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Machine learning models always make a prediction, even when it is likely to be inaccurate. This behavior should be avoided in many decision support applications, where mistakes can have severe consequences. Albeit already studied in 1970, machine learning with rejection recently gained interest. This machine learning subfield enables machine learning models to abstain from making a prediction when likely to make a mistake. This survey aims to provide an overview on machine learning with rejection. We introduce the conditions leading to two types of rejection, ambiguity and novelty rejection, which we carefully formalize. Moreover, we review and categorize strategies to evaluate a model’s predictive and rejective quality. Additionally, we define the existing architectures for models with rejection and describe the standard techniques for learning such models. Finally, we provide examples of relevant application domains and show how machine learning with rejection relates to other machine learning research areas.

Mixed Integer Linear Programming (MILP) is a pillar of mathematical optimization that offers a powerful modeling language for a wide range of applications. The main engine for solving MILPs is the branch-and-bound algorithm. Adding to the enormous algorithmic progress in MILP solving of the past decades, in more recent years there has been an explosive development in the use of machine learning for enhancing all main tasks involved in the branch-and-bound algorithm. These include primal heuristics, branching, cutting planes, node selection and solver configuration decisions. This article presents a survey of such approaches, addressing the vision of integration of machine learning and mathematical optimization as complementary technologies, and how this integration can benefit MILP solving. In particular, we give detailed attention to machine learning algorithms that automatically optimize some metric of branch-and-bound efficiency. We also address appropriate MILP representations, benchmarks and software tools used in the context of applying learning algorithms.

Data mining is able to discover the laws and fixed patterns of data in complex data, which has the advantages that traditional data analysis methods do not have, and has been applied to the analysis of musical works in a large number of applications. Firstly, the historical publication data of vocal works is organized, which is used to outline the historical evolution stages and trends of vocal works. The collected information on Chinese vocal works was analyzed with CiteSpace, and multiple vocal works were clustered into five categories based on the theme keywords, and 11 cluster labels were delineated on the basis of the word frequency results. The timeline mapping results show that the creation of Chinese vocal works can be categorised into three periods: the period of development, the period of prosperity, and the period of adjustment. Finally, based on the different periods, it can be summarized that the historical evolution of Chinese musical works is characterized by diversification of themes, singing styles, and musical styles.

We consider optimal sequential allocation in the context of the so-called stochastic multi-armed bandit model. We describe a generic index policy, in the sense of Gittins [J. R. Stat. Soc. Ser. B Stat. Methodol. 41 (1979) 148—177], based on upper confidence bounds of the arm payoffs computed using the Kullback—Leibler divergence. We consider two classes of distributions for which instances of this general idea are analyzed: the kl-UCB algorithm is designed for one-parameter exponential families and the empirical KL-UCB algorithm for bounded and finitely supported distributions. Our main contribution is a unified finite-time analysis of the regret of these algorithms that asymptotically matches the lower bounds of Lai and Robbins [Adv. in Appl. Math. 6 (1985) 4—22] and Burnetas and Katehakis [Adv. in Appl. Math. 17 (1996) 122—142], respectively. We also investigate the behavior of these algorithms when used with general bounded rewards, showing in particular that they provide significant improvements over the state-of-the-art.

To understand the deep learning model, the author proposed the research of interactive texture replacement of cartoon characters. For image segmentation, if you want to fill a cartoon without any texture in detail, or replace the unsatisfied texture area, first, we need to separate the filled or replaced area from the cartoon. For this reason, the traditional image segmentation algorithm has been carefully studied and analyzed, and the author chooses the Graphcut texture synthesis algorithm, the algorithm is parallelized and improved, and the innovative point of lighting customization is proposed based on the original algorithm, which can affect the synthesis effect according to the input lighting image. In terms of timeliness and synthesis effect, the Graphcut algorithm has been improved. Experimental results show that the algorithm can maintain the brightness distribution of the original cartoon and the practicability and efficiency of the algorithm proposed by the author.

Recently, there has been emerging interest in constructing reproducing kernel Banach spaces (RKBS) for applied and theoretical purposes such as machine learning, sampling reconstruction, sparse approximation and functional analysis. Existing constructions include the reflexive RKBS via a bilinear form, the semi-inner-product RKBS, the RKBS with ℓ 1 norm, the p -norm RKBS via generalized Mercer kernels, etc. The definitions of RKBS and the associated reproducing kernel in those references are dependent on the construction. Moreover, relations among those constructions are unclear. We explore a generic definition of RKBS and the reproducing kernel for RKBS that is independent of construction. Furthermore, we propose a framework of constructing RKBSs that leads to new RKBSs based on Orlicz spaces and unifies existing constructions mentioned above via a continuous bilinear form and a pair of feature maps. Finally, we develop representer theorems for machine learning in RKBSs constructed in our framework, which also unifies representer theorems in existing RKBSs.

Artificial intelligence technology is increasingly being employed within educational contexts, markedly enhancing the dynamics of instruction through the integration of intelligent algorithms. This study endeavors to revitalize the BOPPPS model in blended college English teaching by amalgamating it with the Bayesian knowledge tracking model and a reinforcement learning algorithm. The objective is to establish a refined, blended teaching framework based on the BOPPPS model and to investigate its efficacy along with the variables that influence its outcomes. The findings affirm a positive correlation between the effectiveness of blended English teaching at the university level and the student's cognitive abilities, skillsets, and affective attitudes, with all p-values demonstrating significance at the 0.001 level. Comparative analysis of pre-and post-test data from the blended teaching experiment revealed no initial significant differences between the experimental and control groups across six dimensions of English proficiency and five dimensions of interest in learning English. However, post-experiment results indicated substantial enhancements in the experimental group's overall English scores, listening, reading comprehension, writing, translation, and speaking abilities relative to the control group, with p-values falling below 0.05. Additionally, the p-values for the paired sample tests concerning the dimensions of interest in learning English in both groups were also below 0.05. These results suggest that blended English teaching at the university level not only significantly boosts students’ performance in English but also augments their interest in the language. This study underscores the potential of integrated teaching models that incorporate AI-driven algorithms to significantly enhance educational outcomes.

In this paper, the synthesis of ginsenoside Rh2 was catalyzed by using heterologous expression of Bacillus subtilis SL-44 glycosyltransferase. The synthesis parameters of ginsenoside Rh2 were optimized by the selection of strains and chemical supplies, the establishment of kinetic equations for the respiration rate of UGT enzyme, the effect of storage temperature on the model, and the glycosylation reaction of ginsenoside PPD with UGT. The effect of Rh2 saturation on the thermal denaturation temperature of the protein was analyzed along with the kinetic properties of the enzyme GE02773 (GE03484) while varying the saturation of Rh2. The results showed that the conversion of ginsenoside Rh2 reached 84% at a temperature of 35℃, pH 8, 5% DMSO, 0.4 of M-UDPG, and 1M-PPD in reaction with GE02773. In this paper, we successfully achieved the efficient synthesis of ginsenoside Rh2, which provides new ways and ideas for the application and synthesis of ginsenoside Rh2, with important practical significance and scientific value.

We review machine learning methods employing positive definite kernels. These methods formulate learning and estimation problems in a reproducing kernel Hilbert space (RKHS) of functions defined on the data domain, expanded in terms of a kernel. Working in linear spaces of function has the benefit of facilitating the construction and analysis of learning algorithms while at the same time allowing large classes of functions. The latter include nonlinear functions as well as functions defined on nonvectorial data. We cover a wide range of methods, ranging from binary classifiers to sophisticated methods for estimation with structured data.

Since the 21st century, with the rapid development of network information technology, network technology has begun to be widely used in education. Seizing the opportunity of developing new generation information network technology, establishing a broad coverage education informatization system, strengthening the construction and application of network teaching resource system, and accelerating the development and sharing of high-quality educational resources are the current development goals of education informatization. The open sharing platform of physical education curriculum resources based on MOOC network is an important way to “promote the construction, popularization, and sharing of high-quality physical education resources”. The open-sharing platform of teaching course resources based on MOOC network construction and application has increased access to course resources by 10%. It can promote the exchange and sharing of high-quality teaching resources among universities, realize inter-college cooperation and optimal integration of resources, enrich physical education course resources, and promote the overall improvement of the teaching environment. Furthermore, such improvement in physical education teaching mode improves students’ interest, promotes overall physical education development, guarantees the quality of course teaching, and finally enhances the overall teaching level of the school.