The rise of unstructured data retrieval in the AI era has created an urgent need for vector databases that manage high-dimensional vector embeddings and provide efficient vector search capabilities for AI applications. Performance, elasticity, and isolation are the key factors for vector databases to serve modern AI applications effectively. Disaggregation of storage and compute is widely recognized as the most effective approach in both academia and industry. Existing work either redesigns specialized vector databases according to the disaggregated architecture or integrates vector search into generalized databases that already use this architecture. However, challenges still remain in building elastic and efficient vector search systems within the disaggregated architecture, such as higher data fetching latency and the highly stateful nature of vector index, which hinder the system's ability to simultaneously achieve high performance, high elasticity and resource isolation. Additionally, a recent trend has emerged to integrate vector search into general-purpose databases, yet the extensibility and generality of integration methodologies have not been systematically studied. In this paper, we present BlendHouse, a cloud-native and generalized vector database system built on top of the disaggregated storage and computation architecture. BlendHouse achieves high performance, high elasticity and resource isolation simultaneously via a suite of optimizations specific to the vector search workload regarding the disaggregated architecture and the relational database. Experimental results demonstrate that BlendHouse outperforms Milvus and pgvector in terms of read and write performance. The integration methodology illustrated in this paper is extensible and general, paving the way for more powerful data management systems in the AI era.

One of the central tasks of genome research is to predict phenotypes and discover some important gene biomarkers. However, there are three main problems in analyzing genomics data to predict phenotypes and gene marker selection. Such as large p and small n, low reproducibility of the selected biomarkers, and high noise. To provide a unified solution to alleviate the problems as mentioned above, we propose a self-paced learning L 1 / 2 ${{\rm{L}}}_{1/2}$ absolute network-based logistic regression model, called SLNL. Through the L 1 / 2 ${L}_{1/2}$ regularization, the model can get a more sparse result, which provides better interpretability. The absolute network-based penalty enables the model to integrate the feature network knowledge and helps select higher reproducibility genes. Moreover, this proposed penalty overcomes the drawback of a traditional network penalty without considering the sign of the coefficient. By the self-paced learning strategy, the model can now consider the noise level in gene expression data, lower the impact of high noise samples in data to model training, and provide better prediction accuracy. We compare the proposed method with six alternative approaches in various experimental scenarios, including a comprehensive simulation, four benchmark gene expression datasets, one lung cancer data set, and three lung cancer validation sets. Results show that SLNL can identify fewer meaningful biomarkers and obtain the best or equivalent prediction performance. Moreover, biological analysis shows that the genes selected by the SLNL might be helpful to tumor diagnosis and treatment.

In genome research, it is a fundamental issue to identify few but important survival-related biomarkers. The Cox model is a widely used survival analysis technique, which is used to study the relationship between characteristics and survival response. However, limitations of the existing Cox methods for genomic data are as follows: (1) a typical gene expression data set consists of tens of thousands of genes, and the result of current methods may not be sparse enough; (2) a wealth of structural information about many biological processes, such as regulatory networks and pathways, has often been ignored; (3) genomic data is usually considered as high noise, which is usually ignored in current methods. To alleviate the above problems, in this paper, we study a novel sparse Cox regression model, called SPLSN, which combines self-paced learning (SPL) and a log-sum absolute network-based penalty (Logsum-Net), especially for biomarker selection in survival analysis. SPL is embedded in curriculum design, and the model is trained by gradually increasing samples from low noise to high noise during the training process. The Logsum-Net encourages smoothness among the coefficients of adjacent genes on a specific biological network. We compare the proposed method with five alternative approaches in various experimental scenarios, including a comprehensive simulation, seven benchmark gene expression data sets, and one large validation data set. Results show that the SPLSN can identify fewer meaningful biomarkers and obtain the best or equivalent prediction performance. Moreover, the biological analysis shows that the genes selected by the SPLSN might be helpful to tumor treatment.

Most modern multi-object tracking (MOT) systems for videos follow the tracking-by-detection paradigm, where objects of interest are first located in each frame then associated correspondingly to form their intact trajectories. In this setting, the appearance features of objects usually provide the most important cues for data association, but it is very susceptible to occlusions, illumination variations, and inaccurate detections, thus easily resulting in incorrect trajectories. To address this issue, in this study we propose to make full use of the neighboring information. Our motivations derive from the observations that people tend to move in a group. As such, when an individual target's appearance is remarkably changed, the observer can still identify it with its neighbor context. To model the contextual information from neighbors, we first utilize the spatiotemporal relations among trajectories to efficiently select suitable neighbors for targets. Subsequently, we construct neighbor graph for each target and corresponding neighbors then employ the graph convolutional networks (GCNs) to model their relations and learn the graph features. To the best of our knowledge, it is the first time to explicitly leverage neighbor cues via GCN in MOT. Finally, standardized evaluations on the MOT16 and MOT17 data sets demonstrate that our approach can remarkably reduce the identity switches whilst achieve state-of-the-art overall performance.

Mobile edge caching scheme (MECS) can determine where, how, and what to cache on user equipment by employing its own storage. When considering the performance of MECS, it is often full of uncertainty. The q-rung orthopair fuzzy set (q-ROFS), characterized by membership and nonmembership degrees with adjustable parameter q, is quite a high-efficiency way to capture uncertainty. In this paper, first, information measure (entropy, distance measure, and similarity measure)-based area difference under the q-rung orthopair fuzzy (q-ROF) circumstance is studied along with their detailed proofs. Then, we present a comprehensive weight-determination method by combining objective weights (determining by entropy) and subjective weights (given by experts) as combined weights, which can effectually alleviate the unconscionable influence of extreme data on evaluation results and simultaneously reflect objective data and subjective emotion. Moreover, q-ROF score function-based distance measure is presented for dealing with a value comparison problem. Later, q-ROF multicriteria decision-making (MCDM) method called total area based on orthogonal vector (TAOV) is introduced. Moreover, its feasibility is illustrated by MECS selection problem. Finally, a comparison of some existing MCDM methods and the proposed method is constructed for displaying their effectiveness. This proposed method can effectively avoid counterintuitive phenomena, eliminate antilogarithm by negative and zero issue, and has no division by zero issue.

In the process of online shopping, consumers usually compare the review information of the same product in different e-commerce platforms. The sentiment orientation of online reviews from different platforms interactively influences on consumers’ purchase decision. However, due to the limitation of the ability to process information manually, it is difficult for a consumer to accurately identify the sentiment orientation of all reviews one by one and describe the process of their interactive influence. To this end, we proposed an online shopping support model using deep-learning–based opinion mining and q-rung orthopair fuzzy interaction weighted Heronian mean (q-ROFIWHM) operators. First, in the proposed method, the deep-learning model is used to automatically extract different product attribute words and opinion words from online reviews, and match the corresponding attribute-opinion pairs; meanwhile, the sentiment dictionary is used to calculate sentiment orientation, including positive, negative, and neutral sentiments. Second, the proportions of the three kinds of sentiments about each attribute of the same product are calculated. According to the proportion value of attribute sentiment from different platforms, the sentiment information is converted into multiple cross-decision matrices, which are represented by the q-rung orthopair fuzzy set. Third, considering the interactive characteristics of decision matrix, the q-ROFIWHM operators are proposed to aggregate this cross-decision information, and then the ranking result was determined by score function to support consumers' purchase decisions. Finally, an actual example of mobile phone purchase is given to verify the rationality of the proposed method, and the sensitivity and the comparison analysis are used to show its effectiveness and superiority.

The assessment of classroom teaching quality is critically important for producing a positive incentive and guidance role to improve service and management of universities, stimulating the enthusiasm of teachers, enhancing the teacher’s teaching ability, and improving the quality of talent training. In considering the case of teaching quality evaluation, the essential question that arises concerns strong ambiguity, fuzziness, and inexactness. The q-rung orthopair fuzzy sets ( q-ROFSs) dealing the indeterminacy characterized by membership degrees and nonmembership degrees are a more flexible and effective way to capture indeterminacy. In this paper, firstly, the new score function for q-rung orthopair fuzzy number is initiated for tackling the comparison problem. Subsequently, a new distance measure for q-ROFSs with multiple parameters is studied along with their detailed proofs. The various desirable properties among the developed similarity measures and distance measures have also been derived. Then, the objective weights of various attributes are determined via antientropy weighting method. Also, we develop the combined weights, which can reveal both the subjective information and the objective information. Moreover, two algorithms to solve q-rung orthopair fuzzy decision-making problem by combinative distance-based assessment and multiparametric similarity measure are presented. Later, the feasibility of approaches is demonstrated by a classroom teaching quality problem, along with the effect of the different parameters on the ordering. Finally, a comparison between the proposed and the existing decision-making methods has been performed for showing their effectiveness. The salient features of the proposed methods, compared to the existing q-ROFS decision-making methods, are as follows: (a) it can obtain the optimal alternative without counterintuitive phenomena and (b) it has a lower computational complexity.

With the intensification of global warming trends, the frequent occurrence of natural disasters has brought severe challenges to the sustainable development of society. Emergency decision-making (EDM) in natural disasters is playing an increasingly important role in improving disaster response capacity. In the case of EDM evaluation, the essential problem arises serious incompleteness, impreciseness, subjectivity, and incertitude. The q-rung orthopair fuzzy set (q-ROFS), disposing the indeterminacy portrayed by membership and nonmembership with the sum of qth power of them, is a more viable and effective means to seize indeterminacy. The aim of paper is to present a new score function of q-rung orthopair fuzzy number (q-ROFN) for solving the failure problems when comparing two q-ROFNs. Firstly, we introduce some basic set operations for q-ROFS. The properties of these operations are also discussed in detail. Later, we propose a q-rung orthopair fuzzy decision-making method based on weighted distance-based approximation (WDBA), in which the weights of decision-makers are obtained from a nonliner optimization model according to the deviation-based method. Finally, some examples are investigated to illustrate the feasibility and validity of the proposed approach. The salient features of the proposed method, compared to the existing q-rung orthopair fuzzy decision-making methods, are as follows: (a) it can obtain the optimal alternative without counterintuitive phenomena and (b) it has a great power in distinguishing the optimal alternative.

The q-rung orthopair fuzzy set (q-ROFS), originally developed by Yager, is more capable than that of Pythagorean fuzzy set to deal uncertainty in real life. The main goal of this paper is to investigate the relationship between the distance measure, the similarity measure, the entropy, and the inclusion measure for q-ROFSs. The primary purpose of the study is to develop the systematic transformation of information measures (distance measure, similarity measure, entropy, and inclusion measure) for q-ROFSs. For obtaining this goal, some new formulae for information measures of q-ROFSs are presented. To show the validity of the explored similarity measure, we apply it to pattern recognition, clustering analysis, and medical diagnosis. Some illustrative examples are given to support the findings, and also demonstrate their practicality and availability of similarity measure between q-ROFSs.

q-Rung orthopair fuzzy set (q-ROFS) is a powerful tool that attracts the attention of many scholars in dealing with uncertainty and vagueness. The aim of paper is to present a new score function of q-rung orthopair fuzzy number (q-ROFN) for solving the failure problems when comparing two q-ROFNs. Then a new exponential operational law about q-ROFNs is defined, in which the bases are positive real numbers and the exponents are q-ROFNs. Meanwhile, some properties of the operational law are investigated. Later, we apply them to derive the q-rung orthopair fuzzy weighted exponential aggregation operator. Additionally, an approach for multicriteria decision-making problems under the q-rung orthopair fuzzy data is explored by applying proposed aggregation operator. Finally, an example is investigated to illustrate the feasibility and validity of the proposed approach. The salient features of the proposed method, compared to the existing q-rung orthopair fuzzy decision-making methods, are (1) it can obtain the optimal alternative without counterintuitive phenomena; (2) it has a great power in distinguishing the optimal alternative.

In this paper, we initiate a new axiomatic definition of Pythagorean fuzzy distance measure, which is expressed by Pythagorean fuzzy number that will reduce the information loss and remain more original information. Then, the objective weights of various criteria are determined via grey system theory. Combining objective weights with subjective weights, we present the combined weights, which can reflect both the subjective considerations of the decision maker and the objective information. Meanwhile, a novel score function is proposed. Later, we present two algorithms to solve stochastic multicriteria decision making problem, which takes prospect preference and regret aversion of decision makers into consideration in the decision process. Finally, the effectiveness and feasibility of approach is demonstrated by a numerical example.

Pythagorean fuzzy set (PFS), originally proposed by Yager, is more capable than intuitionistic fuzzy set (IFS) to handle vagueness in the real world. The main purpose of this paper is to investigate the relationship between the distance measure, the similarity measure, the entropy, and the inclusion measure for PFSs. The primary goal of the study is to suggest the systematic transformation of information measures (distance measure, similarity measure, entropy, inclusion measure) for PFSs. For achieving this goal, some new formulae for information measures of PFSs are introduced. To show the efficiency of the proposed similarity measure, we apply it to pattern recognition, clustering analysis, and medical diagnosis. Some illustrative examples are given to support the findings and also demonstrate their practicality and effectiveness of similarity measure between PFSs.

In this paper, we investigate the multiple attribute group decision making (MAGDM) problems with interval-valued Pythagorean fuzzy sets (IVPFSs). First, the concept, operational laws, score function, and accuracy function of IVPFSs are defined. Then, based on the operational laws, two interval-valued Pythagorean fuzzy aggregation operators are developed for aggregating the interval-valued Pythagorean fuzzy information, such as interval-valued Pythagorean fuzzy weighted average (IVPFWA) operator and interval-valued Pythagorean fuzzy weighted geometric (IVPFWG) operator. A series of inequalities of aggregation operators are studied. Later, we develop some interval-valued Pythagorean fuzzy point operators. Moreover, combining the interval-valued Pythagorean fuzzy point operators with IVPFWA operator, we present some interval-valued Pythagorean fuzzy point weighted averaging (IVPFPWA) operators, which can adjust the degree of the aggregated arguments with some parameters. Then, we propose an interval-valued Pythagorean fuzzy ELECTRE method to solve uncertainty MAGDM problem. Finally, an illustrative example for evaluating the software developments is given to verify the developed approach and to demonstrate its practicality and effectiveness.

In this paper, we define the Choquet integral operator for Pythagorean fuzzy aggregation operators, such as Pythagorean fuzzy Choquet integral average (PFCIA) operator and Pythagorean fuzzy Choquet integral geometric (PFCIG) operator. The operators not only consider the importance of the elements or their ordered positions but also can reflect the correlations among the elements or their ordered positions. It is worth pointing out that most of the existing Pythagorean fuzzy aggregation operators are special cases of our operators. Meanwhile, some basic properties are discussed in detail. Later, we propose two approaches to multiple attribute group decision making with attributes involving dependent and independent by the PFCIA operator and multi-attributive border approximation area comparison (MABAC) in Pythagorean fuzzy environment. Finally, two illustrative examples have also been taken in the present study to verify the developed approaches and to demonstrate their practicality and effectiveness.

Pythagorean fuzzy sets (PFSs), originally proposed by Yager (Yager, Abbasov. Int J Intell Syst 2013;28:436–452), are a new tool to deal with vagueness considering the membership grades are pairs satisfying the condition . As a generalized set, PFSs have close relationship with intuitionistic fuzzy sets (IFSs). PFSs can be reduced to IFSs satisfying the condition . However, the related operations of PFSs do not take different conditions into consideration. To better understand PFSs, we propose two operations: division and subtraction, and discuss their properties in detail. Then, based on Pythagorean fuzzy aggregation operators, their properties such as boundedness, idempotency, and monotonicity are investigated. Later, we develop a Pythagorean fuzzy superiority and inferiority ranking method to solve uncertainty multiple attribute group decision making problem. Finally, an illustrative example for evaluating the Internet stocks performance is given to verify the developed approach and to demonstrate its practicality and effectiveness.