Using genetically-defined rodent strains for the identification of hippocampal traits relevant for two-way avoidance behavior: a non-invasive approach

Summary Genetically-defined rodent strains permit the identification of hippocampal traits which are of functional relevance for the performance of two-way avoidance behavior. This is exemplified here by analyzing the relationship between infrapyramidal mossy fibers (a tiny projection terminating upon the basal dendrites of hippocampal pyramidal neurons) and two-way avoidance learning in about 800 animals. The necessary steps include 1) identification of structural traits sensitive to selective breeding for extremes in two-way avoidance, 2) testing the robustness of the associations found by studying individual and genetical correlations between hippocampal traits and behavior, 3) establishing causal relationships by Mendelian crossing of strains with extreme structural traits and studying the behavioral consequences of such structural randomization, 4) confirming causal relationships by manipulating the structural variable in inbred (isogenic) strains, thereby eliminating the possibility of genetic linkage, and 5) ruling out the possibility of spurious associations by studying the correlations between the hippocampal trait and other behaviors known to depend on hippocampal functioning.In comparison with the classical lesion approach for identifying relationships between brain and behavior, the present procedure appears to be superior in two aspects: it is non-invasive, and it focuses automatically on those brain traits which are used by natural selection to shape behaviorally-defined animal populations, i.e., it reveals the natural regulators of behavior.     

原子外层空轨道相对最稳定顺序及周期性规则

本文提出原子外层空轨道相对稳定性可作为电子填充的粗略判据;给出由中性原子轨道能数据直接导出原子外层轨道相对最稳定顺序的图表及周期性规则;用以简明、清晰、合理地推引解释元素基组态和解释元素周期律。提出在中心势场模型基础上推引全部元素基组态的方法。     

能耗率极值原理和重力理论交互作用下粗质颗粒的浓度垂线分布

本文同时运用能耗率极值原理和重力理论研究了固液两相流在粗质颗粒时固相浓度的垂线分布,通过探讨能耗率极值原理对“非单向性”作用问题,进一步揭示了两相流运动的机理。计算结果与实测数据有较好的吻合。其研究方法、原理、步骤也极易推广到其它各种粒径的固相。     

Learning attention-based strategies to cooperate for multi-agent path finding

Multi-agent path finding (MAPF) is a challenging multi-agent systems problem where all agents are required to effectively reach their goals concurrently with not colliding with each other and avoiding obstacles. In MAPF, it is a challenge to effectively express the observation of agents, utilize historical information, and effectively communicate with neighbor agents. To tackle these issues, in this work, we proposed a well-designed model that utilizes the local states of nearby agents and outputs an optimal action for each agent to execute. We build the local observation encoder by using residual attention CNN to extract local observations and use the Transformer architecture to build an interaction layer to combine local observations of agents. With the purpose of overcoming the deficiency of success rate, we also designed a new evaluation index, namely extra time rate (ETR). The experimental results show that our model is superior to most previous models in terms of success rate and ETR. In addition, we also completed the ablation study on the model, and the effectiveness of each component of the model was proved.     

基于深度学习的红外与可见光图像融合综述

图像融合技术是指从不同的源图像中提取并融合互补的信息,生成一幅信息量更丰富、对后续高级视觉任务提供足够支持的图像.红外与可见光图像融合（Infrared and Visible Image Fusion,IVIF）是图像融合领域的一个重要分支.近年来,深度学习技术在视觉计算领域表现出了良好的性能,尤其是基于自编码器、卷积神经网络、生成对抗网络等几种基于深度学习的IVIF技术得到了蓬勃发展.为此,对基于深度学习的IVIF算法的方法、数据集和评估指标等进行了总结和阐述;通过大量的实验,进行定性和定量的结果分析,对比了各类基于深度学习IVIF算法的性能;最后,讨论了该领域未来发展的一些前景和研究方向.     

数据驱动下交通异常行为的双层识别模型

为实现对车辆异常行为的准确识别,提高车辆行驶的安全性,提出了一种基于支持向量机(support vector machine, SVM)、监督学习与长短期记忆(long short term memory, LSTM)深度学习的交通异常驾驶行为双层识别模型。首先,对车辆轨迹数据筛除和滤波,构建异常行为数据集;其次,从异常行为轨迹特征中提取出特定异常行为的特征标签,并人为标定在训练集中;再次,构建SVM模型对训练集进行粗识别,基于SVM的二分法原理,从测试集中筛选出异常行为;最后,通过LSTM时间序列模型构建具体种类的异常行为模型,并通过深度学习的方法,从异常行为数据中细分为蛇形驾驶、急速变向、侧滑、大半径转弯、快速U型转弯、急刹车等具体的异常驾驶行为。本次实验选用下一代仿真(next generation simulation, NGSIM)数据中US-101高速公路和peachtree城市道路的数据集的轨迹数据验证SVM和LSTM双层识别模型的性能,包括均方根误差、识别准确率等。结果表明,构建的双层识别模型在第一层有98%的识别准确率,第二层有超过80%的识别准确率,可以较为准确地识...     

一种基于空间域和频谱域特征的图象分割方法

提出了一种基于空间域和频谱域特征的神经网络的医学图像分割方法．本方法不需要教师监督,能自动生成最优的网络输出节点,即分类数．对ＣＴ,ＭＲＩ和超声图像的分割表明本方法有效方便．     

利用Jourdain原理研究二阶非完整系统的守恒律

目的研究具有二阶非完整约束的力学系统守恒律．方法利用Ｊｏｕｒｄａｉｎ变分原理∑Ｎｉ＝１（Ｆｉ－ｍｉｒ¨ｉ）·δ１ｒ．ｉ＝０,引入Ｊｏｕｒｄａｉｎ生成元（Δ１ｑｓ）．＝εＦｓ（ｔ,ｑ,ｑ．）,（Δ１ｔ）．＝εｆ（ｔ,ｑ,ｑ．）和推广的Четаев定义∑ｎｓ＝１φβｑ¨ｓδ１ｑ．ｓ＝０,得到无限小变换下列的不变性条件．结果当无限小生成元ｆ,Ｆｓ满足一定条件时可得到系统的守恒律．结论推广了完整系统和一阶非完整系统的相应结果,二阶非完整系统也存在守恒律．     

基于实例的工艺知识获取模型

介绍了耦合神经网络的工艺设计实例推理系统的实现方法论,提出了一种基于神经网络的工艺设计实例推理索引模型．与现存大多数实例推理系统不同,该方法用神经网络实现实例的动态分类和索引．实例层次分类的三层结构和基于特征的聚类模板概念,为实现基于符号处理的实例推理求解模式向基于神经计算的模式识别求解模式映射提供了条件．提出了基于实例的工艺知识获取模型,采用新实例的入库操作实现工艺知识的隐式获取,从而使知识获取得以简化．神经网络的自适应、自学习能力将减少系统的日常维护工作．基于实例的系统可望解决知识获取的难题．     

基于神经网络的无监督自学习控制系统

所论系统的控制由两个神经网络完成,即对象放识器和系统控制器。并且提出了一种 新的学习算法,它能利用存贮于辨识网络的信息改进控制策略。该控制系统可以对非线性系统,不确定系统等进行无监督的学习控制。