Partition Tree Research Articles

Tree-Partitioning as an Emergency Measure to Contain Cascading Line Failures

This paper proposes to replace controlled islanding, which is a defense mechanism against cascading failures, by tree partitioning whereby some of the tie-lines connecting the clusters are still connected in such a way that the cluster-level graph forms a tree. Tree-partitioning prevents line failures from spreading between clusters, similarly as for islanding, but keeps the clusters connected. That results in three main advantages. Power transfers between the clusters can still take place, helping to balance each cluster and limiting any necessary load shedding. Fewer lines are cut, which reduces the shock to the system. There is no need to re-synchronize the clusters after the emergency. This paper offers a simple graph-theoretic justification for tree-partitioning, rather than one based on the spectral analysis of network Laplacian proposed in the literature. It also proposes a two-stage methodology, which utilizes spectral clustering for splitting a network into tree-connected clusters. Test results performed on the 118 node IEEE test network have confirmed the usefulness of the methodology.

Blind Dual Watermarking Scheme Using Stucki Kernel and SPIHT for Image Self-Recovery

In this paper we propose a blind dual watermarking scheme using Set Partitioning in Hierarchical Trees (SPIHT) and Stucki Kernel halftone technique for the tamper detection and image self-recovery. The watermark consists of authentication bits for tampering area location and recovery bits for image restoration. We generate two recovery bits to ensure the high-quality recovery of the tampered image. The primary recovery bit is generated by the SPIHT encoding, and the secondary recovery bit is generated by the Stucki Kernel halftone technique. Then the authentication bit is generated based on the recovery bits. Before embedding the watermark, we shuffle the watermark bits through Arnold cat mapping and diagonal mapping to improve the security and quality of the restored image. LSB-based watermarking technique is used to embed the watermark into the original image to ensure the invisibility of the watermarked image. Experiments have been conducted on two datasets, BOW2 and USC-SIPI, and results show that the proposed scheme can achieve high restoration quality. Comparison with the existing works demonstrate the good performance and superiority of the proposed scheme.

Carbohydrate Partitioning and Vegetative Growth of Citrus Nursery Trees Influenced by Varying Photoperiods Under LED Lighting

Carbohydrate Partitioning and Vegetative Growth of Citrus Nursery Trees Influenced by Varying Photoperiods Under LED Lighting

Research on Lossless Compression Coding Algorithm of N-Band Parametric Spectral Integer Reversible Transformation Combined With the Lifting Scheme for Hyperspectral Images

After considering the spectral correlation of structure and statistics of the hyperspectral image, the redundancy reduction is optimized using reversible transformation of integer matrices in conjunction with the N-band integer reversible transformation of spectral matrices. This paper proposes a new quantization algorithm, XCJRCT, that uses invertible matrix transformation to remove spectral redundancy, optimize bit allocation in structure, the lifting scheme of the discrete wavelet transform (DWT) is used in conjunction with other algorithms to reduce redundancy and set partitioning in hierarchical trees (SPIHT) coding. The experimental results show that lossless compression is significantly better than JPEG-LS, WinZip, ARJ, and DPCM. Using the Jet Propulsion Laboratory (JPL) Canal test image (Band Sequential) as an example data set, the average compression ratio increases by about 73.691598%, 67.713276%, 65.175242%, and 59.107580%, respectively, compared to the above algorithms.

High Efficiency Intra CU Partition and Mode Decision Method for VVC

Versatile video coding (VVC/H.266) is the newest video compression standard, which is developed by the Joint Video Experts Team. Compared with previous encoding schemes, VVC achieves higher compression efficiency by introducing a new partition structure and additional intra prediction modes but results in high computational complexity. To efficiently solve this problem of redundant processing in quad-tree with nested multi-type tree structures and intra mode prediction, we propose a texture analysis–based ternary tree (TT) and binary tree (BT) partition strategy, and a gradient-based intra mode decision method to accelerate TT and BT partition and intra mode prediction, separately. The texture complexity and prediction direction of coding unit (CU) is calculated by texture detection method. A texture analysis–based TT and BT partition strategy is established by using the regression method based on analyzing the texture complexity of the CU. Then, a texture analysis–based TT and BT partition strategy is applied to reduce the redundant partition for each CU. By using the prediction direction of CU, a gradient-based intra mode decision method is established for skipping the impossible modes for each CU. Experimental results revealed that the proposed method could save 49.49% in encoding time and increase the Bjontegaard delta bit rate (BDBR) by only 0.56%. It confirms that the proposed method achieved high efficiency and a good balance between the BDBR and time saving.

Fast CU Partition for VVC Using Texture Complexity Classification Convolutional Neural Network

Versatile video coding (H.266/VVC), which was newly released by the Joint Video Exploration Team (JVET), introduces quad-tree plus multi-type tree (QTMT) partition structure on the basis of quad-tree (QT) partition structure in High Efficiency Video Coding (H.265/HEVC). More complicated coding unit (CU) partitioning processes in H.266/VVC significantly improve video compression efficiency, but greatly increase the computational complexity compared. The ultra-high encoding complexity has obstructed its real-time applications. In order to solve this problem, a CU partition algorithm using convolutional neural network (CNN) is proposed in this paper to speed up the H.266/VVC CU partition process. Firstly, 64 × 64 CU is divided into smooth texture CU, mildly complex texture CU and complex texture CU according to the CU texture characteristics. Second, CU texture complexity classification convolutional neural network (CUTCC-CNN) is proposed to classify CUs. Finally, according to the classification results, the encoder is guided to skip different RDO search process. And optimal CU partition results will be determined. Experimental results show that the proposed method reduces the average coding time by 32.2% with only 0.55% BD-BR loss compared with VTM 10.2.

Hyperspectral Pansharpening via Local Intensity Component and Local Injection Gain Estimation

Hyperspectral (HS) pansharpening is an attractive topic in the field of remote sensing, which has attracted the attention of many researchers. Component substitution (CS)-based HS pansharpening algorithms are of great interest due to their simplicity and high spatial quality, and they mainly consist of two phases: detail extraction and detail injection. Detail extraction is performed by estimating the intensity component, whereas detail injection depends on the definition of injection gain. In the classic CS-based pansharpening methods, the intensity component is estimated through a global synthesis scheme, and injection gains can be obtained by a context-adaptive or a global approach. In this letter, we propose an improved CS-based HS pansharpening method in which the intensity component and the injection gain are estimated locally achieved by the binary partition tree (BPT) image segmentation algorithm. The proposed method is applied to two credible CS-based HS pansharpening algorithms, including the Gram–Schmidt adaptive (GSA) and the Brovey transform (Brovey). The experimental results show that the proposed method improves the performance of GSA and Brovey and creates promising results perceptually and quantitatively.

LEARNING HIGH-DIMENSIONAL PROBABILITY DISTRIBUTIONS USING TREE TENSOR NETWORKS

We consider the problem of the estimation of a high-dimensional probability distribution from i.i.d. samples of the distribution using model classes of functions in tree-based tensor formats, a particular case of tensor networks associated with a dimension partition tree. The distribution is assumed to admit a density with respect to a product measure, possibly discrete for handling the case of discrete random variables. After discussing the representation of classical model classes in tree-based tensor formats, we present learning algorithms based on empirical risk minimization using a $L^2$ contrast. These algorithms exploit the multilinear parametrization of the formats to recast the nonlinear minimization problem into a sequence of empirical risk minimization problems with linear models. A suitable parametrization of the tensor in tree-based tensor format allows to obtain a linear model with orthogonal bases, so that each problem admits an explicit expression of the solution and cross-validation risk estimates. These estimations of the risk enable the model selection, for instance when exploiting sparsity in the coefficients of the representation. A strategy for the adaptation of the tensor format (dimension tree and tree-based ranks) is provided, which allows to discover and exploit some specific structures of high-dimensional probability distributions such as independence or conditional independence. We illustrate the performances of the proposed algorithms for the approximation of classical probabilistic models (such as Gaussian distribution, graphical models, Markov chain).

Efficient Partition Decision Based on Visual Perception and Machine Learning for H.266/Versatile Video Coding

H.266/Versatile Video Coding (VVC) is the latest international video coding standard to encode ultra-high-definition video effectively. The quadtree with nested multi-type tree (QT-MTT) structure provides various sizes of coding tree partitioning and allows the nested binary tree (BT) split and ternary tree (TT) split at each QT level. Furthermore, numerous advanced coding tools are equipped in the H.266/VVC encoder. However, the encoding time increases tremendously. Previous researches regarding the fast coding algorithm of H.266/VVC seldom mention perceptual redundancy. This paper utilizes the human vision model of just noticeable difference to extract the visually distinguishable pixels that may affect the visual perception. We observe that the distributions acquired by the horizontal and vertical projections of visually distinguishable pixels within the coding unit are related to their corresponding MTT splitting modes. Therefore, the distributions representing the perceptual information of human vision are used to be the input features of machine learning. Fast MTT decision determined by the random forest models of machine learning is proposed to quickly select the partition for intra coding. Experimental results demonstrate that the proposed method can effectively accelerate intra coding process while maintaining good bitrate and video quality based on the properties of the visual perception. The proposed algorithm provides better performance than the previous work.

Alternative expectation approaches for expectation-maximization missing data imputations in cox regression

Missing data is common in survival analysis. It is either removed or imputed using various methods. Expectation-maximization (EM) imputation is a popular method in Cox regression studies. This paper investigated the effect of different regression methods on Cox regression modeling within the framework of EM. A stratified Cox regression model was derived from a dataset of categorical and numerical variables. Missing data were imputed using the EM framework with five machine learning algorithms and then were compared to the full model. The results show that the recursive partition and regression tree (RPART) method performed better than others. However, all regression methods performed poorly in categorical covariate imputation. R code is available online.

Forest system hydraulic conductance: partitioning tree and soil components.

Soil-leaf hydraulic conductance determines canopy-atmosphere coupling in vegetation models, but it is typically derived from ex-situ measurements of stem segments and soil samples. Using a novel approach, we derive robust in-situ estimates for whole-tree conductance (ktree ), 'functional' soil conductance (ksoil ), and 'system' conductance (ksystem , water table to canopy), at two climatically different tropical rainforest sites. Hydraulic 'functional rooting depth', determined for each tree using profiles of soil water potential (Ψsoil ) and sap flux data, enabled a robust determination of ktree and ksoil . ktree was compared across species, size classes, seasons, height above nearest drainage (HAND), two field sites, and to alternative representations of ktree ; ksoil was analysed with respect to variations in site, season and HAND. ktree was lower and changed seasonally at the site with higher vapour pressure deficit (VPD) and rainfall; ktree differed little across species but scaled with tree circumference; rsoil (1/ksoil ) ranged from 0 in the wet season to 10× less than rtree (1/ktree ) in the dry season. VPD and not rainfall may influence plot-level k; leaf water potentials and sap flux can be used to determine ktree , ksoil and ksystem ; Ψsoil profiles can provide mechanistic insights into ecosystem-level water fluxes.

Partitioning tree water usage into storage and transpiration in a mixed forest

BackgroundWater migration and use are important processes in trees. However, it is possible to overestimate transpiration by equating the water absorbed through the plant roots to that diffused back to the atmosphere through stomatal transpiration. Therefore, it is necessary to quantify the water transpired and stored in plants.MethodThe δ2H/δ18O technique and heat ratio method were used to explore the water usage of coniferous and broad-leaved tree species, including the proportions of water used for transpiration and water storage.ResultsPlatycladus orientalis and Quercus variabilis had strong plasticity in their water usage from different sources. Platycladus orientalis primarily used groundwater (30.5%) and the 60–100-cm soil layer (21.6%) throughout the experimental period and was sensitive to precipitation, absorbing water from the 0–20-cm layer (26.6%) during the rainy season. Quercus variabilis absorbed water from all sources (15.7%–36.5%) except from the 40–60-cm soil layer during the dry season. In addition, it did not change its water source but increased its groundwater uptake during the rainy season. The annual mean water fluxes of P. orientalis and Q. variabilis were 374.69 and 469.50 mm·year− 1, with 93.49% and 93.91% of the water used for transpiration, respectively. However, nocturnal sap flow in P. orientalis and Q. variabilis was mainly used for water storage in the trunk rather than transpiration, which effectively alleviated drought stress and facilitated the transport of nutrients.ConclusionsThe water stored in both species comprised 6%–7% of the total water fluxes and, therefore, should be considered in water balance models.

An effective image compression technique based on burrows wheeler transform with set partitioning in hierarchical trees

AbstractIn this article, the efficient image compression which consists of Burrows–Wheeler transform (BWT) with set partitioning in hierarchical trees (SPIHT). The main phases of the proposed system are: partitioning, compression of non‐ROI areas, Fusion and compression of ROI areas. To enhance the propose of the proposed methodology, the morphological functions are updated by dividing two types of images with the consideration of dilation and erosion control. After that, the convolution and correlation in the deformation provide good accuracy of the segmentation at the fastest speed. In this proposed methodology, SPIHT encryption is a lossy compression technique aimed at understanding the non‐ROI area, while the BWT is a lossless compression technique performed to achieve a summary of the ROI area. Finally, separating these two parts of the image merges the image and reconstructs it to the desired quality. The test sends a variety of images and analyzes the performance of the proposed system using compression ratio and PSNR measurements.

Dysregulated ferroptosis-related genes indicate potential clinical benefits for anti-PD-1/PD-L1 immunotherapy in lung adenocarcinoma.

BackgroundFerroptosis is an iron‐dependent programmed cell death mechanism that influences the development of malignancy. Lung adenocarcinoma (LUAD) is the most common type of lung cancer with no known cure. Anti–PD‐1/PD‐L immunotherapy is effective for patients with partial LUAD. Therefore, there is an immediate requirement of novel markers to predict the individualised benefits of immunotherapy.MethodsWe manually collected the ferroptosis‐related gene (FERG) set and employed the Wilcoxon rank‐sum test to identify the differentially expressed FERGs. Subsequently, we constructed a recursive partitioning and regression tree (RPART) model to predict the benefits of anti–PD‐1/PD‐L1 immunotherapy. Subsequently, the ROC curve and AUC were used to evaluate the model efficiency in an independent dataset.ResultsIn this study, we found that the dysregulated FERGs were closely associated with multiple metabolic processes in LUAD. Furthermore, we identified three ferroptosis‐related tumour subtypes (F1, F3 and F3). The F3 subtype exhibited higher immunoactivity and lower tumour purity, mutation count and aneuploidy and had better survival outcomes compared with the other two subtypes, implying that FERGs played an important role in intertumoral immune heterogeneity. We further explored the role of FERGs in the anti–PD‐1/PD‐L1 immunotherapy. We identified a set of three‐FERGs signature (CD44, G6PD and ZEB1) that acted as a promising indicator (AUC = 0.697) for the prediction of the benefits of anti–PD‐1/PD‐L1 immunotherapy.ConclusionFerroptosis, as emerging programmed cell death mechanism, was associated with cancer development. We used ferroptosis‐related genes to predict the immunotherapy benefits that may facilitate the development of individualised anti‐cancer treatment strategies.

Decision tree accelerated CTU partition algorithm for intra prediction in versatile video coding.

Versatile video coding (VVC) achieves enormous improvement over the advanced high efficiency video coding (HEVC) standard due to the adoption of the quadtree with nested multi-type tree (QTMT) partition structure and other coding tools. However, the computational complexity increases dramatically as well. To tackle this problem, we propose a decision tree accelerated coding tree units (CTU) partition algorithm for intra prediction in VVC. Firstly, specially designated image features are extracted to characterize the coding unit (CU) complexity. Then, the trained decision tree is employed to predict the partition results. Finally, based on our newly designed intra prediction framework, the partition process is early terminated or redundant partition modes are screened out. The experimental results show that the proposed algorithm could achieve around 52% encoding time reduction for various test video sequences on average with only 1.75% Bjontegaard delta bit rate increase compared with the reference test model VTM9.0 of VVC.

Nonlinear price transmission in the rice market in Senegal: a model-based recursive partitioning approach

ABSTRACT This paper analyzes the nonlinear effects in price transmission from international markets to the local rice market of Dakar (Senegal) focusing on asymmetries through threshold effects. We use recent machine learning methods (model-based recursive partitioning trees) to detect asymmetries in the price transmission mechanism. Using a model based recursive partitioning algorithm, we identify a threshold and confirm the asymmetry in the price transmission. Local retail prices are more sensitive to world price increases than to declines. Only 11.80% of positive deviations (international prices go down) are eliminated at the end of the subsequent month, while 39.50% of negative deviations (world prices go up) are eliminated after one month. These results highlight the role of transaction costs and the market power of commercial intermediaries in price transmission in the sense that margins are corrected more rapidly when they are squeezed relative to their long run level than when they are stretched. Our results are confirmed by the traditional Threshold Autoregressive (TAR) model.

Compressed domain image retargeting in hierarchical wavelet trees

This paper presents an efficient algorithm with content aware spatial scalability for effective displaying on various screens of different sizes. It combines the set partitioning in hierarchical trees (SPIHT) algorithm and the multiscale seam carving (MSC) algorithm in the compressed domain. As seams of insignificant wavelet trees can be discarded as early as possible, the burden of unnecessary computation and memory space is alleviated. Experimental results show that the proposed algorithm is preferable to other existing pixel-domain algorithms.

Evaluation of Hidden Surface Removal Methods Using Open GL

The research aims to address one of the complex problems that may be encountered when generating computer images for three-dimensional (3D) objects, which is the hidden surface detection and elimination. Several methods have been devised, through the last three decades, to solve this problem. However, the most popular methods used widely nowadays are depth or Z-Buffer (ZB) and Binary Space Partition Tree (BSPT). The first method (ZB) is very simple and more general but requires additional memory to store depth values in addition to intensity. On the other hand, (BSPT) method is more complex to implement but requires memory for pixel intensity only. Modelling graphical database in a binary tree make the dealing with parts of the database feasible. This is so important for clipping a part or more of the database when outside the viewing zone. The focus of this paper is to study these two methods, design an algorithm to implement each of them. After that a reasonable procedure is to found and applied for testing the performance of each of the two methods using exactly the same graphic load. A step by step increase of this load should be possible so that the behavior of the execution time at different complexity level and on average bases can be reported. The outcome results of the two methods should be compared and a recommendation to use which one of them is to be concluded [1]. Since one of the most popular graphics library which is being used during the last ten years and nowadays is the Open Graphic Library( OpenGL) from(Silicon Graphic Incorporation (SGI)[2], the algorithms of this work are implemented and the related software is carried out using this library with visual C platform.

Compression of MRI brain images based on automatic extraction of tumor region

In the compression of medical images, region of interest (ROI) based techniques seem to be promising, as they can result in high compression ratios while maintaining the quality of region of diagnostic importance, the ROI, when image is reconstructed. In this article, we propose a set-up for compression of brain magnetic resonance imaging (MRI) images based on automatic extraction of tumor. Our approach is to first separate the tumor, the ROI in our case, from brain image, using support vector machine (SVM) classification and region extraction step. Then, tumor region (ROI) is compressed using Arithmetic coding, a lossless compression technique. The non-tumorous region, non-region of interest (NROI), is compressed using a lossy compression technique formed by a combination of discrete wavelet transform (DWT), set partitioning in hierarchical trees (SPIHT) and arithmetic coding (AC). The classification performance parameters, like, dice coefficient, sensitivity, positive predictive value and accuracy are tabulated. In the case of compression, we report, performance parameters like mean square error and peak signal to noise ratio for a given set of bits per pixel (bpp) values. We found that the compression scheme considered in our setup gives promising results as compared to other schemes.

Ultra low bitrate retinal image compression using integer lifting scheme and subband encoder

Recently, ophthalmic clinics have seen many complaints related to retinal diseases. The degree of clarity of the blood vessels (BV) in the eye can be an important indicator of some diseases affecting the retina such as diabetic retinopathy. To diagnose it, we need to intervene more than a medical team, especially in some difficult cases, through the exchange of medical images obtained by photography. This method has contributed significantly to the production of large data that can quickly saturate transmission, storage systems and increase processing time, so the need to compress images efficiently without modifying the content before transmission represents a major challenge. This paper provides an effective method for compressing color retinal images (CRI), which relies on the use of an integer lifting scheme (ILS) based on cohen daubechies-feaveau wavelet (CDFW9/7) and the set partitioning in hierarchical trees (SPIHT) to encode large coefficients. The obtained results demonstrate that the suggested method reduce algorithmic complexity, improve the retinal image quality and achieves high objective parameters values for ultra-low bitrate compared to the conventional methods.