Advances in Learning Bayesian Networks of Bounded Treewidth

This work presents novel algorithms for learning Bayesian network structures with bounded treewidth. Both exact and approximate methods are developed. The exact method combines mixed-integer linear programming formulations for structure learning and treewidth computation. The approximate method consists in uniformly sampling $k$-trees (maximal graphs of treewidth $k$), and subsequently selecting, exactly or approximately, the best structure whose moral graph is a subgraph of that $k$-tree. Some properties of these methods are discussed and proven. The approaches are empirically compared to each other and to a state-of-the-art method for learning bounded treewidth structures on a collection of public data sets with up to 100 variables. The experiments show that our exact algorithm outperforms the state of the art, and that the approximate approach is fairly accurate.Comment: 23 pages, 2 figures, 3 table

Efficient learning of Bayesian networks with bounded tree-width

Learning Bayesian networks with bounded tree-width has attracted much attention recently, because low tree-width allows exact inference to be performed efficiently. Some existing methods [24,29] tackle the problem by using k-trees to learn the optimal Bayesian network with tree-width up to k. Finding the best k-tree, however, is computationally intractable. In this paper, we propose a sampling method to efficiently find representative k-trees by introducing an informative score function to characterize the quality of a k-tree. To further improve the quality of the k-trees, we propose a probabilistic hill climbing approach that locally refines the sampled k-trees. The proposed algorithm can efficiently learn a quality Bayesian network with tree-width at most k. Experimental results demonstrate that our approach is more computationally efficient than the exact methods with comparable accuracy, and outperforms most existing approximate methods

Analysis of factors influencing spatiotemporal differentiation of the NDVI in the upper and middle reaches of the Yellow River from 2000 to 2020

Surface vegetation represents a link between the atmosphere, water, and human society. The quality of the ecological environment in the upper and middle reaches of the Yellow River (UMRYR) has a direct impact on the downstream basin. However, only few studies have investigated vegetation changes in the UMRYR. Therefore, we used the coefficient of variation and linear regression analyses to investigate spatiotemporal variations in the normalized difference vegetation index (NDVI). Further, we used the geographical detector model (GDM) to determine the spatial heterogeneity of the NDVI and its driving factors and then investigated the factors driving the spatial distribution of the NDVI in different climatic zones and vegetation types. The results showed that the NDVI in the UMRYR was high during the study period. The NDVI was distributed in a spatially heterogeneous manner, and it decreased from the southeast to the northwest. We observed severe degradation in the southeast, mild degradation in the northwest and the Yellow River source region, and substantial vegetation recovery in the central basin. Precipitation and vegetation type drove the spatial distribution of the NDVI. Natural factors had higher influence than that of anthropogenic factors, but the interactions between the natural and anthropogenic factors exhibited non-linear and bivariate enhancements. Inter-annual variations in precipitation were the main natural factor influencing inter-annual NDVI variations, while precipitation and anthropogenic ecological restoration projects jointly drove NDVI changes in the UMRYR. This study provides a better understanding of the current status of the NDVI and mechanisms driving vegetation restoration in the UMRYR

Recent progress in anodic oxidation of TiO2 nanotubes and enhanced photocatalytic performance: a short review

© 2021 World Scientific Publishing Company. This is the accepted version of the final published version found at https://doi.org/10.1142/S1793292021300024By adjusting the oxidation voltage, electrolyte, anodizing time and other parameters, TiO2 nanotubes with high aspect ratio can be prepared by oxidation in organic system because anodic oxidation method has the advantage of simple preparation process, low material cost and controllable morphology. Low material cost and controllable morphology by anodizing. This review focuses on the influence of anodizing parameters on the morphology of TiO2 nanotube arrays prepared by anodizing. In order to improve the photocatalytic activity of TiO2 nanotubes under visible light and prolong the life of photo-generated carriers, the research status of improving the photocatalytic activity of TiO2 nanotubes in recent years is reviewed. This review focuses on the preparation and modification of TiO2 nanotubes by anodic oxidation, which is helpful to understand the best structure of TiO2 nanotubes and the appropriate modification methods, thus guiding the application of TiO2 nanotubes in practical photocatalysis. Finally, the development of TiO2 nanotubes is prospected.Peer reviewe

RCooper: A Real-world Large-scale Dataset for Roadside Cooperative Perception

The value of roadside perception, which could extend the boundaries of autonomous driving and traffic management, has gradually become more prominent and acknowledged in recent years. However, existing roadside perception approaches only focus on the single-infrastructure sensor system, which cannot realize a comprehensive understanding of a traffic area because of the limited sensing range and blind spots. Orienting high-quality roadside perception, we need Roadside Cooperative Perception (RCooper) to achieve practical area-coverage roadside perception for restricted traffic areas. Rcooper has its own domain-specific challenges, but further exploration is hindered due to the lack of datasets. We hence release the first real-world, large-scale RCooper dataset to bloom the research on practical roadside cooperative perception, including detection and tracking. The manually annotated dataset comprises 50k images and 30k point clouds, including two representative traffic scenes (i.e., intersection and corridor). The constructed benchmarks prove the effectiveness of roadside cooperation perception and demonstrate the direction of further research. Codes and dataset can be accessed at: https://github.com/AIR-THU/DAIR-RCooper.Comment: Accepted by CVPR2024. 10 pages with 6 figure

Shift in the submucosal microbiome of diseased peri-implant sites after non-surgical mechanical debridement treatment

ObjectivesThe object of this prospective study was to assess the submucosal microbiome shifts in diseased peri-implant sites after non-surgical mechanical debridement therapy.Materials and methodsSubmucosal plaques were collected from 14 healthy implants and 42 diseased implants before and eight weeks after treatment in this prospective study. Mechanical debridement was performed using titanium curettes, followed by irrigation with 0.2% (w/v) chlorhexidine. Subsequently, 16S rRNA gene sequencing was used to analyze the changes in the submucosal microbiome before and after the non-surgical treatment.ResultsClinical parameters and the submucosal microbiome were statistically comparable before and after mechanical debridement. The Alpha diversity decreased significantly after mechanical debridement. However, the microbial richness varied between the post-treatment and healthy groups. In network analysis, the post-treatment increased the complexity of the network compared to pre-treatment. The relative abundances of some pathogenic species, such as Porphyromonas gingivalis, Tannerella forsythia, Peptostreptococcaceae XIG-6 nodatum, Filifactor alocis, Porphyromonas endodontalis, TM7 sp., and Desulfobulbus sp. HMT 041, decreased significantly following the non-surgical treatment.ConclusionsNon-surgical treatment for peri-implant diseases using mechanical debridement could provide clinical and microbiological benefits. The microbial community profile tended to shift towards a healthy profile, and submucosal dysbiosis was relieved following mechanical debridement

Large expert-curated database for benchmarking document similarity detection in biomedical literature search

Document recommendation systems for locating relevant literature have mostly relied on methods developed a decade ago. This is largely due to the lack of a large offline gold-standard benchmark of relevant documents that cover a variety of research fields such that newly developed literature search techniques can be compared, improved and translated into practice. To overcome this bottleneck, we have established the RElevant LIterature SearcH consortium consisting of more than 1500 scientists from 84 countries, who have collectively annotated the relevance of over 180 000 PubMed-listed articles with regard to their respective seed (input) article/s. The majority of annotations were contributed by highly experienced, original authors of the seed articles. The collected data cover 76% of all unique PubMed Medical Subject Headings descriptors. No systematic biases were observed across different experience levels, research fields or time spent on annotations. More importantly, annotations of the same document pairs contributed by different scientists were highly concordant. We further show that the three representative baseline methods used to generate recommended articles for evaluation (Okapi Best Matching 25, Term Frequency-Inverse Document Frequency and PubMed Related Articles) had similar overall performances. Additionally, we found that these methods each tend to produce distinct collections of recommended articles, suggesting that a hybrid method may be required to completely capture all relevant articles. The established database server located at https://relishdb.ict.griffith.edu.au is freely available for the downloading of annotation data and the blind testing of new methods. We expect that this benchmark will be useful for stimulating the development of new powerful techniques for title and title/abstract-based search engines for relevant articles in biomedical research.Peer reviewe

Learning Bayesian Networks with Bounded Tree-Width via Guided Search

Bounding the tree-width of a Bayesian network can reduce the chance of overfitting, and allows exact inference to be performed efficiently. Several existing algorithms tackle the problem of learning bounded tree-width Bayesian networks by learning from k-trees as super-structures, but they do not scale to large domains and/or large tree-width. We propose a guided search algorithm to find k-trees with maximum Informative scores, which is a measure of quality for the k-tree in yielding good Bayesian networks. The algorithm achieves close to optimal performance compared to exact solutions in small domains, and can discover better networks than existing approximate methods can in large domains. It also provides an optimal elimination order of variables that guarantees small complexity for later runs of exact inference. Comparisons with well-known approaches in terms of learning and inference accuracy illustrate its capabilities