BooleanOCT: Optimal Classification Trees based on multivariate Boolean Rules

The global optimization of classification trees has demonstrated considerable promise, notably in enhancing accuracy, optimizing size, and thereby improving human comprehensibility. While existing optimal classification trees substantially enhance accuracy over greedy-based tree models like CART, they still fall short when compared to the more complex black-box models, such as random forests. To bridge this gap, we introduce a new mixed-integer programming (MIP) formulation, grounded in multivariate Boolean rules, to derive the optimal classification tree. Our methodology integrates both linear metrics, including accuracy, balanced accuracy, and cost-sensitive cost, as well as nonlinear metrics such as the F1-score. The approach is implemented in an open-source Python package named BooleanOCT. We comprehensively benchmark these methods on the 36 datasets from the UCI machine learning repository. The proposed models demonstrate practical solvability on real-world datasets, effectively handling sizes in the tens of thousands. Aiming to maximize accuracy, this model achieves an average absolute improvement of 3.1\% and 1.5\% over random forests in small-scale and medium-sized datasets, respectively. Experiments targeting various objectives, including balanced accuracy, cost-sensitive cost, and F1-score, demonstrate the framework's wide applicability and its superiority over contemporary state-of-the-art optimal classification tree methods in small to medium-scale datasets

MixNet: Towards Effective and Efficient UHD Low-Light Image Enhancement

With the continuous advancement of imaging devices, the prevalence of Ultra-High-Definition (UHD) images is rising. Although many image restoration methods have achieved promising results, they are not directly applicable to UHD images on devices with limited computational resources due to the inherently high computational complexity of UHD images. In this paper, we focus on the task of low-light image enhancement (LLIE) and propose a novel LLIE method called MixNet, which is designed explicitly for UHD images. To capture the long-range dependency of features without introducing excessive computational complexity, we present the Global Feature Modulation Layer (GFML). GFML associates features from different views by permuting the feature maps, enabling efficient modeling of long-range dependency. In addition, we also design the Local Feature Modulation Layer (LFML) and Feed-forward Layer (FFL) to capture local features and transform features into a compact representation. This way, our MixNet achieves effective LLIE with few model parameters and low computational complexity. We conducted extensive experiments on both synthetic and real-world datasets, and the comprehensive results demonstrate that our proposed method surpasses the performance of current state-of-the-art methods. The code will be available at \url{https://github.com/zzr-idam/MixNet}

A systems biology approach identifies a regulator, BplERF1, of cold tolerance in Betula platyphylla

Cold is an abiotic stress that can greatly affect the growth and survival of plants. Here, we reported that an AP2/ERF family gene, BplERF1, isolated from Betula platyphylla played a contributing role in cold stress tolerance. Overexpression of BplERF1 in B. platyphylla transgenic lines enhanced cold stress tolerance by increasing the scavenging capability and reducing H2O2 and malondialdehyde (MDA) content in transgenic plants. Construction of BplERF-mediated multilayered hierarchical gene regulatory network (ML-hGRN), using Top-down GGM algorithm and the transcriptomic data of BplERF1 overexpression lines, led to the identification of five candidate target genes of BplERF1 which include MPK20, ERF9, WRKY53, WRKY70, and GIA1. All of them were then verified to be the true target genes of BplERF1 by chromatin-immunoprecipitation PCR (ChIP-PCR) assay. Our results indicate that BplERF1 is a positive regulator of cold tolerance and is capable of exerting regulation on the expression of cold signaling and regulatory genes, causing mitigation of reactive oxygen species

Mining and Predicting Smart Device User Behavior

Three types of user behavior are mined in this paper: application usage, smart device usage and periodicity of user behavior. When mining application usage, the application installation, most frequently used applications and application correlation are analyzed. The application usage is long-tailed. When mining the device usage, the mean, variance and autocorrelation are calculated both for duration and interval. Both the duration and interval are long-tailed but only duration satisfies power-law distribution. Meanwhile, the autocorrelation of both duration and interval is weak, which makes predicting user behavior based on adjacent behavior not so reasonable in related works. Then DFT (Discrete Fourier Transform) is utilized to analyze the periodicity of user behavior and results show that the most obvious periodicity is 24 hours, which is in agreement with related works. Based on the results above, an improved user behavior predicting model is proposed based on Chebyshev inequality. Experiment results show that the performance is good in accurate rate and recall rate

The diverse roles of cytokinins in regulating leaf development

Leaves provide energy for plants, and consequently for animals, through photosynthesis. Despite their important functions, plant leaf developmental processes and their underlying mechanisms have not been well characterized. Here, we provide a holistic description of leaf developmental processes that is centered on cytokinins and their signaling functions. Cytokinins maintain the growth potential (pluripotency) of shoot apical meristems, which provide stem cells for the generation of leaf primordia during the initial stage of leaf formation; cytokinins and auxins, as well as their interaction, determine the phyllotaxis pattern. The activities of cytokinins in various regions of the leaf, especially at the margins, collectively determine the final leaf morphology (e.g., simple or compound). The area of a leaf is generally determined by the number and size of the cells in the leaf. Cytokinins promote cell division and increase cell expansion during the proliferation and expansion stages of leaf cell development, respectively. During leaf senescence, cytokinins reduce sugar accumulation, increase chlorophyll synthesis, and prolong the leaf photosynthetic period. We also briefly describe the roles of other hormones, including auxin and ethylene, during the whole leaf developmental process. In this study, we review the regulatory roles of cytokinins in various leaf developmental stages, with a focus on cytokinin metabolism and signal transduction processes, in order to shed light on the molecular mechanisms underlying leaf development

Hierarchical Pruning of Deep Ensembles with Focal Diversity

Deep neural network ensembles combine the wisdom of multiple deep neural networks to improve the generalizability and robustness over individual networks. It has gained increasing popularity to study deep ensemble techniques in the deep learning community. Some mission-critical applications utilize a large number of deep neural networks to form deep ensembles to achieve desired accuracy and resilience, which introduces high time and space costs for ensemble execution. However, it still remains a critical challenge whether a small subset of the entire deep ensemble can achieve the same or better generalizability and how to effectively identify these small deep ensembles for improving the space and time efficiency of ensemble execution. This paper presents a novel deep ensemble pruning approach, which can efficiently identify smaller deep ensembles and provide higher ensemble accuracy than the entire deep ensemble of a large number of member networks. Our hierarchical ensemble pruning approach (HQ) leverages three novel ensemble pruning techniques. First, we show that the focal diversity metrics can accurately capture the complementary capacity of the member networks of an ensemble, which can guide ensemble pruning. Second, we design a focal diversity based hierarchical pruning approach, which will iteratively find high quality deep ensembles with low cost and high accuracy. Third, we develop a focal diversity consensus method to integrate multiple focal diversity metrics to refine ensemble pruning results, where smaller deep ensembles can be effectively identified to offer high accuracy, high robustness and high efficiency. Evaluated using popular benchmark datasets, we demonstrate that the proposed hierarchical ensemble pruning approach can effectively identify high quality deep ensembles with better generalizability while being more time and space efficient in ensemble decision making.Comment: To appear on ACM Transactions on Intelligent Systems and Technolog