Refined Sparse Representation Based Similar Category Image Retrieval

Given one specific image,it would be quite significant if humanity could simply retrieve all those pictures that fall into a similar category of images.However,traditional methods are inclined to achieve high-quality retrieval by utilizing adequate learning instances,ignoring the extraction of the image’s essential information which leads to difficulty in the retrieval of similar category images just using one reference image.Aiming to solve this problem above,we proposed in this paper one refined sparse representation based similar category image retrieval model.On the one hand,saliency detection and multi-level decomposition could contribute to taking salient and spatial information into consideration more fully in the future.On the other hand,the cross mutual sparse coding model aims to extract the image’s essential feature to the maximumextent possible.At last,we set up a database concluding a large number of multi-source images.Adequate groups of comparative experiments show that our method could contribute to retrieving similar category images effectively.Moreover,adequate groups of ablation experiments show that nearly all procedures play their roles,respectively.

A Hybrid Ensemble Learning Approach Utilizing Light Gradient Boosting Machine and Category Boosting Model for Lifestyle-Based Prediction of Type-II Diabetes Mellitus

Addressing classification and prediction challenges, tree ensemble models have gained significant importance. Boosting ensemble techniques are commonly employed for forecasting Type-II diabetes mellitus. Light Gradient Boosting Machine (LightGBM) is a widely used algorithm known for its leaf growth strategy, loss reduction, and enhanced training precision. However, LightGBM is prone to overfitting. In contrast, CatBoost utilizes balanced base predictors known as decision tables, which mitigate overfitting risks and significantly improve testing time efficiency. CatBoost’s algorithm structure counteracts gradient boosting biases and incorporates an overfitting detector to stop training early. This study focuses on developing a hybrid model that combines LightGBM and CatBoost to minimize overfitting and improve accuracy by reducing variance. For the purpose of finding the best hyperparameters to use with the underlying learners, the Bayesian hyperparameter optimization method is used. By fine-tuning the regularization parameter values, the hybrid model effectively reduces variance (overfitting). Comparative evaluation against LightGBM, CatBoost, XGBoost, Decision Tree, Random Forest, AdaBoost, and GBM algorithms demonstrates that the hybrid model has the best F1-score (99.37%), recall (99.25%), and accuracy (99.37%). Consequently, the proposed framework holds promise for early diabetes prediction in the healthcare industry and exhibits potential applicability to other datasets sharing similarities with diabetes.

End-to-end aspect category sentiment analysis based on type graph convolutional networks

For the existing aspect category sentiment analysis research,most of the aspects are given for sentiment extraction,and this pipeline method is prone to error accumulation,and the use of graph convolutional neural network for aspect category sentiment analysis does not fully utilize the dependency type information between words,so it cannot enhance feature extraction.This paper proposes an end-to-end aspect category sentiment analysis(ETESA)model based on type graph convolutional networks.The model uses the bidirectional encoder representation from transformers(BERT)pretraining model to obtain aspect categories and word vectors containing contextual dynamic semantic information,which can solve the problem of polysemy;when using graph convolutional network(GCN)for feature extraction,the fusion operation of word vectors and initialization tensor of dependency types can obtain the importance values of different dependency types and enhance the text feature representation;by transforming aspect category and sentiment pair extraction into multiple single-label classification problems,aspect category and sentiment can be extracted simultaneously in an end-to-end way and solve the problem of error accumulation.Experiments are tested on three public datasets,and the results show that the ETESA model can achieve higher Precision,Recall and F1 value,proving the effectiveness of the model.

Asterix Category 033协议的分析与应用

介绍了Asterix Category 033协议的数据项组成、编码的内容以及在监视应用中的关键作用,研究了该协议的CRC编码方式以及报文的组装方式,提出了每个数据项的内容在监视终端和地面接收处理基站中的处理方法。所研究和分析成果为Asterix Category 033协议生成与解析报文提供了依据。

基于特征加权的Category ART网络及应用

特征加权是特征选择的一般情况,它能更加细致地区分特征对结果影响的程度,往往能够获得比特征选择更好的或者至少相等的性能。该文采用自适应遗传算法来优化Category ART网络的特征权值,提出了一种改进的Category ART网络FWART。在UCI标准数据集上的实验表明,FWART网络获得了比Category ART网络更好的泛化能力。将该网络应用在地震震型预报上,取得了很好的预报效果。

Asterix Category021协议的应用与分析

介绍了Asterix Category 021协议的数据项内容、用户应用程序框架(UAP)的结构、报文数据块的布局,以及ADS-B系统应用该协议的方式,分析了该协议的实例应用和协议报文的解析,提出了处理报文的思路。研究与分析结果可为如何使用Asterix Category 021协议生成与解析报文提供参考。

基于分类技术的搜索引擎排名算法——CategoryRank

提出了一种基于分类技术的搜索引擎新排名算法CategoryRank。该算法能够借助类别信息,更加准确地计算网页的排名得分,提高搜索引擎排名的准确性。算法基于任意两个网页之间的类别信息,对链接图进行了分析和计算,并且与PageRank等算法进行相比,该算法能够更加准确地模拟用户浏览网页的习惯。同时针对Web中的每个网页,算法计算出它的类别属性,直接体现了该页面针对不同用户的重要程度。最后,把该算法的离线模型和在线模型统一起来,阐明了算法在搜索引擎排名中的运行机制。

A Study of English Neologisms in 2015 Based on the Prototype Category Theory

With the development of science and technology, large quantities of neologisms are created. Neologisms are the result of people's conceptualization of new things based on their life experiences. Cognitive linguistics views that categorization of organisms and concrete objects are based on the prototype category. This paper takes 167 English Neologisms from Wordspy in2015 as the data for analysis and finds that these English Neologisms are characterized by family resemblances, degree of membership in category and boundary fuzziness from the view of prototype category theory. This study also has some implications for the research of English language teaching and learning.

Monoidal Category Approach to Dual Hom-quasi-Hopf Algebras

In this paper, we categorify a Hom-associative algebra by imposing the Homassociative law up to some isomorphisms on the multiplication map and requiring that these isomorphisms satisfy the Pentagon axiom, and obtain a 2-Hom-associative algebra. On the other hand, we introduce the dual Hom-quasi-Hopf algebra and show that any dual Homquasi-Hopf algebras can be viewed as a 2-Hom-associative algebra.

Stable t-structures and Homotopy Category of Strongly Copure Projective Modules

In this paper, we study the homotopy category of unbounded complexes of strongly copure projective modules with bounded relative homologies K;（SCP）.We show that the existence of a right recollement of K;（SCP） with respect to K;（SCP）, K;（SCP） and K;（SCP） has the homotopy category of strongly copure projective acyclic complexes as a triangulated subcategory in some case.

ADS-B Category 021报文解析软件的设计与实现

由于ADS-B设备相比雷达和遥测遥控具有低成本、小型化和通用性的优点,且能够对长航时、远距离的飞行目标进行监控,文章规划了Category 021报文的引入方案,对Category 021报文格式进行分析,重点设计开发了Category 021报文解析软件,通过接收并处理ADS-B地面站传送的目标航迹数据,实现了ADS-B信号中合作目标航迹数据的提取与显示,并进行了测试验证。测试结果表明:该方案能有效地实现对飞行目标的快速跟踪监视,增强了对飞行目标的监控手段。

Equivalence of Dynamicity and Balance of Category in “INCEPTION”

"INCEPTION" has become a blockbuster recently and this word reflects the phenomenon:meaning expanding in a specific context,which,as a matter of fact,lives in many fields such as literature,films and speeches.Such words or phrases have both surface and deep meanings whose translation research is mainly placed on literal or literary transfer,leaving out their dynamicity and balance in meaning category.This paper aims to give a brief analysis on such phenomenon and puts forward to a new translation principle:category equivalence in dynamicity and balance.

Research on Category of Maritime English Vocabulary

Through the research on the maritime English vocabulary, the author analyzes the category of Maritime English vocabulary, stresses an important application of Maritime English vocabulary in maritime field.

THE TWO FUNCTORS IN THE FUZZY MODULAR CATEGORY

After defining Hom(chi (A), eta (B)) and chi (A) circle times eta (B) in the fuzzy modular category Fm, the sufficient conditions of the existence for exact Hom functors Hom(delta (M),), and Hom(, delta (M)), as well as exact Tensor functors delta (M)circle times and circle times delta (M) are given in this paper. Finally the weak isomorphisms relations between Horn functors and Tensor functors are displayed.

Relationship between Category of Weight and Threshold of Maximum Oxidation of Lipids in Congolese Judoists

Background: One of the goals for training in judo is to mimic real situations. In subsaharian countries;there is a lack of knowledge considering relation between category of weight and threshold maximum oxidation of lipids. Objectives: The main objectives of this study were to analyze the effect of category of weight on lipoxmax and to identify the zones of target intensities corresponding to lipoxmax. Materials and Methods:Twenty four male well-training judoists, divided in two groups according to weight, were evaluated during calorimetry effort’ test. For this situation, cardiorespiratory parameters (HR, VO2, VO2max), blood parameter (lactatemia), anthropometric variables and zones of target corresponding to lipox were evaluated. For statistical analysis, Shapiro-Wilks test was used to compare all the data and differences were confirmed by one-way variance. Results: No significant differences were observed among two groups for target intensities to lipox. The judoists of group 1 were aged to 24.1 ± 5.9 years with 1.78 ± 0.07 m versus 20.4 ± 1.6 years with 1.86 ± 0.04 m. Primary results indicated higher intensities for lipoxmax. Also, values of power (% LDC) and VO2 and were significantly higher in the judoists for group 2, with respectively: 45.3 ± 10.2 and 46.4 ± 8.6 ml/min versus 38.2 ± 7.2 and 31.3 ± 7.6 ml/min. Conclusion: High weight in judoists induces high intensities of lipoxmax and change oxidation of lipids markers following training.

Do unemployment and payor category impact length of stay and hospital charges of spine fusion patients?

Introduction: In this paper we investigate the possible connection between socioeconomic status as demarcated by employment and in-surance status and consumption of healthcare resources in spine surgery patients. Methods: The clinical records of 1599 spine surgery pa-tients counted from 2008-2009 were reviewed. The largest groups of patients belonged to MS-DRG 460 (N = 585) and to MS-DRG 473 (N = 700). These two MS-DRG patient groups were used as the study cohort representing patients who, by definition, did not have serious comor-bidities or complications. Results: Unemployed non-cervical patients tended to stay on average 1.8 days longer in hospital and had on average $5800 higher hospital charges. No major differ-ences were noted in length of stay and hospital cost between government and private insurance patients. However, self-pay non-cervical fusion patients had notable increases in length of stay and hospital cost, especially in the >39 and <60 age group with the difference in length of stay amounting to 5 days and in hospital charges to $10,000. Univariate analysis with DRG (460 or 473) as a covariate showed significant impact from employment status on length of stay (F = 4.259, P = 0.014) and less significant impact from payor category on hospital charges (F = 2.229, P = 0.064) in the economically-productive 40 -59 age group. Conclusions: In general, no increase in hospital resource consumption was noted except among self-pay patients, the same group seemingly least able to afford expensive healthcare.

Using Category Theory to Explore and Model Label Event Structures

The development of a concurrent system poses unique challenges, especially those related to correctness and consistency, as such a system usually involves several interactive processes executing simultaneously. To deal with some of these challenges, we resorted to Labeled Event Structures (LES) and category theory as the formal methods to model concurrent systems. Specifically, in this paper, we proposed an idea to define categories and corresponding constructs, such as product and sum, to model events and relationships among events represented by LES. To explain the idea, several examples are developed. Though a mathematical proof, the proposed idea helped to build a correct-by-construction approach for formalizing LES models of concurrent systems.

Category of Attractor and Its Application

In this paper, we provide a new approach to study the geometry of attractor. By applying category, we investigate the relationship between attractor and its attraction basin. In a complete metric space, we prove that the categories of attractor and its attraction basin are always equal. Then we apply this result to both autonomous and non-autonomous systems, and obtain a number of corresponding results.

Fuzzy Sets and Rough Sets:a Comparison through an Extension of Obtulowicz's Category

This paper is a purely mathematical one dealing with a common possible foundation of Fuzzy Set Theory and Rough Set Theory.It begins with a generalization of Obtulowicz's paper,rough sets and Heyting algebra valued sets,published in Bull.Polish Acad Sc.(math),198.In this paper Obtulowicz proposes a special subcategory

“Opinion Editorial”category attracts more attention

Opinion Editorial(Op-Ed)was introduced in 2017 as a new category of Advances in Polar Science(APS),and is attracting the attention of the international polar community.One scientist from School of Environment,Education and Development,the University of Manchester gave the comments that this is a most welcome initiative.