Fungal wood decay is a complex biophysical phenomenon that involves the degradation of a variety of structural wood components, ranging from lignin and carbohydrates to defensive chemical agents. All these substrates serve as varying resources with different material properties that determine the rate of fungal propagation and the structural integrity and color of decaying wood. We propose a novel approach to simulate the dynamic interactions between the biological and mechanical components of wood decay, including fungal colonization, chemical defense, and moisture-driven fracture. We propose a novel volumetric representation of trees that includes grain-aligned mesh generation, internal moisture dynamics, and tissue-specific health states. Furthermore, we model the anisotropic diffusion, consumption, and resulting material failure caused by white and brown rot fungi. This allows simulating and rendering 3D volumetric decaying trees that realistically capture key aspects of the process, such as the progression of cuboid fracture patterns, the hollowing of trunks, and the effects of environmental moisture on structural stability.

We introduce Arenite, a novel physics-based approach for modeling sandstone structures. The key insight of our work is that simulating a combination of stress and multi-factor erosion enables the generation of a wide variety of sandstone structures observed in nature. We isolate the key shape-forming phenomena: multi-physics fabric interlocking, wind and fluvial erosion, and particle-based deposition processes. Complex 3D structures such as arches, alcoves, hoodoos, or buttes can be achieved by creating simple 3D structures with user-painted erodable areas and vegetation and running the simulation. We demonstrate the algorithm on a wide variety of structures, and our GPU-based implementation achieves the simulation in less than 5 minutes on a desktop computer for our most complex example.

Software defect prediction (SDP) aims to identify potentially defective modules early in the development phase, thereby enhancing testing and improving the overall quality of software. Deep learning has advanced SDP by improving accuracy. However, its lack of interpretability remains a critical limitation. Although some existing methods offer explanations for SDP models, they typically focus on assigning feature importance within model decisions. However, these explanations often remain superficial, failing to clarify the specific roles that features play in the decision-making process. To address these challenges, this article presents counterfactual contrastive explanations for SDP (CCE-SDP), a novel framework that generates counterfactual contrastive explanations to enhance model transparency. Leveraging genetic algorithms, CCE-SDP constructs optimal counterfactual examples to reveal how specific changes in software metrics influence predictions, offering fine-grained, instance-level insights. The core of the CCE-SDP method is to offer counterfactual explanations that minimize the number of altered features while maximizing the counterfactual trust score. In addition, by incorporating synthetic counterfactual samples with defect labels into the training process, the ability to handle imbalanced data is also enhanced. Experiments on 36 benchmark software projects showed that the CCE-SDP model demonstrated to users how specific feature changes impact predictive outcomes, thereby allowing for a better understanding of the model’s decision-making. Furthermore, by embedding counterfactual analysis within the model training, we successfully boosted the predictive performance of the SDP models.

Abstract While the past of terrain cannot be known precisely because an effect can result from many different causes, exploring these possible pasts opens the way to numerous applications ranging from movies and games to paleogeography. We introduce unerosion, an attempt to recover plausible past topographies from an input terrain represented as a height field. Our solution relies on novel algorithms for the backward simulation of different processes: fluvial erosion, sedimentation, and thermal erosion. This is achieved by re‐formulating the equations of erosion and sedimentation so that they can be simulated back in time. These algorithms can be combined to account for a succession of climate changes backward in time, while the possible ambiguities provide editing options to the user. Results show that our solution can approximately reverse different types of erosion while enabling users to explore a variety of alternative pasts. Using a chronology of climatic periods to inform us about the main erosion phenomena, we also went back in time using real measured terrain data. We checked the consistency with geological findings, namely the height of river beds hundreds of thousands of years ago.

In this article, a novel pinning control method, only requiring information from partial nodes, is developed to synchronize drive-response memristor-based neural networks (MNNs) with time delay. An improved mathematical model of MNNs is established to describe the dynamic behaviors of MNNs accurately. In the existing literature, pinning controllers for synchronization of drive-response systems were designed based on information of all nodes, but in some specific situations, the control gains may be very large and challenging to realize in practice. To overcome this problem, a novel pinning control policy is developed to achieve synchronization of delayed MNNs, which depends only on local information of MNNs, for reducing communication and calculation burdens. Furthermore, sufficient conditions for synchronization of delayed MNNs are provided. Finally, numerical simulation and comparative experiments are conducted to verify the effectiveness and superiority of the proposed pinning control method.

Rank-oriented software defect prediction (ROSDP) aims to establish a model to predict the testing priority of software modules according to defect severity for the reasonable allocation of limited testing resources. Some ROSDP methods construct the prediction model by a linear model with respect to software features. However, in software repositories, the linear condition between testing priority and software feature is not satisfied, and the ranking performance of the linear prediction model is limited. Thus, in order to relax the limitation of the linear prediction model and improve the ranking performance, ensemble kernel-mapping-based ranking support vector machine (EKMRSVM) is developed based on the theories of ranking SVM, which builds a nonlinear ranking function approximated by the kernel-mapping-based method. Furthermore, the sequential minimal optimization algorithm is developed to derive the ideal parameters of the nonlinear ranking function, and ensemble learning is introduced to reduce time costs and guarantee ranking performance. Experimental results on 20 open source datasets indicate that introducing the kernel mapping method in EKMRSVM is very effective in performance improvement, and ensemble learning makes the proposed ranking algorithm very competitive in terms of time costs. Thus, based on the comparative results of some baseline methods, EKMRSVM with the appropriate kernel function can achieve better ranking performance.

Numerous software defect prediction methods utilize semantic information and software metrics as code features, neglecting the structural knowledge inherent in the source code.Other studies improve feature completeness by simply combining different types of defect indicators, which causes information redundancy.To address these challenges, this paper proposes a novel software defect prediction method that incorporates multitype features and performs feature selection.Firstly, semantic and structural features are extracted by Text Convolutional Neural Network (TextCNN) and Graph Isomorphism Network (GIN) from Abstract Syntax Tree (AST) and Program Dependency Graph (PDG), respectively, which are combined with software metrics to build a multi-type feature set.Then, Recursive Feature Elimination with Cross-Validation (RFECV) integrating a novel feature importance measure is utilized to remove redundant features and generate a feature subset.Finally, a prediction model for classification is established based on the feature subset.The experiments validated the effectiveness of multi-type features and the improved RFECV.Overall our proposed method outperforms state-of-the-art techniques on nine Java open-source projects.

Software Defect Prediction (SDP) aims to identify defect-prone modules in advance to ensure software quality.In SDP research based on deep learning, the mainstream approach is to extract deep semantic features from an Abstract Syntax Tree (AST).Theoretically, the AST as a bi-dimensional structure encloses information at the node level, fragment level, and entire tree level.However, most existing research serializes the whole AST without considering the expression at different granularities.To address this limitation, we introduce a positional hierarchical attention network (PHAN) that acquires semantic features by simultaneously considering contexts between nodes and paths.Specifically, our model incorporates attention mechanisms to capture information of varying importance at separate hierarchies, and relative position representations to distinguish the contributions of different paths.Experimental results demonstrate that PHAN significantly outperforms existing baseline methods.

Cross-Project Defect Prediction (CPDP) refers to transferring knowledge from source software projects to a target software project. Previous research has shown that the impacts of knowledge transferred from different source projects differ on the target task. Therefore, one of the fundamental challenges in CPDP is how to measure the amount of knowledge transferred from each source project to the target task. This article proposed a novel CPDP method called Multi-source defect prediction with Joint Wasserstein Distance and Ensemble Learning (MJWDEL) to learn transferred weights for evaluating the importance of each source project to the target task. In particular, first of all, applying the TCA technique and Logistic Regression (LR) train a sub-model for each source project and the target project. Moreover, the article designs joint Wassertein distance to understand the source-target relationship and then uses this as a basis to compute the transferred weights of different sub-models. After that, the transferred weights can be used to reweight these sub-models to determine their importance in knowledge transfer to the target task. We conducted experiments on 19 software projects from PROMISE, NASA and AEEEM datasets. Compared with several state-of-the-art CPDP methods, the proposed method substantially improves CPDP performance in terms of four evaluation indicators (i.e., F-measure, Balance, G-measure and MMC).

In this paper, robust exponential synchronization of memristor-based neural networks (MNNs) with nonidentical characteristics is investigated. Coefficient mismatch, time-varying delay mismatch, and activation function mismatch are considered between the drive and the response MNNs. Pinning control strategy is developed to realize robust exponential synchronization and the stability criteria is established by using the Lyapunov function method and differential inclusion theory. Furthermore, the stable region of controller parameters is computed to guarantee that the synchronization errors enter a predetermined error bound within given settling time. Finally, the effectiveness of the proposed methods is verified by the numerical simulations. The methods presented in this paper offer novel schemes for robust exponential synchronization of nonidentical MNNs.

A novel policy iteration algorithm, called the continuous-time time-varying (CTTV) policy iteration algorithm, is presented in this paper to obtain the optimal control laws for infinite horizon CTTV nonlinear systems. The adaptive dynamic programming (ADP) technique is utilized to obtain the iterative control laws for the optimization of the performance index function. The properties of the CTTV policy iteration algorithm are analyzed. Monotonicity, convergence, and optimality of the iterative value function have been analyzed, and the iterative value function can be proven to monotonically converge to the optimal solution of the Hamilton-Jacobi-Bellman (HJB) equation. Furthermore, the iterative control law is guaranteed to be admissible to stabilize the nonlinear systems. In the implementation of the presented CTTV policy algorithm, the approximate iterative control laws and iterative value function are obtained by neural networks. Finally, the numerical results are given to verify the effectiveness of the presented method.

In this paper, the drive-response synchronization of the delayed memristive neural networks (MNNs) with unknown parameters is studied. With the realization of practical memristors, more and more researchers start to investigate MNNs, and their synchronization problem has became a hot topic. However, the majority of the existing works are based on the strict condition that the weights of MNNs are known and determined. When the parameters are unknown, the obtained results may be inapplicable. Thus, it is worthwhile to investigate the synchronization problem of the delayed MNNs with unknown parameters. Due to the parameter uncertainties of MNNs, a novel response system and an adaptive control method are proposed under different assumptions. The update laws for weights in the response system and the gains of adaptive controllers are developed to synchronize the proposed response system with the delayed MNNs. Furthermore, the proposed methods can be applied to various cases and the corresponding stability theories are established. Finally, the numerical simulations are conducted to verify the effectiveness of the developed methods.

In this paper, the asymptotic synchronization for drive-response memristive neural networks(MNNs) with nonidentical parameters is investigated. Parameter inconformity is ubiquitous between drive and response systems due to environmental or internal influence. However, the majority of previous results were based on the well-matched MNNs. Thus, it is meaningful to study the synchronization problem of MNNs with nonidentical parameters. First, coefficient mismatches are dealt within the framework of set-valued maps and differential inclusions. Furthermore, in order to reduce the control cost, a pinning control strategy is adopted to drive two nonidentical MNNs to achieve asymptotic synchronization. And the sufficient stability conditions are given based on Lyapunov functional method. Finally, the effectiveness of proposed pinning controller is verified by a numerical example.