Inspires effective alternatives to backpropagation:predictive coding helps understand and build learning

Artificial neural networks are capable of machine learning by simulating the hiera rchical structure of the human brain.To enable learning by brain and machine,it is essential to accurately identify and correct the prediction errors,referred to as credit assignment(Lillicrap et al.,2020).It is critical to develop artificial intelligence by understanding how the brain deals with credit assignment in neuroscience.

Glorious Missions Assigned by the Human History

Frontiers of Engineering Management,after two years’preparation,came,in spring2014,to offer its readers,with the mission and the editor inclination to promote the advancement of engineering management,so as to propel the advancement of engineering science and technology.It is a general consensus of the scientific communities that engineering science is

不变性原则与Python亮点

针对目前Python教学中“以C语言知识解释Python语法”的倾向及其所导致的一些基本概念错误,回顾“软件危机”发生以来人们在“去变量赋值”副作用和函数式编程两个方面的努力,从不变性原则的高度阐述这些举措对于提高程序质量的意义,在此基础上提出Python程序设计语言的亮点并强调对于回归Python本质的重要性,目的是为进一步学习和应用打下坚实基础。

Channel assignment and power allocation for throughput improvement with PPO in B5G heterogeneous edge networks

In Beyond the Fifth Generation(B5G)heterogeneous edge networks,numerous users are multiplexed on a channel or served on the same frequency resource block,in which case the transmitter applies coding and the receiver uses interference cancellation.Unfortunately,uncoordinated radio resource allocation can reduce system throughput and lead to user inequity,for this reason,in this paper,channel allocation and power allocation problems are formulated to maximize the system sum rate and minimum user achievable rate.Since the construction model is non-convex and the response variables are high-dimensional,a distributed Deep Reinforcement Learning(DRL)framework called distributed Proximal Policy Optimization(PPO)is proposed to allocate or assign resources.Specifically,several simulated agents are trained in a heterogeneous environment to find robust behaviors that perform well in channel assignment and power allocation.Moreover,agents in the collection stage slow down,which hinders the learning of other agents.Therefore,a preemption strategy is further proposed in this paper to optimize the distributed PPO,form DP-PPO and successfully mitigate the straggler problem.The experimental results show that our mechanism named DP-PPO improves the performance over other DRL methods.

Development of SNP parentage assignment techniques in the yellowfin seabream Acanthopagrus latus

Acanthopagrus latus is an essential aquaculture species on the south coast of China.However,there is a lack of systematic breeding of A.latus,which considerably limits the sustainable development of A.latus.As a result,genetic improvements are urgently needed to breed new strains of A.latus with rapid growth and strong resistance to disease.During selective breeding,it is necessary to estimate the genetic parameters of the target trait,which in turn depends on an accurate disentangled pedigree for the selective population.Therefore,it is necessary to establish the parentage assignment technique for A.latus.In this study,95 individuals selected from their parents and their 14 families were used as experimental material.SNPs were developed by genome resequencing,and highly polymorphic SNPs were screened on the basis of optimized filtering parameters.A total of 14392738 SNPs were discovered and 205 SNPs were selected for parentage assignment using the CERVUS software.In the model where the gender of the parents is known,the assignment success rate is 98.61%for the male parent,97.22%for the female parent,and 95.83%for the parent pair.In the model where the gender of the parents is unknown,the assignment success rate is 100%for a single parent and 90.28%for the parent pair.The results of this study were expected to serve as a reference for the breeding of new varieties of A.latus.

A self-organization formation configuration based assignment probability and collision detection

The formation control of multiple unmanned aerial vehicles(multi-UAVs)has always been a research hotspot.Based on the straight line trajectory,a multi-UAVs target point assignment algorithm based on the assignment probability is proposed to achieve the shortest overall formation path of multi-UAVs with low complexity and reduce the energy consumption.In order to avoid the collision between UAVs in the formation process,the concept of safety ball is introduced,and the collision detection based on continuous motion of two time slots and the lane occupation detection after motion is proposed to avoid collision between UAVs.Based on the idea of game theory,a method of UAV motion form setting based on the maximization of interests is proposed,including the maximization of self-interest and the maximization of formation interest is proposed,so that multi-UAVs can complete the formation task quickly and reasonably with the linear trajectory assigned in advance.Finally,through simulation verification,the multi-UAVs target assignment algorithm based on the assignment probability proposed in this paper can effectively reduce the total path length,and the UAV motion selection method based on the maximization interests can effectively complete the task formation.

Multi-UAV Collaborative Mission Planning Method for Self-Organized Sensor Data Acquisition

In recent years,sensor technology has been widely used in the defense and control of sensitive areas in cities,or in various scenarios such as early warning of forest fires,monitoring of forest pests and diseases,and protection of endangered animals.Deploying sensors to collect data and then utilizing unmanned aerial vehicle(UAV)to collect the data stored in the sensors has replaced traditional manual data collection as the dominant method.The current strategies for efficient data collection in above scenarios are still imperfect,and the low quality of the collected data and the excessive energy consumed by UAV flights are still the main problems faced in data collection.With regards this,this paper proposes a multi-UAV mission planning method for self-organized sensor data acquisition by comprehensively utilizing the techniques of self-organized sensor clustering,multi-UAV mission area allocation,and sub-area data acquisition scheme optimization.The improvedα-hop clustering method utilizes the average transmission distance to reduce the size of the collection sensors,and the K-Dimensional method is used to form a multi-UAV cooperative workspace,and then,the genetic algorithm is used to trade-off the speed with the age of information(AoI)of the collected information and the energy consumption to form the multi-UAV data collection operation scheme.The combined optimization scheme in paper improves the performance by 95.56%and 58.21%,respectively,compared to the traditional baseline model.In order to verify the excellent generalization and applicability of the proposed method in real scenarios,the simulation test is conducted by introducing the digital elevation model data of the real terrain,and the results show that the relative error values of the proposed method and the performance test of the actual flight of the UAV are within the error interval of±10%.Then,the advantages and disadvantages of the present method with the existing mainstream schemes are tested,and the results show that the present method has a huge advantage in terms of space and time complexity,and at the same time,the accuracy for data extraction is relatively improved by 10.46%and 12.71%.Finally,by eliminating the clustering process and the subtask assignment process,the AoI performance decreases by 3.46×and 4.45×,and the energy performance decreases by 3.52×and 4.47×.This paper presents a comprehensive and detailed proactive optimization of the existing challenges faced in the field of data acquisition by means of a series of combinatorial optimizations.

Reinforcement Learning Based Quantization Strategy Optimal Assignment Algorithm for Mixed Precision

The quantization algorithm compresses the original network by reducing the numerical bit width of the model,which improves the computation speed. Because different layers have different redundancy and sensitivity to databit width. Reducing the data bit width will result in a loss of accuracy. Therefore, it is difficult to determinethe optimal bit width for different parts of the network with guaranteed accuracy. Mixed precision quantizationcan effectively reduce the amount of computation while keeping the model accuracy basically unchanged. In thispaper, a hardware-aware mixed precision quantization strategy optimal assignment algorithm adapted to low bitwidth is proposed, and reinforcement learning is used to automatically predict the mixed precision that meets theconstraints of hardware resources. In the state-space design, the standard deviation of weights is used to measurethe distribution difference of data, the execution speed feedback of simulated neural network accelerator inferenceis used as the environment to limit the action space of the agent, and the accuracy of the quantization model afterretraining is used as the reward function to guide the agent to carry out deep reinforcement learning training. Theexperimental results show that the proposed method obtains a suitable model layer-by-layer quantization strategyunder the condition that the computational resources are satisfied, and themodel accuracy is effectively improved.The proposed method has strong intelligence and certain universality and has strong application potential in thefield of mixed precision quantization and embedded neural network model deployment.

Structural Characteristics and Influencing Factors of Carbon Emission Spatial Association Network:A Case Study of Yangtze River Delta City Cluster,China

City cluster is an effective platform for encouraging regionally coordinated development.Coordinated reduction of carbon emissions within city cluster via the spatial association network between cities can help coordinate the regional carbon emission management,realize sustainable development,and assist China in achieving the carbon peaking and carbon neutrality goals.This paper applies the improved gravity model and social network analysis(SNA)to the study of spatial correlation of carbon emissions in city clusters and analyzes the structural characteristics of the spatial correlation network of carbon emissions in the Yangtze River Delta(YRD)city cluster in China and its influencing factors.The results demonstrate that:1)the spatial association of carbon emissions in the YRD city cluster exhibits a typical and complex multi-threaded network structure.The network association number and density show an upward trend,indicating closer spatial association between cities,but their values remain generally low.Meanwhile,the network hierarchy and network efficiency show a downward trend but remain high.2)The spatial association network of carbon emissions in the YRD city cluster shows an obvious‘core-edge’distribution pattern.The network is centered around Shanghai,Suzhou and Wuxi,all of which play the role of‘bridges’,while cities such as Zhoushan,Ma'anshan,Tongling and other cities characterized by the remote location,single transportation mode or lower economic level are positioned at the edge of the network.3)Geographic proximity,varying levels of economic development,different industrial structures,degrees of urbanization,levels of technological innovation,energy intensities and environmental regulation are important influencing factors on the spatial association of within the YRD city cluster.Finally,policy implications are provided from four aspects:government macro-control and market mechanism guidance,structural characteristics of the‘core-edge’network,reconfiguration and optimization of the spatial layout of the YRD city cluster,and the application of advanced technologies.

Spatial network characteristics and drivers of carbon emission reduction capability in urban agglomerations:A case study of Shandong Province

Synergistic carbon emission reduction at the urban scale is an inherent requirement for China to realize its carbon emission reduction commitments and promote sustainable and regional synergistic development.Using 16 prefectural cities in Shandong Province as an example,this study defines and quantifies the regional carbon emission reduction capacity(CERC)based on the synergistic development of carbon emission reduction,environmental protection,and economic growth objectives.The spatial network characteristics of the(CERC)and its drivers were analyzed using social network analysis and quadratic assignment procedure regression methods.The results revealed that the spatial correlation of the CERC among prefectural cities has been increasing over the years.Jinan−Zibo−Qingdao is the center of the network,while the spatial linkage strength in the southwest and northeast of Shandong Province is weak.Geospatial distance and scientific development differences have a significant negative effect on the intensity of spatial association,while differences in economic and informatization developments have a considerable positive impact.Environmental regulatory differences and transportation differences are not significant.This study offers a methodological reference for similar studies in other countries or regions.At the same time,the findings provide a scientific basis for the government to rationally allocate urban resources and promote regional synergistic carbon emission reduction.

基于模糊多目标规划的防空反导火力分配

针对现有多目标火力分配(weapon target assignment,WTA)方法很难适用于不确定情况下防空反导作战的问题,提出了基于模糊多目标规划的防空反导WTA方法。首先,采用三角模糊数刻画不确定的目标威胁度,在考虑防空反导作战特点的基础上,基于模糊多目标规划建立了WTA模型;然后,根据必要性测度原理将含有模糊参数的目标函数进行了等价清晰化;接着,提出了具有单/双势阱的多目标量子行为粒子群算法用于求解WTA模型,该算法采用了单/双势阱位置更新方式、粒子混合随机变异方法、领导粒子两阶段选取方法;最后,通过实例仿真验证了模型的合理性和算法的有效性。

计算机系统基础课程的实验设计与分析

分析实验对计算机系统基础教育的重要性,结合课程需求和实际教学经验提出实验设计的几条基本原则,结合南京大学计算机科学与技术系计算机系统基础实验近3年的教学,分析实验设计的有效性和存在的问题,并提出解决思路。

Curb parking pricing method based on parking choice behavior

In order to improve the use efficiency of curb parking, a reasonable curb parking pricing is evaluated by considering individual parking choice behavior. The parking choice behavior is analyzed from micro-aspects, and the choice behavior utility function is established combining trip time, search time, waiting time, access time and parking fee. By the utility function, a probit-based parking choice behavior model is constructed. On the basis of these, the curb parking pricing model is deduced by considering the constrained conditions, and an incremental assignment algorithm of the model is also designed. Finally, the model is applied to the parking planning of Tongling city. It is pointed out that the average parking time of curb parking decreases 34%, and the average turnover rate increases 67% under the computed parking price system. The results show that the model can optimize the utilization of static traffic facilities.

Research Progress and the Main Achievements of The Regional Geology of China Preface

Introduction to The Regional Geology of China In 2008, a new project concerning the recompilation of The Regional Geology of China (RGC) was assigned by the Chinese Geological Survey (CGS) and undertaken by the Institute of Geology, Chinese Academy of Geological Sciences (CAGS). Li Tingdong, an academician of the Chinese Academy of Sciences (CAS), is the chief leader and chief scientist of the project. The last time The Regional Geology of China was compiled was in the 1980s (Cheng, 1994).

正压生物防护服检测技术

正压生物防护服检测的主要研究对象一个是防护服的防护因子。美国的呼吸防护委员会将它定义为防护服内部污染物浓度Co与外部环境中污染物浓度Ci的比值,可以分为设计防护因子（assigned protection factor,APF）、工作防护因子（workplace protection factor,WPF）、特殊状态防护因子（program protection factor,PPF）等。另一个是人体工效学检测,包括散热性能检测、透湿性能检测和内部二氧化碳浓度检测等。

The mean age of gender dysphoria diagnosis is decreasing

INTRODUCTION Gender dysphoria(GD),or gender identity disorder,is defined as persistent distress stemming from the incongruence between one's assigned sex at birth and gender identity.'GD has traditionally been introduced as a rare condition predominant in assigned males at birth(AMABs).2 However,recent studies have shown an upward trend in assigned females at birth(AFABs)with a dramatic reversal of the AMAB:AFAB ratio-.5-The actual AMAB:AFAB ratio varies by age group and study population.4-Questions have been raised concerning the increasing number of youth who seek professional care for GD,especially adolescent AFABs.

Synaptic Vesicle Protein2A Decreases in Amygdaloid-kindling Pharmcoresistant Epileptic Rats

Synaptic vesicle protein 2A（SV2A） involvement has been reported in the animal models of epilepsy and in human intractable epilepsy. The difference between pharmacosensitive epilepsy and pharmacoresistant epilepsy remains poorly understood. The present study aimed to observe the hippocampus SV2 A protein expression in amygdale-kindling pharmacoresistant epileptic rats. The pharmacosensitive epileptic rats served as control. Amygdaloid-kindling model of epilepsy was established in 100 healthy adult male Sprague-Dawley rats. The kindled rat model of epilepsy was used to select pharmacoresistance by testing their seizure response to phenytoin and phenobarbital. The selected pharmacoresistant rats were assigned to a pharmacoresistant epileptic group（PRE group）. Another 12 pharmacosensitive epileptic rats（PSE group） served as control. Immunohistochemistry,real-time PCR and Western blotting were used to determine SV2 A expression in the hippocampus tissue samples from both the PRE and the PSE rats. Immunohistochemistry staining showed that SV2 A was mainly accumulated in the cytoplasm of the neurons,as well as along their dendrites throughout all subfields of the hippocampus. Immunoreactive staining level of SV2A-positive cells was 0.483±0.304 in the PRE group and 0.866±0.090 in the PSE group（P〈0.05）. Real-time PCR analysis demonstrated that 2-ΔΔCt value of SV2 A m RNA was 0.30±0.43 in the PRE group and 0.76±0.18 in the PSE group（P〈0.05）. Western blotting analysis obtained the similar findings（0.27±0.21 versus 1.12±0.21,P〈0.05）. PRE rats displayed a significant decrease of SV2 A in the brain. SV2 A may be associated with the pathogenesis of intractable epilepsy of the amygdaloid-kindling rats.

~1H and ^(13)C NMR Assignments for Amlodipine and Risperidone

Aim To investigate the NMR spectroscopy of amlodipine and risperidone.Methods 1D NMR and 2D NMR experimental techniques of gCOSY, gHSQC and gHMBC were wsed. Results Theassignments of the ~1H and ^(13) C NMR data for the two drugs were performed and confirmed by theevidence of J_(HF) and J_(CF). Conclusion The structures of amlodipine and risperidone wereconfirmed by careful analysis of regular 1D and 2D NMR spectroscopy.

An Efficient Test Data Compression Technique Based on Codes

This paper presents a new test data compression/decompression method for SoC testing,called hybrid run length codes. The method makes a full analysis of the factors which influence test parameters：compression ratio,test application time, and area overhead. To improve the compression ratio, the new method is based on variable-to-variable run length codes,and a novel algorithm is proposed to reorder the test vectors and fill the unspecified bits in the pre-processing step. With a novel on-chip decoder, low test application time and low area overhead are obtained by hybrid run length codes. Finally, an experimental comparison on ISCAS 89 benchmark circuits validates the proposed method

ACS-based resource assignment and task scheduling in grid

To solve the deadlock problem of tasks that the interdependence between tasks fails to consider during the course of resource assignment and task scheduling based on the heuristics algorithm, an improved ant colony system （ACS） based algorithm is proposed. First, how to map the resource assignment and task scheduling （RATS） problem into the optimization selection problem of task resource assignment graph （TRAG） and to add the semaphore mechanism in the optimal TRAG to solve deadlocks are explained. Secondly, how to utilize the grid pheromone system model to realize the algorithm based on ACS is explicated. This refers to the construction of TRAG by the random selection of appropriate resources for each task by the user agent and the optimization of TRAG through the positive feedback and distributed parallel computing mechanism of the ACS. Simulation results show that the proposed algorithm is effective and efficient in solving the deadlock problem.