Dynamic Bandits with an Auto-Regressive Temporal Structure

Multi-armed bandit (MAB) problems are mainly studied under two extreme settings known as stochastic and adversarial. These two settings, however, do not capture realistic environments such as search engines and marketing and advertising, in which rewards stochastically change in time. Motivated by that, we introduce and study a dynamic MAB problem with stochastic temporal structure, where the expected reward of each arm is governed by an auto-regressive (AR) model. Due to the dynamic nature of the rewards, simple "explore and commit" policies fail, as all arms have to be explored continuously over time. We formalize this by characterizing a per-round regret lower bound, where the regret is measured against a strong (dynamic) benchmark. We then present an algorithm whose per-round regret almost matches our regret lower bound. Our algorithm relies on two mechanisms: (i) alternating between recently pulled arms and unpulled arms with potential, and (ii) restarting. These mechanisms enable the algorithm to dynamically adapt to changes and discard irrelevant past information at a suitable rate. In numerical studies, we further demonstrate the strength of our algorithm under non-stationary settings.Comment: 41 pages, 4 figure

Fair Assortment Planning

Many online platforms, ranging from online retail stores to social media platforms, employ algorithms to optimize their offered assortment of items (e.g., products and contents). These algorithms tend to prioritize the platforms' short-term goals by solely featuring items with the highest popularity or revenue. However, this practice can then lead to undesirable outcomes for the rest of the items, making them leave the platform, and in turn hurting the platform's long-term goals. Motivated by that, we introduce and study a fair assortment planning problem, which requires any two items with similar quality/merits to be offered similar outcomes. We show that the problem can be formulated as a linear program (LP), called (FAIR), that optimizes over the distribution of all feasible assortments. To find a near-optimal solution to (FAIR), we propose a framework based on the Ellipsoid method, which requires a polynomial-time separation oracle to the dual of the LP. We show that finding an optimal separation oracle to the dual problem is an NP-complete problem, and hence we propose a series of approximate separation oracles, which then result in a $1/2$-approx. algorithm and a PTAS for the original Problem (FAIR). The approximate separation oracles are designed by (i) showing the separation oracle to the dual of the LP is equivalent to solving an infinite series of parameterized knapsack problems, and (ii) taking advantage of the structure of the parameterized knapsack problems. Finally, we conduct a case study using the MovieLens dataset, which demonstrates the efficacy of our algorithms and further sheds light on the price of fairness.Comment: 86 pages, 7 figure

Fault Diagnosis of Supervision and Homogenization Distance Based on Local Linear Embedding Algorithm

In view of the problems of uneven distribution of reality fault samples and dimension reduction effect of locally linear embedding (LLE) algorithm which is easily affected by neighboring points, an improved local linear embedding algorithm of homogenization distance (HLLE) is developed. The method makes the overall distribution of sample points tend to be homogenization and reduces the influence of neighboring points using homogenization distance instead of the traditional Euclidean distance. It is helpful to choose effective neighboring points to construct weight matrix for dimension reduction. Because the fault recognition performance improvement of HLLE is limited and unstable, the paper further proposes a new local linear embedding algorithm of supervision and homogenization distance (SHLLE) by adding the supervised learning mechanism. On the basis of homogenization distance, supervised learning increases the category information of sample points so that the same category of sample points will be gathered and the heterogeneous category of sample points will be scattered. It effectively improves the performance of fault diagnosis and maintains stability at the same time. A comparison of the methods mentioned above was made by simulation experiment with rotor system fault diagnosis, and the results show that SHLLE algorithm has superior fault recognition performance

Increasing Interest in Inclusive Education in the Context of Action Plan for the Development and Enhancement of Special Education during the Fourteenth Five-Year Period in China: An Analysis of Baidu Index Data

Objective: Current evidence shows that public interest in inclusive education has been rising since the implementation of Action Plan for the development and enhancement of special education during the Fourteenth Five-Year period in China. The aim of this study was to quantify recent trends in public interest and related online search behavior for inclusive education in the context of this Action Plan. Methods: Baidu Index, a database of search engines with massive information, was employed. By searching for the keyword inclusive education, and using content analysis to understand the data information related to inclusive education. This study also extracted the search trend data of Chinese netizens on the related terms "Law on the Protection of Persons with Disabilities " and "Regulation on the Education of Persons with Disabilities" from January 1, 2022 to October 27, 2022. Finally, it compares the search trend of public search interests of inclusive education with related terms. Results: The public's interest in "inclusive education" and the related terms "Law on the Protection of Persons with Disabilities " and "Regulation on the Education of Persons with Disabilities" has been on the rise since the implementation of the Action Plan. The search trend reached its peak in February and May 2022, the valley in January 2022, and the search volume in other time periods tended to be stable. Conclusion: Baidu Index can understand the public's interest in inclusive education. The study shows that the rising search trend of inclusive education is closely related to the implementation of the Action Plan. The search volume of the "Law on the Protection of Persons with Disabilities " and "Regulation on the Education of Persons with Disabilities" is basically the same as that of "inclusive education", but the average search volume daily of "inclusive education" is slightly higher than that of "Regulation on the Education of Persons with Disabilities"

Boosting Semi-Supervised Learning with Contrastive Complementary Labeling

Semi-supervised learning (SSL) has achieved great success in leveraging a large amount of unlabeled data to learn a promising classifier. A popular approach is pseudo-labeling that generates pseudo labels only for those unlabeled data with high-confidence predictions. As for the low-confidence ones, existing methods often simply discard them because these unreliable pseudo labels may mislead the model. Nevertheless, we highlight that these data with low-confidence pseudo labels can be still beneficial to the training process. Specifically, although the class with the highest probability in the prediction is unreliable, we can assume that this sample is very unlikely to belong to the classes with the lowest probabilities. In this way, these data can be also very informative if we can effectively exploit these complementary labels, i.e., the classes that a sample does not belong to. Inspired by this, we propose a novel Contrastive Complementary Labeling (CCL) method that constructs a large number of reliable negative pairs based on the complementary labels and adopts contrastive learning to make use of all the unlabeled data. Extensive experiments demonstrate that CCL significantly improves the performance on top of existing methods. More critically, our CCL is particularly effective under the label-scarce settings. For example, we yield an improvement of 2.43% over FixMatch on CIFAR-10 only with 40 labeled data.Comment: typos corrected, 5 figures, 3 tables

Exploring Multi-Programming-Language Commits and Their Impacts on Software Quality: An Empirical Study on Apache Projects

Context: Modern software systems (e.g., Apache Spark) are usually written in multiple programming languages (PLs). There is little understanding on the phenomenon of multi-programming-language commits (MPLCs), which involve modified source files written in multiple PLs. Objective: This work aims to explore MPLCs and their impacts on development difficulty and software quality. Methods: We performed an empirical study on eighteen non-trivial Apache projects with 197,566 commits. Results: (1) the most commonly used PL combination consists of all the four PLs, i.e., C/C++, Java, JavaScript, and Python; (2) 9% of the commits from all the projects are MPLCs, and the proportion of MPLCs in 83% of the projects goes to a relatively stable level; (3) more than 90% of the MPLCs from all the projects involve source files in two PLs; (4) the change complexity of MPLCs is significantly higher than that of non-MPLCs; (5) issues fixed in MPLCs take significantly longer to be resolved than issues fixed in non-MPLCs in 89% of the projects; (6) MPLCs do not show significant effects on issue reopen; (7) source files undergoing MPLCs tend to be more bug-prone; and (8) MPLCs introduce more bugs than non-MPLCs. Conclusions: MPLCs are related to increased development difficulty and decreased software quality.Comment: Preprint accepted for publication in Journal of Systems and Software, 2022. arXiv admin note: substantial text overlap with arXiv:2103.1169

Application of statins in management of glioma: Recent advances

Gliomas are common primary intra-cerebral tumors in adults, and seriously threaten the health and life of affected patients, especially highly-malignant gliomas, such as glioblastoma multiforme. The clinical prognosis of glioma patients is poor, even for those who have received comprehensive treatment including surgery and concurrent chemo- and/or radio-therapy. As a structural analog of β-hydroxy-β- methylglutaryl coenzyme A (HMG CoA) reductase, statins are a restrictive enzyme in the metabolism of cholesterol. Recent laboratory studies and clinical trials have demonstrated that statins can exert antitumor effect, improve clinical prognosis and significantly prolong the survival time of glioma patients. This article is aimed to highlight the mechanisms of the anti-glioma effect of statins and review recent advances in the management of the disease.Keywords: Glioma, Glioblastoma multiforme, Intra-cerebral tumors, Statins, Prognosis, Survival time, β-Hydroxy-β-methylglutaryl coenzyme A (HMG CoA) reductas

Optimization of pneumonia CT classification model using RepVGG and spatial attention features

IntroductionPneumonia is a common and widespread infectious disease that seriously affects the life and health of patients. Especially in recent years, the outbreak of COVID-19 has caused a sharp rise in the number of confirmed cases of epidemic spread. Therefore, early detection and treatment of pneumonia are very important. However, the uneven gray distribution and structural intricacy of pneumonia images substantially impair the classification accuracy of pneumonia. In this classification task of COVID-19 and other pneumonia, because there are some commonalities between this pneumonia, even a small gap will lead to the risk of prediction deviation, it is difficult to achieve high classification accuracy by directly using the current network model to optimize the classification model.MethodsConsequently, an optimization method for the CT classification model of COVID-19 based on RepVGG was proposed. In detail, it is made up of two essential modules, feature extraction backbone and spatial attention block, which allows it to extract spatial attention features while retaining the benefits of RepVGG.ResultsThe model’s inference time is significantly reduced, and it shows better learning ability than RepVGG on both the training and validation sets. Compared with the existing advanced network models VGG-16, ResNet-50, GoogleNet, ViT, AlexNet, MobileViT, ConvNeXt, ShuffleNet, and RepVGG_b0, our model has demonstrated the best performance in a lot of indicators. In testing, it achieved an accuracy of 0.951, an F1 score of 0.952, and a Youden index of 0.902.DiscussionOverall, multiple experiments on the large dataset of SARS-CoV-2 CT-scan dataset reveal that this method outperforms most basic models in terms of classification and screening of COVID-19 CT, and has a significant reference value. Simultaneously, in the inspection experiment, this method outperformed other networks with residual structures