基于Count Sketch的预处理贪婪Kaczmarz方法

在贪婪Kaczmarz方法中,通过对系数矩阵进行正交三角分解引入右预处理子能够提高贪婪Kaczmarz方法的收敛速率。但在系数矩阵的行数远大于列数的情况下,正交三角分解的成本过高。为降低预处理的成本,通过引入Count Sketch变换,提出了基于Count Sketch的预处理贪婪Kaczmarz方法,并对新方法进行了收敛性分析。理论分析说明了新方法在系数矩阵条件数较大时比已有方法具有更好的收敛速率。数值实验验证了新方法的有效性。

基于LIWC文本情感分析的高校大学生二语写作研究

研究采用LIWC文本情感分析词典,对不同性别及不同外语写作水平的高校非英语专业大二学生的英语同题作文进行了情感分析。结果显示,在认知过程维度上,男性学习者认知参与度高于女性学习者,意愿、要求表达强烈,且逻辑性更强;不同性别的学生在第一人称代词使用上并未表现出显著差异;第一人称代词的使用与作文水平成正相关;不同性别及不同水平组的学生在积极和消极情绪表达上没有显著差异。

Lightweight Res-Connection Multi-Branch Network for Highly Accurate Crowd Counting and Localization

Crowd counting is a promising hotspot of computer vision involving crowd intelligence analysis,achieving tremendous success recently with the development of deep learning.However,there have been stillmany challenges including crowd multi-scale variations and high network complexity,etc.To tackle these issues,a lightweight Resconnection multi-branch network(LRMBNet)for highly accurate crowd counting and localization is proposed.Specifically,using improved ShuffleNet V2 as the backbone,a lightweight shallow extractor has been designed by employing the channel compression mechanism to reduce enormously the number of network parameters.A light multi-branch structure with different expansion rate convolutions is demonstrated to extract multi-scale features and enlarged receptive fields,where the information transmission and fusion of diverse scale features is enhanced via residual concatenation.In addition,a compound loss function is introduced for training themethod to improve global context information correlation.The proposed method is evaluated on the SHHA,SHHB,UCF-QNRF and UCF_CC_50 public datasets.The accuracy is better than those of many advanced approaches,while the number of parameters is smaller.The experimental results show that the proposed method achieves a good tradeoff between the complexity and accuracy of crowd counting,indicating a lightweight and high-precision method for crowd counting.

Deep Learning Based Efficient Crowd Counting System

Estimation of crowd count is becoming crucial nowadays,as it can help in security surveillance,crowd monitoring,and management for different events.It is challenging to determine the approximate crowd size from an image of the crowd’s density.Therefore in this research study,we proposed a multi-headed convolutional neural network architecture-based model for crowd counting,where we divided our proposed model into two main components:(i)the convolutional neural network,which extracts the feature across the whole image that is given to it as an input,and(ii)the multi-headed layers,which make it easier to evaluate density maps to estimate the number of people in the input image and determine their number in the crowd.We employed the available public benchmark crowd-counting datasets UCF CC 50 and ShanghaiTech parts A and B for model training and testing to validate the model’s performance.To analyze the results,we used two metrics Mean Absolute Error(MAE)and Mean Square Error(MSE),and compared the results of the proposed systems with the state-of-art models of crowd counting.The results show the superiority of the proposed system.

Rapid Detection of Somatic Cell Count Based on Hybrid Variable Selection Method

Somatic cell count detection is the daily work of dairy farms to monitor the health of cows.The feasibility of applying near-infrared spectroscopy to somatic cell count detection was researched in this paper.Milk samples with different somatic cell counts were collected and preprocessing methods were studied.Variable selection algorithm based on hybrid strategy and modelling method based on ensemble learning were explored for somatic cell count detection.Detection model was used to diagnose subclinical mastitis and the results showed that near-infrared spectroscopy could be a tool to realize rapid detection of somatic cell count in milk.

A multi-source information fusion layer counting method for penetration fuze based on TCN-LSTM

When employing penetration ammunition to strike multi-story buildings,the detection methods using acceleration sensors suffer from signal aliasing,while magnetic detection methods are susceptible to interference from ferromagnetic materials,thereby posing challenges in accurately determining the number of layers.To address this issue,this research proposes a layer counting method for penetration fuze that incorporates multi-source information fusion,utilizing both the temporal convolutional network(TCN)and the long short-term memory(LSTM)recurrent network.By leveraging the strengths of these two network structures,the method extracts temporal and high-dimensional features from the multi-source physical field during the penetration process,establishing a relationship between the multi-source physical field and the distance between the fuze and the target plate.A simulation model is developed to simulate the overload and magnetic field of a projectile penetrating multiple layers of target plates,capturing the multi-source physical field signals and their patterns during the penetration process.The analysis reveals that the proposed multi-source fusion layer counting method reduces errors by 60% and 50% compared to single overload layer counting and single magnetic anomaly signal layer counting,respectively.The model's predictive performance is evaluated under various operating conditions,including different ratios of added noise to random sample positions,penetration speeds,and spacing between target plates.The maximum errors in fuze penetration time predicted by the three modes are 0.08 ms,0.12 ms,and 0.16 ms,respectively,confirming the robustness of the proposed model.Moreover,the model's predictions indicate that the fitting degree for large interlayer spacings is superior to that for small interlayer spacings due to the influence of stress waves.

Diagnostic significance of complete blood cell count and hemogramderived markers for neonatal sepsis at Southwest Public Hospitals,Ethiopia

BACKGROUND Neonatal sepsis is defined as an infection-related condition characterized by signs and symptoms of bacteremia within the first month of life.It is the leading cause of mortality and morbidity among newborns.While several studies have been conducted in other parts of world to assess the usefulness of complete blood count parameters and hemogram-derived markers as early screening tools for neonatal sepsis,the associations between sepsis and its complications with these blood parameters are still being investigated in our setting and are not yet part of routine practice.AIM To evaluate the diagnostic significance of complete blood cell count hemogramderived novel markers for neonatal sepsis among neonates attending public hospitals in the southwest region of Oromia,Ethiopia,through a case control study.METHODS A case control study was conducted from October 2021 to October 2023 Sociodemographic,clinical history,and laboratory test results data were collected using structured questionnaires.The collected data were entered into Epi-data 3.1 version and exported to SPSS-25 for analysis.Chi-square,independent sample ttest,and receiver operator characteristics curve of curve were used for analysis.A P-value of less than 0.05 was considered statistically significant.RESULTS In this study,significant increases were observed in the following values in the case group compared to the control group:In white blood cell(WBC)count,neutrophils,monocyte,mean platelet volume(MPV),neutrophils to lymphocyte ratio,monocyte to lymphocyte ratio(MLR),red blood cell width to platelet count ratio(RPR),red blood width coefficient variation,MPV to RPR,and platelet to lymphocyte ratio.Regarding MLR,a cut-off value of≥0.26 was found,with a sensitivity of 68%,a specificity of 95%,a positive predictive value(PPV)of 93.2%,and a negative predictive value(NPV)of 74.8%.The area under the curve(AUC)was 0.828(P<0.001).For WBC,a cutoff value of≥11.42 was identified,with a sensitivity of 55%,a specificity of 89%,a PPV of 83.3%,and a NPV of 66.4%.The AUC was 0.81(P<0.001).Neutrophils had a sensitivity of 67%,a specificity of 81%,a PPV of 77.9%,and a NPV of 71.1%.The AUC was 0.801,with a cut-off value of≥6.76(P=0.001).These results indicate that they were excellent predictors of neonatal sepsis diagnosis.CONCLUSION The findings of our study suggest that certain hematological parameters and hemogram-derived markers may have a potential role in the diagnosis of neonatal sepsis.

A Study on the Correlation Between LIWC Word Categories and Chinese Composition Writing Performance of Fourth,Fifth,and Sixth Grade Students

This study focuses on the analysis of the Chinese composition writing performance of fourth,fifth,and sixth grade students in 16 selected schools in Longhua District,Shenzhen during the spring semester of 2023.Using LIWC(Linguistic Inquiry and Word Count)as a text analysis tool,the study explores the impact of LIWC categories on writing performance which is scaled by score.The results show that the simple LIWC word categories have a significant positive influence on the composition scores of lower-grade students;while complex LIWC word categories have a significant negative influence on the composition scores of lower-grade students but a significant positive influence on the composition scores of higher-grade students.Process word categories have a positive influence on the composition scores of all three grades,but the impact of complex process word categories increases as the grade level rises.

COUNT评分对于特发性肺纤维化病人肺移植早期预后的预测价值

目的:研究控制营养状态(COUNT)评分对于特发性肺纤维化(IPF)病人肺移植早期预后的预测价值。方法:回顾性收集在无锡市人民医院行肺移植术的154名IPF病人,收集术前资料包括人口统计学资料、术前合并症以及最后一次的实验室检查结果,术中以及术后并发症情况。使用ROC曲线评估COUNT评分及其他营养评估工具预测30 d生存情况的能力,使用Kaplan-Meier法绘制低COUNT组和高COUNT组生存曲线,Log-rank比较两组生存率差异。并使用COX回归分析IPF病人术后30d预后不良的独立危险因素。结果:根据COUNT评分划分,IPF病人术前合并营养不良的有101例(65.6%)。COUNT评分对于IPF肺移植病人早期30d预后不佳的预测能力要高于BMI、Alb、PNI指数。使用ROC确定的cutoff值为2.5划分高低COUNT组,高COUNT组30d、90d生存率低于低COUNT组(P<0.05)。并且高COUNT组入ICU前24hAPACHEⅡ评分更高,术后AKI发生率更高,术后机械通气时间延长、ECMO转流时间延长(P<0.05)。多因素COX回归分析提示高COUNT评分及肥胖是IPF病人肺移植术后30d预后不佳的独立危险因素。结论:COUNT评分是肺移植早期预后不佳的预测因素,在IPF病人肺移植术前进行营养评估至关重要。

Counting of alpha particle tracks on imaging plate based on a convolutional neural network

Imaging plates are widely used to detect alpha particles to track information,and the number of alpha particle tracks is affected by the overlapping and fading effects of the track information.In this study,an experiment and a simulation were used to calibrate the efficiency parameter of an imaging plate,which was used to calculate the grayscale.Images were created by using grayscale,which trained the convolutional neural network to count the alpha tracks.The results demonstrated that the trained convolutional neural network can evaluate the alpha track counts based on the source and background images with a wider linear range,which was unaffected by the overlapping effect.The alpha track counts were unaffected by the fading effect within 60 min,where the calibrated formula for the fading effect was analyzed for 132.7 min.The detection efficiency of the trained convolutional neural network for inhomogeneous ^(241)Am sources(2π emission)was 0.6050±0.0399,whereas the efficiency curve of the photo-stimulated luminescence method was lower than that of the trained convolutional neural network.

Accuracy Assessment and Guidelines for Manual Traffic Counts from Pre-Recorded Video Data

Traffic count is the fundamental data source for transportation planning, management, design, and effectiveness evaluation. Recording traffic flow and counting from the recorded videos are increasingly used due to convenience, high accuracy, and cost-effectiveness. Manual counting from pre-recorded video footage can be prone to inconsistencies and errors, leading to inaccurate counts. Besides, there are no standard guidelines for collecting video data and conducting manual counts from the recorded videos. This paper aims to comprehensively assess the accuracy of manual counts from pre-recorded videos and introduces guidelines for efficiently collecting video data and conducting manual counts by trained individuals. The accuracy assessment of the manual counts was conducted based on repeated counts, and the guidelines were provided from the experience of conducting a traffic survey on forty strip mall access points in Baton Rouge, Louisiana, USA. The percentage of total error, classification error, and interval error were found to be 1.05 percent, 1.08 percent, and 1.29 percent, respectively. Besides, the percent root mean square errors (RMSE) were found to be 1.13 percent, 1.21 percent, and 1.48 percent, respectively. Guidelines were provided for selecting survey sites, instruments and timeframe, fieldwork, and manual counts for an efficient traffic data collection survey.

Udder Health Status of First Parity Dairy Cows in Early-Lactation Based on Somatic Cell Count Categories

The main aim of this study was to investigate the prevalence of intramammary infection (IMI) in early-lactation of primiparous cows using milk recording cow composite somatic cell count (CSCC) categories (combining the first 2 milk recording results after calving). Another aim was to evaluate the milk urea (MU) content as a potential supplementary indicator to SCC or CSCC for the identification of IMI in primiparous cows after calving. This retrospective observational study was conducted on records of test-day of primiparous cows over a period of 6 years (January 2016 to December 2021. The SCC data for 158 Holstein Friesian primiparous cows, with their first milk recording 5 to 35 days after calving and their second milk recording 28 to 56 days in milk (DIM), were identified. Each primiparous cow was assigned a CSCC category (low-low, low-high, high-low or high-high) based on the CSCC at the first 2 milking recordings using the following cut-offs: ≤150,000 cells/ml (low), >150,000 cells/ml (high). The association between CSCC categories and MV content was analyzed using correlation models. At the first milk recording, a proportion of 63.29% was in the low SCC category, and the rest (36.71%) was in the high SCC category. At the second milk recording, a proportion of primiparous cows in CSCC categories was 59.49%, 3.80%, 27.85% and 8.86% in low-low, low-high, high-low and high-high, respectively. At the second milk recording, a proportion of 12.66% of primiparous cows was in the high CSCC category and a proportion of 87.34% of primiparous cows was in the low CSCC category, indicating a poor and a good udder health, respectively. The association of SCC with MU content in low and in high SCC categories at the first milk recording was positive and moderate (+0.49) and negative and strong (-0.97), respectively. The association of CSCC categories with MU contents at the second milk recording was inconclusive. We concluded that CSCC categories may be a useful tool for identifying success and problems regarding the udder health of primiparous cows in early lactation.

RPNet: Rice plant counting after tillering stage based on plant attention and multiple supervision network

Rice is a major food crop and is planted worldwide. Climatic deterioration, population growth, farmland shrinkage, and other factors have necessitated the application of cutting-edge technology to achieve accurate and efficient rice production. In this study, we mainly focus on the precise counting of rice plants in paddy field and design a novel deep learning network, RPNet, consisting of four modules: feature encoder, attention block, initial density map generator, and attention map generator. Additionally, we propose a novel loss function called RPloss. This loss function considers the magnitude relationship between different sub-loss functions and ensures the validity of the designed network. To verify the proposed method, we conducted experiments on our recently presented URC dataset, which is an unmanned aerial vehicle dataset that is quite challenged at counting rice plants. For experimental comparison, we chose some popular or recently proposed counting methods, namely MCNN, CSRNet, SANet, TasselNetV2, and FIDTM. In the experiment, the mean absolute error(MAE), root mean squared error(RMSE), relative MAE(rMAE) and relative RMSE(rRMSE) of the proposed RPNet were 8.3, 11.2, 1.2% and 1.6%, respectively,for the URC dataset. RPNet surpasses state-of-the-art methods in plant counting. To verify the universality of the proposed method, we conducted experiments on the well-know MTC and WED datasets. The final results on these datasets showed that our network achieved the best results compared with excellent previous approaches. The experiments showed that the proposed RPNet can be utilized to count rice plants in paddy fields and replace traditional methods.

Lightweight Fish Bait Particle Counting Method Based on Pruning and Shift Quantization

In the process of aquaculture,monitoring the number of fish bait particles is of great significance to improve the growth and welfare of fish.Although the counting method based on onvolutional neural network(CNN)achieve good accuracy and applicability,it has a high amount of parameters and computation,which limit the deployment on resource-constrained hardware devices.In order to solve the above problems,this paper proposes a lightweight bait particle counting method based on shift quantization and model pruning strategies.Firstly,we take corresponding lightweight strategies for different layers to flexibly balance the counting accuracy and performance of the model.In order to deeply lighten the counting model,the redundant and less informative weights of the model are removed through the combination of model quantization and pruning.The experimental results show that the compression rate is nearly 9 times.Finally,the quantization candidate value is refined by introducing a power-of-two addition term,which improves the matches of the weight distribution.By analyzing the experimental results,the counting loss at 3 bit is reduced by 35.31%.In summary,the lightweight bait particle counting model proposed in this paper achieves lossless counting accuracy and reduces the storage and computational overhead required for running convolutional neural networks.

Deep learning method for cell count from transmitted-light microscope

Automatic cell counting provides an effective tool for medical research and diagnosis.Currently,cell counting can be completed by transmitted-light microscope,however,it requires expert knowledge and the counting accuracy which is unsatisfied for overlapped cells.Further,the image-translation-based detection method has been proposed and the potential has been shown to accomplish cell counting from transmitted-light microscope,automatically and effectively.In this work,a new deep-learning(DL)-based two-stage detection method(cGAN-YOLO)is designed to further enhance the performance of cell counting,which is achieved by combining a DL-based fluorescent image translation model and a DL-based cell detection model.The various results show that cGAN-YOLO can effectively detect and count some different types of cells from the acquired transmitted-light microscope images.Compared with the previously reported YOLO-based one-stage detection method,high recognition accuracy(RA)is achieved by the cGAN-YOLO method,with an improvement of 29.80%.Furthermore,we can also observe that cGAN-YOLO obtains an improvement of 12.11%in RA compared with the previously reported image-translation-based detection method.In a word,cGAN-YOLO makes it possible to implement cell counting directly from the experimental acquired transmitted-light microscopy images with high flexibility and performance,which extends the applicability in clinical research.

Blood Count Characteristics of Children Hospitalized in General Pediatrics at the Gabriel Touré Teaching Hospital

Objective: The main objective was to study the disturbances of the Blood Count of children hospitalized in the general pediatric of the Gabriel Touré teaching hospital. Methods: This was a prospective and descriptive study conducted from September 1 to November 30, 2018 in the general pediatrics department of the Gabriel Touré teaching hospital in Bamako. Data were collected on patient records using a survey sheet. Results: We collected 512 files of children out of 1030 admissions during the study period;the rate of completion of the blood count is 50%. The male sex was predominant with a sex ratio of 1.3. The majority of patients were under 5 years old (58%). The majority of fathers (56%) and mothers (64%) of children had no education;they are mainly farmers (61%) and housewives (88%). Pallor was the reason for consultation in 29% of patients and present in 60%. On blood count, anemia was present in 92% of patients, half of whom (50%) had severe anemia with a hemoglobin level below 7 g/dL. The anemia was mainly microcytic (72%) and hypochromic (66%). Hyperleukocytosis (62%), eosinophilia (68%) and basophilia (58%) were the abnormalities observed in the white line. Thrombocytopenia accounted for 40%. Severe malaria (53%) was the main discharge diagnosis and almost all patients (99%) were alive at discharge. Conclusion: The characteristics of anemia require a study of the complete blood count in healthy children with dosage of serum iron and ferritin for a better understanding of the phenomenon.

A new software for automated counting of glistenings in intraocular lenses in vivo

AIM:To assess the performance of a bespoke software for automated counting of intraocular lens(IOL)glistenings in slit-lamp images.METHODS:IOL glistenings from slit-lamp-derived digital images were counted manually and automatically by the bespoke software.The images of one randomly selected eye from each of 34 participants were used as a training set to determine the threshold setting that gave the best agreement between manual and automatic grading.A second set of 63 images,selected using randomised stratified sampling from 290 images,were used for software validation.The images were obtained using a previously described protocol.Software-derived automated glistenings counts were compared to manual counts produced by three ophthalmologists.RESULTS:A threshold value of 140 was determined that minimised the total deviation in the number of glistenings for the 34 images in the training set.Using this threshold value,only slight agreement was found between automated software counts and manual expert counts for the validating set of 63 images(κ=0.104,95%CI,0.040-0.168).Ten images(15.9%)had glistenings counts that agreed between the software and manual counting.There were 49 images(77.8%)where the software overestimated the number of glistenings.CONCLUSION:The low levels of agreement show between an initial release of software used to automatically count glistenings in in vivo slit-lamp images and manual counting indicates that this is a non-trivial application.Iterative improvement involving a dialogue between software developers and experienced ophthalmologists is required to optimise agreement.The results suggest that validation of software is necessary for studies involving semi-automatic evaluation of glistenings.

A Computer Vision-Based System for Metal Sheet Pick Counting

Inventory counting is crucial to manufacturing industries in terms of inventory management,production,and procurement planning.Many companies currently require workers to manually count and track the status of materials,which are repetitive and non-value-added activities but incur significant costs to the companies as well as mental fatigue to the employees.This research aims to develop a computer vision system that can automate the material counting activity without applying any marker on the material.The type of material of interest is metal sheet,whose shape is simple,a large rectangular shape,yet difficult to detect.The use of computer vision technology can reduce the costs incurred fromthe loss of high-value materials,eliminate repetitive work requirements for skilled labor,and reduce human error.A computer vision system is proposed and tested on a metal sheet picking process formultiple metal sheet stacks in the storage area by using one video camera.Our results show that the proposed computer vision system can count the metal sheet picks under a real situation with a precision of 97.83%and a recall of 100%.

A Deep Learning-Based Crowd Counting Method and System Implementation on Neural Processing Unit Platform

In this paper, a deep learning-based method is proposed for crowdcountingproblems. Specifically, by utilizing the convolution kernel densitymap, the ground truth is generated dynamically to enhance the featureextractingability of the generator model. Meanwhile, the “cross stage partial”module is integrated into congested scene recognition network (CSRNet) toobtain a lightweight network model. In addition, to compensate for the accuracydrop owing to the lightweight model, we take advantage of “structuredknowledge transfer” to train the model in an end-to-end manner. It aimsto accelerate the fitting speed and enhance the learning ability of the studentmodel. The crowd-counting system solution for edge computing is alsoproposed and implemented on an embedded device equipped with a neuralprocessing unit. Simulations demonstrate the performance improvement ofthe proposed solution in terms of model size, processing speed and accuracy.The performance on the Venice dataset shows that the mean absolute error(MAE) and the root mean squared error (RMSE) of our model drop by32.63% and 39.18% compared with CSRNet. Meanwhile, the performance onthe ShanghaiTech PartB dataset reveals that the MAE and the RMSE of ourmodel are close to those of CSRNet. Therefore, we provide a novel embeddedplatform system scheme for public safety pre-warning applications.

Improvement of Counting Sorting Algorithm

By analyzing the internal features of counting sorting algorithm. Two improvements of counting sorting algorithms are proposed, which have a wide range of applications and better efficiency than the original counting sort while maintaining the original stability. Compared with the original counting sort, it has a wider scope of application and better time and space efficiency. In addition, the accuracy of the above conclusions can be proved by a large amount of experimental data.