Collective Movement Simulation:Methods and Applications

Collective movement simulations are challenging and important in many areas,including life science,mathematics,physics,information science and public safety.In this survey,we provide a comprehensive review of the state-of-the-art techniques for collective movement simulations.We start with a discussion on certain concepts to help beginners understand it more systematically.Then,we analyze the intelligence among different collective objects and the emphasis in different fields.Next,we classify existing collective movement simulation methods into four categories according to their effects,namely versatility,accuracy,dynamic adaptability,and assessment feedback capability.Furthermore,we introduce five applications of layout optimization,emergency control,dispatching,unmanned systems,and other derivative applications.Finally,we summarize possible future research directions.

RCAnalyzer:visual analytics of rare categories in dynamic networks

A dynamic network refers to a graph structure whose nodes and/or links dynamically change over time.Existing visualization and analysis techniques focus mainly on summarizing and revealing the primary evolution patterns of the network structure.Little work focuses on detecting anomalous changing patterns in the dynamic network,the rare occurrence of which could damage the development of the entire structure.In this study,we introduce the first visual analysis system RCAnalyzer designed for detecting rare changes of sub-structures in a dynamic network.The proposed system employs a rare category detection algorithm to identify anomalous changing structures and visualize them in the context to help oracles examine the analysis results and label the data.In particular,a novel visualization is introduced,which represents the snapshots of a dynamic network in a series of connected triangular matrices.Hierarchical clustering and optimal tree cut are performed on each matrix to illustrate the detected rare change of nodes and links in the context of their surrounding structures.We evaluate our technique via a case study and a user study.The evaluation results verify the effectiveness of our system.

Cognition-Driven Traffic Simulation for Unstructured Road Networks

Dynamic changes of traffic features in unstructured road networks challenge the scene-cognitive abilities of drivers,which brings various heterogeneous traffic behaviors.Modeling traffic with these heterogeneous behaviors would have significant impact on realistic traffic simulation.Most existing traffic methods generate traffic behaviors by adjust-ing parameters and cannot describe those heterogeneous traffic flows in detail.In this paper,a cognition-driven traffic-simulation method inspired by the theory of cognitive psychology is introduced.We first present a visual-filtering model and a perceptual-information fusion model to describe drivers'heterogeneous cognitive processes.Then,logistic regression is used to model drivers'heuristic decision-making processes based on the above cognitive results.Lastly,we apply the high-level cognitive decision-making results to low-level traffic simulation.The experimental results show that our method can provide realistic simulations for the traffic with those heterogeneous behaviors in unstructured road networks and has nearly the same efficiency as that of existing methods.

Probability-Based Channel Pruning for Depthwise Separable Convolutional Networks

Channel pruning can reduce memory consumption and running time with least performance damage,and is one of the most important techniques in network compression.However,existing channel pruning methods mainly focus on the pruning of standard convolutional networks,and they rely intensively on time-consuming fine-tuning to achieve the performance improvement.To this end,we present a novel efficient probability-based channel pruning method for depthwise separable convolutional networks.Our method leverages a new simple yet effective probability-based channel pruning criterion by taking the scaling and shifting factors of batch normalization layers into consideration.A novel shifting factor fusion technique is further developed to improve the performance of the pruned networks without requiring extra time-consuming fine-tuning.We apply the proposed method to five representative deep learning networks,namely MobileNetV1,MobileNetV2,ShuffleNetV1,ShuffleNetV2,and GhostNet,to demonstrate the efficiency of our pruning method.Extensive experimental results and comparisons on publicly available CIFAR10,CIFAR100,and ImageNet datasets validate the feasibility of the proposed method.

Wheat ear growth modeling based on a polygon

Visual inspection of wheat growth has been a useful tool for understanding and implementing agricultural techniques and a way to accurately predict the growth status of wheat yields for economists and policy decision makers.In this paper,we present a polygonal approach for modeling the growth process of wheat ears.The grain,lemma,and palea of wheat ears are represented as editable polygonal models,which can be re-polygonized to detect collision during the growth process.We then rotate and move the colliding grain to resolve the collision problem.A linear interpolation and a spherical interpolation are developed to simulate the growth of wheat grain,performed in the process of heading and growth of wheat grain.Experimental results show that the method has a good modeling effect and can realize the modeling of wheat ears at different growth stages.

Mapping of Defense Response Gene Homologs and Their Association with Resistance Loci in Maize

Defense response genes in higher plant species are involved in a variety of signal transduction pathways and biochemical reactions to counterattack invading pathogens. In this study, a total of 366 non-redundant defense response gene homologs （DRHs）, including 124 unigenes/expressed sequence tags, 226 tentative consensuses, and 16 DRH contigs have been identified by mining the Maize Genetics and Genomics and The Institute for Genomic Research maize databases using 35 essential defense response genes. Of 366 DRHs, 202 are mapped to 152 loci across ten maize chromosomes via both the genetic and in silico mapping approaches. The mapped DRHs seem to cluster together rather than be evenly distributed along the maize genome. Approximately half of these DHRs are located in regions harboring either major resistance genes or quantitative trait loci （QTL）. Therefore, this comprehensive DRH linkage map will provide reference sequences to identify either positional candidate genes for resistance genes and/or QTLs or to develop makers for fine-mapping and marker-assisted selection of resistance genes and/or QTLs.

Mechanical Assembly Packing Problem Using Joint Constraints

The three-dimensional packing problem is generally on how to pack a set of models into a given bounding box using the smallest packaging volume. It is known as an NP-hard problem. When discussing the packing problem in mechanical field, the space utilization of a mechanism is low due to the constraint of mechanical joints between different mechanical parts. Although such a situation can be improved by breaking the mechanism into components at every joint, it burdens the user when reassembling the mechanism and may also reduce the service life of mechanical parts. In this paper, we propose a novel mechanism packing algorithm that deliberately considers the DOFs （degrees of freedom） of mechanical joints. With this algorithm, we construct the solution space according to each joint. While building the search tree of the splitting scheme, we do not break the joint, but move the joint. Therefore, the algorithm proposed in this paper just requires the minimal number of splits to meet the goal of space utilization. Numerical examples show that the proposed method is convenient and efficient to pack three-dimensional models into a given bounding box with high space utilization.