Tree Structure Research Articles

A Study of Disjoint Set Union in Programming Competitions

Disjoint Set Union (DSU) is a tree data structure, which is used to effectively deal with the problems of merging and querying disjoint sets. The DSU algorithm can be used to merge sets and query to which set the node belongs. The DSU algorithm is usually implemented using arrays and tree structures, but there are also methods that use hash tables. The DSU algorithm has a wide range of applications in the connectivity of graph theory, social network and image processing. It helps researchers better understand and analyze set operations, provides a basis for subsequent work, and promotes the solution and optimization of various problems in the field. At the same time, in the programming competition of college students, the use of DSU is more frequent, usually the use of set query is more, resulting in high time complexity and unable to solve the problem quickly. Therefore, path compression strategy is introduced to significantly improve the efficiency of set query. Finally, the application of path compression in union search is introduced in the form of programming competition.

An Adaptive Metadata Management Scheme Based on Deep Reinforcement Learning for Large-Scale Distributed File Systems

A major challenge confronting today’s distributed metadata management schemes is how to meet the dynamic requirements of various applications through effectively mapping and migrating metadata nodes to different metadata servers (MDS’s). Most of the existing works dynamically reallocate nodes to different servers adopting history-based coarse-grained methods, failing to make a timely and efficient update on the distribution of nodes. In this paper, we present the first deep reinforcement learning-leveraged distributed metadata management scheme, AdaM, to address the aforementioned dilemma. AdaM is an adaptive fine-grained metadata management scheme that trains an actor-critic network to migrate “hot” metadata nodes to different MDS’s based on its observations of the current “states” (i.e., access pattern, the structure of namespace tree and current distribution of nodes on MDS’s). Adaptive to varying access patterns, AdaM can automatically migrate hot metadata nodes among servers to keep load balancing while maintaining metadata locality. Besides, we propose a self-adaptive metadata cache policy, which dynamically combines the two strategies of managing caches on the server side and the client side to gain better query performance. Last but not least, we design a distributed metadata processing 2PC Protocol called MST-based 2PC to ensure data consistency. Experiments on a real-world dataset demonstrate the superiority of our proposed method over other schemes.

Crown structure, growth, and drought tolerance of true service tree (Sorbus domestica L.) in forests and urban environments

Crown structure, growth, and drought tolerance of true service tree (Sorbus domestica L.) in forests and urban environments

Strahler number of natural language sentences in comparison with random trees

The Strahler number was originally proposed to characterize the complexity of river bifurcation and has found various applications. This article proposes a computation of the Strahler number’s upper and lower limits for natural language sentence tree structures. Through empirical measurements across grammatically annotated data, the Strahler number of natural language sentences is shown to be almost 3 or 4, similar to the case of river bifurcation as reported by Strahler (1957 Eos Trans. Am. Geophys. Union 38 913–20). Based on the theory behind this number, we show that there is a kind of lower limit on the amount of memory required to process sentences. We consider the Strahler number to provide reasoning that explains reports showing that the number of required memory areas to process sentences is 3–4 for parsing (Schuler et al 2010 Comput. Linguist. 36 1–30), and reports indicating a psychological ‘magical number’ of 3–5 (Cowan 2001 Behav. Brain Sci. 24 87–114). An analytical and empirical analysis shows that the Strahler number is not constant but grows logarithmically. Therefore, the Strahler number of sentences is derived from the range of sentence lengths. Furthermore, the Strahler number is not different for random trees, which could suggest that its origin is not specific to natural language.

GoTreeScape: Navigate and Explore the Tree Visualization Design Space.

Declarative grammar is becoming an increasingly important technique for understanding visualization design spaces. The GoTreeScape system presented in the paper allows users to navigate and explore the vast design space implied by GoTree, a declarative grammar for visualizing tree structures. To provide an overview of the design space, GoTreeScape, which is based on an encoder-decoder architecture, projects the tree visualizations onto a 2D landscape. Significantly, this landscape takes the relationships between different design features into account. GoTreeScape also includes an exploratory framework that allows top-down, bottom-up, and hybrid modes of exploration to support the inherently undirected nature of exploratory searches. Two case studies demonstrate the diversity with which GoTreeScape expands the universe of designed tree visualizations for users. The source code associated with GoTreeScape is available at https://github.com/bitvis2021/gotreescape.

Towards Efficient Robotic Software Development by Reusing Behavior Tree Structures for Task Planning Paradigms

Towards Efficient Robotic Software Development by Reusing Behavior Tree Structures for Task Planning Paradigms

CoTree: A Side-Channel Collision Tool to Push the Limits of Conquerable Space

By introducing collision information into divide-and-conquer distinguishers, the existing collision-optimized side-channel attacks transform the given candidate space into a significantly smaller collision space, thus achieving more efficient key recovery. However, the candidates of the first several sub-keys shared by collision chains are still repeatedly detected, which happens very frequently and brings huge computational overhead. To alleviate this, we propose a highly-efficient collision-optimized attack named Collision Tree (CoTree). This collision detection tool exploits tree structure to store the chains created from the same sub-chain on the same branch, thus significantly reducing the storage requirements. It then benefits from the properties of both tree and collisions, and exploits a top-down tree building procedure and traverses each node only once when detecting their collisions with a candidate of the sub-key currently under consideration. Finally, unlike the traditional top-down node removal, CoTree launches a bottom-up branch removal procedure to remove the chains unsatisfying the collision conditions from the tree after traversing all the considered candidates of this sub-key, thus avoiding the traversal of the branches satisfying the collision condition. These strategies make our CoTree significantly alleviate the repetitive collision detection, and our experiments verify that it significantly outperforms the existing works.

Towards efficient multi-granular anomaly detection in distributed systems

Distributed systems often consist of a large number of computing and data nodes, which makes it both significant and challenging to detect anomalies efficiently and accurately in distributed systems. Generally, we not only need to determine whether an anomaly has occurred at a certain time (the time level anomaly), but also need to detect whether anomalies occur in a node (the node level anomaly) and which key performance indicators (KPIs) are anomalies (the KPI level anomaly), that is, to perform multi-granular anomaly detection in distributed systems. However, most existing algorithms only focus on the time level anomalies in centralized systems. For distributed systems, a simple way is to train a model for each node and then detect anomalies independently. An obvious disadvantage is that the cost of model inferring is unacceptable in practice. Therefore, we propose a Multi-Granular Anomaly Detection (MGAD) framework that utilizes a tree structure to perform anomaly detection hierarchically from the node level to time and KPI levels, which greatly reduces the cost of model inferring. Specifically, at the time level, we propose a novel model named Masked Sliding Spatial-Temporal Adversarial Network (MS2TAN) that considers spatial and temporal dependencies simultaneously. Extensive experiments with real-world data offer insights into the performance of the proposals, showing that MGAD is at least 5× faster for inferring when compared with the baselines.

Classification of Nutrition Problems for Indonesian Toddler With Decision Tree Algorithm C4.5

Indonesia continues to encounter numerous challenges, particularly in the health and economic sectors. As the future of the nation, the quality of human resources is crucial for Indonesia's development. The development of Indonesia is key to improving the quality of life of its people, and a focus on this development can positively impact the health and economy of the community. A healthy and educated generation is fundamental for the country's expected progress, as nutritional status is one of the factors significantly affecting the quality of human resources. Nutritional problems can cause serious impacts, such as improper physical growth, decreased IQ quality, and even death. The goal is to analyze the factors affecting the nutritional status of toddlers by classifying each variable using a decision tree. A decision tree is a flow chart that resembles a branching tree structure. The C4.5 algorithm was utilized in this study. It can process both numeric and categorical data, handle missing attribute values, and generate easy-to-interpret rules. After conducting the analysis, it was found that there are 392 districts/cities in Indonesia where the prevalence of stunted toddler nutritional status is less than 20%. The model created using the C4.5 algorithm was evaluated and achieved an accuracy of 99.8% and a kappa value close to 1. This indicates that the model can accurately classify toddler nutrition problems in Indonesia.

Construction of Transformer Fault Diagnosis and Prediction Model Based on Deep Learning

The current intelligent diagnosis and prediction methods for transformer faults are prone to low diagnostic accuracy and insufficient trend prediction ability when the fault categories are imbalanced. Therefore, a fault diagnosis and prediction model for transformers was constructed using a deep learning framework. The fault diagnosis model was constructed using a focus loss stack sparse noise reduction autoencoder on the deep learning framework. The prediction model was constructed by fusing long and short term memory networks on the basis of tree structure Parzen optimization, and the two models were validated. The results obtained through validation of the diagnostic model indicate that, when the actual hidden layer is set to 3 and the quantity of neurons is 58, the model accuracy during training and testing reaches 97.5% and 92.5% respectively. After adding 0.001 times the Gaussian white noise, the model accuracy was significantly lifted, so this study set the Gaussian noise coefficient to 0.001. In the comparison with baseline models, the actual classification ability of the research model samples is strong, significantly improving the fault diagnosis ability. In the validation of the prediction model, the three error index values of the research model in the single prediction step of CH4 concentration were 0.0699, 0.0540, and 0.8481%, respectively, and proved to be were lower than in the case of the baseline model. The three error values in the two-step prediction are 0.0194, 0.0161, and 0.6535%, which are also lower than in case of the baseline model. Overall, the diagnosis and prediction model proposed in this paper can provide real-time future numerical predictions of dissolved gas analysis and monitoring data in transformer oil. Furthermore, the research outilnes the future development trend of monitoring and measurement through application of tensor flow deep learning framework in transformer fault diagnosis. The attained prediction results are innovative, and could well complete the purpose of actual transformer fault diagnosis and early warning.

Maximum capture problem based on paired combinatorial weibit model to determine park-and-ride facility locations

Park-and-ride (P&R) facilities are key components in encouraging people to use the transit system by allowing them to leave their private vehicles at certain locations. The well-known multinomial logit (MNL) model is often used to develop a random utility maximization–based mathematical programming formulation to determine P&R facility locations. According to the independently and identically distributed (IID) assumption, the MNL model cannot account for the route similarity and user heterogeneity. This study provides a new mixed integer linear programming (MILP) formulation by incorporating a newly developed paired combinatorial weibit (PCW) model to relax the IID assumption for determining the optimal P&R facility location. Specifically, the incorporation of a copula derived from a generalized extreme value (GEV) model addresses the issue of route overlap within the context of the PCW model. In addition, using the Weibull distribution permits the consideration of heterogeneous perception variance. Its two-level tree structure for evaluating the marginal and conditional probabilities allows a linearization scheme to obtain a set of linear constraints. Numerical examples reveal the influence of the IID assumption relaxation on the results. The two probabilities from the tree structure and the binary location variables are combined to present a corresponding PCW model under open/close P&R facility solution. According to the degree of route overlapping and route-specific perception variance, the fare structure, particularly the distance-based scheme, has an impact on the number of P&R users and location at optimum.

Risk-informed design of advanced nuclear reactor: A target unavailability approach

Incorporating specified safety and production targets during the design phase of an engineered facility can effectively support reductions in construction and operation costs of nuclear power plants. In addition, this supports efforts toward making nuclear power technology more competitive and profitable than other electric power production forms by incorporating operational and design features into the design phase that directly support the increased likelihood of meeting such originating targets. Unavailability is the key parameter to establish plant-level capabilities that cascade down to function, system, and component levels to ensure that all sources of unavailability are addressed in the facility's design phases. In this article, two methods are presented to propose unavailability targets for nuclear reactor systems to risk-inform and optimize the design features, from an availability perspective: the first method is a simple method, and the second is a non-dominated sorting genetic algorithm III (NSGA-III) multi-objective optimization. These iterative methods are demonstrated in this paper for a hypothetical facility with a design maturity such that there is knowing the main components to comply with the engineering and security functions of the systems. With an assumed mature design, it is implied that generic data to execute quantitative reliability, and availability analyses are available at the systems levels that are sufficient to support a design-specific probabilistic risk assessment (PRA). The proposed methods modify the probabilities of basic events within the fault tree structures that model system and component unavailability sources that will be required for the facility’s operating license. The methods provided are applied to visualize the characteristics of the problem and the quantitative results. This work forms the basis for future work in which design alternatives affecting unavailabilities can be estimated and compared to their associated unavailability targets at different levels of granularity.

A new subfamily of ulmarid scyphomedusae, the Santjordiinae, with a description of Santjordia pagesi gen. et sp. nov. (Cnidaria: Scyphozoa: Discomedusae: Semaeostomeae: Ulmaridae) from the Sumisu Caldera, Ogasawara Islands, Japan.

An undescribed species of ulmarid medusa was observed in situ and captured at 812 m depth within the Sumisu Caldera, Ogasawara Islands, Japan. Morphological and molecular evidence points to it being distinct from other ulmarid medusae and a new species (pagesi), genus (Santjordia) and subfamily (Santjordiinae) are herein erected to contain it. This new subfamily of semaeostome ulmarid medusae has both marginal and subumbrellar rhopalia, making it unique within the order Semaeostomeae. Although the combination of subumbrellar tentacles and the lack of branched canals should warrant the erection of a new family within the Semaeostomeae, a lack of information on the gonad structure and poor bootstrap support in the molecular phylogenetic tree cause us to relegate it to the catch-all family Ulmaridae, until greater taxon sampling and phylogenetic analyses are carried out for the Semaeostomeae.

Stochastic modeling of turbulent mixing based on a hierarchical swapping of fluid parcels

AbstractTurbulent mixing is an omnipresent phenomenon that constantly affects our everyday life and plays an important role in a variety of industrial applications. The simulation of turbulent mixing poses great challenges, since the full resolution of all relevant length and time scales is associated with an immense computational effort. This limitation can be overcome by only resolving the large‐scale effects and completely model the sub‐grid scales. The development of an accurate sub‐grid mixing model is therefore a key challenge to capture all interactions in the sub‐grid scales. At this place, the hierarchical parcel‐swapping (HiPS) model formulated by A.R. Kerstein [J. Stat. Phys. 153, 142–161 (2013)] represents a computationally efficient and scale‐resolving turbulent mixing model. HiPS mimics the effects of turbulence on time‐evolving, diffusive scalar fields. In HiPS, the diffusive scalar fields or a state space is interpreted as a binary tree structure, which is an alternative approach compared to the most common mixing models. Every level of the tree represents a specific length and time scale, which is based on turbulence inertial range scaling. The state variables are only located at the base of the tree and are treated as fluid parcels. The effects of turbulent advection are represented by stochastic swaps of sub‐trees at rates determined by turbulent time scales associated with the sub‐trees. The mixing only takes places between adjacent fluid parcels and at rates consistent with the prevailing diffusion time scales. In this work, the HiPS model formulation for the simulation of passive scalar mixing is detailed first. Preliminary results for the mean square displacement, passive scalar probability density function (PDF) and scalar dissipation rate are given and reveal the strengths of the HiPS model considering the reduced order and computational efficiency. These model investigations are an important step of further HiPS advancements. The integrated auxiliary binary tree structure allows HiPS to satisfy a large number of criteria for a good mixing model. From this point of view, HiPS is an attractive candidate for modeling the mixing in transported PDF methods.

Rolling horizon policies for multi-stage stochastic assemble-to-order problems

Assemble-to-order approaches deal with randomness in demand for end items by producing components under uncertainty, but assembling them only after demand is observed. Such planning problems can be tackled by stochastic programming, but true multistage models are computationally challenging and only a few studies apply them to production planning. Solutions based on two-stage models are often short-sighted and unable to effectively deal with non-stationary demand. A further complication may be the scarcity of available data, especially in the case of correlated and seasonal demand. In this paper, we compare different scenario tree structures. In particular, we enrich a two-stage formulation by introducing a piecewise linear approximation of the value of the terminal inventory, to mitigate the two-stage myopic behaviour. We compare the out-of-sample performance of the resulting models by rolling horizon simulations, within a data-driven setting, characterised by seasonality, bimodality, and correlations in the distribution of end item demand. Computational experiments suggest the potential benefit of adding a terminal value function and illustrate interesting patterns arising from demand correlations and the level of available capacity. The proposed approach can provide support to typical MRP/ERP systems, when a two-level approach is pursued, based on master production and final assembly scheduling.

TreeJ: an ImageJ plugin for interactive cell lineage reconstruction from static images

BackgroundWith the emergence of deep-learning methods, tools are needed to capture and standardize image annotations made by experimentalists. In developmental biology, cell lineages are generally reconstructed from time-lapse data. However, some tissues need to be fixed to be accessible or to improve the staining. In this case, classical software do not offer the possibility of generating any lineage. Because of their rigid cell walls, plants present the advantage of keeping traces of the cell division history over successive generations in the cell patterns. To record this information despite having only a static image, dedicated tools are required.ResultsWe developed an interface to assist users in the building and editing of a lineage tree from a 3D labeled image. Each cell within the tree can be tagged. From the created tree, cells of a sub-tree or cells sharing the same tag can be extracted. The tree can be exported in a format compatible with dedicated software for advanced graph visualization and manipulation.ConclusionsThe TreeJ plugin for ImageJ/Fiji allows the user to generate and manipulate a lineage tree structure. The tree is compatible with other software to analyze the tree organization at the graphical level and at the cell pattern level. The code source is available at https://github.com/L-EL/TreeJ.

Tree-Invent: A Novel Multipurpose Molecular Generative Model Constrained with a Topological Tree.

De novo molecular design plays an important role in drug discovery. Here, a novel generative model, Tree-Invent, was proposed to integrate topological constraints in the generation of a molecular graph. In this model, a molecular graph is represented as a topological tree in which a ring system, a nonring atom, and a chemical bond are regarded as the ring node, single node, and edge, respectively. The molecule generation is driven by three independent submodels for carrying out operations of node addition, ring generation, and node connection. One unique feature of the generative model is that the topological tree structure can be specified as a constraint for structure generation, which provides more precise control of structure generation. Combined with reinforcement learning, the Tree-Invent model could efficiently explore targeted chemical space. Moreover, the Tree-Invent model is flexible enough to be used in versatile molecule design settings such as scaffold decoration, scaffold hopping, and linker generation.

Closeness of some tree structures

Closeness of some tree structures

A tree-like structured perceptron for transition-based biomedical event extraction

Event extraction in the biomedical domain has many complex situations, such as nested events, overlapping events, and multiple processing streams. For the problem of massive nested and multi-level nested events in the corpus, existing approaches generally transform these nested events into judgments of the relationships between triggers, resulting ineffective utilization of the complete event information. Similarly, when confronted with the issue of overlapping events in the corpus, the prevailing approach often recognizes only one of the overlapping events while neglecting the other. Due to the complexity of the biomedical events, their effectiveness is unsatisfactory. Biomedical events, which contain nested and overlapping events, can be conceptualized as a tree structure. In light of this, we present a novel tree-like structured perceptron designed for transition-based biomedical event extraction, which converts event predictions into sequences of transition operations. This approach enables the incremental construction of nested events, with each operation relying on the already constructed event and surrounding contextual features. We evaluate the proposed model on two tasks: Genia 2011 and Genia 2013. The experimental results show a 0.22% and 0.04% improvement in F1 score, respectively, compared to the currently better performing baseline models without using external resources.

Deep matched filtering for retinal vessel segmentation

The structure of the retinal vascular tree can reflect many indicators of ophthalmic health status. Retinal vessel segmentation, an important basis for the quantitative analysis of vascular structure, has been developed over decades. However, in the areas of imaging quality and contrast, there are still vascular ruptures and missed detection by existing methods. Moreover, the problem of false positives caused by existing lesions has not been properly addressed. Inspired by the primary visual cortex of the biological visual system, this paper proposes Anisotropic Perceptive Convolution (APC) and Anisotropic Enhancement Module (AEM) to model visual cortex cells and their orientation selection mechanism, respectively, as well as a novel network named W-shaped Deep Matched Filtering (WS-DMF) having a W-shaped overall framework. In the feature extraction backbone, a DMF based on a multilayer aggregation of APC is designed to enhance vascular features and inhibit the expression of pathological features. In addition, AEMs are embedded into DMF to enhance the orientation and position information of high-dimensional features. Furthermore, in order to enhance the ability of APC for perceiving linear textures (such as blood vessels), an Orientation Anisotropic Loss (OAL) is introduced. Based on experiments on several widely used datasets and several professional vessel segmentation metrics, the proposed method exhibits strong abilities of vessel extraction and lesion inhibition, surpassing many state-of-the-art algorithms. The results indicate the potential value of WS-DMF in clinical practice. The source code is available at https://www.github.com/tyb311/WS-DMF.