GIS-BASED TRENDING ANALYSIS OF CROPLAND ASSOCIATED WITH RIPARIAN FROM 2008 TO 2018 IN WISCONSIN

One of important characteristics of riparian area is to protect surface and underground water from nutrient contaminated runoff from nearby farmland. Comparing with the traditional fixed-width riparian buffer delineation, the Riparian Buffer Delineation Model (RBDM) to be used in this study, is a GIS-based tool designed to map variable-width riparian buffers by taking into consideration the watercourse and its associated floodplain. As the use of biofuels increases especially derived from corn, riparian areas are often converted to agricultural fields due to the nutrient rich soils and flat topography commonly found in these locations. Meanwhile, it also gives rise to nonpoint source pollution since massive amounts of fertilizer applied to corn yearly, excess nitrogen and phosphorus commonly wash into adjacent streams and lakes. Wisconsin (WI) is one of the states with this typical issue of planting plenty of corns, so how does the corn acreage changes over years and how does it relate to the riparian area function? This study developed two tools utilizing Python and ArcMap GIS were coded to facilitate geoprocessing and to insure and maintain data correctness. To illustration how to visualize the crop acreage changes in riparian area, nine watersheds with highest increase in corn acreage from 2008 to 2018 were selected as samples for analysis. The results indicate if corn acreage continuous to grow, the use of petroleum based fertilizer will also grow as well as soil erosion

The down/up crossing properties of Markov branching processes

It is well-known that 0 is the absorbing state for a branching system. Each particle in the system lives a random long time and gives a random number of new particles at its death time. It stops when the system has no particle. This paper is devoted to studying the fixed range crossing numbers until any time t. The joint probability distribution of fixed range crossing numbers of such processes until time t is obtained by using a new method. In particular, the probability distribution of total death number is given for Markov branching processes until time t.Comment: 19 pages. arXiv admin note: text overlap with arXiv:2001.0235

A New Dynamic Population Variation in Genetic Programming

A dynamic population variation (DPV) in genetic programming (GP) with four innovations is proposed for reducing computational effort and accelerating convergence during the run of GP. Firstly, we give a new stagnation phase definition and the characteristic measure for it. Secondly, we propose an exponential pivot function (EXP) in conjunction with the new stagnation phase definition. Thirdly, we propose an appropriate population variation formula for EXP. Finally, we introduce a scheme using an instruction matrix for producing new individuals to maintain diversity of the population. The efficacy of these innovations in our DPV is examined using four typical benchmark problems. Comparisons among the different characteristic measures have been conducted for regression problems and the proposed measure performed best in all characteristic measures. It is demonstrated that the proposed population variation scheme is superior to fixed and proportionate population variation schemes for sequence induction. It is proved that the new DPV has the capacity to provide solutions at a lower computational effort compared with previously proposed population variation methods and standard genetic programming in most problems

Maximal reliability of controlled Markov systems

This paper concentrates on the reliability of a discrete-time controlled Markov system with finite states and actions, and aims to give an efficient algorithm for obtaining an optimal (control) policy that makes the system have the maximal reliability for every initial state. After establishing the existence of an optimal policy, for the computation of optimal policies, we introduce the concept of an absorbing set of a stationary policy, and find some characterization and a computational method of the absorbing sets. Using the largest absorbing set, we build a novel optimality equation (OE), and prove the uniqueness of a solution of the OE. Furthermore, we provide a policy iteration algorithm of optimal policies, and prove that an optimal policy and the maximal reliability can be obtained in a finite number of iterations. Finally, an example in reliability and maintenance problems is given to illustrate our results

Weakly Supervised Semantic Segmentation for Large-Scale Point Cloud

Existing methods for large-scale point cloud semantic segmentation require expensive, tedious and error-prone manual point-wise annotations. Intuitively, weakly supervised training is a direct solution to reduce the cost of labeling. However, for weakly supervised large-scale point cloud semantic segmentation, too few annotations will inevitably lead to ineffective learning of network. We propose an effective weakly supervised method containing two components to solve the above problem. Firstly, we construct a pretext task, \textit{i.e.,} point cloud colorization, with a self-supervised learning to transfer the learned prior knowledge from a large amount of unlabeled point cloud to a weakly supervised network. In this way, the representation capability of the weakly supervised network can be improved by the guidance from a heterogeneous task. Besides, to generate pseudo label for unlabeled data, a sparse label propagation mechanism is proposed with the help of generated class prototypes, which is used to measure the classification confidence of unlabeled point. Our method is evaluated on large-scale point cloud datasets with different scenarios including indoor and outdoor. The experimental results show the large gain against existing weakly supervised and comparable results to fully supervised methods\footnote{Code based on mindspore: https://github.com/dmcv-ecnu/MindSpore\_ModelZoo/tree/main/WS3\_MindSpore}

BEV-DG: Cross-Modal Learning under Bird's-Eye View for Domain Generalization of 3D Semantic Segmentation

Cross-modal Unsupervised Domain Adaptation (UDA) aims to exploit the complementarity of 2D-3D data to overcome the lack of annotation in a new domain. However, UDA methods rely on access to the target domain during training, meaning the trained model only works in a specific target domain. In light of this, we propose cross-modal learning under bird's-eye view for Domain Generalization (DG) of 3D semantic segmentation, called BEV-DG. DG is more challenging because the model cannot access the target domain during training, meaning it needs to rely on cross-modal learning to alleviate the domain gap. Since 3D semantic segmentation requires the classification of each point, existing cross-modal learning is directly conducted point-to-point, which is sensitive to the misalignment in projections between pixels and points. To this end, our approach aims to optimize domain-irrelevant representation modeling with the aid of cross-modal learning under bird's-eye view. We propose BEV-based Area-to-area Fusion (BAF) to conduct cross-modal learning under bird's-eye view, which has a higher fault tolerance for point-level misalignment. Furthermore, to model domain-irrelevant representations, we propose BEV-driven Domain Contrastive Learning (BDCL) with the help of cross-modal learning under bird's-eye view. We design three domain generalization settings based on three 3D datasets, and BEV-DG significantly outperforms state-of-the-art competitors with tremendous margins in all settings.Comment: Accepted by ICCV 202