Fast Partitioning Research Articles

A bio-guided search of anti-steatotic compounds in Opuntia stricta var. dillenii by fast centrifugal partition chromatography

The fruit extract of Opuntia stricta var. dillenii (OSDE) has been recognized for its effects on hepatic steatosis, but the compounds responsible for this activity have yet to be precisely identified. This work aimed to evaluate the anti-steatotic effect of OSDE and its different fractions obtained by fast centrifugal-partition chromatography (FCPC) to identify the compounds potentially responsible for this biological activity. Hepatic lipid accumulation and triglyceride content were evaluated, as well as cellular antioxidant activity and inhibition of lipid peroxidation. Pool 1-Pool 4 showed lower lipid accumulation than OSDE in liver cells, while a greater reduction in triglyceride levels, even lower than OSDE and lovastatin (LOV), was observed for Pool 1, 9, and 10. Compared to OSDE, Pools 1,6, 7, and 12 showed higher cellular antioxidant effects, whereas OSDE showed better lipid peroxidation inhibition than all of Pools. Quinic and piscidic acids were the main bioactive present in Pool 1, exhibiting +1597 % and + 997 % increases in their content related to OSDE, respectively. Likewise, the most abundant compounds in Pool 2- Pool 4 were betalains such as betanin and isobetanin, with +163 % and + 162 % of increases in their concentration related to OSDE, respectively. Antioxidant effects in Pools 6 and 7 correlated with higher phenolic acid concentration. OSDE significantly reduced triglyceride levels in a steatotic-induced model. Although OSDE showed anti-steatotic effects, these effects were more pronounced for some of its constituents in FCPC Pools. Results suggested that these compounds might be potentially responsible for this anti-steatotic effect. FCPC fractionation facilitated the separate biological evaluation of OSDE constituents and thus identified them. Future studies should focus on validating these anti-steatotic effects in in vivo models.

Fast CU decision method based on texture characteristics and decision tree for depth map intra-coding

As the demand for higher quality 3D videos continues to grow, the 3D extensions of the Efficient Video Coding (3D-HEVC) standard are gradually unable to meet the needs of users. Versatile Video Coding Standard (H. 266/VVC), as an advanced video coding standard, adopts the nested Multi-Type Tree quadtree (QTMT) partitioning structure that current fast depth map coding unit (CU) partitioning methods cannot apply. Therefore, we have designed a fast intra-frame CU partitioning algorithm for VVC 3D video depth maps. Our proposed algorithm in this article consists of two steps, including two sub-algorithms. First, by analyzing the relationship between image entropy and variance and depth map CU division, we establish a bi-criterion decision algorithm to determine whether the texture complexity of the current CU is low enough to terminate its partitioning process. Then, for CUs that have been determined by the first algorithm to need further partitioning, we use a decision tree model based on Light Gradient Boosting Machine (LGBM) to predict which direction of Rate-Distortion Optimization (RDO) calculation can be skipped, which can avoid some unnecessary RDO calculations in a certain direction. The final experiment demonstrated the effect of the proposed algorithm, which can reduce 47.65% complexity of VVC 3D video intra-coding with negligible 0.23% Bjøntegaard Delta Bitrate (BDBR) increase, superior to other advanced methods.

Leveraging occupancy map to accelerate video-based point cloud compression

Video-based Point Cloud Compression enables point cloud streaming over the internet by converting dynamic 3D point clouds to 2D geometry and attribute videos, which are then compressed using 2D video codecs like H.266/VVC. However, the complex encoding process of H.266/VVC, such as the quadtree with nested multi-type tree (QTMT) partition, greatly hinders the practical application of V-PCC. To address this issue, we propose a fast CU partition method dedicated to V-PCC to accelerate the coding process. Specifically, we classify coding units (CUs) of projected images into three categories based on the occupancy map of a point cloud: unoccupied, partially occupied, and fully occupied. Subsequently, we employ either statistic-based rules or machine-learning models to manage the partition of each category. For unoccupied CUs, we terminate the partition directly; for partially occupied CUs with explicit directions, we selectively skip certain partition candidates; for the remaining CUs (partially occupied CUs with complex directions and fully occupied CUs), we train an edge-driven LightGBM model to predict the partition probability of each partition candidate automatically. Only partitions with high probabilities are retained for further Rate–Distortion (R–D) decisions. Comprehensive experiments demonstrate the superior performance of our proposed method: under the V-PCC common test conditions, our method reduces encoding time by 52% and 44% in geometry and attribute, respectively, while incurring only 0.68% (0.66%) BD-Rate loss in D1 (D2) measurements and 0.79% (luma) BD-Rate loss in attribute, significantly surpassing state-of-the-art works.

Fast partitioning of Pauli strings into commuting families for optimal expectation value measurements of dense operators

Fast partitioning of Pauli strings into commuting families for optimal expectation value measurements of dense operators

Improved Just Noticeable Difference Model Based Algorithm for Fast CU Partition in V-PCC

Improved Just Noticeable Difference Model Based Algorithm for Fast CU Partition in V-PCC

Novel Approaches for the Analysis and Isolation of Benzylisoquinoline Alkaloids in Chelidonium majus

AbstractBenzylisoquinoline alkaloids are the major bioactive components in Chelidonium majus, a plant that has a long usage history for the treatment of gastrointestinal ailments in European and Asian phytomedicine. This study reports on the development and application of a supercritical fluid chromatography technique for the simultaneous qualitative and quantitative determination of seven benzylisoquinoline alkaloids in under six minutes using a Viridis BEH 2-EP column and a modifier comprising methanol with 30% acetonitrile and 20 mM ammonium formate. The method was fully validated according to ICH guidelines showing, e.g., excellent linearity (≥ 0.9997) and maximum deviations for intraday and inter-day precision of 2.99 and 2.76%, respectively. The new supercritical fluid chromatography assay was not only employed for the analysis of several C. majus samples but was also used for the subsequent development of a fast centrifugal partition chromatography technique, whereby five benzylisoquinoline alkaloids could be isolated within approximately 2.5 h, with only two of them, protopine and chelidonine, requiring an additional purification step. To achieve this, a solvent system composed of chloroform/methanol/0.3 M hydrochloric acid was used in descending mode. By injecting 500 mg of crude extract, stylopine (1.93 mg), sanguinarine (0.57 mg), chelidonine (1.29 mg), protopine (1.95 mg), and coptisine (7.13 mg) could be obtained. The purity of compounds was confirmed by supercritical fluid chromatography and MS.

Fast Coding Unit Partitioning Algorithm for Video Coding Standard Based on Block Segmentation and Block Connection Structure and CNN

The recently introduced Video Coding Standard, VVC, presents a novel Quadtree plus Nested Multi-Type Tree (QTMTT) block structure. This structure enables a more flexible block partition and demonstrates enhanced compression performance compared to its predecessor, HEVC. However, The introduction of the new structure has led to a more complex partition search process, resulting in a considerable increase in time complexity. The QTMTT structure yields diverse Coding Unit (CU) block sizes, posing challenges for CNN model inference. In this study, we propose a representation structure termed Block Segmentation and Block Connection (BSC), rooted in texture features. This ensures that partial CU blocks are uniformly represented in size. To address different-sized CUs, various levels of CNN models are designed for prediction. Moreover, we introduce a post-processing method and a multi-thresholding scheme to further mitigate errors introduced by CNNs. This allows for flexible and adjustable acceleration, achieving a trade-off between coding time complexity and performance. Experimental results indicate that, in comparison to VTM-10.0, our “Fast” scheme reduces the average complexity by 57.14% with a 1.86% increase in BDBR. Meanwhile, the “Moderate” scheme reduces average complexity by 50.14% with only a 1.39% increase in BDBR.

Fast CU partition algorithm based on swin-transformer for depth intra coding in 3D-HEVC

Fast CU partition algorithm based on swin-transformer for depth intra coding in 3D-HEVC

Fast partition algorithm in depth map intra coding unit based on multi-deep convolution neural network

Fast partition algorithm in depth map intra coding unit based on multi-deep convolution neural network

A Fast CU Partition Algorithm for AVS3 Based on Adaptive Tree Search and Pruning Optimization

A Fast CU Partition Algorithm for AVS3 Based on Adaptive Tree Search and Pruning Optimization

Fast CU Partitioning Algorithm for VVC Based on CNN and FSVM

Fast CU Partitioning Algorithm for VVC Based on CNN and FSVM

Fast CU Partition Algorithm for Intra Frame Coding Based on Joint Texture Classification and CNN.

High-efficiency video coding (HEVC/H.265) is one of the most widely used video coding standards. HEVC introduces a quad-tree coding unit (CU) partition structure to improve video compression efficiency. The determination of the optimal CU partition is achieved through the brute-force search rate-distortion optimization method, which may result in high encoding complexity and hardware implementation challenges. To address this problem, this paper proposes a method that combines convolutional neural networks (CNN) with joint texture recognition to reduce encoding complexity. First, a classification decision method based on the global and local texture features of the CU is proposed, efficiently dividing the CU into smooth and complex texture regions. Second, for the CUs in smooth texture regions, the partition is determined by terminating early. For the CUs in complex texture regions, a proposed CNN is used for predictive partitioning, thus avoiding the traditional recursive approach. Finally, combined with texture classification, the proposed CNN achieves a good balance between the coding complexity and the coding performance. The experimental results demonstrate that the proposed algorithm reduces computational complexity by 61.23%, while only increasing BD-BR by 1.86% and decreasing BD-PSNR by just 0.09 dB.

Fast CU partition strategy based on texture and neighboring partition information for Versatile Video Coding Intra Coding

Fast CU partition strategy based on texture and neighboring partition information for Versatile Video Coding Intra Coding

Enhanced Density Peak-Based Power Grid Reactive Voltage Partitioning

Clustering-based reactive voltage partitioning is successful in reducing grid cascading faults, by using clustering methods to categorize different power-consuming entities in the power grid into distinct regions. In reality, each power-consuming entity has different electrical characteristics. Additionally, due to the irregular and uneven distribution of the population, the distribution of electricity consumption is also irregular and uneven. However, the existing method neglects the electrical difference among each entity and the irregular and uneven density distribution of electricity consumption, resulting in poor accuracy and adaptability of these methods. To address these problems, an enhanced density peak model-based power grid reactive voltage partitioning method is proposed in this paper, called EDPVP. First, the power grid is modeled as a weighted reactive network to consider entity electrical differences. Second, the novel local density and density following distance are designed to enhance the density peak model to address the problem that the traditional density peak model cannot adapt to weighted networks. Finally, the enhanced density peak model is further equipped with an optimized cluster centers selection strategy and an updated remaining node assignment strategy, to better identify irregular and uneven density distribution of electricity consumption, and to achieve fast and accurate reactive voltage partition. Experiments on two real power grids demonstrate the effectiveness of the EDPVP.

A Low-Complexity Fast CU Partitioning Decision Method Based on Texture Features and Decision Trees

The rapid advancement of information technology, particularly in artificial intelligence and communication, is driving significant transformations in video coding. There is a steadily increasing demand for high-definition video in society. The latest video coding standard, versatile video coding (VVC), offers significant improvements in coding efficiency compared with its predecessor, high-efficiency video coding (HEVC). The improvement in coding efficiency is achieved through the introduction of a quadtree with nested multi-type tree (QTMT). However, this increase in coding efficiency also leads to a rise in coding complexity. In an effort to decrease the computational complexity of VVC coding, our proposed algorithm utilizes a decision tree (DT)-based approach for coding unit (CU) partitioning. The algorithm uses texture features and decision trees to efficiently determine CU partitioning. The algorithm can be summarized as follows: firstly, a statistical analysis of the new features of the VVC is carried out. More representative features are considered to extract to train classifiers that match the framework. Secondly, we have developed a novel framework for rapid CU decision making that is specifically designed to accommodate the distinctive characteristics of QTMT partitioning. The framework predicts in advance whether the CU needs to be partitioned and whether QT partitioning is required. The framework improves the efficiency of the decision-making process by transforming the partition decision of QTMT into multiple binary classification problems. Based on the experimental results, it can be concluded that our method significantly reduces the coding time by 55.19%, whereas BDBR increases it by only 1.64%. These findings demonstrate that our method is able to maintain efficient coding performance while significantly saving coding time.

Fast block-wise partitioning for extreme multi-label classification

Fast block-wise partitioning for extreme multi-label classification

Fast CU Decision Algorithm Based on CNN and Decision Trees for VVC

Compared with the previous generation of High Efficiency Video Coding (HEVC), Versatile Video Coding (VVC) introduces a quadtree and multi-type tree (QTMT) partition structure with nested multi-class trees so that the coding unit (CU) partition can better match the video texture features. This partition structure makes the compression efficiency of VVC significantly improved, but the computational complexity is also significantly increased, resulting in an increase in encoding time. Therefore, we propose a fast CU partition decision algorithm based on DenseNet network and decision tree (DT) classifier to reduce the coding complexity of VVC and save more coding time. We extract spatial feature vectors based on the DenseNet network model. Spatial feature vectors are constructed by predicting the boundary probabilities of 4 × 4 blocks in 64 × 64 coding units. Then, using the spatial features as the input of the DT classifier, through the classification function of the DT classifier model, the top N division modes with higher prediction probability are selected, and other division modes are skipped to reduce the computational complexity. Finally, the optimal partition mode is selected by comparing the RD cost. Our proposed algorithm achieves 47.6% encoding time savings on VTM10.0, while BDBR only increases by 0.91%.

A novel fast intra algorithm for VVC based on histogram of oriented gradient

The latest Versatile Video Coding (VVC) standard incorporates a series of effective and complex new intra coding tools, which obtains superior coding efficiency than the High Efficiency Video Coding (HEVC). However, this makes the intra coding more complicated and time-consuming. A fast algorithm for VVC from two aspects of fast mode decision and fast partition decision is proposed in this paper. For the fast mode decision, the relationship between bins with Histogram of Oriented Gradient (HOG) and intra modes is created for the mode selection, decreasing the planar modes for SATD and RDO. Moreover, we analyze the maximum bins to determine the final modes, and we use the modes of left and upper blocks as a reference for the current CU, which can early terminate RDO. Moreover, a two-step fast partition algorithm is proposed based on HOG for fast partition decision, in which two thresholds are investigated to control the uniformity of textures. The proposed fast algorithm is implemented on the VVC test model, and the experimental results show that it can achieve 69.07% time savings with only 2.96% BDBR increases averagely, which outperforms other relatively existing state-of-the-art methods. Moreover, to convince the universality of our algorithm, we further implement our method in Fraunhofer Versatile Video Encoder (VVenc) and Fraunhofer Versatile Video Decoder (VVdec), which have five settings to control the trade-off between encoding quality and efficiency for intra coding. The fast intra mode decision algorithm and fast partition algorithm decrease the complexity of intra coding for both VTM and VVenc, which shows the efficiency and universality of the proposed fast partition and fast mode decision algorithms.

Effective isolation of cannabidiol and cannabidiolic acid free of psychotropic phytocannabinoids from hemp extract by fast centrifugal partition chromatography

Cannabidiol (CBD), together with its precursor cannabidiolic acid (CBDA), is the major phytocannabinoid occurring in most hemp cultivars. To ensure the safe use of these compounds, their effective isolation from hemp extract is required, with special emphasis on the elimination of ∆9-tetrahydrocannabinol (∆9-THC) and ∆9-tetrahydrocannabinolic acid (∆9-THCA-A). In this study, we demonstrate the applicability of fast centrifugal partition chromatography (FCPC) as a challenging format of counter-current preparative chromatography for the isolation of CBD and CBDA free of psychotropic compounds that may occur in Cannabis sativa L. plant extracts. Thirty-eight solvent mixtures were tested to identify a suitable two-phase system for this purpose. Based on the measured partition coefficients (KD) and separation factors (α), the two-phase system consisting of n-heptane:ethyl acetate:ethanol:water (1.5:0.5:1.5:0.5; v:v:v:v) was selected as an optimal solvent mixture. Employing UHPLC-HRMS/MS for target analysis of collected fractions, the elution profiles of 17 most common phytocannabinoids were determined. Under experimental conditions, the purity of isolated CBD and CBDA was 98.9 and 95.1% (w/w), respectively. Neither of ∆9-THC nor of ∆9-THCA-A were present; only trace amounts of other biologically active compounds contained in hemp extract were detected by screening against in-house spectral library using UHPLC-HRMS.Graphical abstract

An adaptive cross-class transfer learning framework with two-level alignment

Because of insufficient data, cross-class transfer learning has potential prospects in motor imagery electroencephalogram (MI-EEG) based rehabilitation engineering, and it has not been effectively addressed to reduce cross-class variability between source and target domains and find the best class-to-class transitive correspondence (CCTC). In this paper, we propose an adaptive cross-class transfer learning (ACTL) framework with two-level alignment (TLA) for MI decoding. By using the fast partitioning around medoids method, the central samples of each class are acquired from the source domain and target domain respectively. Then, they are applied to construct the central alignment matrix to realize the 1st-level domain alignment for any possible CCTC case. The 2nd-level subject alignment is performed by Euclidean alignment for each class of aligned source domain. Furthermore, the central samples of the target domain together with the two-level aligned source domain are mapped into tangent space, the resulting features are fed to a teacher convolutional neural network (TCNN), it and the transferred student CNN (SCNN) are trained successively, and their parameters of distillation loss are optimized automatically by a scaling-based grid search method. Experiments are conducted on a public MI-EEG dataset with multiple subjects and MI-tasks, the proposed framework can find the optimal SCNN associated with the best CCTC, which achieves a statistically significant average classification accuracy of 86.37%. The results suggest that TLA is helpful for increasing the distribution similarity between different domains, and the knowledge distillation embedded in ACTL framework greatly simplifies the SCNN and outperforms the complicated TCNN.