Distributed Stochastic Optimization with Compression for Non-Strongly Convex Objectives

We are investigating the distributed optimization problem,where a network of nodes works together to minimize a global objective that is a finite sum of their stored local functions.Since nodes exchange optimization parameters through the wireless network,large-scale training models can create communication bottlenecks,resulting in slower training times.To address this issue,CHOCO-SGD was proposed,which allows compressing information with arbitrary precision without reducing the convergence rate for strongly convex objective functions.Nevertheless,most convex functions are not strongly convex(such as logistic regression or Lasso),which raises the question of whether this algorithm can be applied to non-strongly convex functions.In this paper,we provide the first theoretical analysis of the convergence rate of CHOCO-SGD on non-strongly convex objectives.We derive a sufficient condition,which limits the fidelity of compression,to guarantee convergence.Moreover,our analysis demonstrates that within the fidelity threshold,this algorithm can significantly reduce transmission burden while maintaining the same convergence rate order as its no-compression equivalent.Numerical experiments further validate the theoretical findings by demonstrating that CHOCO-SGD improves communication efficiency and keeps the same convergence rate order simultaneously.And experiments also show that the algorithm fails to converge with low compression fidelity and in time-varying topologies.Overall,our study offers valuable insights into the potential applicability of CHOCO-SGD for non-strongly convex objectives.Additionally,we provide practical guidelines for researchers seeking to utilize this algorithm in real-world scenarios.

Effect of low-degree astigmatism on the objective visual quality

AIM:To evaluate the effect of low-degree astigmatism on objective visual quality through the Optical Quality Analysis System(OQAS).METHODS:This study enrolled 46 participants(aged 23 to 30y,90 eyes)with normal or corrected-to-normal vision.The cylindrical lenses(0,0.5,0.75,1.0,and 1.25 D)were placed at the axial direction(180°,45°,90°,and 135°)in front of the eyes with the best correction to form 16 types of regular low-degree astigmatism.OQAS was used to detect the objective visual quality,recorded as the objective scattering index(OSI),OQAS values at contrasts of 100%,20%,and 9%predictive visual acuity(OV100%,OV20%,and OV9%),modulation transfer function cut-off(MTFcut-off)and Strehl ratio(SR).The mixed effect linear model was used to compare objective visual quality differences between groups and examine associations between astigmatic magnitude and objective visual quality parameters.RESULTS:Apparent negative relationships between the magnitude of low astigmatism and objective visual quality were observed.The increase of OSI per degree of astigmatism at 180°,45°,90°,and 135°axis were 0.38(95%CI:0.35,0.42),0.50(95%CI:0.46,0.53),0.49(95%CI:0.45,0.54)and 0.37(95%CI:0.34,0.41),respectively.The decrease of MTFcut-off per degree of astigmatism at 180°,45°,90°,and 135°axis were-10.30(95%CI:-11.43,-9.16),-12.73(95%CI:-13.62,-11.86),-12.75(95%CI:-13.79,-11.70),and-9.97(95%CI:-10.92,-9.03),respectively.At the same astigmatism degree,OSI at 45°and 90°axis were higher than that at 0°and 135°axis,while MTFcut-off were lower.CONCLUSION:Low astigmatism of only 0.50 D can significantly reduce the objective visual quality.

Objective Model Selection in Physics: Exploring the Finite Information Quantity Approach

Traditional methods for selecting models in experimental data analysis are susceptible to researcher bias, hindering exploration of alternative explanations and potentially leading to overfitting. The Finite Information Quantity (FIQ) approach offers a novel solution by acknowledging the inherent limitations in information processing capacity of physical systems. This framework facilitates the development of objective criteria for model selection (comparative uncertainty) and paves the way for a more comprehensive understanding of phenomena through exploring diverse explanations. This work presents a detailed comparison of the FIQ approach with ten established model selection methods, highlighting the advantages and limitations of each. We demonstrate the potential of FIQ to enhance the objectivity and robustness of scientific inquiry through three practical examples: selecting appropriate models for measuring fundamental constants, sound velocity, and underwater electrical discharges. Further research is warranted to explore the full applicability of FIQ across various scientific disciplines.

Eclipse计划系统MU Objective工具在鼻咽癌容积调强放射治疗计划中的应用研究

目的:探讨Varian Eclipse计划系统MU Objective工具在鼻咽癌容积调强放射治疗(VMAT)计划中的应用,旨在保证治疗计划质量的同时降低治疗计划的机器跳数(MU)。方法:选取已接受容积调强放射治疗技术的21例鼻咽癌患者,在未使用MU Objective工具的情况下给每例患者设计治疗参考计划(MU_(ref));将使用MU Objective工具对MU_(ref)进行重新优化,分别设置MU_(ref)的MU的30%、50%、70%、90%和120%,分别命名为MU_(30%)、MU_(50%)、MU_(70%)、MU_(90%)和MU_(120%)。将治疗计划MU_(30%)、MU_(50%)、MU_(70%)、MU_(90%)和MU_(120%)分别与MU_(ref)进行配对t检验统计分析,比较两种治疗计划的靶区、危及器官(OAR)剂量分布和计划的MU。结果:使用MU Objective工具对参考计划优化MU后,与参考计划相比部分靶区和OAR剂量体积参数差异虽有统计学意义,但无临床意义。治疗计划参数为MU_(30%)、MU_(50%)、MU_(70%)和MU_(90%)时,与MU_(ref)比较计划MU平均分别减少21.5%、19.5%、16.6%和8%,差异有统计学意义(t=9.652,t=8.843,t=8.963,t=11.323;P<0.05)。在MU_(120%)时,治疗计划MU平均增加1.7%,但未显著提高靶区剂量覆盖。结论:对于鼻咽癌VMAT计划,使用Eclipse计划系统的MU Objective工具在减少计划MU的同时可获得与参考计划相似的剂量分布。MU Objective工具可作为鼻咽癌或其他部位VMAT计划的常规使用工具,以保证计划质量同时减少放射治疗计划的MU。

Objective Identification and Climatic Characteristics of Heavy-Precipitation Northeastern China Cold Vortexes

The northeastern China cold vortex(NCCV)plays an important role in regional rainstorms over East Asia.Using the National Centers for Environmental Prediction Final reanalysis dataset and the Global Precipitation Measurement product,an objective algorithm for identifying heavy-precipitation NCCV(HPCV)events was designed,and the climatological features of 164 HPCV events from 2001 to 2019 were investigated.The number of HPCV events showed an upward linear trend,with the highest frequency of occurrence in summer.The most active region of HPCV samples was the Northeast China Plain between 40°–55°N.Most HPCV events lasted 3–5 days and had radii ranging from 250 to 1000 km.The duration of HPCV events with larger sizes was longer.About half of the HPCV events moved into(moved out of)the definition region(35°–60°N,115°–145°E),and half initiated(dissipated)within the region.The initial position was close to the western boundary of the definition region,and the final position was mainly near the eastern boundary.The locations associated with the precipitation were mostly concentrated within 2000 km southeast of the HPCV systems,and they were farther from the center in the cold season than in the warm season.

Multi‐objective particle swarm optimisation of complex product change plan considering service performance

Design change is an inevitable part of the product development process.This study proposes an improved binary multi‐objective PSO algorithm guided by problem char-acteristics(P‐BMOPSO)to solve the optimisation problem of complex product change plan considering service performance.Firstly,a complex product multi‐layer network with service performance is established for the first time to reveal the impact of change effect propagation on the product service performance.Secondly,the concept of service performance impact(SPI)is defined by decoupling the impact of strongly associated nodes on the service performance in the process of change affect propagation.Then,a triple‐objective selection model of change nodes is established,which includes the three indicators:SPI degree,change cost,and change time.Furthermore,an integer multi‐objective particle swarm optimisation algorithm guided by problem characteristics is developed to solve the model above.Experimental results on the design change problem of a certain type of Skyworth TV verify the effectiveness of the established optimisation model and the proposed P‐BMOPSO algorithm.

Multi-Objective Optimization with Artificial Neural Network Based Robust Paddy Yield Prediction Model

Agriculture plays a vital role in the food production process that occupies nearly one-third of the total surface of the earth.Rice is propagated from the seeds of paddy and it is a stable food almost used byfifty percent of the total world population.The extensive growth of the human population alarms us to ensure food security and the country should take proper food steps to improve the yield of food grains.This paper concentrates on improving the yield of paddy by predicting the factors that influence the growth of paddy with the help of Evolutionary Computation Techniques.Most of the researchers used to relay on historical records of meteorological parameters to predict the yield of paddy.There is a lack in analyzing the day to day impact of meteorological parameters such as direction of wind,relative humidity,Instant Wind Speed in paddy cultivation.The real time meteorological data collected and analysis the impact of weather parameters from the day of paddy sowing to till the last day of paddy harvesting with regular time series.A Robust Optimized Artificial Neural Network(ROANN)Algorithm with Genetic Algorithm(GA)and Multi Objective Particle Swarm Optimization Algorithm(MOPSO)proposed to predict the factors that to be concentrated by farmers to improve the paddy yield in cultivation.A real time paddy data collected from farmers of Tamilnadu and the meteorological parameters were matched with the cropping pattern of the farmers to construct the database.The input parameters were optimized either by using GA or MOPSO optimization algorithms to reconstruct the database.Reconstructed database optimized by using Artificial Neural Network Back Propagation Algorithm.The reason for improving the growth of paddy was identified using the output of the Neural Network.Performance metrics such as Accuracy,Error Rate etc were used to measure the performance of the proposed algorithm.Comparative analysis made between ANN with GA and ANN with MOPSO to identify the recommendations for improving the paddy yield.

Salient Object Detection Based on a Novel Combination Framework Using the Perceptual Matching and Subjective-Objective Mapping Technologies

The integrity and fineness characterization of non-connected regions and contours is a major challenge for existing salient object detection.The key to address is how to make full use of the subjective and objective structural information obtained in different steps.Therefore,by simulating the human visual mechanism,this paper proposes a novel multi-decoder matching correction network and subjective structural loss.Specifically,the loss pays different attentions to the foreground,boundary,and background of ground truth map in a top-down structure.And the perceived saliency is mapped to the corresponding objective structure of the prediction map,which is extracted in a bottom-up manner.Thus,multi-level salient features can be effectively detected with the loss as constraint.And then,through the mapping of improved binary cross entropy loss,the differences between salient regions and objects are checked to pay attention to the error prone region to achieve excellent error sensitivity.Finally,through tracking the identifying feature horizontally and vertically,the subjective and objective interaction is maximized.Extensive experiments on five benchmark datasets demonstrate that compared with 12 state-of-the-art methods,the algorithm has higher recall and precision,less error and strong robustness and generalization ability,and can predict complete and refined saliency maps.

Overview of multi-objective optimization methods

To assist readers to have a comprehensive understanding, the classical and intelligent methods roundly based on precursory research achievements are summarized in this paper. First, basic conception and description about multi-objective (MO) optimization are introduced. Then some definitions and related terminologies are given. Furthermore several MO optimization methods including classical and current intelligent methods are discussed one by one succinctly. Finally evaluations on advantages and disadvantages about these methods are made at the end of the paper.

基于Stingray Objective Studio的防空C^3I显控系统界面开发

文中提出了用 Stingray Objective Studio 工具集来进行防空显控系统界面开发,使得开发人员将大量的时间和精力用于系统结构和数据处理算法上,这样不光降低了开发难度,也增强了系统的功能和稳定性。

China English——An Objective Existence as a Performance Variety

China English is an objective existence as a performance variety used in China.It is based on standard English and possesses Chinese characteristics.The Chinese characteristics of China English are reflected in the phonology,vocabulary,syntax,rhetoric and text,particularly in the vocabulary.This essay will discuss the features of China English from the following three aspects: the development of English in China;the comparison between China English,Pidgin English and Chinese English and the Chinese words borrowed by English.

Multi-objective route planning approach for timely searching tasks of a supervised robot

To performance efficient searching for an operator-supervised mobile robot, a multiple objectives route planning approach is proposed considering timeliness and path cost. An improved fitness function for route planning is proposed based on the multi-objective genetic algorithm （MOGA） for multiple objectives traveling salesman problem （MOTSP）. Then, the path between two route nodes is generated based on the heuristic path planning method A ＊. A simplified timeliness function for route nodes is proposed to represent the timeliness of each node. Based on the proposed timeliness function, experiments are conducted using the proposed two-stage planning method. The experimental results show that the proposed MOGA with improved fitness function can perform the searching function well when the timeliness of the searching task needs to be taken into consideration.

Closed-loop supply chain network equilibrium strategy model with environmental protection objectives

With the increasing popularity of ecological civilization and sustainable development,enterprises should consider environmental protection measures in their operations in addition to pursue their economic interests.This paper establsihes a closed-loop supply chain network model composed of multiple suppliers,manufacturers,retailers,recyclers,and demand markets—regarding their dual goals of the profit maximization and the minimization of carbon emissions.The conditions necessary for establishing overall equilibrium and an equilibrium model of the entire closed-loop supply chain network are determined by applying variational inequality and dual theory.A modified projection contraction algorithm is used to design a model-solving program.Finally,using numerical examples,the paper conducts a comparative static analysis on important parameters such as the weight coefficients of environmental protection objectives and consumers'awareness of low-carbon environmental protection and attains some beneficial enlightenment on management.The results indicate that when the environmental protection objectives of a certain type of enterprise increases,both the economic benefits and environmental protection performance will improve;when the environmental protection objectives of all enterprises increases simultaneously,environmental protection performance improves significantly,but the changes in economic benefits of different enterprises are inconsistent and profit coordination is more complex.Although consumers’awareness of low-carbon preference could improve environmental performance,it reduces the overall profits of network members and the entire closed-loop supply chain network as a whole.The above conclusions can be used as a reference for the government in designing low-carbon environmental protection policy and in closed-loop supply chain research.

An Objective Penalty Functions Algorithm for Multiobjective Optimization Problem

By using the penalty function method with objective parameters, the paper presents an interactive algorithm to solve the inequality constrained multi-objective programming (MP). The MP is transformed into a single objective optimal problem (SOOP) with inequality constrains;and it is proved that, under some conditions, an optimal solution to SOOP is a Pareto efficient solution to MP. Then, an interactive algorithm of MP is designed accordingly. Numerical examples show that the algorithm can find a satisfactory solution to MP with objective weight value adjusted by decision maker.

A Penalty Function Algorithm with Objective Parameters and Constraint Penalty Parameter for Multi-Objective Programming

In this paper, we present an algorithm to solve the inequality constrained multi-objective programming (MP) by using a penalty function with objective parameters and constraint penalty parameter. First, the penalty function with objective parameters and constraint penalty parameter for MP and the corresponding unconstraint penalty optimization problem (UPOP) is defined. Under some conditions, a Pareto efficient solution (or a weakly-efficient solution) to UPOP is proved to be a Pareto efficient solution (or a weakly-efficient solution) to MP. The penalty function is proved to be exact under a stable condition. Then, we design an algorithm to solve MP and prove its convergence. Finally, numerical examples show that the algorithm may help decision makers to find a satisfactory solution to MP.

Sediment distribution pattern mapped from the combination of objective analysis and geostatistics in the large shallow Taihu Lake, China

Investigation was made into sediment depth at 723 irregularly scattered measurement points which cover all the regions in Taihu Lake, China. The combination of successive correction scheme and geostatistical method was used to get all the values of recent sediment thickness at the 69×69 grids in the whole lake. The results showed that there is the significant difference in sediment depth between the eastern area and the western region, and most of the sediments are located in the western shore-line and northern regimes but just a little in the center and eastern parts. The notable exception is the patch between the center and Xishan Island where the maximum sediment depth is more than 4.0 m. This sediment distribution pattern is more than likely related to the current circulation pattern induced by the prevailing wind-forcing in Taihu Lake. The numerical simulation of hydrodynamics can strong support the conclusion. Sediment effects on water quality was also studied and the results showed that the concentrations of TP, TN and SS in the western part are obviously larger than those in the eastern regime, which suggested that more nutrients can be released from thicker sediment areas.

Retrieval Single-Doppler Radar Wind with Variational Assimilation Method-Part I: Objective Selection of Functional Weighting Factors

In variational problem, the selection of functional weighting factors (FWF) is one of the key points for discussing many relevant studies. To overcome arbitrariness and subjectivity of the empirical selecting methods used widely at present, this paper tries to put forward an optimal objective selecting method of FWF. The focus of the study is on the weighting factors optimal selection in the variation retrieval single-Doppler radar wind field with the simple adjoint models. Weighting factors in the meaning of minimal variance are calculated out with the matrix theory and the finite difference method of partial differential equation. Experiments show that the result is more objective comparing with the factors obtained with the empirical method.

Multi Objective Multireservoir Optimization in Fuzzy Environment for River Sub Basin Development and Management

In this paper, a multi objective, multireservoir operation model is proposed using Genetic algorithm (GA) under fuzzy environment. A monthly Multi Objective Genetic Algorithm Fuzzy Optimization (MOGAFU-OPT) model for the present study is developed in ‘C’ Language. The GA parameters i.e. population size, number of generations, crossover probability, and mutation probability are decided based on optimized val-ues of fitness function. The GA operators adopted are stochastic remainder selection, one point crossover and binary mutation. Initially the model is run for maximization of irrigation releases. Then the model is run for maximization of hydropower production. These objectives are fuzzified by assuming a linear membership function. These fuzzified objectives are simultaneously maximized by defining level of satisfaction (?) and then maximizing it. This approach is applied to a multireservoir system in Godavari river sub basin in Ma-harashtra State, India. Problem is formulated with 4 reservoirs and a barrage. The optimal operation policy for maximization of irrigation releases, maximization of hydropower production and maximization of level of satisfaction is presented for existing demand in command area. This optimal operation policy so deter-mined is compared with the actual average operation policy for Jayakwadi Stage-I reservoir.

OBJECTIVE ANALYSIS OVER INDIAN REGION BY BRATSETH'SSUCCESSIVE CORRECTION SCHEME

Bratseth (1986) has proposed a successive correction scheme for numerical analysis where the solution converges towards the solution obtained by optimum interpolation. This paper presents the results of some experiments carried out over indian reston during summer monsoon period using this Bratseth’s method. The convergence of the method is investigated.

Selection Method of Multi-Objective Problems Using Genetic Algorithm in Motion Plan of AUV

To research the effect of the selection method of multi-objects genetic algorithm problem on optimizing result, thismethod is analyzed theoretically and discussed by using an autonomous underwater vehicle(AUV) as an object. A changingweight vtlue method is put forward and a selection formula is modified. Some experiments were implemented on an AUV.TwinBurger. The results shows that this method is effective and feasible.