Effects of pooling,specialization,and discretionary task completion on queueing performance

Pooling,unpooling/specialization,and discretionary task completion are typical operational strategies in queueing systems that arise in healthcare,call centers,and online sales.These strategies may have advantages and disadvantages in different operational environments.This paper uses the M/M/1 and M/M/2 queues to study the impact of pooling,specialization,and discretionary task completion on the average queue length.Closed-form solutions for the average M/M/2 queue length are derived.Computational examples illustrate how the average queue length changes with the strength of pooling,specialization,and discretionary task completion.Finally,several conjectures are made in the paper.

Bayesian partial pooling to reduce uncertainty in overcoring rock stress estimation

The state of in situ stress is a crucial parameter in subsurface engineering,especially for critical projects like nuclear waste repository.As one of the two ISRM suggested methods,the overcoring(OC)method is widely used to estimate the full stress tensors in rocks by independent regression analysis of the data from each OC test.However,such customary independent analysis of individual OC tests,known as no pooling,is liable to yield unreliable test-specific stress estimates due to various uncertainty sources involved in the OC method.To address this problem,a practical and no-cost solution is considered by incorporating into OC data analysis additional information implied within adjacent OC tests,which are usually available in OC measurement campaigns.Hence,this paper presents a Bayesian partial pooling(hierarchical)model for combined analysis of adjacent OC tests.We performed five case studies using OC test data made at a nuclear waste repository research site of Sweden.The results demonstrate that partial pooling of adjacent OC tests indeed allows borrowing of information across adjacent tests,and yields improved stress tensor estimates with reduced uncertainties simultaneously for all individual tests than they are independently analysed as no pooling,particularly for those unreliable no pooling stress estimates.A further model comparison shows that the partial pooling model also gives better predictive performance,and thus confirms that the information borrowed across adjacent OC tests is relevant and effective.

A Pooling Method Developed for Use in Convolutional Neural Networks

In convolutional neural networks,pooling methods are used to reduce both the size of the data and the number of parameters after the convolution of the models.These methods reduce the computational amount of convolutional neural networks,making the neural network more efficient.Maximum pooling,average pooling,and minimum pooling methods are generally used in convolutional neural networks.However,these pooling methods are not suitable for all datasets used in neural network applications.In this study,a new pooling approach to the literature is proposed to increase the efficiency and success rates of convolutional neural networks.This method,which we call MAM(Maximum Average Minimum)pooling,is more interactive than other traditional maximum pooling,average pooling,and minimum pooling methods and reduces data loss by calculating the more appropriate pixel value.The proposed MAM pooling method increases the performance of the neural network by calculating the optimal value during the training of convolutional neural networks.To determine the success accuracy of the proposed MAM pooling method and compare it with other traditional pooling methods,training was carried out on the LeNet-5 model using CIFAR-10,CIFAR-100,and MNIST datasets.According to the results obtained,the proposed MAM pooling method performed better than the maximum pooling,average pooling,and minimum pooling methods in all pool sizes on three different datasets.

U-Net Based Dual-Pooling Segmentation of Bone Metastases in Thoracic SPECT Bone Scintigrams

In order to enhance the performance of the CNN-based segmentation models for bone metastases, this study proposes a segmentation method that integrates dual-pooling, DAC, and RMP modules. The network consists of distinct feature encoding and decoding stages, with dual-pooling modules employed in encoding stages to maintain the background information needed for bone scintigrams diagnosis. Both the DAC and RMP modules are utilized in the bottleneck layer to address the multi-scale problem of metastatic lesions. Experimental evaluations on 306 clinical SPECT data have demonstrated that the proposed method showcases a substantial improvement in both DSC and Recall scores by 3.28% and 6.55% compared the baseline. Exhaustive case studies illustrate the superiority of the methodology.

辛空间的排列问题及具有容错能力的pooling设计的紧界

该文利用辛空间上的子空间构造了一类新的d^z析取矩阵,然后研究了如下排列问题:对于给定的整数m,r,s,v,d,q和辛空间F_q^(2v)中的一个(m,s)型子空间S,这里v+s≥m>r≥2s-1≥1,d≥2,q是一个素数的幂,作者从S中找到d个(m-1,s-1)型子空间H_1,…H_d,使包含在这些(m-1,s-1)型子空间中的(r,s-1)型子空间个数达到最大.然后利用这个排列的有关结论,给出了一类pooling设计的紧界.

基于DNA pooling技术的全基因组关联研究筛选主动脉夹层等位基因遗传位点

目的:筛选与主动脉夹层发病机制相关的遗传易感基因。方法:从主动脉夹层患者(150例)及对照组(250例)外周血白细胞提取基因组DNA,采用DNA Pooling为基础的Illumina Human660W-Quad芯片扫描,筛选与主动脉夹层发病相关的遗传易感基因。结果:(1)对照组女性数量明显多于病例组(P<0.01);年龄、吸烟、高血压、糖尿病人数无统计学差异(P>0.05)。(2)遗传变异位点SNPrs2298491(位于TBCEL基因),SNP rs6080720(位于BFSP1基因),SNP rs7653410(位于SNTN基因),SNP rs2345106(位于COLQ基因)和位于ABCA13基因上的SNPrs4024044可能与主动脉夹层的发病有关。结论:SNPsrs2298491,rs6080720,rs7653410,rs2345106和rs4024044可能是主动脉夹层发病机制相关遗传变异位点的一部分。

利用伪辛子空间构造的一类pooling设计及其纠错能力的紧界分析

利用伪辛空间上的子空间构造了一类新的dz-析取矩阵,证明了它的析取性,讨论了反映dz-析取矩阵纠错能力z值的紧界.

奇特征正交空间上可检错Pooling设计的构作

Pooling设计的数学模型是一个d-disjunct矩阵.利用奇特征正交空间中全迷向子空间构作了d-disjunct矩阵,并通过计算它的Hamming距离分析了它的检纠错能力,根据Kautz-Singleton定理对d的范围作了估算.

奇异线性空间上的Pooling设计及其紧界(英文)

基于有限域上的奇异线性空间的两种不同形式的子空间,构造一族具有纠错能力的Pooling设计,讨论其析取性质,给出Pooling设计的紧界。

采用Pyrosequencing和Pooling技术对SNP位点多态性分析

目的建立基于pyrosequencing和Pooling技术进行SNP位点的法医学多态性分析技术。方法对50名无关个体样本建立一适合pyrosequencing检测的组池;采用PyroMark Assay Design 2.0软件进行SNP位点等位基因定量分析的引物设计;对组池样本PCR产物进行焦磷酸测序检测。结果检测的3个SNP位点多态性良好,其中位点rs220028与以往人群调查后频率数据无显著差异。结论采用pyrosequencing和Pooling技术对SNP位点进行多态性分析,适合于位点的初筛及大规模群体调查。该技术准确可靠,方便快捷。

特征不为2的正交空间上的一类Pooling设计的构作

非适应性群验在DNA序列筛选等方面有许多实际应用.构作容错和纠错能力强的pooling设计是非适应性群验的中心问题之一.利用正交空间上的一类(m,2s,s)型子空间构作了一个dz-析取矩阵,并证明了当d≤q+1时,z值是最佳的.

辛空间上可检错Pooling设计的讨论

利用辛空间上全迷向子空间的性质构作了Pooling设计的一种重要的数学模型d-disjunct矩阵并计算了它的Hamming距离,分析了它的检纠错能力,对d的范围作了估算。

可纠错pooling设计的一个构作

一个非时序性群试(NGT)算法在DNA筛选等领域都有重要应用,而NGT算法的一个数学模型是d-disjunct矩阵.通过Bd(δ**(n,d,k))的Hamming距离构作一个d-disjunct矩阵,其中δ**(n,d,k)是在δ(n,d,k)的基础上加上δc(n,,αk)构成的,1≤α≤m+1且α∈Z;证明了所构作的矩阵是可纠正1个错误、检测2个错误的d-disjunct矩阵.

A multivariate grey incidence model for different scale data based on spatial pyramid pooling

In order to solve the problem that existing multivariate grey incidence models cannot be applied to time series on different scales, a new model is proposed based on spatial pyramid pooling.Firstly, local features of multivariate time series on different scales are pooled and aggregated by spatial pyramid pooling to construct n levels feature pooling matrices on the same scale. Secondly,Deng's multivariate grey incidence model is introduced to measure the degree of incidence between feature pooling matrices at each level. Thirdly, grey incidence degrees at each level are integrated into a global incidence degree. Finally, the performance of the proposed model is verified on two data sets compared with a variety of algorithms. The results illustrate that the proposed model is more effective and efficient than other similarity measure algorithms.

Deep Rank-Based Average Pooling Network for Covid-19 Recognition

(Aim)To make a more accurate and precise COVID-19 diagnosis system,this study proposed a novel deep rank-based average pooling network(DRAPNet)model,i.e.,deep rank-based average pooling network,for COVID-19 recognition.(Methods)521 subjects yield 1164 slice images via the slice level selection method.All the 1164 slice images comprise four categories:COVID-19 positive;community-acquired pneumonia;second pulmonary tuberculosis;and healthy control.Our method firstly introduced an improved multiple-way data augmentation.Secondly,an n-conv rankbased average pooling module(NRAPM)was proposed in which rank-based pooling—particularly,rank-based average pooling(RAP)—was employed to avoid overfitting.Third,a novel DRAPNet was proposed based on NRAPM and inspired by the VGGnetwork.Grad-CAM was used to generate heatmaps and gave our AI model an explainable analysis.(Results)Our DRAPNet achieved a micro-averaged F1 score of 95.49%by 10 runs over the test set.The sensitivities of the four classes were 95.44%,96.07%,94.41%,and 96.07%,respectively.The precisions of four classes were 96.45%,95.22%,95.05%,and 95.28%,respectively.The F1 scores of the four classes were 95.94%,95.64%,94.73%,and 95.67%,respectively.Besides,the confusion matrix was given.(Conclusions)The DRAPNet is effective in diagnosing COVID-19 and other chest infectious diseases.The RAP gives better results than four other methods:strided convolution,l2-norm pooling,average pooling,and max pooling.

A Decision-Support System for the Car Pooling Problem

The continuous increase of human mobility combined with a relevant use of private vehicles contributes to increase the ill effects of vehicle externalities on the environment, e.g. high levels of air pollution, toxic emissions, noise pollution, and on the quality of life, e.g. parking problem, traffic congestion, and increase in the number of crashes and accidents. Transport demand management plays a very critical role in achieving greenhouse gas emission reduction targets. This study demonstrates that car pooling (CP) is an effective strategy to reduce transport volumes, transportation costs and related hill externalities in agreement with EU programs of emissions reduction targets. This paper presents an original approach to solve the CP problem. It is based on hierarchical clustering models, which have been adopted by an original decision support system (DSS). The DSS helps mobility managers to generate the pools and to design feasible paths for shared vehicles. A significant case studies and obtained results by the application of the proposed models are illustrated. They demonstrate the effectiveness of the approach and the supporting decisions tool.

Term-Based Pooling in Convolutional Neural Networks for Text Classification

To achieve good results in convolutional neural networks(CNN) for text classification task, term-based pooling operation in CNNs is proposed. Firstly, the convolution results of several convolution kernels are combined by this method, and then the results after combination are made pooling operation, three sorts of CNN models(we named TBCNN, MCT-CNN and MMCT-CNN respectively) are constructed and then corresponding algorithmic thought are detailed on this basis. Secondly, relevant experiments and analyses are respectively designed to show the effects of three key parameters(convolution kernel, combination kernel number and word embedding) on three kinds of CNN models and to further demonstrate the effect of the models proposed. The experimental results show that compared with the traditional method of text classification in CNNs, term-based pooling method is addressed that not only the availability of the way is proved, but also the performance shows good superiority.

Clinical features of multiple gastrointestinal stromal tumors:A pooling analysis combined with evidence and gap map

BACKGROUND Multiple gastrointestinal stromal tumors(MGISTs)are a very rare type of gastrointestinal stromal tumor(GIST)and are usually observed in syndrome.AIM The paper aimed to describe the clinical and oncological features of MGISTs and to offer evidence for the diagnosis and treatment.METHODS Data of consecutive patients with MGISTs who were diagnosed at Peking University People’s Hospital(PKUPH)from 2008 to 2019 were retrospectively evaluated.Further,a literature search was conducted by retrieving data from PubMed,EMBASE,and the Cochrane library databases from inception up to November 30,2019.RESULTS In all,12 patients were diagnosed with MGISTs at PKUPH,and 43 published records were ultimately included following the literature review.Combined analysis of the whole individual patient data showed that female(59.30%),young(14.45%),and syndromic GIST(63.95%)patients comprised a large proportion of the total patient population.Tumors were mainly located in the small intestine(58.92%),and both CD117 and CD34 were generally positive.After a mean 78.32-mo follow-up,the estimated median overall survival duration(11.5 years)was similar to single GISTs,but recurrence-free survival was relatively poorer.CONCLUSION The clinical and oncological features are potentially different between MGISTs and single GIST.Further studies are needed to explore appropriate surgical approach and adjuvant therapy.

Sentiment Classification Based on Piecewise Pooling Convolutional Neural Network

Recently,the effectiveness of neural networks,especially convolutional neural networks,has been validated in the field of natural language processing,in which,sentiment classification for online reviews is an important and challenging task.Existing convolutional neural networks extract important features of sentences without local features or the feature sequence.Thus,these models do not perform well,especially for transition sentences.To this end,we propose a Piecewise Pooling Convolutional Neural Network(PPCNN)for sentiment classification.Firstly,with a sentence presented by word vectors,convolution operation is introduced to obtain the convolution feature map vectors.Secondly,these vectors are segmented according to the positions of transition words in sentences.Thirdly,the most significant feature of each local segment is extracted using max pooling mechanism,and then the different aspects of features can be extracted.Specifically,the relative sequence of these features is preserved.Finally,after processed by the dropout algorithm,the softmax classifier is trained for sentiment classification.Experimental results show that the proposed method PPCNN is effective and superior to other baseline methods,especially for datasets with transition sentences.

Narrow Pooling Clothing Classification Based on Attention Mechanism

In recent years,with the rapid development of e-commerce,people need to classify the wide variety and a large number of clothing images appearing on e-commerce platforms.In order to solve the problems of long time consumption and unsatisfactory classification accuracy arising from the classification of a large number of clothing images,researchers have begun to exploit deep learning techniques instead of traditional learning methods.The paper explores the use of convolutional neural networks(CNNs)for feature learning to enhance global feature information interactions by adding an improved hybrid attention mechanism(HAM)that fully utilizes feature weights in three dimensions:channel,height,and width.Moreover,the improved pooling layer not only captures local feature information,but also fuses global and local information to improve the misclassification problem that occurs between similar categories.Experiments on the Fashion-MNIST and DeepFashion datasets show that the proposed method significantly improves the accuracy of clothing classification(93.62%and 67.9%)compared with residual network(ResNet)and convolutional block attention module(CBAM).