Advanced lithography materials: From fundamentals to applications.

The semiconductor industry has long been driven by advances in a nanofabrication technology known as lithography, and the fabrication of nanostructures on chips relies on an important coating, the photoresist layer. Photoresists are typically spin-coated to form a film and have a photolysis solubility transition and etch resistance that allow for rapid fabrication of nanostructures. As a result, photoresists have attracted great interest in both fundamental research and industrial applications. Currently, the semiconductor industry has entered the era of extreme ultraviolet lithography (EUVL) and expects photoresists to be able to fabricate sub-10 nm structures. In order to realize sub-10 nm nanofabrication, the development of photoresists faces several challenges in terms of sensitivity, etch resistance, and molecular size. In this paper, three types of lithographic mechanisms are reviewed to provide strategies for designing photoresists that can enable high-resolution nanofabrication. The discussion of the current state of the art in optical lithography is presented in depth. Practical applications of photoresists and related recent advances are summarized. Finally, the current achievements and remaining issues of photoresists are discussed and future research directions are envisioned.

Goal-Conditioned Hierarchical Reinforcement Learning With High-Level Model Approximation.

Hierarchical reinforcement learning (HRL) exhibits remarkable potential in addressing large-scale and long-horizon complex tasks. However, a fundamental challenge, which arises from the inherently entangled nature of hierarchical policies, has not been understood well, consequently compromising the training stability and exploration efficiency of HRL. In this article, we propose a novel HRL algorithm, high-level model approximation (HLMA), presenting both theoretical foundations and practical implementations. In HLMA, a Planner constructs an innovative high-level dynamic model to predict the k -step transition of the Controller in a subtask. This allows for the estimation of the evolving performance of the Controller. At low level, we leverage the initial state of each subtask, transforming absolute states into relative deviations by a designed operator as Controller input. This approach facilitates the reuse of subtask domain knowledge, enhancing data efficiency. With this designed structure, we establish the local convergence of each component within HLMA and subsequently derive regret bounds to ensure global convergence. Abundant experiments conducted on complex locomotion and navigation tasks demonstrate that HLMA surpasses other state-of-the-art single-level RL and HRL algorithms in terms of sample efficiency and asymptotic performance. In addition, thorough ablation studies validate the effectiveness of each component of HLMA.

Structure-Aware DropEdge Toward Deep Graph Convolutional Networks.

It has been discovered that graph convolutional networks (GCNs) encounter a remarkable drop in performance when multiple layers are piled up. The main factor that accounts for why deep GCNs fail lies in oversmoothing, which isolates the network output from the input with the increase of network depth, weakening expressivity and trainability. In this article, we start by investigating refined measures upon DropEdge-an existing simple yet effective technique to relieve oversmoothing. We term our method as DropEdge ++ for its two structure-aware samplers in contrast to DropEdge: layer-dependent (LD) sampler and feature-dependent (FD) sampler. Regarding the LD sampler, we interestingly find that increasingly sampling edges from the bottom layer yields superior performance than the decreasing counterpart as well as DropEdge. We theoretically reveal this phenomenon with mean-edge-number (MEN), a metric closely related to oversmoothing. For the FD sampler, we associate the edge sampling probability with the feature similarity of node pairs and prove that it further correlates the convergence subspace of the output layer with the input features. Extensive experiments on several node classification benchmarks, including both full-and semi-supervised tasks, illustrate the efficacy of DropEdge ++ and its compatibility with a variety of backbones by achieving generally better performance over DropEdge and the no-drop version.

Echo-Enhanced Embodied Visual Navigation.

Visual navigation involves a movable robotic agent striving to reach a point goal (target location) using vision sensory input. While navigation with ideal visibility has seen plenty of success, it becomes challenging in suboptimal visual conditions like poor illumination, where traditional approaches suffer from severe performance degradation. We propose E3VN (echo-enhanced embodied visual navigation) to effectively perceive the surroundings even under poor visibility to mitigate this problem. This is made possible by adopting an echoer that actively perceives the environment via auditory signals. E3VN models the robot agent as playing a cooperative Markov game with that echoer. The action policies of robot and echoer are jointly optimized to maximize the reward in a two-stream actor-critic architecture. During optimization, the reward is also adaptively decomposed into the robot and echoer parts. Our experiments and ablation studies show that E3VN is consistently effective and robust in point goal navigation tasks, especially under nonideal visibility.

Learning Representations by Graphical Mutual Information Estimation and Maximization.

The rich content in various real-world networks such as social networks, biological networks, and communication networks provides unprecedented opportunities for unsupervised machine learning on graphs. This paper investigates the fundamental problem of preserving and extracting abundant information from graph-structured data into embedding space without external supervision. To this end, we generalize conventional mutual information computation from vector space to graph domain and present a novel concept, Graphical Mutual Information (GMI), to measure the correlation between input graph and hidden representation. Except for standard GMI which considers graph structures from a local perspective, our further proposed GMI++ additionally captures global topological properties by analyzing the co-occurrence relationship of nodes. GMI and its extension exhibit several benefits: First, they are invariant to the isomorphic transformation of input graphs-an inevitable constraint in many existing methods; Second, they can be efficiently estimated and maximized by current mutual information estimation methods; Lastly, our theoretical analysis confirms their correctness and rationality. With the aid of GMI, we develop an unsupervised embedding model and adapt it to the specific anomaly detection task. Extensive experiments indicate that our GMI methods achieve promising performance in various downstream tasks, such as node classification, link prediction, and anomaly detection.

Channel Exchanging Networks for Multimodal and Multitask Dense Image Prediction.

Multimodal fusion and multitask learning are two vital topics in machine learning. Despite the fruitful progress, existing methods for both problems are still brittle to the same challenge-it remains dilemmatic to integrate the common information across modalities (resp. tasks) meanwhile preserving the specific patterns of each modality (resp. task). Besides, while they are actually closely related to each other, multimodal fusion and multitask learning are rarely explored within the same methodological framework before. In this paper, we propose Channel-Exchanging-Network (CEN) which is self-adaptive, parameter-free, and more importantly, applicable for multimodal and multitask dense image prediction. At its core, CEN adaptively exchanges channels between subnetworks of different modalities. Specifically, the channel exchanging process is self-guided by individual channel importance that is measured by the magnitude of Batch-Normalization (BN) scaling factor during training. For the application of dense image prediction, the validity of CEN is tested by four different scenarios: multimodal fusion, cycle multimodal fusion, multitask learning, and multimodal multitask learning. Extensive experiments on semantic segmentation via RGB-D data and image translation through multi-domain input verify the effectiveness of CEN compared to state-of-the-art methods. Detailed ablation studies have also been carried out, which demonstrate the advantage of each component we propose. Our code is available at https://github.com/yikaiw/CEN.

Fine-Grained Multilevel Fusion for Anti-Occlusion Monocular 3D Object Detection.

We propose a deep fine-grained multi-level fusion architecture for monocular 3D object detection, with an additionally designed anti-occlusion optimization process. Conventional monocular 3D object detection methods usually leverage geometry constraints such as keypoints, object shape relationships, and 3D to 2D optimizations to offset the lack of accurate depth information. However, these methods still struggle against directly extracting rich information for fusion from the depth estimation. To solve the problem, we integrate the monocular 3D features with the pseudo-LiDAR filter generation network between fine-grained multi-level layers. Our network utilizes the inherent multi-scale and promotes depth and semantic information flow in different stages. The new architecture can obtain features that incorporate more reliable depth information. At the same time, the problem of occlusion among objects is prevalent in natural scenes yet remains unsolved mainly. We propose a novel loss function that aims at alleviating the problem of occlusion. Extensive experiments have proved that the framework demonstrates a competitive performance, especially for the complex scenes with occlusion.

Cross-dependent graph neural networks for molecular property prediction.

MOTIVATION: The crux of molecular property prediction is to generate meaningful representations of the molecules. One promising route is to exploit the molecular graph structure through graph neural networks (GNNs). Both atoms and bonds significantly affect the chemical properties of a molecule, so an expressive model ought to exploit both node (atom) and edge (bond) information simultaneously. Inspired by this observation, we explore the multi-view modeling with GNN (MVGNN) to form a novel paralleled framework, which considers both atoms and bonds equally important when learning molecular representations. In specific, one view is atom-central and the other view is bond-central, then the two views are circulated via specifically designed components to enable more accurate predictions. To further enhance the expressive power of MVGNN, we propose a cross-dependent message-passing scheme to enhance information communication of different views. The overall framework is termed as CD-MVGNN. RESULTS: We theoretically justify the expressiveness of the proposed model in terms of distinguishing non-isomorphism graphs. Extensive experiments demonstrate that CD-MVGNN achieves remarkably superior performance over the state-of-the-art models on various challenging benchmarks. Meanwhile, visualization results of the node importance are consistent with prior knowledge, which confirms the interpretability power of CD-MVGNN. AVAILABILITY AND IMPLEMENTATION: The code and data underlying this work are available in GitHub at https://github.com/uta-smile/CD-MVGNN. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Multimode Grasping Soft Gripper Achieved by Layer Jamming Structure and Tendon-Driven Mechanism.

Robotic grasping has become increasingly important in many application areas such as industrial manufacturing and logistics. Because of the diversity and uncertainty of objects and environments, common grippers with one single grasping mode face difficulties to fulfill all the tasks. Hence, we proposed a soft gripper with multiple grasping modes in this study. The gripper consists of four modular soft fingers integrated with layer jamming structure and tendon-driven mechanism. Each finger's rotating shaft of the base uses a torsional spring to decouple the bending deformation and relative rotation. An octopus-mimicking vacuum sucker is installed in the fingertip to generate suction. The effectiveness of the bending deformation and variable stiffness of the design were proved by finite element simulation. Thus, the control model of the finger was built, and the control strategy of multimode grasping of the gripper was proposed. Three control modes were designed to realize the four anthropomorphic grasping modes, including wrap, pinch, hook, and suck. Furthermore, the grasping performance was evaluated to show the abilities. The experiments indicated the superior performance of the proposed gripper and the multimode grasping ability that satisfies various grasping tasks.

Graph Convolutional Module for Temporal Action Localization in Videos.

Temporal action localization, which requires a machine to recognize the location as well as the category of action instances in videos, has long been researched in computer vision. The main challenge of temporal action localization lies in that videos are usually long and untrimmed with diverse action contents involved. Existing state-of-the-art action localization methods divide each video into multiple action units (i.e., proposals in two-stage methods and segments in one-stage methods) and then perform action recognition/regression on each of them individually, without explicitly exploiting their relations during learning. In this paper, we claim that the relations between action units play an important role in action localization, and a more powerful action detector should not only capture the local content of each action unit but also allow a wider field of view on the context related to it. To this end, we propose a general graph convolutional module (GCM) that can be easily plugged into existing action localization methods, including two-stage and one-stage paradigms. To be specific, we first construct a graph, where each action unit is represented as a node and their relations between two action units as an edge. Here, we use two types of relations, one for capturing the temporal connections between different action units, and the other one for characterizing their semantic relationship. Particularly for the temporal connections in two-stage methods, we further explore two different kinds of edges, one connecting the overlapping action units and the other one connecting surrounding but disjointed units. Upon the graph we built, we then apply graph convolutional networks (GCNs) to model the relations among different action units, which is able to learn more informative representations to enhance action localization. Experimental results show that our GCM consistently improves the performance of existing action localization methods, including two-stage methods (e.g., CBR [15] and R-C3D [47]) and one-stage methods (e.g., D-SSAD [22]), verifying the generality and effectiveness of our GCM. Moreover, with the aid of GCM, our approach significantly outperforms the state-of-the-art on THUMOS14 (50.9 percent versus 42.8 percent). Augmentation experiments on ActivityNet also verify the efficacy of modeling the relationships between action units. The source code and the pre-trained models are available at https://github.com/Alvin-Zeng/GCM.

68Ga-PSMA PET/CT targeted biopsy for the diagnosis of clinically significant prostate cancer compared with transrectal ultrasound guided biopsy: a prospective randomized single-centre study.

PURPOSE: 68Ga-prostate-specific membrane antigen (PSMA) positron emission tomography/computed tomography (PET/CT) is valuable for detecting primary and recurrent prostatic lesions. This study aimed to evaluate the efficacy of 68Ga-PSMA-11 PET/CT as a triage tool for prostate biopsy (PSMA-TB) and compare with transrectal ultrasound-guided biopsy (TRUS-GB) for the diagnosis of clinically significant prostate cancer (csPCa). METHODS: This single-centre study randomly allocated 120 patients with elevated serum prostate-specific antigen (PSA) levels (> 4 ng/ml) to PSMA-PET or TRUS group. Patients with PSMA-avid lesions (SUVmax ≥ 8.0) underwent PSMA-TB via a single-puncture percutaneous transgluteal approach (n = 25), whilst patients with negative PSMA-PET underwent systematic TRUS-GB (n = 35). All patients in the TRUS group underwent TRUS-GB directly (n = 60). RESULTS: PCa and csPCa were detected in 26/60 (43.3%) and 24/60 (40.0%) patients in the PSMA-PET group and 19/60 (31.6%) and 15/60 (25.0%) in the TRUS group, respectively. In the PSMA-PET group, the detection rate of PCa and csPCa were significantly higher in PSMA-PET-positive than negative patients (PCa, 23/25 (92.0%) vs 3/35 (8.6%), P < 0.01; csPCa, 22/25 (88.0%) vs 2/35 (5.7%), P < 0.01). PSMA-TB detected significantly more PCa and csPCa than TRUS-GB in the TRUS controls (PCa, 21/25 (84.0%) vs 19/60 (31.6%), P < 0.01; csPCa, 20/25 (80.0%) vs 15/60 (25.0%), P < 0.01). PSMA-PET detected significantly more cases of csPCa amongst patients with PSA 4.0-20.0 ng/ml than TRUS (27.02% vs 8.82%, P < 0.05). No haematuria, urinary retention or pelvic infection was observed after PSMA-TB compare with TRUS-GB. CONCLUSIONS: 68Ga-PSMA-11 PET/CT is a feasible imaging technique that may serve as a triage tool for prostate biopsy, and may improve the detection rate of csPCa compared with TRUS-GB, especially in patients with serum PSA 4.0-20.0 ng/ml.

Breaking Winner-Takes-All: Iterative-Winners-Out Networks for Weakly Supervised Temporal Action Localization.

We address the challenging problem of weakly supervised temporal action localization from unconstrained web videos, where only the video-level action labels are available during training. Inspired by the adversarial erasing strategy in weakly supervised semantic segmentation, we propose a novel iterative-winners-out network. Specifically, we make two technical contributions: we propose an iterative training strategy, namely, winners-out, to select the most discriminative action instances in each training iteration and remove them in the next training iteration. This iterative process alleviates the "winner-takes-all" phenomenon that existing approaches tend to choose the video segments that strongly correspond to the video label but neglects other less discriminative video segments. With this strategy, our network is able to localize not only the most discriminative instances but also the less discriminative ones. To better select the target action instances in winners-out, we devise a class-discriminative localization technique. By employing the attention mechanism and the information learned from data, our technique is able to identify the most discriminative action instances effectively. The two key components are integrated into an end-to-end network to localize actions without using the frame-level annotations. Extensive experimental results demonstrate that our method outperforms the state-of-the-art weakly supervised approaches on ActivityNet1.3 and improves mAP from 16.9% to 20.5% on THUMOS14. Notably, even with weak video-level supervision, our method attains comparable accuracy to those employing frame-level supervisions.

Feature Pyramid Reconfiguration with Consistent Loss for Object Detection.

Taking the feature pyramids into account has become a crucial way to boost the object detection performance. While various pyramid representations have been developed, previous works are still inefficient to integrate the semantical information over different scales. Moreover, recent object detectors are suffering from accurate object location applications, mainly due to the coarse definition of the "positive" examples at training and predicting phases. In this paper, we begin by analyzing current pyramid solutions, and then propose a novel architecture by reconfiguring the feature hierarchy in a flexible yet effective way. In particular, our architecture consists of two lightweight and trainable processes: global attention and local reconfiguration. The global attention is to emphasize the global information of each feature scale, while the local reconfiguration is to capture the local correlations across different scales. Both the global attention and local reconfiguration are non-linear and thus exhibit more expressive ability. Then, we discover that the loss function for object detectors during training is the central cause of the inaccurate location problem. We propose to address this issue by reshaping the standard cross entropy loss such that it focuses more on accurate predictions. Both the feature reconfiguration and the consistent loss could be utilized in popular one-stage (SSD, RetinaNet) and two-stage (Faster R-CNN) detection frameworks. Extensive experimental evaluations on PASCAL VOC 2007, PASCAL VOC 2012 and MS COCO datasets demonstrate that, our models achieve consistent and significant boosts compared with other state-of-the-art methods.

Toward Efficient Action Recognition: Principal Backpropagation for Training Two-Stream Networks.

In this paper, we propose the novel principal backpropagation networks (PBNets) to revisit the backpropagation algorithms commonly used in training two-stream networks for video action recognition. We content that existing approaches always take all the frames/snippets for the backpropagation not optimal for video recognition since the desired actions only occur in a short period within a video. To remedy these drawbacks, we design a watch-and-choose mechanism. In particular, the watching stage exploits a dense snippet-wise temporal pooling strategy to discover the global characteristic for each input video, while the choosing phase only backpropagates a small number of representative snippets that are selected with two novel strategies, i.e., Max-rule and KL-rule. We prove that with the proposed selection strategies, performing the backpropagation on the selected subset is capable of decreasing the loss of the whole snippets as well. The proposed PBNets are evaluated on two standard video action recognition benchmarks UCF101 and HMDB51, where it surpasses the state of the arts consistently, but requiring less memory and computation to achieve high performance.

[Binding interaction of harpagoside and bovine serum albumin: spectroscopic methodologies and molecular docking].

Scrophularia ningpoensis has exhibited a variety of biological activities and been used as a pharmaceutical product for the treatment of inflammatory ailment, rheumatoid arthritis, osteoarthritis and so on. Harpagoside (HAR) is considerer as a main bioactive compound in this plant. Serum albumin has important physiological roles in transportation, distribution and metabolism of many endogenous and exogenous substances in body. It is of great significance to study the interaction mechanism between HAR and bovine serum albumin (BSA). The mechanism of interaction between HAR and BSA was investigated using 2D and 3D fluorescence, synchronous florescence, ultraviolet spectroscopy and molecular docking. According to the analysis of fluorescence spectra, HAR could strongly quench the fluorescence of BSA, and the static quenching process indicated that the decrease in the quenching constant was observed with the increase in temperature. The magnitude of binding constants (KA) was more than 1×105 L·mol⁻¹, and the number of binding sites(n) was approximate to 1. The thermodynamic parameters were calculated through analysis of fluorescence data with Stern-Volmer and Van't Hoff equation. The calculated enthalpy change (ΔH) and entropy change (ΔS) implied that the main interaction forces of HAR with BSA were the bonding interaction between van der Waals forces and hydrogen. The negative values of energy (ΔG) demonstrated that the binding of HAR with BSA was a spontaneous and exothermic process. The binding distance(r) between HAR and BSA was calculated to be about 2.80 nm based on the theory of Frster's non-radiation energy transfer, which indicated that energy is likely to be transfer from BSA to HAR. Both synchronous and 3D florescence spectroscopy clearly revealed that the microenvironment and conformation of BSA changed during the binding interaction between HAR and BSA. The molecular docking analysis revealed HAR is more inclined to BSA and human serum albumin (HSA) in subdomain ⅡA (Sudlow's site I). This study will provide valuable information for understanding the action mechanism of HAR.

Changes in choroidal thickness after trabeculectomy in primary angle closure glaucoma.

PURPOSE: The purpose of this study was to evaluate changes in choroidal thickness (CT) in advanced or late-stage primary angle closure glaucoma (PACG) patients who had undergone trabeculectomy. METHODS: This study included 23 eyes with PACG that required trabeculectomy. Enhanced depth imaging optical coherence tomography was used to measure CT before and 7 days after trabeculectomy. The relationships between changes in CT and changes in IOP and axial length were explored. RESULTS: At all nine macular locations, CT was significantly higher after trabeculectomy, with the exception of two sites. CT at locations close to the macula exhibited a greater increase after surgery (except for the inferior location), although there were no significant differences compared to locations farther from the macula. The mean CTs (± SD) under the fovea before and after the surgery were 282.3 (± 42.4) µm and 311.6 (± 59.9) µm, respectively. The mean IOP (± SD) decreased from 25.9 (± 11.0) mm Hg to 11.8 (± 3.2) mm Hg (without antiglaucoma medications), which positively related to the shortened axial length after surgery (P = 0.019). However, the changes in CT were not correlated with either IOP or axial length. CONCLUSIONS: Short-term CT increased following trabeculectomy in PACG, but it was not related to decreased IOP or shortened axial length. The potential role for and significance of CT increase after trabeculectomy remains to be interpreted.

Downregulation of L-type amino acid transporter 1 expression inhibits the growth, migration and invasion of gastric cancer cells.

Gastric cancer is the second leading cause of cancer-related mortality worldwide. Identifying the molecules that play critical roles in the development of gastric cancer, and clarifying their mechanisms, will contribute to the development of novel molecularly targeted therapeutic drugs. Recently, the large (L)-type amino acid transporter 1 (LAT1), a glycoprotein that transports amino acids through the cell membrane when associated with CD98hc, has been demonstrated to be overexpressed in various types of cancer, and to regulate multiple biological process, including cell growth, migration and invasion. However, the involvement of LAT1 in gastric cancer remains unclear. In the present study, stable gastric cancer cell lines with a LAT1 knockdown were established by transfection of constructs with inserted short (sh) RNAs, in order to clarify the role of LAT1 in gastric caner. A significant decrease in LAT1 expression was observed in the established LAT1-silenced SGC7901 cells compared with the corresponding control cells; however, the expression levels of its partner, CD98hc, were not altered. Furthermore, downregulation of LAT1 expression inhibited the proliferation, migration and invasion of gastric cancer cells. In addition, the decreased expression of LAT1 induced cell cycle arrest in the G1/M phase. These findings suggested that LAT1 may be significant in the progression and metastasis of gastric cancer, and may be developed as a therapeutic target for cancer therapy.

Calcifying nested stroma-epithelial tumor of the liver: a case report and review of literature.

Calcifying nested stromal-epithelial tumor (CNSET) of the liver is extremely rare. This tumor is characterized by nests of epithelial and spindle cells, an associated desmoplastic stroma, as well as variable calcifications and ossifications. Only 24 cases have been reported in the literature whereas none has been reported in Asian descendants. The authors report the first case of CNSET in a 34-year-old Asian woman and provide detailed histological and clinical follow-up data. Compared with those reported earlier, the present case with a history of oral contraceptive use displayed most typical features and the oldest age of onset. A retrospective study was made and the characteristics of CNSET were summarized.

MHC class II expression by beta2 integrin (CD18)-positive microglia, macrophages and macrophage-like cells in rabbit retina.

The aim of this study was to investigate the developmental expression of major histocompatibility complex class II (MHCII) by microglia and macrophages and their relationship to blood vessels in the retina, a representative tissue of the central nervous system. Such information is crucial to understanding the role of these cells in immune surveillance. Wholemount preparations of retinas from late embryonic, postnatal and adult rabbits were subjected to three-colour fluorescence microscopy using beta2 integrin (CD18) and MHCII antibodies and biotinylated Griffonia simplicifolia B4 isolectin labelling of blood vessels. CD18+ cells consistently exhibited characteristics of macrophages or microglia in the vascularized and non-vascularized regions of the retina, respectively. At all ages, MHCII was expressed by a high proportion of cells in the vascularized region, which contained macrophage-like 'parenchymal cells' as well as typical perivascular macrophages. MHCII expression by ramified microglia, first detected on postnatal day 30, was lower in the peripheral retina and intermediate in the avascular region of the myelinated streak. The observed localization of MHCII+ cells in relation to blood vessels and location-dependent differences in MHCII expression point to the possibility that these cells may be distributed strategically within the retina to provide multiple lines of defence against immune challenge arriving via the retinal vasculature.

Competitive regulation of plant allometry and a generalized model for the plant self-thinning process.

Taking into account the individual growth form (allometry) in a plant population and the effects of intraspecific competition on allometry under the population self-thinning condition, and adopting Ogawa's allometric equation 1/y = 1/axb + 1/c as the expression of complex allometry, the generalized model describing the change mode of r (the self-thinning exponential in the self-thinning equation, log M = K + log N, where M is mean plant mass, K is constant, and N is population density) was constructed. Meanwhile, with reference to the changing process of population density to survival curve type B, the exponential, r, was calculated using the software MATHEMATICA 4.0. The results of the numerical simulation show that (1) the value of the self-thinning exponential, r, is mainly determined by allometric parameters; it is most sensitive to change of b of the three allometric parameters, and a and c take second place; (2) the exponential, r, changes continuously from about -3 to the asymptote -1; the slope of -3/2 is a transient value in the population self-thinning process; (3) it is not a 'law' that the slope of the self-thinning trajectory equals or approaches -3/2, and the long-running dispute in ecological research over whether or not the exponential, r, equals -3/2 is meaningless. So future studies on the plant self-thinning process should focus on investigating how plant neighbor competition affects the phenotypic plasticity of plant individuals, what the relationship between the allometry mode and the self-thinning trajectory of plant population is and, in the light of evolution, how plants have adapted to competition pressure by plastic individual growth.