a key aspect of research on network controllability and its robustness. In this paper, we propose a Leaf Node Neighbor-based Attack (LNNA) strategy that can effectively disrupt the controllability of undirected networks. The LNNA strategy targets the neighbors of leaf nodes, and when there are no leaf nodes in the network, the strategy attacks the neighbors of nodes with a higher degree to produce the leaf nodes. Results from simulations on synthetic and real-world networks demonstrate the effectiveness of the proposed method. In particular, our findings suggest that removing neighbors of low-degree nodes (i.e., nodes with degree 1 or 2) can significantly reduce the controllability robustness of networks. Thus, protecting such low-degree nodes and their neighbors during network construction can lead to networks with improved controllability robustness.

Distributed representation learning with skip-gram model for trained random forests

The random forest family has been extensively studied due to its wide applications in machine learning and data analytics. However, the representation abilities of forests have not been explored yet. The existing forest representation is mainly based on feature hashing on the indices of leaf nodes. Feature hashing typically disregards the information from tree structures, i.e., the relationships between leaf nodes. Furthermore, the visualisation abilities of feature hashing are limited. On the contrary, the Skip-Gram model has been widely explored in word and node embedding due to its excellent representation ability. This paper proposes distributed representation learning for trained forests (DRL-TF) to extract co-occurrence relationships of samples and tree structures, and further boost the representation abilities of the trained forest using the Skip-Gram model. The experimental results demonstrate that the proposed DRL-TF outperforms the challenging baselines. To the best of the authors’ knowledge, the visualisation by DRL-TF is the first tool to analyse the trained forests. The code is available at: https://github.com/machao199271/DRL-TF.

RDIMM: Revocable and dynamic identity-based multi-copy data auditing for multi-cloud storage

Multi-copy data possession on multi-cloud storage service provides resource-constrained users with a secure and effective way to process data maintenance. To enable integrity for the outsourced data, numerous multi-copy data auditing schemes for multi-cloud storage have been proposed. Nevertheless, existing solutions suffer from the following limitations: (i) cannot support dynamic data updates; (ii) fail to consider the user revocation feature; (iii) may leak the privacy of user identity. To address these limitations, we propose a new identity-based multi-copy data auditing scheme for multi-cloud storage, termed RDIMM. We introduce a new variant of Merkle Tree to enable fully dynamic data updates, in which all copies of a raw block are kept in the same leaf node. In addition, we design a new key generation strategy and a private key update technique, where the cost of user revocation is independent of the total number of file blocks owned by the revoked user. Moreover, we conduct a comprehensive correctness analysis and security analysis of our proposed RDIMM. In addition, we provide detailed theoretical analysis and experimental simulations, which illustrate the effective functionality and practical performance compared to state-of-the-art works.

Optimize and analysis of drilling PMSM using hybrid technique based on BN, FFT, FCE, and FMA for risk assessment

A failure risk assessment must be carried out and potential drilling equipment failure risks must be promptly addressed in order to prevent drilling fluid pollution during offshore oil drilling. The qualitative, comprehensive, and quantitative failure risks for Drilling Permanent Magnetic Synchronous Motors (DPMSM) are examined in this article using a hybrid methodology. First, the Drilling PMSM using Failure Mode Analysis (FMA) method is combined with the Risk Matrix (RM) approach to analyse the risk levels of risk factors individually. Next, the Borda number is introduced to compare the risk levels exactly. To execute a Fuzzy Comprehensive Evaluation (FCE) of the system failure risk, a fuzzy relation matrix of risk factors is generated, and the weight of each risk component is calculated using importance analysis. The failure rate is then determined using fuzzy inference, and the Fault Tree (FT) is then built based on the risk variables. Fault tree analysis is used to compute the system failure rate, and the significance of the bottom event is evaluated. The Bayesian network (BN) is used to depict the Fuzzy Fault Tree (FFT) analysis. By utilizing Bayesian forward causal inference and reverse diagnostic inference to calculate the leaf node failure rate and root node posterior probability, the system’s weak points and potential failure causes are determined.

Simulation of English part-of-speech classification based on artificial intelligence and additive logistic regression

English part-of-speech classification technology is a technology that can process text data, can effectively solve the problem of messy data in text information categories, make data structured and organized, and facilitate people to obtain effective information implicit in the text. This article transforms the original polynomial distribution into a generalized linear model and uses logistic regression algorithm for specific implementation. Moreover, the model proposed in this paper inherits the good explanatory characteristics of the decision tree, and it locally uses logistic regression to fit the data, which greatly improves the function space that logistic regression can fit. In addition, due to changes in the decision theory of logistic regression leaf nodes, the corresponding tree branch theory also needs to be changed accordingly. Finally, this paper designs experiments to study the performance of the model constructed in this paper. The research results show that the model constructed in this paper has high accuracy in the extraction and classification of English part-of-speech features.

An energy-efficient command broadcast protocol in dynamic WSNs

In wireless sensor networks (WSNs), a sink must broadcast periodically a command message to a group of nodes or all nodes to control the operation of the network. However, the sparse distribution and limited resources of nodes bring difficulties for efficient broadcasting. In this paper, we propose an energy-efficient command broadcast protocol based on the construction of a spanning tree which includes maximizing the number of leaf nodes, and the allocation of one sharable slot to each tree level. The sharable slot that allocates to tree level will be divided into multiple mini slots, and non-leaf nodes at the same level will compete for broadcasting; in this way, collision can be reduced. Moreover, a node that moves to another level can use the sharable slot allocated to the new level without reallocating the slots. Extensive simulations prove that this approach not only achieves high reliability of message delivery but also energy efficiency.

Semantic guided level-category hybrid prediction network for hierarchical image classification

Hierarchical classification (HC) assigns each object with multiple labels organized into a hierarchical structure. The existing deep learning-based HC methods usually predict an instance starting from the root node until a leaf node is reached. However, in the real world, images impaired by noise, occlusion, blur, or low resolution may not provide sufficient information for the classification at subordinate levels. To address this issue, we propose a novel Semantic Guided level-category Hybrid Prediction Network (SGHPN) that can jointly perform the level and category prediction in an end-to-end manner. SGHPN comprises two modules: a visual transformer that extracts feature vectors from the input images, and a semantic guided cross-attention module that uses categories word embeddings as queries to guide learning category-specific representations. In order to evaluate the proposed method, we construct two new datasets in which images are at a broad range of quality and thus are labeled to different levels (depths) in the hierarchy according to their individual quality. Experimental results demonstrate the effectiveness of our proposed HC method.

Interpretable ontology extension in chemistry

Reference ontologies provide a shared vocabulary and knowledge resource for their domain. Manual construction and annotation enables them to maintain high quality, allowing them to be widely accepted across their community. However, the manual ontology development process does not scale for large domains. We present a new methodology for automatic ontology extension for domains in which the ontology classes have associated graph-structured annotations, and apply it to the ChEBI ontology, a prominent reference ontology for life sciences chemistry. We train Transformer-based deep learning models on the leaf node structures from the ChEBI ontology and the classes to which they belong. The models are then able to automatically classify previously unseen chemical structures, resulting in automated ontology extension. The proposed models achieved an overall F1 scores of 0.80 and above, improvements of at least 6 percentage points over our previous results on the same dataset. In addition, the models are interpretable: we illustrate that visualizing the model’s attention weights can help to explain the results by providing insight into how the model made its decisions. We also analyse the performance for molecules that have not been part of the ontology and evaluate the logical correctness of the resulting extension.

A traceable and revocable decentralized multi-authority privacy protection scheme for social metaverse

Intelligent identification of close friends is the mainstream approach to protect users’ multi-dimensional data in the social metaverse. It provides a fine-grained matching service for users. However, users enjoy these benefits at the cost of leaking personal multi-dimensional data. The existing social metaverse-based intelligent identification of close friends privacy protection models typically use centralized mechanisms. This will limit users’ control over their data and make them vulnerable to privacy leakage and data misuse. To address these problems, this paper proposes a traceable and revocable decentralized multi-authority privacy protection scheme (TRDM) for social metaverse. The scheme establishes multiple authority centers based on users’ multi-dimensional data, overcoming single point of failure and performance bottlenecks. In addition, TRDM partially hides access policies and supports multi-dimensional attribute access control in social metaverse environment. By introducing leaf node values of binary trees, TRDM achieves the traceability and completes the revocation of malicious users by updating part of the ciphertext. Security analysis and experiments show that TRDM satisfies the requirement of static security under the random prediction model and has better performance in the revocation of malicious users.

DILI: A Distribution-Driven Learned Index

Targeting in-memory one-dimensional search keys, we propose a novel DIstribution-driven Learned Index tree ( DILI ), where a concise and computation-efficient linear regression model is used for each node. An internal node's key range is equally divided by its child nodes such that a key search enjoys perfect model prediction accuracy to find the relevant leaf node. A leaf node uses machine learning models to generate searchable data layout and thus accurately predicts the data record position for a key. To construct DILI, we first build a bottom-up tree with linear regression models according to global and local key distributions. Using the bottom-up tree, we build DILI in a top-down manner, individualizing the fanouts for internal nodes according to local distributions. DILI strikes a good balance between the number of leaf nodes and the height of the tree, two critical factors of key search time. Moreover, we design flexible algorithms for DILI to efficiently insert and delete keys and automatically adjust the tree structure when necessary. Extensive experimental results show that DILI outperforms the state-of-the-art alternatives on different kinds of workloads.

Multi-Objective Automatic Design of Permanent Magnet Motor Using Monte Carlo Tree Search

This study proposes a novel multi-objective design method based on Monte Carlo tree search (MCTS) for the design of permanent magnet motors. The global configurations that define the entire structure are represented by nodes in a tree structure. After MCTS is performed to select a route extending from the root to leaf node, multi-objective topology optimization (TO) is performed at the leaf node to determine the detailed shape, considering a trade-off relationship among objective functions. The novelty of this work lies in the integration of MCTS with multi-objective TO where the score of a node is provided by the number of Pareto solutions obtained by selecting that node. This enables the scoring of nodes and the determination of the node selection criterion. The proposed method is applied to the multi-objective optimization of a permanent magnet motor with respect to average torque, and either torque ripple or iron loss. The proposed method successfully obtained Pareto solutions, which comprise various global configurations and shapes.

Effects of the Random Forests Hyper-Parameters in Surrogate Models for Multi-Objective Combinatorial Optimization: A Case Study using MOEA/D-RFTS

Surrogate models are techniques to approximate the objective functions of expensive optimization problems. Recently, Random Forests have been studied as a surrogate model technique for combinatorial optimization problems. Nonetheless, Random Forests contain several hyper-parameters that are used to control the prediction process. Despite their importance, research on the effects of these hyper-parameters is scarce. Therefore, this paper performs a systematic investigation of the effects of different combinations of values for the Random Forest hyper-parameters on the approximation of well-known multi-objective combinatorial benchmark problems. The results show that the number of samples to consider when building each tree and the minimum number of samples to be at the leaf node are the two most important hyper-parameters in this context.

Trajectory privacy data publishing scheme based on local optimisation and R-tree

The proliferation of location-based service applications has led to a substantial surge in the amount of life trajectory data produced by mobile devices. And these data frequently contain confidential personal details. Simultaneously, the corresponding relatively lagging privacy protection technology and the improper trajectory data handling method will make tremendous problems with privacy breach. Therefore, this paper presents a trajectory privacy data publishing scheme, denoted as LORDP, which is based on local optimisation and R-tree. The proposed scheme aims to handle sensitive data while improves trajectory protection effectiveness. Firstly, the scheme combines the LKC-privacy model requirement to filter out the minimum violating sequences set, to reduce data sensitivity and the amount of injected noise. Secondly, R-tree is constructed based on trajectory similarity. Finally, Laplacian noise is added to the R-tree’s leaf nodes constrained by differential privacy. The experiments show that the proposed LORDP algorithm significantly enhances the utility of data compared to other algorithms, and reduces the loss rate of about approximately 2% for per trajectory data, which shows that the present algorithm is extremely effective.

Reconstruction of integrated hydraulic turbine characteristics curve based on classification weight neural network

To address the shortcomings in the drawing and processing of comprehensive characteristic curves of hydraulic turbines and the problems in the operation and management of small hydropower stations, this paper proposes a method for reconstructing the comprehensive characteristic curves based on classification weight neural network. Firstly, this study discretizes the comprehensive characteristic curve of the target turbine factory package to obtain multiple sets of discrete data points, and compares them with the actual operating data of small hydropower plants, and then pre-processes the operating data by the type and operating characteristics of the unit. Next, this paper uses XGBoost to classify the two types of data to obtain the leaf node optimal scores and uses the scores as the prediction weights of the deep neural network to grid the discrete points of the hydraulic turbine for prediction, and finally obtains the specific integrated characteristic graph of the unit. The method based on classification weight neural network can substantially improve the accuracy and reliability of composite characteristic curves while avoiding multivariate nonlinear mathematical problems, which provides a new method and idea for the reconstruction and expansion of the hydraulic turbine characteristic curve, and also provides a theoretical basis for the safe and economic operation of hydropower units.

Status-aware and energy-efficient data aggregation for inter-tidal monitoring systems

The Inter-tidal area is an important habitat for marine organisms and the wireless sensor network is a good candidate for data collection and environmental monitoring, yet faces challenges. Due to the poor link condition, our observations in three deployed inter-tidal wireless sensor networks (IT-WSN) reveal that only around 40% of packets can be received with extremely long transmission delays. On the other hand, relay nodes run out of energy much early than leaf nodes, which significantly shorten the system lifespan. Based on these observations in real systems, we specifically design a compressive-sensing-based data aggregation (CS-DA) mechanism and an adaptive operation strategy (AOS) for the system to mitigate the above problems. The real-trace-driven evaluation demonstrates that we can collect all data packets with a 94.46% accuracy with only 40% of packets received, while the lifespan of the system can be extended by 54.9% with AOS.