Tree-based Structure Research Articles

Cclib 2.0: An updated architecture for interoperable computational chemistry.

Interoperability in computational chemistry is elusive, impeded by the independent development of software packages and idiosyncratic nature of their output files. The cclib library was introduced in 2006 as an attempt to improve this situation by providing a consistent interface to the results of various quantum chemistry programs. The shared API across programs enabled by cclib has allowed users to focus on results as opposed to output and to combine data from multiple programs or develop generic downstream tools. Initial development, however, did not anticipate the rapid progress of computational capabilities, novel methods, and new programs; nor did it foresee the growing need for customizability. Here, we recount this history and present cclib 2, focused on extensibility and modularity. We also introduce recent design pivots-the formalization of cclib's intermediate data representation as a tree-based structure, a new combinator-based parser organization, and parsed chemical properties as extensible objects.

ResFaultyMan: An intelligent fault detection predictive model in power electronics systems using unsupervised learning isolation forest

Intelligent fault detection considered as a paramount importance in Power Electronics Systems (PELS) to ensure operational reliability along with rising complexities and critical application demands. However, most of the developed methods in real-world scenarios can have better detection, and accurate diagnosis. In this regard, ResFaultyMan, a novel unsupervised isolation forest-based model, is presented in this paper, for real-world fault/anomaly detection in PELS. Capitalizing on the dynamics of faults, ResFaultyMan utilizes a tree-based structure for effective anomaly isolation, demonstrating adaptability to diverse fault scenarios. The test bench, comprising a load, Triac switch, resistor, voltage source, and Pyboard microcontroller, provides a dynamic setting for performance evaluation. The integration of a Pyboard microcontroller and a Python-to-Python interface facilitates fast data transfer and sampling, enhancing the efficiency of ResFaultyMan in real-time fault detection scenarios. Comparative analysis with OneClassSVM and LocalOutlierFactor, utilizing Key Performance Indicators (KPIs) of Accuracy, Precision, and Recall, as well as F1 Score, manifest ResFaultyMan's fault detection capabilities for fault detection in PELSs, and its performance in the related applications.

Multitask Linear Genetic Programming with Shared Individuals and its Application to Dynamic Job Shop Scheduling

Multitask genetic programming methods have been applied to various domains, such as classification, regression, and combinatorial optimization problems. Most existing multitask genetic programming methods are designed based on tree-based structures, which are not good at reusing building blocks since each sub-tree passes its outputs to only one parent. It may limit the design and performance of knowledge sharing in multitask optimization. Different from tree-based genetic programming, building blocks in linear genetic programming can be easily reused by more than one parent. Besides, existing multitask genetic programming methods always allocate each individual to a specific task and have to duplicate genetic materials from task to task in knowledge transfer, which is inefficient and often produces redundancy. Contrarily, it is natural for a linear genetic programming individual to produce multiple distinct outputs, which enables each linear genetic programming individual to solve multiple tasks simultaneously. With this in mind, we propose a new multitask linear genetic programming method that transfers knowledge via multi-output individuals (i.e., shared individuals among tasks). By integrating different solutions into one multi-output individual, the proposed method efficiently reuses common knowledge among tasks and maintains distinct behaviors for each task. The empirical results show that the proposed method has a significantly better test performance than state-of-the-art multitask genetic programming methods. Further analyses verify that the new knowledge transfer mechanism can adjust the transfer rate automatically and thus improves its effectiveness.

SEMtree: tree-based structure learning methods with structural equation models.

With the exponential growth of expression and protein-protein interaction (PPI) data, the identification of functional modules in PPI networks that show striking changes in molecular activity or phenotypic signatures becomes of particular interest to reveal process-specific information that is correlated with cellular or disease states. This requires both the identification of network nodes with reliability scores and the availability of an efficient technique to locate the network regions with the highest scores. In the literature, a number of heuristic methods have been suggested. We propose SEMtree(), a set of tree-based structure discovery algorithms, combining graph and statistically interpretable parameters together with a user-friendly R package based on structural equation models framework. Condition-specific changes from differential expression and gene-gene co-expression are recovered with statistical testing of node, directed edge, and directed path difference between groups. In the end, from a list of seed (i.e. disease) genes or gene P-values, the perturbed modules with undirected edges are generated with five state-of-the-art active subnetwork detection methods. The latter are supplied to causal additive trees based on Chu-Liu-Edmonds' algorithm (Chow and Liu, Approximating discrete probability distributions with dependence trees. IEEE Trans Inform Theory 1968;14:462-7) in SEMtree() to be converted in directed trees. This conversion allows to compare the methods in terms of directed active subnetworks. We applied SEMtree() to both Coronavirus disease (COVID-19) RNA-seq dataset (GEO accession: GSE172114) and simulated datasets with various differential expression patterns. Compared to existing methods, SEMtree() is able to capture biologically relevant subnetworks with simple visualization of directed paths, good perturbation extraction, and classifier performance. SEMtree() function is implemented in the R package SEMgraph, easily available at https://CRAN.R-project.org/package=SEMgraph.

TreeNet: Structure preserving multi-class 3D point cloud completion

Generating the missing data of 3D object point clouds from partial observations is a challenging task. Existing state-of-the-art learning-based 3D point cloud completion methods tend to use a limited number of categories/classes of training data and regenerate the entire point cloud based on the training datasets. As a result, output 3D point clouds generated by such methods may lose details (i.e. sharp edges and topology changes) due to the lack of multi-class training. These methods also lose the structural and spatial details of partial inputs due to the models do not separate the reconstructed partial input from missing points in the output.In this paper, we propose a novel deep learning network - TreeNet for 3D point cloud completion. TreeNet has two networks in hierarchical tree-based structures: TreeNet-multiclass focuses on multi-class training with a specific class of the completion task on each sub-tree to improve the quality of point cloud output; TreeNet-binary focuses on generating points in missing areas and fully preserving the original partial input. TreeNet-multiclass and TreeNet-binary are both network decoders and can be trained independently. TreeNet decoder is the combination of TreeNet-multiclass and TreeNet-binary and is trained with an encoder from existing methods (i.e. PointNet encoder). We compare the proposed TreeNet with five state-of-the-art learning-based methods on fifty classes of the public Shapenet dataset and unknown classes, which shows that TreeNet provides a significant improvement in the overall quality and exhibits strong generalization to unknown classes that are not trained.

Compressed Geometric Arrays for Point Cloud Processing

The ever-increasing demand for 3D modeling in the emerging immersive applications has made point clouds an essential class of data for 3D image and video processing. Tree based structures are commonly used for representing point clouds where pointers are used to realize the connection between nodes. Tree-based structures significantly suffer from irregular access patterns for large point clouds. Memory access indirection in such structures is disruptive to bandwidth efficiency and performance. In this paper, we propose a point cloud representation format based on compressed geometric arrays (CGA). Then, we examine new methods for point cloud processing based on CGA. The proposed format enables a higher bandwidth efficiency via eliminating memory access indirections (i.e., pointer chasing at the nodes of tree) thereby improving the efficiency of point cloud processing. Our experimental results show that using CGA for point cloud operations achieves 1328x speed up, 1321x better bandwidth utilization, and 54% reduction in the volume of transferred data as compared to the state-of-the-art tree-based format from point cloud library (PCL).

Signature-based Tree for Finding Frequent Itemsets

The efficiency of a data mining process depends on the data structure used to find frequent itemsets. Two approaches are possible: use the original transaction dataset or transform it into another more compact structure. Many algorithms use trees as compact structure, like FP-Tree and the associated algorithm FP-Growth. Although this structure reduces the number of scans (only 2), its efficiency depends on two criteria: (i) the size of the support (small or large); (ii) the type of transaction dataset (sparse or dense). But these two criteria can generate very large trees. In this paper, we propose a new tree-based structure that emphasizes on transactions and not on itemsets. Hence, we avoid the problem of support values that have a negative impact on the generated tree.

Structure-Free Broadcast Scheduling for Duty-Cycled Multihop Wireless Sensor Networks

Broadcasting is an essential operation in wireless networks for disseminating the message from the source node to all other nodes. Unfortunately, the problem of Minimum Latency Broadcast Scheduling (MLBS) in duty-cycled wireless sensor networks is not well studied. In existing works, the construction of broadcast tree and the scheduling of transmissions are conducted separately, where a tree-based structure is used as the input of the scheduling algorithm. Relying on a pre-determined tree may result in a much large latency even using the optimal scheduling method. Thus, the MLBS problem in duty-cycled WSNs without the above limitation is investigated in this paper. First, to avoid relying on a pre-determined structure, a two-step scheduling algorithm is proposed to construct the broadcast tree and compute a collision-free schedule simultaneously. To the best of our knowledge, this is the first work that can integrate these two kinds of operations together. Second, a novel transmission mode, i.e., concurrent broadcasting, is first introduced for wireless networks and several techniques are designed to further improve the broadcast latency. Third, the multiple messages broadcasting and all-to-all broadcasting algorithms, which can generate a series of broadcast schedules independently without a pre-determined tree, are also proposed by taking care of the collisions in both the current and the previous broadcast schedules. Finally, the theoretical analysis and experimental results demonstrate the efficiency of the proposed algorithms in terms of latency.

Predicting and interpreting financial distress using a weighted boosted tree-based tree

Financial distress prediction aims at providing an early warning solution of financial distress to help business participants, investors, and regulators to achieve better profit growth and financial risk management. Extreme gradient boosting (XGBoost), has been recognized as a favorable competitor compared with machine learning-based individual classifiers. However, its commercial value for FDP is hindered by two reasons. First, FDP is a classical imbalance issue, traditional XGBoost is considered a cost-insensitive approach that yields skew-sensitive FDP results. Second, XGBoost is a complex ensemble approach that faces the performance-interpretability dilemma, making the decision logic of XGBoost cannot be easily understood. To solve the above limitations, in this study, we first focus on addressing the imbalance issue in FDP by introducing a weighted cost-sensitive XGBoost, reducing the error of misclassifying financial distress firms. Next, we merge the decision rules extracted from the optimized weighted XGBoost to reconstruct a new tree as the approximation of the cost-sensitive ensemble model, making the proposed weighted XGBoost-based tree (XGBoost-W-BT) an accurate and interpretable solution for imbalanced FDP. Experimental results on a Chinese FDP dataset collected from China Security Market Accounting Research Database (CSMARD) showed that XGBoost-W-BT can be an alternative to weighted XGBoost to predict financial distress at an early stage. Besides, the transparent tree-based structure provides an explicit explanation to help industry participants and regulators make scientific policies, guiding investors to make rational investments.

EE-MSWSN: Energy-Efficient Mobile Sink Scheduling in Wireless Sensor Networks

Data gathering using mobile sink (MS) based on rendezvous points (RPs) is a need in several Internet of Things (IoT) applications. However, devising energy-efficient and reliable tour planning strategies for MS is a challenging issue, considering that sensors have finite buffer space and disparate sensing rates. This is even more challenging in delay-tolerant networks, where it is more desirable to select the shortest traveling path. There exist several algorithms on MS scheduling, which are based on hierarchical protocols for data forwarding and data collection. These algorithms are lacking efficient tradeoff between the Quality-of-Service (QoS) requirements in terms of energy efficiency, reliability, and computational cost. Besides, these algorithms have shown high packet losses while jointly performing MS tour planning and buffer overflow management. To address these limitations, we propose EE-MSWSN, an energy-efficient MS wireless sensor network that reliably collects data by implementing efficient buffer management. It forms novel clustered tree-based structures to cover all the network, and select each RP based on 1) hop count; 2) number of accumulated data in each clustered tree; and 3) distance to the stationary sink. The extensive simulation results verify that the EE-MSWSN minimizes tour length for various network configurations and incurs less energy consumption while reliably gathering data without packet losses as compared with existing protocols.

Privacy-Preserving Context-Based Electric Vehicle Dispatching for Energy Scheduling in Microgrids: An Online Learning Approach

Electric Vehicles (EVs) are beginning to play a key role in the fast developing area of Internet of Things (IoT). Numerous results have shown the feasibility of vehicle-to-building (V2B) mode of charge/discharge, where EVs are considered as dynamically configurable dispersed energy storage units. When properly incorporated into the building energy system, EVs are able to provide ancillary services to the power grid during high demand periods or outage situations. The arising challenge is how to act intelligent behaviors in complex and changing microgrid environments. With the aim of minimizing the cost and maximizing satisfaction degree, this paper, unlike previous works, jointly considers the building energy need and the safety/willingness of EVs to find and dispatch the optimal vehicle to conduct auxiliary or supportive actions. To realize that, we propose an intelligent Privacy-preserving Context-based Online EV Dispatching System (PCOEDS), using a tree-based structure which supports the ever-increasing big metering datasets with context-awareness. Moreover, privacy preservation and security protection on both sides of the the energy transmission process are well guaranteed in our work. Theoretical results validate that our intelligent dispatching system achieves sublinear regret and differential privacy, which outperforms other online learning method when applied to a huge city-level dataset.

Development of Monthly Reference Evapotranspiration Machine Learning Models and Mapping of Pakistan—A Comparative Study

Accurate estimation of reference evapotranspiration (ETo) plays a vital role in irrigation and water resource planning. The Penman–Monteith method recommended by the Food and Agriculture Organization (FAO PM56) is widely used and considered a standard to calculate ETo. However, FAO PM56 cannot be used with limited meteorological variables, so it is compulsory to choose an alternative model for ETo estimation, which requires fewer variables. This study built ten machine learning (ML) models based on multi-function, neural network, and tree-based structure against the FAO PM56 method. For this purpose, time series temperature data on a monthly scale are only used to train ML models. The developed ML models were applied to estimate ETo at different test stations and the obtained results were compared with the FAO PM56 method to verify and validate their performance in ETo estimation for the selected stations. In addition, multiple statistical indicators, including root-mean-square error (RMSE), coefficient of determination (R2), mean absolute error (MAE), Nash–Sutcliffe efficiency (NSE), and correlation coefficient (r) were calculated to compare the performance of each ML model on ETo estimation. Among the applied ML models, the ETo tree boost (TB) ML model outperformed the other ML models in estimating ETo in diverse climatic conditions based on statistical indicators (R2, NSE, r, RMSE, and MAE). Moreover, the observed R2, NSE, and r were the highest for the TB ML model, while RMSE and MAE were found to be the lowest at the study sites compared to other applied ML models. Lastly, ETo point data yielded from the TB ML model was used in an interpolation process to create monthly and annual ETo maps. Based on the ETo maps, this study suggests mainly a focus on areas with high ETo values and proper irrigation scheduling of crops to ensure water sustainability.

Faster-LIO: Lightweight Tightly Coupled Lidar-Inertial Odometry Using Parallel Sparse Incremental Voxels

This letter presents an incremental voxel-based lidar-inertial odometry (LIO) method for fast-tracking spinning and solid-state lidar scans. To achieve the high tracking speed, we neither use complicated tree-based structures to divide the spatial point cloud nor the strict k nearest neighbor (k-NN) queries to compute the point matching. Instead, we use the incremental voxels (iVox) as our point cloud spatial data structure, which is modified from the traditional voxels and supports incremental insertion and parallel approximated k-NN queries. We propose the linear iVox and PHC (Pseudo Hilbert Curve) iVox as two alternative underlying structures in our algorithm. The experiments show that the speed of iVox reaches 1000–2000 Hz per scan in solid-state lidars and over 200 Hz for 32 lines spinning lidars only with a modern CPU while still preserving the same level of accuracy.

RRT* and Goal-Driven Variable Admittance Control for Obstacle Avoidance in Manual Guidance Applications

In manual guidance robotic applications, like the handling of large and heavy objects in a cluttered environment, it is important to guarantee that the operator accurately reaches the goal position without collisions with working isles or other obstacles in the surrounding environment. When the transported object is bulky, the operator’s view is obstructed and the situation becomes more critical. In this work, a novel variable admittance control provides the operator with a directional haptic feedback about the best motion direction towards the goal. This feedback allows the user to accurately reach the target position in a cluttered environment, also in case his/her view is partially or totally obstructed. To select the best motion direction in a cluttered workspace, a tree-based structure rooted in the goal is optimally built offline to fully explore the environment free-space based on the workspace layout and regardless of the initial position. Then, at each time instant, the optimal motion direction is determined based on the current position with respect to the exploring structure and on the user motion intention. In this work, to build the tree structure, we adapt RRT* algorithm to the manual guidance context and we define a tailored cost function. The performance is evaluated in many scenarios with a variable number of obstacles of different shapes involving several subjects and a Comau Smart Six robot.

A Dynamic Distributed Deterministic Load-Balancer for Decentralized Hierarchical Infrastructures

In this work, we propose D3-Tree, a dynamic distributed deterministic structure for data management in decentralized networks, by engineering and extending an existing decentralized structure. Conducting an extensive experimental study, we verify that the implemented structure outperforms other well-known hierarchical tree-based structures since it provides better complexities regarding load-balancing operations. More specifically, the structure achieves an O(logN) amortized bound (N is the number of nodes present in the network), using an efficient deterministic load-balancing mechanism, which is general enough to be applied to other hierarchical tree-based structures. Moreover, our structure achieves O(logN) worst-case search performance. Last but not least, we investigate the structure’s fault tolerance, which hasn’t been sufficiently tackled in previous work, both theoretically and through rigorous experimentation. We prove that D3-Tree is highly fault-tolerant and achieves O(logN) amortized search cost under massive node failures, accompanied by a significant success rate. Afterwards, by incorporating this novel balancing scheme into the ART (Autonomous Range Tree) structure, we go one step further to achieve sub-logarithmic complexity and propose the ART+ structure. ART+ achieves an O(logb2logN) communication cost for query and update operations (b is a double-exponentially power of 2 and N is the total number of nodes). Moreover, ART+ is a fully dynamic and fault-tolerant structure, which supports the join/leave node operations in O(loglogN) expected WHP (with high proability) number of hops and performs load-balancing in O(loglogN) amortized cost.

A Tree-Based Architecture for High-Performance Ultra-Low-Voltage Amplifiers

In this paper, we introduce a novel tree-based architecture which allows the implementation of Ultra-Low-Voltage (ULV) amplifiers. The architecture exploits a body-driven input stage to guarantee a rail-to-rail input common mode range and body-diode loading to avoid Miller compensation, thanks to the absence of high-impedance internal nodes. The tree-based structure improves the CMRR of the proposed amplifier with respect to the conventional OTA architectures and allows achievement of a reasonable CMRR even at supply voltages as low as 0.3 V and without tail current generators which cannot be used in ULV circuits. The bias currents and the static output voltages of all the stages implementing the architecture are accurately set through the gate terminals of biasing transistors in order to guarantee good robustness against PVT variations. The proposed architecture and the implementing stages are investigated from an analytical point of view and design equations for the main performance metrics are presented to provide insight into circuit behavior. A 0.3 V supply voltage, subthreshold, ultra-low-power (ULP) OTA, based on the proposed tree-based architecture, was designed in a commercial 130 nm CMOS process. Simulation results show a dc gain higher than 52 dB with a gain-bandwidth product of about 35 kHz and reasonable values of CMRR and PSRR, even at such low supply voltages and considering mismatches. The power consumption is as low as 21.89 nW and state-of-the-art small-signal and large-signal FoMs are achieved. Extensive parametric and Monte Carlo simulations show the robustness of the proposed circuit to PVT variations and mismatch. These results confirm that the proposed OTA is a good candidate to implement ULV, ULP, high performance analog building blocks for directly harvested IoT nodes.

A framework for designing power-efficient inference accelerators in tree-based learning applications

Machine Learning techniques (ML) are being widely adopted in embedded devices due to their efficiency and flexibility. However, the strict power limitations in such devices, combined with the variable resource requirements of ML models, require further understanding of how model complexity affects power and performance. This paper proposes a framework that facilitates the design space exploration of dedicated decision trees (DT) and random forests (RF) accelerators by enabling a joint assessment of power dissipation and prediction accuracy. The proposed framework translates tree-based structures to hardware description languages (HDL). The HDL modules are submitted to logic and physically-aware hardware synthesis flows, allowing a detailed power-performance analysis of VLSI DTs and RFs. Using four data sets of embedded applications as case studies, we found that quantizing the input features leads to accuracy gains of up to 6.3% compared with the precise versions. We also show that using shallower trees may lead to small prediction loss with significant reductions in power, which is favorable for power-constrained applications. Our translator achieves better results in terms of energy/inference w.r.t. prior related works under comparison, one of which employed standard methods for hardware translation such as High-Level Synthesis. The proposed solution presents a power reduction of 10 times or more for the same inference throughput reported in prior works.

Automated e-Assessment: Students’ Needs and e-Evaluation Solution Possibilities

E-learning is increasingly gaining its usage. In the COVID-19 period, its importance even increased. One of the important e-learning processes is e-assessment. Suitable e-evaluation implementation depends on multiple factors. One of the factors for suitable testing implementation is alignment between users’ needs for a specific purpose testing and its implementation possibilities. Therefore in this paper, we analyze what are students’ needs for e-evaluation system in knowledge level testing and self-evaluation testing cases. Student survey and multi-criteria decision-making were used to find out how students rank different criteria of e-assessment and how linear, graph-based, and tree-based testing structures rank based on these criteria.

Reservoir Flood Routing Simulation for Dam Safety Management in Thailand

A reservoir flood routing simulation software with spillway operation rules that are readable and configurable by the spillway operator is developed in this study. The software is part of the Dam Safety Remote Monitoring System used by the Electricity Generating Authority of Thailand. The flood routing simulation is implemented using a storage-indication routing method, which is a hydrologic method. The spillway operation rules are exhibited in a tree-based structure, in which the spillway gate opening is derived from the current reservoir water level (RWL), spillway gate opening, and flood situation if the peak inflow has passed. The simulation results show that the simulated RWL is similar to the RWL data in the dam construction manual. This verifies the accuracy of the reservoir flood routing simulation, which is useful for planning the spillway operation.

Efficient Retrieval of Music Recordings Using Graph-Based Index Structures

Flexible retrieval systems are required for conveniently browsing through large music collections. In a particular content-based music retrieval scenario, the user provides a query audio snippet, and the retrieval system returns music recordings from the collection that are similar to the query. In this scenario, a fast response from the system is essential for a positive user experience. For realizing low response times, one requires index structures that facilitate efficient search operations. One such index structure is the K-d tree, which has already been used in music retrieval systems. As an alternative, we propose to use a modern graph-based index, denoted as Hierarchical Navigable Small World (HNSW) graph. As our main contribution, we explore its potential in the context of a cross-version music retrieval application. In particular, we report on systematic experiments comparing graph- and tree-based index structures in terms of the retrieval quality, disk space requirements, and runtimes. Despite the fact that the HNSW index provides only an approximate solution to the nearest neighbor search problem, we demonstrate that it has almost no negative impact on the retrieval quality in our application. As our main result, we show that the HNSW-based retrieval is several orders of magnitude faster. Furthermore, the graph structure also works well with high-dimensional index items, unlike the tree-based structure. Given these merits, we highlight the practical relevance of the HNSW graph for music information retrieval (MIR) applications.